PredictProtein - Predicting Protein Structure and Function for 29 Years.

Since 1992 PredictProtein (https://predictprotein.org) is a one-stop online resource for protein sequence analysis with its main site hosted at the Luxembourg Centre for Systems Biomedicine (LCSB) and queried monthly by over 3,000 users in 2020. PredictProtein was the first Internet server for protein predictions. It pioneered combining evolutionary information and machine learning. Given a protein sequence as input, the server outputs multiple sequence alignments, predictions of protein structure in 1D and 2D (secondary structure, solvent accessibility, transmembrane segments, disordered regions, protein flexibility, and disulfide bridges) and predictions of protein function (functional effects of sequence variation or point mutations, Gene Ontology (GO) terms, subcellular localization, and protein-, RNA-, and DNA binding). PredictProtein's infrastructure has moved to the LCSB increasing throughput; the use of MMseqs2 sequence search reduced runtime five-fold (apparently without lowering performance of prediction methods); user interface elements improved usability, and new prediction methods were added. PredictProtein recently included predictions from deep learning embeddings (GO and secondary structure) and a method for the prediction of proteins and residues binding DNA, RNA, or other proteins. PredictProtein.org aspires to provide reliable predictions to computational and experimental biologists alike. All scripts and methods are freely available for offline execution in high-throughput settings.

MSAViewer: interactive JavaScript visualization of multiple sequence alignments.

The MSAViewer is a quick and easy visualization and analysis JavaScript component for Multiple Sequence Alignment data of any size. Core features include interactive navigation through the alignment, application of popular color schemes, sorting, selecting and filtering. The MSAViewer is 'web ready': written entirely in JavaScript, compatible with modern web browsers and does not require any specialized software. The MSAViewer is part of the BioJS collection of components. AVAILABILITY AND IMPLEMENTATION: The MSAViewer is released as open source software under the Boost Software License 1.0. Documentation, source code and the viewer are available at http://msa.biojs.net/Supplementary information: Supplementary data are available at Bioinformatics online. CONTACT: msa@bio.sh.

Anatomy of BioJS, an open source community for the life sciences.

BioJS is an open source software project that develops visualization tools for different types of biological data. Here we report on the factors that influenced the growth of the BioJS user and developer community, and outline our strategy for building on this growth. The lessons we have learned on BioJS may also be relevant to other open source software projects.

BioJS: an open source standard for biological visualisation - its status in 2014.

BioJS is a community-based standard and repository of functional components to represent biological information on the web. The development of BioJS has been prompted by the growing need for bioinformatics visualisation tools to be easily shared, reused and discovered. Its modular architecture makes it easy for users to find a specific functionality without needing to know how it has been built, while components can be extended or created for implementing new functionality. The BioJS community of developers currently provides a range of functionality that is open access and freely available. A registry has been set up that categorises and provides installation instructions and testing facilities at http://www.ebi.ac.uk/tools/biojs/. The source code for all components is available for ready use at https://github.com/biojs/biojs.

HeatMapViewer: interactive display of 2D data in biology.

SUMMARY: The HeatMapViewer is a BioJS component that lays-out and renders two-dimensional (2D) plots or heat maps that are ideally suited to visualize matrix formatted data in biology such as for the display of microarray experiments or the outcome of mutational studies and the study of SNP-like sequence variants. It can be easily integrated into documents and provides a powerful, interactive way to visualize heat maps in web applications. The software uses a scalable graphics technology that adapts the visualization component to any required resolution, a useful feature for a presentation with many different data-points. The component can be applied to present various biological data types. Here, we present two such cases - showing gene expression data and visualizing mutability landscape analysis. AVAILABILITY: https://github.com/biojs/biojs; http://dx.doi.org/10.5281/zenodo.7706.

PredictProtein--an open resource for online prediction of protein structural and functional features.

PredictProtein is a meta-service for sequence analysis that has been predicting structural and functional features of proteins since 1992. Queried with a protein sequence it returns: multiple sequence alignments, predicted aspects of structure (secondary structure, solvent accessibility, transmembrane helices (TMSEG) and strands, coiled-coil regions, disulfide bonds and disordered regions) and function. The service incorporates analysis methods for the identification of functional regions (ConSurf), homology-based inference of Gene Ontology terms (metastudent), comprehensive subcellular localization prediction (LocTree3), protein-protein binding sites (ISIS2), protein-polynucleotide binding sites (SomeNA) and predictions of the effect of point mutations (non-synonymous SNPs) on protein function (SNAP2). Our goal has always been to develop a system optimized to meet the demands of experimentalists not highly experienced in bioinformatics. To this end, the PredictProtein results are presented as both text and a series of intuitive, interactive and visually appealing figures. The web server and sources are available at http://ppopen.rostlab.org.

LocTree3 prediction of localization.

The prediction of protein sub-cellular localization is an important step toward elucidating protein function. For each query protein sequence, LocTree2 applies machine learning (profile kernel SVM) to predict the native sub-cellular localization in 18 classes for eukaryotes, in six for bacteria and in three for archaea. The method outputs a score that reflects the reliability of each prediction. LocTree2 has performed on par with or better than any other state-of-the-art method. Here, we report the availability of LocTree3 as a public web server. The server includes the machine learning-based LocTree2 and improves over it through the addition of homology-based inference. Assessed on sequence-unique data, LocTree3 reached an 18-state accuracy Q18=80±3% for eukaryotes and a six-state accuracy Q6=89±4% for bacteria. The server accepts submissions ranging from single protein sequences to entire proteomes. Response time of the unloaded server is about 90 s for a 300-residue eukaryotic protein and a few hours for an entire eukaryotic proteome not considering the generation of the alignments. For over 1000 entirely sequenced organisms, the predictions are directly available as downloads. The web server is available at http://www.rostlab.org/services/loctree3.

FeatureViewer, a BioJS component for visualization of position-based annotations in protein sequences.

SUMMARY: FeatureViewer is a BioJS component that lays out, maps, orients, and renders position-based annotations for protein sequences. This component is highly flexible and customizable, allowing the presentation of annotations by rows, all centered, or distributed in non-overlapping tracks. It uses either lines or shapes for sites and rectangles for regions. The result is a powerful visualization tool that can be easily integrated into web applications as well as documents as it provides an export-to-image functionality. AVAILABILITY: https://github.com/biojs/biojs/blob/master/src/main/javascript/Biojs.FeatureViewer.js; http://dx.doi.org/10.5281/zenodo.7719.

Cloud prediction of protein structure and function with PredictProtein for Debian.

We report the release of PredictProtein for the Debian operating system and derivatives, such as Ubuntu, Bio-Linux, and Cloud BioLinux. The PredictProtein suite is available as a standard set of open source Debian packages. The release covers the most popular prediction methods from the Rost Lab, including methods for the prediction of secondary structure and solvent accessibility (profphd), nuclear localization signals (predictnls), and intrinsically disordered regions (norsnet). We also present two case studies that successfully utilize PredictProtein packages for high performance computing in the cloud: the first analyzes protein disorder for whole organisms, and the second analyzes the effect of all possible single sequence variants in protein coding regions of the human genome.

New in protein structure and function annotation: hotspots, single nucleotide polymorphisms and the 'Deep Web'.

The rapidly increasing quantity of protein sequence data continues to widen the gap between available sequences and annotations. Comparative modeling suggests some aspects of the 3D structures of approximately half of all known proteins; homology- and network-based inferences annotate some aspect of function for a similar fraction of the proteome. For most known protein sequences, however, there is detailed knowledge about neither their function nor their structure. Comprehensive efforts towards the expert curation of sequence annotations have failed to meet the demand of the rapidly increasing number of available sequences. Only the automated prediction of protein function in the absence of homology can close the gap between available sequences and annotations in the foreseeable future. This review focuses on two novel methods for automated annotation, and briefly presents an outlook on how modern web software may revolutionize the field of protein sequence annotation. First, predictions of protein binding sites and functional hotspots, and the evolution of these into the most successful type of prediction of protein function from sequence will be discussed. Second, a new tool, comprehensive in silico mutagenesis, which contributes important novel predictions of function and at the same time prepares for the onset of the next sequencing revolution, will be described. While these two new sub-fields of protein prediction represent the breakthroughs that have been achieved methodologically, it will then be argued that a different development might further change the way biomedical researchers benefit from annotations: modern web software can connect the worldwide web in any browser with the 'Deep Web' (ie, proprietary data resources). The availability of this direct connection, and the resulting access to a wealth of data, may impact drug discovery and development more than any existing method that contributes to protein annotation.

Structural genomics reveals EVE as a new ASCH/PUA-related domain.

We report on several proteins recently solved by structural genomics consortia, in particular by the Northeast Structural Genomics consortium (NESG). The proteins considered in this study differ substantially in their sequences but they share a similar structural core, characterized by a pseudobarrel five-stranded beta sheet. This core corresponds to the PUA domain-like architecture in the SCOP database. By connecting sequence information with structural knowledge, we characterize a new subgroup of these proteins that we propose to be distinctly different from previously described PUA domain-like domains such as PUA proper or ASCH. We refer to these newly defined domains as EVE. Although EVE may have retained the ability of PUA domains to bind RNA, the available experimental and computational data suggests that both the details of its molecular function and its cellular function differ from those of other PUA domain-like domains. This study of EVE and its relatives illustrates how the combination of structure and genomics creates new insights by connecting a cornucopia of structures that map to the same evolutionary potential. Primary sequence information alone would have not been sufficient to reveal these evolutionary links.

Improved disorder prediction by combination of orthogonal approaches.

UNLABELLED: Disordered proteins are highly abundant in regulatory processes such as transcription and cell-signaling. Different methods have been developed to predict protein disorder often focusing on different types of disordered regions. Here, we present MD, a novel META-Disorder prediction method that molds various sources of information predominantly obtained from orthogonal prediction methods, to significantly improve in performance over its constituents. In sustained cross-validation, MD not only outperforms its origins, but it also compares favorably to other state-of-the-art prediction methods in a variety of tests that we applied. AVAILABILITY: http://www.rostlab.org/services/md/

SNAP predicts effect of mutations on protein function.

Many non-synonymous single nucleotide polymorphisms (nsSNPs) in humans are suspected to impact protein function. Here, we present a publicly available server implementation of the method SNAP (screening for non-acceptable polymorphisms) that predicts the functional effects of single amino acid substitutions. SNAP identifies over 80% of the non-neutral mutations at 77% accuracy and over 76% of the neutral mutations at 80% accuracy at its default threshold. Each prediction is associated with a reliability index that correlates with accuracy and thereby enables experimentalists to zoom into the most promising predictions.

Create and assess protein networks through molecular characteristics of individual proteins.

MOTIVATION: The study of biological systems, pathways and processes relies increasingly on analyses of networks. Most often, such analyses focus on network topology, thereby treating all proteins or genes as identical, featureless nodes. Integrating molecular data and insights about the qualities of individual proteins into the analysis may enhance our ability to decipher biological pathways and processes. RESULTS: Here, we introduce a novel platform for data integration that generates networks on the macro system-level, analyzes the molecular characteristics of each protein on the micro level, and then combines the two levels by using the molecular characteristics to assess networks. It also annotates the function and subcellular localization of each protein and displays the process on an image of a cell, rendering each protein in its respective cellular compartment. By thus visualizing the network in a cellular context we are able to analyze pathways and processes in a novel way. As an example, we use the system to analyze proteins implicated with Alzheimers disease and show how the integrated view corroborates previous observations and how it helps in the formulation of new hypotheses regarding the molecular underpinnings of the disease. AVAILABILITY: http://www.rostlab.org/services/pinat.

PROFbval: predict flexible and rigid residues in proteins.

UNLABELLED: The mobility of a residue on the protein surface is closely linked to its function. The identification of extremely rigid or flexible surface residues can therefore contribute information crucial for solving the complex problem of identifying functionally important residues in proteins. Mobility is commonly measured by B-value data from high-resolution three-dimensional X-ray structures. Few methods predict B-values from sequence. Here, we present PROFbval, the first web server to predict normalized B-values from amino acid sequence. The server handles amino acid sequences (or alignments) as input and outputs normalized B-value and two-state (flexible/rigid) predictions. The server also assigns a reliability index for each prediction. For example, PROFbval correctly identifies residues in active sites on the surface of enzymes as particularly rigid. AVAILABILITY: http://www.rostlab.org/services/profbval CONTACT: profbval@rostlab.org SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Epitome: database of structure-inferred antigenic epitopes.

Immunoglobulin molecules specifically recognize particular areas on the surface of proteins. These areas are commonly dubbed B-cell epitopes. The identification of epitopes in proteins is important both for the design of experiments and vaccines. Additionally, the interactions between epitopes and antibodies have often served as a model for protein-protein interactions. One of the main obstacles in creating a database of antigen-antibody interactions is the difficulty in distinguishing between antigenic and non-antigenic interactions. Antigenic interactions involve specific recognition sites on the antibody's surface, while non-antigenic interactions are between a protein and any other site on the antibody. To solve this problem, we performed a comparative analysis of all protein-antibody complexes for which structures have been experimentally determined. Additionally, we developed a semi-automated tool that identified the antigenic interactions within the known antigen-antibody complex structures. We compiled those interactions into Epitome, a database of structure-inferred antigenic residues in proteins. Epitome consists of all known antigen/antibody complex structures, a detailed description of the residues that are involved in the interactions, and their sequence/structure environments. Interactions can be visualized using an interface to Jmol. The database is available at http://www.rostlab.org/services/epitome/.

The PredictProtein server.

PredictProtein (http://www.predictprotein.org) is an Internet service for sequence analysis and the prediction of protein structure and function. Users submit protein sequences or alignments; PredictProtein returns multiple sequence alignments, PROSITE sequence motifs, low-complexity regions (SEG), nuclear localization signals, regions lacking regular structure (NORS) and predictions of secondary structure, solvent accessibility, globular regions, transmembrane helices, coiled-coil regions, structural switch regions, disulfide-bonds, sub-cellular localization and functional annotations. Upon request fold recognition by prediction-based threading, CHOP domain assignments, predictions of transmembrane strands and inter-residue contacts are also available. For all services, users can submit their query either by electronic mail or interactively via the World Wide Web.