Methods for the Refinement of Protein Structure 3D Models

Molecular dynamics simulations: Insights into protein and protein ligand interactions

Molecular dynamics (MD) simulations are a powerful tool for studying biomolecular systems, offering in-depth insights into the dynamic behaviors of proteins and their interactions with ligands. This chapter delves into the fundamental principles and methodologies of MD simulations, exploring how they contribute to our understanding of protein structures, conformational changes, and the mechanisms underlying protein-ligand interactions. We discuss the computational techniques, force fields, and algorithms that drive MD simulations, highlighting their applications in drug discovery and design. Through case studies and practical examples, we illustrate the capabilities and limitations of MD simulations, emphasizing their role in predicting binding affinities, elucidating binding pathways, and optimizing lead compounds. This chapter offers a thorough understanding of how MD simulations can be leveraged to advance the study of protein-ligand interactions.

Beyond AlphaFold2: The Impact of AI for the Further Improvement of Protein Structure Prediction

Protein structure prediction is fundamental to molecular biology and has numerous applications in areas such as drug discovery and protein engineering. Machine learning techniques have greatly advanced protein 3D modeling in recent years, particularly with the development of AlphaFold2 (AF2), which can analyze sequences of amino acids and predict 3D structures with near experimental accuracy. Since the release of AF2, numerous studies have been conducted, either using AF2 directly for large-scale modeling or building upon the software for other use cases. Many reviews have been published discussing the impact of AF2 in the field of protein bioinformatics, particularly in relation to neural networks, which have highlighted what AF2 can and cannot do. It is evident that AF2 and similar approaches are open to further development and several new approaches have emerged, in addition to older refinement approaches, for improving the quality of predictions. Here we provide a brief overview, aimed at the general biologist, of how machine learning techniques have been used for improvement of 3D models of proteins following AF2, and we highlight the impacts of these approaches. In the most recent experiment on the Critical Assessment of Techniques for Protein Structure Prediction (CASP15), the most successful groups all developed their own tools for protein structure modeling that were based at least in some part on AF2. This improvement involved employing techniques such as generative modeling, changing parameters such as dropout to generate more AF2 structures, and data-driven approaches including using alternative templates and MSAs.

Computational studies on metabolic pathways of Coxiella burnetii to combat Q fever: A roadmap to vaccine development

Coxiella burnetii (Cbu) is the gram-negative intracellular pathogen responsible for deadly zoonotic infection, Q fever. The pathogen is environmentally stable and distributed throughout the world which is sustained in nature by chronic infection of ruminants. The epidemiological studies on Q fever indicates it as emerging public health problem in various countries and it is imperative to promptly identify an appropriate therapeutic solution for this pathogen. In the current study, metabolic pathways of Cbu were analysed by the combination of multiple computational tools for the prediction of suitable therapeutic candidates. We have identified 25 metabolic pathways which were specific to Cbu containing 287 unique proteins. A total of 141 proteins which were either virulent, essential or resistant were shortlisted that do not show homology with the host proteins and considered as potential targets for drug and vaccine development. The potential therapeutic targets were classified in to seven functional classes, i.e., metabolism, transport, gene expression and regulation, signal transduction, antimicrobial resistance, stress response regulator and unknown. The majority of the proteins were found to be present in metabolism and transport class. The functional annotation showed the predominant presence of proteins containing HATPase_c, Beta-lactamase, GerE, ACR_tran, PP-binding, CsrA domains. We have identified Type I secretion outer membrane protein for the design of multi-epitope subunit vaccine using reverse vacciniology approach. Four B cell epitopes, six MHC-I epitopes and four MHC-II epitopes were identified which are non-toxic, non-allergen and highly antigenic. The multi-epitope subunit vaccine construct was 327 amino acid residues long which include adjuvant, B cell epitopes, MHC-I epitopes and MHC-II epitopes. The Cholera enterotoxin subunit B is included as an adjuvant in the N terminal of vaccine construct which will help to produce a strong immune response to the vaccine. The multi-epitope vaccine construct was non-toxic, non-allergen and probable antigen having molecular weight 35.13954 kDa, aliphatic index 85.50, theoretical PI 9.65, GRAVY -0.001, and instability index of 28.37. The tertiary structure of the vaccine construct was modeled and physiochemical properties were predicted. After validation and refinement of tertiary structure the molecular docking of vaccine exhibited strong binding with TLR2, TLR3, TLR4, TLR5 and TLR8. The TLRs and vaccine construct formed hydrogen bonds, salt bridges and non-bonded contacts with all TLR receptors. The in-silico immune simulations showed the ability to trigger primary immune response as shown by increment in B-cell and T-cell population. The research paves the way for more effective control of zoonotic disease Q fever.

Molecular docking and molecular dynamics of hypoxia-inducible factor (HIF-1alpha): towards potential inhibitors

HIF-1α is a primary regulator in the adaptation of cancer cells to hypoxia. The aim was to find out new inhibitors of the HIF-1α. A molecular dynamic (MD) simulation performed on HIF-1α showed stable dynamic features. Virtual screening of 217 anticancer drugs was performed along with a positive control (2-Methoxyestradiolm, 2-ME2) on an optimized HIF-1α and dynamically simulated structure. Docking results produced two compounds namely pycnidione and nilotinib of high binding affinity -9.34 kcal/mol and -9.04 kcal/mol respectively, whereas 2-ME2 displayed a relatively lower affinity (-6.68 kcal/mol). For the three complexes, MD of 200 ns simulation was run. Data analysis showed that the three medications behaved similarly in the MD simulation. Nilotinib had a lower RMSD and higher SASA than the other complexes. In addition, the Nilotinib-HIF-1α combination had a lower RMSF value, a flatter Rg, and a number of hydrogen bonds similar to other complexes. MM-GBSA analysis revealed that nilotinib, pycnidione and 2-ME2 compounds had free binding energy of -23.77 ± 5.29, -21.85 ± 4.24 and -7.53 ± 6.62 kcal/mol respectively. Nilotinib and pycnidione bind competitively to HIF-1α, with nilotinib showing consistent molecular-dynamic properties. They relatively pass the blood-brain barrier, non-carcinogenic, and have IV-category acute oral toxicity. They have low CYP inhibitory characteristics. Further investigations are therefore warranted to elucidate their implications in hypoxia pathways, cell proliferation, apoptosis, survival, and metastatic potential.

Computational exploration of SLC14A1 genetic variants through structure modeling, protein-ligand docking, and molecular dynamics simulation

The urea transporter UT-B1, encoded by the SLC14A1 gene, has been hypothesized to be a significant protein whose deficiency and dysfunction contribute to the pathogenesis of bladder cancer and many other diseases. Several studies reported the association of genetic alterations in the SLC14A1 (UT-B1) gene with bladder carcinogenesis, suggesting a need for thorough characterization of the UT-B1 protein's coding and non-coding variants. This study used various computational techniques to investigate the commonly occurring germ-line missense and non-coding SNPs (ncSNPs) of the SLC14A1 gene (UT-B1) for their structural, functional, and molecular implications for disease susceptibility and dysfunctionality. SLC14A1 missense variants, primarily identified from the ENSEMBL genome browser, were screened through twelve functionality prediction tools leading to two variants D280Y (predicted detrimental by maximum tools) and D280N (high global MAF) for rs1058396. Subsequently, the ConSurf and NetSurf tools revealed the D280 residue to be in a variable site and exposed on the protein surface. According to I-Mutant2.0 and MUpro, both variants are predicted to cause a significant effect on protein stability. Analysis of molecular docking anticipated these two variants to decrease the binding affinity of UT-B1 protein for the examined ligands to a significant extent. Molecular dynamics also disclosed the possible destabilization of the UT-B1 protein due to single nucleotide polymorphism compared to wild-type protein which may result in impaired protein function. Furthermore, several non-coding SNPs were estimated to affect transcription factor binding and regulation of SLC14A1 gene expression. Additionally, two ncSNPs were found to affect miRNA-based post-transcriptional regulation by creating new seed regions for miRNA binding. This comprehensive in-silico study of SLC14A1 gene variants may serve as a springboard for future large-scale investigations examining SLC14A1 polymorphisms.

Molecular interactions of paraben family of pollutants with embryonic neuronal proteins of Danio rerio: A step ahead in computational toxicity towards adverse outcome pathway

The paraben family of endocrine disruptors exhibit persistent behaviours in aquatic matrices, having bio-accumulative effects and necessitating toxicity analysis and safe use, as well as prevention of food web penetration. In this study, the toxicity effects of 9 different parabens (Methyl, Ethyl, Propyl, Butyl, Heptyl, Isopropyl, Isobutyl, benzyl parabens and p-hydroxybenzoic acid) were studied against 17 neuronal proteins (Neurog1, Ascl1a, DLA, Syn2a, Ntn1a, Pitx2, and SoxB1, Her/Hes, Zic family) expressed during the early embryonic developmental stage of Danio rerio. The neuronal genes were selected as a biomarker to study the inhibitory effects on the cascade of genes expressed in the early developmental stage. The study uses trRossetta software to predict protein structures of neuronal genes, followed by structural refinement, energy minimisation, and active site prediction, evaluated using energy value, RC plot and ERRAT scores of PROCHECK and ERRAT programs. Compared to raw structures, highly confident predicted structures and quality scores were observed for refined protein with few exceptions. Based on the polarity and charge of the aminoacids, the probable pockets were identified using active site prediction, which were then used for molecular docking analysis. Further, the ADMET analysis, ligand likeliness and toxicological test revealed the paraben family of compounds as one of the most susceptible toxic and mutagenic compounds. The molecular docking results showed an interesting pattern of increasing binding affinity with increase in the carbon chains of paraben molecules. Benzyl Paraben showed higher binding affinities across all 17 neuronal proteins. Finally, gene co-occurrence/co-expression and protein-protein interaction studies using the STRING database depict that all proteins are functionally related and play essential roles in standard biological processes or pathways, conserved and expressed in diverse organisms. The interaction between paraben compounds and neuronal genes indicates high risks of inhibiting reactions in embryonic stages, emphasising the need for effective treatment measures and strict regulations.

Integrated virtual screening and molecular dynamics simulation approaches revealed potential natural inhibitors for DNMT1 as therapeutic solution for triple negative breast cancer

Triple negative breast cancers (TNBC) are clinically heterogeneous but mostly aggressive malignancies devoid of expression of the estrogen, progesterone, and HER2 (ERBB2 or NEU) receptors. It accounts for 15-20% of all cases. Altered epigenetic regulation including DNA hypermethylation by DNA methyltransferase 1 (DNMT1) has been implicated as one of the causes of TNBC tumorigenesis. The antitumor effect of DNMT1 has also been explored in TNBC that currently lacks targeted therapies. However, the actual treatment for TNBC is yet to be discovered. This study is attributed to the identification of novel drug targets against TNBC. A comprehensive docking and simulation analysis was performed to optimize promising new compounds by estimating their binding affinity to the target protein. Molecular dynamics simulation of 500 ns well complemented the binding affinity of the compound and revealed strong stability of predicted compounds at the docked site. Calculation of binding free energies using MMPBSA and MMGBSA validated the strong binding affinity between compound and binding pockets of DNMT1. In a nutshell, our study uncovered that Beta-Mangostin, Gancaonin Z, 5-hydroxysophoranone, Sophoraflavanone L, and Dorsmanin H showed maximum binding affinity with the active sites of DNMT1. Furthermore, all of these compounds depict maximum drug-like properties. Therefore, the proposed compounds can be a potential candidate for patients with TNBC, but, experimental validation is needed to ensure their safety.Communicated by Ramaswamy H. Sarma.

Ab Initio Prediction of 3-D Conformations for Protein Long Loops with High Accuracy and Applications to Antibody CDRH3 Modeling

Residue-level potentials of mean force were widely used for protein backbone refinements to avoid simultaneous sampling of side-chain conformations. The interaction energy between the reduced side chains and backbone atoms was not considered explicitly. In this study, we developed novel methods to calculate the residue-atom interaction energy in combination with atomic and residue-level terms. The parameters were optimized step by step to remove the overcounting or overlap problem between different energy terms. The mixing energy functions were then used to evaluate the generated backbone conformations at the initial sampling stage of protein loop modeling (OSCAR-loop), including the interaction energy between the reduced loop residues and full atoms of the protein framework. The accuracies of top-ranked decoys were 1.18 and 2.81 Å for 8-residue and 12-residue loops, respectively. We then selected diverse decoys for side-chain modeling, backbone refinement, and energy minimization. The procedure was repeated multiple times to select one prediction with the lowest energy. Consequently, we obtained an accuracy of 0.74 Å for a prevailing test set of 12-residue loops, compared with >1.4 Å reported by other researchers. The OSCAR-loop was also effective for modeling the H3 loops of antibody complementary determining regions (CDRs) in the crystal environment. The prediction accuracy of OSCAR-loop (1.74 Å) was better than the accuracy of the Rosetta NGK method (3.11 Å) or those achieved by deep learning methods (>2.2 Å) for the CDRH3 loops of 49 targets in the Rosetta antibody benchmark. The performance of OSCAR-loop in a model environment was also discussed.

Comparative homology of Pleurotus ostreatus laccase enzyme: Swiss model or Modeller?

Laccases stand out in the industrial context due to their versatile biotechnological applications. Although these enzymes are frequently investigated, currently, Pleurotus ostreatus laccase structural model is unknown. Therefore, this research aims to predict and validate a P. ostreatus laccase theoretical model by means of comparative homology. The laccase target's primary structure (AOM73725.1) was obtained from the NCBI database, the model was predicted from homologous structures obtained from the PDB (PDB-ID: 5A7E, 2HRG, 4JHU, 1GYC) using the Swiss-Model and Modeller, and was refined in GalaxyRefine. The models were validated using PROCHECK, VERIFY 3D, ERRAT, PROVE and QMEAN Z-score servers. Moreover, molecular docking between the laccase model (Lacc4MN) and ABTS was performed on AutoDock Vina. The models that were generated by the Modeller showed superior stereochemical and structural characteristics to those predicted by the Swiss Model. The refinement made it difficult to stabilize the copper atoms which are typical of laccases. The Lacc4MN model showed the interactions between the amino acids in the active site of the laccase and the copper atoms, thereby hinting the stabilization of the metal through electrostatic interactions with histidine and cysteine. The molecular docking between Lacc4MN and ABTS showed negative free energy and the formation of two hydrogen bonds involving the amino acids ASP 208 and GLY 268, and a Pi-sulfur bond between residue HIS 458 and ABTS, which demonstrates the typical catalytic functionality of laccases. Furthermore, the theoretical model Lacc4MN presented stereochemical and structural characteristics that allow its use in silico tests.Communicated by Ramaswamy H. Sarma.

Improvement of protein tertiary and quaternary structure predictions using the ReFOLD refinement method and the AlphaFold2 recycling process

Motivation

The accuracy gap between predicted and experimental structures has been significantly reduced following the development of AlphaFold2 (AF2). However, for many targets, AF2 models still have room for improvement. In previous CASP experiments, highly computationally intensive MD simulation-based methods have been widely used to improve the accuracy of single 3D models. Here, our ReFOLD pipeline was adapted to refine AF2 predictions while maintaining high model accuracy at a modest computational cost. Furthermore, the AF2 recycling process was utilized to improve 3D models by using them as custom template inputs for tertiary and quaternary structure predictions.

Results

According to the Molprobity score, 94% of the generated 3D models by ReFOLD were improved. AF2 recycling showed an improvement rate of 87.5% (using MSAs) and 81.25% (using single sequences) for monomeric AF2 models and 100% (MSA) and 97.8% (single sequence) for monomeric non-AF2 models, as measured by the average change in lDDT. By the same measure, the recycling of multimeric models showed an improvement rate of as much as 80% for AF2-Multimer (AF2M) models and 94% for non-AF2M models.

Availability and implementation

Refinement using AlphaFold2-Multimer recycling is available as part of the MultiFOLD docker package (https://hub.docker.com/r/mcguffin/multifold). The ReFOLD server is available at https://www.reading.ac.uk/bioinf/ReFOLD/ and the modified scripts can be downloaded from https://www.reading.ac.uk/bioinf/downloads/.

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

In silico and experimental methods for designing a potent anticancer arazyme-herceptin fusion protein in HER2-positive breast cancer

CONTEXT

Breast cancer is the most prevalent type of malignancies among women worldwide and is associated with serious physical and mental consequences. Current chemotherapies may lack successful outcomes; thus, the development of targeted recombinant immunotoxins is plausible. The predicted B cell and T cell epitopes of arazyme of the fusion protein are able to elicit immune response. The results of codon adaptation tool of herceptin-arazyme have improved from 0.4 to 1. The in silico immune simulation results showed significant response for immune cells. In conclusion, our findings show that the known multi-epitope fusion protein may activate humoral and cellular immune responses and maybe a possible candidate for breast cancer treatment.

METHODS

In this study, the selected monoclonal antibody constituting herceptin and the bacterial metalloprotease, arazyme, was used with different peptide linkers to design a novel fusion protein to predict different B cell and T cell epitopes by the means of the relevant databases. Modeler 10.1 and I-TASSER online server were used to predict and validate the 3D structure and then docked to HER2-receptor using HADDOCK2.4 web server. The molecular dynamics (MD) simulations of the arazyme-linker-herceptin-HER2 complex were performed by GROMACS 2019.6 software. The sequence of arazyme-herceptin was optimized for the expression in prokaryotic host using online servers and cloned into pET-28a plasmid. The recombinant pET28a was transferred into the Escherichia coli BL21DE3. Expression and binding affinity of arazyme-herceptin and arazyme to human breast cancer cell lines (SK-BR-3/HER2 + and MDA-MB-468/HER2 -) were validated by the SDS-PAGE and cell‑ELISA, respectively.

In Silico Characterization of Withania coagulans Bioactive Compounds as Potential Inhibitors of Hydroxymethylglutaryl (HMG-CoA) Reductase of Mus musculus

Hypercholesterolemia is a mediator for the etiology of cardiovascular diseases, which are characterized as the global leading cause of mortality. We aimed to investigate the inhibitory activity of Withania coagulans compounds against 3-hydroxy-3-methylglutaryl-coenzyme A reductase (Hmgcr) of Mus musculus using an extensive in silico approach. The 3D structure of the Hmgcr protein is not yet known, so we performed the homology modeling using MODELLER and SWISS-MODEL tools, followed with structural validation and assessment. The PROCHECK web server showed that the top-ranked homology model from SWISS-MODEL has 93.4% of residues in the most-favorable region, the quality factor was 98%, and the Verify3D score was 91.43%, compared to the other generated models. The druggable protein-binding cavities in a 3D model of Hmgcr were investigated with the aid of commonly prescribed statin compounds using the CB-dock approach. We compiled a 3D compound library of W. coagulans, followed by drug-likeness evaluation, and found 20 eligible compounds. The pattern of consensus residues obtained from the CB-dock procedure was then used for grid-box docking of W. coagulans compounds and statin drugs using AutoDock 4.2, respectively. The results showed that withanolide R (-10.77 kcal/mol), withanolide Q (-10.56 kcal/mol), withanolide J (-10.52 kcal/mol), atorvastatin (-8.99 kcal/mol), simvastatin (-8.66 kcal/mol), and rosuvastatin (-8.58 kcal/mol) were promising candidates that bind Hmgcr protein. The key residues involved in protein-ligand (withanolide R) interactions were Y516, C526, V529, I530, M533, I535, and V537, and the formation of a H-bond was at C526, M533, and I535 residues. M533 was the consensus residue having a tendency to form a H-bond with withanolide Q, too. Molecular dynamics simulations were used to validate the top-ranked docked complexes for the stability of the modeled protein. We also predicted the pharmacokinetic properties of binding affinity-based top-ranked compounds and concluded that they could be used as potential inhibitors of Hmgcr. However, further in vitro and in vivo studies are essential to completing the drug development process.

In Silico Structural and Functional Analyses of NLRP3 Inflammasomes to Provide Insights for Treating Neurodegenerative Diseases

Inflammasomes are cytoplasmic intracellular multiprotein complexes that control the innate immune system's activation of inflammation in response to derived chemicals. Recent advancements increased our molecular knowledge of activation of NLRP3 inflammasomes. Although several studies have been done to investigate the role of inflammasomes in innate immunity and other diseases, structural, functional, and evolutionary investigations are needed to further understand the clinical consequences of NLRP3 gene. The purpose of this study is to investigate the structural and functional impact of the NLRP3 protein by using a computational analysis to uncover putative protein sites involved in the stabilization of the protein-ligand complexes with inhibitors. This will allow for a deeper understanding of the molecular mechanism underlying these interactions. It was found that human NLRP3 gene coexpresses with PYCARD, NLRC4, CASP1, MAVS, and CTSB based on observed coexpression of homologs in other species. The NACHT, LRR, and PYD domain-containing protein 3 is a key player in innate immunity and inflammation as the sensor subunit of the NLRP3 inflammasome. The inflammasome polymeric complex, consisting of NLRP3, PYCARD, and CASP1, is formed in response to pathogens and other damage-associated signals (and possibly CASP4 and CASP5). Comprehensive structural and functional analyses of NLRP3 inflammasome components offer a fresh approach to the development of new treatments for a wide variety of human disorders.

Bioinformatics evaluation of anticancer properties of GP63 protein-derived peptides on MMP2 protein of melanoma cancer

Background

GP63, also known as Leishmanolysin, is a multifunctional virulence factor abundant on the surface of Leishmania spp. small peptides with anticancer capabilities that are selective and toxic to cancer cells are known as anticancer peptides. We aimed to demonstrate the activity of GP63 and its anticancer properties on melanoma using a range of in silico tools and screening methods to identify predicted and designed anticancer peptides.

Methods

Various in silico modeling methodologies are used to establish the three-dimensional (3D) structure of GP63. Refinement and re-evaluation of the modeled structures and the built models' quality evaluated using the different docking used to find the interacting amino acids between MMP2 and GP63 and its anticancer peptides. AntiCP2.0 is used for screening anticancer peptides. 2D interaction plots of protein-ligand complexes evaluated by Protein-Ligand Interaction Profiler server. It is for the first time that used anticancer peptides of GP63 and the predicted and designed peptides.

Results

We used 3 peptides of GP63 based on the AntiCP 2.0 server with scores of 0.63, 0.53, and 0.49, and common peptides of GP63/MMP2 (continues peptide: mean the completely selected peptide after docking with non-anticancer effect, predicted with 0.58 score and designed peptides with 0.47 and 0.45 scores by AntiCP 2.0 server).

Conclusions

The antileishmanial and anticancer peptide research topics exemplify the multidisciplinary nature of peptide research. The advancement of therapeutics targeting cancer and/or Leishmania requires an interconnected research strategy shown in this work.

Protein-Protein Interaction Studies Using Molecular Dynamics Simulation

Protein-protein interaction (PPI) is a crucial event for many biological functions. Studying the molecular details of PPI requires structure determination using X-ray crystallography, nuclear magnetic resistance (NMR), and single particle Cryo-EM. However, sometimes it is not easy to solve the complex structure for various reasons. For example, complex may be unstable, not enough protein expression for structural studies, etc. Further, PPI are intricate processes, and its molecular details cannot be fully explained by experimental observations. Here, we describe a quick and simple method to study the PPI using the combinatorial approach of molecular dynamics simulation and biophysical methods.

Illuminating the "Twilight Zone": Advances in Difficult Protein Modeling

Homology modeling was long considered a method of choice in tertiary protein structure prediction. However, it used to provide models of acceptable quality only when templates with appreciable sequence identity with a target could be found. The threshold value was long assumed to be around 20-30%. Below this level, obtained sequence identity was getting dangerously close to values that can be obtained by chance, after aligning any random, unrelated sequences. In these cases, other approaches, including ab initio folding simulations or fragment assembly, were usually employed. The most recent editions of the CASP and CAMEO community-wide modeling methods assessment have brought some surprising outcomes, proving that much more clues can be inferred from protein sequence analyses than previously thought. In this chapter, we focus on recent advances in the field of difficult protein modeling, pushing the threshold deep into the "twilight zone", with particular attention devoted to improvements in applications of machine learning and model evaluation.

Using Local Protein Model Quality Estimates to Guide a Molecular Dynamics-Based Refinement Strategy

The refinement of predicted 3D models aims to bring them closer to the native structure by fixing errors including unusual bonds and torsion angles and irregular hydrogen bonding patterns. Refinement approaches based on molecular dynamics (MD) simulations using different types of restraints have performed well since CASP10. ReFOLD, developed by the McGuffin group, was one of the many MD-based refinement approaches, which were tested in CASP 12. When the performance of the ReFOLD method in CASP12 was evaluated, it was observed that ReFOLD suffered from the absence of a reliable guidance mechanism to reach consistent improvement for the quality of predicted 3D models, particularly in the case of template-based modelling (TBM) targets. Therefore, here we propose to utilize the local quality assessment score produced by ModFOLD6 to guide the MD-based refinement approach to further increase the accuracy of the predicted 3D models. The relative performance of the new local quality assessment guided MD-based refinement protocol and the original MD-based protocol ReFOLD are compared utilizing many different official scoring methods. By using the per-residue accuracy (or local quality) score to guide the refinement process, we are able to prevent the refined models from undesired structural deviations, thereby leading to more consistent improvements. This chapter will include a detailed analysis of the performance of the local quality assessment guided MD-based protocol versus that deployed in the original ReFOLD method.

Immunoinformatics Approach to Design Novel Subunit Vaccine against the Epstein-Barr Virus

Epstein-Barr virus (EBV) is a lymphotropic virus responsible for numerous epithelial and lymphoid cell malignancies, including gastric carcinoma, Hodgkin's lymphoma, nasopharyngeal carcinoma, and Burkitt lymphoma. Hundreds of thousands of people worldwide get infected with this virus, and in most cases, this viral infection leads to cancer. Although researchers are trying to develop potential vaccines and drug therapeutics, there is still no effective vaccine to combat this virus. In this study, the immunoinformatics approach was utilized to develop a potential multiepitope subunit vaccine against the two most common subtypes of EBV, targeting three of their virulent envelope glycoproteins. Eleven cytotoxic T lymphocyte (CTL) epitopes, 11 helper T lymphocyte (HTL) epitopes, and 10 B-cell lymphocyte (BCL) epitopes were predicted to be antigenic, nonallergenic, nontoxic, and fully conserved among the two subtypes, and nonhuman homologs were used for constructing the vaccine after much analysis. Later, further validation experiments, including molecular docking with different immune receptors (e.g., Toll-like receptors [TLRs]), molecular dynamics simulation analyses (including root means square deviation [RMSD], root mean square fluctuation [RMSF], radius of gyration [Rg], principal-component analysis [PCA], dynamic cross-correlation [DCC], definition of the secondary structure of proteins [DSSP], and Molecular Mechanics Poisson-Boltzmann Surface Area [MM-PBSA]), and immune simulation analyses generated promising results, ensuring the safe and stable response of the vaccine with specific immune receptors after potential administration within the human body. The vaccine's high binding affinity with TLRs was revealed in the docking study, and a very stable interaction throughout the simulation proved the potential high efficacy of the proposed vaccine. Further, in silico cloning was also conducted to design an efficient mass production strategy for future bulk industrial vaccine production. IMPORTANCE Epstein-Barr virus (EBV) vaccines have been developing for over 30 years, but polyphyletic and therapeutic vaccines have failed to get licensed. Our vaccine surpasses the limitations of many such vaccines and remains very promising, which is crucial because the infection rate is higher than most viral infections, affecting a whopping 90% of the adult population. One of the major identifications covers a holistic analysis of populations worldwide, giving us crucial information about its effectiveness for everyone's unique immunological system. We targeted three glycoproteins that enhance the virulence of the virus to design an epitope-based polyvalent vaccine against two different strains of EBV, type 1 and 2. Our methodology in this study is nonconventional yet swift to show effective results while designing vaccines.

AI-Based Protein Structure Prediction in Drug Discovery: Impacts and Challenges

Proteins are the molecular machinery of the human body, and their malfunctioning is often responsible for diseases, making them crucial targets for drug discovery. The three-dimensional structure of a protein determines its biological function, its conformational state determines substrates, cofactors, and protein binding. Rational drug discovery employs engineered small molecules to selectively interact with proteins to modulate their function. To selectively target a protein and to design small molecules, knowing the protein structure with all its specific conformation is critical. Unfortunately, for a large number of proteins relevant for drug discovery, the three-dimensional structure has not yet been experimentally solved. Therefore, accurately predicting their structure based on their amino acid sequence is one of the grant challenges in biology. Recently, AlphaFold2, a machine learning application based on a deep neural network, was able to predict unknown structures of proteins with an unprecedented accuracy. Despite the impressive progress made by AlphaFold2, nature still challenges the field of structure prediction. In this Perspective, we explore how AlphaFold2 and related methods help make drug design more efficient. Furthermore, we discuss the roles of predicting domain-domain orientations, all relevant conformational states, the influence of posttranslational modifications, and conformational changes due to protein binding partners. We highlight where further improvements are needed for advanced machine learning methods to be successfully and frequently used in the pharmaceutical industry.

Identification of the most damaging nsSNPs in the human CFL1 gene and their functional and structural impacts on cofilin-1 protein

The cofilin-1 protein, encoded by CFL1, is an actin-binding protein that regulates F-actin depolymerization and nucleation activity through phosphorylation and dephosphorylation. CFL1 has been implicated in the development of neurodegenerative diseases (Alzheimer's disease and Huntington's disease), neuronal migration disorders (lissencephaly, epilepsy, and schizophrenia), and neural tube closure defects. Mutations in CFL1 have been associated with impaired neural crest cell migration and neural tube closure defects. In our study, various computational approaches were utilized to explore single-nucleotide polymorphisms (SNPs) in CFL1. The Variation Viewer and gnomAD databases were used to retrieve CFL1 SNPs, including 46 nonsynonymous SNPs (nsSNPs). The functional and structural annotation of SNPs was performed using 12 sequence-based web applications, which identified 20 nsSNPs as being the most likely to be deleterious or disease-causing. The conservation of cofilin-1 protein structures was illustrated using the ConSurf and PROSITE web servers, which projected the 12 most deleterious nsSNPs onto conserved domains, with the potential to disrupt the protein's functionality. These 12 nsSNPs were selected for protein structure construction, and the DynaMut/DUET servers predicted that the protein variants V7G, L84P, and L99A were the most likely to be damaging to the cofilin-1 protein structure or function. The evaluation of molecular docking studies demonstrated that the L99A and L84P cofilin-1 variants reduce the binding affinity for actin compared with the native cofilin-1 structure, and molecular dynamic simulation studies confirmed that these variants might destabilize the protein structure. The consequences of putative mutations on protein-protein interactions and post-translational modification sites in the cofilin-1 protein structure were analyzed. This study represents the first complete approach to understanding the effects of nsSNPs within the actin-depolymerizing factor/cofilin family, which suggested that SNPs resulting in L84P (rs199716082) and L99A (rs267603119) variants represent significant CFL1 mutations associated with disease development.

Development of multivalent vaccine targeting M segment of Crimean Congo Hemorrhagic Fever Virus (CCHFV) using immunoinformatic approaches

Crimean-Congo Hemorrhagic Fever (CCHF) is a tick-borne viral infection with no licensed vaccine or therapeutics available for its treatment. In the present study we have developed the first multi-epitope subunit vaccine effective against all the seven genotypes of CCHF virus (CCHFV). The vaccine contains five B-cell, two MHC-II (HTL), and three MHC-I (CTL) epitopes screened from two structural glycoproteins (Gc and Gn in M segment) of CCHFV with an N-terminus human β-defensin as an adjuvant, as well as an N-terminus EAAAK sequence. The epitopes were rigorously investigated for their antigenicity, allergenicity, IFN gamma induction, anti-inflammatory responses, stability, and toxicity. The three-dimensional structure of the vaccine was predicted and docked with TLR-3, TLR-8, and TLR-9 receptors to find the strength of the binding complexes via molecular dynamics simulation. After codon adaptation, the subunit vaccine construct was developed in a pDual-GC plasmid and has population coverage of 98.47% of the world's population (HLA-I & II combined). The immune simulation studies were carried out on the C-ImmSim in-silico interface showing a marked increase in the production of cellular and humoral response (B-cell and T-cell) as well as TGFβ, IL-2, IL-10, and IL-12 indicating that the proposed vaccine would be able to sufficiently provoke both humoral and cell-mediated immune responses. Thus, making it a new and promising vaccine candidate against CCHFV.

How much metagenome data is needed for protein structure prediction: The advantages of targeted approach from the ecological and evolutionary perspectives

It has been proven that three-dimensional protein structures could be modeled by supplementing homologous sequences with metagenome sequences. Even though a large volume of metagenome data is utilized for such purposes, a significant proportion of proteins remain unsolved. In this review, we focus on identifying ecological and evolutionary patterns in metagenome data, decoding the complicated relationships of these patterns with protein structures, and investigating how these patterns can be effectively used to improve protein structure prediction. First, we proposed the metagenome utilization efficiency and marginal effect model to quantify the divergent distribution of homologous sequences for the protein family. Second, we proposed that the targeted approach effectively identifies homologous sequences from specified biomes compared with the untargeted approach's blind search. Finally, we determined the lower bound for metagenome data required for predicting all the protein structures in the Pfam database and showed that the present metagenome data is insufficient for this purpose. In summary, we discovered ecological and evolutionary patterns in the metagenome data that may be used to predict protein structures effectively. The targeted approach is promising in terms of effectively extracting homologous sequences and predicting protein structures using these patterns.

Characterization and Evaluation of Cell-Penetrating Activity of Brevinin-2R: An Amphibian Skin Antimicrobial Peptide

Natural peptides have been the source of some important tools to address challenges in protein therapy of diseases. Bypassing cell plasma membrane has been a bottleneck in the intracellular delivery of biomolecules. Among others, cell-penetrating peptides (CPPs) provide an efficient strategy for intracellular delivery of various cargos. Brevinin-2R peptide is an antimicrobial peptide isolated from the skin secretions of marsh frog, Rana ridibunda with semi-selective anticancer properties. Here, we investigated cell-penetrating properties of Brevinin-2R peptide and its ability to deliver functional protein cargos. Bioinformatics studies showed that Brevinin-2R is a cationic peptide with a net charge of + 5 with an alpha-helix structure and a heptameric ring at the carboxylic terminal due to disulfide bond between C19 and C25 amino acids and a hinge region at A10. To evaluate the ability of this peptide as a CPP, β-galactosidase protein and GFP were transfected into HeLa cells. The entry pathway of the peptide/protein complex into the cell was investigated by inhibiting endocytic pathways at 4 °C. It was observed that Brevinin-2R can efficiently transfer β-galactosidase and GFP with 21% and 90% efficacy, respectively. Brevinin-2R opts for endocytosis pathways to enter cells. The cytotoxicity of this peptide against HeLa cells was studied using MTT assay. The results showed that at the concentration of 131.5 μg/ml of Brevinin-2R peptide, the proliferation of 50% of HeLa cells was inhibited. The results of this study suggest that Brevinin-2R peptide can act as a CPP of natural origin and low cytotoxicity.

Computational design and characterization of a multiepitope vaccine against carbapenemase-producing Klebsiella pneumoniae strains, derived from antigens identified through reverse vaccinology

Klebsiella pneumoniae is a Gram-negative pathogen of clinical relevance, which can provoke serious urinary and blood infections and pneumonia. This bacterium is a major public health threat due to its resistance to several antibiotic classes. Using a reverse vaccinology approach, 7 potential antigens were identified, of which 4 were present in most of the sequences of Italian carbapenem-resistant K. pneumoniae clinical isolates. Bioinformatics tools demonstrated the antigenic potential of these bacterial proteins and allowed for the identification of T and B cell epitopes. This led to a rational design and in silico characterization of a multiepitope vaccine against carbapenem-resistant K. pneumoniae strains. As adjuvant, the mycobacterial heparin-binding hemagglutinin adhesin (HBHA), which is a Toll-like receptor 4 (TLR-4) agonist, was included, to increase the immunogenicity of the construct. The multiepitope vaccine candidate was analyzed by bioinformatics tools to assess its antigenicity, solubility, allergenicity, toxicity, physical and chemical parameters, and secondary and tertiary structures. Molecular docking binding energies to TLR-2 and TLR-4, two important innate immunity receptors involved in the immune response against K. pneumoniae infections, and molecular dynamics simulations of such complexes supported active interactions. A codon optimized multiepitope sequence cloning strategy is proposed, for production of recombinant vaccine in classical bacterial vectors. Finally, a 3 dose-immunization simulation with the multiepitope construct induced both cellular and humoral immune responses. These results suggest that this multiepitope construct has potential as a vaccination strategy against carbapenem-resistant K. pneumoniae and deserves further validation.

Computational insights into differential interaction of mammalian angiotensin-converting enzyme 2 with the SARS-CoV-2 spike receptor binding domain

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of the COVID-19 pandemic. Angiotensin-converting enzyme 2 (ACE2) has been identified as the host cell receptor that binds to the receptor-binding domain (RBD) of the SARS-COV-2 spike protein and mediates cell entry. Because the ACE2 proteins are widely available in mammals, it is important to investigate the interactions between the RBD and the ACE2 of other mammals. Here we analyzed the sequences of ACE2 proteins from 16 mammals, predicted the structures of ACE2-RBD complexes by homology modeling, and refined the complexes using molecular dynamics simulation. Analyses on sequence, structure, and dynamics synergistically provide valuable insights into the interactions between ACE2 and RBD. The analysis outcomes suggest that the ACE2 of bovine, cat, and panda form strong binding interactions with RBD, while in the cases of rat, least horseshoe bat, horse, pig, mouse, and civet, the ACE2 proteins interact weakly with RBD.

A conserved Neurite Outgrowth and Guidance motif with biomimetic potential in neuronal Cell Adhesion Molecules

The discovery of conserved protein motifs can, in turn, unveil important regulatory signals, and when properly designed, synthetic peptides derived from such motifs can be used as biomimetics for biotechnological and therapeutic purposes. We report here that specific Ig-like repeats from the extracellular domains of neuronal Cell Adhesion Molecules share a highly conserved Neurite Outgrowth and Guidance (NOG) motif, which mediates homo- and heterophilic interactions crucial in neural development and repair. Synthetic peptides derived from the NOG motif of such proteins can boost neuritogenesis, and this potential is also retained by peptides with recombinant sequences, when fitting the NOG sequence pattern. The NOG motif discovery not only provides one more tile to the complex puzzle of neuritogenesis, but also opens the route to new neural regeneration strategies via a tunable biomimetic toolbox.

ReFOLD3: refinement of 3D protein models with gradual restraints based on predicted local quality and residue contacts

ReFOLD3 is unique in its application of gradual restraints, calculated from local model quality estimates and contact predictions, which are used to guide the refinement of theoretical 3D protein models towards the native structures. ReFOLD3 achieves improved performance by using an iterative refinement protocol to fix incorrect residue contacts and local errors, including unusual bonds and angles, which are identified in the submitted models by our leading ModFOLD8 model quality assessment method. Following refinement, the likely resulting improvements to the submitted models are recognized by ModFOLD8, which produces both global and local quality estimates. During the CASP14 prediction season (May-Aug 2020), we used the ReFOLD3 protocol to refine hundreds of 3D models, for both the refinement and the main tertiary structure prediction categories. Our group improved the global and local quality scores for numerous starting models in the refinement category, where we ranked in the top 10 according to the official assessment. The ReFOLD3 protocol was also used for the refinement of the SARS-CoV-2 targets as a part of the CASP Commons COVID-19 initiative, and we provided a significant number of the top 10 models. The ReFOLD3 web server is freely available at https://www.reading.ac.uk/bioinf/ReFOLD/.

Pairing a high-resolution statistical potential with a nucleobase-centric sampling algorithm for improving RNA model refinement

Refining modelled structures to approach experimental accuracy is one of the most challenging problems in molecular biology. Despite many years’ efforts, the progress in protein or RNA structure refinement has been slow because the global minimum given by the energy scores is not at the experimentally determined “native” structure. Here, we propose a fully knowledge-based energy function that captures the full orientation dependence of base–base, base–oxygen and oxygen–oxygen interactions with the RNA backbone modelled by rotameric states and internal energies. A total of 4000 quantum-mechanical calculations were performed to reweight base–base statistical potentials for minimizing possible effects of indirect interactions. The resulting BRiQ knowledge-based potential, equipped with a nucleobase-centric sampling algorithm, provides a robust improvement in refining near-native RNA models generated by a wide variety of modelling techniques.

Predicting RNA structure from sequence is challenging due to the relative sparsity of experimentally-determined RNA 3D structures for model training. Here, the authors propose a way to incorporate knowledge on interactions at the atomic and base–base level to refine the prediction of RNA structures.

Can molecular dynamics simulations improve the structural accuracy and virtual screening performance of GPCR models?

The determination of G protein-coupled receptor (GPCR) structures at atomic resolution has improved understanding of cellular signaling and will accelerate the development of new drug candidates. However, experimental structures still remain unavailable for a majority of the GPCR family. GPCR structures and their interactions with ligands can also be modelled computationally, but such predictions have limited accuracy. In this work, we explored if molecular dynamics (MD) simulations could be used to refine the accuracy of in silico models of receptor-ligand complexes that were submitted to a community-wide assessment of GPCR structure prediction (GPCR Dock). Two simulation protocols were used to refine 30 models of the D3 dopamine receptor (D3R) in complex with an antagonist. Close to 60 μs of simulation time was generated and the resulting MD refined models were compared to a D3R crystal structure. In the MD simulations, the receptor models generally drifted further away from the crystal structure conformation. However, MD refinement was able to improve the accuracy of the ligand binding mode. The best refinement protocol improved agreement with the experimentally observed ligand binding mode for a majority of the models. Receptor structures with improved virtual screening performance, which was assessed by molecular docking of ligands and decoys, could also be identified among the MD refined models. Application of weak restraints to the transmembrane helixes in the MD simulations further improved predictions of the ligand binding mode and second extracellular loop. These results provide guidelines for application of MD refinement in prediction of GPCR-ligand complexes and directions for further method development.

In silico Structural, Functional and Phylogenetic Analyses of cellulase from Ruminococcus albus

Background

Cellulose is the primary component of the plant cell wall and an important source of energy for the ruminant and microbial protein synthesis in the rumen. Cell wall content is digested by anaerobic fermentation activity mainly of bacteria belonging to species Fibrobacter succinogenes, Ruminicoccus albus, Ruminococcus flavefaciens, and Butyrivibrio fibrisolvens. Bacteria belonging to the species Ruminococcus albus contain cellulosomes that enable it to adhere to and digest cellulose, and its genome encodes cellulases and hemicellulases.

This study aimed to perform an in silico comparative characterization and functional analysis of cellulase from Ruminococcus albus to explore physicochemical properties and to estimate primary, secondary, and tertiary structure using various bio-computational tools.

The protein sequences of cellulases belonging to 6 different Ruminococcus albus strains were retrieved using UniProt. In in silico composition of amino acids, basic physicochemical characteristics were analyzed using ProtParam and Protscale. Multiple sequence alignment of retrieved sequences was performed using Clustal Omega and the phylogenetic tree was constructed using Mega X software. Bioinformatics tools are used to better understand and determine the 3D structure of cellulase. The predicted model was refined by ModRefiner. Structure alignment between the best-predicted model and the template is applied to evaluate the similarity between structures.

Results

In this study are demonstrated several physicochemical characteristics of the cellulase enzyme. The instability index values indicate that the proteins are highly stable. Proteins are dominated by random coils and alpha helixes. The aliphatic index was higher than 71 providing information that the proteins are highly thermostable. No transmembrane domain was found in the protein, and the enzyme is extracellular and moderately acidic. The best tertiary structure model of the enzyme was obtained by the use of Raptor X, which was refined by ModRefiner. Raptor X suggested the 6Q1I_A as one of the best homologous templates for the predicted 3D protein structure. Ramachandran plot analysis showed that 90.1% of amino acid residues are within the most favored regions.

Conclusions

This study provides for the first time insights about the physicochemical properties, structure, and function of cellulase, from Ruminococcus albus, that will help for detection and identification of such enzyme in vivo or in silico.

Supplementary Information

The online version contains supplementary material available at 10.1186/s43141-021-00162-x.

Molecular free energy optimization on a computational graph

Free energy is arguably the most important property of molecular systems. Despite great progress in both its efficient estimation by scoring functions/potentials and more rigorous computation based on extensive sampling, we remain far from accurately predicting and manipulating biomolecular structures and their interactions. There are fundamental limitations, including accuracy of interaction description and difficulty of sampling in high dimensional space, to be tackled. Computational graph underlies major artificial intelligence platforms and is proven to facilitate training, optimization and learning. Combining autodifferentiation, coordinates transformation and generalized solvation free energy theory, we construct a computational graph infrastructure to realize seamless integration of fully trainable local free energy landscape with end to end differentiable iterative free energy optimization. This new framework drastically improves efficiency by replacing local sampling with differentiation. Its specific implementation in protein structure refinement achieves superb efficiency and competitive accuracy when compared with state of the art all-atom mainstream methods.

Computational Methods for the Elucidation of Protein Structure and Interactions

Biologists are increasingly aware of the importance of protein structure in revealing function. The computational tools now exist which allow researchers to model unknown proteins simply on the basis of their primary sequence. However, for the non-specialist bioinformatician, there is a dazzling array of terminology, acronyms, and competing computer software available for this process. This review is intended to highlight the key stages of computational protein structure prediction, as well as explain the reasons behind some of the procedures and list some established workarounds for common pitfalls. Thereafter follows a review of five one-stop servers for start-to-finish structure prediction.

A comprehensive screening of the whole proteome of hantavirus and designing a multi-epitope subunit vaccine for cross-protection against hantavirus: Structural vaccinology and immunoinformatics study

Hantaviruses are an emerging zoonotic group of rodent-borne viruses that are having serious implications on global public health due to the increase in outbreaks. Since there is no permanent cure, there is increasing interest in developing a vaccine against the hantavirus. This research aimed to design a robust cross-protective subunit vaccine using a novel immunoinformatics approach. After careful evaluation, the best predicted cytotoxic & helper T-cell and B-cell epitopes from nucleocapsid proteins, glycoproteins, RdRp proteins, and non-structural proteins were considered as potential vaccine candidates. Among the four generated vaccine models with different adjuvant, the model with toll-like receptor-4 (TLR-4) agonist adjuvant was selected because of its high antigenicity, non-allergenicity, and structural quality. The selected model was 654 amino acids long and had a molecular weight of 70.5 kDa, which characterizes the construct as a good antigenic vaccine candidate. The prediction of the conformational B-lymphocyte (CBL) epitope secured its ability to induce the humoral response. Thereafter, disulfide engineering improved vaccine stability. Afterwards, the molecular docking confirmed a good binding affinity of -1292 kj/mol with considered immune receptor TLR-4 and the dynamics simulation showed high stability of the vaccine-receptor complex. Later, the in silico cloning confirmed the better expression of the constructed vaccine protein in E. coli K12. Finally, in in silico immune simulation, significantly high levels of immunoglobulin M (IgM), immunoglobulin G1 (IgG1), cytotoxic & helper T lymphocyte (CTL & HTL) populations, and numerous cytokines such as interferon-γ (IFN-γ), interleukin-2 (IL-2) etc. were found as coherence with actual immune response and also showed faster antigen clearance for repeated exposures. Nonetheless, experimental validation can demonstrate the safety and cross-protective ability of the proposed vaccine to fight against hantavirus infection.

Improved Protein Model Quality Assessment By Integrating Sequential And Pairwise Features Using Deep Learning

MOTIVATION

Accurately estimating protein model quality in the absence of experimental structure is not only important for model evaluation and selection, but also useful for model refinement. Progress has been steadily made by introducing new features and algorithms (especially deep neural networks), but the accuracy of quality assessment (QA) is still not very satisfactory, especially local QA on hard protein targets.

RESULTS

We propose a new single-model-based QA method ResNetQA for both local and global quality assessment. Our method predicts model quality by integrating sequential and pairwise features using a deep neural network composed of both 1 D and 2 D convolutional residual neural networks (ResNet). The 2 D ResNet module extracts useful information from pairwise features such as model-derived distance maps, co-evolution information, and predicted distance potential from sequences. The 1 D ResNet is used to predict local (global) model quality from sequential features and pooled pairwise information generated by 2 D ResNet. Tested on the CASP12 and CASP13 datasets, our experimental results show that our method greatly outperforms existing state-of-the-art methods. Our ablation studies indicate that the 2 D ResNet module and pairwise features play an important role in improving model quality assessment.

AVAILABILITY

https://github.com/AndersJing/ResNetQA.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Structure of fish Toll-like receptors (TLR) and NOD-like receptors (NLR)

Innate immunity driven by pattern recognition receptor (PRR) protects the host from invading pathogens. Aquatic animals like fish where the adaptive immunity is poorly developed majorly rely on their innate immunity modulated by PRRs like toll-like receptors (TLR) and NOD-like receptors (NLR). However, current development to improve the fish immunity via TLR/NLR signaling is affected by a poor understanding of its mechanistic and structural features. This review discusses the structure of fish TLRs/NLRs and its interaction with pathogen associated molecular patterns (PAMPs) and downstream signaling molecules. Over the past one decade, significant progress has been done in studying the structure of TLRs/NLRs in higher eukaryotes; however, structural studies on fish innate immune receptors are undermined. Several novel TLR genes are identified in fish that are absent in higher eukaryotes, but the function is still poorly understood. Unlike the fundamental progress achieved in developing antagonist/agonist to modulate human innate immunity, analogous studies in fish are nearly lacking due to structural inadequacy. This underlies the importance of exploring the structural and mechanistic details of fish TLRs/NLRs at an atomic and molecular level. This review outlined the mechanistic and structural basis of fish TLR and NLR activation.

Deciphering effectual binding potential of xylo-substrates towards xylose isomerase and xylokinase through molecular docking and molecular dynamic simulation

Xylooligosaccharides (XOS) such as xylobiose and xylotriose are prebiotics with important functions and relevance and the study of interaction mechanism between these substrate and their respective enzymes has scope and applications. Thus, the present study aimed to decipher the interaction mechanisms of xylose isomerase (XylA) and xylokinase (XylB) towards their xylo-substrates namely xylobiose and xylotriose by computational modeling and molecular dynamic simulation studies. The three-dimensional structures of XylA and XylB, not available in their native forms, were predicted, energy minimized and validated by various computational biology tools and software. The binding mechanisms of xylobiose and xylotriose towards XylA and XylB were modeled by molecular docking and the stability of the docked complexes was confirmed by molecular dynamic (MD) simulation. The current study suggested that the theoretical models of XylA and XylB possessed good stereo-chemical validity, structural stabilities and minimum energy conformers. The molecular docking studied showed that xylotriose showed better binding interactions to XylA than xylobiose and xylobiose showed better binding interaction to XylB than xylotriose with ideal root mean square deviation (RMS), minimum binding energy (kcal/mol), hydrogen bonding and weak interactions. The MD simulation confirmed the stabilities of the docked complexes predicted by docking studies. The study suggested that interactions between the probiotics and prebiotics and provides the novel insights in exploring synbiotics as functional foods towards their futuristic applications. [Formula: see text]HighlightsThis study deciphers the interactions of xylosubstrates to XylA and XylB.The XylA and XylB possessed ideal structural stability and stereochemistryXylotriose and Xylobiose showed significant interactionsThe interactions of Xylotriose-XylA and Xylobiose-XylB were found stable in MD studies.Communicated by Ramaswamy H. Sarma.

Identification and Modeling of a GT-A Fold in the α-Dystroglycan Glycosylating Enzyme LARGE1

The acetylglucosaminyltransferase-like protein LARGE1 is an enzyme that is responsible for the final steps of the post-translational modifications of dystroglycan (DG), a membrane receptor that links the cytoskeleton with the extracellular matrix in the skeletal muscle and in a variety of other tissues. LARGE1 acts by adding the repeating disaccharide unit [-3Xyl-α1,3GlcAβ1-] to the extracellular portion of the DG complex (α-DG); defects in the LARGE1 gene result in an aberrant glycosylation of α-DG and consequent impairment of its binding to laminin, eventually affecting the connection between the cell and the extracellular environment. In the skeletal muscle, this leads to degeneration of the muscular tissue and muscular dystrophy. So far, a few missense mutations have been identified within the LARGE1 protein and linked to congenital muscular dystrophy, and because no structural information is available on this enzyme, our understanding of the molecular mechanisms underlying these pathologies is still very limited. Here, we generated a 3D model structure of the two catalytic domains of LARGE1, combining different molecular modeling approaches. Furthermore, by using molecular dynamics simulations, we analyzed the effect on the structure and stability of the first catalytic domain of the pathological missense mutation S331F that gives rise to a severe form of muscle–eye–brain disease.

Ten quick tips for homology modeling of high-resolution protein 3D structures

The purpose of this quick guide is to help new modelers who have little or no background in comparative modeling yet are keen to produce high-resolution protein 3D structures for their study by following systematic good modeling practices, using affordable personal computers or online computational resources. Through the available experimental 3D-structure repositories, the modeler should be able to access and use the atomic coordinates for building homology models. We also aim to provide the modeler with a rationale behind making a simple list of atomic coordinates suitable for computational analysis abiding to principles of physics (e.g., molecular mechanics). Keeping that objective in mind, these quick tips cover the process of homology modeling and some postmodeling computations such as molecular docking and molecular dynamics (MD). A brief section was left for modeling nonprotein molecules, and a short case study of homology modeling is discussed.

Systematic and Comparative Evaluation of Software Programs for Template-Based Modeling of Protein Structures

Modeling protein structures is critical for understanding protein functions in various biological and biotechnological studies. Among representative protein structure modeling approaches, template-based modeling (TBM) is by far the most reliable and most widely used approach to model protein structures. However, it still remains as a challenge to select appropriate software programs for pairwise alignments and model building, two major steps of the TBM. In this paper, pairwise alignment methods for TBM are first compared with respect to the quality of structure models built using these methods. This comparative study is conducted using comprehensive datasets, which cover 6185 domain sequences from Structural Classification of Proteins extended for soluble proteins, and 259 Protein Data Bank entries (whole protein sequences) from Orientations of Proteins in Membranes database for membrane proteins. Overall, a profile-based method, especially PSI-BLAST, consistently shows high performance across the datasets and model evaluation metrics used. Next, use of two model building programs, MODELLER and SWISS-MODEL, does not seem to significantly affect the quality of protein structure models built except for the Hard group (a group of relatively less homologous proteins) of membrane proteins. The results presented in this study will be useful for more accurate implementation of TBM.