VarMap: a web tool for mapping genomic coordinates to protein sequence and structure and retrieving protein structural annotations

Abstract Motivation Understanding the protein structural context and patterning on proteins of genomic variants can help to separate benign from pathogenic variants and reveal molecular consequences. However, mapping genomic coordinates to protein structures is non-trivial, complicated by alternative splicing and transcript evidence. Results Here we present VarMap, a web tool for mapping a list of chromosome coordinates to canonical UniProt sequences and associated protein 3D structures, including validation checks, and annotating them with structural information. Availability and implementation https://www.ebi.ac.uk/thornton-srv/databases/VarMap. Supplementary information Supplementary data are available at Bioinformatics online.

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CorGAT: a tool for the functional annotation of SARS-CoV-2 genomes

Bioinformatics ◽

10.1093/bioinformatics/btaa1047 ◽

2020 ◽

Author(s):

Matteo Chiara ◽

Federico Zambelli ◽

Marco Antonio Tangaro ◽

Pietro Mandreoli ◽

David S Horner ◽

...

Keyword(s):

Functional Annotation ◽

Ad Hoc ◽

State Of The Art ◽

Supplementary Information ◽

Genomic Sequences ◽

Supplementary Data ◽

Evolutionary Patterns ◽

Genomic Variants ◽

Art Methods ◽

Available Resources

Abstract Summary While over 200 000 genomic sequences are currently available through dedicated repositories, ad hoc methods for the functional annotation of SARS-CoV-2 genomes do not harness all currently available resources for the annotation of functionally relevant genomic sites. Here, we present CorGAT, a novel tool for the functional annotation of SARS-CoV-2 genomic variants. By comparisons with other state of the art methods we demonstrate that, by providing a more comprehensive and rich annotation, our method can facilitate the identification of evolutionary patterns in the genome of SARS-CoV-2. Availabilityand implementation Galaxy http://corgat.cloud.ba.infn.it/galaxy; software: https://github.com/matteo14c/CorGAT/tree/Revision_V1; docker: https://hub.docker.com/r/laniakeacloud/galaxy_corgat. Supplementary information Supplementary data are available at Bioinformatics online.

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A Basic Molecular Analysis of the Diabetic Antigen GAD by Homology Modelling. Principles of the Method and Understanding of Antigenicity and Binding Sites

Pteridines ◽

10.1515/pteridines.2007.18.1.79 ◽

2007 ◽

Vol 18 (1) ◽

pp. 79-94

Author(s):

Marco Wiltgen ◽

Gernot P. Tilz

Keyword(s):

Active Site ◽

Protein Sequence ◽

Structure Prediction ◽

Structural Information ◽

Homology Modelling ◽

Protein Structures ◽

Dopa Decarboxylase ◽

Gad 65 ◽

Unknown Structure ◽

Introductory Paper

Abstract Functional specificity of a protein is linked to its structure. A growing section of bioinformatics deals with the prediction and visualization of protein 3D structures. In homology modelling, a protein sequence with an unknown structure is aligned with sequences of known protein structures. By exploiting structural information from the known configurations, the new structure can be predicted. In this introductory paper, we will present the principles of homology modelling and demonstrate the method used, by determining the structure of the enzyme glutamic decarboxylase (GAD 65). This protein is an autoantigen involved in several human autoimmune diseases. We will illustrate the different steps in structure prediction of GAD 65 by use of two experimentally determined structures of pig kidney DOPA decarboxylase (one structure in complex with the inhibitor carbidopa) as templates. The resulting model of GAD 65 provides detailed information about the active site of the protein and selected epitopes. By analysis of the interactions between the DOPA decarboxylase with the inhibitor carbidopa, the residues of the GAD 65 active site can be identified via the sequence alignment between DOPA and GAD 65. The locations of known epitopes in the molecule are visualized in special representations giving insights into mechanisms of antigenicity. Hydrophobicity analysis gives first hints for the adherence ability of GAD 65 to the cell membrane. Homology modelling is at present one of the most efficient techniques to provide accurate structural models of proteins. It is expected that in few years, for every new determined protein sequence, at least one member with a known structure of the same protein family will be available, which will steadily increase the importance and applicability of homology modelling.

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MAJIQ-SPEL: web-tool to interrogate classical and complex splicing variations from RNA-Seq data

Bioinformatics ◽

10.1093/bioinformatics/btx565 ◽

2017 ◽

Vol 34 (2) ◽

pp. 300-302 ◽

Cited By ~ 2

Author(s):

Christopher J Green ◽

Matthew R Gazzara ◽

Yoseph Barash

Keyword(s):

Experimental Validation ◽

Ucsc Genome Browser ◽

Supplementary Information ◽

Supplementary Data ◽

Rna Seq ◽

Web Tool ◽

Rt Pcr ◽

Design Algorithm ◽

Gene Isoforms ◽

Downstream Analysis

Abstract Summary Analysis of RNA sequencing (RNA-Seq) data have highlighted the fact that most genes undergo alternative splicing (AS) and that these patterns are tightly regulated. Many of these events are complex, resulting in numerous possible isoforms that quickly become difficult to visualize, interpret and experimentally validate. To address these challenges we developed MAJIQ-SPEL, a web-tool that takes as input local splicing variations (LSVs) quantified from RNA-Seq data and provides users with visualization and quantification of gene isoforms associated with those. Importantly, MAJIQ-SPEL is able to handle both classical (binary) and complex, non-binary, splicing variations. Using a matching primer design algorithm it also suggests to users possible primers for experimental validation by RT-PCR and displays those, along with the matching protein domains affected by the LSV, on UCSC Genome Browser for further downstream analysis. Availability and implementation Program and code will be available athttp://majiq.biociphers.org/majiq-spel. Supplementary information Supplementary data are available atBioinformatics online.

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Streamlined use of protein structures in variant analysis

10.1101/2021.09.10.459756 ◽

2021 ◽

Author(s):

Sandeep Kaur ◽

Neblina Sikta ◽

Andrea Schafferhans ◽

Nicola Bordin ◽

Mark J. Cowley ◽

...

Keyword(s):

Protein Function ◽

Molecular Mechanisms ◽

Structural Information ◽

Protein Structures ◽

Structural Data ◽

Supplementary Information ◽

3D Structures ◽

Link Type ◽

Variant Analysis ◽

Many Sources

AbstractMotivationVariant analysis is a core task in bioinformatics that requires integrating data from many sources. This process can be helped by using 3D structures of proteins, which can provide a spatial context that can provide insight into how variants affect function. Many available tools can help with mapping variants onto structures; but each has specific restrictions, with the result that many researchers fail to benefit from valuable insights that could be gained from structural data.ResultsTo address this, we have created a streamlined system for incorporating 3D structures into variant analysis. Variants can be easily specified via URLs that are easily readable and writable, and use the notation recommended by the Human Genome Variation Society (HGVS). For example, ‘https://aquaria.app/SARS-CoV-2/S/?N501Y’ specifies the N501Y variant of SARS-CoV-2 S protein. In addition to mapping variants onto structures, our system provides summary information from multiple external resources, including COSMIC, CATH-FunVar, and PredictProtein. Furthermore, our system identifies and summarizes structures containing the variant, as well as the variant-position. Our system supports essentially any mutation for any well-studied protein, and uses all available structural data — including models inferred via very remote homology — integrated into a system that is fast and simple to use. By giving researchers easy, streamlined access to a wealth of structural information during variant analysis, our system will help in revealing novel insights into the molecular mechanisms underlying protein function in health and disease.AvailabilityOur resource is freely available at the project home page (https://aquaria.app). After peer review, the code will be openly available via a GPL version 2 license at https://github.com/ODonoghueLab/Aquaria. PSSH2, the database of sequence-to-structure alignments, is also freely available for download at https://zenodo.org/record/[email protected] informationNone.

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Analysis of several key factors influencing deep learning-based inter-residue contact prediction

Bioinformatics ◽

10.1093/bioinformatics/btz679 ◽

2019 ◽

Cited By ~ 1

Author(s):

Tianqi Wu ◽

Jie Hou ◽

Badri Adhikari ◽

Jianlin Cheng

Keyword(s):

Deep Learning ◽

Structural Information ◽

Protein Structures ◽

Supplementary Information ◽

Prediction Methods ◽

Key Factors ◽

Sequence Alignments ◽

Multiple Sequence ◽

Contact Prediction ◽

Ab Initio Approach

Abstract Motivation Deep learning has become the dominant technology for protein contact prediction. However, the factors that affect the performance of deep learning in contact prediction have not been systematically investigated. Results We analyzed the results of our three deep learning-based contact prediction methods (MULTICOM-CLUSTER, MULTICOM-CONSTRUCT and MULTICOM-NOVEL) in the CASP13 experiment and identified several key factors [i.e. deep learning technique, multiple sequence alignment (MSA), distance distribution prediction and domain-based contact integration] that influenced the contact prediction accuracy. We compared our convolutional neural network (CNN)-based contact prediction methods with three coevolution-based methods on 75 CASP13 targets consisting of 108 domains. We demonstrated that the CNN-based multi-distance approach was able to leverage global coevolutionary coupling patterns comprised of multiple correlated contacts for more accurate contact prediction than the local coevolution-based methods, leading to a substantial increase of precision by 19.2 percentage points. We also tested different alignment methods and domain-based contact prediction with the deep learning contact predictors. The comparison of the three methods showed deeper sequence alignments and the integration of domain-based contact prediction with the full-length contact prediction improved the performance of contact prediction. Moreover, we demonstrated that the domain-based contact prediction based on a novel ab initio approach of parsing domains from MSAs alone without using known protein structures was a simple, fast approach to improve contact prediction. Finally, we showed that predicting the distribution of inter-residue distances in multiple distance intervals could capture more structural information and improve binary contact prediction. Availability and implementation https://github.com/multicom-toolbox/DNCON2/. Supplementary information Supplementary data are available at Bioinformatics online.

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A structural homology approach for computational protein design with flexible backbone

Bioinformatics ◽

10.1093/bioinformatics/bty975 ◽

2018 ◽

Vol 35 (14) ◽

pp. 2418-2426 ◽

Cited By ~ 2

Author(s):

David Simoncini ◽

Kam Y J Zhang ◽

Thomas Schiex ◽

Sophie Barbe

Keyword(s):

Amino Acid ◽

Protein Design ◽

Protein Sequence ◽

Critical Role ◽

Protein Structures ◽

Amino Acid Sequences ◽

Computational Protein Design ◽

Supplementary Information ◽

Structural Homology ◽

Homologous Proteins

Abstract Motivation Structure-based Computational Protein design (CPD) plays a critical role in advancing the field of protein engineering. Using an all-atom energy function, CPD tries to identify amino acid sequences that fold into a target structure and ultimately perform a desired function. Energy functions remain however imperfect and injecting relevant information from known structures in the design process should lead to improved designs. Results We introduce Shades, a data-driven CPD method that exploits local structural environments in known protein structures together with energy to guide sequence design, while sampling side-chain and backbone conformations to accommodate mutations. Shades (Structural Homology Algorithm for protein DESign), is based on customized libraries of non-contiguous in-contact amino acid residue motifs. We have tested Shades on a public benchmark of 40 proteins selected from different protein families. When excluding homologous proteins, Shades achieved a protein sequence recovery of 30% and a protein sequence similarity of 46% on average, compared with the PFAM protein family of the target protein. When homologous structures were added, the wild-type sequence recovery rate achieved 93%. Availability and implementation Shades source code is available at https://bitbucket.org/satsumaimo/shades as a patch for Rosetta 3.8 with a curated protein structure database and ITEM library creation software. Supplementary information Supplementary data are available at Bioinformatics online.

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eFORGE v2.0: updated analysis of cell type-specific signal in epigenomic data

Bioinformatics ◽

10.1093/bioinformatics/btz456 ◽

2019 ◽

Vol 35 (22) ◽

pp. 4767-4769 ◽

Cited By ~ 9

Author(s):

Charles E Breeze ◽

Alex P Reynolds ◽

Jenny van Dongen ◽

Ian Dunham ◽

John Lazar ◽

...

Keyword(s):

Supplementary Information ◽

Supplementary Data ◽

Cell Type ◽

Web Tool ◽

Methylation Analysis ◽

450K Array ◽

Composition Effects ◽

Epigenome Editing ◽

Cell Type Specific ◽

Dna Methylation Analysis

Abstract Summary The Illumina Infinium EPIC BeadChip is a new high-throughput array for DNA methylation analysis, extending the earlier 450k array by over 400 000 new sites. Previously, a method named eFORGE was developed to provide insights into cell type-specific and cell-composition effects for 450k data. Here, we present a significantly updated and improved version of eFORGE that can analyze both EPIC and 450k array data. New features include analysis of chromatin states, transcription factor motifs and DNase I footprints, providing tools for epigenome-wide association study interpretation and epigenome editing. Availability and implementation eFORGE v2.0 is implemented as a web tool available from https://eforge.altiusinstitute.org and https://eforge-tf.altiusinstitute.org/. Supplementary information Supplementary data are available at Bioinformatics online.

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A novel sequence alignment algorithm based on deep learning of the protein folding code

Bioinformatics ◽

10.1093/bioinformatics/btaa810 ◽

2020 ◽

Cited By ~ 1

Author(s):

Mu Gao ◽

Jeffrey Skolnick

Keyword(s):

Protein Folding ◽

Deep Learning ◽

Sequence Alignment ◽

Protein Sequence ◽

Protein Structures ◽

Supplementary Information ◽

Alignment Algorithm ◽

Sequence Alignments ◽

Alignment Algorithms ◽

Structural Alignments

Abstract Motivation From evolutionary interference, function annotation to structural prediction, protein sequence comparison has provided crucial biological insights. While many sequence alignment algorithms have been developed, existing approaches often cannot detect hidden structural relationships in the ‘twilight zone’ of low sequence identity. To address this critical problem, we introduce a computational algorithm that performs protein Sequence Alignments from deep-Learning of Structural Alignments (SAdLSA, silent ‘d’). The key idea is to implicitly learn the protein folding code from many thousands of structural alignments using experimentally determined protein structures. Results To demonstrate that the folding code was learned, we first show that SAdLSA trained on pure α-helical proteins successfully recognizes pairs of structurally related pure β-sheet protein domains. Subsequent training and benchmarking on larger, highly challenging datasets show significant improvement over established approaches. For challenging cases, SAdLSA is ∼150% better than HHsearch for generating pairwise alignments and ∼50% better for identifying the proteins with the best alignments in a sequence library. The time complexity of SAdLSA is O(N) thanks to GPU acceleration. Availability and implementation Datasets and source codes of SAdLSA are available free of charge for academic users at http://sites.gatech.edu/cssb/sadlsa/. Contact [email protected] or [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

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ssbio: a Python framework for structural systems biology

Bioinformatics ◽

10.1093/bioinformatics/bty077 ◽

2018 ◽

Vol 34 (12) ◽

pp. 2155-2157 ◽

Cited By ~ 15

Author(s):

Nathan Mih ◽

Elizabeth Brunk ◽

Ke Chen ◽

Edward Catoiu ◽

Anand Sastry ◽

...

Keyword(s):

Structural Information ◽

Protein Structures ◽

Structural Data ◽

Third Party ◽

Supplementary Information ◽

Scale Models ◽

Protein Properties ◽

Scale Network ◽

Structural Systems Biology ◽

Genome Scale

Abstract Summary Working with protein structures at the genome-scale has been challenging in a variety of ways. Here, we present ssbio, a Python package that provides a framework to easily work with structural information in the context of genome-scale network reconstructions, which can contain thousands of individual proteins. The ssbio package provides an automated pipeline to construct high quality genome-scale models with protein structures (GEM-PROs), wrappers to popular third-party programs to compute associated protein properties, and methods to visualize and annotate structures directly in Jupyter notebooks, thus lowering the barrier of linking 3D structural data with established systems workflows. Availability and implementation ssbio is implemented in Python and available to download under the MIT license at http://github.com/SBRG/ssbio. Documentation and Jupyter notebook tutorials are available at http://ssbio.readthedocs.io/en/latest/. Interactive notebooks can be launched using Binder at https://mybinder.org/v2/gh/SBRG/ssbio/master?filepath=Binder.ipynb. Supplementary information Supplementary data are available at Bioinformatics online.

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MMseqs2 desktop and local web server app for fast, interactive sequence searches

Bioinformatics ◽

10.1093/bioinformatics/bty1057 ◽

2019 ◽

Vol 35 (16) ◽

pp. 2856-2858 ◽

Cited By ~ 17

Author(s):

Milot Mirdita ◽

Martin Steinegger ◽

Johannes Söding

Keyword(s):

Protein Sequence ◽

Response Times ◽

Web Server ◽

Supplementary Information ◽

Supplementary Data ◽

Server Application ◽

The Web

Abstract Summary The MMseqs2 desktop and web server app facilitates interactive sequence searches through custom protein sequence and profile databases on personal workstations. By eliminating MMseqs2’s runtime overhead, we reduced response times to a few seconds at sensitivities close to BLAST. Availability and implementation The app is easy to install for non-experts. GPLv3-licensed code, pre-built desktop app packages for Windows, MacOS and Linux, Docker images for the web server application and a demo web server are available at https://search.mmseqs.com. Supplementary information Supplementary data are available at Bioinformatics online.

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