ClusPro PeptiDock: efficient global docking of peptide recognition motifs using FFT

PepBinding: A Workflow for Predicting Peptide Binding Structures by Combining Peptide Docking and Peptide Gaussian Accelerated Molecular Dynamics Simulations

Predicting protein-peptide interactions is crucial for understanding peptide binding processes and designing peptide drugs. However, traditional computational modeling approaches face challenges in accurately predicting peptide-protein binding structures due to the slow dynamics and high flexibility of the peptides. Here, we introduce a new workflow termed "PepBinding" for predicting peptide binding structures, which combines peptide docking, all-atom enhanced sampling simulations using the Peptide Gaussian accelerated Molecular Dynamics (Pep-GaMD) method, and structural clustering. PepBinding has been demonstrated on seven distinct model peptides. In peptide docking using HPEPDOCK, the peptide backbone root-mean-square deviations (RMSDs) of their bound conformations relative to X-ray structures ranged from 3.8 to 16.0 Å, corresponding to the medium to inaccurate quality models according to the Critical Assessment of PRediction of Interactions (CAPRI) criteria. The Pep-GaMD simulations performed for only 200 ns significantly improved the docking models, resulting in five medium and two acceptable quality models. Therefore, PepBinding is an efficient workflow for predicting peptide binding structures and is publicly available at https://github.com/MiaoLab20/PepBinding.

Computational investigation of the inhibitory interaction of IRF3 and SARS-CoV-2 accessory protein ORF3b

ORF3b is one of the SARS-CoV-2 accessory proteins. Previous experimental study suggested that ORF3b prevents IRF3 translocating to nucleus. However, the biophysical mechanism of ORF3b-IRF3 interaction is elusive. Here, we explored the conformation ensemble of ORF3b using all-atom replica exchange molecular dynamics simulation. Disordered ORF3b has mixed α-helix, β-turn and loop conformers. The potential ORF3b-IRF3 binding modes were searched by docking representative ORF3b conformers with IRF3, and 50 ORF3b-IRF3 complex poses were screened using molecular dynamics simulations ranging from 500 to 1000 ns. We found that ORF3b binds IRF3 predominantly on its CBP binding and phosphorylated pLxIS motifs, with CBP binding site has the highest binding affinity. The ORF3b-IRF3 binding residues are highly conserved in SARS-CoV-2. Our results provided biophysics insights into ORF3b-IRF3 interaction and explained its interferon antagonism mechanism.

Therapeutic peptides for coronary artery diseases: in silico methods and current perspectives

Many drug formulations containing small active molecules are used for the treatment of coronary artery disease, which affects a significant part of the world's population. However, the inadequate profile of these molecules in terms of therapeutic efficacy has led to the therapeutic use of protein and peptide-based biomolecules with superior properties, such as target-specific affinity and low immunogenicity, in critical diseases. Protein‒protein interactions, as a consequence of advances in molecular techniques with strategies involving the combined use of in silico methods, have enabled the design of therapeutic peptides to reach an advanced dimension. In particular, with the advantages provided by protein/peptide structural modeling, molecular docking for the study of their interactions, molecular dynamics simulations for their interactions under physiological conditions and machine learning techniques that can work in combination with all these, significant progress has been made in approaches to developing therapeutic peptides that can modulate the development and progression of coronary artery diseases. In this scope, this review discusses in silico methods for the development of peptide therapeutics for the treatment of coronary artery disease and strategies for identifying the molecular mechanisms that can be modulated by these designs and provides a comprehensive perspective for future studies.

Efficient Refinement of Complex Structures of Flexible Histone Peptides Using Post-Docking Molecular Dynamics Protocols

Histones are keys to many epigenetic events and their complexes have therapeutic and diagnostic importance. The determination of the structures of histone complexes is fundamental in the design of new drugs. Computational molecular docking is widely used for the prediction of target-ligand complexes. Large, linear peptides like the tail regions of histones are challenging ligands for docking due to their large conformational flexibility, extensive hydration, and weak interactions with the shallow binding pockets of their reader proteins. Thus, fast docking methods often fail to produce complex structures of such peptide ligands at a level appropriate for drug design. To address this challenge, and improve the structural quality of the docked complexes, post-docking refinement has been applied using various molecular dynamics (MD) approaches. However, a final consensus has not been reached on the desired MD refinement protocol. In this present study, MD refinement strategies were systematically explored on a set of problematic complexes of histone peptide ligands with relatively large errors in their docked geometries. Six protocols were compared that differ in their MD simulation parameters. In all cases, pre-MD hydration of the complex interface regions was applied to avoid the unwanted presence of empty cavities. The best-performing protocol achieved a median of 32% improvement over the docked structures in terms of the change in root mean squared deviations from the experimental references. The influence of structural factors and explicit hydration on the performance of post-docking MD refinements are also discussed to help with their implementation in future methods and applications.

Development of multi epitope subunit vaccines against emerging carp viruses Cyprinid herpesvirus 1 and 3 using immunoinformatics approach

Cyprinid herpesvirus is a causative agent of a destructive disease in common and koi carp (Cyprinus carpio), which leads to substantial global financial losses in aquaculture industries. Among the strains of C. herpesvirus, C. herpesvirus 1 (CyHV-1) and C. herpesvirus 3 (CyHV-3) are known as highly pathogenic to carp fishes in Europe, Asia, and Africa. To date, no effective vaccine has been developed to combat these viruses. This study aimed to develop unique multi-epitope subunit vaccines targeting the CyHV-1 and CyHV-3 using a reverse vaccinology approach. The study began with a comprehensive literature review to identify the most critical proteins, which were then subjected to in silico analyses to predict highly antigenic epitopes. These analyses involved assessing antigenicity, transmembrane topology screening, allergenecity, toxicity, and molecular docking approaches. We constructed two multi-epitope-based vaccines incorporating a suitable adjuvant and appropriate linkers. It revealed that both the vaccines are non-toxic and immunogenic. The tertiary structures of the vaccine proteins were generated, refined, and validated to ensure their suitability. The binding affinity between the vaccine constructs and TLR3 and TLR5 receptors were assessed by molecular docking studies. Molecular dynamics simulations indicated that vaccine construct V1 exhibited greater stability with both TLR3 and TLR5 based on RMSD analysis. Hydrogen bond analysis revealed a stronger binding affinity between the vaccine constructs and TLR5 compared to TLR3. Furthermore, MM-PBSA analysis suggested that both vaccine constructs exhibited a better affinity for TLR5. Considering all aspects, the results suggest that in silico development of CyHV vaccines incorporating multiple epitopes holds promise for management of diseases caused by CyHV-1 and CyHV-3. However, further in vivo trials are highly recommended to validate the efficacies of these vaccines.

Simulation and Computational Study of RING Domain Mutants of BRCA1 and Ube2k in AD/PD Pathophysiology

Lysine-based post-translational modification (PTM) such as acylation, acetylation, deamination, methylation, SUMOylation, and ubiquitination has proven to be a major regulator of gene expression, chromatin structure, protein stability, protein-protein interaction, protein degradation, and cellular localization. However, besides all the PTMs, ubiquitination stands as the second most common PTM after phosphorylation that is involved in the etiology of neurodegenerative diseases (NDDs) namely, Alzheimer's disease (AD) and Parkinson's disease (PD). NDDs are characterized by the accumulation of misfolded protein aggregates in the brain that lead to disease-related gene mutation and irregular protein homeostasis. The ubiquitin-proteasome system (UPS) is in charge of degrading these misfolded proteins, which involve an interplay of E1, E2, E3, and deubiquitinase enzymes. Impaired UPS has been commonly observed in NDDs and E3 ligases are the key members of the UPS, thus, dysfunction of the same can accelerate the neurodegeneration process. Therefore, the aim of this study is firstly, to find E3 ligases that are common in both AD and PD through data mining. Secondly, to study the impact of mutation on its structure and function. The study deciphered 74 E3 ligases that were common in both AD and PD. Later, 10 hub genes were calculated of which protein-protein interaction, pathway enrichment, lysine site prediction, domain, and motif analysis were performed. The results predicted BRCA1, PML, and TRIM33 as the top three putative lysine-modified E3 ligases involved in AD and PD pathogenesis. However, based on structural characterization, BRCA1 was taken further to study RING domain mutation that inferred K32Y, K32L, K32C, K45V, K45Y, and K45G as potential mutants that alter the structural and functional ability of BRCA1 to interact with Ube2k, E2-conjugating enzyme. The most probable mutant observed after molecular dynamics simulation of 50 ns is K32L. Therefore, our study concludes BRCA1, a potential E3 ligase common in AD and PD, and RING domain mutation at sites K32 and K45 possibly disturbs its interaction with its E2, Ube2k.

Molecular Dynamics Simulations on Spike Protein Mutants Binding with Human β Defensin Type 2

Human β defensin type 2 (hBD-2), a cationic cysteine-rich peptide secreted from the human innate immune system, can bind Spike-RBD at the same site as receptor ACE2, thus blocking viral entry into ACE2-expressing cells. In order to find out the impact of CoV-2 mutations on hBD-2's antiviral activity, it is important to investigate the binding and interaction of hBD-2 with RBD mutants. All-atom molecular dynamics simulations were conducted on typical RBD mutants, including N501Y, E484K, P479S, T478I, S477N, N439K, K417N, and N501Y-E484K-K417N, binding with hBD-2. Starting from the stable binding structure of hBD-2 and wt-RBD and ClusPro and HADDOCK docking-predicted initial structures, the RBD variants bound with hBD-2 simulations were set up, and NAMD simulations were conducted. Based on the structure and dynamics analysis, it was found that most RBD variants can still form a similar number of hydrogen bonds with hBD-2, in addition to having a similar-sized buried surface area (BSA) and a similar binding interface to the RBD wildtype. However, the RBD triple mutant formed a less stable binding structure with hBD-2 than other variants. Additionally, the free energy perturbation (FEP) method was applied to calculate the contribution of key mutant residues to the binding and the free energy change caused by the mutations. The result shows that N439K, K417N, and the trimutation increase the binding free energy of RBD with hBD-2; thus, RBD should bind less stably with hBD-2. E484K decreases the binding free energy, thus it should bind more stably with hBD-2, while N501Y, S477N, T478I, and P479S almost do not change the binding free energy with hBD-2. The MM-GBSA method predicted the binding interaction energy which shows that the trimutant should be able to escape the binding with hBD-2 but N501Y should not. The result can provide insight into understanding the functional mechanism of hBD-2 combating SARS-CoV-2 mutants.

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.

Interaction of Tau with G-Protein-Coupled Purinergic P2Y12 Receptor by Molecular Docking and Molecular Dynamic Simulation

Alzheimer's disease, a progressive neurological disorder, is characterized by the accumulation of neurofibrillary tangles and senile plaques by Tau and amyloid-β, respectively, in the brain microenvironment. The misfolded protein aggregates interact with several components of neuronal and glial cells such as membrane lipids, receptors, transporters, enzymes, cytoskeletal proteins, etc. Under pathological conditions, Tau interacts with several G-protein-coupled receptors (GPCRs), which undergoes either receptor signaling or desensitization followed by internalization of the protein complex. The purinergic GPCR, P2Y12 which is expressed in microglial cells, plays a key role in its activation and migration. Microglial cells sense and migrate to the site of injury aided by P2Y12 receptor that interacts with ADP released from damaged cells. P2Y12 receptor also interacts with misfolded Tau accumulated at the extracellular space and promotes receptor-mediated internalization. Immunocolocalization and co-immunoprecipitation studies demonstrated the interaction of Tau species with the P2Y12 receptor. Later, in-silico analyses were carried out with the repeat domain of Tau (TauRD), which has been identified as the interacting partner of P2Y12 receptor by in-vitro studies. Molecular docking and molecular dynamics simulation studies show the stability and the type of interaction in TauRD-receptor complex. Tau interaction with P2Y12 receptor plays a significant role in maintaining the active state of microglia which could lead to neuroinflammation and neuronal damage in AD brain. Hence, blocking P2Y12-Tau interaction and P2Y12-mediated Tau internalization in microglial cells could be possible therapeutic strategies in downregulating the severity of neuroinflammation in AD.

A New Advanced Approach: Design and Screening of Affinity Peptide Ligands Using Computer Simulation Techniques

Peptides acquire target affinity based on the combination of residues in their sequences and the conformation formed by their flexible folding, an ability that makes them very attractive biomaterials in therapeutic, diagnostic, and assay fields. With the development of computer technology, computer-aided design and screening of affinity peptides has become a more efficient and faster method. This review summarizes successful cases of computer-aided design and screening of affinity peptide ligands in recent years and lists the computer programs and online servers used in the process. In particular, the characteristics of different design and screening methods are summarized and categorized to help researchers choose between different methods. In addition, experimentally validated sequences are listed, and their applications are described, providing directions for the future development and application of computational peptide screening and design.

ComDock: A novel approach for protein-protein docking with an efficient fusing strategy

Protein-protein interaction plays an important role in studying the mechanism of protein functions from the structural perspective. Molecular docking is a powerful approach to detect protein-protein complexes using computational tools, due to the high cost and time-consuming of the traditional experimental methods. Among existing technologies, the template-based method utilizes the structural information of known homologous 3D complexes as available and reliable templates to achieve high accuracy and low computational complexity. However, the performance of the template-based method depends on the quality and quantity of templates. When insufficient or even no templates, the ab initio docking method is necessary and largely enriches the docking conformations. Therefore, it's a feasible strategy to fuse the effectivity of the template-based model and the universality of ab initio model to improve the docking performance. In this study, we construct a new, diverse, comprehensive template library derived from PDB, containing 77,685 complexes. We propose a template-based method (named TemDock), which retrieves the evolutionary relationship between the target sequence and samples in the template library and transfers similar structural information. Then, the target structure is built by superposing on the homologous template complex with TM-align. Moreover, we develop a consensus-based method (named ComDock) to integrate our TemDock and an existing ab initio method (ZDOCK). On 105 targets with templates from Benchmark 5.0, the TemDock and ComDock achieve a success rate of 68.57 % and 71.43 % in the top 10 conformations, respectively. Compared with the HDOCK, ComDock obtains better I-RMSD of hit configurations on 9 targets and more hit models in the top 100 conformations. As an efficient method for protein-protein docking, the ComDock is expected to study protein-protein recognition and reveal the various biological passways that are critical for developing drug discovery. The final results are stored at https://github.com/guofei-tju/mqz_ComDock_docking.

Specific Genetic Polymorphisms Contributing in Differential Binding of Gliadin Peptides to HLA-DQ and TCR to Elicit Immunogenicity in Celiac Disease

Immunogenicity of gliadin peptides in celiac disease (CD) is majorly determined by the pattern of molecular interactions with HLA-DQ and T-cell receptors (TCR). Investigation of the interactions between immune-dominant gliadin peptides, DQ protein, and TCR are warranted to unravel the basis of immunogenicity and variability contributed by the genetic polymorphisms. Homology modeling of HLA and TCR done using Swiss Model and iTASSER, respectively. Molecular interactions of eight common deamidated immune-dominant gliadin with HLA-DQ allotypes and specific TCR gene pairs were evaluated. Docking of the three structures was performed with ClusPro2.0 and ProDiGY was used to predict binding energies. Effects of known allelic polymorphisms and reported susceptibility SNPs were predicted on protein-protein interactions. CD susceptible allele, HLA-DQ2.5 was shown to have considerable binding affinity to 33-mer gliadin (ΔG = - 13.9; Kd = 1.5E - 10) in the presence of TRAV26/TRBV7. Higher binding affinity was predicted (ΔG = - 14.3, Kd = 8.9E - 11) when TRBV28 was replaced with TRBV20 paired with TRAV4 suggesting its role in CD predisposition. SNP rs12722069 at HLA-DQ8 that codes Arg76α forms three H-bonds with Glu12 and two H-bonds with Asn13 of DQ2 restricted gliadin in the presence of TRAV8-3/TRBV6. None of the HLA-DQ polymorphisms was found to be in linkage disequilibrium with reported CD susceptibility markers. Haplotypic presentations of rs12722069-G, rs1130392-C, rs3188043-C and rs4193-A with CD reported SNPs were observed in sub-ethnic groups. Highly polymorphic sites of HLA alleles and TCR variable regions could be utilized for better risk prediction models in CD. Therapeutic strategies by identifying inhibitors or blockers targeting specific gliadin:HLA-DQ:TCR binding sites could be investigated.

Is the BNT162b2 vaccine still effective against the latest variant: XBB.1.5?

Background

The XBB.1.5 sub-variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron now continues to spread rapidly due to the increased transmission rate as a result of increased affinity of the virus binding over the ACE-2 receptor - a gained property due to the mutation that occurred in spike protein.

Aim

The protectivity of BNT162b2 antibodies produced in the serum of patients is an important parameter for preventing transmission. However, the affinity of the antibodies of patients vaccinated with BNT162b2 over the latest SARS-CoV-2 variant, XBB.1.5, is not well established. This study aimed to evaluate the efficacy of the BNT162b2 vaccine-induced antibody on XBB.1.5 by comparing the X-ray crystallographic structures and spike protein mutations of BA.5 and XBB.1.5 using in silico methods.

Materials and Methods

Binding points and binding affinity values of the BNT162b2 antibody with BA.5 and XBB.1.5 spike protein were calculated using ClusPro 2.0 protein-protein docking and Discovery Studio 2021 Client software. Mutations in the genetic code of the spike protein for SARS-CoV-2 BA.5 and XBB.1.5 sub-variants were screened using the GISAID database.

Results

Binding affinity values showed that BNT162b2 had higher negative values in the XBB.1.5 sub-variant than BA.5 at the mutation sites at the binding region. The results suggested that BNT162b2 may retain its activity despite mutations and conformational changes in the binding site of the XBB.1.5.

Conclusion

The findings of this study shed light on the importance and usability of the current BNT162b2 vaccine for XBB.1.5 and future variants of concern.

Significant destabilization of human telomeric G-quadruplex upon peptide binding: dramatic effect of flanking bases

Human telomere is composed of highly repeated hexanucleotide sequence TTAGGG and a 3' single-stranded DNA tail. Many telomere G4 topologies characterized at atomic level by X-ray crystallography and NMR studies. Until now, various small ligands developed to interact with G-quadruplex mainly to stabilize the structure and least is known for its destabilization. In this study, we provide the first evidence of human telomeric G4 destabilization upon peptide binding in dilute and cell-mimicking molecular crowing conditions due to the changes in flanking bases of human telomeric sequences. Hence, our findings will open the new ways to target diseases related with increasing the efficiency of DNA replication, transcription or duplex reannealing.Communicated by Ramaswamy H. Sarma.

Design of Cyclic Peptides Targeting Protein-Protein Interactions Using AlphaFold

More than 930,000 protein-protein interactions (PPIs) have been identified in recent years, but their physicochemical properties differ from conventional drug targets, complicating the use of conventional small molecules as modalities. Cyclic peptides are a promising modality for targeting PPIs, but it is difficult to predict the structure of a target protein-cyclic peptide complex or to design a cyclic peptide sequence that binds to the target protein using computational methods. Recently, AlphaFold with a cyclic offset has enabled predicting the structure of cyclic peptides, thereby enabling de novo cyclic peptide designs. We developed a cyclic peptide complex offset to enable the structural prediction of target proteins and cyclic peptide complexes and found AlphaFold2 with a cyclic peptide complex offset can predict structures with high accuracy. We also applied the cyclic peptide complex offset to the binder hallucination protocol of AfDesign, a de novo protein design method using AlphaFold, and we could design a high predicted local-distance difference test and lower separated binding energy per unit interface area than the native MDM2/p53 structure. Furthermore, the method was applied to 12 other protein-peptide complexes and one protein-protein complex. Our approach shows that it is possible to design putative cyclic peptide sequences targeting PPI.

Design of Tetra-Peptide Ligands of Antibody Fc Regions Using In Silico Combinatorial Library Screening

Peptides, or short chains of amino-acid residues, are becoming increasingly important as active ingredients of drugs and as crucial probes and/or tools in medical, biotechnological, and pharmaceutical research. Situated at the interface between small molecules and larger macromolecular systems, they pose a difficult challenge for computational methods. We report an in silico peptide library generation and prioritization workflow using CmDock for identifying tetrapeptide ligands that bind to Fc regions of antibodies that is analogous to known in vitro recombinant peptide libraries' display and expression systems. The results of our in silico study are in accordance with existing scientific literature on in vitro peptides that bind to antibody Fc regions. In addition, we postulate an evolving in silico library design workflow that will help circumvent the combinatorial problem of in vitro comprehensive peptide libraries by focusing on peptide subunits that exhibit favorable interaction profiles in initial in silico peptide generation and testing.

Quinacrine inhibits HIF-1α/VEGF-A mediated angiogenesis by disrupting the interaction between cMET and ABCG2 in patient-derived breast cancer stem cells

BACKGROUND

Breast cancer stem cells (BCSCs) have a critical role in progression of breast cancer by inducing angiogenesis. Several therapeutic strategies have been designed for the treatment of breast cancer by specifically preventing angiogenesis. But there is a dearth of study regarding the treatment procedure which can specifically target and kill the BCSCs and cause lesser harm to healthy cells of the body. A plant-based bioactive compound Quinacrine (QC) specifically kills cancer stem cells (CSCs) without harming healthy cells and also inhibits cancer angiogenesis but the detailed mechanistic study of its anti-CSCs and anti-angiogenic activity is yet to explore.

HYPOTHESIS

Earlier report showed that both cMET and ABCG2 play an essential role in cancer angiogenesis. Both are present on the cell surface of CSCs and share an identical ATP-binding domain. Interestingly, QC a plant based and bioactive compound which was found to inhibit the function of CSCs marker cMET and ABCG2. These relevant evidence led us to hypothesize that cMET and ABCG2 may interact with each other and induce the production of angiogenic factors, resulting in activation of cancer angiogenesis and QC might disrupt the interaction between them to stop this phenomena.

METHODS

Co-immunoprecipitation assay, immunofluorescence assay, and western blotting were performed by using ex vivo patient-derived breast cancer-stem-cells (PDBCSCs) and human umbilical vein endothelial cells (HUVECs). In silico study was carried out to check the interaction between cMET and ABCG2 in presence or absence of QC. Tube formation assay using HUVECs and in ovo Chorioallantoic membrane (CAM) assay using chick fertilized eggs were performed to monitor angiogenesis. In vivo patient-derived xenograft (PDX) mice model was used to validate in silico and ex vivo results.

RESULTS

Data revealed that in a hypoxic tumor microenvironment (TME), cMET and ABCG2 interact with each other and upregulate HIF-1α/VEGF-A axis to induce breast cancer angiogenesis. In silico and ex vivo study showed that QC disrupted the interaction between cMET and ABCG2 to inhibit the angiogenic response in endothelial cells by reducing the secretion of VEGF-A from PDBCSCs within the TME. Knockdown of cMET, ABCG2 or both, significantly downregulated the expression of HIF-1α and reduced the secretion of pro-angiogenic factor VEGF-A in the TME of PDBCSCs. Additionally, when PDBCSCs were treated with QC, similar experimental results were obtained.

CONCLUSION

In silico, in ovo, ex vivo and in vivo data confirmed that QC inhibited the HIF-1α/VEGF-A mediated angiogenesis in breast cancer by disrupting the interaction between cMET and ABCG2.

Synthetic peptides derived from predicted B cell epitopes of nervous necrosis virus (NNV) show antigenicity and elicit immunogenic responses in Asian seabass (Lates calcarifer)

Nervous necrosis virus (NNV) has spread throughout the world, affecting more than 120 freshwater and marine fish species. While vaccination effectively prevents disease outbreaks, the difficulty of producing sufficient viruses using cell lines continues to be a significant disadvantage for producing inactivated vaccines. This study, therefore, explored the application of synthetic peptides as potential vaccine candidates for the prevention of NNV in Asian seabass (Lates calcarifer). Using the epitope prediction tool and molecular docking, three predicted immunogenic B cell epitopes (30-32 aa) derived from NNV coat protein were selected and synthesised, corresponding to amino acid positions 5 to 34 (P1), 133 to 162 (P2) and 181 to 212 (P3). All the predicted peptides interact with Asian sea bass's MHC class II by docking. The antigenicity of these peptides was determined through ELISA and all peptides were able to react with NNV-specific antibodies. Subsequently, the immunogenicity of these synthetic peptides was investigated by immunisation of Asian seabass with individual peptides (30 μg/fish) and a peptide cocktail (P1+P2+P3, 10 μg each/fish) by intraperitoneal injection, followed by a booster dose at day 28 post-primary immunisation. There was a subset of immunised fish that were able to induce upregulation of immune genes (IL-1β, TNFα, MHCI, MHCII β, CD4, CD8, and IgM-like) in the head kidney and spleen post immunization. Importantly, antibodies derived from fish immunised with synthetic peptides reacted with whole NNV virions, and sera from P1 group could neutralise NNV in an in vitro assay. Taken together, these findings indicate that synthetic linear peptides based on predicted B cell epitopes exhibited both antigenic and immunogenic properties, suggesting that they could be potential vaccine candidates for the prevention of NNV in fish.

Blockade of Activin Receptor IIB Protects Arthritis Pathogenesis by Non-Amplification of Activin A-ACVR2B-NOX4 Axis Pathway

Although activin receptor IIB (ACVR2B) is emerging as a novel pathogenic receptor, its ligand and assembled components (or assembly) are totally unknown in the context of osteoarthritis (OA) pathogenesis. The present results suggest that upregulation of ACVR2B and its assembly could affect osteoarthritic cartilage destruction. It is shown that the ACVR2B ligand, activin A, regulates catabolic factor expression through ACVR2B in OA development. Activin A Tg mice (Col2a1-Inhba) exhibit enhanced cartilage destruction, whereas heterozygous activin A KO mice (Inhba^+/- ) show protection from cartilage destruction. In silico analysis suggests that the Activin A-ACVR2B axis is involved in Nox4-dependent ROS production. Activin A Tg:Nox4 KO (Col2a1-Inhba:Nox4^-/- ) mice show inhibition of experimental OA pathogenesis. NOX4 directly binds to the C-terminal binding site on ACVR2B-ACVR1B and amplifies the pathogenic signal for cartilage destruction through SMAD2/3 signaling. Together, the findings reveal that the ACVR2B assembly, which comprises Activin A, ACVR2B, ACVR1B, Nox4, and AP-1-induced HIF-2α, accelerates OA development. Furthermore, it is shown that shRNA-mediated ACVR2B knockdown or trapping ligands of ACVR2B abrogate OA development by competitively disrupting the ACVR2B-Activin A interaction. These results suggest that the ACVR2B assembly is required to amplify osteoarthritic cartilage destruction and could be a potential therapeutic target in efforts to treat OA.

Computational approaches for the design of modulators targeting protein-protein interactions

BACKGROUND

Protein-protein interactions (PPIs) are intriguing targets for designing novel small-molecule inhibitors. The role of PPIs in various infectious and neurodegenerative disorders makes them potential therapeutic targets . Despite being portrayed as undruggable targets, due to their flat surfaces, disorderedness, and lack of grooves. Recent progresses in computational biology have led researchers to reconsider PPIs in drug discovery.

AREAS COVERED

In this review, we introduce in-silico methods used to identify PPI interfaces and present an in-depth overview of various computational methodologies that are successfully applied to annotate the PPIs. We also discuss several successful case studies that use computational tools to understand PPIs modulation and their key roles in various physiological processes.

EXPERT OPINION

Computational methods face challenges due to the inherent flexibility of proteins, which makes them expensive, and result in the use of rigid models. This problem becomes more significant in PPIs due to their flexible and flat interfaces. Computational methods like molecular dynamics (MD) simulation and machine learning can integrate the chemical structure data into biochemical and can be used for target identification and modulation. These computational methodologies have been crucial in understanding the structure of PPIs, designing PPI modulators, discovering new drug targets, and predicting treatment outcomes.

Integration of immunoinformatics and cheminformatics to design and evaluate a multitope vaccine against Klebsiella pneumoniae and Pseudomonas aeruginosa coinfection

Introduction: Klebsiella pneumoniae (K. pneumoniae) and Pseudomonas aeruginosa (P. aeruginosa) are the most common Gram-negative bacteria associated with pneumonia and coinfecting the same patient. Despite their high virulence, there is no effective vaccine against them. Methods: In the current study, the screening of several proteins from both pathogens highlighted FepA and OmpK35 for K. pneumonia in addition to HasR and OprF from P. aeruginosa as promising candidates for epitope mapping. Those four proteins were linked to form a multitope vaccine, that was formulated with a suitable adjuvant, and PADRE peptides to finalize the multitope vaccine construct. The final vaccine's physicochemical features, antigenicity, toxicity, allergenicity, and solubility were evaluated for use in humans. Results: The output of the computational analysis revealed that the designed multitope construct has passed these assessments with satisfactory scores where, as the last stage, we performed a molecular docking study between the potential vaccine construct and K. pneumonia associated immune receptors, TLR4 and TLR2, showing affinitive to both targets with preferentiality for the TLR4 receptor protein. Validation of the docking studies has proceeded through molecular dynamics simulation, which estimated a strong binding and supported the nomination of the designed vaccine as a putative solution for K. pneumoniae and P. aeruginosa coinfection. Here, we describe the approach for the design and assessment of our potential vaccine.

HBD-2 variants and SARS-CoV-2: New insights into inter-individual susceptibility

Background

A deep understanding of the causes of liability to SARS-CoV-2 is essential to develop new diagnostic tests and therapeutics against this serious virus in order to overcome this pandemic completely. In the light of the discovered role of antimicrobial peptides [such as human b-defensin-2 (hBD-2) and cathelicidin LL-37] in the defense against SARS-CoV-2, it became important to identify the damaging missense mutations in the genes of these molecules and study their role in the pathogenesis of COVID-19.

Methods

We conducted a comprehensive analysis with multiple in silico approaches to identify the damaging missense SNPs for hBD-2 and LL-37; moreover, we applied docking methods and molecular dynamics analysis to study the impact of the filtered mutations.

Results

The comprehensive analysis reveals the presence of three damaging SNPs in hBD-2; these SNPs were predicted to decrease the stability of hBD-2 with a damaging impact on hBD-2 structure as well. G51D and C53G mutations were located in highly conserved positions and were associated with differences in the secondary structures of hBD-2. Docking-coupled molecular dynamics simulation analysis revealed compromised binding affinity for hBD-2 SNPs towards the SARS-CoV-2 spike domain. Different protein-protein binding profiles for hBD-2 SNPs, in relation to their native form, were guided through residue-wise levels and differential adopted conformation/orientation.

Conclusions

The presented model paves the way for identifying patients prone to COVID-19 in a way that would guide the personalization of both the diagnostic and management protocols for this serious disease.

Reverse engineering approach: a step towards a new era of vaccinology with special reference to Salmonella

INTRODUCTION

Salmonella is responsible for causing enteric fever, septicemia, and gastroenteritis in humans. Due to high disease burden and emergence of multi- and extensively drug-resistant Salmonella strains, it is becoming difficult to treat the infection with existing battery of antibiotics as we are not able to discover newer antibiotics at the same pace at which the pathogens are acquiring resistance. Though vaccines against Salmonella are available commercially, they have limited efficacy. Advancements in genome sequencing technologies and immunoinformatics approaches have solved the problem significantly by giving rise to a new era of vaccine designing, i.e. 'Reverse engineering.' Reverse engineering/vaccinology has expedited the vaccine identification process. Using this approach, multiple potential proteins/epitopes can be identified and constructed as a single entity to tackle enteric fever.

AREAS COVERED

This review provides details of reverse engineering approach and discusses various protein and epitope-based vaccine candidates identified using this approach against typhoidal Salmonella.

EXPERT OPINION

Reverse engineering approach holds great promise for developing strategies to tackle the pathogen(s) by overcoming the limitations posed by existing vaccines. Progressive advancements in the arena of reverse vaccinology, structural biology, and systems biology combined with an improved understanding of host-pathogen interactions are essential components to design new-generation vaccines.

Computer- and NMR-Aided Design of Small-Molecule Inhibitors of the Hub1 Protein

By binding to the spliceosomal protein Snu66, the human ubiquitin-like protein Hub1 is a modulator of the spliceosome performance and facilitates alternative splicing. Small molecules that bind to Hub1 would be of interest to study the protein-protein interaction of Hub1/Snu66, which is linked to several human pathologies, such as hypercholesterolemia, premature aging, neurodegenerative diseases, and cancer. To identify small molecule ligands for Hub1, we used the interface analysis, peptide modeling of the Hub1/Snu66 interaction and the fragment-based NMR screening. Fragment-based NMR screening has not proven sufficient to unambiguously search for fragments that bind to the Hub1 protein. This was because the Snu66 binding pocket of Hub1 is occupied by pH-sensitive residues, making it difficult to distinguish between pH-induced NMR shifts and actual binding events. The NMR analyses were therefore verified experimentally by microscale thermophoresis and by NMR pH titration experiments. Our study found two small peptides that showed binding to Hub1. These peptides are the first small-molecule ligands reported to interact with the Hub1 protein.

LCK-Mediated RIPK3 Activation Controls Double-Positive Thymocyte Proliferation and Restrains Thymic Lymphoma by Regulating the PP2A-ERK Axis

Receptor-interacting protein kinase 3 (RIPK3) is the primary regulator of necroptotic cell death. RIPK3 expression is often silenced in various cancer cells, which suggests that it may have tumor suppressor properties. However, the exact mechanism by which RIPK3 negatively regulates cancer development and progression remains unclear. This report indicates that RIPK3 acts as a potent regulator of the homeostatic proliferation of CD4⁺ CD8⁺ double-positive (DP) thymocytes. Abnormal proliferation of RIPK3-deficient DP thymocytes occurs independently of the well-known role for RIPK3 in necroptosis (upstream of MLKL activation), and is associated with an incidental thymic mass, likely thymic hyperplasia. In addition, Ripk3-null mice develop increased thymic tumor formation accompanied by reduced host survival in the context of an N-ethyl-N-nitrosourea (ENU)-induced tumor model. Moreover, RIPK3 deficiency in p53-null mice promotes thymic lymphoma development via upregulated extracellular signal-regulated kinase (ERK) signaling, which correlates with markedly reduced survival rates. Mechanistically, lymphocyte-specific protein tyrosine kinase (LCK) activates RIPK3, which in turn leads to increases in the phosphatase activity of protein phosphatase 2 (PP2A), thereby suppressing hyper-activation of ERK in DP thymocytes. Overall, these findings suggest that a RIPK3-PP2A-ERK signaling axis regulates DP thymocyte homeostasis and may provide a potential therapeutic target to improve thymic lymphoma therapies.

Improving peptide-protein docking with AlphaFold-Multimer using forced sampling

Protein interactions are key in vital biological processes. In many cases, particularly in regulation, this interaction is between a protein and a shorter peptide fragment. Such peptides are often part of larger disordered regions in other proteins. The flexible nature of peptides enables the rapid yet specific regulation of important functions in cells, such as their life cycle. Consequently, knowledge of the molecular details of peptide-protein interactions is crucial for understanding and altering their function, and many specialized computational methods have been developed to study them. The recent release of AlphaFold and AlphaFold-Multimer has led to a leap in accuracy for the computational modeling of proteins. In this study, the ability of AlphaFold to predict which peptides and proteins interact, as well as its accuracy in modeling the resulting interaction complexes, are benchmarked against established methods. We find that AlphaFold-Multimer predicts the structure of peptide-protein complexes with acceptable or better quality (DockQ ≥0.23) for 66 of the 112 complexes investigated—25 of which were high quality (DockQ ≥0.8). This is a massive improvement on previous methods with 23 or 47 acceptable models and only four or eight high quality models, when using energy-based docking or interaction templates, respectively. In addition, AlphaFold-Multimer can be used to predict whether a peptide and a protein will interact. At 1% false positives, AlphaFold-Multimer found 26% of the possible interactions with a precision of 85%, the best among the methods benchmarked. However, the most interesting result is the possibility of improving AlphaFold by randomly perturbing the neural network weights to force the network to sample more of the conformational space. This increases the number of acceptable models from 66 to 75 and improves the median DockQ from 0.47 to 0.55 (17%) for first ranked models. The best possible DockQ improves from 0.58 to 0.72 (24%), indicating that selecting the best possible model is still a challenge. This scheme of generating more structures with AlphaFold should be generally useful for many applications involving multiple states, flexible regions, and disorder.

Tools and techniques for rational designing of antimicrobial peptides for aquaculture

Fisheries and aquaculture industries remain essential sources of food and nutrition for millions of people worldwide. Indiscriminate use of antibiotics has led to the emergence of antimicrobial-resistant bacteria and posed a severe threat to public health. Researchers have opined that antimicrobial peptides (AMPs) can be the best possible alternative to curb the rising tide of antimicrobial resistance in aquaculture. AMPs may also help to achieve the objectives of one health approach. The natural AMPs are associated with several shortcomings, like less in vivo stability, toxicity to host cell, high cost of production and low potency in a biological system. In this review, we have provided a comprehensive outline about the strategies for designing synthetic mimics of natural AMPs with high potency. Moreover, the freely available AMP databases and the information about the molecular docking tools are enlisted. We also provided in silico template for rationally designing the AMPs from fish piscidins or other peptides. The rationally designed piscidin (rP1 and rp2) may be used to tackle microbial infections in aquaculture. Further, the protocol can be used to develop the truncated mimics of natural AMPs having more potency and protease stability.

A plausible contributor to multiple sclerosis; presentation of antigenic myelin protein epitopes by major histocompatibility complexes

BACKGROUND

Multiple sclerosis (MS) can be induced upon successful presentation of myelin antigens by MHC I/II. Antigenic similarity between the myelin and viral proteins may worsen the immunological responses.

METHODOLOGY

Antigenic regions within myelin proteins; PLP1, MBP, MOG, and MAG were analyzed using SVMTrip and EMBOSS. Homology search identified sequence similarity between the predicted host epitopes and viral proteins. NetMHCpan predicted MHC I/II binding followed by peptide-protein docking through the HPEPDOCK server. Thereafter we analyzed conformational flexibility and stability of 15 protein-peptide complexes based on high docking scores. The binding free energy was calculated using conventional (MD) and Gaussian accelerated molecular dynamics simulation.

RESULTS

PLP1, MBP, MAG and MOG contained numerous antigenic epitopes. MBP and MOG epitopes had sequence similarity to HHV-6 BALF5; EBNA1 and CMV glycoprotein M (gM), and EBV LMP2B, gp350/220; HHV-8 ORFs respectively. Many herpes virus proteins like tegument, envelope glycoproteins, and ORFs of EBV, CMV, HHV-6, and HHV-8 demonstrated sequence similarity with MAG and PLP1. Some antigenic peptides were also linear B-cell epitopes and influenced cytokine production by T-cell. MHC I allele HLA-B*57:01 bound to PLP1 peptide and HLA-A*68:02 bound to a MAG peptide strongly. MHC II alleles HLA-DRB1*04:05 and HLA-DR1*01:01 associated with MAG- and MOG-derived peptides, respectively, demonstrating high HPEPDOCK scores. MD simulations established stable binding of certain peptides with the MHC namely HLA-B*51:01-MBP(DYKSAHKGFKGVDAQGTLSKIFKL), HLA-B*57:01-PLP1(PDKFVGITYALTVVWLLVFACSAVPVYIYF), HLA-DR1*01:01-MOG(VEDPFYWVSPGVLVLLAVLPVLLLQITVGLVFLCLQYR) and HLA-DRB1*04:05-MAG(TWVQVSLLHFVPTREA).

CONCLUSIONS

Cross-reactivity between self-antigens and pathogen derived immunodominant epitopes may induce MS. Our study supported the role of specific MHC alleles as a contributing MS risk factor.

Inhibition of SARS-CoV-2 pathogenesis by potent peptides designed by the mutation of ACE2 binding region

The outbreak of COVID-19 has resulted in millions of deaths. Despite all attempts that have been made to combat the pandemic, the re-emergence of new variants complicated SARS-CoV-2 eradication. The ongoing global spread of COVID-19 demands the incessant development of novel agents in vaccination, diagnosis, and therapeutics. Targeting receptor-binding domain (RBD) of spike protein by which the virus identifies host receptor, angiotensin-converting enzyme (ACE2), is a promising strategy for curbing viral infection. This study aims to discover novel peptide inhibitors against SARS-CoV-2 entry using computational approaches. The RBD binding domain of ACE2 was extracted and docked against the RBD. MMPBSA calculations revealed the binding energies of each residue in the template. The residues with unfavorable binding energies were considered as mutation spots by OSPREY. Binding energies of the residues in RBD-ACE2 interface was determined by molecular docking. Peptide inhibitors were designed by the mutation of RBD residues in the virus-receptors complex which had unfavorable energies. Peptide tendency for RBD binding, safety, and allergenicity were the criteria based on which the final hits were screened among the initial library. Molecular dynamics simulations also provided information on the mechanisms of inhibitory action in peptides. The results were finally validated by molecular docking simulations to make sure the peptides are capable of hindering virus-host interaction. Our results introduce three peptides P7 (RAWTFLDKFNHEAEDLRYQSSLASWN), P13 (RASTFLDKFNHEAEDLRYQSSLASWN), and P19 (RADTFLDKFNHEAEDLRYQSSLASWN) as potential effective inhibitors of SARS-CoV-2 entry which could be considered in drug development for COVID-19 treatment.

Immunological investigation of a multiepitope peptide vaccine candidate based on main proteins of SARS-CoV-2 pathogen

Multiepitope vaccines could induce multiantigenic immunity against large complex pathogens with different strain variants. Herein, the in silico, in vitro and in vivo studies were used to design and develop a novel candidate antigenic multiepitope vaccine against SARS-CoV-2 pathogen. The designed multiepitope construct targets the spike glycoprotein (S), membrane protein (M), and nucleocapsid phosphoprotein (N) of SARS-CoV-2 (i.e., the S-N-M construct). This construct contains the cytotoxic T lymphocyte (CTL)-, helper T lymphocyte (HTL)-, and linear B lymphocyte (LBL)-inducing epitopes. The multiepitope s-n-m fusion gene was subcloned in prokaryotic (pET24a) and eukaryotic (pcDNA3.1) expression vectors. Its expression was evaluated in mammalian cell line using LL37 cell penetrating peptide. Moreover, the recombinant multiepitope S-N-M peptide was produced in E. coli strain. Finally, mice were immunized using homologous and heterologous regimens for evaluation of immune responses. Our data indicated that the multiepitope S-N-M peptide construct combined with Montanide 720 in homologous regimen significantly stimulated total IgG, IgG2a, IFN-γ, TNF-α, IL-15, IL-21 and IL-6, and Granzyme B secretion as compared to other groups. Moreover, the pcDNA-s-n-m/ LL37 nanoparticles significantly induced higher immune responses than the naked DNA in both homologous and heterologous regimens. In general, our designed multiepitope vaccine construct can be considered as a vaccine candidate in SARS-CoV-2 infection model.

HBD-2 binds SARS-CoV-2 RBD and blocks viral entry: Strategy to combat COVID-19

New approaches to complement vaccination are needed to combat the spread of SARS-CoV-2 and stop COVID-19-related deaths and medical complications. Human beta defensin 2 (hBD-2) is a naturally occurring epithelial cell-derived host defense peptide that has anti-viral properties. Our comprehensive in-silico studies demonstrate that hBD-2 binds the site on the CoV-2-RBD that docks with the ACE2 receptor. Biophysical measurements confirm that hBD-2 indeed binds to the CoV-2-receptor-binding domain (RBD) (K_D ∼ 2μM by surface plasmon resonance), preventing it from binding to ACE2-expressing cells. Importantly, hBD-2 shows specificity by blocking CoV-2/spike pseudoviral infection, but not VSVG-mediated infection, of ACE2-expressing human cells with an IC₅₀ of 2.8 ± 0.4 μM. These promising findings offer opportunities to develop hBD-2 and/or its derivatives and mimetics to safely and effectively use as agents to prevent SARS-CoV-2 infection.

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HBD-2 binds spike-RBD at the ACE2 interaction site in silico

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Biophysical and biological assays confirm hBD-2 binding to spike-RBD

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HBD-2 blocks spike-RBD:ACE2 binding

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HBD-2 prevents CoV-2/spike pseudovirions from infecting ACE2-expressing human cells

In silico and in vivo approaches to recombinant multi-epitope immunogen of GroEL provides efficient cross protection against S. Typhimurium, S. flexneri, and S. dysenteriae

OBJECTIVES

Stress or Heat Shock Proteins (HSPs) have been included in various operations like protein folding, autophagy, and apoptosis. HSP families recognize as protective antigens in a wide range of bacteria because they have been conserved through evolution. Due to their homology as well as antigenicity they are competent for applying in cross-protection against bacterial diseases.

METHODS

In the present study, bioinformatics approaches utilized to design epitope-based construction of Hsp60 (or GroEL) protein. In this regard, potential B-cell and T-cell epitopes except for allergenic sequences were selected by immunoinformatic tools. The structural and functional aspects of the DNA, RNA, and protein levels were assessed by bioinformatics software. Following in silico investigations, recombinant GroEL multi-epitope of Salmonella typhi was expressed, purified, and validated. Mouse groups were immunized with recombinant protein and humoral immune response was measured by enzyme linked immunosorbent assay (ELISA). Animal challenge against Salmonella Typhimurium, Shigella flexneri, and Shigella dysenteriae was evaluated.

RESULTS

recombinant protein expression and purification with 14.3 kilodaltons (kDa) was confirmed by SDS-PAGE and western blotting. After animal administration, the immunoglobulins evaluated increase after each immunization. Immunized antisera exhibited 80%, 40%, and 40% protection against the lethal dose infection by S. Typhimurium, S. flexneri, and S. dysenteriae respectively. Passive immunization conferred 50%, 30%, and 30% protection in mice against S. Typhimurium, S. flexneri and S. dysentery respectively. In addition, bacterial organ load had exhibited a significant decrease in colony forming unit (CFU) in the liver and spleen of the immunized mice compared to the control.

CONCLUSION

Our study demonstrates the efficacy of S. Typhi recombinant GroEL multi-epitope to consider as a universal immunogen candidate versus multiple bacterial pathogens.

Harnessing protein folding neural networks for peptide-protein docking

Highly accurate protein structure predictions by deep neural networks such as AlphaFold2 and RoseTTAFold have tremendous impact on structural biology and beyond. Here, we show that, although these deep learning approaches have originally been developed for the in silico folding of protein monomers, AlphaFold2 also enables quick and accurate modeling of peptide-protein interactions. Our simple implementation of AlphaFold2 generates peptide-protein complex models without requiring multiple sequence alignment information for the peptide partner, and can handle binding-induced conformational changes of the receptor. We explore what AlphaFold2 has memorized and learned, and describe specific examples that highlight differences compared to state-of-the-art peptide docking protocol PIPER-FlexPepDock. These results show that AlphaFold2 holds great promise for providing structural insight into a wide range of peptide-protein complexes, serving as a starting point for the detailed characterization and manipulation of these interactions.

Predicting Protein-Peptide Complex Structures by Accounting for Peptide Flexibility and the Physicochemical Environment

Predicting protein-peptide complex structures is crucial to the understanding of a vast variety of peptide-mediated cellular processes and to peptide-based drug development. Peptide flexibility and binding mode ranking are the two major challenges for protein-peptide complex structure prediction. Peptides are highly flexible molecules, and therefore, brute-force modeling of peptide conformations of interest in protein-peptide docking is beyond current computing power. Inspired by the fact that the protein-peptide binding process is like protein folding, we developed a novel strategy, named MDockPeP2, which tries to address these challenges using physicochemical information embedded in abundant monomeric proteins with an exhaustive search strategy, in combination with an integrated global search and a local flexible minimization method. Only the peptide sequence and the protein crystal structure are required. The method was systemically assessed using a newly constructed structural database of 89 nonredundant protein-peptide complexes with the peptide sequence length ranging from 5 to 29 in which about half of the peptides are longer than 15 residues. MDockPeP2 yielded a total success rate of 58.4% (70.8, 79.8%) for the bound docking (i.e., with the bound receptor and fully flexible peptides) and 19.0% (44.8, 70.7%) for the challenging unbound docking when top 10 (100, 1000) models were considered for each prediction. MDockPeP2 achieved significantly higher success rates on two other datasets, peptiDB and LEADS-PEP, which contain only short- and medium-size peptides (≤ 15 residues). For peptiDB, our method obtained a success rate of 62.0% for the bound docking and 35.9% for the unbound docking when the top 10 models were considered. For LEADS-PEP, MDockPeP2 achieved a success rate of 69.8% when the top 10 models were considered. The program is available at https://zougrouptoolkit.missouri.edu/mdockpep2/download.html.

Challenges and frontiers of computational modelling of biomolecular recognition

Biomolecular recognition including binding of small molecules, peptides and proteins to their target receptors plays a key role in cellular function and has been targeted for therapeutic drug design. However, the high flexibility of biomolecules and slow binding and dissociation processes have presented challenges for computational modelling. Here, we review the challenges and computational approaches developed to characterise biomolecular binding, including molecular docking, molecular dynamics simulations (especially enhanced sampling) and machine learning. Further improvements are still needed in order to accurately and efficiently characterise binding structures, mechanisms, thermodynamics and kinetics of biomolecules in the future.

Recent advancement in functional properties and toxicity assessment of plant-derived bioactive peptides using bioinformatic approaches

Nowadays, biopeptides have gained considerable interest by the food industries, given their potent biological effect on health. BPs, when released from the sequence of their precursors by proteolytic enzymes, improved the various physiological functions of the body. Diabetic and hypertension are the two most common life-threatening diseases linked to dietary patterns. Angiotensin-converting enzyme (ACE) (hypertension-responsible glycoprotein) and dipeptidyl peptidase IV (DPP-IV) (proline-specific dimeric aminopeptidase) have been widely used as molecular target sites of action of bioactive compounds possessing antihypertensive and antidiabetic effects. Although, BPs possess considerable biological properties (antioxidant, antimicrobial, antiviral, immunomodulating, antiproliferative, antidiabetic, and antihypertensive effects), most of them possess inherent lacunae such as toxicity, allergenicity, bitterness, and lack of detailed mechanistic investigation, limiting their commercial application. The present review provides an overview on various sources of bioactive peptides, conventional and modern methods of extraction, and challenges that need to be addressed before its commercial application. In addition, bioinformatics' role in exploring the functional properties of biopeptides (ACE and DPP-IV inhibitory effects) toxicity, the target site of action with special reference to plant-based peptides, and recent burgeoning proficiencies in biopeptide research have been discussed.

Divide-and-link peptide docking: a fragment-based peptide docking protocol

Protein-peptide interactions are crucial for various important cellular regulations, and are also a basis for understanding protein-protein interactions, protein folding and peptide drug design. Due to the limited structural data obtained using experimental methods, it is necessary to predict protein-peptide interaction modes using computational methods. In the present work, we designed a fragment-based docking protocol, Divide-and-Link Peptide Docking (DLPepDock), to predict protein-peptide binding modes. This protocol contains the following steps: dividing the peptide into fragments and separately docking the fragments using a third-party small molecular docking tool, linking the docked fragmental poses to form the whole peptide conformations via fragmental coordinate transformation using our in-house program, removing unreasonable poses according to several geometrical filters, extracting representative conformations after clustering for further minimization using the steepest descent and conjugation gradient methods based on a full-atom molecular force field and finally scoring using the MM/PBSA binding energy calculation implemented in Amber. When tested on the LEADS-PEP benchmark data set of 26 diverse complexes with peptides of 6-12 residues, FlexPepDock ab initio and AutoDock CrankPep achieved superior results. DLPepDock performed better than the other 15 docking protocols implemented in nine docking programs (HPepDock, DockThor, rDock, Glide, LeDock, AutoDock, AutoDock Vina, Surflex, and GOLD). The Linux scripts to call the third-party tools and run all the calculations.

Gaussian accelerated molecular dynamics (GaMD): principles and applications

Gaussian accelerated molecular dynamics (GaMD) is a robust computational method for simultaneous unconstrained enhanced sampling and free energy calculations of biomolecules. It works by adding a harmonic boost potential to smooth biomolecular potential energy surface and reduce energy barriers. GaMD greatly accelerates biomolecular simulations by orders of magnitude. Without the need to set predefined reaction coordinates or collective variables, GaMD provides unconstrained enhanced sampling and is advantageous for simulating complex biological processes. The GaMD boost potential exhibits a Gaussian distribution, thereby allowing for energetic reweighting via cumulant expansion to the second order (i.e., "Gaussian approximation"). This leads to accurate reconstruction of free energy landscapes of biomolecules. Hybrid schemes with other enhanced sampling methods, such as the replica exchange GaMD (rex-GaMD) and replica exchange umbrella sampling GaMD (GaREUS), have also been introduced, further improving sampling and free energy calculations. Recently, new "selective GaMD" algorithms including the ligand GaMD (LiGaMD) and peptide GaMD (Pep-GaMD) enabled microsecond simulations to capture repetitive dissociation and binding of small-molecule ligands and highly flexible peptides. The simulations then allowed highly efficient quantitative characterization of the ligand/peptide binding thermodynamics and kinetics. Taken together, GaMD and its innovative variants are applicable to simulate a wide variety of biomolecular dynamics, including protein folding, conformational changes and allostery, ligand binding, peptide binding, protein-protein/nucleic acid/carbohydrate interactions, and carbohydrate/nucleic acid interactions. In this review, we present principles of the GaMD algorithms and recent applications in biomolecular simulations and drug design.

Computational Modeling as a Tool to Investigate PPI: From Drug Design to Tissue Engineering

Protein-protein interactions (PPIs) mediate a large number of important regulatory pathways. Their modulation represents an important strategy for discovering novel therapeutic agents. However, the features of PPI binding surfaces make the use of structure-based drug discovery methods very challenging. Among the diverse approaches used in the literature to tackle the problem, linear peptides have demonstrated to be a suitable methodology to discover PPI disruptors. Unfortunately, the poor pharmacokinetic properties of linear peptides prevent their direct use as drugs. However, they can be used as models to design enzyme resistant analogs including, cyclic peptides, peptide surrogates or peptidomimetics. Small molecules have a narrower set of targets they can bind to, but the screening technology based on virtual docking is robust and well tested, adding to the computational tools used to disrupt PPI. We review computational approaches used to understand and modulate PPI and highlight applications in a few case studies involved in physiological processes such as cell growth, apoptosis and intercellular communication.

In silico Approaches for the Design and Optimization of Interfering Peptides Against Protein-Protein Interactions

Large contact surfaces of protein-protein interactions (PPIs) remain to be an ongoing issue in the discovery and design of small molecule modulators. Peptides are intrinsically capable of exploring larger surfaces, stable, and bioavailable, and therefore bear a high therapeutic value in the treatment of various diseases, including cancer, infectious diseases, and neurodegenerative diseases. Given these promising properties, a long way has been covered in the field of targeting PPIs via peptide design strategies. In silico tools have recently become an inevitable approach for the design and optimization of these interfering peptides. Various algorithms have been developed to scrutinize the PPI interfaces. Moreover, different databases and software tools have been created to predict the peptide structures and their interactions with target protein complexes. High-throughput screening of large peptide libraries against PPIs; "hotspot" identification; structure-based and off-structure approaches of peptide design; 3D peptide modeling; peptide optimization strategies like cyclization; and peptide binding energy evaluation are among the capabilities of in silico tools. In the present study, the most recent advances in the field of in silico approaches for the design of interfering peptides against PPIs will be reviewed. The future perspective of the field and its advantages and limitations will also be pinpointed.

From Proteomic Mapping to Invasion-Metastasis-Cascade Systemic Biomarkering and Targeted Drugging of Mutant BRAF-Dependent Human Cutaneous Melanomagenesis

Simple Summary

Despite the recent advances in human malignancy therapy, metastasis and chemoresistance remain the principal causes of cancer-derived deaths. Given the fatal forms of cutaneous metastatic melanoma, we herein employed primary (WM115) and metastatic (WM266-4) melanoma cells, both obtained from the same patient, to identify novel biomarkers and therapeutic agents. Through state-of-the-art technologies including deep proteome landscaping, immunofluorescence phenotyping, and drug toxicity screening, we were able to describe new molecular programs, oncogenic drivers, and drug regimens, controlling the invasion-metastasis cascade during BRAF^V600D-dependent melanomagenesis. It proved that proteomic navigation could foster the development of systemic biomarkering and targeted drugging for successful treatment of advanced disease.

Abstract

Melanoma is classified among the most notoriously aggressive human cancers. Despite the recent progress, due to its propensity for metastasis and resistance to therapy, novel biomarkers and oncogenic molecular drivers need to be promptly identified for metastatic melanoma. Hence, by employing nano liquid chromatography-tandem mass spectrometry deep proteomics technology, advanced bioinformatics algorithms, immunofluorescence, western blotting, wound healing protocols, molecular modeling programs, and MTT assays, we comparatively examined the respective proteomic contents of WM115 primary (n = 3955 proteins) and WM266-4 metastatic (n = 6681 proteins) melanoma cells. It proved that WM115 and WM266-4 cells have engaged hybrid epithelial-to-mesenchymal transition/mesenchymal-to-epithelial transition states, with TGF-β controlling their motility in vitro. They are characterized by different signatures of SOX-dependent neural crest-like stemness and distinct architectures of the cytoskeleton network. Multiple signaling pathways have already been activated from the primary melanoma stage, whereas HIF1α, the major hypoxia-inducible factor, can be exclusively observed in metastatic melanoma cells. Invasion-metastasis cascade-specific sub-routines of activated Caspase-3-triggered apoptosis and LC3B-II-dependent constitutive autophagy were also unveiled. Importantly, WM115 and WM266-4 cells exhibited diverse drug response profiles, with epirubicin holding considerable promise as a beneficial drug for metastatic melanoma clinical management. It is the proteome navigation that enables systemic biomarkering and targeted drugging to open new therapeutic windows for advanced disease.

Effect and mechanism analysis of different linkers on efficient catalysis of subunit-fused nitrile hydratase

Protein fusion using a linker plays an important role for protein evolution. However, designing suitable linkers for protein evolution is yet challenging and under-explored. To further clarify the regular pattern of suitable type of linker for fusion proteins, one nitrile hydratase (NHase) was used as a target protein and subunit fusion strategy was carried out to improve its efficient catalysis. Subunit-fused variants with three different types of linkers were constructed and characterized. All variants exhibited higher stability than that of the wild type. The longer the linker was, the higher stability NHase showed, however, too long linker affected NHase activity and expression. Among the three types of linkers, the α-helical linker seemed more suitable for NHase than flexible or rigid linkers. Though it is not clear how the linkers affecting the activity, structure analysis indicated that the stability improvement is dependent on the additional salt bridge, H-bond, and the subunit interface area increasing due to the linker insertion, among which the additional salt bridge and interface area were more important factors. The results described here may be useful for redesigning other enzymes through subunit fusion.

Predicting Immunogenicity Risk in Biopharmaceuticals

The assessment of immunogenicity of biopharmaceuticals is a crucial step in the process of their development. Immunogenicity is related to the activation of adaptive immunity. The complexity of the immune system manifests through numerous different mechanisms, which allows the use of different approaches for predicting the immunogenicity of biopharmaceuticals. The direct experimental approaches are sometimes expensive and time consuming, or their results need to be confirmed. In this case, computational methods for immunogenicity prediction appear as an appropriate complement in the process of drug design. In this review, we analyze the use of various In silico methods and approaches for immunogenicity prediction of biomolecules: sequence alignment algorithms, predicting subcellular localization, searching for major histocompatibility complex (MHC) binding motifs, predicting T and B cell epitopes based on machine learning algorithms, molecular docking, and molecular dynamics simulations. Computational tools for antigenicity and allergenicity prediction also are considered.

Therapeutic peptides for the treatment of cystic fibrosis: Challenges and perspectives

Cystic fibrosis (CF) is the most common amongst rare genetic diseases, affecting more than 70.000 people worldwide. CF is characterized by a dysfunctional chloride channel, termed cystic fibrosis conductance regulator (CFTR), which leads to the production of a thick and viscous mucus layer that clogs the lungs of CF patients and traps pathogens, leading to chronic infections and inflammation and, ultimately, lung damage. In recent years, the use of peptides for the treatment of respiratory diseases, including CF, has gained growing interest. Therapeutic peptides for CF include antimicrobial peptides, inhibitors of proteases, and modulators of ion channels, among others. Peptides display unique features that make them appealing candidates for clinical translation, like specificity of action, high efficacy, and low toxicity. Nevertheless, the intrinsic properties of peptides, together with the need of delivering these compounds locally, e.g. by inhalation, raise a number of concerns in the development of peptide therapeutics for CF lung disease. In this review, we discuss the challenges related to the use of peptides for the treatment of CF lung disease through inhalation, which include retention within mucus, proteolysis, immunogenicity and aggregation. Strategies for overcoming major shortcomings of peptide therapeutics will be presented, together with recent developments in peptide design and optimization, including computational analysis and high-throughput screening.

Molecular dynamics simulations and functional studies reveal that hBD-2 binds SARS-CoV-2 spike RBD and blocks viral entry into ACE2 expressing cells

New approaches to complement vaccination are needed to combat the spread of SARS-CoV-2 and stop COVID-19 related deaths and long-term medical complications. Human beta defensin 2 (hBD-2) is a naturally occurring epithelial cell derived host defense peptide that has antiviral properties. Our comprehensive in-silico studies demonstrate that hBD-2 binds the site on the CoV-2-RBD that docks with the ACE2 receptor. Biophysical and biochemical assays confirm that hBD-2 indeed binds to the CoV-2-receptor binding domain (RBD) (KD ~ 300 nM), preventing it from binding to ACE2 expressing cells. Importantly, hBD-2 shows specificity by blocking CoV-2/spike pseudoviral infection, but not VSV-G mediated infection, of ACE2 expressing human cells with an IC50 of 2.4± 0.1 μM. These promising findings offer opportunities to develop hBD-2 and/or its derivatives and mimetics to safely and effectively use as novel agents to prevent SARS-CoV-2 infection.

Binding Ensembles of p53-MDM2 Peptide Inhibitors by Combining Bayesian Inference and Atomistic Simulations

Designing peptide inhibitors of the p53-MDM2 interaction against cancer is of wide interest. Computational modeling and virtual screening are a well established step in the rational design of small molecules. But they face challenges for binding flexible peptide molecules that fold upon binding. We look at the ability of five different peptides, three of which are intrinsically disordered, to bind to MDM2 with a new Bayesian inference approach (MELD × MD). The method is able to capture the folding upon binding mechanism and differentiate binding preferences between the five peptides. Processing the ensembles with statistical mechanics tools depicts the most likely bound conformations and hints at differences in the binding mechanism. Finally, the study shows the importance of capturing two driving forces to binding in this system: the ability of peptides to adopt bound conformations (ΔGconformation) and the interaction between interface residues (ΔGinteraction).

Methods for Molecular Modelling of Protein Complexes

Biological processes are often mediated by complexes formed between proteins and various biomolecules. The 3D structures of such protein-biomolecule complexes provide insights into the molecular mechanism of their action. The structure of these complexes can be predicted by various computational methods. Choosing an appropriate method for modelling depends on the category of biomolecule that a protein interacts with and the availability of structural information about the protein and its interacting partner. We intend for the contents of this chapter to serve as a guide as to what software would be the most appropriate for the type of data at hand and the kind of 3D complex structure required. Particularly, we have dealt with protein-small molecule ligand, protein-peptide, protein-protein, and protein-nucleic acid interactions.Most, if not all, model building protocols perform some sampling and scoring. Typically, several alternate conformations and configurations of the interactors are sampled. Each such sample is then scored for optimization. To boost the confidence in these predicted models, their assessment using other independent scoring schemes besides the inbuilt/default ones would prove to be helpful. This chapter also lists such software and serves as a guide to gauge the fidelity of modelled structures of biomolecular complexes.