Flexible docking of peptides to proteins using CABS-dock

Discovery of Bioactive Peptides Through Peptide Scanning

Therapeutic peptides targeted at various diseases are becoming increasingly relevant for the pharmaceutical industry. Several of these drugs were originally designed by mimicking a segment of a protein of interest. As such, protein mimicry represents a promising strategy both in immunology, for the identification of B- and T-cell epitopes, as well as for the modulation of protein activity, including the disruption of protein-protein interactions (PPIs) and the interference with biological or pathological cellular functions. Several methods have been developed to pinpoint the (binding) epitopes of a protein or the regions responsible for biological activity. One of such strategies is the scanning of the protein or selected domains with synthetic overlapping peptides. As the mechanism of action of a mimetic peptide can be similar to that of the whole protein, this method offers a powerful tool for the investigation of protein function, along with providing a solid basis for the development of therapeutic candidates. This review gives a general overview of different applications of the peptide scanning methodology, describing a comparison of the preparation and use of solid-phase libraries (peptide arrays) with isolated peptide libraries and highlighting their strengths and most common applications.

Introducing the Potential Binding Interface between the TRAIL-Mimicking Peptide and DR5 via Alanine Scan

Here we harnessed the unexplored binding interface between the 16-residue peptide (P) agonist and death receptor 5 (DR5). P is a solitary peptide ligand that mimics TRAIL (the natural ligand to death receptor) and is reported to control cancer growth in vivo selectively. We delved into the strategic merging of experimental and in silico structure-activity studies via the alanine scanning mutagenesis of P, wherein the disulfide bond was kept intact for structural integrity. Antiproliferative activity studies with these synthetic mutants on HCT116 cells enabled the mapping of the interaction engagement of each residue. Further, in silico docking and MD simulations led us to interpret and model the 3D interface of the binding site. Notably, Trp1, Leu4, Arg7, Ile8, Gln12, and Arg15 were projected experimentally as "hot-spot" residues crucial for primary interactions with DR5, which is predominantly supported via in silico investigations. This study is pivotal for developing new-generation peptide agonists that induce death receptor-mediated apoptosis.

Immunogenicity of Trypanosoma cruzi Multi-Epitope Recombinant Protein as an Antigen Candidate for Chagas Disease Vaccine in Humans

Chagas disease, caused by the protozoan Trypanosoma cruzi (T. cruzi), is the most significant neglected tropical disease affecting individuals in the Americas. Currently, available drugs, such as nifurtimox and benznidazole (BZN), are both toxic and ineffective in the chronic phase of the disease. A promising alternative is the development of a Chagas disease vaccine, although this effort is hampered by the complexity of the parasite and HLA polymorphisms. In addition, the activation of epitope-specific CD8+ T cells is critical to conferring a robust cell-mediated immune response and protection by producing IFN-γ and perforin. Thus, the antigen (s) for the development of a Chagas vaccine or immunotherapy must include CD8+ T cell epitopes. In this study, we aimed to develop a multi-epitope recombinant protein as a novel human vaccine for Chagas disease. Sixteen database programs were used to predict de novo 40 potential epitopes for the HLA-A*02:01 allele. Nine out of the 40 predicted epitopes were able to elicit IFN-γ production in Peripheral Blood Mononuclear Cells (PBMCs) from Chagas patients. Molecular docking revealed a good binding affinity among the epitopes with diverse HLA molecules. Therefore, a recombinant multi-epitope protein including these nine T. cruzi CD8+ epitopes was expressed and demonstrated to recall an antigen-specific immune response in ex-vivo assays using PBMCs from Chagas patients with the HLA-A*02 allele. These findings support the development of this multi-epitope protein as a promising candidate human vaccine against Chagas disease.

Molecular Modelling in Bioactive Peptide Discovery and Characterisation

Molecular modelling is a vital tool in the discovery and characterisation of bioactive peptides, providing insights into their structural properties and interactions with biological targets. Many models predicting bioactive peptide function or structure rely on their intrinsic properties, including the influence of amino acid composition, sequence, and chain length, which impact stability, folding, aggregation, and target interaction. Homology modelling predicts peptide structures based on known templates. Peptide-protein interactions can be explored using molecular docking techniques, but there are challenges related to the inherent flexibility of peptides, which can be addressed by more computationally intensive approaches that consider their movement over time, called molecular dynamics (MD). Virtual screening of many peptides, usually against a single target, enables rapid identification of potential bioactive peptides from large libraries, typically using docking approaches. The integration of artificial intelligence (AI) has transformed peptide discovery by leveraging large amounts of data. AlphaFold is a general protein structure prediction tool based on deep learning that has greatly improved the predictions of peptide conformations and interactions, in addition to providing estimates of model accuracy at each residue which greatly guide interpretation. Peptide function and structure prediction are being further enhanced using Protein Language Models (PLMs), which are large deep-learning-derived statistical models that learn computer representations useful to identify fundamental patterns of proteins. Recent methodological developments are discussed in the context of canonical peptides, as well as those with modifications and cyclisations. In designing potential peptide therapeutics, the main outstanding challenge for these methods is the incorporation of diverse non-canonical amino acids and cyclisations.

Hydrodynamic characterization of the FtsZ protein from Escherichia coli demonstrates the presence of linear and lateral trimers

FtsZ is a bacterial protein that plays a crucial role in cytokinesis by forming the Z-ring. This ring acts as a scaffold to recruit other division proteins and guide the synthesis of septal peptidoglycan, which leads to cell constriction. In its native state, the FtsZ protein from Escherichia coli (EcFtsZ) is a multi-oligomer comprising dimers, trimers, tetramers, and hexamers in a dynamic self-association equilibrium depending on its concentration. This study employed classical methods of analytical biochemistry that included native polyacrylamide gel electrophoresis, size-exclusion chromatography, sedimentation through sucrose gradients, and chemical cross-linking with formaldehyde to characterize the EcFtsZ. The dimers, trimers, and tetramers are the most prevalent oligomers of the EcFtsZ protein; however, the trimer has been understudied compared to the dimer. In this study, we characterized uncross-linked trimers by exclusion chromatography and crosslinked trimers by sedimentation. The results of size-exclusion chromatography demonstrated that the uncross-linked trimer of EcFtsZ has a mass of 128.8 kDa and a frictional ratio f/fo of 1.96, which coincides with the theoretical frictional ratio of 1.80 for a linear trimer. The EcFtsZ protein treated with formaldehyde resulted in a polypeptide band of 128 kDa recognized by anti-FtsZ antibodies and a frictional ratio Smax/S20,w equal to 1.95, which agrees with the theoretical calculation of the frictional ratio of a lateral trimer. The protein-protein interaction prediction program (PEPPI) identified a contact site between subunits in the C-terminal linker region of the EcFtsZ protein, which has the potential to interfere with the recognition of the C-terminal linker by the ClpX(P) protease.

Designing a multi-epitope vaccine against Pseudomonas aeruginosa via integrating reverse vaccinology with immunoinformatics approaches

Pseudomonas aeruginosa is a typically opportunistic pathogen responsible for a wide range of nosocomial infections. In this study, we designed two multi-epitope vaccines targeting P. aeruginosa proteins, incorporating cytotoxic T lymphocyte (CTL), helper T lymphocyte (HTL), and linear B lymphocyte (LBL) epitopes identified using reverse vaccinology and immunoinformatics approaches. The vaccines exhibited favorable physicochemical properties, including stability, solubility, and optimal molecular weight, suggesting their potential as viable candidates for vaccine development. Molecular docking studies revealed strong binding affinity to Toll-like receptors 1 (TLR1) and 2 (TLR2). Furthermore, molecular dynamics simulations confirmed the stability of the vaccine-TLR complexes over time. Immune simulation analyses indicated that the vaccines could induce robust humoral and cellular immune responses, providing a promising new approach for combating P. aeruginosa infections, particularly in the face of increasing antibiotic resistance.

Protein-Peptide Docking with ESMFold Language Model

Designing peptide therapeutics requires precise peptide docking, which remains a challenge. We assessed the ESMFold language model, originally designed for protein structure prediction, for its effectiveness in protein-peptide docking. Various docking strategies, including polyglycine linkers and sampling-enhancing modifications, were explored. The number of acceptable-quality models among top-ranking results is comparable to traditional methods and generally lower than AlphaFold-Multimer or Alphafold 3, though ESMFold surpasses it in some cases. The combination of result quality and computational efficiency underscores ESMFold's potential value as a component in a consensus approach for high-throughput peptide design.

Insight into Protein Engineering: From In silico Modelling to In vitro Synthesis

Protein engineering alters the polypeptide chain to obtain a novel protein with improved functional properties. This field constantly evolves with advanced in silico tools and techniques to design novel proteins and peptides. Rational incorporating mutations, unnatural amino acids, and post-translational modifications increases the applications of engineered proteins and peptides. It aids in developing drugs with maximum efficacy and minimum side effects. Currently, the engineering of peptides is gaining attention due to their high stability, binding specificity, less immunogenic, and reduced toxicity properties. Engineered peptides are potent candidates for drug development due to their high specificity and low cost of production compared with other biologics, including proteins and antibodies. Therefore, understanding the current perception of designing and engineering peptides with the help of currently available in silico tools is crucial. This review extensively studies various in silico tools available for protein engineering in the prospect of designing peptides as therapeutics, followed by in vitro aspects. Moreover, a discussion on the chemical synthesis and purification of peptides, a case study, and challenges are also incorporated.

Mechanisms for translating chiral enantiomers separation research into macroscopic visualization

Chirality is a common phenomenon in nature, including the dominance preference of small biomolecules, the special spatial conformation of biomolecules, and the biological and physiological processes triggered by chirality. The selective chiral recognition of molecules in nature from up-bottom or bottom-up is of great significance for living organisms. Such as the transcription of DNA, the recognition of membrane proteins, and the catalysis of enzymes all involve chiral recognition processes. The selective recognition between these macromolecules is mainly achieved through non covalent interactions such as hydrophobic interactions, ammonia bonding, electrostatic interactions, metal coordination, van der Waals forces, and π-π stacking. Researchers have been committed to studying how to convert this weak non covalent interaction into macroscopic visualization, which has further understood of the interactions between chiral molecules and is of great significance for simulating the interactions between molecules in living organisms. This article reviews several models of chiral recognition mechanisms, the interaction forces involved in the chiral recognition process, and the research progress of chiral recognition mechanisms. The outlook in this review points out that studying chiral recognition interactions provides an important bridge between chiral materials and the life sciences, providing an ideal platform for studying chiral phenomena in biological systems.

Multiscale modeling of protofilament structures: A case study on insulin amyloid aggregates

Under certain conditions, proteins may undergo misfolding and form long insoluble aggregates called amyloid fibrils. The presence of these aggregates is often associated with various diseases. The molecular mechanisms governing the aggregation process are yet to be fully understood. The self-assembly of amyloid protofilaments occurs over extended time frames, making the simulation of such events problematic. In this work, we describe a pipeline for multiscale modeling protofilament structures. In the first stage, the self-assembly of short fibrillar oligomers occurs during coarse-grained docking simulations of multiple copies of aggregating peptides. Subsequently, symmetry criteria are used to select the highest-ranked oligomer structures. Selected models are then reconstructed to an all-atom representation and used for the assembly of longer protofilaments. Models are optimized using molecular dynamics. Final structures are selected using various scoring protocols. We evaluated this modeling procedure through the test prediction of insulin amyloid protofilaments whose experimental structures have been published recently. The resulting insulin protofilament models closely resemble the experimental structures. This work provides a proof of concept for the proposed modeling procedure aiming to predict amyloid protofilament structures that exhibit in-register and parallel arrangement of β-sheets based solely on the amino acid sequence of aggregating peptides.

Exploring immunogenic CD8 + T-cell epitopes for peptide-based vaccine development against evolving SARS-CoV-2 variants: An immunoinformatics approach

The COVID-19 pandemic originated in Wuhan in 2019 due to a novel SARS-COV-2 (Severe Acute Respiratory Syndrome Coronavirus 2) responsible for the massive number of deaths across the globe. So far, several vaccines have been developed using highly antigenic Spike protein and authorized for emergency use, reducing the severity of the infection. Nonetheless, the virus continues to evolve through multiple mutations, resulting in numerous variants with enhanced transmission that evade the vaccine-induced immune response. Given the persistently mutating nature of the SARS-COV-2 virus, peptide-based vaccines with highly conserved epitopes may offer lasting protection against evolving variants. This study presents an immunoinformatics-based identification of potentially immunogenic CD8 + T-cell epitopes (CTLs) of Spike (S), Membrane (M), Nucleocapsid (N) and Envelope (E) proteins of SARS-COV-2. By utilizing the immunoinformatic approach, 21 epitopes have successfully been evaluated, where 15, 3, 2, and 1 epitopes are respectively from Spike, Membrane, Envelope and Nucleocapsid proteins. Out of these, 20 are found to be identical with experimentally verified immunogenic epitopes, except for the novel NTQEVFAQV epitope from spike protein. These epitopes show a high degree of conservation in both former variants of concerns (VOCs), variants of interest (VOIs) and current variants under monitoring (VUMs), are non-toxic, non-homologous to humans and have a wide range of global population coverage. Furthermore, utilizing molecular docking analysis followed by molecular dynamics simulation, these epitopes have been verified as having stable interactions with their respective HLA molecules. The described framework and projected immunogenic epitopes could significantly impact the development of SARS-COV-2 vaccines based on peptides.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13337-024-00894-7.

Predicting Immunogenic Epitopes Variation of Envelope 2 Gene Among Chikungunya Virus Clonal Lineages by an In Silico Approach

Chikungunya virus (CHIKV), responsible for a mosquito-borne viral illness, has rapidly spread worldwide, posing a significant global health threat. In this study, we explored the immunogenic variability of CHIKV envelope 2 (E2), a pivotal component in the anti-CHIKV immune response, using an in silico approach. After extracting the representative sequence types of the CHIKV E2 antigen, we predicted the structure-based B-cell epitopes and MHC I and II binding T-cell epitopes. Variations in key T-cell epitopes were further analyzed using molecular docking simulations. We extracted 258 E2 gene sequences from a pool of 1660 blast hits, displaying homology levels ranging from 93.6% to 100%. This revealed 44 sequence types, each representing a unique genetic variant. Phylogenetic analysis revealed distinct geographically distributed clonal lineages (clades I-IV). The B-cell linear and discontinuous epitopes demonstrated a similar distribution across the E2 protein of different strains, spanning domains A, B, and C, with some slight variations. Moreover, T-cell epitope prediction revealed eight conserved MHC class I hot spots and three MHC II hot spots, displaying variations among lineages. Among clade II strains, there were significant variations (N5H, S118G, G194S, L248F/S, and I255V/T) observed in epitopes, distinct from strains belonging to other lineages. Additionally, molecular docking showed that variations in MHC I epitopes across clonal lineages induced changes in the structure of the peptide-MHC complexes, potentially resulting in immunogenic disparities. We expect that this in silico approach will serve as a complementary tool to experimental platforms for exploring immunogenic variation or developing biomarkers for vaccine design and other related studies.

Exploring protein functions from structural flexibility using CABS-flex modeling

Understanding protein function often necessitates characterizing the flexibility of protein structures. However, simulating protein flexibility poses significant challenges due to the complex dynamics of protein systems, requiring extensive computational resources and accurate modeling techniques. In response to these challenges, the CABS-flex method has been developed as an efficient modeling tool that combines coarse-grained simulations with all-atom detail. Available both as a web server and a standalone package, CABS-flex is dedicated to a wide range of users. The web server version offers an accessible interface for straightforward tasks, while the standalone command-line program is designed for advanced users, providing additional features, analytical tools, and support for handling large systems. This paper examines the application of CABS-flex across various structure-function studies, facilitating investigations into the interplay among protein structure, dynamics, and function in diverse research fields. We present an overview of the current status of the CABS-flex methodology, highlighting its recent advancements, practical applications, and forthcoming challenges.

Efficient improvement of the proliferation, differentiation, and anti-arthritic capacity of mesenchymal stem cells by simply culturing on the immobilized FGF2 derived peptide, 44-ERGVVSIKGV-53

INTRODUCTION

The stem cell microenvironment has been evidenced to robustly affect its biological functions and clinical grade. Natural or synthetic growth factors, especially, are essential for modulating stem cell proliferation, metabolism, and differentiation via the interaction with specific extracellular receptors. Fibroblast growth factor-2 (FGF-2) possesses pleiotropic functions in various tissues and organs. It interacts with the FGF receptor (FGFR) and activates FGFR signaling pathways, which involve numerous biological functions, such as angiogenesis, wound healing, cell proliferation, and differentiation.

OBJECTIVES

Here, we aim to explore the molecular functions, mode of action, and therapeutic activity of yet undetermined function, FGF-2-derived peptide, FP2 (44-ERGVVSIKGV-53) in promoting the proliferation, differentiation, and therapeutic application of human Wharton's jelly mesenchymal stem cells (hWJ-MSCs) in comparison to other test peptides, canofin1 (FP1), hexafin2 (FP3), and canofin3 (FP4) with known functions.

METHODS

The immobilization of test peptides that are fused with mussel adhesive proteins (MAP) on the culture plate was carried out via EDC/NHS chemistry. Cell Proliferation assay, colony-forming unit, western blotting analysis, gene expression analysis, RNA-Seq. analysis, osteogenic, and chondrogenic differentiation capacity were applied to test the activity of the test peptides. We additionally utilized three-dimensional (3D) structural analysis and artificial intelligence (AI)-based AlphaFold2 and CABS-dock programs for receptor interaction prediction of the peptide receptor. We also verified the in vivo therapeutic capacity of FP2-cultured hWJ-MSCs using an osteoarthritis mice model.

RESULTS

Culture of hWJ-MSC onto an FP2-immobilized culture plate showed a significant increase in cell proliferation (n = 3; *p < 0.05, **p < 0.01) and the colony-forming unit (n = 3; *p < 0.05, **p < 0.01) compared with the test peptides. FP2 showed a significantly upregulated phosphorylation of FRS2α and FGFR1 and activated the AKT and ERK signaling pathways (n = 3; *p < 0.05, **p < 0.01, ***p < 0.001). Interestingly, we detected efficient FP2 receptor binding that was predicted using AI-based tools. Treatment with an AKT inhibitor significantly abrogated the FP2-mediated enhancement of cell differentiation (n = 3; *p < 0.05, **p < 0.01, ***p < 0.001). Intra-articular injection of FP2-cultured MSCs significantly mitigated arthritis symptoms in an osteoarthritis mouse model, as shown through the functional tests (n = 10; *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001), modulation of the expression level of the pro-inflammatory and anti-inflammatory genes, and improved osteochondral regeneration as demonstrated by tissue sections.

CONCLUSION

Our study identified the FGF-2-derived peptide FP2 as a promising candidate peptide to improve the therapeutic potential of hWJ-MSCs, especially in bone and cartilage regeneration.

Universal peptide-based potential vaccine design against canine distemper virus (CDV) using a vaccinomic approach

Canine distemper virus (CDV) affects many domestic and wild animals. Variations among CDV genome linages could lead to vaccination failure. To date, there are several vaccine alternatives, such as a modified live virus and a recombinant vaccine; however, most of these alternatives are based on the ancestral strain Onderstepoort, which has not been circulating for years. Vaccine failures and the need to update vaccines have been widely discussed, and the development of new vaccine candidates is necessary to reduce circulation and mortality. Current vaccination alternatives cannot be used in wildlife animals due to the lack of safety data for most of the species, in addition to the insufficient immune response against circulating strains worldwide in domestic species. Computational tools, including peptide-based therapies, have become essential for developing new-generation vaccines for diverse models. In this work, a peptide-based vaccine candidate with a peptide library derived from CDV H and F protein consensus sequences was constructed employing computational tools. The molecular docking and dynamics of the selected peptides with canine MHC-I and MHC-II and with TLR-2 and TLR-4 were evaluated. In silico safety was assayed through determination of antigenicity, allergenicity, toxicity potential, and homologous canine peptides. Additionally, in vitro safety was also evaluated through cytotoxicity in cell lines and canine peripheral blood mononuclear cells (cPBMCs) and through a hemolysis potential assay using canine red blood cells. A multiepitope CDV polypeptide was constructed, synthetized, and evaluated in silico and in vitro by employing the most promising peptides for comparison with single CDV immunogenic peptides. Our findings suggest that predicting immunogenic CDV peptides derived from most antigenic CDV proteins could aid in the development of new vaccine candidates, such as multiple single CDV peptides and multiepitope CDV polypeptides, that are safe in vitro and optimized in silico. In vivo studies are being conducted to validate potential vaccines that may be effective in preventing CDV infection in domestic and wild animals.

Study on the structure-activity relationship of rice immunopeptides based on molecular docking

Research on food-derived immunoregulatory peptides has attracted increasing attention of scientists worldwide. However, the structure-activity relationship of rice immunopeptides was not clearly. Herein, 114 rice immunopeptides were obtained by simulating the enzymatic hydrolysis of rice proteins and were further analyzed by NetMHCIipan-4.0. Subsequently, the molecular docking was used to simulate the binding of immunoreactive peptides to major histocompatibility complex class II (MHC-II) molecules. Results show that S, R, D, E, and T amino acid could easily form hydrogen bonds with MHC-II molecules, thus enhancing innate and adaptive immunity. Finally, glucose-modified rice immunopeptides were to investigate the binding of the peptides with MHC-II molecules after glycosylation modification; this provided a theoretical basis for the targeted modification of the generated immunopeptides. All in all, the present study provides a theoretical foundation to further utilize rice processing byproducts and other food products to enhance immunity.

Bioinformatics and bioactive peptides from foods: Do they work together?

We live in the Big Data Era which affects many aspects of science, including research on bioactive peptides derived from foods, which during the last few decades have been a focus of interest for scientists. These two issues, i.e., the development of computer technologies and progress in the discovery of novel peptides with health-beneficial properties, are closely interrelated. This Chapter presents the example applications of bioinformatics for studying biopeptides, focusing on main aspects of peptide analysis as the starting point, including: (i) the role of peptide databases; (ii) aspects of bioactivity prediction; (iii) simulation of peptide release from proteins. Bioinformatics can also be used for predicting other features of peptides, including ADMET, QSAR, structure, and taste. To answer the question asked "bioinformatics and bioactive peptides from foods: do they work together?", currently it is almost impossible to find examples of peptide research with no bioinformatics involved. However, theoretical predictions are not equivalent to experimental work and always require critical scrutiny. The aspects of compatibility of in silico and in vitro results are also summarized herein.

Virtual Screening of Peptide Libraries: The Search for Peptide-Based Therapeutics Using Computational Tools

Over the last few decades, we have witnessed growing interest from both academic and industrial laboratories in peptides as possible therapeutics. Bioactive peptides have a high potential to treat various diseases with specificity and biological safety. Compared to small molecules, peptides represent better candidates as inhibitors (or general modulators) of key protein-protein interactions. In fact, undruggable proteins containing large and smooth surfaces can be more easily targeted with the conformational plasticity of peptides. The discovery of bioactive peptides, working against disease-relevant protein targets, generally requires the high-throughput screening of large libraries, and in silico approaches are highly exploited for their low-cost incidence and efficiency. The present review reports on the potential challenges linked to the employment of peptides as therapeutics and describes computational approaches, mainly structure-based virtual screening (SBVS), to support the identification of novel peptides for therapeutic implementations. Cutting-edge SBVS strategies are reviewed along with examples of applications focused on diverse classes of bioactive peptides (i.e., anticancer, antimicrobial/antiviral peptides, peptides blocking amyloid fiber formation).

Structure prediction of linear and cyclic peptides using CABS-flex

The structural modeling of peptides can be a useful aid in the discovery of new drugs and a deeper understanding of the molecular mechanisms of life. Here we present a novel multiscale protocol for the structure prediction of linear and cyclic peptides. The protocol combines two main stages: coarse-grained simulations using the CABS-flex standalone package and an all-atom reconstruction-optimization process using the Modeller program. We evaluated the protocol on a set of linear peptides and two sets of cyclic peptides, with cyclization through the backbone and disulfide bonds. A comparison with other state-of-the-art tools (APPTEST, PEP-FOLD, ESMFold and AlphaFold implementation in ColabFold) shows that for most cases, AlphaFold offers the highest resolution. However, CABS-flex is competitive, particularly when it comes to short linear peptides. As demonstrated, the protocol performance can be further improved by combination with the residue-residue contact prediction method or more efficient scoring. The protocol is included in the CABS-flex standalone package along with online documentation to aid users in predicting the structure of peptides and mini-proteins.

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

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

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.

Disordered regions endow structural flexibility to shell proteins and function towards shell-enzyme interactions in 1,2-propanediol utilization microcompartment

Intrinsically disordered regions in proteins have been functionally linked to the protein-protein interactions and genesis of several membraneless organelles. Depending on their residual makeup, hydrophobicity or charge distribution they may remain in extended form or may assume certain conformations upon biding to a partner protein or peptide. The present work investigates the distribution and potential roles of disordered regions in the integral proteins of 1,2-propanediol utilization microcompartments. We use bioinformatics tools to identify the probable disordered regions in the shell proteins and enzyme of the 1,2-propanediol utilization microcompartment. Using a combination of computational modelling and biochemical techniques we elucidate the role of disordered terminal regions of a major shell protein and enzyme. Our findings throw light on the importance of disordered regions in the self-assembly, providing flexibility to shell protein and mediating its interaction with a native enzyme.Communicated by Ramaswamy H. Sarma.

Integrative modeling of diverse protein-peptide systems using CABS-dock

The CABS model can be applied to a wide range of protein-protein and protein-peptide molecular modeling tasks, such as simulating folding pathways, predicting structures, docking, and analyzing the structural dynamics of molecular complexes. In this work, we use the CABS-dock tool in two diverse modeling tasks: 1) predicting the structures of amyloid protofilaments and 2) identifying cleavage sites in the peptide substrates of proteolytic enzymes. In the first case, simulations of the simultaneous docking of amyloidogenic peptides indicated that the CABS model can accurately predict the structures of amyloid protofilaments which have an in-register parallel architecture. Scoring based on a combination of symmetry criteria and estimated interaction energy values for bound monomers enables the identification of protofilament models that closely match their experimental structures for 5 out of 6 analyzed systems. For the second task, it has been shown that CABS-dock coarse-grained docking simulations can be used to identify the positions of cleavage sites in the peptide substrates of proteolytic enzymes. The cleavage site position was correctly identified for 12 out of 15 analyzed peptides. When combined with sequence-based methods, these docking simulations may lead to an efficient way of predicting cleavage sites in degraded proteins. The method also provides the atomic structures of enzyme-substrate complexes, which can give insights into enzyme-substrate interactions that are crucial for the design of new potent inhibitors.

Glycomimetic Peptides as Therapeutic Tools

The entry of peptides into glycobiology has led to the development of a unique class of therapeutic tools. Although numerous and well-known peptides are active as endocrine regulatory factors that bind to specific receptors, and peptides have been used extensively as epitopes for vaccine production, the use of peptides that mimic sugars as ligands of lectin-type receptors has opened a unique approach to modulate activity of immune cells. Ground-breaking work that initiated the use of peptides as tools for therapy identified sugar mimetics by screening phage display libraries. The peptides that have been discovered show significant potential as high-avidity, therapeutic tools when synthesized as multivalent structures. Advantages of peptides over sugars as drugs for immune modulation will be illustrated in this review.

Structure of the SHOC2-MRAS-PP1C complex provides insights into RAF activation and Noonan syndrome

SHOC2 acts as a strong synthetic lethal interactor with MEK inhibitors in multiple KRAS cancer cell lines. SHOC2 forms a heterotrimeric complex with MRAS and PP1C that is essential for regulating RAF and MAPK-pathway activation by dephosphorylating a specific phosphoserine on RAF kinases. Here we present the high-resolution crystal structure of the SHOC2-MRAS-PP1C (SMP) complex and apo-SHOC2. Our structures reveal that SHOC2, MRAS, and PP1C form a stable ternary complex in which all three proteins synergistically interact with each other. Our results show that dephosphorylation of RAF substrates by PP1C is enhanced upon interacting with SHOC2 and MRAS. The SMP complex forms only when MRAS is in an active state and is dependent on SHOC2 functioning as a scaffolding protein in the complex by bringing PP1C and MRAS together. Our results provide structural insights into the role of the SMP complex in RAF activation and how mutations found in Noonan syndrome enhance complex formation, and reveal new avenues for therapeutic interventions.

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.

Automated Protein Secondary Structure Assignment from Cα Positions Using Neural Networks

The assignment of secondary structure elements in protein conformations is necessary to interpret a protein model that has been established by computational methods. The process essentially involves labeling the amino acid residues with H (Helix), E (Strand), or C (Coil, also known as Loop). When particular atoms are absent from an input protein structure, the procedure becomes more complicated, especially when only the alpha carbon locations are known. Various techniques have been tested and applied to this problem during the last forty years. The application of machine learning techniques is the most recent trend. This contribution presents the HECA classifier, which uses neural networks to assign protein secondary structure types. The technique exclusively employs Cα coordinates. The Keras (TensorFlow) library was used to implement and train the neural network model. The BioShell toolkit was used to calculate the neural network input features from raw coordinates. The study’s findings show that neural network-based methods may be successfully used to take on structure assignment challenges when only Cα trace is available. Thanks to the careful selection of input features, our approach’s accuracy (above 97%) exceeded that of the existing methods.

Matching protein surface structural patches for high-resolution blind peptide docking

Modeling interactions between short peptides and their receptors is a challenging docking problem due to the peptide flexibility, resulting in a formidable sampling problem of peptide conformation in addition to its orientation. Alternatively, the peptide can be viewed as a piece that complements the receptor monomer structure. Here, we show that the peptide conformation can be determined based on the receptor backbone only and sampled using local structural motifs found in solved protein monomers and interfaces, independent of sequence similarity. This approach outperforms current peptide docking protocols and promotes new directions for peptide interface design.

Peptide docking can be perceived as a subproblem of protein–protein docking. However, due to the short length and flexible nature of peptides, many do not adopt one defined conformation prior to binding. Therefore, to tackle a peptide docking problem, not only the relative orientation, but also the bound conformation of the peptide needs to be modeled. Traditional peptide-centered approaches use information about peptide sequences to generate representative conformer ensembles, which can then be rigid-body docked to the receptor. Alternatively, one may look at this problem from the viewpoint of the receptor, namely, that the protein surface defines the peptide-bound conformation. Here, we present PatchMAN (Patch-Motif AligNments), a global peptide-docking approach that uses structural motifs to map the receptor surface with backbone scaffolds extracted from protein structures. On a nonredundant set of protein–peptide complexes, starting from free receptor structures, PatchMAN successfully models and identifies near-native peptide–protein complexes in 58%/84% within 2.5 Å/5 Å interface backbone RMSD, with corresponding sampling in 81%/100% of the cases, outperforming other approaches. PatchMAN leverages the observation that structural units of peptides with their binding pocket can be found not only within interfaces, but also within monomers. We show that the bound peptide conformation is sampled based on the structural context of the receptor only, without taking into account any sequence information. Beyond peptide docking, this approach opens exciting new avenues to study principles of peptide–protein association, and to the design of new peptide binders. PatchMAN is available as a server at https://furmanlab.cs.huji.ac.il/patchman/.

Multiscale Modeling of Amyloid Fibrils Formed by Aggregating Peptides Derived from the Amyloidogenic Fragment of the A-Chain of Insulin

Computational prediction of molecular structures of amyloid fibrils remains an exceedingly challenging task. In this work, we propose a multi-scale modeling procedure for the structure prediction of amyloid fibrils formed by the association of ACC_1-13 aggregation-prone peptides derived from the N-terminal region of insulin’s A-chain. First, a large number of protofilament models composed of five copies of interacting ACC_1-13 peptides were predicted by application of CABS-dock coarse-grained (CG) docking simulations. Next, the models were reconstructed to all-atom (AA) representations and refined during molecular dynamics (MD) simulations in explicit solvent. The top-scored protofilament models, selected using symmetry criteria, were used for the assembly of long fibril structures. Finally, the amyloid fibril models resulting from the AA MD simulations were compared with atomic force microscopy (AFM) imaging experimental data. The obtained results indicate that the proposed multi-scale modeling procedure is capable of predicting protofilaments with high accuracy and may be applied for structure prediction and analysis of other amyloid fibrils.

Antisense Peptide Technology for Diagnostic Tests and Bioengineering Research

Antisense peptide technology (APT) is based on a useful heuristic algorithm for rational peptide design. It was deduced from empirical observations that peptides consisting of complementary (sense and antisense) amino acids interact with higher probability and affinity than the randomly selected ones. This phenomenon is closely related to the structure of the standard genetic code table, and at the same time, is unrelated to the direction of its codon sequence translation. The concept of complementary peptide interaction is discussed, and its possible applications to diagnostic tests and bioengineering research are summarized. Problems and difficulties that may arise using APT are discussed, and possible solutions are proposed. The methodology was tested on the example of SARS-CoV-2. It is shown that the CABS-dock server accurately predicts the binding of antisense peptides to the SARS-CoV-2 receptor binding domain without requiring predefinition of the binding site. It is concluded that the benefits of APT outweigh the costs of random peptide screening and could lead to considerable savings in time and resources, especially if combined with other computational and immunochemical methods.

AMIDE v2: High-Throughput Screening Based on AutoDock-GPU and Improved Workflow Leading to Better Performance and Reliability

Molecular docking is widely used in computed drug discovery and biological target identification, but getting fast results can be tedious and often requires supercomputing solutions. AMIDE stands for AutoMated Inverse Docking Engine. It was initially developed in 2014 to perform inverse docking on High Performance Computing. AMIDE version 2 brings substantial speed-up improvement by using AutoDock-GPU and by pulling a total revision of programming workflow, leading to better performances, easier use, bug corrections, parallelization improvements and PC/HPC compatibility. In addition to inverse docking, AMIDE is now an optimized tool capable of high throughput inverse screening. For instance, AMIDE version 2 allows acceleration of the docking up to 12.4 times for 100 runs of AutoDock compared to version 1, without significant changes in docking poses. The reverse docking of a ligand on 87 proteins takes only 23 min on 1 GPU (Graphics Processing Unit), while version 1 required 300 cores to reach the same execution time. Moreover, we have shown an exponential acceleration of the computation time as a function of the number of GPUs used, allowing a significant reduction of the duration of the inverse docking process on large datasets.

Protein-Protein Docking with Large-Scale Backbone Flexibility Using Coarse-Grained Monte-Carlo Simulations

Most of the protein-protein docking methods treat proteins as almost rigid objects. Only the side-chains flexibility is usually taken into account. The few approaches enabling docking with a flexible backbone typically work in two steps, in which the search for protein-protein orientations and structure flexibility are simulated separately. In this work, we propose a new straightforward approach for docking sampling. It consists of a single simulation step during which a protein undergoes large-scale backbone rearrangements, rotations, and translations. Simultaneously, the other protein exhibits small backbone fluctuations. Such extensive sampling was possible using the CABS coarse-grained protein model and Replica Exchange Monte Carlo dynamics at a reasonable computational cost. In our proof-of-concept simulations of 62 protein-protein complexes, we obtained acceptable quality models for a significant number of cases.

New peptides with immunomodulatory activity identified from rice proteins through peptidomic and in silico analysis

The new food-derived bio-functional peptides are urgently needed globally, but the separation and purification process for obtaining the immunopeptides from food is low efficiency and highly time-consuming. In the present study, rice proteins were extracted and identified by using liquid chromatography/tandem mass spectrometry (LC-MS/MS). Furthermore, a strategy combining immuno-prediction and in silico simulation was used to screen for peptides showing immunomodulatory activity, including inhibition of the release of nitric oxide, tumor necrosis factor-α, and the interleukins IL-6 and IL-1β in lipopolysaccharide-induced RAW264.7 mouse macrophages. This LC-MS/MS identification and immuno-prediction method may provide insights for the potential identification of more food-derived immunopeptides.

CK1δ-Derived Peptides as Novel Tools Inhibiting the Interactions between CK1δ and APP695 to Modulate the Pathogenic Metabolism of APP

Alzheimer’s disease (AD) is the major cause of dementia, and affected individuals suffer from severe cognitive, mental, and functional impairment. Histologically, AD brains are basically characterized by the presence of amyloid plaques and neurofibrillary tangles. Previous reports demonstrated that protein kinase CK1δ influences the metabolism of amyloid precursor protein (APP) by inducing the generation of amyloid-β (Aβ), finally contributing to the formation of amyloid plaques and neuronal cell death. We therefore considered CK1δ as a promising therapeutic target and suggested an innovative strategy for the treatment of AD based on peptide therapeutics specifically modulating the interaction between CK1δ and APP. Initially, CK1δ-derived peptides manipulating the interactions between CK1δ and APP695 were identified by interaction and phosphorylation analysis in vitro. Selected peptides subsequently proved their potential to penetrate cells without inducing cytotoxic effects. Finally, for at least two of the tested CK1δ-derived peptides, a reduction in Aβ levels and amyloid plaque formation could be successfully demonstrated in a complex cell culture model for AD. Consequently, the presented results provide new insights into the interactions of CK1δ and APP695 while also serving as a promising starting point for further development of novel and highly innovative pharmacological tools for the treatment of AD.

Can molecular mimicry explain the cytokine storm of SARS-CoV-2?: An in silico approach

PARP14 and PARP9 play a key role in macrophage immune regulation. SARS‐CoV‐2 is an emerging viral disease that triggers hyper‐inflammation known as a cytokine storm. In this study, using in silico tools, we hypothesize about the immunological phenomena of molecular mimicry between SARS‐CoV‐2 Nsp3 and the human PARP14 and PARP9. The results showed an epitope of SARS‐CoV‐2 Nsp3 protein that contains consensus sequences for both human PARP14 and PARP9 that are antigens for MHC Classes 1 and 2, which can potentially induce an immune response against human PARP14 and PARP9; while its depletion causes a hyper‐inflammatory state in SARS‐CoV‐2 patients.

Molecular mimicry could produce an inflammatory state in SARS‐CoV‐2 patients.

Human PARP14 and PARP9 are the proteins involves in this phenomena.

Molecular Dynamics Scoring of Protein-Peptide Models Derived from Coarse-Grained Docking

One of the major challenges in the computational prediction of protein-peptide complexes is the scoring of predicted models. Usually, it is very difficult to find the most accurate solutions out of the vast number of sometimes very different and potentially plausible predictions. In this work, we tested the protocol for Molecular Dynamics (MD)-based scoring of protein-peptide complex models obtained from coarse-grained (CG) docking simulations. In the first step of the scoring procedure, all models generated by CABS-dock were reconstructed starting from their original C-alpha trace representations to all-atom (AA) structures. The second step included geometry optimization of the reconstructed complexes followed by model scoring based on receptor-ligand interaction energy estimated from short MD simulations in explicit water. We used two well-known AA MD force fields, CHARMM and AMBER, and a CG MARTINI force field. Scoring results for 66 different protein-peptide complexes show that the proposed MD-based scoring approach can be used to identify protein-peptide models of high accuracy. The results also indicate that the scoring accuracy may be significantly affected by the quality of the reconstructed protein receptor structures.

Protein Docking Model Evaluation by Graph Neural Networks

Physical interactions of proteins play key functional roles in many important cellular processes. To understand molecular mechanisms of such functions, it is crucial to determine the structure of protein complexes. To complement experimental approaches, which usually take a considerable amount of time and resources, various computational methods have been developed for predicting the structures of protein complexes. In computational modeling, one of the challenges is to identify near-native structures from a large pool of generated models. Here, we developed a deep learning-based approach named Graph Neural Network-based DOcking decoy eValuation scorE (GNN-DOVE). To evaluate a protein docking model, GNN-DOVE extracts the interface area and represents it as a graph. The chemical properties of atoms and the inter-atom distances are used as features of nodes and edges in the graph, respectively. GNN-DOVE was trained, validated, and tested on docking models in the Dockground database and further tested on a combined dataset of Dockground and ZDOCK benchmark as well as a CAPRI scoring dataset. GNN-DOVE performed better than existing methods, including DOVE, which is our previous development that uses a convolutional neural network on voxelized structure models.

hTLR2 interacting peptides of pathogenic leptospiral outer membrane proteins

To adapt into the host system from moist environment Leptospira alter their gene expression by inducing differential expression of the genes encoding virulence factors. Knowledge about the molecular pathogenesis and virulent evolution remains limited to Leptospira. The pathogenic organism sense the environmental changes mainly through their outer membrane proteins that in-turn activates the signal transduction pathways to overcome the stress to adaptation into host system and to evade immunity. In this present study, we analyzed the expression profile of virulence associated OMPs regulated under various stress conditions like temperatures, iron deprivation, osmotic stress and low to high passages in single scale and characterized the selected proteins by MALDI-TOF MS/MS and their role in pathogenesis were predicted by implying in-silico analysis. To identify differential expression profile, the extracted OMPs were resolved through 2DE and compared the OMPs profile from various in-vivo like conditions in single scale and found 61 upregulated OMPs and three potentially virulent proteins were earmarked for their significance in pathogenesis. Further, the in-silico analysis revealed that differentially expressed protein has MHC-I T-cell, MHC-II T-cell and B-cell epitopes which showed an interaction between human TLR2 proteins confirmed by CABS docking and interaction network unveiled to understand the leptospiral virulent mechanism and host adaptation.

Related parameters of affinity and stability prediction of HLA-A*2402 restricted antigen peptides based on molecular docking

Background

Major histocompatibility complex class I (MHC-I) plays an important role in cell immune response, and stable interaction between polypeptides and MHC-I ensures efficient presentation of polypeptide-MHC-I (pMHC-I) molecular complexes to T cells. The aim of this study was to explore ways to improve the affinity and stability of the p-Human Leukocyte Antigen (HLA)-A*2402 complex.

Methods

The peptide sequences of the restricted antigen peptides for HLA-A*2402 and the results of the in vitro competitive binding test were retrieved from the literature. The affinity values were predicted using NetMHCpan v4.1 server, and the stability values were predicted using the NetMHCstab v1.0 server. Auto Vina was used to dock peptides to HLA-A*2402 protein in a flexible docking manner, while Flexpepdock was employed to optimize the docking morphology. Maestro was used to analyze the intermolecular forces and the binding affinity of the complex, while MM-GBSA was used to calculate the binding free energy values.

Results

The intermolecular interactions that maintained the affinity and stability of peptide-HLA-A*2402 complex relied mainly on HB, followed by pi stack. The binding affinity values of molecular docking were associated with the predicted values of affinity and stability, the binding affinity and the binding free energy, as well as the intermolecular force pi-stack. The pi stack had a significant negative correlation with binding affinity and binding free energy. The replacement of the residues of the polypeptides that did not form pi-stack interactions with HLA-A*2402 improved the affinity and/or stability compared to before replacement.

Conclusions

The generation and increase in the number of pi-stacks between peptides and HLA-A*2402 molecules may help improve the affinity and stability of p-HLA-A*2402 complexes. The prediction of intermolecular forces and binding affinity of peptide-HLA by means of molecular docking is a supplement to the current commonly used prediction databases.

Purification, identification, and computational analysis of xanthine oxidase inhibitory peptides from kidney bean

Hyperuricemia is related to plenty of diseases, seriously damaging human health. Current clinical drugs used to treat hyperuricemia have many adverse effects. In this study, kidney bean hydrolysate (KBH) was found to exert high xanthine oxidase inhibitory (XOI) activity. Compared to KBH (50.31 ± 2.73%), XOI activities of three fractions (M_w <5 kDa, M_w <3 kDa, M_w  < 1 kDa) by ultrafiltration were higher and increased to 58.58 ± 3.57%, 59.34 ± 1.78%, and 55.05 ± 5.00%, respectively (P < 0.05). A total of 69 peptides were identified by HPLC-ESI-MS/MS and analyzed binding affinities with XO with the help of molecular docking. AVDSLVPIGR, DWYDIK, LDNLLR, ISPIPVLK, ISSLEMTR showed well binding affinities with XO and DWYDIK presented the highest XOI activity (68.63 ± 5.07%) among five synthetic peptides (P < 0.05). Additionally, visual analysis results indicated that DWYDIK was pushed into the hydrophobic channel and formed hydrogen bonds with pivotal amino acids of xanthine oxidase. Overall, KBH could be a promising candidate as anti- hyperuricemia functional food. PRACTICAL APPLICATION: This research initially revealed that kidney bean peptides could significantly inhibit the activity of xanthine oxidase, indicating kidney bean peptides could be a treatment for hyperuricemia. Kidney bean peptides may have commercial potentials as a safer alternative with few side effects to drugs.

The Mechanism of NEDD8 Activation of CUL5 Ubiquitin E3 Ligases

Cullin RING E3 ligases (CRLs) ubiquitylate hundreds of important cellular substrates. Here we have assembled and purified the Ankyrin repeat and SOCS Box protein 9 CUL5 RBX2 ligase (ASB9-CRL) in vitro and show how it ubiquitylates one of its substrates, CKB. CRLs occasionally collaborate with RING between RING E3 ligases (RBRLs), and indeed, mass spectrometry analysis showed that CKB is specifically ubiquitylated by the ASB9-CRL-ARIH2-UBE2L3 complex. Addition of other E2s such as UBE2R1 or UBE2D2 contributes to polyubiquitylation but does not alter the sites of CKB ubiquitylation. Hydrogen–deuterium exchange mass spectrometry (HDX-MS) analysis revealed that CUL5 neddylation allosterically exposes its ARIH2 binding site, promoting high-affinity binding, and it also sequesters the NEDD8 E2 (UBE2F) binding site on RBX2. Once bound, ARIH2 helices near the Ariadne domain active site are exposed, presumably relieving its autoinhibition. These results allow us to propose a model of how neddylation activates ASB-CRLs to ubiquitylate their substrates.

•

ARIH2 is required for ASB9CRL to polyubiquitylate 4/18 lysines on one creatine kinase subunit.

•

HDX-MS reveals long-range allosteric opening of a cleft in CUL5 where the ARIH2 RBRL binds.

•

HDX-MS reveals that neddylation of CUL5 alters the RBX2 conformation away from binding the E2∼NEDD8.

•

HDX-MS reveals opening of the ARIH2 active site upon binding CUL5, thus releasing its autoinhibition.

Identification and evaluation of immunogenic MHC-I and MHC-II binding peptides from Mycobacterium tuberculosis

Due to several limitations of the only available BCG vaccine, to generate adequate protective immune responses, it is important to develop potent and cost-effective vaccines against tuberculosis (TB). In this study, we have used an immune-informatics approach to identify potential peptide based vaccine targets against TB. The proteome of Mycobacterium tuberculosis (Mtb), the causative agent of TB, was analyzed for secretory or surface localized antigenic proteins as potential vaccine candidates. The T- and B-cell epitopes as well as MHC molecule binding efficiency were identified and mapped in the modelled structures of the selected proteins. Based on antigenicity score and molecular dynamic simulation (MD) studies two peptides namely Pep-9 and Pep-15 were analyzed, modelled and docked with MHC-I and MHC-II structures. Both peptides exhibited no cytotoxicity and were able to induce proinflammatory cytokine secretion in stimulated macrophages. The molecular docking, MD and in-vitro studies of the predicted B and T-cell epitopes of Pep-9 and Pep-15 peptides with the modelled MHC structures exhibited strong binding affinity and antigenic properties, suggesting that the complex is stable, and that these peptides can be considered as a potential candidates for the development of vaccine against TB.

Docking of peptides to GPCRs using a combination of CABS-dock with FlexPepDock refinement

The structural description of peptide ligands bound to G protein-coupled receptors (GPCRs) is important for the discovery of new drugs and deeper understanding of the molecular mechanisms of life. Here we describe a three-stage protocol for the molecular docking of peptides to GPCRs using a set of different programs: (1) CABS-dock for docking fully flexible peptides; (2) PD2 method for the reconstruction of atomistic structures from C-alpha traces provided by CABS-dock and (3) Rosetta FlexPepDock for the refinement of protein–peptide complex structures and model scoring. We evaluated the proposed protocol on the set of seven different GPCR–peptide complexes (including one containing a cyclic peptide), for which crystallographic structures are available. We show that CABS-dock produces high resolution models in the sets of top-scored models. These sets of models, after reconstruction to all-atom representation, can be further improved by Rosetta high-resolution refinement and/or minimization, leading in most of the cases to sub-Angstrom accuracy in terms of interface root-mean-square-deviation measure.

Bioactive peptides from beans with the potential to decrease the risk of developing noncommunicable chronic diseases

Several studies have demonstrated that peptides obtained from the proteins of different bean species have the potential to act on therapeutic targets of noncommunicable chronic diseases or NCDs. However, peptides with great structural diversity can be obtained from the hydrolysis of proteins present in foods. Therefore, the present review had the objective of identifying, in silico, the possibility of obtaining peptides with potential biological activity from the storage globulin proteins of the bean species Phaseolus vulgaris (L.), Vigna angularis (Willd.), Vigna radiata (L.) and Vigna unguiculata (L.) Walp., using the UniProtKB, BIOPEP and PeptideRanker databases, as well as reviewing available research reports that showed evidence bioactive properties of peptides obtained from beans via in vitro assays. For all the species studied, the highest frequency of the occurrence of bioactive fragments was found for the inhibition of dipeptidyl peptidase-IV, followed by the inhibition of the angiotensin-converting enzyme and by antioxidant activity. The inhibition of the two enzymes is the therapeutic target of drugs used for type 2 diabetes mellitus (T2DM) and for hypertension, respectively, while the antioxidant activity can prevent the development of several chronic diseases related to oxidative stress.

Docking interactions determine early cleavage events in insulin proteolysis by pepsin: Experiment and simulation

In silico modelling of cascade enzymatic proteolysis is an exceedingly complex and challenging task. Here, we study partial proteolysis of insulin by pepsin: a process leading to the release of a highly amyloidogenic two chain 'H-fragment'. The H-fragment retains several cleavage sites for pepsin. However, under favorable conditions H-monomers rapidly self-assemble into proteolysis-resistant amyloid fibrils whose composition provides snapshots of early and intermediate stages of the proteolysis. In this work, we report a remarkable agreement of experimentally determined and simulation-predicted cleavage sites on different stages of the proteolysis. Prediction of cleavage sites was based on the comprehensive analysis of the docking interactions from direct simulation of coupled folding and binding of insulin (or its cleaved derivatives) to pepsin. The most frequent interactions were found to be between the pepsin's active site, or its direct vicinity, and the experimentally determined insulin cleavage sites, which suggest that the docking interactions govern the proteolytic process.

Flexible docking of peptides to proteins using CABS-dock

Molecular docking of peptides to proteins can be a useful tool in the exploration of the possible peptide binding sites and poses. CABS-dock is a method for protein-peptide docking that features significant conformational flexibility of both the peptide and the protein molecules during the peptide search for a binding site. The CABS-dock has been made available as a web server and a standalone package. The web server is an easy to use tool with a simple web interface. The standalone package is a command-line program dedicated to professional users. It offers a number of advanced features, analysis tools and support for large-sized systems. In this article, we outline the current status of the CABS-dock method, its recent developments, applications, and challenges ahead.

Protocols for All-Atom Reconstruction and High-Resolution Refinement of Protein-Peptide Complex Structures

Structural characterizations of protein-peptide complexes may require further improvements. These may include reconstruction of missing atoms and/or structure optimization leading to higher accuracy models. In this work, we describe a workflow that generates accurate structural models of peptide-protein complexes starting from protein-peptide models in C-alpha representation generated using CABS-dock molecular docking. First, protein-peptide models are reconstructed from their C-alpha traces to all-atom representation using MODELLER. Next, they are refined using Rosetta FlexPepDock. The described workflow allows for reliable all-atom reconstruction of CABS-dock models and their further improvement to high-resolution models.