Binding Protein-protein Interactions Research Articles

Development of a novel papain gel formulation: Exploring different concentrations for smear-layer deproteinization and enhanced dentin bonding

BackgroundThe self-etch adhesive system modifies but does not completely remove the smear layer, leading to the weakening of the bond strength due to the formation of a hybridized layer. Smear-layer deproteinization with papain enzyme partially removes the smear layer, and increases the bond strength with self-etch adhesive. The aim was to develop a deproteinizing agent with a high papain enzyme concentration to enhance dentin bonding with self-etch adhesives. MethodsPapain enzyme gel formulations (15 and 30 IU/g) were prepared and tested for physical stability, viscosity, pH, homogeneity, and organoleptic properties. Moreover, 64 teeth were used to test the deproteinization efficiency of the formed gel. Fourier transform infrared was used to calculate the ratio of organic to inorganic components of smear-layer after deproteinization with 15 and 30 IU/g papain gel and a 6 IU/g commercial papain gel. Moreover, tensile bond strength was measured after deproteinization and dentin bonding with self-etching adhesive for the same groups. A molecular modeling simulation was also performed to evaluate the protein-protein binding interaction, predict the conformational/orientation patterns, and estimate the binding energies of papain with collagen target protein. ResultsBoth 15 and 30 IU/g gels exhibited similar viscosity, pH, homogeneity, and organoleptic properties. However, after 60 s, the 15 IU/g gel was solid, while the 30 IU/g gel was half-solid. All tested groups decreased the amide:phosphate ratio and increased tensile bond strength. Binding complexes between papain and three deposited collagen-1 structures formed strong binding energies with high negative values and residue-wise binding patterns. ConclusionsThe production of the papain enzyme gel with a concentration of 15 IU/g was successful. In addition, it demonstrated promising results when used as a smear-layer deproteinization agent. Clinical significanceEnzymatic smear-layer deproteinization may improve dentin adhesion, and high concertation papain enzyme gels may improve dentin adhesion with the use of self-etch adhesive

ProBASS - a language model with sequence and structural features for predicting the effect of mutations on binding affinity.

Protein-protein interactions (PPIs) govern virtually all cellular processes. Even a single mutation within PPI can significantly influence overall protein functionality and potentially lead to various types of diseases. To date, numerous approaches have emerged for predicting the change in free energy of binding (ΔΔGbind) resulting from mutations, yet the majority of these methods lack precision. In recent years, protein language models (PLMs) have been developed and shown powerful predictive capabilities by leveraging both sequence and structural data from protein-protein complexes. Yet, PLMs have not been optimized specifically for predicting ΔΔGbind. We developed an approach to predict effects of mutations on PPI binding affinity based on two most advanced protein language models ESM2 and ESM-IF1 that incorporate PPI sequence and structural features, respectively. We used the two models to generate embeddings for each PPI mutant and subsequently fine-tuned our model by training on a large dataset of experimental ΔΔGbind values. Our model, ProBASS (Protein Binding Affinity from Structure and Sequence) achieved a correlation with experimental ΔΔGbind values of 0.83 ± 0.05 for single mutations and 0.69 ± 0.04 for double mutations when model training and testing was done on the same PDB. Moreover, ProBASS exhibited very high correlation (0.81 ± 0.02) between prediction and experiment when training and testing was performed on a dataset containing 2325 single mutations in 132 PPIs. ProBASS surpasses the state-of-the-art methods in correlation with experimental data and could be further trained as more experimental data becomes available. Our results demonstrate that the integration of extensive datasets containing ΔΔGbind values across multiple PPIs to refine the pre-trained PLMs represents a successful approach for achieving a precise and broadly applicable model for ΔΔGbind prediction, greatly facilitating future protein engineering and design studies.

DDMut-PPI: predicting effects of mutations on protein-protein interactions using graph-based deep learning.

Protein-protein interactions (PPIs) play a vital role in cellular functions and are essential for therapeutic development and understanding diseases. However, current predictive tools often struggle to balance efficiency and precision in predicting the effects of mutations on these complex interactions. To address this, we present DDMut-PPI, a deep learning model that efficiently and accurately predicts changes in PPI binding free energy upon single and multiple point mutations. Building on the robust Siamese network architecture with graph-based signatures from our prior work, DDMut, the DDMut-PPI model was enhanced with a graph convolutional network operated on the protein interaction interface. We used residue-specific embeddings from ProtT5 protein language model as node features, and a variety of molecular interactions as edge features. By integrating evolutionary context with spatial information, this framework enables DDMut-PPI to achieve a robust Pearson correlation of up to 0.75 (root mean squared error: 1.33 kcal/mol) in our evaluations, outperforming most existing methods. Importantly, the model demonstrated consistent performance across mutations that increase or decrease binding affinity. DDMut-PPI offers a significant advancement in the field and will serve as a valuable tool for researchers probing the complexities of protein interactions. DDMut-PPI is freely available as a web server and an application programming interface at https://biosig.lab.uq.edu.au/ddmut_ppi.

Small Molecule Antagonists of the DNA Repair ERCC1/XPA Protein-Protein Interaction.

The DNA excision repair protein ERCC1 and the DNA damage sensor protein, XPA are highly overexpressed in patient samples of cisplatin-resistant solid tumors including lung, bladder, ovarian, and testicular cancer. The repair of cisplatin-DNA crosslinks is dependent upon nucleotide excision repair (NER) that is modulated by protein-protein binding interactions of ERCC1, the endonuclease, XPF, and XPA. Thus, inhibition of their function is a potential therapeutic strategy for the selective sensitization of tumors to DNA-damaging platinum-based cancer therapy. Here, we report on new small-molecule antagonists of the ERCC1/XPA protein-protein interaction (PPI) discovered using a high-throughput competitive fluorescence polarization binding assay. We discovered a unique structural class of thiopyridine-3-carbonitrile PPI antagonists that block a truncated XPA polypeptide from binding to ERCC1. Preliminary hit-to-lead studies from compound 1 reveal structure-activity relationships (SAR) and identify lead compound 27 o with an EC50 of 4.7 μM. Furthermore, chemical shift perturbation mapping by NMR confirms that 1 binds within the same site as the truncated XPA67-80 peptide. These novel ERCC1 antagonists are useful chemical biology tools for investigating DNA damage repair pathways and provide a good starting point for medicinal chemistry optimization as therapeutics for sensitizing tumors to DNA damaging agents and overcoming resistance to platinum-based chemotherapy.

Fluorescent carbazole-derived α-amino acids: structural mimics of tryptophan.

Fluorescent tags are commonly used for imaging of proteins and peptides during biological events; however, the large size of dyes can disrupt protein structure and function, and typically require the use of a chemical spacer. Herein, we report the synthesis of a new class of fluorescent unnatural α-amino acid, containing carbazole side-chains designed to mimic l-tryptophan and thus, readily incorporated into peptides. The amino acids were constructed using a Negishi cross-coupling reaction as the key step and exhibited strong fluorescent emission, with high quantum yields in both organic solvents and water. Compatible with solid phase peptide synthesis, the carbazole amino acids were used to replace tryptophan in a β-hairpin model peptide and shown to be a close structural mimic with retention of conformation. They were also found to be effective fluorescent molecular reporters for biological events. Incorporation into a proline-rich ligand of the WW domain protein demonstrated that the fluorescent properties of a carbazole amino acid could be used to measure the protein-protein binding interaction of this important biological signalling process.

Structure and computation-guided yeast surface display for the evolution of TIMP-based matrix metalloproteinase inhibitors.

The study of protein-protein interactions (PPIs) and the engineering of protein-based inhibitors often employ two distinct strategies. One approach leverages the power of combinatorial libraries, displaying large ensembles of mutant proteins, for example, on the yeast cell surface, to select binders. Another approach harnesses computational modeling, sifting through an astronomically large number of protein sequences and attempting to predict the impact of mutations on PPI binding energy. Individually, each approach has inherent limitations, but when combined, they generate superior outcomes across diverse protein engineering endeavors. This synergistic integration of approaches aids in identifying novel binders and inhibitors, fine-tuning specificity and affinity for known binding partners, and detailed mapping of binding epitopes. It can also provide insight into the specificity profiles of varied PPIs. Here, we outline strategies for directing the evolution of tissue inhibitors of metalloproteinases (TIMPs), which act as natural inhibitors of matrix metalloproteinases (MMPs). We highlight examples wherein design of combinatorial TIMP libraries using structural and computational insights and screening these libraries of variants using yeast surface display (YSD), has successfully optimized for MMP binding and selectivity, and conferred insight into the PPIs involved.

Discovery of 14-3-3 PPI Stabilizers by Extension of an Amidine-Substituted Thiophene Fragment.

Protein-protein interaction (PPI) modulation is a promising approach in drug discovery with the potential to expand the 'druggable' proteome and develop new therapeutic strategies. While there have been significant advancements in methodologies for developing PPI inhibitors, there is a relative scarcity of literature describing the 'bottom-up' development of PPI stabilizers (Molecular Glues). The hub protein 14-3-3 and its interactome provide an excellent platform for exploring conceptual approaches to PPI modulation, including evolution of chemical matter for Molecular Glues. In this study, we employed a fragment extension strategy to discover stabilizers for the complex of 14-3-3 protein and an Estrogen Receptor alpha-derived peptide (ERα). A focused library of analogues derived from an amidine-substituted thiophene fragment enhanced the affinity of the 14-3-3/ERα complex up to 6.2-fold. Structure-activity relationship (SAR) analysis underscored the importance of the newly added, aromatic side chain with a certain degree of rigidity. X-ray structural analysis revealed a unique intermolecular π-π stacking binding mode of the most active analogues, resulting in the simultaneous binding of two molecules to the PPI binding pocket. Notably, analogue 11 displayed selective stabilization of the 14-3-3/ERα complex.

Temporal control by cofactors prevents kinetic trapping in retroviral Gag lattice assembly

For retroviruses like HIV to proliferate, they must form virions shaped by the self-assembly of Gag polyproteins into a rigid lattice. This immature Gag lattice has been structurally characterized and reconstituted in vitro, revealing the sensitivity of lattice assembly to multiple cofactors. Due to this sensitivity, the energetic criterion for forming stable lattices is unknown, as are their corresponding rates. Here, we use a reaction-diffusion model designed from the cryo-ET structure of the immature Gag lattice to map a phase diagram of assembly outcomes controlled by experimentally constrained rates and free energies, over experimentally relevant timescales. We find that productive assembly of complete lattices in bulk solution is extraordinarily difficult due to the large size of this ∼3700 monomer complex. Multiple Gag lattices nucleate before growth can complete, resulting in loss of free monomers and frequent kinetic trapping. We therefore derive a time-dependent protocol to titrate or “activate” the Gag monomers slowly within the solution volume, mimicking the biological roles of cofactors. This general strategy works remarkably well, yielding productive growth of self-assembled lattices for multiple interaction strengths and binding rates. By comparing to the in vitro assembly kinetics, we can estimate bounds on rates of Gag binding to Gag and the cellular cofactor IP6. Our results show that Gag binding to IP6 can provide the additional time delay necessary to support smooth growth of the immature lattice with relatively fast assembly kinetics, mostly avoiding kinetic traps. Our work provides a foundation for predicting and disrupting formation of the immature Gag lattice via targeting specific protein-protein binding interactions.

Reversible Dual-Covalent Molecular Locking of the 14-3-3/ERRγ Protein-Protein Interaction as a Molecular Glue Drug Discovery Approach.

Molecules that stabilize protein-protein interactions (PPIs) are invaluable as tool compounds for biophysics and (structural) biology, and as starting points for molecular glue drug discovery. However, identifying initial starting points for PPI stabilizing matter is highly challenging, and chemical optimization is labor-intensive. Inspired by chemical crosslinking and reversible covalent fragment-based drug discovery, we developed an approach that we term "molecular locks" to rapidly access molecular glue-like tool compounds. These dual-covalent small molecules reversibly react with a nucleophilic amino acid on each of the partner proteins to dynamically crosslink the protein complex. The PPI between the hub protein 14-3-3 and estrogen-related receptor γ (ERRγ) was used as a pharmacologically relevant case study. Based on a focused library of dual-reactive small molecules, a molecular glue tool compound was rapidly developed. Biochemical assays and X-ray crystallographic studies validated the ternary covalent complex formation and overall PPI stabilization via dynamic covalent crosslinking. The molecular lock approach is highly selective for the specific 14-3-3/ERRγ complex, over other 14-3-3 complexes. This selectivity is driven by the interplay of molecular reactivity and molecular recognition of the composite PPI binding interface. The long lifetime of the dual-covalent locks enabled the selective stabilization of the 14-3-3/ERRγ complex even in the presence of several other competing 14-3-3 clients with higher intrinsic binding affinities. The molecular lock approach enables systematic, selective, and potent stabilization of protein complexes to support molecular glue drug discovery.

Persistent Tor-algebra for protein-protein interaction analysis.

Protein-protein interactions (PPIs) play crucial roles in almost all biological processes from cell-signaling and membrane transport to metabolism and immune systems. Efficient characterization of PPIs at the molecular level is key to the fundamental understanding of PPI mechanisms. Even with the gigantic amount of PPI models from graphs, networks, geometry and topology, it remains as a great challenge to design functional models that efficiently characterize the complicated multiphysical information within PPIs. Here we propose persistent Tor-algebra (PTA) model for a unified algebraic representation of the multiphysical interactions. Mathematically, our PTA is inherently algebraic data analysis. In our PTA model, protein structures and interactions are described as a series of face rings and Tor modules, from which PTA model is developed. The multiphysical information within/between biomolecules are implicitly characterized by PTA and further represented as PTA barcodes. To test our PTA models, we consider PTA-based ensemble learning for PPI binding affinity prediction. The two most commonly used datasets, i.e. SKEMPI and AB-Bind, are employed. It has been found that our model outperforms all the existing models as far as we know. Mathematically, our PTA model provides a highly efficient way for the characterization of molecular structures and interactions.

In silico de novo drug design of a therapeutic peptide inhibitor against UBE2C in breast cancer.

The World Health Organization (WHO) declared breast cancer (BC) as the most prevalent cancer in the world. With its prevalence and severity, there have been several breakthroughs in developing treatments for the disease. Targeted therapy treatments limit the damage done to healthy tissues. These targeted therapies are especially potent for luminal and HER-2 positive type breast cancer. However, for triple negative breast cancer (TNBC), the lack of defining biomarkers makes it hard to approach with targeted therapy methods. Protein-protein interactions (PPIs) have been studied as possible targets for drug action. However, small molecule drugs are not able to cover the entirety of the PPI binding interface. Peptides were found to be more suited to the large or flat PPI surfaces, in addition to their better pharmacokinetic properties. In this study, computational methods was used in order to verify whether peptide drug inhibitors are good drug candidates against the ubiquitin protein, UBE2C by conducting docking, MD and MMPBSA analyses. Results show that while the lead peptide, T20-M shows good potential as a peptide drug, its binding affinity towards UBE2C is not enough to overcome the natural UBE2C-ANAPC2 interaction. Further studies on modification of T20-M and the analysis of other peptide leads are recommended.

Hom-Complex-Based Machine Learning (HCML) for the Prediction of Protein-Protein Binding Affinity Changes upon Mutation.

Protein-protein interactions (PPIs) are involved in almost all biological processes in the cell. Understanding protein-protein interactions holds the key for the understanding of biological functions, diseases and the development of therapeutics. Recently, artificial intelligence (AI) models have demonstrated great power in PPIs. However, a key issue for all AI-based PPI models is efficient molecular representations and featurization. Here, we propose Hom-complex-based PPI representation, and Hom-complex-based machine learning models for the prediction of PPI binding affinity changes upon mutation, for the first time. In our model, various Hom complexes Hom(G1, G) can be generated for the graph representation G of protein-protein complex by using different graphs G1, which reveal G1-related inner connections within the graph representation G of protein-protein complex. Further, for a specific graph G1, a series of nested Hom complexes are generated to give a multiscale characterization of the PPIs. Its persistent homology and persistent Euler characteristic are used as molecular descriptors and further combined with the machine learning model, in particular, gradient boosting tree (GBT). We systematically test our model on the two most-commonly used data sets, that is, SKEMPI and AB-Bind. It has been found that our model outperforms all the existing models as far as we know, which demonstrates the great potential of our model for the analysis of PPIs. Our model can be used for the analysis and design of efficient antibodies for SARS-CoV-2.

Structure-based assessment and druggability classification of protein–protein interaction sites

The featureless interface formed by protein–protein interactions (PPIs) is notorious for being considered a difficult and poorly druggable target. However, recent advances have shown PPIs to be druggable, with the discovery of potent inhibitors and stabilizers, some of which are currently being clinically tested and approved for medical use. In this study, we assess the druggability of 12 commonly targeted PPIs using the computational tool, SiteMap. After evaluating 320 crystal structures, we find that the PPI binding sites have a wide range of druggability scores. This can be attributed to the unique structural and physiochemical features that influence their ligand binding and concomitantly, their druggability predictions. We then use these features to propose a specific classification system suitable for assessing PPI targets based on their druggability scores and measured binding-affinity. Interestingly, this system was able to distinguish between different PPIs and correctly categorize them into four classes (i.e. very druggable, druggable, moderately druggable, and difficult). We also studied the effects of protein flexibility on the computed druggability scores and found that protein conformational changes accompanying ligand binding in ligand-bound structures result in higher protein druggability scores due to more favorable structural features. Finally, the drug-likeness of many published PPI inhibitors was studied where it was found that the vast majority of the 221 ligands considered here, including orally tested/marketed drugs, violate the currently acceptable limits of compound size and hydrophobicity parameters. This outcome, combined with the lack of correlation observed between druggability and drug-likeness, reinforces the need to redefine drug-likeness for PPI drugs. This work proposes a PPI-specific classification scheme that will assist researchers in assessing the druggability and identifying inhibitors of the PPI interface.

Reverse vaccinology-based prediction of a multi-epitope SARS-CoV-2 vaccine and its tailoring to new coronavirus variants

The genome feature of SARS-CoV-2 leads the virus to mutate and creates new variants of concern. Tackling viral mutations is also an important challenge for the development of a new vaccine. Accordingly, in the present study, we undertook to identify B- and T-cell epitopes with immunogenic potential for eliciting responses to SARS-CoV-2, using computational approaches and its tailoring to coronavirus variants. A total of 47 novel epitopes were identified as immunogenic triggering immune responses and no toxic after investigation with in silico tools. Furthermore, we found these peptide vaccine candidates showed a significant binding affinity for MHC I and MHC II alleles in molecular docking investigations. We consider them to be promising targets for developing peptide-based vaccines against SARS-CoV-2. Subsequently, we designed two efficient multi-epitopes vaccines against the SARS-CoV-2, the first one based on potent MHC class I and class II T-cell epitopes of S (FPNITNLCPF–NYNYLYRLFR–MFVFLVLLPLVSSQC), M (MWLSYFIASF–GLMWLSYFIASFRLF), E (LTALRLCAY–LLFLAFVVFLLVTLA), and N (SPRWYFYYL–AQFAPSASAFFGMSR). The second candidate is the result of the tailoring of the first designed vaccine according to three classes of SARS-CoV-2 variants. Molecular docking showed that the protein-protein binding interactions between the vaccines construct and TLR2–TLR4 immune receptors are stable complexes. These findings confirmed that the final multi-epitope vaccine could be easily adapted to new viral variants. Our study offers a shortlist of promising epitopes that can accelerate the development of an effective and safe vaccine against the virus and its adaptation to new variants. Communicated by Ramaswamy H. Sarma

A systems genomics approach to uncover patient-specific pathogenic pathways and proteins in ulcerative colitis

We describe a precision medicine workflow, the integrated single nucleotide polymorphism network platform (iSNP), designed to determine the mechanisms by which SNPs affect cellular regulatory networks, and how SNP co-occurrences contribute to disease pathogenesis in ulcerative colitis (UC). Using SNP profiles of 378 UC patients we map the regulatory effects of the SNPs to a human signalling network containing protein-protein, miRNA-mRNA and transcription factor binding interactions. With unsupervised clustering algorithms we group these patient-specific networks into four distinct clusters driven by PRKCB, HLA, SNAI1/CEBPB/PTPN1 and VEGFA/XPO5/POLH hubs. The pathway analysis identifies calcium homeostasis, wound healing and cell motility as key processes in UC pathogenesis. Using transcriptomic data from an independent patient cohort, with three complementary validation approaches focusing on the SNP-affected genes, the patient specific modules and affected functions, we confirm the regulatory impact of non-coding SNPs. iSNP identified regulatory effects for disease-associated non-coding SNPs, and by predicting the patient-specific pathogenic processes, we propose a systems-level way to stratify patients.

Importance of interface and surface areas in protein-protein binding affinity prediction: A machine learning analysis based on linear regression and artificial neural network

Protein-protein interaction plays an important role in all biological systems. The binding affinity between two protein binding partners reflects the strength of their association, which is crucial to the elucidation of the biological functions of these proteins and also to the design of protein-based therapeutic agents. In recent years, many studies have been conducted in an effort to improve the ability to predict the binding affinity of a protein-protein complex. Different sequence and structural features have been adopted in the prediction, but the surface or interface areas of the protein-protein complex were often not given adequate consideration. In the present study, different types of interface and surface areas in the protein-protein complex were used to construct or train linear, nonlinear or mixed models using linear regression and artificial neural network to predict the binding affinity of protein-protein interactions. The relative importance of the different types of areas in the selected models for affinity prediction was analyzed using variable-controlling approach. In terms of performance, the best area-based binding affinity predictors appeared to be superior or at least comparable to the widely-used predictors PRODIGY (a contacts-based predictor) and LISA (Local Interaction Signal Analysis). This work highlights the importance of interface and surface areas in protein-protein binding interactions. It also sheds light on the more suitable computational approaches that may aid in solving some of the scientific and technical issues associated with protein-protein binding affinity prediction. SignificanceProtein-protein interactions are ubiquitous in living systems. Protein-protein binding affinity is a metric that estimates the binding strength between two protein binding partners. Reliable information on their binding affinity is of great value in understanding complex biological processes as well as in designing protein-based therapeutics. In this work, the interface and surface areas in protein-protein interaction are explored with respect to their relative importance in better predicting the protein-protein binding affinity. The results from this study showed that different types of areas contribute importantly to protein-protein interactions and thus should be jointly considered in an explicit manner to improve affinity predictions. In addition, the effective application of interface and surface areas may also facilitate the simulation of the protein folding and binding processes.

Quantifying thermodynamic parameters of multi-step binding using a novel approach to Bayesian inference

Multistep protein-protein or protein-ligand binding interactions are of critical biological importance in numerous cellular processes. However, probing the thermodynamics of complex binding by isothermal titration calorimetry (ITC) or other methods often relies on applications of simple models with strong assumptions not representative of the underlying interactions, which can lead to overly narrow uncertainty estimates. Focusing on ITC, standard practice in the field is to fit binding curves to simple models which return a single enthalpy and free energy term representing an overall picture of binding thermodynamics, but offering minimal information on intermediate steps.

Generation of Monoubiquitin and K63-Linked Polyubiquitin Chains for Protein Interaction Studies.

Ubiquitylation is a posttranslational modification that utilizes protein-protein binding interactions to regulate cellular processes. In ubiquitin signaling, a vast array of mono- and polyubiquitin modifications to substrate proteins are recognized by a diverse group of ubiquitin-binding proteins. Identifying ubiquitin-binding proteins and characterizing their binding properties is necessary for understanding the structural basis of ubiquitin signaling. This chapter provides a means of studying ubiquitin-binding interactions in vitro by describing how to generate monoubiquitin and K63-linked polyubiquitin chains and perform pull-down assays with ubiquitin-binding proteins, which is of particular relevance for DNA damage and other signaling pathways.

Optimized Molecular Interaction Networks for the Study of Skeletal Muscle.

Molecular interaction networks (MINs) aim to capture the complex relationships between interacting molecules within a biological system. MINs can be constructed from existing knowledge of molecular functional associations, such as protein-protein binding interactions (PPI) or gene co-expression, and these different sources may be combined into a single MIN. A given MIN may be more or less optimal in its representation of the important functional relationships of molecules in a tissue. The aim of this study was to establish whether a combined MIN derived from different types of functional association could better capture muscle-relevant biology compared to its constituent single-source MINs. MINs were constructed from functional association databases for both protein-binding and gene co-expression. The networks were then compared based on the capture of muscle-relevant genes and gene ontology (GO) terms, tested in two different ways using established biological network clustering algorithms. The top performing MINs were combined to test whether an optimal MIN for skeletal muscle could be constructed. The STRING PPI network was the best performing single-source MIN among those tested. Combining STRING with interactions from either the MyoMiner or CoXPRESSdb gene co-expression sources resulted in a combined network with improved performance relative to its constituent networks. MINs constructed from multiple types of functional association can better represent the functional relationships of molecules in a given tissue. Such networks may be used to improve the analysis and interpretation of functional genomics data in the study of skeletal muscle and neuromuscular diseases. Networks and clusters described by this study, including the combinations of STRING with MyoMiner or with CoXPRESSdb, are available for download from https://www.sys-myo.com/myominer/download.php.

Evaluation and Designing of Epitopic-Peptide Vaccine Against Bunyamwera orthobunyavirus Using M-Polyprotein Target Sequences.

Bunyamwera orthobunyavirus and its serogroup can cause several diseases in humans, cattle, ruminants, and birds. The viral M-polyprotein helps the virus to enter the host body. Therefore, this protein might serve as a potential vaccine target against Bunyamwera orthobunyavirus. The present study applied the immunoinformatics technique to design an epitopic vaccine component that could protect against Bunyamwera infection. Phylogenetic analysis revealed the presence of conserved patterns of M-polyprotein within the viral serogroup. Three epitopes common for both B-cell and T-cell were identified, i.e., YQPTELTRS, YKAHDKEET, and ILGTGTPKF merged with a specific linker peptide to construct an active vaccine component. The low atomic contact energy value of docking complex between human TLR4 (TLR4/MD2 complex) and vaccine construct confirms the elevated protein–protein binding interaction. Molecular dynamic simulation and normal mode analysis illustrate the docking complex’s stability, especially by the higher Eigenvalue. In silico cloning of the vaccine construct was applied to amplify the desired vaccine component. Structural allocation of both the vaccine and epitopes also show the efficacy of the developed vaccine. Hence, the computational research design outcomes support that the peptide-based vaccine construction is a crucial drive target to limit the infection of Bunyamwera orthobunyavirus to an extent.Supplementary InformationThe online version contains supplementary material available at 10.1007/s10989-021-10322-9.