The Consensus Constrained TOPology prediction (CCTOP; http://cctop.enzim.ttk.mta.hu) server is a web-based application providing transmembrane topology prediction. In addition to utilizing 10 different state-of-the-art topology prediction methods, the CCTOP server incorporates topology information from existing experimental and computational sources available in the PDBTM, TOPDB and TOPDOM databases using the probabilistic framework of hidden Markov model. The server provides the option to precede the topology prediction with signal peptide prediction and transmembrane-globular protein discrimination. The initial result can be recalculated by (de)selecting any of the prediction methods or mapped experiments or by adding user specified constraints. CCTOP showed superior performance to existing approaches. The reliability of each prediction is also calculated, which correlates with the accuracy of the per protein topology prediction. The prediction results and the collected experimental information are visualized on the CCTOP home page and can be downloaded in XML format. Programmable access of the CCTOP server is also available, and an example of client-side script is provided.

Short Linear Motifs (SLiMs) are the smallest structural and functional components of modular eukaryotic proteins. They are also the most abundant, especially when considering post-translational modifications. As well as being found throughout the cell as part of regulatory processes, SLiMs are extensively mimicked by intracellular pathogens. At the heart of the Eukaryotic Linear Motif (ELM) Resource is a representative (not comprehensive) database. The ELM entries are created by a growing community of skilled annotators and provide an introduction to linear motif functionality for biomedical researchers. The 2024 ELM update includes 346 novel motif instances in areas ranging from innate immunity to both protein and RNA degradation systems. In total, 39 classes of newly annotated motifs have been added, and another 17 existing entries have been updated in the database. The 2024 ELM release now includes 356 motif classes incorporating 4283 individual motif instances manually curated from 4274 scientific publications and including >700 links to experimentally determined 3D structures. In a recent development, the InterPro protein module resource now also includes ELM data. ELM is available at: http://elm.eu.org.

The Database of Intrinsically Disordered Proteins (DisProt, URL: https://disprot.org) is the major repository of manually curated annotations of intrinsically disordered proteins and regions from the literature. We report here recent updates of DisProt version 9, including a restyled web interface, refactored Intrinsically Disordered Proteins Ontology (IDPO), improvements in the curation process and significant content growth of around 30%. Higher quality and consistency of annotations is provided by a newly implemented reviewing process and training of curators. The increased curation capacity is fostered by the integration of DisProt with APICURON, a dedicated resource for the proper attribution and recognition of biocuration efforts. Better interoperability is provided through the adoption of the Minimum Information About Disorder (MIADE) standard, an active collaboration with the Gene Ontology (GO) and Evidence and Conclusion Ontology (ECO) consortia and the support of the ELIXIR infrastructure.

Transmembrane proteins have important roles in cells, as they are involved in energy production, signal transduction, cell-cell interaction, cell-cell communication and more. In human cells, they are frequently targets for pharmaceuticals; therefore, knowledge about their properties and structure is crucial. Topology of transmembrane proteins provide a low resolution structural information, which can be a starting point for either laboratory experiments or modelling their 3D structures. Here, we present a database of the human α-helical transmembrane proteome, including the predicted and/or experimentally established topology of each transmembrane protein, together with the reliability of the prediction. In order to distinguish transmembrane proteins in the proteome as well as for topology prediction, we used a newly developed consensus method (CCTOP) that incorporates recent state of the art methods, with tested accuracies on a novel human benchmark protein set. CCTOP utilizes all available structure and topology data as well as bioinformatical evidences for topology prediction in a probabilistic framework provided by the hidden Markov model. This method shows the highest accuracy (98.5 % for discrinimating between transmembrane and non-transmembrane proteins and 84 % for per protein topology prediction) among the dozen tested topology prediction methods. Analysis of the human proteome with the CCTOP indicates that it contains 4998 (26 %) transmembrane proteins. Besides predicting topology, reliability of the predictions is estimated as well, and it is demonstrated that the per protein prediction accuracies of more than 60 % of the predictions are over 98 % on the benchmark sets and most probably on the predicted human transmembrane proteome too. Here, we present the most accurate prediction of the human transmembrane proteome together with the experimental topology data. These data, as well as various statistics about the human transmembrane proteins and their topologies can be downloaded from and can be visualized at the website of the human transmembrane proteome ( http://htp.enzim.hu ). This article was reviewed by Dr. Sandor Pongor, Dr. Michael Galperin and Dr. Pascale Gaudet (nominated by Dr Michael Galperin).

Low complexity regions (LCRs) in protein sequences are characterized by a less diverse amino acid composition compared to typically observed sequence diversity. Recent studies have shown that LCRs may co-occur with intrinsically disordered regions, are highly conserved in many organisms, and often play important roles in protein functions and in diseases. In previous decades, several methods have been developed to identify regions with LCRs or amino acid bias, but most of them as stand-alone applications and currently there is no web-based tool which allows users to explore LCRs in protein sequences with additional functional annotations. We aim to fill this gap by providing PlaToLoCo - PLAtform of TOols for LOw COmplexity—a meta-server that integrates and collects the output of five different state-of-the-art tools for discovering LCRs and provides functional annotations such as domain detection, transmembrane segment prediction, and calculation of amino acid frequencies. In addition, the union or intersection of the results of the search on a query sequence can be obtained. By developing the PlaToLoCo meta-server, we provide the community with a fast and easily accessible tool for the analysis of LCRs with additional information included to aid the interpretation of the results. The PlaToLoCo platform is available at: http://platoloco.aei.polsl.pl/.

The Topology Data Bank of Transmembrane Proteins (TOPDB, http://topdb.enzim.ttk.mta.hu) contains experimentally determined topology data of transmembrane proteins. Recently, we have updated TOPDB from several sources and utilized a newly developed topology prediction algorithm to determine the most reliable topology using the results of experiments as constraints. In addition to collecting the experimentally determined topology data published in the last couple of years, we gathered topographies defined by the TMDET algorithm using 3D structures from the PDBTM. Results of global topology analysis of various organisms as well as topology data generated by high throughput techniques, like the sequential positions of N- or O-glycosylations were incorporated into the TOPDB database. Moreover, a new algorithm was developed to integrate scattered topology data from various publicly available databases and a new method was introduced to measure the reliability of predicted topologies. We show that reliability values highly correlate with the per protein topology accuracy of the utilized prediction method. Altogether, more than 52 000 new topology data and more than 2600 new transmembrane proteins have been collected since the last public release of the TOPDB database.