firestar—prediction of functionally important residues using structural templates and alignment reliability

Here we present firestar, an expert system for predicting ligand-binding residues in protein structures. The server provides a method for extrapolating from the large inventory of functionally important residues organized in the FireDB database and adds information about the local conservation of potential-binding residues. The interface allows users to make queries by protein sequence or structure. The user can access pairwise and multiple alignments with structures that have relevant functionally important binding sites. The results are presented in a series of easy to read displays that allow users to compare binding residue conservation across homologous proteins. The binding site residues can also be viewed with molecular visualization tools. One feature of firestar is that it can be used to evaluate the biological relevance of small molecule ligands present in PDB structures. With the server it is easy to discern whether small molecule binding is conserved in homologous structures. We found this facility particularly useful during the recent assessment of CASP7 function prediction. Availability: http://firedb.bioinfo.cnio.es/Php/FireStar.php

Proteomics studies confirm the presence of alternative protein isoforms on a large scale

Stably expressed alternatively-spliced protein isoforms are produced on a genome-wide scale in Drosophila

Clinical variant interpretation and biologically relevant reference transcripts.

Clinical variant interpretation is highly dependent on the choice of reference transcript. Although the longest transcript has traditionally been chosen as the reference, APPRIS principal and MANE Select transcripts, biologically supported reference sequences, are now available. In this study, we show that MANE Select and APPRIS principal transcripts are the best reference transcripts for clinical variation. APPRIS principal and MANE Select transcripts capture almost all ClinVar pathogenic variants, and they are particularly powerful over the 94% of coding genes in which they agree. We find that a vanishingly small number of ClinVar pathogenic variants affect alternative protein products. Alternative isoforms that are likely to be clinically relevant can be predicted using TRIFID scores, the highest scoring alternative transcripts are almost 700 times more likely to house pathogenic variants. We believe that APPRIS, MANE and TRIFID are essential tools for clinical variant interpretation.Research reported in this publication was supported by the National Human Genome Research Institute of the National Institutes of Health under Award Number U24HG007234. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Research was also supported by the following grants: PGC2018-097019-B-I00/Ministry of Science, Innovation and Universities; IPT17/0019/Carlos III Institute of Health-Fondo de Investigación Sanitaria; HR17-00247/'la Caixa' Foundation. We would like to thank the referees for their constructive suggestions. We believe they have improved the paper considerably.S

firestar—advances in the prediction of functionally important residues

firestar is a server for predicting catalytic and ligand-binding residues in protein sequences. Here, we present the important developments since the first release of firestar. Previous versions of the server required human interpretation of the results; the server is now fully automatized. firestar has been implemented as a web service and can now be run in high-throughput mode. Prediction coverage has been greatly improved with the extension of the FireDB database and the addition of alignments generated by HHsearch. Ligands in FireDB are now classified for biological relevance. Many of the changes have been motivated by the critical assessment of techniques for protein structure prediction (CASP) ligand-binding prediction experiment, which provided us with a framework to test the performance of firestar. URL: http://firedb.bioinfo.cnio.es/Php/FireStar.php

APPRIS principal isoforms and MANE Select transcripts define reference splice variants.

Selecting the splice variant that best represents a coding gene is a crucial first step in many experimental analyses, and vital for mapping clinically relevant variants. This study compares the longest isoforms, MANE Select transcripts, APPRIS principal isoforms, and expression data, and aims to determine which method is best for selecting biological important reference splice variants for large-scale analyses. Proteomics analyses and human genetic variation data suggest that most coding genes have a single main protein isoform. We show that APPRIS principal isoforms and MANE Select transcripts best describe these main cellular isoforms, and find that using the longest splice variant as the representative is a poor strategy. Exons unique to the longest splice isoforms are not under selective pressure, and so are unlikely to be functionally relevant. Expression data are also a poor means of selecting the main splice variant. APPRIS principal and MANE Select exons are under purifying selection, while exons specific to alternative transcripts are not. There are MANE and APPRIS representatives for almost 95% of genes, and where they agree they are particularly effective, coinciding with the main proteomics isoform for over 98.2% of genes. APPRIS principal isoforms for human, mouse and other model species can be downloaded from the APPRIS database (https://appris.bioinfo.cnio.es), GENCODE genes (https://www.gencodegenes.org/) and the Ensembl website (https://www.ensembl.org). MANE Select transcripts for the human reference set are available from the Ensembl, GENCODE and RefSeq databases (https://www.ncbi.nlm.nih.gov/refseq/). Lists of splice variants where MANE and APPRIS coincide are available from the APPRIS database. Supplementary data are available at Bioinformatics online.This paper was published as part of a special issue financially supported by ECCB2022. This work was supported by the National Human Genome Research Institute of the National Institutes of Health under Award Number U24HG007234. This work was also supported by the following grants: PGC2018-097019-B-I00/Ministry of Science, Innovation and Universities; IPT17/0019/Carlos III Institute of Health-Fondo de Investigacio´n Sanitaria and HR17-00247/‘la Caixa’ Foundation. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.S

APPRIS: selecting functionally important isoforms.

APPRIS (https://appris.bioinfo.cnio.es) is a well-established database housing annotations for protein isoforms for a range of species. APPRIS selects principal isoforms based on protein structure and function features and on cross-species conservation. Most coding genes produce a single main protein isoform and the principal isoforms chosen by the APPRIS database best represent this main cellular isoform. Human genetic data, experimental protein evidence and the distribution of clinical variants all support the relevance of APPRIS principal isoforms. APPRIS annotations and principal isoforms have now been expanded to 10 model organisms. In this paper we highlight the most recent updates to the database. APPRIS annotations have been generated for two new species, cow and chicken, the protein structural information has been augmented with reliable models from the EMBL-EBI AlphaFold database, and we have substantially expanded the confirmatory proteomics evidence available for the human genome. The most significant change in APPRIS has been the implementation of TRIFID functional isoform scores. TRIFID functional scores are assigned to all splice isoforms, and APPRIS uses the TRIFID functional scores and proteomics evidence to determine principal isoforms when core methods cannot.National Human Genome Research Institute of the National Institutes of Health [2 U41 HG007234]; Spanish Ministry of Science, Innovation and Universities [PGC2018-097019-B-I00]; Carlos III Institute of Health-Fondo de Investigacion Sanitaria [PRB3 ´ (IPT17/0019––ISCIII-SGEFI/ERDF, ProteoRed]; ‘la Caixa’ Banking Foundation [HR17-00247]. Funding for open access charge: National Human Genome Research Institute.S

Loose ends: almost one in five human genes still have unresolved coding status

Seventeen years after the sequencing of the human genome, the human proteome is still under revision. One in eight of the 22 210 coding genes listed by the Ensembl/GENCODE, RefSeq and UniProtKB reference databases are annotated differently across the three sets. We have carried out an in-depth investigation on the 2764 genes classified as coding by one or more sets of manual curators and not coding by others. Data from large-scale genetic variation analyses suggests that most are not under protein-like purifying selection and so are unlikely to code for functional proteins. A further 1470 genes annotated as coding in all three reference sets have characteristics that are typical of non-coding genes or pseudogenes. These potential non-coding genes also appear to be undergoing neutral evolution and have considerably less supporting transcript and protein evidence than other coding genes. We believe that the three reference databases currently overestimate the number of human coding genes by at least 2000, complicating and adding noise to large-scale biomedical experiments. Determining which potential non-coding genes do not code for proteins is a difficult but vitally important task since the human reference proteome is a fundamental pillar of most basic research and supports almost all large-scale biomedical projects.National Institutes of Health [2 U41 HG007234 to I.J., L.M., J.M.R. and M.L.T., R01 HG004037 to I.J.]. Funding for open access charge: NIH [2 U41 HG007234].S

Sequence-based feature prediction and annotation of proteins

The combination of prediction tools in complex workflows and pipelines facilitates prediction of protein features from sequence

APPRIS 2017: principal isoforms for multiple gene sets

The APPRIS database (http://appris-tools.org) uses protein structural and functional features and information from cross-species conservation to annotate splice isoforms in protein-coding genes. APPRIS selects a single protein isoform, the ‘principal’ isoform, as the reference for each gene based on these annotations. A single main splice isoform reflects the biological reality for most protein coding genes and APPRIS principal isoforms are the best predictors of these main proteins isoforms. Here, we present the updates to the database, new developments that include the addition of three new species (chimpanzee, Drosophila melangaster and Caenorhabditis elegans), the expansion of APPRIS to cover the RefSeq gene set and the UniProtKB proteome for six species and refinements in the core methods that make up the annotation pipeline. In addition APPRIS now provides a measure of reliability for individual principal isoforms and updates with each release of the GENCODE/Ensembl and RefSeq reference sets. The individual GENCODE/Ensembl, RefSeq and UniProtKB reference gene sets for six organisms have been merged to produce common sets of splice variants.National Institutes of Health [U41 HG007234, 2U41 HG007234]; Spanish Ministry of Economics and Competitiveness [BIO2015-67580-P]; SpanishNational Institute of Bioinformatics (www.inab.org) [INB-ISCIII, PRB2 to J.M.R.]; ProteoRed [IPT13/0001-ISCIII-SGEFI/FEDER to J.V.]; Joint BSC-IRB-CRG Program in Computation Biology and Award Severo Ochoa [SEV 2015-0493 to A.V.]. Funding for open access charge: U.S. Department of Health and Human Services; National Institutes of Health; National Human Genome Research Institute [2U41 HG007234].Peer ReviewedPostprint (published version