Gynura procumbens is an edible flowering plant that has been utilized as traditional therapy for numerous diseases. The current experiment investigates the hepatoprotective potentials of the ethanol extract of Gynura procumbens leaf (EEGPL) against thioacetamide (TAA)-induced liver cirrhosis in rats. Thirty Sprague Dawley rats were randomly divided into 5 clusters: A, rats received orally 10% Tween 80 and intraperitoneal (i.p) inoculation of sterile distal water; B, rats received orally10% Tween 80; C, rats received orally daily 50 mg/kg of silymarin, while groups; D and E, rats received orally daily doses of 200 and 400 mg/kg of EEGPL, respectively. Furthermore, B-E clusters received 200 mg/kg thioacetamide (i.p) three times a week for 60 days.The liver gross morphology of rats that received only TAA (B) revealed irregular rough surface layers compared to smoother livers of rats that received EEGPL. Histopathology of group B revealed clear hepatic necrosis and fibrous connective tissue, which were significantly reduced in C-E groups. EEGPL treatment caused a significant down-regulation of PCNA and α-SMA protein expressions. Antioxidant (SOD and CAT) enzymes in hepatic homogeneity were meaningfully lower, and MDA levels were significantly higher in TAA controls compared to those of C-E groups. Moreover, EEGPL treatment caused a reduction of TNF-α and IL-6 and increased expression of IL-10 cytokines. Therefore, the hepatoprotective potentials of EEGPL might be contributed to its modulation of detoxification enzymes, anti-inflammatory, and antioxidant activities. In recent decades, liver dysfunctionality and fibrosis have notably increased, mainly due to environmental carcinogens and lifestyle changes. New data analysis in 2020 has shown that the prevalence of hepatic injury and fibrosis has increased by 13% since 2000, reaching more than 1.5 billion [ in 2016 .Another cause of liver cirrhosis is thioacetamide (TAA), a laboratory chemical used by researchers to enhance the formation of hepatocyte injury and cirrhosis in rats, which resembles liver injury detected in humans . The patGynura [Herbal medicine and its derivatives have a long history as a traditional remedy for liver dysfunctionalities, and in today's world, these products are gaining more interest as alternative medicine due to numerous drawbacks related to synthetic chemicals. In last few decades, numerous research studies have reported the hepatoprotective of traditional herbal medicines including many species of the genus Gynura ,8.G. procumbens (Merr.) which is known in Malaysia as Sambung nyawa, is broadly dispersed in South-East Asian countries. Various parts of this medicinal herb have been conventionally used for dealing with eruptive fevers, rash, constipation, hypertension, diabetes mellitus, rheumatism, viral diseases of the skin, kidney diseases, migraine, and cancer [d cancer . The lead cancer  and haved cancer .kB). Moreover, scientists revealed that pro-inflammatory cytokines can induce the pathway actions of NF-kB, thereby creating a continuous auto-controlling cycle that can duplicate the inflammatory process for a prolonged time [Inflammatory cytokines are major contributors of the progression or suppression of liver cirrhosis in any stage of this disease. The cytokines tumor necrosis factor (TNF-α), interleukin-1 β (IL-1 β), and interleukin-6 (IL-6) have been well-known as pro-inflammatory biomarkers that were significantly elevated during hepatocyte damage, which also facilitate the further progression of the disease through their contribution in many biological pathways, including lipid metabolism, protein synthesis (positive and negative acute phase), biliary system obstruction, and fibrosis progression . In contged time . Therefoged time .Quite a lot of studies have revealed that EEGPL displays numerous biological activities due to its phytocontents  . Despite22.1G. procumbens leaves were found at Ethno Resources Sdn Bhd, Malaysia. The plant identification and authentication were done according to criteria recorded at the Herbarium of Rimba Ilmu, Foundation of Natural Science, and the University of Malaya. The GP leaves were desiccated and ground into ﬁne dust. Two hundred grams of the residue were immersed in 1 L of 95% ethanol for 4 days. At that point, the combination was sieved via Whatman paper (#1) and cleansed by condensed pressure in a spinning evaporator. The obtained plant extract was dissolved in 10% Tween 80 and delivered to rats through oral gavage [The l gavage .2.2Thioacetamide was bought from a Swiss company (Sigma-Aldrich)and then it was dissolved in 10% Tween 80 and mixed well till wholly quartzes were liquefied. At that time, 200 mg/kg body mass was given to rats by intraperitoneal injection in 3 dosages weekly for 60 days. The injection of TAA will produce significant tissue damage and biochemical modulations in rats, analogous to liver cirrhosis occurring in humans .2.3The mature male Sprague Dawley rats, aged 7–8 weeks and weighted 170–180 g, were obtained from the Animal House, Cihan University-Erbil. Rats were kept in separate cages (bottom designed with wide-mesh wire) to prevent coprophagia and ingested with tap water and standard rat diets (pellets). The experiment began after 7 days of adaptation. For the acute toxicity procedure, rats were divided randomly into three groups; A, normal controls received 10% Tween 80 (5 mL/kg): B, rats received 2 g/kg of EEGPL; C, rats treated with 4 g/kg of EEGPL. Rats were fasting for overnight before treatment delivery. After supplementation, rats were fasting (food and water)foranother 3–4 h. The observation procedure started after supplementation and continued for 48 h (every 8 h) for any possible toxic signs or abnormal changes. After two weeks, rats received an over dose of anaesthesia (ketamine and xylazine) and sacrificed. The intercardial blood puncture were obtained for biochemical analysis and rat organs (liver and kidney) dissected for histological evaluations .2.4The mature rats were aimlessly distributed into 5 clusters (6 rats each):A, normal control rats had daily oral dose of 10% Tween 80 (5 mL/kg) and weekly three injections (5 mL/kg) of sterile distilled water.B, cirrhosis rats received daily oral of 10% Tween 80 (5 mL/kg) and weekly three injections of 200 mg/kg of TAA.C, Reference rats received daily oral dose of silymarin (50 mg/kg) and weekly three injections (200 mg/kg) of TAA.Gynura procumbens leaf)-treated rats were given orally 200 and 400 mg/kg, respectively, and weekly three injections (200 mg/kg) of TAA. The experiment continued for 8 weeks, and the body masses of laboratory animals in all groups were taken weekly [D and E, EEGPL , and placed on the ﬁlter papers for the inspection of weight and gross pathological alterations .2.6The sliced liver tissues transferred into10% phosphate buﬀered formalin for 24 h for the fixation purpose and then transferred into mechanical tissue processing machine . Liver tissue slices (3–5 μm thickness) were fixed on slides for histological evaluations by using hematoxylin, eosin, and Masson Trichrome stains .2.7The liver fibrosis evaluation was also studied by immunohistochemical technique via estimation of PCNA and α -smooth muscle actin (α-SMA) protein expression. The intensity of stains in liver tissue was determined by calculating values of stained cells divided by 1000 liver cells and mitotic index was found based on detected cellular mitosis .2.8Liver tissue samples obtained from both lobes of the liver. Hepatic tissues (1 g) were placed in a flask containing 10 mL (10%) of PBS solution (pH 7.2) before normalization and homogenization procedure by a homogenizer machine at 5000 rpm (15 min at −4 °C). The supernatant part was obtained and stored in a −80 °C freezer. Antioxidant kits were bought from German company (Merk) for the evolution of and superoxide dismutase (SOD)), and malondialdehyde (MDA) contents .2.9The evaluation of pro-inflammatory cytokines (TNF-α and IL-6) and anti-inflammatory cytokines (IL-10) in serum samples were possible via the application of an easily accessible ELISA kit (Cusabio Biotech Co. China). ELISA inter and intra-assay consistency is calculated via wells obtained from exact plate/assay kit.Intra-Assay Precision, samples (3) of determined intensity were run three times on one plate. Intra-Assay: CV<8%Inter-Assay Precision, samples (3) of known concentration were tested in several separate assays. CV (%) = SD/meanX100. Inter-Assay: CV<12%The procedure began with centrifugation of the blood samples at 3000 g (15 min), and the obtained supernatant (serum) was separated and evaluated for the cytokine contents using a common enzyme-linked immunosorbent assay kit following the producer's guidelines as written in the ELISA kit for rats, TNF-α, IL-6, and IL-10. The concentrations were determined based on the purified reference cytokines .2.10The blood samples centrifuged and the isolated serum was investigated for the amount of liver enzyme contents including ALT, alanine aminotransferase; ALP, alkaline phosphatase; AST, aspartate aminotransferase. Furthermore, the synthetic and excretory efficiency of the liver were evaluated via detection of the total protein, albumin, and bilirubin levels.2.11The statistical procedure included one-way analysis of variance (ANOVA) and Graph Pad Prism (version 9.0). Values were presented as MEAN ± S.E.M., and the significant level was set at p < 0.05.33.1The present outcomes revealed the absence of any behavioural or physiological changes in rats after two-week administration of 2 g/kg and 4 g/kg of EEGPL. Furthermore, rats consumed equal amounts of food and water without significant differences in their body weight compared to normal controls. The biochemical analysis showed non-significant changes in the serum profiles represented by comparable levels of liver and renal parameters between normal control and supplemented rats. The histopathological investigations of liver and kidneys from EEGPL-treated rats showed the absence of any structure changes, which were very comparable to that of normal control rats. A–C. IndiThe present data outcomes for liver  A and kiThe oral supplementation of EEGPL (2 and 4 g/kg) to rats revealed non-significant biochemical changes in the kidney based on the estimated parameters compared to normal controls .3.23.2.1The current results have detected significant differentiation between the body weight (BW) of experimental rats compared to those of normal controls. Rats treated with TAA only had statistically lower BW (178.33 ± 2.41 gm) than that 322 ± 5.74, 295.42 ± 2.82, 226.30 ± 3.75, 263.40 ± 4.33 gm of normal control, silymarin, 200 and 400 mg/kg of EEGPL-treated rats, respectively.The liver weightiness of rats in the TAA control group was significantly higher (13.29 ± 0.06 gm) than that of the normal control, silymarin or EEGPL. Animals nourished with silymarin or EEGPL had positive augmentation of their body mass and liver index . The liv3.2.2The morphological appearance of livers obtained from normal control rats demonstrate smooth surface of hepatic tissues with regular tissue layers. However, the microscopic views of liver tissue dissected form TAA control rats showed numerous micronodules with rough irregularities on the hepatic tissues. Rats treated with standard drug (silymarin) or EEGPL (200 mg/kg and 400 mg/kg) had significantly lower tissue damage (induced by TAA) in their parenchymal tissue, as shown by uniform tissue structure and fewer micronodules , GA.Fig.3.2.3The normal control rats (A) had a normal liver structure in the absence of any notable signs of inflammation or necrosis. The histological results of liver dissection from TAA control rats (B) showed significant tissue damage represented by disrupted endothelium, unclear nuclei, and increased cytoplasmic vacuoles, indicating severe tissue necrosis and inflammation. The results also revealed significant modification of parenchymal cells by fibrous septa that align the collagen linkage in the hepatic triangles, indicating numerous micro-, macro-nodules in the hepatocytes. Such nodules were surrounded by bundles of connective tissues that divide the liver into lobules, which exhibited significant inflammation and tissue necrosis. The liver histology of silymarin-treated rats (C) showed significant recovery from TAA-induced tissue damage as shown by lower infiltrated cells, hepatic micronodules (necrosis), and reduced tissue disruption compared to TAA control. Thus, the structure of the liver was well protected, as shown by normal hepatic lobules and numerous veins spreading throughout connective tissue. The liver tissue analysis of rats receiving 200 mg/kg of EEGPL (D) demonstrated significant retrieval from TAA-induced damage represented by lower scores for fibrotic tissues, lower nucleus damage, and vacuolization, fewer parenchymal cell damage with less tissue fibrosis and micro-nodules than that of TAA control, but not as significant as rats ingested the standard drug silymarin. Finally, the microscopic views of liver tissue showed fewer tissue penetration and parenchymal cell regeneration as expressed by fewer necrotic zones and fewer vacuoles in their endothelial and sub-endothelial layers , H&E.The Mason trachoma technique is utilized to evaluate the collagen fibrous deposition in the hepatic tissues. The current results of liver tissue coloured with Masson's trichrome revealed a lack of any collagen deposition in the hepatocyte tissues of normal control rats , C or EE3.2.4The current results of the liver fibrosis rate according to the expression of immunohistochemical staining of αSMA protein in the liver tissue significantly differed between all rat groups. Histological evaluation showed the lowest α-SMA intensity in the liver parenchymal tissues of normal control rats A and F. The results of evaluating the hepatocyte proliferation were determined based on the expression of immunohistochemical PCNA stain in the parenchymal cells by utilizing a typical anti-PCNA antibody .Fig. 4EfThe normal control rats showed zero expression of PCNA stains, indicating the absence of cell renewal A. Contra3.2.5The current results of antioxidants in liver homogenates significantly varied between the treated and control rats. The normal control rats (A) had the highest concentration of antioxidant activity in their homogenized hepatic tissues, represented by an increased level of SOD (17.87 U/mg), CAD (36.84 U/mg), and reduced lipid peroxidation activity, represented by the lowest MDA level (1.18 U/mg) compared to all other rat groups. In contrast, TAA control rats (B) experienced the lowest antioxidant status expressed by decreased SOD (8.35 U/mg) and CAT (19.95 U/mg) levels and an increased lipid peroxidation status indicated by increased MDA (4.62 U/mg) values a and b. 3.2.6The results of inflammatory and anti-inflammatory biomarkers were significantly different between all rat groups due to differentiation in the liver damage grades. The normal rats (A) showed the lowest TNF-α and IL-6 and the highest IL-10 cytokines compared to all other rat groups. While rats receiving only TAA had significant up-regulation of TNF-α and IL-6 values, and down-regulation of IL-10 cytokines, indicating severe inflammation initiated by TAA. In contrast, rats treated with silymarin (C) or EEGPL (D and E) had a significant reduction in the TNF-α and IL-6 values and a significant increase in the IL-10 in the serum samples compared to those of TAA control rats a-c.Fig. 3.2.7The serum biochemical results revealed significant up-regulation in the TAA control rats (B) compared to the normal control rats (A), indicating clear enzyme leakage due to severe hepatic tissue penetration induced by TAA. Rats who had oral ingestion of silymarin (C) or EEGPL (D and E) supplementation showed lower liver enzymes and bilirubin concentrations in their serum compared to those of TAA control rats . The liv4The present EEGPL supplementation (2 and 4 g/kg) to rats in a 14-day toxicity trial did not cause any abnormalities in the serum biochemical (liver and kidney) parameters or their organtissue structure, with a zero mortality rate even after the experimental period. Suggesting that the toxic dose for this plant exceeds the 4 g/kg dosage. Similar results were reported in the absence of any physiological change or mortality among rats administered 5 g/kg of EEGPL for 2 weeks .The present work revealed that rats treated only with TAA (B) had significantly lower body weight and increased liver weight compared to the normal control. Similar results have been reported by numerous researchers during their investigations of different liver cirrhosis trials ,21,22. HThe current study showed that TAA control rats experienced significant hepatomegaly and augmented the weightiness/body mass ratio, which could be correlated with the accumulation of fat and deterioration in the hepatocytes . This effect was in line with a previous statement on the improved liver heaviness/body heaviness relation in hepatotoxic rats ,22. The In the current study, the TAA injection caused severe liver cirrhosis in rats, which could be due to its effects on liver enzymes, as results indicated significant up-regulation of ALP, ALT, and AST enzymes in TAA control rats. Studies have linked the TAA efficacy in the elevation of liver enzyme production with its reaction with the nucleic acids (DNA and RNA), thereby causing extracellular liver damage that accelerates the hepatic enzyme formation and their leakage into the extracellular fluid . In contThe biochemical evaluation also revealed that TAA injection caused a significant reduction in the levels of albumin and total proteins in TAA control rats. This has been linked with the effect of this chemical on lowering the transcriptional processes (mRNA) and significantly with the RNA program from the nucleus to the cytoplasm, initiating severe injury in the cell membranes and leading to significant enzyme leakages. Prolonged liver leakage consequently leads to diminished proteins in cells and extracellular fluids . SimilarThe current gross study showed increased production of micro-, macro-nodules on the parenchymal tissues of the liver obtained from TAA control rats. Similarly, numerous studies have shown the efficacy of TAA in the reformation of the liver surface tissues (irregular surface), indicating proliferative cells (tumors) and presence of severe hepatocyte injury. In contrast, silymarin or EEGPL-treated rats had livers with a more regular surface smooth area, indicating significant protection against TAA-induced liver damage. Similar outcomes were reported previously ,35.The histological examination of liver tissue stained with H and E stains revealed significant liver injury in TAA control rats who received only TAA (TAA controls). Moreover, livers obtained from the TAA control group showed significant collagen deposition (using Masson's trichrome stain), indicating severe alteration of the membrane permeability of hepatocytes. In contrast, rats administered silymarin or EEGPL showed lower colour intensity in their liver tissue, meaning less collagen deposition occurred. Accordingly, numerous scientists have reported the efficacy of TAA in releasing collagens from liver cells and the potential of herbal medicines in turning over this action ,36.The immunohistochemical outcomes have shown that TAA controls experienced severe liver cirrhosis, fibrosis, and cellular proliferation, as shown by increased concentrations of α-SMA and PCNA proteins in their liver tissue. In contrast, silymarin or EEGPL-treated rats revealed significantly lower expression of these two proteins, indicating less liver tissue damage. Similar results were reported on the up-regulation of these proteins by TAA and their down-regulation by medicinal plants or their natural products ,37. PrevO-hexoside, coumaroylquinic acid, and caffeoyl-O-hexoside) as previously reported in details [The evaluation of antioxidants (SOD and CAT) in the liver homogenates has become one of the main indicators of oxidative stress-causing liver injury . The cur details ,41.G. procumbens. The current outcome scientifically backup the therapeutic efficacy of this plant that could serve as new medicinal source for better management of patients with liver cirrhosis.The immune defence changes are another physiological alteration caused by TAA in hepatotoxic rats. Immunity modulation mainly includes increased production of pro-inflammatory cytokines (TNF-α and IL-6) and decreased anti-inflammatory cytokines (IL-10), thereby causing more ROS formation and oxidative tension . Studies5Gynura procumbens have shown significant hepatoprotective potentials based on the histological, immunohistochemical and biochemical evaluations. The molecular mechanism behind these action can be explained through its positive augmentation on the inflammatory cytokines, antioxidant enzymes, immunohistochemical proteins (α-SMA and PCNA), altogether resulted in decreased liver cell proliferation, and lower mitotic index. Hepatoprotection of EEGPL can be also correlated with its inducing actions on the SOD and CAT enzymes and its inhibitory potentials on MDA levels. The current study faced many obstacles including, a small sample size, unavailability and expensiveness of laboratory reagents, and inability to extract specific active ingredients of this plant due to poor facility and lack of specialized instruments.The ethanolic leave extracts of The study protocol approved by ethics committee in Cihan University-Erbil . The procedure was according to the standards set by national and international organization for animal use in the laboratory .Ahmed A.j. Jabbar: Conceived and designed the experiments; Performed the experiments; Analyzed and interpreted the data; Wrote the paper.Zaenah Zuhair Alamri, Nur Ain Salehen, Zakia Salim Amur Al Sinawi, Soliman Mohammed Alfaif: Contributed reagents, materials, analysis tools or data.Mahmood Ameen Abdulla: Conceived and designed the experiments; Performed the experiments; Analyzed and interpreted the data.This work received no specific funding or grant.Data will be made available on request.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


Streptococcus pneumoniae, ComA is a conserved efflux pump responsible for the maturation and secretion of peptide signals, including the competence-stimulating peptide (CSP), yet its structure and function remain unclear. Here, we functionally characterize ComA as an ABC transporter with high ATP affinity and determined its cryo-EM structures in the presence or absence of CSP or nucleotides. Our findings reveal a network of strong electrostatic interactions unique to ComA at the intracellular gate, a putative binding pocket for two CSP molecules, and negatively charged residues facilitating CSP translocation. Mutations of these residues affect ComA’s peptidase activity in-vitro and prevent CSP export in-vivo. We demonstrate that ATP-Mg2+ triggers the outward-facing conformation of ComA for CSP release, rather than ATP alone. Our study provides molecular insights into the QS signal peptide secretion, highlighting potential targets for QS-targeting drugs.Quorum sensing (QS) is a crucial regulatory mechanism controlling bacterial signalling and holds promise for novel therapies against antimicrobial resistance. In Gram-positive bacteria, such as  Quorum sensing is a regulatory mechanism controlling bacterial signaling and ComA, a conserved efflux pump, is responsible for the maturation and secretion of peptide signals. Here, authors determine the 3D structure and demonstrate its function as an ABC transporter. Processes regulated by QS include antibiotic production6, the CRISPR system7, biofilm formation10, sporulation12, and competence16. Unlike QS signaling molecules in Gram-negative bacteria that can freely diffuse across the cell membrane, an active transport mechanism is required to export peptide signals in the Gram-positive QS systems4. For example, in the competence pathway in S. pneumoniae, the QS signal competence stimulating peptide (CSP) is processed and secreted by a bi-functional efflux pump ComA  allows pathogens to synchronize gene expression in response to the ever-changing environmentomA Fig. 17–19. Co25. One potential approach is to intercept QS, which can inhibit virulence and reduce the severity of infections. Among the early Gram-positive QS systems targeted is the accessory gene regulator (agr) QS system in Staphylococcus aureus, which led to the development of autoinducing peptide-based agr QS inhibitors27. Another system being explored for its therapeutic potential is the fsr QS circuit in Enterococcus faecalis27. Additionally, efforts are underway to attenuate virulence in pneumococcal infections24. However, current QS modulator development mainly focuses on disrupting the interaction between signal peptides and their receptors, rather than targeting the upstream biosynthesis process, due to the lack of underpinning molecular structures. ComA, a highly conserved membrane transporter in Gram-positive bacteria, represents an ideal drug target since signal peptide secretion is ubiquitous.The discovery of quorum sensing (QS) in bacteria has led to the development of new strategies for combating bacterial infections30. During this process, the immature peptide binds to the cytosolic protease domain with the leader peptide and the core peptide located is positioned in the central cavity of the TMD. The mature peptide is generated by cutting off the leader peptide with the peptidase domain, followed by secretion across the membrane. The fact that all PCATs have the same architecture with a conserved peptidase domain suggests that the mechanism of peptide processing is conserved29. However, the process of peptide secretion may differ due to variations in the shape and size of the substrates, and this process is not yet fully understood because no structure has been obtained with the matured peptide binding.ComA is a member of the widespread PCAT (peptidase-containing ATP-binding cassette transporter) family. It consists of three domains: an N-terminal C39 cysteine peptidase domain (PEP), a C-terminal nucleotide-binding domain , and a transmembrane domain (TMD). The PCAT family is known for its role in peptide processing, which has been extensively studied in PCAT133, elucidating the mechanism of CSP binding and secretion without a full-length transporter structure is challenging, thereby limiting the development of drugs targeting the QS signal peptide secretion process. In this study, we reconstitute full-length ComA, characterize it biochemically, and solved its structure by cryo-EM. ComA is unique because it has a high affinity towards ATP. Additionally, we determined multiple structures of ComA in various conformational states, with and without CSP and nucleotides, covering critical stages of CSP export. We identify a putative noncanonical peptide binding site at the outer leaflet of the membrane. Critical residues in ComA were also validated by in-vitro peptidase assays and in-vivo CSP secretion assays in S. pneumoniae. Additionally, we report ComA activities were inhibited by Zn2+, which trapped the molecule in the outward-facing conformation. ATP-Mg2+, but not ATP, triggers the outward-facing conformation of ComA and facilitates CSP release. Our work provides a comprehensive mechanistic insight underlying the export of peptide signals for quorum sensing in gram-positive bacteria.Despite the availability of partial structures of the ComA PEP and NBD domains over a decade of studies34. We did not detect a significant difference in ATPase activity between LMNG solubilized ComA and ComA reconstituted in peptidiscs or nanodiscs . The peptidase activity of full-length ComA was also notably higher than the truncated peptidase domain  abolished the ComA peptidase activity, confirming their role in substrate cleavage  and the turnover rate of ComA were determined at 25  oC Fig. . The ATPain Fig. . We alsoage Fig. . In summWe further investigated the coordination between the catalytic activities of the PEP and NBD domains of ComA. First, the ATPase activity was measured in the presence and absence of the substrate peptide. Neither ComC nor matured CSP affected the ATPase activity of ComA Fig. . On the 2+, compared to the enzyme in the presence of ComC alone  ComA enzyme in the presence of ATP-Mgone Fig. . We sugg28, where it was noted that the PEP domain is associated with the primary structure of the ABC transporter when the two NBD domains are isolated from one another. Concurrently, the ATP binding initiates the dissociation of the PEP domains, subsequently leading to their flexibility. We aimed to examine if this hypothesis is applicable to ComA; hence, a cryo-electron microscopy (cryo-EM) study of ComA mutants was conducted in the presence or absence of substrate CSP or ATP  experiments on ComA in the presence and absence of ATP, cross-linked with disuccinimidyl sulfoxide (DSSO) with 2 biological replicates digested with trypsin and 2 biological replicates digested with chymotrypsin. Our CLMS analysis with 1% false discovery rate (FDR) cut-off identified a total of 46 unique cross-link sites across both states, with 27 cross-links found in ATP-free state only, 6 cross-links found in ATP-bound state only and 13 cross-links found in both ATP-free and ATP-bound states  mutant sample was in the absence of Mg39, Sav186640, PCAT129, and MsbA43 33 28. Though an apparent correlation between the central cavity size and substrate size may exist, it does not dictate a strict rule. For instance, smaller molecule transporters, such as rV1819c (transporting Bleomycin and vitamin B12)45 and IrtAB 46, have cavity sizes of 3780 Å³ and 4600 Å³, respectively. Thus, while cavity size might affect substrate accommodation, it’s not always proportional to the size of the transported substrate.Our structural analysis, utilizing the 3V serverIn addition, this cavity also has a remarkable set of charged residues, which can be divided into three distinct parts: inner, middle and outer regions. The negatively charged inner region is closed to the IC-gate Fig.  and contWe then performed cryo-EM experiments with ComA and ComC to understand the interaction between the mature CSP peptide and ComA  and the observation that 13 out of the 17 residues of the CSP were fitted acceptably into the density  mutant Figs. a and b. 2+ binding, which was further stabilized by hydrogen bonds with residue S420 in TM6. As D194 is likely crucial for CSP binding, its movement may disrupt the interaction between ComA and CSP. Moreover, TM2 squeezes inward by about 2 Å toward the central translocation pathway, which may destabilize the CSP binding pocket.State 2 drew our attention because it was in an OF-conformation with the EC gate open, which may allow the bound CSPs to escape Figs. a and b. 2+, rather than ATP alone, instigates the opening of the extracellular (EC) gate, we determined a structure of the ComA (E647Q) mutant in the presence of both ATP and Mg2+  . The bacteriocin transporter BlpA was then inactivated to prevent its interference in the assay52. Subsequently, the corresponding comA mutants were constructed in the ∆blpA comC-HiBiT background. comC-HiBiT expression was induced by adding CSP to the culture. If ComC-HiBiT was processed and exported by ComA or its variants, the CSP-HiBiT fusion would be detected in the culture supernatant when LgBiT and a luminescent substrate were added. Otherwise, the ComC-HiBiT peptide would be trapped in the cytoplasm. Thus, the CSP-HiBiT fusion could only be detected in the cell pellet fraction.We next validated the critical residues of ComA and ComC by measuring CSP export in S. pneumoniae.As expected, changing residues Y216 and Y433 in ComA to alanine reduced CSP export by ~2-fold, likely because these variants are defective in CSP-binding Figs. a, and b.2+, ATP-Mg2+, ATP-Zn2+, and ComC. ComA has several distinct features, including an extensive electrostatic interaction network at the IC gate. This may account for the unusually high ATP affinity and thermostability, perhaps important for enabling robust QS in an ever-changing environment. Moreover, the inner and middle regions of the central cavity are charged, which may push the CSP molecules through the channel after leader peptide cleavage. The remodeling of the central translocation tunnel may also help CSP export  Then, in a process analogous to our presumed ComC-bound structure, which strongly mirrors the peptide-bound PCAT1 structure28, the substrate peptide binds to the PEP domain located at the intracellular site. 3) After cleavage of the leader peptide, the two mature CSP molecules enter the central cavity, a state captured in our ATP-absent CSP-bound ComA structure. The negatively charged residues in the cytosolic region of the conduit facilitate the translocation of CSP molecules towards the outer leaflet binding site. Two potential salt bridges between D194 (ComA) and R9 (CSP) stablize the substrate binding. Once CSP binds, the intracellular gate closes, the PEP domains dissociate from the ABC transporter, and the two NBDs approach each other, preparing for ATP binding. 4) Subsequently, ATP and Mg2+ bind to the two NBDs, causing local reconfiguration of the putative CSP binding pocket, including disruption of the potential D194 (ComA) - R9 (CSP) ion pair, and minor shifts in TM2. The opening at the extracellular gate, with a radius larger than 5.2 Å, enables the release of CSP from the central cavity. 5) Finally, after the CSP molecules are released, Mg2+ dissociate from the ATP binding site, the extracellular gate closes once more, resulted in an OF-occluded state as observed in PCAT1 as well29. This conformation with Mg2+ dissociated may be facilitated by ATP hydrolysis in cell, followed by ADP release from the NBD domain. ComA then reverts to the apo state, ready for the next cycle of CSP processing and secretion.Based on the ComA structures trapped in different states and on the assumption that the peptide processing mechanism is likely conserved among PCATs, which has been elegantly studied in PCAT1del Fig.  for the One limitation of this study is that the PEP domains of ComA could not be clearly resolved to high resolution. The reason why these domains are especially flexible in ComA is unclear, but it may be due to the transient fashion of CSP processing. Future directions will be to trap the pre-cleavage state using noncleavable ComC analog and to investigate how ATP binding inhibits the peptidase activity of ComA. Nevertheless, the peptidase activity seems to couple with the transporter function , and cloned into the pRSFDuet-M2 or pET-15b vector with an MBP tag or His tag at N-terminus. Vectors of ComA mutants  and ComC mutants (E1Q and E1R) were constructed using site-directed mutagenesis method.The ComA, truncated peptidase domain (PEP) of ComA, and ComC genes were amplified from the genomic DNA of E. coli cells. 0.5 mM Isopropyl β-D-1-thiogalactopyranoside (IPTG) was added when cells OD600 reached to 0.8 and cultured for another 20 h at 18°C. Harvested cell pallets were resuspended using Buffer A  in the presence of 1 mM dithiothreitol (DTT) and 1 mM phenylmethylsulfonyl fluoride (PMSF). Cell suspension was broken by a high-pressure homogenizer and ultracentrifuged to get the cell membranes. Buffer A with 2% LMNG (Anatrace) was used to solubilize membranes for 2 h at 4°C, and then the supernatant was subjected to further purification with anti-MBP beads (SMART lifesciences). Buffer A in the presence of 60 mM maltose and 0.01% LMNG was applied to elute ComA proteins. MBP tag was cleaved by tobacco etch virus protease (TEVP) for further size-exclusion chromatography (SEC) purification with Superose 6 Increase 10/300 GL column (Cytiva). Fractions corresponding to ComA were pulled and concentrated to 4.5 mg/ml for the following studies.For ComA and its mutants, the reconstructed pRSFDuet-M2-ComA plasmid was transformed and expressed in BL21(DE3) E. coli cells. 0.5 mM Isopropyl β-D-1-thiogalactopyranoside (IPTG) was added when cells OD600 reached to 0.8 and cultured for another 15 h at 18°C. Cells were collected and resuspended using Buffer B  with 1 mM DTT 1 mM PMSF. Cell suspension was subsequently broken by a high-pressure homogenizer and cell debris was moved using a high-speed centrifuge. The supernatant was collected and applied to MBP affinity purification and eluted with Buffer B in the presence of 60 mM maltose. The MBP-tag was moved by incubation with 1 mg/ml TEVP at 30 °C for 30 min, and concentrated for further purification by SEC with Superdex 200 Increase 10/300 GL column. Fractions corresponding to PEP domain were pulled and concentrated for the peptidase assay.For the truncated PEP domain, the reconstructed PRSFDuet-M2-PEP plasmid was transformed and expressed in BL21(DE3) E. coli DE3 and cultured to OD600 = 0.3–0.4 for expression induced with 0.5 mM IPTG for 1 h at 37 °C. Harvested cell pellets were resuspended with Buffer A and were broken using a high-pressure homogenizer. Supernatant from the high-speed centrifuge was further applied to His-affinity purification. Buffer B in the presence of 300 mM imidazole was used to elute the ComC protein and concentrated for size-exclusion chromatography with Superdex 200 Increase 10/300 GL column (Cytiva). The concentration of purified ComC was determined by microvolume UV-Vis Spectrophotometer (Thermo Fisher) at 257 nm, based on the extinction coefficient of phenylalanine53. Tricine-SDS-PAGE54 was used for ComC characterization.For ComC and its mutants, the corresponding pET-15b-ComC plasmids were transformed in 55. In short, ComA was solubilized using 2% n-dodecyl-β-D-maltoside (DDM) (Anatrace), followed by MBP affinity purification. After binding to the beads, the protein was washed with ten-column volumes (CV) of Buffer B in the presence of 1 mg/ml peptidisc peptide (Peptidisc Biotech), and then eluted in Buffer C . MBP tag was cleaved by TEVP for further SEC purification with Superose 6 Increase 10/300 GL column (Cytiva). Purified peptidisc with ComA reconstituted was further concentrated for functional study.Peptidisc reconstitution was performed as previously described56 for intensities of protein bands. To test the effect of nucleotide on the peptidase activity, ATP, ATP analogs including ATPγS, AMP-PNP, and Vanadate, MgCl2 and EDTA were added at a final concentration around 3 mM.Purified ComC substrate was incubated with ComA enzymes at a molar ratio of 15:1 for indicated time and temperature. Total volume of the reaction system was 10 μL. ComC cleavage was analyzed by 12% Tricine-SDS-PAGE gel and Image J softwareE. coli BL21(DE3) and subsequently purified via Nickel-Nitrilotriacetic acid (Ni-NTA) and Size Exclusion Chromatography (SEC) using a Superdex 200 Increase 10/300 GL column (Cytiva) in a buffer containing 50 mM Tris-HCl (pH 7.5) and 100 mM NaCl. The MSP1D1 was then concentrated to a final concentration of 5 mg/ml. E. coli total lipid was prepared by solubilization to a concentration of 10 mg/ml using 200 mM sodium cholate. In parallel, ComA with a cleaved MBP tag, suspended in 0.02% DDM, was concentrated to 2 mg/ml.The membrane scaffold protein MSP1D1 was expressed in 57. Specifically, for ComA reconstitution, the molar ratio used was ComA: MSP1D1: E. coli total lipid=1:2:150. The final sodium cholate concentration in the mixture was set to 25 mM. Bio-beads SM2 (Bio-Rad) were prepared at a ratio of 100 mg per 1 ml of the mixture, added to initiate the reconstitution process, which works by extracting detergents from the system. After an hour of incubation at 4°C with constant rotation, the mixture was left to incubate overnight under the same conditions. Following the incubation period, the Bio-beads were removed, and the reconstituted sample was clarified via centrifugation. The sample then underwent separation on an SEC Superose 6 Increase 10/300 GL column in the same buffer used previously . The resulting samples were analyzed by SDS-PAGE and subsequently concentrated for further use.The nanodisc reconstitution procedure employed in this study closely follows the previously described method58 with minor modifications. First, 2 µg of purified ComA or ComA mutants was incubated with 50 µl reaction solution of 50 mM HEPES, pH 7.5, 10% glycerol, 100 mM NaCl, 2.5 mM ATP and 5 mM MgCl2, and incubated in a water bath at 37 °C for 30 min. The reaction was stopped by adding 50 µl 12% (w/v) SDS. Then the reaction was incubated at room temperature for 5 min after the addition of 100 µl solution containing 12% (w/v) ascorbic acid in 1 M HCl and 2% (w/v) ammonium molybdate in 1 M HCl. Finally, 150 µl solution containing 25 mM sodium citrate, 2% (w/v) sodium metaarsenite, and 2% (v/v) acetic acid was added, and incubated at room temperature for 10 min. Absorbance at 848 nm was measured using a multimodal microplate reader (HIDEX), and a standard curve of potassium phosphate ranging from 0.05 mM to 0.6 mM was generated to quantitate the amount of released phosphate. Reaction reagents were purchased from Sigma.All ATPase activity assays were performed using a previously described procedure2+ were added and incubated at 37 °C for 25 min before applying the mixture to cryo-EM grids. For the ATP/Zn2+ complex, final concentrations of 2 mM ATP/Zn2+ were added and incubated at 37 °C for 25 min before applying the mixture to cryo-EM grids. Grids were blotted for 3–4.5 s with 100% relative humidity and plunge-frozen in liquid ethane cooled by liquid nitrogen using a Vitrobot System (Gatan). Cryo-EM data were collected at liquid nitrogen temperature on a Titan Krios electron microscope (Thermo Fisher Scientific), equipped with a K3 Summit direct electron detector (Gatan) and GIF Quantum energy filter. All cryo-EM movies were recorded in counting mode with SerialEM459 with a slit width of 20 eV from the energy filter. Movies were acquired at a nominal magnification of 81,000×, corresponding to a calibrated pixel size of 0.858 Å on the specimen level. The dose rate was set to be 7.6 counts per physical pixel per second. The total exposure time of each movie was 6 s, resulting in a total dose of 46.4 electrons per Å2, fractionated into 40 frames (150 ms per frame). More details of electron microscopy data collection parameters are listed in Supplementary Table 3 μl of purified ComA or ComA mutants in detergents at a concentration of 4.5 mg ml/ml was applied to glow-discharged Quantifoil holey carbon grids . For the ATP complex, a final concentration of 20 µM ComC and 2 mM ATP were added into ComA (E647Q) and incubated at room temperature for 15 min before freezing. For the CSP complex, final concentrations of 20 µM ComC were added and incubated at 25 °C for 25 min before applying the mixture to cryo-EM grids. For the ATPγS complex, final concentrations of 20 µM ComC and 2 mM ATPγS/Mg42. Dose-fractionated movies collected using K3 Summit direct electron detector were subjected to motion correction using the program MotionCor260. A sum of all frames of each movie was calculated following a dose-weighting scheme, and used for all image-processing steps except defocus determination. CTFFIND461 was used to calculate defocus values of the summed images from all movie frames without dose weighting. Particle picking was performed using a semi-automated procedure with SAMUEL and SamViewer62.EM data were processed as previously described63, which included five different side views. We increased the contrast of the motion-corrected images by binning them four times, then applied a 48-pixel box for particle picking. An initial set of around 200 images was used for preliminary particle selection, followed by a basic 2D classification using SAMUEL64. The five most accurate 2D averages, exhibiting intact ABC transporter features, were chosen for another round of template-based particle picking on all motion-corrected images. The picked particles underwent screening through a 2D classification using SAMUEL. We selected particles from the 2D averages displaying clear ABC transporter features, specifically a rectangular-shaped TMD region. Averages showing two distinct dots at the center, corresponding to the top or bottom views of the sample, were also chosen, although side views were sufficient for subsequent 2D and 3D reconstructions.For particle picking, we used 2D averages from our prior FtsEX study as templates65. At each step, we only retained particles from the group displaying complete ABC transporter features and relatively high resolution compared to other classes in the results for further processing. All refinements followed the gold-standard procedure, in which two-half data sets were refined independently. The overall resolutions were estimated based on the gold-standard criterion of Fourier shell correlation (FSC) = 0.143. Local resolution variations were estimated from two half-data maps using ResMap66. The amplitude information of the final maps was corrected by “relion_post_process” using the program RELION365.Post-SAMUEL screening, selected particles were extracted from the dose-weighted, unbinned, motion-corrected images using a 256-pixel box size. The subsequent 2D and 3D classifications and 3D refinement were conducted using “relion_refine_mpi” in RELION32+ ComA were processed by CryoSPARC67. Dose-fractionated movies collected using K3 Summit direct electron detector were subjected to motion correction using the program MotionCor260. A sum of all frames of each movie was calculated following a dose-weighting scheme and used for all image-processing steps except defocus determination. CTFFIND461 was used to calculate defocus values of the summed images from all movie frames without dose weighting. Particle picking was performed using the blob picker followed by the template picker. 2D and 3D classification and 3D refinement were carried out using “2D classification”, “Ab-initial Reconstruction” and “Heterogenous Refinement”. Refinements were done using “Homogenous Refinement” and “Non-Uniform Refinement”. The overall resolutions were estimated based on the gold-standard criterion of Fourier shell correlation (FSC) = 0.143. Local resolution was estimated by “Local Resolution Estimation”.EM data of ATP/Zn68 using the crystal structure of PCAT1 (PDB ID: 4S0F) as the template. This initial model was rigid-body fitted to our cryo-EM maps in UCSF Chimera69, extensively rebuilt in Coot70, and refined using real space refinement in Phenix71. Restraints for ATP and ATPγS were generated with phenix_elbow program using its isomeric SMILES string files obtained from the PDB Chemical Component Dictionary through Ligand Expo. Ligands were manually docked into the cryo-EM maps in Coot, followed by iterative real-space refinements in Phenix. Final models were validated with statistics from Ramachandran plots, MolProbity scores, and Clash scores with the program in Phenix  (Thermofisher Scientific) at 37oC in 5% CO2. PCR products were synthesized using high fidelity Phusion DNA polymerase (NEB M0530S) and purified with the QIAquick PCR purification kit (Qiagen 28106) following the manufacturer’s protocol. Cells were transformed with cassettes assembled by isothermal assembly after the induction of natural competence. Transformants were selected on blood plates supplemented with the indicated antibiotics. Allelic replacements were performed using the Janus cassette (P-spec-rpsL)73. The resulting strains were validated by diagnostic PCR using GoTaq DNA polymerase  and Sanger sequencing. Antibiotics were purchased from Sigma-Aldrich and used at final concentrations of 0.3 µg/ml for erythromycin (Erm), 150 µg/ml for spectinomycin (Spec), and 300 µg/ml for streptomycin (Str).Primers used for the synthesis of ComC-HiBiT expressing strains are listed in Supplementary Table oC in 5% CO2. To prevent the secretion of CSP by the BlpAB transporter52, strains expressing ComC-HiBiT are in a ∆blpA background. When the optical density at 600 nm (OD600) reached 0.1 to 0.3, cultures were normalized to OD600 of 0.1, and exogenous CSP was added to induce natural competence. After an hour of induction, cultures were immediately placed on ice for 5 minutes. Cells were pelleted by centrifugation at 16,100x g for 2 minutes at 4oC. The supernatant fraction was collected and stored at 4oC. Pellets were washed with prechilled BHI twice and the washed pellets were then resuspended in 1 ml of prechilled BHI. The suspension was transferred to the lysis matric column homogenizer and disrupted 10X for 3 rounds of lysis. Each round consists of 3 cycles at 6 M/s for 40 seconds. Suspensions were placed on ice for 5 minutes after every round of homogenization. Cell suspensions were centrifuged at 16,100x g for 2 minutes at 4 oC to remove cellular debris. The ΔcomA and ΔcomC strains were used as negative controls. Supernatant and cell lysate samples of the ComC-HiBiT-expressing strains were aliquoted into a white 96-well plate (Corning). The HiBiT tag in the samples were quantified by adding an equal volume of the HiBiT Extracellular Detection Reagent  to each well. Luminescence was measured using a Tecan plate reader with the following settings: 1 s integration time, gain 135. Experiments were repeated three times, and the differences between mutants were evaluated by the Mann-Whitney U test.Mutants of ComA and ComC expressing ComC-HiBiT were generated with primers listed in Supplementary Table Purified ComA proteins were cross-linked in buffer A for pre-cleavage and buffer B for post-cleavage state with the addition of 1 mM disuccinimidyl sulfoxide , with 26:1 ratio for DSSO:protein by molarity in DMSO  for 60 min at 25 °C with shaking. Cross-linking was then quenched by adding 1 M Tris to the final concentration 80 µM. Cross-linked protein was then run into SDS-PAGE gel and the gel was stained with premixed colloidal coomassie G-250 staining solution (BioRad 1610786). Bands with molecular weight corresponding to cross-linked ComA were excised and proceeded with in-gel digestion. Briefly, gel bands were incubated three times with 50% ethanol in 100 mM TEAB, triethylammonium bicarbonate, with gentle agitation for 5 min. Then the gel was rehydrated with 100 mM TEAB followed by dehydration with 100% ethanol which was performed successively for two times. Excess ethanol was removed by short vacuum centrifugation. The gel pieces were then rehydrated with 20 mM TCEP, tris(2-carboxyethyl) phosphine, in 100 mM TEAB and incubated at 55 °C for 60 min and left to cool to room temperature. Next, 500 mM CAA, chloroacetamide, was added to final 55 mM concentration and incubated in the dark for 30 min. The gel was then washed twice with 100 mM TEAB and dried by vacuum centrifugation. 2 gel replicates were then digested by addition of trypsin (Pierce 90058), 2 µg in 100 mM TEAB, and 2 gel replicates were digested by addition of chymotrypsin (Promega V1061), 2 µg in 100 mM TEAB, 10 mM CaCl2, and incubated at 37 °C for 16 h. Protease digestion was quenched by the addition of TFA, trifluoroacetic acid, to a final concentration of 1% (v/v). Peptides were extracted by the addition of 30% ACN, acetonitrile, followed by 100% ACN. Extracted peptides were pooled and dried by vacuum centrifugation. Dried peptides were then resuspended in water with 0.1% formic acid and desalted with C18 stage tips (Empore C18 discs). Briefly, stage tips were activated with 100% ACN then equilibrated twice with water with 0.1% formic acid. Cross-linked peptides were loaded and the stage tip was washed twice with water with 0.1% formic acid. Peptides were eluted with 65% ACN, 0.1% formic acid and dried by vacuum centrifugation.74. Desalted cross-linked peptides were resuspended in 0.5% acetic acid, 0.06% TFA, 2% acetonitrile in water. 1 µg of cross-linked peptide was injected on a Easy-nLC 1200 (Thermo) chromatography system coupled to a Orbitrap Fusion Lumos mass spectrometer (Thermo) using a 50 cm × 75 µm inner diameter Easy-Spray reverse phase column  over a 60 min gradient from 0.1% formic acid in water to 40% acetonitrile with 0.1% formic acid. MS acquisition was performed with MS1 using Orbitrap 60 K resolution with scan range 350–1650 m/z. Precursor ions with 3–8 positive charge were selected for MS2 CID fragmentation with normalized collision energy of 30% and Orbitrap analyzer at 30 K resolution. MS3 HCD fragmentation was triggered based on targeted mass difference of DSSO (31.9721 Da) for 4 dependent scans with normalized collision energy of 30% and Ion Trap analyzer in rapid mode.Liquid chromatography-mass spectrometry (LC-MS) acquisition of cross-linked peptides was performed as previously describedq value ≤ 0.01. Cross-links were visualized by xiVIEW. Raw mass spectrometry spectra and search data were uploaded to the jPost repository75.Thermo raw files were searched against fasta file containing ComA protein sequence using Metamorpheus v0.0.318 with a calibration search with precursor mass tolerance of 10ppm and product mass tolerance of 20ppm. Cross-link search was performed for DSSO on K, S, T, Y amino acids for MS2 CID and MS3 HCD scans with 3 maximum missed cleavages, trypsin protease and fixed modification for carbamidomethyl (C) and variable modifications for oxidation (M), deamidation  DSSO hydrolyzed by water and hydrolyzed by Tris , DSSO alkene and thiol . Crosslinks from intralinks result files were filtered for Further information on research design is available in the Supplementary InformationPeer Review FileReporting SummarySource Data


Modern machine learning (ML) and deep learning (DL) techniques using high-dimensional data representations have helped accelerate the materials discovery process by efficiently detecting hidden patterns in existing datasets and linking input representations to output properties for a better understanding of the scientific phenomenon. While a deep neural network comprised of fully connected layers has been widely used for materials property prediction, simply creating a deeper model with a large number of layers often faces with vanishing gradient problem, causing a degradation in the performance, thereby limiting usage. In this paper, we study and propose architectural principles to address the question of improving the performance of model training and inference under fixed parametric constraints. Here, we present a general deep-learning framework based on branched residual learning (BRNet) with fully connected layers that can work with any numerical vector-based representation as input to build accurate models to predict materials properties. We perform model training for materials properties using numerical vectors representing different composition-based attributes of the respective materials and compare the performance of the proposed models against traditional ML and existing DL architectures. We find that the proposed models are significantly more accurate than the ML/DL models for all data sizes by using different composition-based attributes as input. Further, branched learning requires fewer parameters and results in faster model training due to better convergence during the training phase than existing neural networks, thereby efficiently building accurate models for predicting materials properties. The process has been catalyzed by the increase in the availability of large-scale datasets through experiments and first-principles calculations such as high throughput density functional theory (DFT) computations17 and the ease to access and analyze them by using various data mining tools19. Such application of ML techniques has attracted significant attention throughout the materials science research community and therefore led to the new paradigm of materials informatics25 which has helped materials scientists better understand materials and predict their properties.Modern machine learning (ML) techniques using high-dimensional data representations have seen widespread success in the field of materials science owing to their ability to efficiently detect hidden patterns in existing datasets and link input representations to output properties for a better understanding of the scientific phenomenon and accelerating materials discovery process26. Although limited, we have also seen a growing application of more advanced deep learning (DL) techniques in recent years29. Harvard Energy Clean Project by Pyzer–Knapp et al.8 used a three-layer network for predicting the power conversion efficiency of organic photovoltaic materials. Montavon et al.29 predicted multiple electronic ground-state and excited-state properties using a model trained on a four-layer network on a database of around 7000 organic compounds. Zhou et al.27 used high-dimensional vectors learned using Atom2Vec along with a fully connected network with a single hidden layer to predict formation energy. ElemNet28 used a 17-layered architecture to learn formation energy from elemental composition but has shown performance degradation beyond that depth. Some research performed domain knowledge-based model engineering within a deep learning context in materials science for predictive modeling33. Montavon et al.26 trained a four-layer network on a database of around 7000 organic compounds to predict multiple electronic ground-state and excited-state properties. SchNet30 incorporated continuous filter convolutional layers to model quantum interactions in molecules for the total energy and inter-atomic forces which follow fundamental quantum chemical principles. CheMixNet31 has tried to learn molecular properties from the molecular structures of organic materials by applying deep learning methods. Boomsma and Frellsen introduced the idea of spherical convolution in molecular modeling by making use of the structural environments within proteins. Jha et al.32 developed a deep learning framework to predict the crystal orientations of polycrystalline materials from their electron back-scatter diffraction patterns. Work in33 performs deep learning by making deeper layered architecture ranging from 10-layer to 48-layer composed of skip connections after every layer using composition and structure based representations to predict materials properties across different datasets. There also have been several efforts to learn either the atomic interaction or the material embeddings using graph-based networks from the crystal structure and composition38. SchNet is extended in34 where the authors used an edge update network to allow for neural message passing between atoms for better property prediction for molecules and materials. Crystal graph convolution neural networks (CGCNN)35 directly learn material properties via the connection of atoms in the crystal structure of the crystalline materials, providing an interpretable representation. MatErials Graph Network (MEGNet)36 was developed as a universal model for the property prediction of molecules and crystals. Goodall and Lee37 developed an architecture that takes stoichiometric attributes instead of crystal attributes as inputs along with matscholar embedding obtained from material science literature using advanced natural language processing algorithms to learn appropriate materials descriptors from data using a graph-based neural network composed of message-passing layer and fully-connected layers. Atomistic Line Graph Neural Network (ALIGNN)38 combines atom, bond, and angle-based information obtained from the structure of the materials to obtain high-accuracy models for improved materials property prediction.Conventionally, traditional ML techniques such as Random Forest, Support Vector Machine, and Decision Tree, have often been applied in materials informatics applications39, here, we focus on addressing the general issue of how to efficiently build deep neural network architectures for more robust and accurate predictive performance by imposing a parametric constraint (17-layers in our case) and utilizing the available limited computational resources effectively and efficiently. For that, we analyze and propose design principles for a time and parameter-efficient deep learning framework composed of deep neural networks that can predict materials properties using numerical vector-based representations. Since the model architectures for the regression problem are composed of fully connected layers, it is highly non-linear and learning the mapping from input to output is comparatively more challenging than the classification problem. To maximize accuracy and minimize training time under parametric constraints using a neural network composed of fully connected layers, we present a novel approach based on a combination of residual learning with skip connections around a stack of multiple layers 42 and branched architecture45, which were originally proposed for classification problems for text or image classification.In general, introducing complex input attributes, network components, and architecture design has been shown to produce more accurate predictive models for materials properties prediction tasks. However, these improvements require higher computational resources and training time which is undesirable, making it hard to leverage such complex components to build predictive models. Hence, rather than focusing on introducing complex input attributes, network components, and architectural designs in a bid to boost model performance as done in recent works46 with a branched structure in the initial layers. BRNet uses BNet as the base network and adds residual connections after each stack for better convergence during the training. BNet and BRNet architectures are designed for the prediction task of learning the formation energy from a vector-based material representation composed of 86 features representing a composition-based elemental fraction as the model input. When trained using 15, BNet and BRNet achieved a mean absolute error (MAE) of 0.042 eV/atom and 0.041 eV/atom respectively compared to an MAE of 0.149 eV/atom using AutoML47. A conference version of this work appeared in Gupta et al.48; the current article significantly expands on the conference paper with additional modeling experiments on more datasets, and subsequent analysis of results and insights. We compare our proposed architectures against traditional ML models, and multiple baselines using deep neural network architectures for regression (made using 17 fully connected layers): ElemNet28 with dropout at variable intervals of fully connected layers, and individual residual network (IRNet)33 with residual connections, batch normalization, and ReLU activation function after each layer. We provide a detailed evaluation and analysis of BNet/BRNet on various publicly available DFT-computed and experimental materials datasets and show that branched networks consistently outperform other ML models and DL networks on the materials property prediction tasks. We also observe that the use of branching leads to faster convergence than existing approaches, while reducing the number of model parameters significantly. BRNet and BNet leverage a simple and intuitive approach of introducing branching with/without residual connections after each layer without using any domain-dependent model engineering, which makes it appealing to researchers working not only on materials but other scientific domains to leverage it for their predictive modeling tasks.We introduce a novel approach to leverage branching in neural networks with and without residual connections for each individual layer (BRNet and BNet). BNet comprises of a series of stacks, each composed of a fully connected layer and LeakyReLU49 with four properties, Automatic Flow of Materials Discovery Library (AFLOWLIB)50 with four properties, Materials Project (MP)14 with four properties, Joint Automated Repository for Various Integrated Simulations (JARVIS) with five properties17, Kingsbury Experimental Formation Enthalpy (KEFE)51 with 1 property, and Kingsbury Experimental Band Gap (KEBG)51 with 1 property. DFT-computed datasets  were downloaded from the website of the database, and experimental datasets (KEFE and KEBG) were obtained using Matminer18. The relevant information about the datasets used to evaluate our methods are shown in Table We use six datasets of DFT-computed and experimental properties in this work: Open Quantum Materials Database (OQMD)In each of the datasets, materials property values correspond to the lowest formation energy among all compounds with the same composition, representing its most stable crystal structure. The datasets are randomly split with a fixed random seed into training, validation, and test sets in the ratio of 81:9:10.39. Given that computational resources are usually limited, and oftentimes we only see marginal improvements in the accuracy of the model as compared to the exponential increase in the number of parameters added to the architecture of the deep neural network33, analyzing design principle to improve the accuracy of the model under parametric constraints might be a more practical and useful goal to work towards. We thus explore a novel approach of using branching at the early stage of the deep neural network architecture composed of fully connected layers to maximize the performance of the model under a parametric constraint. Note that parametric constraint in this work refers to using a fixed number of layers for constructing the architecture of the deep neural network, i.e., 17 layers in our case. We design two deep neural networks (BRNet and BNet) which contain branching with/without residual connections where both the proposed networks take a numerical vector-based representation as model input to predict the materials property of interest.Many of the existing DL works in materials science focus on introducing complex input attributes, network components, and architectural designs to boost model performance46 as the activation function with (BRNet) and without (BNet) residual connections. To demonstrate the impact of our approach, we compare our proposed architectures against traditional ML models and multiple existing architectures (ElemNet and IRNet) comprised of the same number of layers (17 fully connected layers in our case) for fair comparison in terms of parametric constraint. In this study, we give ElemNet and IRNet architecture different sets of inputs for model training than what was previously used in their respective works to test the generalized performance of the different architectures. For a detailed description of the existing architectures (ElemNet and IRNet), the reader is referred to their respective publications33. We show the performance comparison of the proposed architectures with other existing deep neural networks for formation energy as the materials property and composition-based elemental fraction as the model input using various datasets in Table BRNet and BNet architectures are designed for the prediction task of learning the formation energy from a numerical vector-based representation composed of 86 features representing a composition-based elemental fraction as the model input. The deep neural network architectures are composed of fully connected layers, where each fully connected layer is followed by LeakyReLUTable Next, we demonstrate the significance of branching on the prediction modeling tasks of “Other materials properties”. We train BRNet and BNet for predicting materials properties from numerical vector-based representation composed of 86 features representing composition-based elemental fractions as the model input. To illustrate the impact of branching, we also compare the performance of our proposed networks against traditional ML algorithms, ElemNet, and IRNet.We observe in Table Figure 3 for model input instead of 86 elemental fractions (EF)28. Table Next, we illustrate the versatility of leveraging branching in the deep neural network architecture by building models with different composition-based attributes as model input. We train BRNet, BNet, ElemNet, and IRNet similar to the previous analysis, but use 145 composition-based physical attributes52. Hence, we will only use the numerical vector-based representation composed of composition-based elemental fractions for further analysis.From Table In our analysis, we generally observe the benefit of leveraging branched deep neural network architecture which tends to perform better than other DL networks and traditional ML models. Here, we investigate the performance of the proposed networks against the experimental datasets which are usually small in size as compared to the DFT-computed datasets. We train traditional ML models and DL models using numerical vector-based representation composed of 86 features representing composition-based elemental fractions as the model input.From Table Next, we perform performance analysis using a bubble chart, prediction error chart, and cumulative distribution function (CDF) of the prediction errors. We mainly compare the accuracy and training time of different deep neural networks comprised of the same number of layers when trained using numerical vector-based representation composed of composition-based elemental fractions on formation energy from four different DFT-computed datasets .Figure Figure We presented a novel approach to leverage the concept of branching in deep neural network architecture to enable better performance for materials property prediction under parametric constraints. To illustrate the benefit of leveraging the proposed approach, we built a general deep learning framework composed of branched deep neural network architectures BRNet and BNet. To compare the performance of the proposed models, we use traditional ML algorithms and existing deep neural networks ElemNet and IRNet, which consist of the same number of layers in their architecture to ensure a fair comparison of parametric constraints. The proposed BRNet and BNet architectures were designed (optimized) for the task of predicting formation energy using a numerical vector-based representation composed of 86 composition-derived elemental fractions as the model input. On the design problem, the proposed models leveraging the proposed design approach significantly outperformed the traditional ML algorithms, ElemNet and IRNet. We demonstrated the efficacy of the proposed approach by evaluating and comparing these DL model architectures against ElemNet, IRNet, and traditional ML algorithms on a variety of materials properties available across multiple materials datasets. Furthermore, we demonstrated that the presented DL model architectures leveraging the proposed approach are versatile in their vector-based model input by evaluating prediction models for different materials properties using different numerical vector-based representations, i.e., composition-derived 145 physical attributes and composition-derived 86 elemental fractions. The proposed approach outperforms other ML/DL models in terms of model accuracy irrespective of the size of the materials property being analyzed, where branching provides a better capability to capture the mapping between the given input material representation and the output property. In general, the training time of a deep neural network model depends on the given prediction task (model inputs and model output), the size of the training dataset, and the architecture and depth of the neural networks (number of model parameters). In our case, as the depth of the neural networks (the number of layers used to construct the architecture) is fixed, the complexity and components used to construct the architecture is the only factor that can affect the training time. We see that the use of branched architecture helps in a significant reduction of training time as compared to other baseline architectures used for comparison. Additionally, to check the robustness of the proposed branched architectures even further, we perform empirical and statistical analysis to explore the benefits of branching in deep neural networks. In the empirical analysis, we perform predictive analysis by changing the location of the branch and the number of branches for a given location under fixed parametric constraints , thus rejecting the null hypothesis at p-value to compare the test MAEs of BNet and BRNet and obtain the p-value < 0.05 for 3 out of 4 cases. For the MP dataset, although BNet performed better as compared to BRNet in terms of mean ± standard deviation of the test MAE, we obtained the p-value > 0.05, which shows that the results are not significantly better statistically. This shows that, in general, BRNet tends to perform at least comparable or better than BNet. Since the proposed approach of leveraging branching the deep neural network architecture in BRNet and BNet does not depend on any particular material representation/embedding as model input, we expect that it can also be used to improve the performance of other DL works leveraging other types of materials representations in materials science and other scientific domains. The proposed approach of branched deep neural network architecture is conceptually simple to implement and build upon. The BRNet framework code is publicly available at https://github.com/GuptaVishu2002/BRNet.Next, we perform statistical analysis where we estimate a one-tailed entclass1pt{minima46 as the activation function. The simplest instantiation of this architecture adds no residual connections and thus learns simply the approximate mapping from input to output which we refer to as Branched Network (BNet). We also create a deep neural network architecture with residual connection after every sequence, so that each sequence needs only to learn the residual mapping between its input and output. The residual connection has the effect of making the regression learning task easier and providing a smooth flow of gradients between layers. We refer to this deep neural network as a branched residual network (BRNet). The implementation of all the models used in this work is publicly available at https://github.com/GuptaVishu2002/BRNet.The design approach and mathematical formulation for branched deep neural network architecture is illustrated in Fig. 54 and Keras55. We found the best hyperparameters to be Adam56 as the optimizer with a mini-batch size of 32, alearning rate of 0.0001, mean absolute error as loss function, and LeakyReLU46 as activation function after every fully connected layer . Rather than training the model for a specific number of epochs, we used early stopping with patience of 100 epochs, meaning that we stopped training when the performance did not improve in 100 epochs. For traditional ML models, we used an AutoML library called hyperopt sklearn47 to find the best-performing ML model implementations and employed mean absolute error (MAE) as loss function and error metric. For the number of model parameters and model size used by each of the deep learning models, please refer to Supplementary Table We implement the deep learning models with Python and TensorFlow 2Supplementary Information.


Nally et al.  conducte2) between intervention and control groups […]” and “Forest plot for standardised mean difference of change in BMI z-score between intervention and control groups […]” [standardized mean differences of change scores, but rather raw mean differences of post-intervention values. The data shown in Figure 4 are post-intervention values and not change scores.First, the results in Figures 3 and 4 do not match the methods and figure captions. The captions of Figures 3 and 4 read “Forest plot for standardised mean difference of change in BMI ” . Althoug