Protein-peptide docking using CABS-dock and contact information

Specific interaction from different Aβ42 peptide fragments to α7nAChR-A study of molecular dynamics simulation

CONTEXT

Existing researches confirmed that β amyloid (Aβ) has a high affinity for the α7 nicotinic acetylcholine receptor (α7nAChR), associating closely to Alzheimer's disease. The majority of related studies focused on the experimental reports on the neuroprotective role of Aβ fragment (Aβx), however, with a lack of investigation into the most suitable binding region and mechanism of action between Aβ fragment and α7nAChR. In the study, we employed four Aβ1-42 fragments Aβx, Aβ1-16, Aβ10-16, Aβ12-28, and Aβ30-42, of which the first three were confirmed to play neuroprotective roles upon directly binding, to interact with α7nAChR.

METHODS

The protein-ligand docking server of CABS-DOCK was employed to obtain the α7nAChR-Aβx complexes. Only the top α7nAChR-Aβx complexes were used to perform all-atom GROMACS dynamics simulation in combination with Charmm36 force field, by which α7nAChR-Aβx interactions' dynamic behavior and specific locations of these different Aβx fragments were identified. MM-PBSA calculations were also done to estimate the binding free energies and the different contributions from the residues in the Aβx. Two distinct results for the first three and fourth Aβx fragments in binding site, strength, key residue, and orientation, account for why the fourth fails to play a neuroprotective role at the molecular level.

Enzyme-cargo encapsulation peptides bind between tessellating tiles of the bacterial microcompartment shell

Bacterial microcompartments are prokaryotic organelles comprising encapsulated enzymes within a thin protein shell. They facilitate metabolic processing including propanediol, choline, glycerol, and ethanolamine utilization, and they accelerate carbon fixation in cyanobacteria. Enzymes targeted to the inside of the microcompartment frequently possess a cargo-encapsulation peptide, but the site to which the peptide binds is unclear. We provide evidence that the encapsulation peptides bind to the hydrophobic groove formed between tessellating subunits of the shell proteins. In silico docking studies provide a compelling model of peptide binding to this prominent hydrophobic groove. This result is consistent with the now widely accepted view that the convex side of the shell oligomers faces the lumen of the microcompartment. The binding of the encapsulation peptide to the groove between tessellating shell protein tiles explains why it has been difficult to define the peptide binding site using other methods, provides a mechanism by which encapsulation-peptide bearing enzymes can promote shell assembly, and explains how the presence of cargo affects the size and shape of the bacterial microcompartment. This knowledge may be exploited in engineering microcompartments or disease prevention by hampering cargo encapsulation.

How the immune response to the structural proteins of SARS-CoV-2 affects the retinal vascular endothelial cells: an immune thrombotic and/or endotheliopathy process with in silico modeling

Thrombotic events associated with SARS-CoV-2 at the vascular endothelium still remains unclear. The aim of the current study is to determine the relationship between cellular proteins on the (ocular) vascular endothelial surface and the immune thrombotic and/or endotheliopathy process elicited by SARS-CoV-2 using an in-silico modeling. The structural S (spike glycoprotein), N (nucleocapsid protein), M (membrane protein), and E (envelope protein) proteins, an accessory protein (ORF1ab) of SARS-CoV-2 and 158 cellular proteins associated with retinal vascular endothelial cell surface or structure were included in this study for comparison of three-dimensional (3D) structure and sequence. Sixty-nine of the retinal proteins were obtained from the Uniprot database. Remaining proteins not included in the database were included in the study after they were converted into 3D structures using the RaptorX web tool. Sequence and three-dimensional structure of SARS-COV-2 S, N, M, E, ORF1ab proteins and retinal vascular endothelial proteins were compared with mTM-align server. Proteins with significant similarity (score above 0.5) were validated with the TM-align web server. Immune and thrombosis-related protein-receptor interactions of similar proteins was checked with CABS-dock. We detected a high level of structural similarity between E protein and ACE, ACE2, LAT1, and TM9SF4 endothelial proteins. In addition, PECAM-1 was found to be structurally similar to ORF1ab and S protein. When we evaluated the likelihood/potential to stimulate an immune responses/a cytokine release, TLR-2 and TLR-3, which are highly susceptible to SARS-CoV2, showed a potential receptor-protein interaction with retinal vascular endothelial proteins. Our study demonstrates that SARS-CoV-2 proteins may have structural similarities with vascular endothelial proteins, and therefore, as immunological target sites, the counterpart proteins on the endothelial surface of many organs may also be secondarily affected by any immune response against SARS-CoV-2 structural proteins.

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.

In Silico Design of a New Epitope-Based Vaccine against Grass Group 1 Allergens

Allergic diseases are a global public health problem that affects up to 30% of the population in industrialized societies. More than 40% of allergic patients suffer from grass pollen allergy. Grass pollen allergens of group 1 and group 5 are the major allergens, since they induce allergic reactions in patients at high rates. In this study, we used immunoinformatic approaches to design an effective epitope-based vaccine against the grass group 1 allergens. After the alignment of all known pollen T-cell and B-cell epitopes from pollen allergens available in the public databases, the epitope GTKSEVEDVIPEGWKADTSY was identified as the most suitable for further analyses. The target sequence was subjected to immunoinformatics analyses to predict antigenic T-cell and B-cell epitopes. Population coverage analysis was performed for CD8+ and CD4+ T-cell epitopes. The selected T-cell epitopes (VEDVIPEGW and TKSEVEDVIPEGWKA) covered 78.87% and 98.20% of the global population and 84.57% and 99.86% of the population of Europe. Selected CD8+, CD4+ T-cell and B-cell epitopes have been validated by molecular docking analysis. CD8+ and CD4+ T-cell epitopes showed a very strong binding affinity to major histocompatibility complex (MHC) class I (MHC I) molecules and MHC class II (MHC II) molecules with global energy scores of -72.1 kcal/mol and -89.59 kcal/mol, respectively. The human IgE-Fc (PDB ID 4J4P) showed a lower affinity with B-cell epitope (ΔG = -34.4 kcal/mol), while the Phl p 2-specific human IgE Fab (PDB ID 2VXQ) had the lowest binding with the B-cell epitope (ΔG = -29.9 kcal/mol). Our immunoinformatics results demonstrated that the peptide GTKSEVEDVIPEGWKADTSY could stimulate the immune system and we performed ex vivo tests showed that the investigated epitope activates T cells isolated from patients with grass pollen allergy, but it is not recognized by IgE antibodies specific for grass pollen allergens. This confirms the importance of such studies to establish universal epitopes to serve as a basis for developing an effective vaccine against a particular group of allergens. Further in vivo studies are needed to validate the effectiveness of such a vaccine against grass pollen allergens.

In Silico Strategies to Predict Anti-aging Features of Whey Peptides

We have analysed the in silico potential of bioactive peptides from cheese whey, the most relevant by-product from the dairy industry, to bind into the active site of collagenase and elastase. The peptides generated from the hydrolysis of bovine β-lactoglobulin with three proteases (trypsin, chymotrypsin, and subtilisin) were docked onto collagenase and elastase by molecular docking. The interaction models were ranked according to their free binding energy using molecular dynamics simulations, which showed that most complexes presented favourable interactions. Interactions with elastase had significantly lower binding energies than those with collagenase. Regarding the interaction site, it was found that four bioactive peptides were positioned in collagenase's active site, while six were found in elastase's active site. Among these, the most we have found one promising collagen-binding peptide produced by chymotrypsin and two for elastase, produced by subtilisin and chymotrypsin. These in silico results can be used as a tool for designing further experiments aiming at testing the in vitro potential of the peptides found in this work.

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

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

Domain-Based Protein Docking with Extremely Large Conformational Changes

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

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.

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.

Immunoinformatics Analysis of SARS-CoV-2 ORF1ab Polyproteins to Identify Promiscuous and Highly Conserved T-Cell Epitopes to Formulate Vaccine for Indonesia and the World Population

SARS-CoV-2 and its variants caused the COVID-19 pandemic. Vaccines that target conserved regions of SARS-CoV-2 and stimulate protective T-cell responses are important for reducing symptoms and limiting the infection. Seven cytotoxic (CTL) and five helper T-cells (HTL) epitopes from ORF1ab were identified using NetCTLpan and NetMHCIIpan algorithms, respectively. These epitopes were generated from ORF1ab regions that are evolutionary stable as reflected by zero Shannon's entropy and are presented by 56 human leukocyte antigen (HLA) Class I and 22 HLA Class II, ensuring good coverage for the Indonesian and world population. Having fulfilled other criteria such as immunogenicity, IFNγ inducing ability, and non-homology to human and microbiome peptides, the epitopes were assembled into a vaccine construct (VC) together with β-defensin as adjuvant and appropriate linkers. The VC was shown to have good physicochemical characteristics and capability of inducing CTL as well as HTL responses, which stem from the engagement of the vaccine with toll-like receptor 4 (TLR4) as revealed by docking simulations. The most promiscuous peptide 899WSMATYYLF907 was shown via docking simulation to interact well with HLA-A*24:07, the most predominant allele in Indonesia. The data presented here will contribute to the in vitro study of T-cell epitope mapping and vaccine design in Indonesia.

A novel multi-objective metaheuristic algorithm for protein-peptide docking and benchmarking on the LEADS-PEP dataset

Protein-peptide interactions have attracted the attention of many drug discovery scientists due to their possible druggability features on most key biological activities such as regulating disease-related signaling pathways and enhancing the immune system's responses. Different studies have utilized some protein-peptide-specific docking algorithms/methods to predict protein-peptide interactions. However, the existing algorithms/methods suffer from two serious limitations which make them unsuitable for protein-peptide docking problems. First, it seems that the prevalent approaches require to be modified and remodeled for weighting the unbounded forces between a protein and a peptide. Second, they do not employ state-of-the-art search algorithms for detecting the 3D pose of a peptide relative to a protein. To address these restrictions, the present study aims to introduce a novel multi-objective algorithm, which first generates some potential 3D poses of a peptide, and then, improves them through its operators. The candidate solutions are further evaluated using Multi-Objective Pareto Front (MOPF) optimization concepts. To this end, van der Waals, electrostatic, solvation, and hydrogen bond energies between the atoms of a protein and designated peptide are computed. To evaluate the algorithm, it is first applied to the LEADS-PEP dataset containing 53 protein-peptide complexes with up to 53 rotatable branches/bonds and then compared with three popular/efficient algorithms. The obtained results indicate that the MOPF-based approaches which reduce the backbone RMSD between the original and predicted states, achieve significantly better results in terms of the success rate in predicting the near-native conditions. Besides, a comparison between the different types of search algorithms reveals that efficient ones like the multi-objective Trader/differential evolution algorithm can predict protein-peptide interactions better than the popular algorithms such as the multi-objective genetic/particle swarm optimization algorithms.

Identification of a peptide binding to cancer antigen Kita-kyushu lung cancer antigen 1 from a phage-display library

Kita‐kyushu lung cancer antigen 1 (KK‐LC‐1) is a kind of cancer‐testis antigen with anti‐tumor potential for clinical application. As a class of small‐molecule antigen conjugate, tumor‐targeting peptides have broad application prospects in gastric cancer diagnosis, imaging, and biological treatment. Here, we screened specific cyclic nonapeptides from a phage‐display library. The targeting peptide with the best affinity was selected and further verified in ex vivo tissue sections. Finally, enrichment of targeting peptides in tumor tissues was observed in vivo, and the dynamic biodistribution process was also observed with micro‐positron emission tomography (micro‐PET)/computed tomography (CT) imaging. Studies showed that the specific cyclic nonapeptide had a high binding capacity for KK‐LC‐1 protein. It has a strong affinity and specificity for KK‐LC‐1‐expressing positive tumor cells. Targeting peptides were significantly enriched at tumor sites in vivo, with very low normal tissue background. These findings demonstrated that the KK‐LC‐1 targeting peptide has high clinical potential.

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.

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.

Pharmacological targets and mechanisms of calycosin against meningitis

This report aimed to identity the potential anti-meningitis targets and mechanisms functioned by calycosin through network pharmacology approach. The bioinformatics databases were used to screen and collect the candidate genes/targets of calycosin and meningitis prior to identification of vital biotargets of calycosin-anti-meningitis. Additionally, the functional processes, signaling pathways of calycosin-anti-meningitis were screened and identified before further data visualization. As a result, all candidate and mapped biotargets of calycosin and meningitis were harvested before the vital targets of epidermal growth factor receptor (EGFR), tumor necrosis factor (TNF), epidermal growth factor (EGF), ataxia telangiectasia mutated protein (ATM), estrogen receptor alpha (ESR1), caspase-8 (CASP8), nerve growth factor (NGF) of calycosin-anti-meningitis were identified. The molecular processes of calycosin-anti-meningitis were screened and identified, including reduction of inflammatory development. Furthermore, the molecular pathways of calycosin-anti-meningitis were revealed, including suppression of NF-kappa B, Toll-like receptor, TNF signaling pathways. Molecular docking findings uncovered the docking capacity of calycosin with meningitis and potential pharmacological activity of calycosin against meningitis. In conclusion, these bioinformatic data uncovered the network targets and mechanisms of calycosin-anti-meningitis. And the current findings indicated that the vital targets might be used as potent biomarkers for detecting meningitis.

CABS-dock standalone: a toolbox for flexible protein-peptide docking

Summary

CABS-dock standalone is a multiplatform Python package for protein–peptide docking with backbone flexibility. The main feature of the CABS-dock method is its ability to simulate significant backbone flexibility of the entire protein–peptide system in a reasonable computational time. In the default mode, the package runs a simulation of fully flexible peptide searching for a binding site on the surface of a flexible protein receptor. The flexibility level of the molecules may be defined by the user. Furthermore, the CABS-dock standalone application provides users with full control over the docking simulation from the initial setup to the analysis of results. The standalone version is an upgrade of the original web server implementation—it introduces a number of customizable options, provides support for large-sized systems and offers a framework for deeper analysis of docking results.

Availability and implementation

CABS-dock standalone is distributed under the MIT licence, which is free for academic and non-profit users. It is implemented in Python and Fortran. The CABS-dock standalone source code, wiki with documentation and examples of use and installation instructions for Linux, macOS and Windows are available in the CABS-dock standalone repository at https://bitbucket.org/lcbio/cabsdock.

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.

TASL is the SLC15A4-associated adaptor for IRF5 activation by TLR7-9

Toll-like receptors (TLRs) have a crucial role in the recognition of pathogens and initiation of immune responses^1–3. Here we show that a previously uncharacterized protein encoded by CXorf21—a gene that is associated with systemic lupus erythematosus^4,5—interacts with the endolysosomal transporter SLC15A4, an essential but poorly understood component of the endolysosomal TLR machinery also linked to autoimmune disease^4,6–9. Loss of this type-I-interferon-inducible protein, which we refer to as ‘TLR adaptor interacting with SLC15A4 on the lysosome’ (TASL), abrogated responses to endolysosomal TLR agonists in both primary and transformed human immune cells. Deletion of SLC15A4 or TASL specifically impaired the activation of the IRF pathway without affecting NF-κB and MAPK signalling, which indicates that ligand recognition and TLR engagement in the endolysosome occurred normally. Extensive mutagenesis of TASL demonstrated that its localization and function relies on the interaction with SLC15A4. TASL contains a conserved pLxIS motif (in which p denotes a hydrophilic residue and x denotes any residue) that mediates the recruitment and activation of IRF5. This finding shows that TASL is an innate immune adaptor for TLR7, TLR8 and TLR9 signalling, revealing a clear mechanistic analogy with the IRF3 adaptors STING, MAVS and TRIF^10,11. The identification of TASL as the component that links endolysosomal TLRs to the IRF5 transcription factor via SLC15A4 provides a mechanistic explanation for the involvement of these proteins in systemic lupus erythematosus^12–14.

Molecular Functionality of Plant Proteins from Low- to High-Solid Systems with Ligand and Co-Solute

In the food industry, proteins are regarded as multifunctional systems whose bioactive hetero-polymeric properties are affected by physicochemical interactions with the surrounding components in formulations. Due to their nutritional value, plant proteins are increasingly considered by the new product developer to provide three-dimensional assemblies of required structure, texture, solubility and interfacial/bulk stability with physical, chemical or enzymatic treatment. This molecular flexibility allows them to form systems for the preservation of fresh food, retention of good nutrition and interaction with a range of microconstituents. While, animal- and milk-based proteins have been widely discussed in the literature, the role of plant proteins in the development of functional foods with enhanced nutritional profile and targeted physiological effects can be further explored. This review aims to look into the molecular functionality of plant proteins in relation to the transport of bioactive ingredients and interaction with other ligands and proteins. In doing so, it will consider preparations from low- to high-solids and the effect of structural transformation via gelation, phase separation and vitrification on protein functionality as a delivery vehicle or heterologous complex. Applications for the design of novel functional foods and nutraceuticals will also be discussed.

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.

Modeling of Disordered Protein Structures Using Monte Carlo Simulations and Knowledge-Based Statistical Force Fields

The description of protein disordered states is important for understanding protein folding mechanisms and their functions. In this short review, we briefly describe a simulation approach to modeling protein interactions, which involve disordered peptide partners or intrinsically disordered protein regions, and unfolded states of globular proteins. It is based on the CABS coarse-grained protein model that uses a Monte Carlo (MC) sampling scheme and a knowledge-based statistical force field. We review several case studies showing that description of protein disordered states resulting from CABS simulations is consistent with experimental data. The case studies comprise investigations of protein⁻peptide binding and protein folding processes. The CABS model has been recently made available as the simulation engine of multiscale modeling tools enabling studies of protein⁻peptide docking and protein flexibility. Those tools offer customization of the modeling process, driving the conformational search using distance restraints, reconstruction of selected models to all-atom resolution, and simulation of large protein systems in a reasonable computational time. Therefore, CABS can be combined in integrative modeling pipelines incorporating experimental data and other modeling tools of various resolution.

Probing the local conformational flexibility in receptor recognition: mechanistic insight from an atomic-scale investigation

Inherent protein conformational flexibility is important for biomolecular recognition, but this critical property is often neglected in several studies. This event can lead to large deviations in the research results. In the current contribution, we disclose the effects of the local conformational flexibility on receptor recognition by using an atomic-scale computational method. The results indicated that both static and dynamic reaction modes have noticeable differences, and these originated from the structural features of the protein molecules. Dynamic interaction results displayed that the structural stability and conformational flexibility of the proteins had a significant influence on the recognition processes. This point related closely to the characteristics of the flexible loop regions where bixin located within the protein structures. The energy decomposition analyses and circular dichroism results validated the rationality of the recognition studies. More importantly, the conformational and energy changes of some residues around the bixin binding domain were found to be vital to biological reactions. These microscopic findings clarified the nature of the phenomenon that the local conformational flexibility could intervene in receptor recognition. Obviously, this report may provide biophysical evidence for the exploration of the structure–function relationships of the biological receptors in the human body.

The local conformational flexibility and dynamics have significant impacts on the receptor recognition processes, and this phenomenon is related closely to the structural characteristics of the flexible loop domains in biomacromolecules.