There is a wide, and continuously widening, gap between the number of proteins known only by their amino acid sequence versus those structurally characterized by direct experiment. To close this gap, we mostly rely on homology-based inference and modeling to reason about the structures of the uncharacterized proteins by using structures of homologous proteins as templates. With the rapidly growing size of the Protein Data Bank, there are often multiple choices of templates, including multiple sets of coordinates from the same protein. The substantial conformational differences observed between different experimental structures of the same protein often reflect function related structural flexibility. Thus, depending on the questions being asked, using distant homologs, or coordinate sets with lower resolution but solved in the appropriate functional form, as templates may be more informative. The ModFlex server (https://modflex.org/) addresses this seldom mentioned gap in the standard homology modeling approach by providing the user with an interface with multiple options and tools to select the most relevant template and explore the range of structural diversity in the available templates. ModFlex is closely integrated with a range of other programs and servers developed in our group for the analysis and visualization of protein structural flexibility and divergence.

RiPPMiner-Genome is a unique bioinformatics resource for identifying Biosynthetic Gene Clusters (BGC) for RiPPs (Ribosomally Synthesized and Post-translationally Modified Peptides) and automated prediction of crosslinked chemical structures of RiPPs starting from genomic sequences. It is a major update of the RiPPMiner webserver, which used only peptide sequence of RiPP precursors as input for predicting RiPP class and crosslinked chemical structures. Other major improvements are, machine learning (ML) based identification of correct RiPP precursor peptide from among multiple small ORFs (Open Reading Frames) in a BGC, prediction of the cleavage site and cross-links in thiopeptides and identification of non-crosslinked modified residues in lanthipeptides. It has been benchmarked on a dataset of 204 experimentally characterized RiPP BGCs. RiPPMiner-Genome also facilitates visualization of the RiPP BGCs and depiction of the chemical structure of crosslinked RiPP. It also has an interface for searching characterized RiPPs, similar to the predicted core peptide sequence or crosslinked chemical structure.

Ensembles of tandem repeats (TRs) in protein sequences expand rapidly to form domains well suited for interactions with proteins. For this reason, they are relatively frequent. Some TRs have known structures and therefore it is advantageous to predict their presence in a protein sequence. However, since most TRs diverge quickly, their detection by classical sequence comparison algorithms is not very accurate. Previously, we developed a method and a web server that used curated profiles and thresholds for the detection of 11 common TRs. Here we present a new web server (REP2) that allows the analysis of TRs in both individual and aligned sequences. We provide currently precomputed analyses for a selection of 78 UniProt reference proteomes. We illustrate how these data can be used to study the evolution of TRs using comparative genomics. REP2 can be accessed at http://cbdm-01.zdv.uni-mainz.de/~munoz/rep/.

FunCoup (https://funcoup.sbc.su.se) is one of the most comprehensive functional association networks of genes/proteins available. Functional associations are inferred by integrating different types of evidence using a redundancy-weighted naïve Bayesian approach, combined with orthology transfer. FunCoup's high coverage comes from using eleven different types of evidence, and extensive transfer of information between species. Since the latest update of the database, the availability of source data has improved drastically, and user expectations on a tool for functional associations have grown. To meet these requirements, we have made a new release of FunCoup with updated source data and improved functionality. FunCoup 5 now includes 22 species from all domains of life, and the source data for evidences, gold standards, and genomes have been updated to the latest available versions. In this new release, directed regulatory links inferred from transcription factor binding can be visualized in the network viewer for the human interactome. Another new feature is the possibility to filter by genes expressed in a certain tissue in the network viewer. FunCoup 5 further includes the SARS-CoV-2 proteome, allowing users to visualize and analyze interactions between SARS-CoV-2 and human proteins in order to better understand COVID-19. This new release of FunCoup constitutes a major advance for the users, with updated sources, new species and improved functionality for analysis of the networks.

Numerous human diseases are caused by mutations in genomic sequences. Since amino acid changes affect protein function through mechanisms often predictable from protein structure, the integration of structural and sequence data enables us to estimate with greater accuracy whether and how a given mutation will lead to disease. Publicly available annotated databases enable hypothesis assessment and benchmarking of prediction tools. However, the results are often presented as summary statistics or black box predictors, without providing full descriptive information. We developed a new semi-manually curated human variant database presenting information on the protein contact-map, sequence-to-structure mapping, amino acid identity change, and stability prediction for the popular UniProt database. We found that the profiles of pathogenic and benign missense polymorphisms can be effectively deduced using decision trees and comparative analyses based on the presented dataset. The database is made publicly available through https://zhanglab.ccmb.med.umich.edu/ADDRESS.

Most cells in multicellular organisms are somehow asymmetric, polarized: maintaining separate membrane domains. Typical examples are the epithelial cells (apical-basal polarization), neurons (dendritic-axonal domains), or migratory cells (with a leading and a trailing edge). Here we present the most comprehensive database containing experimentally verified mammalian proteins that display polarized sorting or secretion, focusing on epithelial polarity. In addition to the source cells or tissues, homology-based inferences and transmembrane topology (if applicable) are all provided. PolarProtDb also offers a detailed interface displaying all information that may be relevant for trafficking: including post-translational modifications (glycosylations and phosphorylations), known or predicted short linear motifs conserved across orthologs, as well as potential interaction partners. Data on polarized sorting has so far been scattered across myriads of publications, hence difficult to access. This information can help researchers in several areas, such as scanning for potential entry points of viral agents like COVID-19. PolarProtDb shall be a useful resource to design future experiments as well as for comparative analyses. The database is available at http://polarprotdb.enzim.hu.

TransMembrane β-Barrel (TMBB) proteins located in the outer membranes of Gram-negative bacteria are crucial for many important biological processes and primary candidates as drug targets. Structure determination of TMBB proteins is challenging and hence computational methods devised for the analysis of TMBB proteins are important for complementing experimental approaches. Here, we present a novel web server called BetAware-Deep that is able to accurately identify the topology of TMBB proteins (i.e. the number and orientation of membrane-spanning segments along the protein sequence) and to discriminate them from other protein types. The method in BetAware-Deep defines new features by exploiting a non-canonical computation of the hydrophobic moment and by adopting sequence-profile weighting of the White&Wimley hydrophobicity scale. These features are processed using a two-step approach based on deep learning and probabilistic graphical models. BetAware-Deep has been trained on a dataset comprising 58 TMBBs and benchmarked on a novel set of 15 TMBB proteins. Results showed that BetAware-Deep outperforms two recently released state-of-the-art methods for topology prediction, predicting correct topologies of 10 out of 15 proteins. TMBB detection was also assessed on a larger dataset comprising 1009 TMBB proteins and 7571 non-TMBB proteins. Even in this benchmark, BetAware-Deep scored at the level of top-performing methods. A web server has been developed allowing users to analyze input protein sequences and providing topology prediction together with a rich set of information including a graphical representation of the residue-level annotations and prediction probabilities. BetAware-Deep is available at https://busca.biocomp.unibo.it/betaware2.

Short aggregation prone sequence motifs can trigger aggregation in peptide and protein sequences. Most algorithms developed so far to identify potential aggregation prone regions (APRs) use amino acid residue composition and/or sequence pattern features. In this work, we have investigated the importance of atomic-level characteristics rather than residue level to understand the initiation of aggregation in proteins and peptides. Using atomic-level features an ensemble-classifier, ANuPP has been developed to predict the aggregation-nucleating regions in peptides and proteins. In a dataset of 1279 hexapeptides, ANuPP achieved an area under the curve (AUC) of 0.831 with 77% accuracy on 10-fold cross-validation and an AUC of 0.883 with 83% accuracy in a blind test dataset of 142 hexapeptides. Further, it showed an average SOV of 48.7% on identifying APR regions in 37 proteins. The performance of ANuPP is better than other methods reported in the literature on both amyloidogenic hexapeptide prediction and APR identification. We have developed a web server for ANuPP and it is available at https://web.iitm.ac.in/bioinfo2/ANuPP/. Insights gained from this work demonstrate the importance of atomic and functional group characteristics towards diversity of atomic level origins as well as mechanisms of protein aggregation.

Non-coding RNA (ncRNA) genes assume increasing biological importance, with growing associations with diseases. Many ncRNA sources are transcript-centric, but for non-coding variant analysis and disease decipherment it is essential to transform this information into a comprehensive set of genome-mapped ncRNA genes. We present GeneCaRNA, a new all-inclusive gene-centric ncRNA database within the GeneCards Suite. GeneCaRNA information is integrated from four community-backed data structures: the major transcript database RNAcentral with its 20 encompassed databases, and the ncRNA entries of three major gene resources HGNC, Ensembl and NCBI Gene. GeneCaRNA presents 219,587 ncRNA gene pages, a 7-fold increase from those available in our three gene mining sources. Each ncRNA gene has wide-ranging annotation, mined from >100 worldwide sources, providing a powerful GeneCards-leveraged search. The latter empowers VarElect, our disease-gene interpretation tool, allowing one to systematically decipher ncRNA variants. The combined power of GeneCaRNA with GeneHancer, our regulatory elements database, facilitates wide-ranging scrutiny of the non-coding terra incognita of gene networks and whole genome analyses.

As a key region, promoter plays a key role in transcription regulation. A eukaryotic promoter database called EPD has been constructed to store eukaryotic POL II promoters. Although there are some promoter databases for specific prokaryotic species or specific promoter type, such as RegulonDB for Escherichia coli K-12, DBTBS for Bacillus subtilis and Pro54DB for sigma 54 promoter, because of the diversity of prokaryotes and the development of sequencing technology, huge amounts of prokaryotic promoters are scattered in numerous published articles, which is inconvenient for researchers to explore the process of gene regulation in prokaryotes. In this study, we constructed a Prokaryotic Promoter Database (PPD), which records the experimentally validated promoters in prokaryotes, from published articles. Up to now, PPD has stored 129,148 promoters across 63 prokaryotic species manually extracted from published papers. We provided a friendly interface for users to browse, search, blast, visualize, submit and download data. The PPD will provide relatively comprehensive resources of prokaryotic promoter for the study of prokaryotic gene transcription. The PPD is freely available and easy accessed at http://lin-group.cn/database/ppd/.

Coarse-grained models have long been considered indispensable tools in the investigation of biomolecular dynamics and assembly. However, the process of simulating such models is arduous because unconventional force fields and particle attributes are often needed, and some systems are not in thermal equilibrium. Although modern molecular dynamics programs are highly adaptable, software designed for preparing all-atom simulations typically makes restrictive assumptions about the nature of the particles and the forces acting on them. Consequently, the use of coarse-grained models has remained challenging. Moltemplate is a file format for storing coarse-grained molecular models and the forces that act on them, as well as a program that converts moltemplate files into input files for LAMMPS, a popular molecular dynamics engine. Moltemplate has broad scope and an emphasis on generality. It accommodates new kinds of forces as they are developed for LAMMPS, making moltemplate a popular tool with thousands of users in computational chemistry, materials science, and structural biology. To demonstrate its wide functionality, we provide examples of using moltemplate to prepare simulations of fluids using many-body forces, coarse-grained organic semiconductors, and the motor-driven supercoiling and condensation of an entire bacterial chromosome.

The US Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB) serves many millions of unique users worldwide by delivering experimentally-determined 3D structures of biomolecules integrated with >40 external data resources via RCSB.org, application programming interfaces (APIs), and FTP downloads. Herein, we present the architectural redesign of RCSB PDB data delivery services that build on existing PDBx/mmCIF data schemas. New data access APIs (data.rcsb.org) enable efficient delivery of all PDB archive data. A novel GraphQL-based API provides flexible, declarative data retrieval along with a simple-to-use REST API. A powerful new search system (search.rcsb.org) seamlessly integrates heterogeneous types of searches across the PDB archive. Searches may combine text attributes, protein or nucleic acid sequences, small-molecule chemical descriptors, 3D macromolecular shapes, and sequence motifs. The new RCSB.org architecture adheres to the FAIR Principles, empowering users to address a wide array of research problems in fundamental biology, biomedicine, biotechnology, bioengineering, and bioenergy.

One of the biggest barriers in drug and vaccine development is to find an effective delivery system. Cell-penetrating peptides (CPPs) play a crucial role for delivery of biological cargoes and pass them through the membranes. Several databases have been developed for therapeutic peptides as potential drug candidates and delivery vehicles. A rapid growth has occurred in many patents and research articles on CPPs as therapeutic peptides. To save time and cost in laboratories, prediction and design of CPPs before in vitro/in vivo experiments using computational methods and online web servers are rational. Various online web servers which provide prediction of CPPs including CellPPD, CPPpred, CPPred-RF and MLCPP, and also different curated databases that present validated information of CPPs such as CPPsite 2.0 have been developed up to now. Two methods including CellPPD and CPPpred were applied to predict and design potent CPPs. CPPsite 2.0 is a user-friendly updated database that provides various information about CPPs and contains 1855 entries. This database provides comprehensive information on experimentally tested CPPs and prediction of their secondary and tertiary structures to realize their structure-function relationship. Furthermore, each entry presents information of a CPP including chirality, origin, nature of peptide, sub-cellular localization, uptake mechanism and efficiency, amino acid composition, hydrophobicity, and physicochemical properties. One of main goals of CPPsite 2.0 database is to provide the latest datasets of CPPs for analysis and development of CPP prediction methods. CPPsite 2.0 is freely available at https://webs.iiitd.edu.in/raghava/cppsite.

Enzymatic assays are widely employed to characterize important allosteric and enzyme modulation effects. The high sensitivity of these assays can represent a serious problem if the occurrence of experimental errors surreptitiously affects the reliability of enzyme kinetics results. We have addressed this problem and found that hidden assay interferences can be unveiled by the graphical representation of progress curves in modified reaction coordinates. To render this analysis accessible to users across all levels of expertise, we have developed a webserver, interferENZY, that allows (i) an unprecedented tight quality control of experimental data, (ii) the automated identification of small and major assay interferences, and (iii) the estimation of bias-free kinetic parameters. By eliminating the subjectivity factor in kinetic data reporting, interferENZY will contribute to solving the "reproducibility crisis" that currently challenges experimental molecular biology. The interferENZY webserver is freely available (no login required) at https://interferenzy.i3s.up.pt.

The ELASPIC web server allows users to evaluate the effect of mutations on protein folding and protein-protein interaction on a proteome-wide scale. It uses homology models of proteins and protein-protein interactions, which have been precalculated for several proteomes, and machine learning models, which integrate structural information with sequence conservation scores, in order to make its predictions. Since the original publication of the ELASPIC web server, several advances have motivated a revisiting of the problem of mutation effect prediction. First, progress in neural network architectures and self-supervised pre-trained has resulted in models which provide more informative embeddings of protein sequence and structure than those used by the original version of ELASPIC. Second, the amount of training data has increased several-fold, largely driven by advances in deep mutation scanning and other multiplexed assays of variant effect. Here, we describe two machine learning models which leverage the recent advances in order to achieve superior accuracy in predicting the effect of mutation on protein folding and protein-protein interaction. The models incorporate features generated using pre-trained transformer- and graph convolution-based neural networks, and are trained to optimize a ranking objective function, which permits the use of heterogeneous training data. The outputs from the new models have been incorporated into the ELASPIC web server, available at http://elaspic.kimlab.org.

Protein interactions play a crucial role among the different functions of a cell and are central to our understanding of cellular processes both in health and disease. Here we present Galaxy InteractoMIX (http://galaxy.interactomix.com), a platform composed of 13 different computational tools each addressing specific aspects of the study of protein-protein interactions, ranging from large-scale cross-species protein-wide interactomes to atomic resolution level of protein complexes. Galaxy InteractoMIX provides an intuitive interface where users can retrieve consolidated interactomics data distributed across several databases or uncover links between diseases and genes by analyzing the interactomes underlying these diseases. The platform makes possible large-scale prediction and curation protein interactions using the conservation of motifs, interology, or presence or absence of key sequence signatures. The range of structure-based tools includes modeling and analysis of protein complexes, delineation of interfaces and the modeling of peptides acting as inhibitors of protein-protein interactions. Galaxy InteractoMIX includes a range of ready-to-use workflows to run complex analyses requiring minimal intervention by users. The potential range of applications of the platform covers different aspects of life science, biomedicine, biotechnology and drug discovery where protein associations are studied.

The stability of membrane proteins differs from globular proteins due to the presence of nonpolar membrane-spanning regions. Using a dataset of 929 membrane protein mutations whose effects on thermal stability (ΔTm) were experimentally determined, we found that the average ΔTm due to 190 stabilizing and 232 destabilizing mutations occurring in membrane-spanning regions are 2.43(3.1) °C and -5.48(5.5) °C, respectively. The ΔTm values for mutations occurring in solvent-exposed regions are 2.56(2.82) and - 6.8(7.2) °C. We have systematically analyzed the factors influencing the stability of mutants and observed that changes in hydrophobicity, number of contacts between Cα atoms and frequency of aliphatic residues are important determinants of the stability change induced by mutations occurring in membrane-spanning regions. We have developed structure- and sequence-based machine learning predictors of ΔTm due to mutations specifically for membrane proteins. They showed a correlation and mean absolute error (MAE) of 0.72 and 2.85 °C, respectively, between experimental and predicted ΔTm for mutations in membrane-spanning regions on 10-fold group-wise cross-validation. The average correlation and MAE for mutations in aqueous regions are 0.73 and 3.7 °C, respectively. These MAE values are about 50% lower than standard deviations from the mean ΔTm values. The reliability of the method was affirmed on a test set of mutations occurring in evolutionary independent protein sequences. The developed MPTherm-pred server for predicting thermal stability changes upon mutations in membrane proteins is available at https://web.iitm.ac.in/bioinfo2/mpthermpred/. Our results provide insights into factors influencing the stability of membrane proteins and can aid in designing mutants that are more resistant to thermal stress.

Information on the protein flexibility is essential to understand crucial molecular mechanisms such as protein stability, interactions with other molecules and protein functions in general. B-factor obtained in the X-ray crystallography experiments is the most common flexibility descriptor available for the majority of the resolved protein structures. Since the gap between the number of the resolved protein structures and available protein sequences is continuously growing, it is important to provide computational tools for protein flexibility prediction from amino acid sequence. In the current study, we report a Deep Learning based protein flexibility prediction tool MEDUSA (https://www.dsimb.inserm.fr/MEDUSA). MEDUSA uses evolutionary information extracted from protein homologous sequences and amino acid physico-chemical properties as input for a convolutional neural network to assign a flexibility class to each protein sequence position. Trained on a non-redundant dataset of X-ray structures, MEDUSA provides flexibility prediction in two, three and five classes. MEDUSA is freely available as a web-server providing a clear visualization of the prediction results as well as a standalone utility (https://github.com/DSIMB/medusa). Analysis of the MEDUSA output allows a user to identify the potentially highly deformable protein regions and general dynamic properties of the protein.

In high-throughput omics disciplines like transcriptomics, researchers face a need to assess the quality of an experiment prior to an in-depth statistical analysis. To efficiently analyze such voluminous collections of data, researchers need triage methods that are both quick and easy to use. Such a normalization method for relative quantitation, CONSTANd, was recently introduced for isobarically-labeled mass spectra in proteomics. It transforms the data matrix of abundances through an iterative, convergent process enforcing three constraints: (I) identical column sums; (II) each row sum is fixed (across matrices) and (III) identical to all other row sums. In this study, we investigate whether CONSTANd is suitable for count data from massively parallel sequencing, by qualitatively comparing its results to those of DESeq2. Further, we propose an adjustment of the method so that it may be applied to identically balanced but differently sized experiments for joint analysis. We find that CONSTANd can process large data sets at well over 1 million count records per second whilst mitigating unwanted systematic bias and thus quickly uncovering the underlying biological structure when combined with a PCA plot or hierarchical clustering. Moreover, it allows joint analysis of data sets obtained from different batches, with different protocols and from different labs but without exploiting information from the experimental setup other than the delineation of samples into identically processed sets (IPSs). CONSTANd's simplicity and applicability to proteomics as well as transcriptomics data make it an interesting candidate for integration in multi-omics workflows.

The biological function and evolutionary history of protein-coding genes are not only written in their nucleotide sequences. The comparison of genomic contexts throughout different lineages may highlight genomic mechanisms in the generation of new protein families, while the conservation of gene clusters may unravel, for instance, metabolic pathways. Various tools and databases exist that allow for the analysis and comparison of genomic contexts, but each has its own limitations. Online databases allow for quick comparisons, but only for those genomes for which data were pre-calculated. More advanced tools may allow for the comparison of any genome, but are often limited to a given phylogenetic kingdom or provide only a snapshot of the genomic contexts without further information about the genes involved. Here, we introduce GCsnap, a flexible Python-based tool that allows for the interactive comparison of the genomic contexts of protein-coding genes from any genome at any taxonomic level, integrating them with functional and structural information. By connecting the output to different protein databases, users can navigate through the different genomic contexts from a simple interactive platform, facilitating the further analysis of the contexts found. GCsnap is not limited to a single input format, can perform batch jobs and accepts protein classification maps. Results are stored in detailed, human and machine-readable files, and customizable, publication-ready figures. GCsnap is freely available from https://github.com/JoanaMPereira/GCsnap and can be set up easily on any computer.

Deleterious single amino acid variation (SAV) is one of the leading causes of human diseases. Evaluating the functional impact of SAVs is crucial for diagnosis of genetic disorders. We previously developed a deep convolutional neural network predictor, DeepSAV, to evaluate the deleterious effects of SAVs on protein function based on various sequence, structural, and functional properties. DeepSAV scores of rare SAVs observed in the human population are aggregated into a gene-level score called GTS (Gene Tolerance of rare SAVs) that reflects a gene's tolerance to deleterious missense mutations and serves as a useful tool to study gene-disease associations. In this study, we aim to enhance the performance of DeepSAV by using expanded datasets of pathogenic and benign variants, more features, and neural network optimization. We found that multiple sequence alignments built from vertebrate-level orthologs yield better prediction results compared to those built from mammalian-level orthologs. For multiple sequence alignments built from BLAST searches, optimal performance was achieved with a sequence identify cutoff of 50% to remove distant homologs. The new version of DeepSAV exhibits the best performance among standalone predictors of deleterious effects of SAVs. We developed the DBSAV database (http://prodata.swmed.edu/DBSAV) that reports GTS scores of human genes and DeepSAV scores of SAVs in the human proteome, including pathogenic and benign SAVs, population-level SAVs, and all possible SAVs by single nucleotide variations. This database serves as a useful resource for research of human SAVs and their relationships with protein functions and human diseases.

Membrane proteins (MPs) are the target of numerous structural and functional studies in biological and medical/pharmaceutical sciences. Strategies for the high-throughput structural analysis of MPs and of their perturbations driven by ligands having potential therapeutic applications are uncommon, often requiring scaled up crystallization, electron microscopy, and nuclear magnetic resonance (NMR) efforts. Small-angle X-ray scattering (SAXS) provides a rapid means to study low resolution structures and conformational changes of native MPs in solution without cumbersome sample preparations/treatment. The method requires the MPs solubilized in an appropriate medium (eg. detergents, mixed micelles and nanodiscs) and reliable and robust models are needed to describe the relevant complexes. Here we present MPBuilder, a simple and versatile tool for the generation and refinement of all-atom MP systems in the popular software PyMOL, an environment familiar to most biologists. MPBuilder provides building capability for protein-detergent, bicelle, and lipid-scaffold (saposin nanoparticles, nanodiscs) complexes and links this to the ATSAS software package modules for model refinement and validation against the SAXS data.

In diploid organisms, two copies of each allele are normally inherited from parents. Paternal and maternal alleles can be regulated and expressed unequally, which is referred to as allele-specific expression (ASE). In this work, we present aScan, a novel method for the identification of ASE from the analysis of matched individual genomic and RNA sequencing data. By performing extensive analyses of both real and simulated data, we demonstrate that aScan can correctly identify ASE with high accuracy and sensitivity in different experimental settings. Additionally, by applying our method to a small cohort of individuals that are not included in publicly available databases of human genetic variation, we outline the value of possible applications of ASE analysis in single individuals for deriving a more accurate annotation of "private" low-frequency genetic variants associated with regulatory effects on transcription. All in all, we believe that aScan will represent a beneficial addition to the set of bioinformatics tools for the analysis of ASE. Finally, while our method was initially conceived for the analysis of RNA-seq data, it can in principle be applied to any quantitative NGS assay for which matched genotypic and expression data are available. AVAILABILITY: aScan is currently available in the form of an open source standalone software package at: https://github.com/Federico77z/aScan/. aScan version 1.0.3, available at https://github.com/Federico77z/aScan/releases/tag/1.0.3, has been used for all the analyses included in this manuscript. A Docker image of the tool has also been made available at https://github.com/pmandreoli/aScanDocker.

High-throughput methods to profile the genome, transcriptome, proteome and metabolome of various systems has become a routine in multiple research laboratories around the world. Hence, to analyse and interpret these heterogenous datasets user-friendly bioinformatics tools are needed. Here, we discuss FunRich tool that enables biologists to perform functional enrichment analysis on the generated datasets. Users can perform enrichment analysis with a variety of background databases and have complete control in updating or modifying the content in most of the databases. Specifically, users can download and update the background database from UniProt at any time thereby allowing a robust background database that can support annotations from >18 taxonomies. Users can create customizable Venn diagrams, pie charts, bar graphs and heatmaps of publication quality for their datasets using FunRich (http://www.funrich.org). Overall, FunRich tool is user-friendly and enables users to perform various analysis on their datasets with minimal or no aid from bioinformaticians.

While variants of noncoding RNAs (ncRNAs) have been experimentally validated as a new class of biomarkers and drug targets, the discovery and interpretation of relationships between ncRNA variants and human diseases become important and challenging. Here we present ncRNAVar (http://www.liwzlab.cn/ncrnavar/), the first database that provides association data between validated ncRNA variants and human diseases through manual curation on 2650 publications and computational annotation. ncRNAVar contains 4565 associations between 711 human disease phenotypes and 3112 variants from 2597 ncRNAs. Each association was reviewed by professional curators, incorporated with valuable annotation and cross references, and designated with an association score by our refined score model. ncRNAVar offers web applications including association prioritization, network visualization, and relationship mapping. ncRNAVar, presenting a landscape of ncRNA variants in human diseases and a useful resource for subsequent software development, will improve our insight of relationships between ncRNA variants and human health.

Genetic variation, gene expression and DNA methylation influence each other in a complex way. To study the impact of sequence variation and DNA methylation on gene expression, we generated geno5mC, a database that contains statistically significant SNP-CpG associations that are biologically classified either through co-localization with known regulatory regions (promoters and enhancers), or through known correlations with the expression levels of nearby genes. The SNP rs727563 can be used to illustrate the usefulness of this approach. This SNP has been associated with inflammatory bowel disease through GWAS, but it is not located near any gene related to this phenotype. However, geno5mC reveals that rs727563 is associated with the methylation state of several CpGs located in promoter regions of genes reported to be involved in inflammatory processes. This case exemplifies how geno5mC can be used to infer relevant and previously unknown interactions between described disease-associated SNPs and their functional targets.

Nucleic acids exhibit a repertoire of conformational preference depending on the sequence and environment. Circular dichroism (CD) is an essential and valuable tool for monitoring such secondary structural conformations of nucleic acids. Nonetheless, the CD spectral diversity associated with these structures poses a challenge in obtaining the quantitative information about the secondary structural content of a given CD spectrum. To this end, the competence of the extreme gradient boosting decision-tree (XGBoost), Kohonen and neural network (nnet) algorithms have been exploited here to predict the diverse secondary structures of nucleic acids. A curated library of 450 CD spectra corresponding to 16 different secondary structures of nucleic acids has been created and used as a training dataset. The hyper-parameters corresponding to the aforementioned algorithms have been optimized using holdout and k-fold (here, k = 5) cross-validation methods. For a test dataset of 150 CD spectra, both the nnet and XGBoost algorithms have exhibited nearly similar prediction accuracy in the range of 85% and 87% (the latter exhibited a slightly higher prediction accuracy). Thus, the nnet and XGBoost algorithms tested here can be employed for predicting the hybrid nucleic acid topologies in future. For the sake of accessibility, the entire process has been automated and implemented as a webserver, called CD-NuSS (CD to nucleic acids secondary structure) and is freely accessible at https://project.iith.ac.in/cdnuss/.

The Covid-19 pandemic is a centenarial global catastrophe. Similar events are likely to be recurring with more frequency in the future. The inability to control the virus' impact is caused by many factors, but the lack of a technology infrastructure to detect and impede the virus at an early stage are principal shortcomings. Using phage display mutagenesis, we have generated a cohort of high performance antibody fragments (Fabs) that can be used in a sensitive point of care (POC) assay and are potent inhibitors (IC50-0.5 nM) to viral entry into cells. The POC assay is based on a split-enzyme (β-lactamase) complementation strategy that detects virus particles at low nM levels. We have shown that this assay is equally effective for detecting other viruses like Ebola and Zika. Importantly, its components can be freeze dried and stored, but becomes fully active when rehydrated.

The COVID-19 pandemic entered its third and most intense to date wave of infections in November 2020. This perspective article describes how combination therapies (polytherapeutics) are a needed focus for helping battle the severity of complications from SARS-CoV-2 infection. It outlines the types of systems that are needed for fast and efficient combinatorial assessment of therapeutic candidates. Proposed are micro-physiological systems using human iPSC as a format for tissue-specific modeling of infection, the use of gene-humanized zebrafish and C. elegans for combinatorial drug screens due to the animals being addressable in liquid multi-well formats, and the use of engineered pseudo-typing systems to safely model infection in the transgenic animals and engineered tissue systems.

Coronaviruses are a major infectious disease threat, and include the zoonotic-origin human pathogens SARS-CoV-2, SARS-CoV, and MERS-CoV (SARS-2, SARS-1, and MERS). Entry of coronaviruses into host cells is mediated by the spike (S) protein. In our previous ESR studies, the local membrane ordering effect of the fusion peptide (FP) of various viral glycoproteins including the S of SARS-1 and MERS has been consistently observed. We previously determined that the sequence immediately downstream from the S2' cleavage site is the bona fide SARS-1 FP. In this study, we used sequence alignment to identify the SARS-2 FP, and studied its membrane ordering effect. Although there are only three residue differences, SARS-2 FP induces even greater membrane ordering than SARS-1 FP, possibly due to its greater hydrophobicity. This may be a reason that SARS-2 is better able to infect host cells. In addition, the membrane binding enthalpy for SARS-2 is greater. Both the membrane ordering of SARS-2 and SARS-1 FPs are dependent on Ca2+, but that of SARS-2 shows a greater response to the presence of Ca2+. Both FPs bind two Ca2+ ions as does SARS-1 FP, but the two Ca2+ binding sites of SARS-2 exhibit greater cooperativity. This Ca2+ dependence by the SARS-2 FP is very ion-specific. These results show that Ca2+ is an important regulator that interacts with the SARS-2 FP and thus plays a significant role in SARS-2 viral entry. This could lead to therapeutic solutions that either target the FP-calcium interaction or block the Ca2+ channel.

Genome-wide protein-protein interaction (PPI) determination remains a significant unsolved problem in structural biology. The difficulty is twofold since high-throughput experiments (HTEs) have often a relatively high false-positive rate in assigning PPIs, and PPI quaternary structures are more difficult to solve than tertiary structures using traditional structural biology techniques. We proposed a uniform pipeline, Threpp, to address both problems. Starting from a pair of monomer sequences, Threpp first threads both sequences through a complex structure library, where the alignment score is combined with HTE data using a naïve Bayesian classifier model to predict the likelihood of two chains to interact with each other. Next, quaternary complex structures of the identified PPIs are constructed by reassembling monomeric alignments with dimeric threading frameworks through interface-specific structural alignments. The pipeline was applied to the Escherichia coli genome and created 35,125 confident PPIs which is 4.5-fold higher than HTE alone. Graphic analyses of the PPI networks show a scale-free cluster size distribution, consistent with previous studies, which was found critical to the robustness of genome evolution and the centrality of functionally important proteins that are essential to E. coli survival. Furthermore, complex structure models were constructed for all predicted E. coli PPIs based on the quaternary threading alignments, where 6771 of them were found to have a high confidence score that corresponds to the correct fold of the complexes with a TM-score >0.5, and 39 showed a close consistency with the later released experimental structures with an average TM-score = 0.73. These results demonstrated the significant usefulness of threading-based homologous modeling in both genome-wide PPI network detection and complex structural construction.

How retroviral Gag proteins recognize the packaging signals (Psi) on their genomic RNA (gRNA) is a key question that we addressed here using Mason-Pfizer monkey virus (MPMV) as a model system by combining band-shift assays and footprinting experiments. Our data show that Pr78Gag selects gRNA against spliced viral RNA by simultaneously binding to two single stranded loops on the MPMV Psi RNA: (1) a large purine loop (ssPurines), and (2) a loop which partially overlaps with a mostly base-paired purine repeat (bpPurines) and extends into a GU-rich binding motif. Importantly, this second Gag binding site is located immediately downstream of the major splice donor (mSD) and is thus absent from the spliced viral RNAs. Identifying elements crucial for MPMV gRNA packaging should help in understanding not only the mechanism of virion assembly by retroviruses, but also facilitate construction of safer retroviral vectors for human gene therapy.

DNA co-crystallization with Dps family proteins is a fundamental mechanism, which preserves DNA in bacteria from harsh conditions. Though many aspects of this phenomenon are well characterized, the spatial organization of DNA in DNA-Dps co-crystals is not completely understood, and existing models need further clarification. To advance in this problem we have utilized atomic force microscopy (AFM) as the main structural tool, and small-angle X-scattering (SAXS) to characterize Dps as a key component of the DNA-protein complex. SAXS analysis in the presence of EDTA indicates a significantly larger radius of gyration for Dps than would be expected for the core of the dodecamer, consistent with the N-terminal regions extending out into solution and being accessible for interaction with DNA. In AFM experiments, both Dps protein molecules and DNA-Dps complexes adsorbed on mica or highly oriented pyrolytic graphite (HOPG) surfaces form densely packed hexagonal structures with a characteristic size of about 9 nm. To shed light on the peculiarities of DNA interaction with Dps molecules, we have characterized individual DNA-Dps complexes. Contour length evaluation has confirmed the non-specific character of Dps binding with DNA and revealed that DNA does not wrap Dps molecules in DNA-Dps complexes. Angle analysis has demonstrated that in DNA-Dps complexes a Dps molecule contacts with a DNA segment of ~6 nm in length. Consideration of DNA condensation upon complex formation with small Dps quasi-crystals indicates that DNA may be arranged along the rows of ordered protein molecules on a Dps sheet.

Aberrant aggregation and amyloid formation of tar DNA binding protein (TDP-43) and α-synuclein (αS) underlie frontotemporal dementia (FTD) and Parkinson's disease (PD), respectively. Amyloid inclusions of TDP-43 and αS are also commonly co-observed in amyotrophic lateral sclerosis (ALS), dementia with Lewy bodies (DLB) and Alzheimer disease (AD). Emerging evidence from cellular and animal models show colocalization of the TDP-43 and αS aggregates, raising the possibility of direct interactions and co-aggregation between the two proteins. In this report, we set out to answer this question by investigating the interactions between αS and prion-like pathogenic C-terminal domain of TDP-43 (TDP-43 PrLD). PrLD is an aggregation-prone fragment generated both by alternative splicing as well as aberrant proteolytic cleavage of full length TDP-43. Our results indicate that two proteins interact in a synergistic manner to augment each other's aggregation towards hybrid fibrils. While monomers, oligomers and sonicated fibrils of αS seed TDP-43 PrLD monomers, TDP-43 PrLD fibrils failed to seed αS monomers indicating selectivity in interactions. Furthermore, αS modulates liquid droplets formed by TDP-43 PrLD and RNA to promote insoluble amyloid aggregates. Importantly, the cross-seeded hybrid aggregates show greater cytotoxicity as compared to the individual homotypic aggregates suggesting that the interactions between the two proteins have a discernable impact on cellular functions. Together, these results bring forth insights into TDP-43 PrLD - αS interactions that could help explain clinical and pathological presentations in patients with co-morbidities involving the two proteins.

Liquid-liquid phase separation (LLPS) of proteins is involved in a growing number of cellular processes. Most proteins with LLPS harbor intrinsically disordered regions (IDR), which serve as a guideline to search for cellular proteins that potentially phase separate. Herein, we reveal that oligomerization lowers the barriers for LLPS and could act as a general mechanism to enhance LLPS of proteins domains independent of IDR. Using TDP43 as a model system, we found that deleting its IDR resulted in LLPS that was dependent on the oligomerization of the N-terminal domain (NTD). Replacing TDP43's NTD with other oligomerization domains enhanced the LLPS proportionately to the state of oligomerization. In addition to TDP43, fusing NTD to other globular proteins without known LLPS behavior also drove their phase separation in a manner dependent on oligomerization. Finally, we demonstrate that heterooligomers composed of NTD-fused proteins can be driven into droplets through NTD interactions. Our results potentiate a new paradigm for using oligomerization domains as a signature to systematically identify cellular proteins with LLPS behavior, thus broadening the scope of this exciting research field.

Macrolide antibiotics, such as erythromycin, bind to the nascent peptide exit tunnel (NPET) of the bacterial ribosome and modulate protein synthesis depending on the nascent peptide sequence. Whereas in vitro biochemical and structural methods have been instrumental in dissecting and explaining the molecular details of macrolide-induced peptidyl-tRNA drop-off and ribosome stalling, the dynamic effects of the drugs on ongoing protein synthesis inside live bacterial cells are far less explored. In the present study, we used single-particle tracking of dye-labeled tRNAs to study the kinetics of mRNA translation in the presence of erythromycin, directly inside live Escherichia coli cells. In erythromycin-treated cells, we find that the dwells of elongator tRNAPhe on ribosomes extend significantly, but they occur much more seldom. In contrast, the drug barely affects the ribosome binding events of the initiator tRNAfMet. By overexpressing specific short peptides, we further find context-specific ribosome binding dynamics of tRNAPhe, underscoring the complexity of erythromycin's effect on protein synthesis in bacterial cells.

Linker histones bind to nucleosomes and modify chromatin structure and dynamics as a means of epigenetic regulation. Biophysical studies have shown that chromatin fibers can adopt a plethora of conformations with varying levels of compaction. Linker histone condensation, and its specific binding disposition, has been associated with directly tuning this ensemble of states. However, the atomistic dynamics and quantification of this mechanism remains poorly understood. Here, we present molecular dynamics simulations of octa-nucleosome arrays, based on a cryo-EM structure of the 30-nm chromatin fiber, with and without the globular domains of the H1 linker histone to determine how they influence fiber structures and dynamics. Results show that when bound, linker histones inhibit DNA flexibility and stabilize repeating tetra-nucleosomal units, giving rise to increased chromatin compaction. Furthermore, upon the removal of H1, there is a significant destabilization of this compact structure as the fiber adopts less strained and untwisted states. Interestingly, linker DNA sampling in the octa-nucleosome is exaggerated compared to its mono-nucleosome counterparts, suggesting that chromatin architecture plays a significant role in DNA strain even in the absence of linker histones. Moreover, H1-bound states are shown to have increased stiffness within tetra-nucleosomes, but not between them. This increased stiffness leads to stronger long-range correlations within the fiber, which may result in the propagation of epigenetic signals over longer spatial ranges. These simulations highlight the effects of linker histone binding on the internal dynamics and global structure of poly-nucleosome arrays, while providing physical insight into a mechanism of chromatin compaction.

Checkpoint Kinase 1 (Chk1) prevents DNA damage by adjusting the replication choreography in the face of replication stress. Chk1 depletion provokes slow and asymmetrical fork movement, yet the signals governing such changes remain unclear. We sought to investigate whether poly(ADP-ribose) polymerases (PARPs), key players of the DNA damage response, intervene in the DNA replication of Chk1-depleted cells. We demonstrate that PARP inhibition selectively alleviates the reduced fork elongation rates, without relieving fork asymmetry in Chk1-depleted cells. While the contribution of PARPs to fork elongation is not unprecedented, we found that their role in Chk1-depleted cells extends beyond fork movement. PARP-dependent fork deceleration induced mild dormant origin firing upon Chk1 depletion, augmenting the global rates of DNA synthesis. Thus, we have identified PARPs as novel regulators of replication fork dynamics in Chk1-depleted cells.

The rod-outer-segment guanylyl cyclase 1 (ROS-GC1) is a key transmembrane protein for retinal phototransduction. Mutations of ROS-GC1 correlate with different retinal diseases that often lead to blindness. No structural data are available for ROS-GC1 so far. We performed a 3D-structural analysis of native ROS-GC1 from bovine retina by cross-linking/mass spectrometry (XL-MS) and computational modeling. Absolute quantification and activity measurements of native ROS-GC1 were performed by MS-based assays directly in bovine retina samples. Our data present the first 3D-structural analysis of active, full-length ROS-GC1 derived from bovine retina. We propose a novel domain organization for the intracellular domain ROS-GC1. Our XL-MS data of native ROS-GC1 from rod-outer-segment preparations of bovine retina agree with a dimeric architecture. Our integrated approach can serve as a blueprint for conducting 3D-structural studies of membrane proteins in their native environment.

The late-acting steps of the pathway responsible for the maturation of mitochondrial [4Fe-4S] proteins are still elusive. Three proteins ISCA1, ISCA2 and NFU1 were shown to be implicated in the assembly of [4Fe-4S] clusters and their transfer into mitochondrial apo proteins. We present here a NMR-based study showing a detailed molecular model of the succession of events performed in a coordinated manner by ISCA1, ISCA2 and NFU1 to make [4Fe-4S] clusters available to mitochondrial apo proteins. We show that ISCA1 is the key player of the [4Fe-4S] protein maturation process because of its ability to interact with both NFU1 and ISCA2, which, instead do not interact each other. ISCA1 works as the promoter of the interaction between ISCA2 and NFU1 being able to determine the formation of a transient ISCA1-ISCA2-NFU1 ternary complex. We also show that ISCA1, thanks to its specific interaction with the C-terminal cluster-binding domain of NFU1, drives [4Fe-4S] cluster transfer from the site where the cluster is assembled on the ISCA1-ISCA2 complex to a cluster binding site formed by ISCA1 and NFU1 in the ternary ISCA1-ISCA2-NFU1 complex. Such mechanism guarantees that the [4Fe-4S] cluster can be safely moved from where it is assembled on the ISCA1-ISCA2 complex to NFU1, thereby resulting the [4Fe-4S] cluster available for the mitochondrial apo proteins specifically requiring NFU1 for their maturation.