The structural states of proteins include ordered globular domains as well as intrinsically disordered protein regions that exist as highly flexible conformational ensembles in isolation. Various computational tools have been developed to discriminate ordered and disordered segments based on the amino acid sequence. However, properties of IDRs can also depend on various conditions, including binding to globular protein partners or environmental factors, such as redox potential. These cases provide further challenges for the computational characterization of disordered segments. In this work we present IUPred2A, a combined web interface that allows to generate energy estimation based predictions for ordered and disordered residues by IUPred2 and for disordered binding regions by ANCHOR2. The updated web server retains the robustness of the original programs but offers several new features. While only minor bug fixes are implemented for IUPred, the next version of ANCHOR is significantly improved through a new architecture and parameters optimized on novel datasets. In addition, redox-sensitive regions can also be highlighted through a novel experimental feature. The web server offers graphical and text outputs, a RESTful interface, access to software download and extensive help, and can be accessed at a new location: http://iupred2a.elte.hu.

IUPred2A is a combined prediction tool designed to discover intrinsically disordered or conditionally disordered proteins and protein regions. Intrinsically disordered regions exist without a well‐defined three‐dimensional structure in isolation but carry out important biological functions. Over the years, various prediction methods have been developed to characterize disordered regions. The existence of disordered segments can also be dependent on different factors such as binding partners or environmental traits like pH or redox potential, and recognizing such regions represents additional computational challenges. In this article, we present detailed instructions on how to use IUPred2A, one of the most widely used tools for the prediction of disordered regions/proteins or conditionally disordered segments, and provide examples of how the predictions can be interpreted in different contexts. © 2020 The Authors.Basic Protocol 1: Analyzing disorder propensity with IUPred2A onlineBasic Protocol 2: Analyzing disordered binding regions using ANCHOR2Support Protocol 1: Interpretation of the resultsBasic Protocol 3: Analyzing redox‐sensitive disordered regionsSupport Protocol 2: Download optionsSupport Protocol 3: REST API for programmatic purposesBasic Protocol 4: Using IUPred2A locally

Intrinsically disordered proteins and protein regions (IDPs/IDRs) carry out important biological functions without relying on a single well-defined conformation. As these proteins are a challenge to study experimentally, computational methods play important roles in their characterization. One of the commonly used tools is the IUPred web server which provides prediction of disordered regions and their binding sites. IUPred is rooted in a simple biophysical model and uses a limited number of parameters largely derived on globular protein structures only. This enabled an incredibly fast and robust prediction method, however, its limitations have also become apparent in light of recent breakthrough methods using deep learning techniques. Here, we present AIUPred, a novel version of IUPred which incorporates deep learning techniques into the energy estimation framework. It achieves improved performance while keeping the robustness of the original method. Based on the evaluation of recent benchmark datasets, AIUPred scored amongst the top three single sequence based methods. With a new web server we offer fast and reliable visual analysis for users as well as options to analyze whole genomes in mere seconds with the downloadable package. AIUPred is available at https://aiupred.elte.hu.

Membraneless organelles (MOs) are dynamic liquid condensates that host a variety of specific cellular processes, such as ribosome biogenesis or RNA degradation. MOs form through liquid–liquid phase separation (LLPS), a process that relies on multivalent weak interactions of the constituent proteins and other macromolecules. Since the first discoveries of certain proteins being able to drive LLPS, it emerged as a general mechanism for the effective organization of cellular space that is exploited in all kingdoms of life. While numerous experimental studies report novel cases, the computational identification of LLPS drivers is lagging behind, and many open questions remain about the sequence determinants, composition, regulation and biological relevance of the resulting condensates. Our limited ability to overcome these issues is largely due to the lack of a dedicated LLPS database. Therefore, here we introduce PhaSePro (https://phasepro.elte.hu), an openly accessible, comprehensive, manually curated database of experimentally validated LLPS driver proteins/protein regions. It not only provides a wealth of information on such systems, but improves the standardization of data by introducing novel LLPS-specific controlled vocabularies. PhaSePro can be accessed through an appealing, user-friendly interface and thus has definite potential to become the central resource in this dynamically developing field.

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.

Protein-protein interactions (PPIs) formed between short linear motifs and globular domains play important roles in many regulatory and signaling processes but are highly underrepresented in current protein-protein interaction databases. These types of interactions are usually characterized by a specific binding motif that captures the key amino acids shared among the interaction partners. However, the computational proteome-level identification of interaction partners based on the known motif is hindered by the huge number of randomly occurring matches from which biologically relevant motif hits need to be extracted. In this work, we established a novel bioinformatic filtering protocol to efficiently explore interaction network of a hub protein. We introduced a novel measure that enabled the optimization of the elements and parameter settings of the pipeline which was built from multiple sequence-based prediction methods. In addition, data collected from PPI databases and evolutionary analyses were also incorporated to further increase the biological relevance of the identified motif hits. The approach was applied to the dynein light chain LC8, a ubiquitous eukaryotic hub protein that has been suggested to be involved in motor-related functions as well as promoting the dimerization of various proteins by recognizing linear motifs in its partners. From the list of putative binding motifs collected by our protocol, several novel peptides were experimentally verified to bind LC8. Altogether 71 potential new motif instances were identified. The expanded list of LC8 binding partners revealed the evolutionary plasticity of binding partners despite the highly conserved binding interface. In addition, it also highlighted a novel, conserved function of LC8 in the upstream regulation of the Hippo signaling pathway. Beyond the LC8 system, our work also provides general guidelines that can be applied to explore the interaction network of other linear motif binding proteins or protein domains. Fine-tuning of many cellular processes relies on weak, transient protein-protein interactions. Such interactions often involve compact functional modules, called short linear motifs (SLiMs) that can bind to specific globular domains. SLiM-mediated interactions can carry out diverse molecular functions by targeting proteins to specific cellular locations, regulating the activity and binding preferences of proteins, or aiding the assembly of macromolecular complexes. The key to the function of SLiMs is their small size and highly flexible nature. At the same time, these properties make their experimental identification challenging. Consequently, only a small portion of SLiM-mediated interactions is currently known. This underlies the importance of novel computational methods that can reliably identify candidate sites involved in binding to linear motif binding domains. Here we present a novel bioinformatic approach that efficiently predicts new binding partners for SLiM-binding domains. We applied this method to the dynein light chain LC8, a protein that was already known to bind many partners in a wide range of organisms. With this method, we not only significantly expanded the interaction network of LC8, but also identified a novel function of LC8 in a highly important pathway controlling organ size in animals.