Zsuzsanna Dosztányi

ANCHOR: web server for predicting protein binding regions in disordered proteins

Summary: ANCHOR is a web-based implementation of an original method that takes a single amino acid sequence as an input and predicts protein binding regions that are disordered in isolation but can undergo disorder-to-order transition upon binding. The server incorporates the result of a general disorder prediction method, IUPred and can carry out simple motif searches as well. Availability: The web server is available at http://anchor.enzim.hu. The program package is freely available for academic users. Contact: zsuzsa@enzim.hu

InterPro in 2017—beyond protein family and domain annotations

InterPro (http://www.ebi.ac.uk/interpro/) is a freely available database used to classify protein sequences into families and to predict the presence of important domains and sites. InterProScan is the underlying software that allows both protein and nucleic acid sequences to be searched against InterPros predictive models, which are provided by its member databases. Here, we report recent developments with InterPro and its associated software, including the addition of two new databases (SFLD and CDD), and the functionality to include residue-level annotation and prediction of intrinsic disorder. These developments enrich the annotations provided by InterPro, increase the overall number of residues annotated and allow more specific functional inferences.

Prediction of Protein Binding Regions in Disordered Proteins

Many disordered proteins function via binding to a structured partner and undergo a disorder-to-order transition. The coupled folding and binding can confer several functional advantages such as the precise control of binding specificity without increased affinity. Additionally, the inherent flexibility allows the binding site to adopt various conformations and to bind to multiple partners. These features explain the prevalence of such binding elements in signaling and regulatory processes. In this work, we report ANCHOR, a method for the prediction of disordered binding regions. ANCHOR relies on the pairwise energy estimation approach that is the basis of IUPred, a previous general disorder prediction method. In order to predict disordered binding regions, we seek to identify segments that are in disordered regions, cannot form enough favorable intrachain interactions to fold on their own, and are likely to gain stabilizing energy by interacting with a globular protein partner. The performance of ANCHOR was found to be largely independent from the amino acid composition and adopted secondary structure. Longer binding sites generally were predicted to be segmented, in agreement with available experimentally characterized examples. Scanning several hundred proteomes showed that the occurrence of disordered binding sites increased with the complexity of the organisms even compared to disordered regions in general. Furthermore, the length distribution of binding sites was different from disordered protein regions in general and was dominated by shorter segments. These results underline the importance of disordered proteins and protein segments in establishing new binding regions. Due to their specific biophysical properties, disordered binding sites generally carry a robust sequence signal, and this signal is efficiently captured by our method. Through its generality, ANCHOR opens new ways to study the essential functional sites of disordered proteins.

PDB_TM: selection and membrane localization of transmembrane proteins in the protein data bank

PDB_TM is a database for transmembrane proteins with known structures. It aims to collect all transmembrane proteins that are deposited in the protein structure database (PDB) and to determine their membrane-spanning regions. These assignments are based on the TMDET algorithm, which uses only structural information to locate the most likely position of the lipid bilayer and to distinguish between transmembrane and globular proteins. This algorithm was applied to all PDB entries and the results were collected in the PDB_TM database. By using TMDET algorithm, the PDB_TM database can be automatically updated every week, keeping it synchronized with the latest PDB updates. The PDB_TM database is available at http://www.enzim.hu/PDB_TM.

D2P2: database of disordered protein predictions

We present the Database of Disordered Protein Prediction (D2P2), available at http://d2p2.pro (including website source code). A battery of disorder predictors and their variants, VL-XT, VSL2b, PrDOS, PV2, Espritz and IUPred, were run on all protein sequences from 1765 complete proteomes (to be updated as more genomes are completed). Integrated with these results are all of the predicted (mostly structured) SCOP domains using the SUPERFAMILY predictor. These disorder/structure annotations together enable comparison of the disorder predictors with each other and examination of the overlap between disordered predictions and SCOP domains on a large scale. D2P2 will increase our understanding of the interplay between disorder and structure, the genomic distribution of disorder, and its evolutionary history. The parsed data are made available in a unified format for download as flat files or SQL tables either by genome, by predictor, or for the complete set. An interactive website provides a graphical view of each protein annotated with the SCOP domains and disordered regions from all predictors overlaid (or shown as a consensus). There are statistics and tools for browsing and comparing genomes and their disorder within the context of their position on the tree of life.

What's in a name? Why these proteins are intrinsically disordered: Why these proteins are intrinsically disordered.

“What’s in a name? That which we call a rose By any other name would smell as sweet.” From “Romeo and Juliet”, William Shakespeare (1594) This article opens a series of publications on disambiguation of the basic terms used in the field of intrinsically disordered proteins. We start from the beginning, namely from the explanation of what the expression “intrinsically disordered protein” actually means and why this particular term has been chosen as the common denominator for this class of proteins characterized by broad structural, dynamic and functional characteristics.

Bioinformatical approaches to characterize intrinsically disordered/unstructured proteins

Intrinsically disordered/unstructured proteins exist without a stable three-dimensional (3D) structure as highly flexible conformational ensembles. The available genome sequences revealed that these proteins are surprisingly common and their frequency reaches high proportions in eukaryotes. Due to their vital role in various biological processes including signaling and regulation and their involvement in various diseases, disordered proteins and protein segments are the focus of many biochemical, molecular biological, pathological and pharmaceutical studies. These proteins are difficult to study experimentally because of the lack of unique structure in the isolated form. Their amino acid sequence, however, is available, and can be used for their identification and characterization by bioinformatic tools, analogously to globular proteins. In this review, we first present a small survey of current methods to identify disordered proteins or protein segments, focusing on those that are publicly available as web servers. In more detail we also discuss approaches that predict disordered regions and specific regions involved in protein binding by modeling the physical background of protein disorder. In our review we argue that the heterogeneity of disordered segments needs to be taken into account for a better understanding of protein disorder.

Prediction of Protein Disorder at the Domain Level

Intrinsically disordered/unstructured proteins exist in a highly flexible conformational state largely devoid of secondary structural elements and tertiary contacts. Despite their lack of a well defined structure, these proteins often fulfill essential regulatory functions. The intrinsic lack of structure confers functional advantages on these proteins, allowing them to adopt multiple conformations and to bind to different binding partners. The structural flexibility of disordered regions hampers efforts solving structures at high resolution by X-ray crystallography and/or NMR. Removing such proteins/regions from high-throughput structural genomics pipelines would be of significant benefit in terms of cost and success rate. In this paper we outline the theoretical background of structural disorder, and review bioinformatic predictors that can be used to delineate regions most likely to be amenable for structure determination. The primary focus of our review is the interpretation of prediction results in a way that enables segmentation of proteins to separate ordered domains from disordered regions.

Prediction of Protein Disorder

The recent advance in our understanding of the relation of protein structure and function cautions that many proteins, or regions of proteins, exist and function without a well-defined three-dimensional structure. These intrinsically disordered/unstructured proteins (IDP/IUP) are frequent in proteomes and carry out essential functions, but their lack of stable structures hampers efforts of solving structures at high resolution by x-ray crystallography and/or NMR. Thus, filtering such proteins/regions out of high-throughput structural genomics pipelines would be of significant benefit in terms of cost and success rate. This chapter outlines the theoretical background of structural disorder, and provides practical advice on the application of advanced bioinformatic predictors to this end, that is to recognize fully/mostly disordered proteins or regions, which are incompatible with structure determination. An emphasis is also given to a somewhat different approach, in which ordered/disordered regions are explicitly delineated to the end of making constructs amenable for structure determination even when disordered regions are present.

Disordered binding regions and linear motifs--bridging the gap between two models of molecular recognition.

Intrinsically disordered proteins (IDPs) exist without the presence of a stable tertiary structure in isolation. These proteins are often involved in molecular recognition processes via their disordered binding regions that can recognize partner molecules by undergoing a coupled folding and binding process. The specific properties of disordered binding regions give way to specific, yet transient interactions that enable IDPs to play central roles in signaling pathways and act as hubs of protein interaction networks. An alternative model of protein-protein interactions with largely overlapping functional properties is offered by the concept of linear interaction motifs. This approach focuses on distilling a short consensus sequence pattern from proteins with a common interaction partner. These motifs often reside in disordered regions and are considered to mediate the interaction roughly independent from the rest of the protein. Although a connection between linear motifs and disordered binding regions has been established through common examples, the complementary nature of the two concepts has yet to be fully explored. In many cases the sequence based definition of linear motifs and the structural context based definition of disordered binding regions describe two aspects of the same phenomenon. To gain insight into the connection between the two models, prediction methods were utilized. We combined the regular expression based prediction of linear motifs with the disordered binding region prediction method ANCHOR, each specialized for either model to get the best of both worlds. The thorough analysis of the overlap of the two methods offers a bioinformatics tool for more efficient binding site prediction that can serve a wide range of practical implications. At the same time it can also shed light on the theoretical connection between the two co-existing interaction models.