LZerD Protein-Protein Docking Webserver Enhanced With de novo Structure Prediction

A Versatile Protein Scaffold Engineered for the Hierarchical Assembly of Robust and Highly Active Enzymes

Scaffold proteins play immense roles in bringing enzymes together to enhance their properties. However, the direct fusion of scaffold with bulky guest enzymes may disrupt the assembly process or diminish catalytic efficiency. Most self-assembling protein scaffolds are engineered to form structures beforehand, and then carry guest proteins via different conjugation strategies in vitro. Here, a robust self-assembling scaffold is presented, engineered from Methanococcus jannaschii using disulfide bonds, which efficiently assembles bulky enzymes into higher-order helices without additional chemistry or bio-conjugation in vitro. When fused directly with monomeric Endo-1,4-beta-xylanase A, the catalytic efficiency of the guest enzyme increased by 2.5 times with enhanced thermostability. Additionally, integrating the scaffold with the multimeric metalloenzyme nitrile hydratase overcame the typical stability-activity trade-off of such industrial enzyme, yielding three-fold higher activity and 28-fold higher thermostability. Structural analyses suggest that the artificially made helical twist structures create new interface interactions and provide a concentration of active sites of guest enzymes. Further fusion of fluorescent protein pairs with the scaffold exhibited a 12-fold higher FRET efficiency, suggesting its potential for dual-enzyme cascade applications. Overall, this study showcases a simple yet powerful protein scaffold that organizes guest enzymes into hierarchical structures with enhanced catalytic performance.

Integrative Protein Assembly With LZerD and Deep Learning in CAPRI 47-55

We report the performance of the protein complex prediction approaches of our group and their results in CAPRI Rounds 47-55, excluding the joint CASP Rounds 50 and 54, as well as the special COVID-19 Round 51. Our approaches integrated classical pipelines developed in our group as well as more recently developed deep learning pipelines. In the cases of human group prediction, we surveyed the literature to find information to integrate into the modeling, such as assayed interface residues. In addition to any literature information, generated complex models were selected by a rank aggregation of statistical scoring functions, by generative model confidence, or by expert inspection. In these CAPRI rounds, our human group successfully modeled eight interfaces and achieved the top quality level among the submissions for all of them, including two where no other group did. We note that components of our modeling pipelines have become increasingly unified within deep learning approaches. Finally, we discuss several case studies that illustrate successful and unsuccessful modeling using our approaches.

Structural basis for the asymmetric binding of coactivator SRC1 to FXR-RXRα and allosteric communication within the complex

Farnesoid X receptor (FXR) is a promising target for treatment of metabolic associated fatty liver disease (MAFLD). In this study, we employed an integrative approach to investigate the interaction between FXR-RXRα-DNA complex and the entire coactivator SRC1-NRID (nuclear receptor interaction domain). We constructed a multi-domain model of FXR-RXRα-DNA, highlighting the interface between FXR-DBD and LBD. Using HDX-MS, XL-MS, and biochemical assays, we revealed the allosteric communications in FXR-RXRα-DNA upon agonist and DNA binding. We then demonstrated that SRC1 binds only to the coactivator binding surface of FXR within the FXR-RXRα heterodimer, with the NR-box2 and NR-box3 of SRC1 as the key binding motifs. Our findings, which provide the first model of SRC1-NRID in complex with FXR-RXRα-DNA, shed light on the molecular mechanism through which the coactivator asymmetrically interacts with nuclear receptors and provide structural basis for further understanding the function of FXR and its implications in diseases.

Unraveling the Binding Mode of TSC2-Rheb through Protein Docking and Simulations

Proteasome inhibitors (PIs) constitute the first line of therapy for multiple myeloma (MM). Despite the impressive clinical efficacy, MM remains fatal due to the development of drug resistance over time. During MM progression, stress responses to hypoxia and PIs suppress mammalian target of rapamycin complex 1 (mTORC1) activity by releasing tuberous sclerosis complex 2 (TSC2), which deactivates Ras homologue enriched in brain (Rheb), a crucial regulator of mTORC1. The efficacy of PIs targeting MM is enhanced when mTORC1 is hyperactivated. We thus propose that the inhibition of TSC2 will improve the efficacy of PIs targeting MM. To the best of our knowledge, no cocrystallized structure of the TSC2-Rheb complex has been reported. We therefore developed a representative model using the individual structures of TSC2 (PDB: 7DL2) and Rheb (PDB: 1XTS). Computational modeling involving an extensive protein-protein docking consensus approach was performed to determine the putative binding mode of TSC2-Rheb. The proposed docking poses were refined, clustered, and evaluated by MD simulations to explore the conformational dynamics and protein mobility, particularly at the drug-binding interface of TSC2-Rheb. Our results agree with the suggested binding mode of TSC2-Rheb previously reported in the literature. The results reported herein establish a basis for the development of new inhibitors blocking the binding of TSC2 and Rheb, aiming to reinstate mTORC1 activation and facilitate improved efficacy of PIs against multiple myeloma.

Computational epitope-based vaccine design with bioinformatics approach; a review

The significance of vaccine development has gained heightened importance in light of the COVID-19 pandemic. In such critical circumstances, global citizens anticipate researchers in this field to swiftly identify a vaccine candidate to combat the pandemic's root cause. It is widely recognized that the vaccine design process is traditionally both time-consuming and costly. However, a specialized subfield within bioinformatics, known as "multi-epitope vaccine design" or "reverse vaccinology," has significantly decreased the time and costs of the vaccine design process. The methodology reverses itself in this subfield and finds a potential vaccine candidate by analyzing the pathogen's genome. Leveraging the tools available in this domain, we strive to pinpoint the most suitable antigen for crafting a vaccine against our target. Once the optimal antigen is identified, the next step involves uncovering epitopes within this antigen. The immune system recognizes particular areas of an antigen as epitopes. By characterizing these crucial segments, we gain the opportunity to design a vaccine centered around these epitopes. Subsequently, after identifying and assembling the vital epitopes with the assistance of linkers and adjuvants, our vaccine candidate can be formulated. Finally, employing computational techniques, we can thoroughly evaluate the designed vaccine. This review article comprehensively covers the entire multi-epitope vaccine development process, starting from obtaining the pathogen's genome to identifying the relevant vaccine candidate and concluding with an evaluation. Furthermore, we will delve into the essential tools needed at each stage, comparing and introducing them.

EquiRank: Improved protein-protein interface quality estimation using protein language-model-informed equivariant graph neural networks

Quality estimation of the predicted interaction interface of protein complex structural models is not only important for complex model evaluation and selection but also useful for protein-protein docking. Despite recent progress fueled by symmetry-aware deep learning architectures and pretrained protein language models (pLMs), existing methods for estimating protein complex quality have yet to fully exploit the collective potentials of these advances for accurate estimation of protein-protein interface. Here we present EquiRank, an improved protein-protein interface quality estimation method by leveraging the strength of a symmetry-aware E(3) equivariant deep graph neural network (EGNN) and integrating pLM embeddings from the pretrained ESM-2 model. Our method estimates the quality of the protein-protein interface through an effective graph-based representation of interacting residue pairs, incorporating a diverse set of features, including ESM-2 embeddings, and then by learning the representation using symmetry-aware EGNNs. Our experimental results demonstrate improved ranking performance on diverse datasets over existing latest protein complex quality estimation methods including the top-performing CASP15 protein complex quality estimation method VoroIF_GNN and the self-assessment module of AlphaFold-Multimer repurposed for protein complex scoring and across different performance evaluation metrics. Additionally, our ablation studies demonstrate the contributions of both pLMs and the equivariant nature of EGNN for improved protein-protein interface quality estimation performance. EquiRank is freely available at https://github.com/mhshuvo1/EquiRank.

Bioinformatic Resources for Exploring Human-virus Protein-protein Interactions Based on Binding Modes

Historically, there have been many outbreaks of viral diseases that have continued to claim millions of lives. Research on human-virus protein-protein interactions (PPIs) is vital to understanding the principles of human-virus relationships, providing an essential foundation for developing virus control strategies to combat diseases. The rapidly accumulating data on human-virus PPIs offer unprecedented opportunities for bioinformatics research around human-virus PPIs. However, available detailed analyses and summaries to help use these resources systematically and efficiently are lacking. Here, we comprehensively review the bioinformatic resources used in human-virus PPI research, and discuss and compare their functions, performance, and limitations. This review aims to provide researchers with a bioinformatic toolbox that will hopefully better facilitate the exploration of human-virus PPIs based on binding modes.

Development and tissue specific expression of RAPGEF1 (C3G) transcripts having exons encoding disordered segments with predicted regulatory function

BACKGROUND

The ubiquitously expressed Guanine nucleotide exchange factor, RAPGEF1 (C3G), is essential for early development of mouse embryos. It functions to regulate gene expression and cytoskeletal reorganization, thereby controlling cell proliferation and differentiation. While multiple transcripts have been predicted, their expression in mouse tissues has not been investigated in detail.

METHODS & RESULTS

Full length RAPGEF1 isoforms primarily arise due to splicing at two hotspots, one involving exon-3, and the other involving exons 12-14 incorporating amino acids immediately following the Crk binding region of the protein. These isoforms vary in expression across embryonic and adult organs. We detected the presence of unannotated, and unpredicted transcripts with incorporation of cassette exons in various combinations, specifically in the heart, brain, testis and skeletal muscle. Isoform switching was detected as myocytes in culture and mouse embryonic stem cells were differentiated to form myotubes, and embryoid bodies respectively. The cassette exons encode a serine-rich polypeptide chain, which is intrinsically disordered, and undergoes phosphorylation. In silico structural analysis using AlphaFold indicated that the presence of cassette exons alters intra-molecular interactions, important for regulating catalytic activity. LZerD based docking studies predicted that the isoforms with one or more cassette exons differ in interaction with their target GTPase, RAP1A.

CONCLUSIONS

Our results demonstrate the expression of novel RAPGEF1 isoforms, and predict cassette exon inclusion as an additional means of regulating RAPGEF1 activity in various tissues and during differentiation.

Quercetin and taxifolin inhibits TMPRSS2 activity and its interaction with EGFR in paclitaxel-resistant breast cancer cells: An in silico and in vitro study

Transmembrane protease/serine (TMPRSS2), a type II transmembrane serine protease, plays a crucial role in different stages of cancer. Recent studies have reported that the triggering epidermal growth factor receptor (EGFR) activation through protease action promotes metastasis. However, there are no reports on the interaction of TMPRSS2 with EGFR, especially in triple-negative triple negative (TNBC). The current study investigates the unexplored interaction between TMPRSS2 and EGFR, which are key partners mediating metastasis. This interaction is explored for potential targeting using quercetin (QUE) and taxifolin (TAX). TMPRSS2 expression patterns in breast cancer (BC) tissues and subtypes have been predicted, with the prognostic significance assessed using the GENT2.0 database. Validation of TMPRSS2 expression was performed in normal and TNBC tissues, including drug-resistant cell lines, utilizing GEO datasets. TMPRSS2 was further validated as a predictive biomarker for FDA-approved chemotherapeutics through transcriptomic data from BC patients. The study demonstrated the association of TMPRSS2 with EGFR through in silico analysis and validates the findings in TNBC cohorts using the TIMER2.0 web server and the TCGA dataset through C-Bioportal. Molecular docking and molecular dynamic simulation studies identified QUE and TAX as best leads targeting TMPRSS2. They inhibited cell-free TMPRSS2 activity like clinical inhibitor of TMPRSS2, Camostat mesylate. In cell-based assays focused on paclitaxel-resistant TNBC (TNBC/PR), QUE and TAX demonstrated potent inhibitory activity against extracellular and membrane-bound TMPRSS2, with low IC50 values. Furthermore, ELISA and cell-based AlphaLISA assays demonstrated that QUE and TAX inhibit the interaction of TMPRSS2 with EGFR. Additionally, QUE and TAX exhibited significant inhibition of proliferation and cell cycle accompanied by notable alterations in the morphology of TNBC/PR cells. This study provides valuable insights into potential of QUE and TAX targeting TMPRSS2 overexpressing TNBC.

Design of a novel multiepitope vaccine with CTLA-4 extracellular domain against Mycoplasma pneumoniae: A vaccine-immunoinformatics approach

BACKGROUND

Community-acquired pneumonia often stems from the macrolide-resistant strain of Mycoplasma pneumoniae, yet no effective vaccine exists against it.

METHODS

This study proposes a vaccine-immunoinformatics strategy for Mycoplasma pneumoniae and other pathogenic microbes. Specifically, dominant B and T cell epitopes of the Mycoplasma pneumoniae P30 adhesion protein were identified through immunoinformatics method. The vaccine sequence was then constructed by coupling with CTLA-4 extracellular region, a novel molecular adjuvant for antigen-presenting cells. Subsequently, the vaccine's physicochemical properties, antigenicity, and allergenicity were verified. Molecular dynamics modeling was employed to confirm interaction with TLR-2, TLR-4, B7-1, and B7-2. Finally, the vaccine underwent in silico cloning for expression.

RESULTS

The vaccine exhibited both antigenicity and non-allergenicity. Molecular dynamics simulation, post-docking with TLR-2, TLR-4, B7-1, and B7-2, demonstrated stable interaction between the vaccine and these molecules. In silico cloning confirmed effective expression of the vaccine gene in insect baculovirus vectors.

CONCLUSION

This vaccine-immunoinformatics approach holds promise for the development of vaccines against Mycoplasma pneumoniae and other pathogenic non-viral and non-bacterial microbes.

Genome-wide characterization of the NBLRR gene family provides evolutionary and functional insights into blast resistance in pearl millet (Cenchrus americanus (L.) Morrone)

MAIN CONCLUSION

The investigation is the first report on genome-wide identification and characterization of NBLRR genes in pearl millet. We have shown the role of gene loss and purifying selection in the divergence of NBLRRs in Poaceae lineage and candidate CaNBLRR genes for resistance to Magnaporthe grisea infection. Plants have evolved multiple integral mechanisms to counteract the pathogens' infection, among which plant immunity through NBLRR (nucleotide-binding site, leucine-rich repeat) genes is at the forefront. The genome-wide mining in pearl millet (Cenchrus americanus (L.) Morrone) revealed 146 CaNBLRRs. The variation in the branch length of NBLRRs showed the dynamic nature of NBLRRs in response to evolving pathogen races. The orthology of NBLRRs showed a predominance of many-to-one orthologs, indicating the divergence of NBLRRs in the pearl millet lineage mainly through gene loss events followed by gene gain through single-copy duplications. Further, the purifying selection (Ka/Ks < 1) shaped the expansion of NBLRRs within the lineage of pear millet and other members of Poaceae. Presence of cis-acting elements, viz. TCA element, G-box, MYB, SARE, ABRE and conserved motifs annotated with P-loop, kinase 2, RNBS-A, RNBS-D, GLPL, MHD, Rx-CC and LRR suggests their putative role in disease resistance and stress regulation. The qRT-PCR analysis in pearl millet lines showing contrasting responses to Magnaporthe grisea infection identified CaNBLRR20, CaNBLRR33, CaNBLRR46 CaNBLRR51, CaNBLRR78 and CaNBLRR146 as putative candidates. Molecular docking showed the involvement of three and two amino acid residues of LRR domains forming hydrogen bonds (histidine, arginine and threonine) and salt bridges (arginine and lysine) with effectors. Whereas 14 and 20 amino acid residues of CaNBLRR78 and CaNBLRR20 showed hydrophobic interactions with 11 and 9 amino acid residues of effectors, Mg.00g064570.m01 and Mg.00g006570.m01, respectively. The present investigation gives a comprehensive overview of CaNBLRRs and paves the foundation for their utility in pearl millet resistance breeding through understanding of host-pathogen interactions.

Investigating the Protein-Based Therapeutic Relationship between Honey Protein SHP-60 and Bevacizumab on Angiogenesis: Exploring the Synergistic Effect through In Vitro and In Silico Analysis

Honey is a natural product produced by honeybees, which has been used not only as food but also as a medicine by humans for thousands of years. In this study, 60 kDa protein was purified from Pakistani Sidr honey named as SHP-60 (Sidr Honey Protein-60), and its antioxidant potential and the effect of Bevacizumab with purified protein on in vitro angiogenesis using human umbilical vein endothelial cells (HUVEC) were investigated. We further validated the molecular protein-protein (SHP-60 with Bevacizumab) interactions through in silico analysis. It showed very promising antioxidant activity by reducing 2,2-diphenyl-1-picrylhydrazyl free radicals with a maximum of 83% inhibition at 50 μM and an IC50 of 26.45 μM statistically significant (**p < 0.01). Angiogenesis is considered a hallmark of cancer, and without it, the tumor cannot grow or metastasize. Bevacizumab, SHP-60, and both in combination were used to treat HUVEC, and the MTT assay was used to assess cell viability. To demonstrate in vitro angiogenesis, HUVEC was grown on Geltrex, and the formation of endotubes was examined using a tube formation assay. HUVEC viability was dose-dependently decreased by Bevacizumab, SHP-60, and both together. Bevacizumab and SHP-60 both inhibited angiogenesis in vitro, and their combination displayed levels of inhibition even higher than those of Bevacizumab alone. We investigated the protein-protein molecular docking interactions and molecular dynamics simulation analysis of MRJP3 (major royal jelly protein 3) similar to SHP-60 in molecular weight with both the heavy chain (HC) and light chain (LC) of Bevacizumab. We found significant interactions between the LC and MRJP3, indicating that ASN468, GLN470, and ASN473 of MRJP3 interact with SER156, SER159, and GLU161 of LC of Bevacizumab. The integration of experimental data and computational techniques is believed to improve the reliability of the findings and aid in future drug design.

Assembly of Protein Complexes in and on the Membrane with Predicted Spatial Arrangement Constraints

Membrane proteins play crucial roles in various cellular processes, and their interactions with other proteins in and on the membrane are essential for their proper functioning. While an increasing number of structures of more membrane proteins are being determined, the available structure data is still sparse. To gain insights into the mechanisms of membrane protein complexes, computational docking methods are necessary due to the challenge of experimental determination. Here, we introduce Mem-LZerD, a rigid-body membrane docking algorithm designed to take advantage of modern membrane modeling and protein docking techniques to facilitate the docking of membrane protein complexes. Mem-LZerD is based on the LZerD protein docking algorithm, which has been constantly among the top servers in many rounds of CAPRI protein docking assessment. By employing a combination of geometric hashing, newly constrained by the predicted membrane height and tilt angle, and model scoring accounting for the energy of membrane insertion, we demonstrate the capability of Mem-LZerD to model diverse membrane protein-protein complexes. Mem-LZerD successfully performed unbound docking on 13 of 21 (61.9%) transmembrane complexes in an established benchmark, more than shown by previous approaches. It was additionally tested on new datasets of 44 transmembrane complexes and 92 peripheral membrane protein complexes, of which it successfully modeled 35 (79.5%) and 15 (16.3%) complexes respectively. When non-blind orientations of peripheral targets were included, the number of successes increased to 54 (58.7%). We further demonstrate that Mem-LZerD produces complex models which are suitable for molecular dynamics simulation. Mem-LZerD is made available at https://lzerd.kiharalab.org.

In vivo biological validation of in silico analysis: A novel approach for predicting the effects of TLR4 exon 3 polymorphisms on brucellosis

The role of the Toll-like receptor 4 (TLR4) is of recognising intracellular and extracellular pathogens and of activating the immune response. This process can be compromised by single nucleotide polymorphisms (SNPs) which might affect the activity of several TLRs. The aim of this study is of ascertaining whether SNPs in the TLR4 of Bubalus bubalis infected by Brucella abortus, compromise the protein functionality. For this purpose, a computational analysis was performed. Next, computational predictions were confirmed by performing genotyping analysis. Finally, NMR-based metabolomics analysis was performed to identify potential biomarkers for brucellosis. The results indicate two SNPs (c. 672 A > C and c. 902 G > C) as risk factor for brucellosis in Bubalus bubalis, and three metabolites (lactate, 3-hydroxybutyrate and acetate) as biological markers for predicting the risk of developing the disease. These metabolites, together with TLR4 structural modifications in the MD2 interaction domain, are a clear signature of the immune system alteration during diverse Gram-negative bacterial infections. This suggests the possibility to extend this study to other pathogens, including Mycobacterium tuberculosis. In conclusion, this study combines multidisciplinary approaches to evaluate the biological and structural effects of SNPs on protein function.

Assessment of Protein-Protein Docking Models Using Deep Learning

Protein-protein interactions are involved in almost all processes in a living cell and determine the biological functions of proteins. To obtain mechanistic understandings of protein-protein interactions, the tertiary structures of protein complexes have been determined by biophysical experimental methods, such as X-ray crystallography and cryogenic electron microscopy. However, as experimental methods are costly in resources, many computational methods have been developed that model protein complex structures. One of the difficulties in computational protein complex modeling (protein docking) is to select the most accurate models among many models that are usually generated by a docking method. This article reviews advances in protein docking model assessment methods, focusing on recent developments that apply deep learning to several network architectures.

Software and Databases for Protein-Protein Docking

Protein-protein interactions (PPIs) provide valuable insights for understanding the principles of biological systems and for elucidating causes of incurable diseases. One of the techniques used for computational prediction of PPIs is protein-protein docking calculations, and a variety of software has been developed. This chapter is a summary of software and databases used for protein-protein docking.

Free-Docking and Template-Based Docking: Physics Versus Knowledge-Based Docking

Docking methods can be used to predict the orientations of two or more molecules with respect of each other using a plethora of various algorithms, which can be based on the physics of interactions or can use information from databases and templates. The usability of these approaches depends on the type and size of the molecules, whose relative orientation will be estimated. The two most important limitations are (i) the computational cost of the prediction and (ii) the availability of the structural information for similar complexes. In general, if there is enough information about similar systems, knowledge-based and template-based methods can significantly reduce the computational cost while providing high accuracy of the prediction. However, if the information about the system topology and interactions between its partners is scarce, physics-based methods are more reliable or even the only choice. In this chapter, knowledge-, template-, and physics-based methods will be compared and briefly discussed providing examples of their usability with a special emphasis on physics-based protein-protein, protein-peptide, and protein-fullerene docking in the UNRES coarse-grained model.

GRAMM Web Server for Protein Docking

Prediction of the structure of protein complexes by docking methods is a well-established research field. The intermolecular energy landscapes in protein-protein interactions can be used to refine docking predictions and to detect macro-characteristics, such as the binding funnel. A new GRAMM web server for protein docking predicts a spectrum of docking poses that characterize the intermolecular energy landscape in protein interaction. A user-friendly interface provides options to choose free or template-based docking, as well as other advanced features, such as clustering of the docking poses, and interactive visualization of the docked models.

Exploring Scoring Function Space: Developing Computational Models for Drug Discovery

BACKGROUND

The idea of scoring function space established a systems-level approach to address the development of models to predict the affinity of drug molecules by those interested in drug discovery.

OBJECTIVE

Our goal here is to review the concept of scoring function space and how to explore it to develop machine learning models to address protein-ligand binding affinity.

METHODS

We searched the articles available in PubMed related to the scoring function space. We also utilized crystallographic structures found in the protein data bank (PDB) to represent the protein space.

RESULTS

The application of systems-level approaches to address receptor-drug interactions allows us to have a holistic view of the process of drug discovery. The scoring function space adds flexibility to the process since it makes it possible to see drug discovery as a relationship involving mathematical spaces.

CONCLUSION

The application of the concept of scoring function space has provided us with an integrated view of drug discovery methods. This concept is useful during drug discovery, where we see the process as a computational search of the scoring function space to find an adequate model to predict receptor-drug binding affinity.

Functional and genomic characterization of a novel probiotic Lactobacillus johnsonii KD1 against shrimp WSSV infection

White Spot syndrome virus (WSSV) causes rapid shrimp mortality and production loss worldwide. This study demonstrates potential use of Lactobacillus johnsonii KD1 as an anti-WSSV agent for post larva shrimp cultivation and explores some potential mechanisms behind the anti-WSSV properties. Treatment of Penaeus vannamei shrimps with L. johnsonii KD1 prior to oral challenge with WSSV-infected tissues showed a significantly reduced mortality. In addition, WSSV copy numbers were not detected and shrimp immune genes were upregulated. Genomic analysis of L. johnsonii KD1 based on Illumina and Nanopore platforms revealed a 1.87 Mb chromosome and one 15.4 Kb plasmid. Only one antimicrobial resistance gene (ermB) in the chromosome was identified. Phylogenetic analysis comparing L. johnsonii KD1 to other L. johnsonii isolates revealed that L. johnsonii KD1 is closely related to L. johnsonii GHZ10a isolated from wild pigs. Interestingly, L. johnsonii KD1 contains isolate-specific genes such as genes involved in a type I restriction-modification system and CAZymes belonging to the GT8 family. Furthermore, genes coding for probiotic survival and potential antimicrobial/anti-viral metabolites such as a homolog of the bacteriocin helveticin-J were found. Protein-protein docking modelling suggests the helveticin-J homolog may be able to block VP28-PmRab7 interactions and interrupt WSSV infection.

Assembly of Protein Complexes In and On the Membrane with Predicted Spatial Arrangement Constraints

Membrane proteins play crucial roles in various cellular processes, and their interactions with other proteins in and on the membrane are essential for their proper functioning. While an increasing number of structures of more membrane proteins are being determined, the available structure data is still sparse. To gain insights into the mechanisms of membrane protein complexes, computational docking methods are necessary due to the challenge of experimental determination. Here, we introduce Mem-LZerD, a rigid-body membrane docking algorithm designed to take advantage of modern membrane modeling and protein docking techniques to facilitate the docking of membrane protein complexes. Mem-LZerD is based on the LZerD protein docking algorithm, which has been constantly among the top servers in many rounds of CAPRI protein docking assessment. By employing a combination of geometric hashing, newly constrained by the predicted membrane height and tilt angle, and model scoring accounting for the energy of membrane insertion, we demonstrate the capability of Mem-LZerD to model diverse membrane protein-protein complexes. Mem-LZerD successfully performed unbound docking on 13 of 21 (61.9%) transmembrane complexes in an established benchmark, more than shown by previous approaches. It was additionally tested on new datasets of 44 transmembrane complexes and 92 peripheral membrane protein complexes, of which it successfully modeled 35 (79.5%) and 15 (16.3%) complexes respectively. When non-blind orientations of peripheral targets were included, the number of successes increased to 54 (58.7%). We further demonstrate that Mem-LZerD produces complex models which are suitable for molecular dynamics simulation. Mem-LZerD is made available at https://lzerd.kiharalab.org.

Modeling protein-nucleic acid complexes with extremely large conformational changes using Flex-LZerD

Proteins and nucleic acids are key components in many processes in living cells, and interactions between proteins and nucleic acids are often crucial pathway components. In many cases, large flexibility of proteins as they interact with nucleic acids is key to their function. To understand the mechanisms of these processes, it is necessary to consider the 3D atomic structures of such protein-nucleic acid complexes. When such structures are not yet experimentally determined, protein docking can be used to computationally generate useful structure models. However, such docking has long had the limitation that the consideration of flexibility is usually limited to small movements or to small structures. We previously developed a method of flexible protein docking which could model ordered proteins which undergo large-scale conformational changes, which we also showed was compatible with nucleic acids. Here, we elaborate on the ability of that pipeline, Flex-LZerD, to model specifically interactions between proteins and nucleic acids, and demonstrate that Flex-LZerD can model more interactions and types of conformational change than previously shown.

Functional Diversity of Homologous Oxidoreductases-Tuning of Substrate Specificity by a FAD-Stacking Residue for Iron Acquisition and Flavodoxin Reduction

Although bacterial thioredoxin reductase-like ferredoxin/flavodoxin NAD(P)⁺ oxidoreductases (FNRs) are similar in terms of primary sequences and structures, they participate in diverse biological processes by catalyzing a range of different redox reactions. Many of the reactions are critical for the growth, survival of, and infection by pathogens, and insight into the structural basis for substrate preference, specificity, and reaction kinetics is crucial for the detailed understanding of these redox pathways. Bacillus cereus (Bc) encodes three FNR paralogs, two of which have assigned distinct biological functions in bacillithiol disulfide reduction and flavodoxin (Fld) reduction. Bc FNR2, the endogenous reductase of the Fld-like protein NrdI, belongs to a distinct phylogenetic cluster of homologous oxidoreductases containing a conserved His residue stacking the FAD cofactor. In this study, we have assigned a function to FNR1, in which the His residue is replaced by a conserved Val, in the reduction of the heme-degrading monooxygenase IsdG, ultimately facilitating the release of iron in an important iron acquisition pathway. The Bc IsdG structure was solved, and IsdG-FNR1 interactions were proposed through protein-protein docking. Mutational studies and bioinformatics analyses confirmed the importance of the conserved FAD-stacking residues on the respective reaction rates, proposing a division of FNRs into four functionally unique sequence similarity clusters likely related to the nature of this residue.

PIQLE: protein-protein interface quality estimation by deep graph learning of multimeric interaction geometries

Motivation

Accurate modeling of protein-protein interaction interface is essential for high-quality protein complex structure prediction. Existing approaches for estimating the quality of a predicted protein complex structural model utilize only the physicochemical properties or energetic contributions of the interacting atoms, ignoring evolutionarily information or inter-atomic multimeric geometries, including interaction distance and orientations.

Results

Here, we present PIQLE, a deep graph learning method for protein-protein interface quality estimation. PIQLE leverages multimeric interaction geometries and evolutionarily information along with sequence- and structure-derived features to estimate the quality of individual interactions between the interfacial residues using a multi-head graph attention network and then probabilistically combines the estimated quality for scoring the overall interface. Experimental results show that PIQLE consistently outperforms existing state-of-the-art methods including DProQA, TRScore, GNN-DOVE and DOVE on multiple independent test datasets across a wide range of evaluation metrics. Our ablation study and comparison with the self-assessment module of AlphaFold-Multimer repurposed for protein complex scoring reveal that the performance gains are connected to the effectiveness of the multi-head graph attention network in leveraging multimeric interaction geometries and evolutionary information along with other sequence- and structure-derived features adopted in PIQLE.

Availability and implementation

An open-source software implementation of PIQLE is freely available at https://github.com/Bhattacharya-Lab/PIQLE.

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

PIQLE: protein-protein interface quality estimation by deep graph learning of multimeric interaction geometries

Accurate modeling of protein-protein interaction interface is essential for high-quality protein complex structure prediction. Existing approaches for estimating the quality of a predicted protein complex structural model utilize only the physicochemical properties or energetic contributions of the interacting atoms, ignoring evolutionarily information or inter-atomic multimeric geometries, including interaction distance and orientations. Here we present PIQLE, a deep graph learning method for protein-protein interface quality estimation. PIQLE leverages multimeric interaction geometries and evolutionarily information along with sequence- and structure-derived features to estimate the quality of the individual interactions between the interfacial residues using a multihead graph attention network and then probabilistically combines the estimated quality of the interfacial residues for scoring the overall interface. Experimental results show that PIQLE consistently outperforms existing state-of-the-art methods on multiple independent test datasets across a wide range of evaluation metrics. Our ablation study reveals that the performance gains are connected to the effectiveness of the multihead graph attention network in leveraging multimeric interaction geometries and evolutionary information along with other sequence- and structure-derived features adopted in PIQLE. An open-source software implementation of PIQLE, licensed under the GNU General Public License v3, is freely available at https://github.com/Bhattacharya-Lab/PIQLE .

Quantitative comparison of protein-protein interaction interface using physicochemical feature-based descriptors of surface patches

Driving mechanisms of many biological functions in a cell include physical interactions of proteins. As protein-protein interactions (PPIs) are also important in disease development, protein-protein interactions are highlighted in the pharmaceutical industry as possible therapeutic targets in recent years. To understand the variety of protein-protein interactions in a proteome, it is essential to establish a method that can identify similarity and dissimilarity between protein-protein interactions for inferring the binding of similar molecules, including drugs and other proteins. In this study, we developed a novel method, protein-protein interaction-Surfer, which compares and quantifies similarity of local surface regions of protein-protein interactions. protein-protein interaction-Surfer represents a protein-protein interaction surface with overlapping surface patches, each of which is described with a three-dimensional Zernike descriptor (3DZD), a compact mathematical representation of 3D function. 3DZD captures both the 3D shape and physicochemical properties of the protein surface. The performance of protein-protein interaction-Surfer was benchmarked on datasets of protein-protein interactions, where we were able to show that protein-protein interaction-Surfer finds similar potential drug binding regions that do not share sequence and structure similarity. protein-protein interaction-Surfer is available at https://kiharalab.org/ppi-surfer.

A knowledge-based protein-protein interaction inhibition (KPI) pipeline: an insight from drug repositioning for COVID-19 inhibition

The inhibition of protein-protein interactions (PPIs) by small molecules is an exciting drug discovery strategy. Here, we aimed to develop a pipeline to identify candidate small molecules to inhibit PPIs. Therefore, KPI, a Knowledge-based Protein-Protein Interaction Inhibition pipeline, was introduced to improve the discovery of PPI inhibitors. Then, phytochemicals from a collection of known Middle Eastern antiviral herbs were screened to identify potential inhibitors of key PPIs involved in COVID-19. Here, the following investigations were sequenced: 1) Finding the binding partner and the interface of the proteins in PPIs, 2) Performing the blind ligand-protein inhibition (LPI) simulations, 3) Performing the local LPI simulations, 4) Simulating the interactions of the proteins and their binding partner in the presence and absence of the ligands, and 5) Performing the molecular dynamics simulations. The pharmacophore groups involved in the LPI were also characterized. Aloin, Genistein, Neoglucobrassicin, and Rutin are our new pipeline candidates for inhibiting PPIs involved in COVID-19. We also propose KPI for drug repositioning studies.Communicated by Ramaswamy H. Sarma.

Pairwise and Multi-chain Protein Docking Enhanced Using LZerD Web Server

Interactions of proteins with other macromolecules have important structural and functional roles in the basic processes of living cells. To understand and elucidate the mechanisms of interactions, it is important to know the 3D structures of the complexes. Proteomes contain numerous protein-protein complexes, for which experimentally determined structures often do not exist. Computational techniques can be a practical alternative to obtain useful complex structure models. Here, we present a web server that provides access to the LZerD and Multi-LZerD protein docking tools, which can perform both pairwise and multi-chain docking. The web server is user-friendly, with options to visualize the distribution and structures of binding poses of top-scoring models. The LZerD web server is available at https://lzerd.kiharalab.org . This chapter dictates the algorithm and step-by-step procedure to model the monomeric structures with AttentiveDist, and also provides the detail of pairwise LZerD docking, and multi-LZerD. This also provided case studies for each of the three modules.

Virus-like Particles as Antiviral Vaccine: Mechanism, Design, and Application

Virus-like particles (VLPs) are viral structural protein that are noninfectious as they do not contain viral genetic materials. They are safe and effective immune stimulators and play important roles in vaccine development because of their intrinsic immunogenicity to induce cellular and humoral immune responses. In the design of antiviral vaccine, VLPs based vaccines are appealing multifunctional candidates with the advantages such as self-assembling nanoscaled structures, repetitive surface epitopes, ease of genetic and chemical modifications, versatility as antigen presenting platforms, intrinsic immunogenicity, higher safety profile in comparison with live-attenuated vaccines and inactivated vaccines. In this review, we discuss the mechanism of VLPs vaccine inducing cellular and humoral immune responses. We outline the impact of size, shape, surface charge, antigen presentation, genetic and chemical modification, and expression systems when constructing effective VLPs based vaccines. Recent applications of antiviral VLPs vaccines and their clinical trials are summarized.

Domain-Based Protein Docking with Extremely Large Conformational Changes

Proteins are key components in many processes in living cells, and physical interactions with other proteins and nucleic acids often form key parts of their functions. In many cases, large flexibility of proteins as they interact is key to their function. To understand the mechanisms of these processes, it is necessary to consider the 3D structures of such protein complexes. When such structures are not yet experimentally determined, protein docking has long been present to computationally generate useful structure models. However, protein docking has long had the limitation that the consideration of flexibility is usually limited to very small movements or very small structures. Methods have been developed which handle minor flexibility via normal mode or other structure sampling, but new methods are required to model ordered proteins which undergo large-scale conformational changes to elucidate their function at the molecular level. Here, we present Flex-LZerD, a framework for docking such complexes. Via partial assembly multidomain docking and an iterative normal mode analysis admitting curvilinear motions, we demonstrate the ability to model the assembly of a variety of protein-protein and protein-nucleic acid complexes.

Staphylococcus aureus Exfoliative Toxin E, Oligomeric State and Flip of P186: Implications for Its Action Mechanism

Staphylococcal exfoliative toxins (ETs) are glutamyl endopeptidases that specifically cleave the Glu381-Gly382 bond in the ectodomains of desmoglein 1 (Dsg1) via complex action mechanisms. To date, four ETs have been identified in different Staphylococcus aureus strains and ETE is the most recently characterized. The unusual properties of ETs have been attributed to a unique structural feature, i.e., the 180° flip of the carbonyl oxygen (O) of the nonconserved residue 192/186 (ETA/ETE numbering), not conducive to the oxyanion hole formation. We report the crystal structure of ETE determined at 1.61 Å resolution, in which P186(O) adopts two conformations displaying a 180° rotation. This finding, together with free energy calculations, supports the existence of a dynamic transition between the conformations under the tested conditions. Moreover, enzymatic assays showed no significant differences in the esterolytic efficiency of ETE and ETE/P186G, a mutant predicted to possess a functional oxyanion hole, thus downplaying the influence of the flip on the activity. Finally, we observed the formation of ETE homodimers in solution and the predicted homodimeric structure revealed the participation of a characteristic nonconserved loop in the interface and the partial occlusion of the protein active site, suggesting that monomerization is required for enzymatic activity.

The archetypal gene transfer agent RcGTA is regulated via direct interaction with the enigmatic RNA polymerase omega subunit

Gene transfer agents (GTAs) are small virus-like particles that indiscriminately package and transfer any DNA present in their host cell, with clear implications for bacterial evolution. The first transcriptional regulator that directly controls GTA expression, GafA, was recently discovered, but its mechanism of action has remained elusive. Here, we demonstrate that GafA controls GTA gene expression via direct interaction with the RNA polymerase omega subunit (Rpo-ω) and also positively autoregulates its own expression by an Rpo-ω-independent mechanism. We show that GafA is a modular protein with distinct DNA and protein binding domains. The functional domains we observe in Rhodobacter GafA also correspond to two-gene operons in Hyphomicrobiales pathogens. These data allow us to produce the most complete regulatory model for a GTA and point toward an atypical mechanism for RNA polymerase recruitment and specific transcriptional activation in the Alphaproteobacteria.

Novel Cyclic Peptides for Targeting EGFR and EGRvIII Mutation for Drug Delivery

The epidermal growth factor-epidermal growth factor receptor (EGF-EGFR) pathway has become the main focus of selective chemotherapeutic intervention. As a result, two classes of EGFR inhibitors have been clinically approved, namely monoclonal antibodies and small molecule kinase inhibitors. Despite an initial good response rate to these drugs, most patients develop drug resistance. Therefore, new treatment approaches are needed. In this work, we aimed to find a new EGFR-specific, short cyclic peptide, which could be used for targeted drug delivery. Phage display peptide technology and biopanning were applied to three EGFR expressing cells, including cells expressing the EGFRvIII mutation. DNA from the internalized phage was extracted and the peptide inserts were sequenced using next-generation sequencing (NGS). Eleven peptides were selected for further investigation using binding, internalization, and competition assays, and the results were confirmed by confocal microscopy and peptide docking. Among these eleven peptides, seven showed specific and selective binding and internalization into EGFR positive (EGFR+ve) cells, with two of them-P6 and P9-also demonstrating high specificity for non-small cell lung cancer (NSCLC) and glioblastoma cells, respectively. These peptides were chemically conjugated to camptothecin (CPT). The conjugates were more cytotoxic to EGFR+ve cells than free CPT. Our results describe a novel cyclic peptide, which can be used for targeted drug delivery to cells overexpressing the EGFR and EGFRvIII mutation.

Protein Structural Modeling for Electron Microscopy Maps Using VESPER and MAINMAST

An increasing number of protein structures are determined by cryo-electron microscopy (cryo-EM) and stored in the Electron Microscopy Data Bank (EMDB). To interpret determined cryo-EM maps, several methods have been developed that model the tertiary structure of biomolecules, particularly proteins. Here we show how to use two such methods, VESPER and MAINMAST, which were developed in our group. VESPER is a method mainly for two purposes: fitting protein structure models into an EM map and aligning two EM maps locally or globally to capture their similarity. VESPER represents each EM map as a set of vectors pointing toward denser points. By considering matching the directions of vectors, in general, VESPER aligns maps better than conventional methods that only consider local densities of maps. MAINMAST is a de novo protein modeling tool designed for EM maps with resolution of 3-5 Å or better. MAINMAST builds a protein main chain directly from a density map by tracing dense points in an EM map and connecting them using a tree-graph structure. This article describes how to use these two tools using three illustrative modeling examples. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Protein structure model fitting using VESPER Alternate Protocol: Atomic model fitting using VESPER web server Basic Protocol 2: Protein de novo modeling using MAINMAST.

Protein-protein interaction networks as miners of biological discovery

Protein-protein interactions (PPIs) form the basis of a myriad of biological pathways and mechanism, such as the formation of protein-complexes or the components of signaling cascades. Here, we reviewed experimental methods for identifying PPI pairs, including yeast two-hybrid, mass spectrometry, co-localization, and co-immunoprecipitation. Furthermore, a range of computational methods leveraging biochemical properties, evolution history, protein structures and more have enabled identification of additional PPIs. Given the wealth of known PPIs, we reviewed important network methods to construct and analyze networks of PPIs. These methods aid biological discovery through identifying hub genes and dynamic changes in the network, and have been thoroughly applied in various fields of biological research. Lastly, we discussed the challenges and future direction of research utilizing the power of PPI networks. This article is protected by copyright. All rights reserved.