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 tools used in human-virus PPIs research, discuss and compare the function, performance, and limitations of these web resources. This study aims to provide researchers with a bioinformatic toolbox that will hopefully better facilitate the exploration of human-virus PPIs based on binding modes.

Exploring the allergenic potential of sesame oleosins: Isolation and bioinformatics analysis.

This study examined two oleosins of 17 kDa and 15 kDa isolated from Yuzhi white sesame seeds through oil body extraction. The allergens were identified as oleosin H1 (Ses i 4) and oleosin L (Ses i 5) using SDS-PAGE, dot blot analysis, and LC-MS/MS. PCR analysis revealed high sequence homology for the oleosin proteins in the sesame seeds. Utilizing AlphaFold2, bioinformatics tools, and protein-protein docking, the structure and function of these oleosins were analyzed. Ten potential B cell epitope peptides were predicted and mapped onto the α-helix and random coil-dominated oleosome membrane conformation. IgE binding simulations identified key epitopes, B3 (FLTSGAFGL) and B4 (KRGVQEGTLY) for oleosin H1, and B8 (GGFGVAALSV) and B9 (DQLESAKTKL) for oleosin L. Mutational analysis highlighted Glu135, Phe102, Tyr128, Tyr139, Gly136, and Gly132 in oleosin H1, and Leu120, Lys119, and Leu113 in oleosin L as critical residues for binding stability, providing insights into the sensitization mechanism of these epitopes. The integration of bioinformatics and immunoinformatics in this study has contributed to a deeper understanding of the allergy properties of sesame oleosins.

In silico-driven identification and experimental confirmation of antifungal proteins (AFPs) against Candidaalbicans.

Mycoses infect millions of people annually across the world. The most common mycosis agent, Candida albicans is responsible for a great deal of illness and death. C. albicans infection is becoming more widespread and the current antifungals polyenes, triazoles, and echinocandins are less efficient against it. Investigating antifungal peptides (AFPs) as therapeutic is gaining momentum. Therefore, we used MALDI-TOF/MS analysis to identify AFPs and protein-protein docking to analyze their interactions with the C. albicans target protein. Some microorganisms with strong antifungal action against C. albicans were selected for the isolation of AFPs. Using MALDI-TOF/MS, we identified 3 AFPs Chitin binding protein (ACW83017.1; Bacillus licheniformis), the bifunctional protein GlmU (BBQ13478.1; Stenotrophomonas maltophilia), and zinc metalloproteinase aureolysin (BBA25172.1; Staphylococcus aureus). These AFPs showed robust interactions with C. albicans target protein Sap5. We deciphered some important residues in identified APFs and highlighted interaction with Sap5 through hydrogen bonds, protein-protein interactions, and salt bridges using protein-protein docking and MD simulations. The three discovered AFPs-Sap5 complexes exhibit different levels of stability, as seen by the RMSD analysis and interaction patterns. Among protein-protein interactions, the remarkable stability of the BBQ25172.1-2QZX complex highlights the role of salt bridges and hydrogen bonds. Identified AFPs could be further studied for developing successful antifungal candidates and peptide-based new antifungal therapeutic strategies as fresh insights into addressing antifungal resistance also.

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.

Machine Learning Methods in Protein-Protein Docking.

An exponential increase in the number of publications that address artificial intelligence (AI) usage in life sciences has been noticed in recent years, while new modeling techniques are constantly being reported. The potential of these methods is vast-from understanding fundamental cellular processes to discovering new drugs and breakthrough therapies. Computational studies of protein-protein interactions, crucial for understanding the operation of biological systems, are no exception in this field. However, despite the rapid development of technology and the progress in developing new approaches, many aspects remain challenging to solve, such as predicting conformational changes in proteins, or more "trivial" issues as high-quality data in huge quantities.Therefore, this chapter focuses on a short introduction to various AI approaches to study protein-protein interactions, followed by a description of the most up-to-date algorithms and programs used for this purpose. Yet, given the considerable pace of development in this hot area of computational science, at the time you read this chapter, the development of the algorithms described, or the emergence of new (and better) ones should come as no surprise.

Molecular Dynamics Simulations in Protein-Protein Docking.

Concerted interactions between all the cell components form the basis of biological processes. Protein-protein interactions (PPIs) constitute a tremendous part of this interaction network. Deeper insight into PPIs can help us better understand numerous diseases and lead to the development of new diagnostic and therapeutic strategies. PPI interfaces, until recently, were considered undruggable. However, it is now believed that the interfaces contain "hot spots," which could be targeted by small molecules. Such a strategy would require high-quality structural data of PPIs, which are difficult to obtain experimentally. Therefore, in silico modeling can complement or be an alternative to in vitro approaches. There are several computational methods for analyzing the structural data of the binding partners and modeling of the protein-protein dimer/oligomer structure. The major problem with in silico structure prediction of protein assemblies is obtaining sufficient sampling of protein dynamics. One of the methods that can take protein flexibility and the effects of the environment into account is Molecular Dynamics (MD). While sampling of the whole protein-protein association process with plain MD would be computationally expensive, there are several strategies to harness the method to PPI studies while maintaining reasonable use of resources. This chapter reviews known applications of MD in the PPI investigation workflows.

Computational Methods to Target Protein-Protein Interactions.

The chapter emphasizes the importance of understanding protein-protein interactions in cellular mechanisms and highlights the role of computational modeling in predicting these interactions. It discusses sequence-based approaches such as evolutionary trace (ET), correlated mutation analysis (CMA), and subtractive correlated mutation (SCM) for identifying crucial amino acid residues, considering interface conservation or evolutionary changes. The chapter also explores methods like differential ET, hidden-site class model, and spatial cluster detection (SCD) for interface specificity and spatial clustering. Furthermore, it examines approaches combining structural and sequential methodologies and evaluates modeled predictions through initiatives like critical assessment of prediction of interactions (CAPRI). Additionally, the chapter provides an overview of various software programs used for molecular docking, detailing their search, sampling, refinement and scoring stages, along with innovative techniques and tools like normal mode analysis (NMA) and adaptive Poisson-Boltzmann solver (APBS) for electrostatic calculations. These computational and experimental approaches are crucial for unraveling protein-protein interactions and aid in developing potential therapeutics for various diseases.

Refinement of Docked Protein-Protein Complexes Using Repulsive Scaling Replica Exchange Simulations.

Accurate prediction and evaluation of protein-protein complex structures is of major importance to understand the cellular interactome. Predicted complex structures based on deep learning approaches or traditional docking methods require often structural refinement and rescoring for realistic evaluation. Standard molecular dynamics (MD) simulations are time-consuming and often do not structurally improve docking solutions. Better refinement can be achieved with our recently developed replica-exchange-based scheme employing different levels of repulsive biasing between proteins in each replica simulation (RS-REMD). The bias acts specifically on the intermolecular interactions based on an increase in effective pairwise van der Waals radii without changing interactions within each protein or with the solvent. It allows for an improvement of the predicted protein-protein complex structure and simultaneous realistic free energy scoring of protein-protein complexes. The setup of RS-REMD simulations is described in detail including the application on two examples (all necessary scripts and input files can be obtained from https://gitlab.com/TillCyrill/mmib ).

Protein-Protein Docking Approach to GPCR Oligomerization.

G-protein-coupled receptors (GPCRs), the largest family of human membrane proteins, play a crucial role in cellular control and are the target of approximately one-third of all drugs on the market. Targeting these complexes with selectivity or formulating small molecules capable of modulating receptor-receptor interactions could potentially offer novel avenues for drug discovery, fostering the development of more refined and safer pharmacotherapies. Due to the lack of experimentally derived X-ray crystallography spectra of GPCR oligomers, there is growing evidence supporting the development of new in silico approaches for predicting GPCR self-assembling structures. The significance of GPCR oligomerization, the challenges in modeling these structures, and the potential of protein-protein docking algorithms to address these challenges are discussed. The study also underscores the use of various software solutions for modeling GPCR oligomeric structures and presents practical cases where these techniques have been successfully applied.

Improving Fertility in Non-obstructive Azoospermia: Results from an Autologous Bone Mar-row-Derived Mesenchymal Stromal/Stem Cell Phase I Clinical Trial.

BACKGROUND: In this phase I clinical trial, our primary objective was to develop an innovative therapeutic approach utilizing autologous bone marrow-derived mesenchymal stromal/stem cells (BM-MSCs) for the treatment of nonobstructive azoospermia (NOA). Additionally, we aimed to assess the feasibility and safety of this approach. MATERIALS AND METHODS: We recruited 80 participants in this non-randomized, open-label clinical trial, including patients undergoing NOA treatment using autologous BM-MSCs (n=40) and those receiving hormone therapy as a control group (n=40). Detailed participant characteristics, such as age, baseline hormonal profiles, etiology of NOA, and medical history, were thoroughly documented. Autotransplantation of BM-MSCs into the testicular network was achieved using microsurgical testicular sperm extraction (microTESE). Semen analysis and hormonal assessments were performed both before and six months after treatment. Additionally, we conducted an in-silico analysis to explore potential protein-protein interactions between exosomes secreted from BM-MSCs and receptors present in human seminiferous tubule cells. RESULTS: Our results revealed significant improvements following treatment, including increased testosterone and inhibin B levels, elevated sperm concentration, and reduced levels of follicle-stimulating hormone (FSH), luteinizing hormone (LH), and prolactin. Notably, in nine patients (22.5%) previously diagnosed with secondary infertility and exhibiting azoospermia before treatment, the proposed approach yielded successful outcomes, as indicated by hormonal profile changes over six months. Importantly, these improvements were achieved without complications. Additionally, our in-silico analysis identified potential binding interactions between the protein content of BM-MSC-derived exosomes and receptors integral to spermatogenesis. CONCLUSION: Autotransplantation of BM-MSCs into the testicular network using microTESE in NOA patients led to the regeneration of seminiferous tubules and the regulation of hormonal profiles governing spermatogenesis. Our findings support the safety and effectiveness of autologous BM-MSCs as a promising treatment modality for NOA, with a particular focus on the achieved outcomes in patients with secondary infertility (registration number: IRCT20190519043634N1).

Molecular docking analysis of α-Synuclein aggregation with Anle138b.

α-Synuclein aggregation into toxic oligomeric species is central to Parkinson's disease pathogenesis. Anle138b is a recently identified inhibitor of α-synuclein oligomerization showing promise in preclinical studies. This study employed computational approaches to elucidate Anle138b's mechanism of oligomer-specific action. The inhibitory potential of Anle138b against α-synuclein oligomers was evaluated by performing molecular docking studies using AutoDock Tools, followed by their binding pocket analysis. Further, protein-protein docking studies were performed using Hex8.0 to validate the aggregation inhibitory potential of Anle138b. Molecular docking revealed increasing binding affinity of Anle138b against higher order α-synuclein oligomers (dimer to decamer). Anle138b occupied oligomeric cavity and interacted with residues Thr54, Gly73, Val74 and Thr75 across several oligomers. Protein-protein docking showed that Anle138b interferes with α-synuclein decamer formation. These results highlight the oligomer-directed inhibitory mechanism of Anle138b, without hindering the monomeric forms and provide molecular insights to advance its therapeutic development for Parkinson's and related synucleinopathies.

The mitochondrial carrier homolog 2 is involved in down-regulation of influenza A virus replication.

BACKGROUND: The mitochondrial carrier homolog 2 (MTCH2) is a mitochondrial outer membrane protein regulating mitochondrial metabolism and functions in lipid homeostasis and apoptosis. Experimental data on the interaction of MTCH2 with viral proteins in virus-infected cells are very limited. Here, the interaction of MTCH2 with PA subunit of influenza A virus RdRp and its effects on viral replication was investigated. METHODS: The human MTCH2 protein was identified as the influenza A virus PA-related cellular factor with the Y2H assay. The interaction between GST.MTCH2 and PA protein co-expressed in transfected HEK293 cells was evaluated by GST-pull down. The effect of MTCH2 on virus replication was determined by quantification of viral transcript and/or viral proteins in the cells transfected with MTCH2-encoding plasmid or MTCH2-siRNA. An interaction model of MTCH2 and PA was predicted with protein modeling/docking algorithms. RESULTS: It was observed that PA and GST.MTCH2 proteins expressed in HEK293 cells were co-precipitated by glutathione-agarose beads. The influenza A virus replication was stimulated in HeLa cells whose MTCH2 expression was suppressed with specific siRNA, whereas the increase of MTCH2 in transiently transfected HEK293 cells inhibited viral RdRp activity. The results of a Y2H assay and protein-protein docking analysis suggested that the amino terminal part of the viral PA (nPA) can bind to the cytoplasmic domain comprising amino acid residues 253 to 282 of the MTCH2. CONCLUSION: It is suggested that the host mitochondrial MTCH2 protein is probably involved in the interaction with the viral polymerase protein PA to cause negative regulatory effect on influenza A virus replication in infected cells.

Protein language model-embedded geometric graphs power inter-protein contact prediction.

Accurate prediction of contacting residue pairs between interacting proteins is very useful for structural characterization of protein-protein interactions. Although significant improvement has been made in inter-protein contact prediction recently, there is still a large room for improving the prediction accuracy. Here we present a new deep learning method referred to as PLMGraph-Inter for inter-protein contact prediction. Specifically, we employ rotationally and translationally invariant geometric graphs obtained from structures of interacting proteins to integrate multiple protein language models, which are successively transformed by graph encoders formed by geometric vector perceptrons and residual networks formed by dimensional hybrid residual blocks to predict inter-protein contacts. Extensive evaluation on multiple test sets illustrates that PLMGraph-Inter outperforms five top inter-protein contact prediction methods, including DeepHomo, GLINTER, CDPred, DeepHomo2, and DRN-1D2D_Inter, by large margins. In addition, we also show that the prediction of PLMGraph-Inter can complement the result of AlphaFold-Multimer. Finally, we show leveraging the contacts predicted by PLMGraph-Inter as constraints for protein-protein docking can dramatically improve its performance for protein complex structure prediction.

A suite of designed protein cages using machine learning and protein fragment-based protocols.

Designed protein cages and related materials provide unique opportunities for applications in biotechnology and medicine, but their creation remains challenging. Here, we apply computational approaches to design a suite of tetrahedrally symmetric, self-assembling protein cages. For the generation of docked conformations, we emphasize a protein fragment-based approach, while for sequence design of the de novo interface, a comparison of knowledge-based and machine learning protocols highlights the power and increased experimental success achieved using ProteinMPNN. An analysis of design outcomes provides insights for improving interface design protocols, including prioritizing fragment-based motifs, balancing interface hydrophobicity and polarity, and identifying preferred polar contact patterns. In all, we report five structures for seven protein cages, along with two structures of intermediate assemblies, with the highest resolution reaching 2.0 Å using cryo-EM. This set of designed cages adds substantially to the body of available protein nanoparticles, and to methodologies for their creation.

In Silico Description of the Direct Inhibition Mechanism of Endothelial Lipase by ANGPTL3.

Angiopoietin-like protein 3 (ANGPTL3) is a plasmatic protein that plays a crucial role in lipoprotein metabolism by inhibiting the lipoprotein lipase (LPL) and the endothelial lipase (EL) responsible for the hydrolysis of phospholipids on high-density lipoprotein (HDL). Interest in developing new pharmacological therapies aimed at inhibiting ANGPTL3 has been growing due to the hypolipidemic and antiatherogenic profile observed in its absence. The goal of this study was the in silico characterization of the interaction between ANGPTL3 and EL. Because of the lack of any structural information on both the trimeric coiled-coil N-terminal domain of ANGPTL3 and the EL homodimer as well as data regarding their interactions, the first step was to obtain the three-dimensional model of these two proteins. The models were then refined via molecular dynamics (MD) simulations and used to investigate the interaction mechanism. The analysis of interactions in different docking poses and their refinement via MD allowed the identification of three specific glutamates of ANGPTL3 that recognize a positively charged patch on the surface of EL. These ANGPTL3 key residues, i.e., Glu154, Glu157, and Glu160, could form a putative molecular recognition site for EL. This study paves the way for future investigations aimed at confirming the recognition site and at designing novel inhibitors of ANGPTL3.

Structural analyzes suggest that MiSSP13 and MiSSP16.5 may act as proteases inhibitors during ectomycorrhiza establishment in Laccaria bicolor.

•The signaling process during mycorrhiza establishment involves intense molecular communication between symbionts. It has been suggested that a group of protein effectors, the so-called MiSSPs, plays a broader function in the symbiosis metabolism, however, many of these remain uncharacterized structurally and functionally. •Herein we used three-dimensional protein structure modeling methods, ligand analysis, and molecular docking to structurally characterize and describe two protein effectors, MiSSP13 and MiSSP16.5, with enhanced expression during the mycorrhizal process in Laccaria bicolor. •MiSSP13 and MiSSP16.5 show structural homology with the cysteine and aspartate protease inhibitor, cocaprin (CCP1). Through structural analysis, it was observed that MiSSP13 and MiSSP16.5 have an active site similar to that observed in CCP1. The protein-protein docking data showed that MiSSP13 and MiSSP16.5 interact with the papain and pepsin proteases at sites that are near to where CCP1 interacts with these same targets, suggesting a function as inhibitor of cysteine and aspartate proteases. The interaction of MiSSP13 with papain and MiSSP16.5 with pepsin was stronger than the interaction of CCP1 with these proteases, suggesting that the MiSSPs had a greater activity in inhibiting these classes of proteases. Based on the data supplied, a model is proposed for the function of MiSSPs 13 and 16.5 during the symbiosis establishment. Our findings, while derived from in silico analyses, enable us formulate intriguing hypothesis on the function of MiSSPs in ectomycorrhization, which will require experimental validation.

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.

Binding site prediction between lysozyme and glucose-regulated protein 78, a hope to fight amyloidosis.

Amyloidosis is an extraordinarily vigorous and heterogeneous group of disorders that causes numerous organ failures due to the precipitation of misfolded proteins. Many of these damaged proteins are discarded before causing any fatal diseases due to the contribution of the protein quality control (PQC) system and its chaperons, including glucose-regulated protein (GRP78). One of the most important enzymatic proteins inside the body is lysozyme, which is reported to have many mutated variants that may cause amyloid fibrils. This study used structural bioinformatics and molecular dynamics simulations to test and suggest binding sites for the human lysozyme protein with GRP78. Multiple sequence alignment (MSA) shows that part of the lysozyme envelope protein (C65-C81 cyclic region) has high similarities (30.77% identity) with the cyclic Pep42. Additionally, the binding between the lysozyme cyclic region (C65-C81) and GRP78 substrate binding domain (SBD) is found favorable. The number and types of interactions vary between each of the mutant isoforms of lysozyme. The more significant the conformational changes in the mutation, the greater its probability of aggregation and the formation of amyloid fibrils. Each mutation leads to different interactions and binding patterns with GRP78. The present computational study suggests a lysozyme-GRP78 binding site, thus paving the way for drug designers to construct suitable carriers that can collect misfolded lysozyme proteins and eliminate them from the body, preventing their aggregation and amyloidogenesis.Communicated by Ramaswamy H. Sarma.