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Abstract Background Biotite is a program library for sequence and structural bioinformatics written for the Python programming language. It implements widely used computational methods into a consistent and accessible package. This allows for easy combination of various data analysis, modeling and simulation methods. Results This article presents major functionalities introduced into Biotite since its original publication. The fields of application are shown using concrete examples. We show that the computational performance of Biotite for bioinformatics tasks is comparable to individual, special purpose software systems specifically developed for the respective single task. Conclusions The results show that Biotite can be used as program library to either answer specific bioinformatics questions and simultaneously allow the user to write entire, self-contained software applications with sufficient performance for general application.

Abstract Background High throughput experiments in cancer and other areas of genomic research identify large numbers of sequence variants that need to be evaluated for phenotypic impact. While many tools exist to score the likely impact of single nucleotide polymorphisms (SNPs) based on sequence alone, the three-dimensional structural environment is essential for understanding the biological impact of a nonsynonymous mutation. Results We present a program, 3DVizSNP, that enables the rapid visualization of nonsynonymous missense mutations extracted from a variant caller format file using the web-based iCn3D visualization platform. The program, written in Python, leverages REST APIs and can be run locally without installing any other software or databases, or from a webserver hosted by the National Cancer Institute. It automatically selects the appropriate experimental structure from the Protein Data Bank, if available, or the predicted structure from the AlphaFold database, enabling users to rapidly screen SNPs based on their local structural environment. 3DVizSNP leverages iCn3D annotations and its structural analysis functions to assess changes in structural contacts associated with mutations. Conclusions This tool enables researchers to efficiently make use of 3D structural information to prioritize mutations for further computational and experimental impact assessment. The program is available as a webserver at https://analysistools.cancer.gov/3dvizsnp or as a standalone python program at https://github.com/CBIIT-CGBB/3DVizSNP .

The DSR-IBUN dextransucrase produced by Leuconostoc mesenteroides strain IBUN 91.2.98 has a short production time (4.5 hours), an enzymatic activity of 24.8 U/mL, and a specific activity of purified enzyme 2 times higher (331.6 U/mg) than that reported for similar enzymes. The aim of this study was to generate a structural model that, from an in silico approach, allows a better understanding, from the structural point of view, of the activity obtained by the enzyme of interest, which is key to continue with its study and industry application. For this, we translated the nucleotide sequence of the dsr_IBUN gene. With the primary structure of DSR-IBUN, the in silico prediction of physicochemical parameters, the possible subcellular localization, the presence of signal peptide, and the location of domains and functional and structural motifs of the protein were established. Subsequently, its secondary and tertiary structure were predicted and a homology model of the dextransucrase under study was constructed using Swiss-Model, performing careful template selection. The values obtained for the model, Global Model Quality Estimation (0.63), Quality Mean (−1.49), and root-mean-square deviation (0.09), allow us to affirm that the model for the enzyme dextransucrase DSR-IBUN is of adequate quality and can be used as a source of information for this protein. [ABSTRACT FROM AUTHOR]

In eukaryotic organisms, genomic DNA associates with histone proteins to form nucleosomes. Nucleosomes provide a basis for genome compaction, epigenetic markup, and mediate interactions of nuclear proteins with their target DNA loci. A negatively charged (acidic) patch located on the H2A-H2B histone dimer is a characteristic feature of the nucleosomal surface. The acidic patch is a common site in the attachment of various chromatin proteins, including viral ones. Acidic patch-binding peptides present perspective compounds that can be used to modulate chromatin functioning by disrupting interactions of nucleosomes with natural proteins or alternatively targeting artificial moieties to the nucleosomes, which may be beneficial for the development of new therapeutics. In this work, we used several computational and experimental techniques to improve our understanding of how peptides may bind to the acidic patch and what are the consequences of their binding. Through extensive analysis of the PDB database, histone sequence analysis, and molecular dynamic simulations, we elucidated common binding patterns and key interactions that stabilize peptide–nucleosome complexes. Through MD simulations and FRET measurements, we characterized changes in nucleosome dynamics conferred by peptide binding. Using fluorescence polarization and gel electrophoresis, we evaluated the affinity and specificity of the LANA1-22 peptide to DNA and nucleosomes. Taken together, our study provides new insights into the different patterns of intermolecular interactions that can be employed by natural and designed peptides to bind to nucleosomes, and the effects of peptide binding on nucleosome dynamics and stability. [ABSTRACT FROM AUTHOR]

In India and other affected countries, Omicron variant of SARS-CoV-2 has shown faster transmission but less clinical severities when compared to Delta strain. Present study was aimed to investigate how molecular changes in the spike proteins of Omicron variant has increased its transmission but reduced the disease severity. We report molecular interactions of Spike proteins of Delta and Omicron variants with ACE-2 receptor to explain how change in chemical and physical nature of mutated amino acids of Omicron variant has affected the internalization competence of virus into host cell. The Research Collaboratory Structural Bioinformatics (RCSB) and Protein Data Bank (PDB) were used to construct ACE2-Spike Protein interaction. The binding affinity of both omicron and delta variant spike proteins with human ACE2 receptor was observed. Spike protein of Omicron variants has revealed total number of 93 dissimilarities of amino acids from Delta strain,15 of which are in its Receptor Binding Domain (RBD). Our study showed that RBD of Delta variant contained only one hydrophobic amino acid whereas there were 6 hydrophobic amino acids in the RBD of Omicron variant. We report that increased number of Hydrophobic Amino Acids in RBD of Omicron variant affects its binding with ACE2 receptor to enter into the cell. The failure of internalization of virus has increased concentration of extracellular virions at nasopharyngeal region leading to faster expulsion of infective droplets during coughing or sneezing to increase transmission but has reduced the severity of infection. The reported observations could prove to be of public health and therapeutic significance. [ABSTRACT FROM AUTHOR]

The RNA‐dependent RNA polymerase (RdRp) complex of SARS‐CoV‐2 lies at the core of its replication and transcription processes. The interfaces between holo‐RdRp subunits are highly conserved, facilitating the design of inhibitors with high affinity for the interaction interface hotspots. We, therefore, take this as a model protein complex for the application of a structural bioinformatics protocol to design peptides that inhibit RdRp complexation by preferential binding at the interface of its core subunit nonstructural protein, nsp12, with accessory factor nsp7. Here, the interaction hotspots of the nsp7‐nsp12 subunit of RdRp, determined from a long molecular dynamics trajectory, are used as a template. A large library of peptide sequences constructed from multiple hotspot motifs of nsp12 is screened in‐silico to determine sequences with high geometric complementarity and interaction specificity for the binding interface of nsp7 (target) in the complex. Two lead designed peptides are extensively characterized using orthogonal bioanalytical methods to determine their suitability for inhibition of RdRp complexation. Binding affinity of these peptides to accessory factor nsp7, determined using a surface plasmon resonance (SPR) assay, is slightly better than that of nsp12: dissociation constant of 133nM and 167nM, respectively, compared to 473nM for nsp12. A competitive ELISA is used to quantify inhibition of nsp7‐nsp12 complexation, with one of the lead peptides giving an IC50 of 25μM. Cell penetrability and cytotoxicity are characterized using a cargo delivery assay and MTT cytotoxicity assay, respectively. Overall, this work presents a proof‐of‐concept of an approach for rational discovery of peptide inhibitors of SARS‐CoV‐2 protein–protein interactions. [ABSTRACT FROM AUTHOR]

Bio3D is a family of R packages for the analysis of biomolecular sequence, structure, and dynamics. Major functionality includes biomolecular database searching and retrieval, sequence and structure conservation analysis, ensemble normal mode analysis, protein structure and correlation network analysis, principal component, and related multivariate analysis methods. Here, we review recent package developments, including a new underlying segregation into separate packages for distinct analysis, and introduce a new method for structure analysis named ensemble difference distance matrix analysis (eDDM). The eDDM approach calculates and compares atomic distance matrices across large sets of homologous atomic structures to help identify the residue wise determinants underlying specific functional processes. An eDDM workflow is detailed along with an example application to a large protein family. As a new member of the Bio3D family, the Bio3D-eddm package supports both experimental and theoretical simulation-generated structures, is integrated with other methods for dissecting sequence-structure-function relationships, and can be used in a highly automated and reproducible manner. Bio3D is distributed as an integrated set of platform independent open source R packages available from: http://thegrantlab.org/bio3d/.

Tuberculosis (TB), also known as the "White Plague", is caused by Mycobacterium tuberculosis (Mtb). Before the COVID-19 epidemic, TB had the highest mortality rate of any single infectious disease. Vaccination is considered one of the most effective strategies for controlling TB. Despite the limitations of the Bacille Calmette-Gue'rin (BCG) vaccine in terms of protection against TB among adults, it is currently the only licensed TB vaccine. Recently, with the evolution of bioinformatics and structural biology techniques to screen and optimize protective antigens of Mtb, the tremendous potential of protein subunit vaccines is being exploited. Multistage subunit vaccines obtained by fusing immunodominant antigens from different stages of TB infection are being used both to prevent and to treat TB. Additionally, the development of novel adjuvants is compensating for weaknesses of immunogenicity, which is conducive to the flourishing of subunit vaccines. With advances in the development of animal models, preclinical vaccine protection assessments are becoming increasingly accurate. This review summarizes progress in the research of protein subunit TB vaccines during the past decades to facilitate the further optimization of protein subunit vaccines that may eradicate TB. [ABSTRACT FROM AUTHOR]

Beta-barrel outer membrane proteins (OMP) are integral components of Gram-negative bacteria, eukaryotic mitochondria, and chloroplasts. They play essential roles in various cellular processes including nutrient transport, membrane stability, host-pathogen interactions, antibiotic resistance and more. The advent of AlphaFold2 for accurate protein structure predictions transformed structural bioinformatic studies. We previously used a QTY code to convert hydrophobic alpha-helices to hydrophilic alpha-helices in over 50 membrane proteins with all alpha-helices. The QTY code systematically replaces hydrophobic leucine (L), isoleucine (I), valine (V), and phenylalanine (F) with hydrophilic glutamine (Q), threonine (T), and tyrosine (Y). We here present a structural bioinformatic analysis of five outer membrane beta-barrel proteins with known molecular structures, including a) BamA, b) Omp85 (also called Sam50), c) FecA, d) Tsx, and e) OmpC. We superposed the structures of five native beta-barrel outer membrane proteins and their AlphaFold2-predicted corresponding QTY variant structures. The superposed structures of OMPs and their QTY variants exhibit remarkable structural similarity, as evidenced by residue mean square distance (RMSD) values between 0.206Å to 0.414Å despite the replacement of at least 22% (Transmembrane variation) of the amino acids in the transmembrane regions. We also show that native outer membrane proteins and QTY variants have different hydrophobicity patches. Our study provides important insights into the differences between hydrophobic and hydrophilic beta-barrels and validates the QTY code for studying beta-barrel membrane proteins and perhaps other hydrophobic aggregated proteins. Our findings demonstrate that the QTY code can be used as a simple tool for designing hydrophobic proteins in various biological contexts. [ABSTRACT FROM AUTHOR]

Over the past decades, many existing drugs and clinical/preclinical compounds have been repositioned as new therapeutic indication from which they were originally intended and to treat off-target diseases by targeting their noncognate protein receptors, such as Sildenafil and Paxlovid, termed drug repurposing (DRP). Despite its significant attraction in the current medicinal community, the DRP is usually considered as a matter of accidents that cannot be fulfilled reliably by traditional drug discovery protocol. In this study, we proposed an integrated computational/experimental (iC/E) strategy to facilitate the DRP within a framework of rational drug design, which was practiced on the identification of new neuronal nitric oxide synthase (nNOS) inhibitors from a structurally diverse, functionally distinct drug pool. We demonstrated that the iC/E strategy is very efficient and readily feasible, which confirmed that the phosphodiesterase inhibitor DB06237 showed a high inhibitory potency against nNOS synthase domain, while other two general drugs, i.e. DB02302 and DB08258, can also inhibit the synthase at nanomolar level. Structural bioinformatics analysis revealed diverse noncovalent interactions such as hydrogen bonds, hydrophobic forces and van der Waals contacts across the complex interface of nNOS active site with these identified drugs, conferring both stability and specificity for the complex recognition and association. [ABSTRACT FROM AUTHOR]

Background: The development of therapeutic agents against Alzheimer's disease (AD) has stalled recently. Drug candidates targeting amyloid-β (Aβ) deposition have often failed clinical trials at different stages, prompting the search for novel targets for AD therapy. The NLRP3 inflammasome is an integral part of innate immunity, contributing to neuroinflammation and AD pathophysiology. Thus, it has become a promising new target for AD therapy. Objective: The study sought to investigate the potential of bioactive compounds derived from Azadirachta-indica to inhibit the NLRP3 protein implicated in the pathophysiology of AD. Methods: Structural bioinformatics via molecular docking and density functional theory (DFT) analysis was utilized for the identification of novel NLRP3 inhibitors from A. indica bioactive compounds. The compounds were further subjected to pharmacokinetic and drug-likeness analysis. Results obtained from the compounds were compared against that of oridonin, a known NLRP3 inhibitor. Results: The studied compounds optimally saturated the binding site of the NLRP3 NACHT domain, forming principal interactions with the different amino acids at its binding site. The studied compounds also demonstrated better bioactivity and chemical reactivity as ascertained by DFT analysis and all the compounds except 7-desacetyl-7-benzoylazadiradione, which had two violations, conformed to Lipinski's rule of five. Conclusion: In silico studies show that A. indica derived compounds have better inhibitory potential against NLRP3 and better pharmacokinetic profiles when compared with the reference ligand (oridonin). These compounds are thus proposed as novel NLRP3 inhibitors for the treatment of AD. Further wet-lab studies are needed to confirm the potency of the studied compounds. [ABSTRACT FROM AUTHOR]

[Objective] The effect of PPP3CA gene on growth traits of the Chishui black-bone silky fowl was explored, which provided the basis for genetic improvement and molecular breeding of Chishui black-bone silky fowl. [Method] All exons of PPP3CA gene of Chishui black-bone silky fowl were screened by PCR amplification and direct sequencing.RNAfold WebServer and Structural Bioinformatics Group were used to predict the secondary and tertiary structure of PPP3CA protein, and to calculate the gene frequency, genotype frequency, homozygosity, heterozygosity, number of effective alleles and polymorphic information content.The Hardy-Weinberg equilibrium state was analyzed, and the correlation between the genotypes corresponding to different polymorphic loci and the body size index were analyzed by SPSS. [Result] Three missense mutation sites (C29T,T50C and T63G) were found at exon 7,and one missense mutation site (G720A) was found at exon 16.The χ² test showed that all the above loci deviated from Hardy-Weinberg equilibrium.CC type with C29T mutation significantly increased body weight, pelvic width and chest circumference.CC type with T50C mutation significantly increased body weight, tibial length and keel length.The TT type at T63G site significantly increased the tibial length and keel length.The AA type at G720A significantly increased the tibial length and keel length, and the CC type significantly increased the body oblique length.The CC type at C29T,CC type at T50C, TT type at T63G and AA type at G720A were all dominant genotypes, but the C29T and T50C base mutations were unfavorable mutations, while the T63G and G720A base mutations were favorable mutations, and G720A was more conducive to the growth of black-bone silky fowl, which could be used as a reference site for molecular marker assisting breeding of Chishui black-bone silky fowl. [Conclusion] The study is the first to investigate the correlation between PPP3CA gene and chicken growth traits, and finds four polymorphic loci in the exon of PPP3CA gene in domestic chickens, indicating that PPP3CA gene has a certain effect on growth traits of Chishui black-bone silky fowl, and can be used as a reference site for molecular marker assisting breeding of Chishui black-bone silky fowl.The results of the study can provide reference for genetic improvement and breeding of Chishui black-bone silky fowl in the future, and lay a foundation for further research on PPP3CA gene of chicken. [ABSTRACT FROM AUTHOR]

Background: Although mmCIF is the current official format for deposition of protein and nucleic acid structures to the protein data bank (PDB) database, the legacy PDB format is still the primary supported format for many structural bioinformatics tools. Therefore, reliable software to convert mmCIF structure files to PDB files is needed. Unfortunately, existing conversion programs fail to correctly convert many mmCIF files, especially those with many atoms and/or long chain identifies. Results: This study proposed BeEM, which converts any mmCIF format structure files to PDB format. BeEM conversion faithfully retains all atomic and chain information, including chain IDs with more than 2 characters, which are not supported by any existing mmCIF to PDB converters. The conversion speed of BeEM is at least ten times faster than existing converters such as MAXIT and Phenix. Part of the reason for the speed improvement is the avoidance of conversion between numerical values and text strings. Conclusion: BeEM is a fast and accurate tool for mmCIF-to-PDB format conversion, which is a common procedure in structural biology. The source code is available under the BSD licence at https://github.com/kad-ecoli/BeEM/. [ABSTRACT FROM AUTHOR]

Background: High throughput experiments in cancer and other areas of genomic research identify large numbers of sequence variants that need to be evaluated for phenotypic impact. While many tools exist to score the likely impact of single nucleotide polymorphisms (SNPs) based on sequence alone, the three-dimensional structural environment is essential for understanding the biological impact of a nonsynonymous mutation. Results: We present a program, 3DVizSNP, that enables the rapid visualization of nonsynonymous missense mutations extracted from a variant caller format file using the web-based iCn3D visualization platform. The program, written in Python, leverages REST APIs and can be run locally without installing any other software or databases, or from a webserver hosted by the National Cancer Institute. It automatically selects the appropriate experimental structure from the Protein Data Bank, if available, or the predicted structure from the AlphaFold database, enabling users to rapidly screen SNPs based on their local structural environment. 3DVizSNP leverages iCn3D annotations and its structural analysis functions to assess changes in structural contacts associated with mutations. Conclusions: This tool enables researchers to efficiently make use of 3D structural information to prioritize mutations for further computational and experimental impact assessment. The program is available as a webserver at https://analysistools.cancer.gov/3dvizsnp or as a standalone python program at https://github.com/CBIIT-CGBB/3DVizSNP. [ABSTRACT FROM AUTHOR]

Background: Biotite is a program library for sequence and structural bioinformatics written for the Python programming language. It implements widely used computational methods into a consistent and accessible package. This allows for easy combination of various data analysis, modeling and simulation methods. Results: This article presents major functionalities introduced into Biotite since its original publication. The fields of application are shown using concrete examples. We show that the computational performance of Biotite for bioinformatics tasks is comparable to individual, special purpose software systems specifically developed for the respective single task. Conclusions: The results show that Biotite can be used as program library to either answer specific bioinformatics questions and simultaneously allow the user to write entire, self-contained software applications with sufficient performance for general application. [ABSTRACT FROM AUTHOR]

Access to the three-dimensional (3D) structural information of macromolecules is of major interest in both fundamental and applied research. Obtaining this experimental data can be complex, time consuming, and costly. Therefore, in silico computational approaches are an alternative of interest, and sometimes present a unique option. In this context, the Protein Structure Prediction method AlphaFold2 represented a revolutionary advance in structural bioinformatics. Named method of the year in 2021, and widely distributed by DeepMind and EBI, it was thought at this time that protein-folding issues had been resolved. However, the reality is slightly more complex. Due to a lack of input experimental data, related to crystallographic challenges, some targets have remained highly challenging or not feasible. This perspective exercise, dedicated to a non-expert audience, discusses and correctly places AlphaFold2 methodology in its context and, above all, highlights its use, limitations, and opportunities. After a review of the interest in the 3D structure and of the previous methods used in the field, AF2 is brought into its historical context. Its spatial interests are detailed before presenting precise quantifications showing some limitations of this approach and finishing with the perspectives in the field. [ABSTRACT FROM AUTHOR]

Louse‐borne relapsing fever (LBRF) with high untreated mortality caused by spirochete Borrelia recurrentis is predominantly endemic to Sub‐Saharan Africa and has re‐emerged in parts of Eastern Europe, Asia and Latin America due to population migrations. Despite subtractive evolution of lice‐borne pathogenic Borrelia spp. from tick‐borne species, there has been no comprehensive report on conservation of protein targets across tick and lice‐borne pathogenic Borrelia nor exploration of phytocompounds that are toxic to tick against lice. From the 19 available whole genomes including B. recurrentis, B. burgdorferi, B. hermsii, B. parkeri and B. miyamotoi, conservation of seven drug targets (>80% domain identity) viz. 30 S ribosomal subunit proteins (RSP) S3, S7, S8, S14, S19, penicillin‐binding protein‐2 and 50 S RSP L16 were deciphered through multiple sequence alignments. Twelve phytocompounds (hydroxy‐tyrosol, baicalein, cis‐2‐decanoic acid, morin, oenin, rosemarinic acid, kaempferol, piceatannol, rottlerin, luteolin, fisetin and monolaurin) previously explored against Lyme disease spirochete B. burgdorferi when targeted against LBRF‐causing B. recurrentis protein targets revealed high multi‐target affinity (2%–20% higher than conventional antibiotics) through molecular docking. However, based on high binding affinity against all target proteins, stable coarse‐grained dynamics (fluctuations <1 Å) and safe pharmacological profile, luteolin was prioritized. The study encourages experimental evaluation of the potent phytocompounds and similar protocols for investigating other emerging vector‐borne diseases. [ABSTRACT FROM AUTHOR]

Molecular dynamics simulations were performed for wild-type purine nucleoside phosphorylase in complexes with two substrates (adenosine and guanosine). The MD simulations were also performed for the mutant form of the enzyme with the same substrates. The free energy changes upon the formation of the complexes were evaluated from the molecular dynamics trajectories by the MM-GBSA method. [ABSTRACT FROM AUTHOR]

The bacteria Vibrio cholerae causes cholera, an acute diarrheal infection that can lead to dehydration and even death. Over 100,000 people die each year as a result of epidemic diseases; vaccination has emerged as a successful strategy for combating cholera. This study uses bioinformatics tools to create a multi-epitope vaccine against cholera infection using five structural polyproteins from the V. cholerae (CTB, TCPA, TCPF, OMPU, and OMPW). The antigenic retrieved protein sequence were analyzed using BCPred and IEDB bioinformatics tools to predict B cell and T cell epitopes, respectively, which were then linked with flexible linkers together with an adjuvant to boost it immunogenicity. The construct has a theoretical PI of 6.09, a molecular weight of 53.85 kDa, and an estimated half-life for mammalian reticulocytes in vitro of 4.4 h. These results demonstrate the construct's longevity. The vaccine design was docked against the human toll-like receptor (TLR) to evaluate compatibility and effectiveness; also other additional post-vaccination assessments were carried out on the designed vaccine. Through in silico cloning, its expression was determined. The results show that it has a CAI value of 0.1 and GC contents of 58.97% which established the adequate expression and downstream processing of the vaccine construct, and our research demonstrated that the multi-epitope subunit vaccine exhibits antigenic characteristics. Additionally, we carried out an in silico immunological simulation to examine the immune reaction to an injection. Our results strongly suggest that the vaccine candidate on further validation would induce immune response against the V. cholerae infection. [ABSTRACT FROM AUTHOR]

Protein modelling plays a vital role in the drug discovery process. TANK-binding kinase 1–binding protein 1 is also called an adapter protein, which is encoded by gene TBK1 present in Homo sapiens. It is found in lungs, small intestine, leukocytes, heart, placenta, muscle, kidney, lower level of thymus, and brain. It has a number of protein-binding sites, to which TBK1 and IKBKE bind and perform different functions as immunomodulatory, antiproliferative, and antiviral innate immunity which release different types of interferons. Our study predicts the comparative model of 3-dimensional (3D) structure through different bioinformatics tools that will be helpful for further studies in future. The reactivity and stability of these proteins were evaluated physicochemically and through domain determination and prediction of secondary structure using bioinformatics methods such as ProtParam, Pfam, and SOPMA, respectively. Robetta, an ab initio approach, I-TASSER, and AlphaFold was used for 3D structure prediction, and the models were validated using the SAVESv6.0 (PROCHECK) server. Conclusively, the best 3D structure of TBK1-binding protein 1 was predicted using Robetta software. After unveiling the 3D structure of the novel protein, we concluded that this structure will help us to find out its role other than in antiviral innate immunity and by producing torsion in its 3D structure researchers will be able to detect either this protein is involved in any disease or not because according to previous studies it was not associated with any disease. [ABSTRACT FROM AUTHOR]

Background: Data management is fast becoming an essential part of scientific practice, driven by open science and FAIR (findable, accessible, interoperable, and reusable) data sharing requirements. Whilst data management plans (DMPs) are clear to data management experts and data stewards, understandings of their purpose and creation are often obscure to the producers of the data, which in academic environments are often PhD students. Methods: Within the RNAct EU Horizon 2020 ITN project, we engaged the 10 RNAct early-stage researchers (ESRs) in a training project aimed at formulating a DMP. To do so, we used the Data Stewardship Wizard (DSW) framework and modified the existing Life Sciences Knowledge Model into a simplified version aimed at training young scientists, with computational or experimental backgrounds, in core data management principles. We collected feedback from the ESRs during this exercise. Results: Here, we introduce our new life-sciences training DMP template for young scientists. We report and discuss our experiences as principal investigators (PIs) and ESRs during this project and address the typical difficulties that are encountered in developing and understanding a DMP. Conclusions: We found that the DS-wizard can also be an appropriate tool for DMP training, to get terminology and concepts across to researchers. A full training in addition requires an upstream step to present basic DMP concepts and a downstream step to publish a dataset in a (public) repository. Overall, the DS-Wizard tool was essential for our DMP training and we hope our efforts can be used in other projects.

This study systematically investigated the differences in allergenicity of casein in cow milk (CM), goat milk (GM), camel milk (CAM), and mare milk (MM) from protein structures using bioinformatics. Primary structure sequence analysis reveals high sequence similarity between the α-casein of CM and GM, while all allergenic subtypes are likely to have good hydrophilicity and thermal stability. By analyzing linear B-cell epitope, T-cell epitope, and allergenic peptides, the strongest casein allergenicity is observed for CM, followed by GM, and the casein of MM has the weakest allergenicity. Meanwhile, 7, 9, and 16 similar or identical amino acid fragments in linear B-cell epitopes, T-cell epitopes, and allergenic peptides, respectively, were observed in different milks. Among these, the same T-cell epitope FLGAEVQNQ was shared by κ-CN in all four different species' milk. Epitope results may provide targets of allergenic fragments for reducing milk allergenicity through physical or/and chemical methods. This study explained the underlying secrets for the high allergenicity of CM to some extent from the perspective of casein and provided new insights for the dairy industry to reduce milk allergy. Furthermore, it provides a new idea and method for comparing the allergenicity of homologous proteins from different species. [ABSTRACT FROM AUTHOR]

Invasive fungal infections represent a public health problem that worsens over the years with the increasing resistance to current antimycotic agents. Therefore, there is a compelling medical need of widening the antifungal drug repertoire, following different methods such as drug repositioning, identification and validation of new molecular targets and developing new inhibitors against these targets. In this work we developed a structure-based strategy for drug repositioning and new drug design, which can be applied to infectious fungi and other pathogens. Instead of applying the commonly accepted off-target criterion to discard fungal proteins with close homologues in humans, the core of our approach consists in identifying fungal proteins with active sites that are structurally similar, but preferably not identical to binding sites of proteins from the so-called "human pharmacolome". Using structural information from thousands of human protein target-inhibitor complexes, we identified dozens of proteins in fungal species of the genera Histoplasma, Candida, Cryptococcus, Aspergillus and Fusarium, which might be exploited for drug repositioning and, more importantly, also for the design of new fungus-specific inhibitors. As a case study, we present the in vitro experiments performed with a set of selected inhibitors of the human mitogen-activated protein kinases 1/2 (MEK1/2), several of which showed a marked cytotoxic activity in different fungal species. [ABSTRACT FROM AUTHOR]

Protein–ligand binding affinity prediction is an important task in structural bioinformatics for drug discovery and design. Although various scoring functions (SFs) have been proposed, it remains challenging to accurately evaluate the binding affinity of a protein–ligand complex with the known bound structure because of the potential preference of scoring system. In recent years, deep learning (DL) techniques have been applied to SFs without sophisticated feature engineering. Nevertheless, existing methods cannot model the differential contribution of atoms in various regions of proteins, and the relationship between atom properties and intermolecular distance is also not fully explored. We propose a novel empirical graph neural network for accurate protein–ligand binding affinity prediction (EGNA). Graphs of protein, ligand and their interactions are constructed based on different regions of each bound complex. Proteins and ligands are effectively represented by graph convolutional layers, enabling the EGNA to capture interaction patterns precisely by simulating empirical SFs. The contributions of different factors on binding affinity can thus be transparently investigated. EGNA is compared with the state-of-the-art machine learning-based SFs on two widely used benchmark data sets. The results demonstrate the superiority of EGNA and its good generalization capability. [ABSTRACT FROM AUTHOR]

Introduction: The plant Pandanus amaryllifolius Roxb (pandan), has been shown to have antidyslipidemic potency. This study explored the potential of several alkaloids from pandan leaf as antidyslipidemia as well as their safety profile in silico. Methods: Analyses were carried out by studying the binding affinity of the alkaloids to 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase, peroxisome proliferator activator receptor (PPAR) alpha and Niemann Pick C1 Like 1 (NPC1L1). The structures of the alkaloids were downloaded from the Pubchem database and optimized using the ChemDraw Professional 16.0 to obtain 3D structures in protein data bank (PDB) format. The in silico testing was based on the interactions of the alkaloids with the HMG-CoA reductase (PDB ID 1HW9), PPAR alpha (PDB ID 6LX4) and NPC1L1 (PDB ID 7DFZ) proteins, downloaded from the Research Collaboratory for Structural Bioinformatics (RSCB) PDB website (http://www.rcsb.org/pdb). The preparation of protein structures was performed using the Discovery studio 2021 client and Gromacs applications, while optimization of the 3D structure of the alkaloids was carried out with the ChemDraw professional 16.0. Finally, validation was completed using AutoDock application. The safety profile was assessed by pkCSM online tool. Results: The respective root mean square deviation (RMSD) values of the 1HW9, 6LX4 and 7DFZ proteins were 1.677, 0.918 and 1.706, respectively. The alkaloids pandanusine B, pandamarilactonine A, pandamarilactonine B had respective values of binding energy for HMG-CoA of -5.52, -5.51 and -5.46 kcal/mol. The binding energy of pandamarilactonine B, pandamarilactonine A and pandanamine for PPAR alpha were -9.14, -9.10 and -8.48 kcal/mol, respectively, with the corresponding energy for t NPC1L1 of -9.63, -9.71 and -8.54 kcal/mol. The toxicity tests indicated that the alkaloids were safe, pandamarilactonines had the highest LD50 (2.736 mol/kg). Conclusion: The studied pandan alkaloids have potential antidyslipidemic activity by interacting with HMG-CoA reductase, PPAR alpha, and NPC1L1, with good safety profile. [ABSTRACT FROM AUTHOR]

The biologically relevant structures of proteins and nucleic acids and their complexes are dynamic. They include a combination of regions ranging from rigid structural segments to structural switches to regions that are almost always disordered, which interact with each other in various ways. Comparing conformational changes and variation in contacts between different conformational states is essential to understand the biological functions of proteins, nucleic acids, and their complexes. Here, we describe a new computational tool, 1D2DSimScore, for comparing contacts and contact interfaces in all kinds of macromolecules and macromolecular complexes, including proteins, nucleic acids, and other molecules. 1D2DSimScore can be used to compare structural features of macromolecular models between alternative structures obtained in a particular experiment or to score various predictions against a defined "ideal" reference structure. Comparisons at the level of contacts are particularly useful for flexible molecules, for which comparisons in 3D that require rigid‐body superpositions are difficult, and in biological systems where the formation of specific inter‐residue contacts is more relevant for the biological function than the maintenance of a specific global 3D structure. Similarity/dissimilarity scores calculated by 1D2DSimScore can be used to complement scores describing 3D structural similarity measures calculated by the existing tools. [ABSTRACT FROM AUTHOR]

The structural and functional diversity of proteins can be enhanced by numerous post‐translational modifications. C‐mannosylation is a rare form of glycosylation consisting of a single alpha or beta D‐mannopyranose forming a carbon–carbon bond with the pyrrole ring of a tryptophan residue. Despite first being discovered in 1994, C‐mannosylation is still poorly understood and 3D structures are available for only a fraction of the total predicted C‐mannosylated proteins. Here, we present the first comprehensive review of C‐mannosylated protein structures by analysing the data for all 10 proteins with C‐mannosylation/s deposited in the Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB). We analysed in detail the WXXW/WXXWXXW consensus motif and the highly conserved pair of arginine residues in thrombospondin type 1 repeat C‐mannosylation sites or homologous arginine residues in other domains. Furthermore, we identified a conserved PXP sequence C‐terminal of the C‐mannosylation site. The PXP motif forms a tight turn region in the polypeptide chain and its universal conservation in C‐mannosylated protein is worthy of further experimental study. The stabilization of C‐mannopyranosyl groups was demonstrated through hydrogen bonding with arginine and other charged or polar amino acids. Where possible, the structural findings were linked to other functional studies demonstrating the role of C‐mannosylation in protein stability, secretion or function. With the current technological advances in structural biology, we hope to see more progress in the study of C‐mannosylation that may correspond to discoveries of novel C‐mannosylation pathways and functions with implications for human health and biotechnology. [ABSTRACT FROM AUTHOR]

Over the past few decades, the number of available structural bioinformatics pipelines, libraries, plugins, web resources and software has increased exponentially and become accessible to the broad realm of life scientists. This expansion has shaped the field as a tangled network of methods, algorithms and user interfaces. In recent years PyMOL, widely used software for biomolecules visualization and analysis, has started to play a key role in providing an open platform for the successful implementation of expert knowledge into an easy-to-use molecular graphics tool. This review outlines the plugins and features that make PyMOL an eligible environment for supporting structural bioinformatics analyses. [ABSTRACT FROM AUTHOR]

Protein intrinsic disorder is found in all kingdoms of life and is known to underpin numerous physiological and pathological processes. Computational methods play an important role in characterizing and identifying intrinsically disordered proteins and protein regions. Herein, we present a new high‐efficiency web‐based disorder predictor named Rapid Intrinsic Disorder Analysis Online (RIDAO) that is designed to facilitate the application of protein intrinsic disorder analysis in genome‐scale structural bioinformatics and comparative genomics/proteomics. RIDAO integrates six established disorder predictors into a single, unified platform that reproduces the results of individual predictors with near‐perfect fidelity. To demonstrate the potential applications, we construct a test set containing more than one million sequences from one hundred organisms comprising over 420 million residues. Using this test set, we compare the efficiency and accessibility (i.e., ease of use) of RIDAO to five well‐known and popular disorder predictors, namely: AUCpreD, IUPred3, metapredict V2, flDPnn, and SPOT‐Disorder2. We show that RIDAO yields per‐residue predictions at a rate two to six orders of magnitude greater than the other predictors and completely processes the test set in under an hour. RIDAO can be accessed free of charge at https://ridao.app. [ABSTRACT FROM AUTHOR]

Sulfated fucan has attracted increasing research interest due to its various biological activities. Endo-1,3-fucanases are favorable tools for structure investigation and structure-activity relationships establishment of sulfated fucan. However, the three-dimensional structure of enzymes from the GH174 family has not been disclosed, which hinders the understanding of the action mechanism. This study reports the first crystal structure of endo-1,3-fucanase from GH174 family (Fun174A) at a resolution of 1.60 Å. Notably, Fun174A exhibited an unusual distorted β-sandwich fold, which is distinct from other known glycoside hydrolase folds. The conserved amino acid residues D119 and H154 were proposed as the catalytic residues in the family. Molecular docking suggested that Fun174A primarily recognized sulfated fucan through a series of polar amino acid residues around the substrate binding pocket. Furthermore, structural bioinformatics analysis suggested that the structural analogs of Fun174A may be extensively implicated in the bacterial metabolism of polysaccharides, which provided opportunities for the discovery of novel glycoside hydrolases. This study offers new insights into the structural diversity of glycoside hydrolases and will contribute to the establishment of a novel clan of glycoside hydrolases. • The first crystal structure of endo-1,3-fucanase from GH174 family is reported. • The structure of Fun174A defines an unusual glycoside hydrolase fold. • Fun174A recognizes sulfated fucan through a series of polar residues. • Structural analogs of Fun174A are widely involved in polysaccharide metabolism. [ABSTRACT FROM AUTHOR]

Ataxia Telangiectasia (AT) is a rare multisystemic neurodegenerative disease caused by biallelic mutations in the ATM gene. Few clinical studies on AT disease have been conducted in Tunisia, however, the mutational landscape is still undefined. Our aim is to determine the clinical and genetic spectrum of AT Tunisian patients and to explore the potential underlying mechanism of variant pathogenicity. Sanger sequencing was performed for nine AT patients. A comprehensive computational analysis was conducted to evaluate the possible pathogenic effect of ATM identified variants. Genetic screening of ATM gene has identified nine different variants from which six have not been previously reported. In silico analysis has predicted a pathogenic effect of identified mutations. This was corroborated by a structural bioinformatics study based on molecular modeling and docking for novel missense mutations. Our findings suggest a profound impact of identified mutations not only on the ATM protein stability, but also on the ATM-ligand interactions. Our study characterizes the mutational landscape of AT Tunisian patients which will allow to set up genetic counseling and prenatal diagnosis for families at risk and expand the spectrum of ATM variants worldwide. Furthermore, understanding the mechanism that underpin variant pathogenicity could provide further insights into disease pathogenesis. [ABSTRACT FROM AUTHOR]

[Display omitted] • ModFOLD9 is a popular tool for independently estimating both the global and local quality of 3D protein models. • The new version integrates 6 additional deep learning-based scoring methods, which have been retrained on high-quality model data. • These improvements have led to significant performance increases over the previous versions of the server. • The server results pages provide user-friendly visualisations of annotated models and a refinement option to fix detected errors at the click of a button. • The server has maintained its position as one of the leading methods according to independent benchmarking results. Accurate models of protein tertiary structures are now available from numerous advanced prediction methods, although the accuracy of each method often varies depending on the specific protein target. Additionally, many models may still contain significant local errors. Therefore, reliable, independent model quality estimates are essential both for identifying errors and selecting the very best models for further biological investigations. ModFOLD9 is a leading independent server for detecting the local errors in models produced by any method, and it can accurately discriminate between high-quality models from multiple alternative approaches. ModFOLD9 incorporates several new scores from deep learning-based approaches, leading to greatly improved prediction accuracy compared with earlier versions of the server. ModFOLD9 is continuously independently benchmarked, and it is shown to be highly competitive with other public servers. ModFOLD9 is freely available at https://www.reading.ac.uk/bioinf/ModFOLD/. [ABSTRACT FROM AUTHOR]

Background: Considering protein mutations in their biological context is essential for understanding their functional impact, interpretation of high-dimensional datasets and development of effective targeted therapies in personalized medicine. Methods: We combined the curated knowledge of biochemical reactions from Reactome with the analysis of interaction-mediating 3D interfaces from Mechismo. In addition, we provided a software tool for users to explore and browse the analysis results in a multi-scale perspective starting from pathways and reactions to protein-protein interactions and protein 3D structures. Results: We analyzed somatic mutations from TCGA, revealing several significantly impacted reactions and pathways in specific cancer types. We found examples of genes not yet listed as oncodrivers, whose rare mutations were predicted to affect cancer processes similarly to known oncodrivers. Some identified processes lack any known oncodrivers, which suggests potentially new cancer-related processes (e.g. complement cascade reactions). Furthermore, we found that mutations perturbing certain processes are significantly associated with distinct phenotypes (i.e. survival time) in specific cancer types (e.g. PIK3CA centered pathways in LGG and UCEC cancer types), suggesting the translational potential of our approach for patient stratification. Our analysis also uncovered several druggable processes (e.g. GPCR signalling pathways) containing enriched reactions, providing support for new off-label therapeutic options. Conclusions: In summary, we have established a multi-scale approach to study genetic variants based on protein-protein interaction 3D structures. Our approach is different from previously published studies in its focus on biochemical reactions and can be applied to other data types (e.g. post-translational modifications) collected for many types of disease.

In the current study, we report computational scores for advancing genomic interpretation of disease-associated genomic variation in members of the RAS family of genes. For this purpose, we applied 31 sequence- and 3D structure-based computational scores, chosen by their breadth of biophysical properties. We parametrized our data by assembling a numerically homogenized experimentally-derived dataset, which when use in our calculations reveal that computational scores using 3D structure highly correlate with experimental measures (e.g., GAP-mediated hydrolysis RSpearman = 0.80 and RAF affinity Rspearman = 0.82), while sequence-based scores are discordant with this data. Performing all-against-all comparisons, we applied this parametrized modeling approach to the study of 935 RAS variants from 7 RAS genes, which led us to identify 4 groups of mutations according to distinct biochemical scores within each group. Each group was comprised of hotspot and non-hotspot KRAS variants, indicating that poorly characterized variants could functionally behave like pathogenic mutations. Combining computational scores using dimensionality reduction indicated that changes to local unfolding propensity associate with changes in enzyme activity by genomic variants. Hence, our systematic approach, combining methodologies from both clinical genomics and 3D structural bioinformatics, represents an expansion for interpreting genomic data, provides information of mechanistic value, and that is transferable to other proteins.

Alkaptonuria (AKU), a rare genetic disorder, is characterized by the accumulation of homogentisic acid (HGA) in the body. Affected individuals lack functional levels of an enzyme required to breakdown HGA. Mutations in the homogentisate 1,2-dioxygenase (HGD) gene cause AKU and they are responsible for deficient levels of functional HGD, which, in turn, leads to excess levels of HGA. Although HGA is rapidly cleared from the body by the kidneys, in the long term it starts accumulating in various tissues, especially cartilage. Over time (rarely before adulthood), it eventually changes the color of affected tissue to slate blue or black. Here we report a comprehensive mutation analysis of 111 pathogenic and 190 non-pathogenic HGD missense mutations using protein structural information. Using our comprehensive suite of graph-based signature methods, mCSM complemented with sequence-based tools, we studied the functional and molecular consequences of each mutation on protein stability, interaction and evolutionary conservation. The scores generated from the structure and sequence-based tools were used to train a supervised machine learning algorithm with 89% accuracy. The empirical classifier was used to generate the variant phenotype for novel HGD missense mutations. All this information is deployed as a user friendly freely available web server called HGDiscovery (https://biosig.lab.uq.edu.au/hgdiscovery/).

Background: Hepatitis C Virus (HCV) is the main causative factor for liver cirrhosis and for the development of liver cancer. E2 is an HCV structural protein responsible for virus entry to the host cell. GRP78 is the master regulator of the unfolded protein response mechanism in the Endoplasmic Reticulum (ER) in normal conditions. Under the stress of HCV infection or carcinogenesis, GRP78 is upregulated. Consequently, it escapes the ER retention and translocates to the cytoplasm and over the plasma membrane. Aim: This study aims to predict the binding mode of HCV E2 to GRP78 protein. Methods: Due to the high sequence and structural conservation between the C554–C566 region of HCV E2 and the Pep42, cyclic peptide that is reported to target GRP78, we propose that this region of E2 can be the recognition site. We predict the possible binding mode between HCV E2 and GRP78 by implementing molecular docking and molecular dynamics simulation to test such proposed binding. Results: The simulations reveal a stable and highly potent (−111.2 docking score) binding of the HCV E2 C554–C566 peptide to GRP78 substrate-binding domain β (SBDβ). Moreover, the full-length HCV E2 also exhibits high binding affinity to GRP78 SBDβ (score = −107.5 ± 3.1), which is better than the association of GRP78 and Pep42. Conclusions: Defining the compulsory mode between HCV E2 and GRP78 is significant, so it would be possible to interfere with such binding to reduce the viral infection.

Background: Considering protein mutations in their biological context is essential for understanding their functional impact, interpretation of high-dimensional datasets and development of effective targeted therapies in personalized medicine. Methods: We combined the curated knowledge of biochemical reactions from Reactome with the analysis of interaction-mediating 3D interfaces from Mechismo. In addition, we provided a software tool for users to explore and browse the analysis results in a multi-scale perspective starting from pathways and reactions to protein-protein interactions and protein 3D structures. Results: We analyzed somatic mutations from TCGA, revealing several significantly impacted reactions and pathways in specific cancer types. We found examples of genes not yet listed as oncodrivers, whose rare mutations were predicted to affect cancer processes similarly to known oncodrivers. Some identified processes lack any known oncodrivers, which suggests potentially new cancer-related processes (e.g. complement cascade reactions). Furthermore, we found that mutations perturbing certain processes are significantly associated with distinct phenotypes (i.e. survival time) in specific cancer types (e.g. PIK3CA centered pathways in LGG and UCEC cancer types), suggesting the translational potential of our approach for patient stratification. Our analysis also uncovered several druggable processes (e.g. GPCR signalling pathways) containing enriched reactions, providing support for new off-label therapeutic options. Conclusions: In summary, we have established a multi-scale approach to study genetic variants based on protein-protein interaction 3D structures. Our approach is different from previously published studies in its focus on biochemical reactions and can be applied to other data types (e.g. post-translational modifications) collected for many types of disease.

Prioritizing genes for their role in drug sensitivity, is an important step in understanding drugs mechanisms of action and discovering new molecular targets for co-treatment. To formalize this problem, we consider two sets of genes X and P respectively composing the gene signature of cell sensitivity at the drug IC50 and the genes involved in its mechanism of action, as well as a protein interaction network (PPIN) containing the products of X and P as nodes. We introduce Genetrank, a method to prioritize the genes in X for their likelihood to regulate the genes in P. Genetrank uses asymmetric random walks with restarts, absorbing states, and a suitable renormalization scheme. Using novel so-called saturation indices, we show that the conjunction of absorbing states and renormalization yields an exploration of the PPIN which is much more progressive than that afforded by random walks with restarts only. Using MINT as underlying network, we apply Genetrank to a predictive gene signature of cancer cells sensitivity to tumor-necrosis-factor-related apoptosis-inducing ligand (TRAIL), performed in single-cells. Our ranking provides biological insights on drug sensitivity and a gene set considerably enriched in genes regulating TRAIL pharmacodynamics when compared to the most significant differentially expressed genes obtained from a statistical analysis framework alone. We also introduce gene expression radars, a visualization tool embedded in MA plots to assess all pairwise interactions at a glance on graphical representations of transcriptomics data. Genetrank is made available in the Structural Bioinformatics Library (https://sbl.inria.fr/doc/Genetrank-user-manual.html). It should prove useful for mining gene sets in conjunction with a signaling pathway, whenever other approaches yield relatively large sets of genes. [ABSTRACT FROM AUTHOR]