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. © The Author(s) 2012. Published by Oxford University Press

A stabilizációs centrumokra vonatkozó információ beépítése fehérje szekvencia-térszerkezet párosító (threading) eljárásba = Incorporation of stabilization centers into threading

A 2003-2006 időszakban végzett kutatómunkám a stabilizációs centrumok azonosítására szolgáló SCIDE nyilvános szerver világhálóra telepítésével kezdődött. A program közepétől tevékenységem kiterjedt a globuláris vízoldható fehérjéken túlra is. Egyrészt kidolgoztuk a TMDET nevű algoritmust, amely alkalmas a transzmembrán fehérjék automatizált megkülönböztetésére és ennek segítségével létrehoztuk a hetente folyamatosan frissülő PDB_TM adatbázist. Emellett elkezdtem foglalkozni az időben állandó térszerkezettel nem rendelkező, úgynevezett rendezetlen fehérjékkel. Ezen a területen a legfontosabb eredményem, hogy kidolgoztam egy eredeti módszert rendezetlen fehérjék illetve fehérje szegmenseknek szekvenciából történő becslésére. | I started the research period of 2003-2006 with setting up the freely accessible web server SCIDE for the identification of stabilization centers. Form the middle of the program my research interest gradually shifted beyond water soluble globular proteins. First, we developed the TMDET algorithm suitable for the automated discrimination of globular and transmembrane proteins, and this allowed establishing the PDB_TM database which is updated on a weekly basis. Furthermore, I become interested in the so-called disordered proteins which do not have a stable well defined three-dimensional structure. My main result on this area was the development of an original method for the prediction of disorder protein and protein segments from the amino acid sequence

Specifikus DNS felismerés molekuláris szintű értelmezése = Molecular basis of specificity in DNA binding

A specifikus DNS felismerés molekuláris hátterét a i) kétértékű fémionok, ii) fehérje-flexibilitás és a iii) domének közötti kommunikáció szempontjából vizsgáltam és ezen tényezők szerepét a DNS kötésében értelmeztem. Kimutattam a finom-elektronszerkezeti tényezők szerepét a bázis-kötés energetikájának kialakításában és így értelmeztem a Mg2+ és Mn2+ ionok szelektivitása között tapasztalható különbséget. Modelleztem a dUTPáz flexibilis karjának a szubsztrát felismerésében játszott szerepét. Magyarázatot találtam egy hibrid EcoRI-RsrI restrikciós endonukleáz különleges viselkedésére. Sikerült továbbá a kétértékű fémionoknak a restrikciós endonukleázok katalízisében betöltött szerepét pontosan felderíteni, mely egy egységes katalitikus modell megalkotását segíti elő. A specifikus felismerés jelenségének mélyebb megértéséhez járul hozzá a rendezetlen fehérje-szakaszok kötődési mechanizmusának értelmezése is. | I investigated the molecular background of specific DNA recognition from the point of i) divalent metal ions, ii) protein flexibility and iii) inter-domain communication and revealed the role of these factors in specific DNA binding. I studied the role of electronic effects in modulating the energetics of metal ion-nucleobase interactions and thus account for the difference in sequence selectivity of Mg2+ and Mn2+ ions. I simulated the contribution of the flexible arm of dUTPase to specific substrate binding. I provided explanation for the peculiar behavior of a hybrid EcoRI-RsrI restriction endonuclease. We unraveled the role and quantitative contribution of bivalent metal ions to restriction endonuclease catalysis that can serve as a framework for a unified catalytic mechanism. The studies on the binding mechanism of intrinsically disordered regions also contribute to better understanding of specific recognition

IUPred2A: context-dependent prediction of protein disorder as a function of redox state and protein binding

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

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

DisCanVis: Visualizing integrated structural and functional annotations to better understand the effect of cancer mutations located within disordered proteins

Intrinsically disordered proteins (IDPs) play important roles in a wide range of biological processes and have been associated with various diseases, including cancer. In the last few years, cancer genome projects have systematically collected genetic variations underlying multiple cancer types. In parallel, the number and different types of disordered proteins characterized by experimental methods have also significantly increased. Nevertheless, the role of IDPs in various types of cancer is still not well understood. In this work, we present DisCanVis, a novel visualization tool for cancer mutations with a special focus on IDPs. In order to aid the interpretation of observed mutations, genome level information is combined with information about the structural and functional properties of proteins. The web server enables users to inspect individual proteins, collect examples with existing annotations of protein disorder and associated function or to discover currently uncharacterized examples with likely disease relevance. Through a REST API interface and precompiled tables the analysis can be extended to a group of proteins

Fehérjék aktiv állapotának elemzése modellezése és predikciója = Analyzing, modeling and predicting of the active state of proteins

A 2008. április 1. és 2011. december 31. között végzett munka része volt az munkatársaimmal több, mint húsz éve végzett elméleti fehérje szerkezet kutató tevékenységnek, melynek hosszú távú célja a fehérjék szerkezet szerveződésének korszerű leírása. Új molekulamechanikai és molekuladinamikai módszereket fejlesztettünk ki. Ezeket és a már korábban is ismert módszereket felhasználtuk fehérjék egymás közötti, illetve fehérje működése szempontjából releváns folyamatok vizsgálatára. Az általános fehérje szerkezeti vizsgálatokhoz kapcsolódóan létrehoztuk és a világhálóra telepítettünk egy adatbázist a EPIC-DB-t Befejeztük a transzmembrán fehérjék topológiájának vizsgálatát. Ezen a területen is létrehoztunk egy adatbázist, a TOPDOM-ot, amit szintén feltelepítettünk a világhálóra. Transzmembrán fehérjékről több összefoglalót publikáltunk és megkezdtük a munkálatokat ezen fehérjék harmadlagos szerkezetének becsléséhez. A legtöbb munkát a rendezetlen fehérjék témakörében végeztük. Létrehoztuk a jelenleg egyetlen módszert, az ANCHOR-t, rendezetlen fehérjék fehérje kötőhelyeinek a szekvenciából történő becslésére. Jelentős eredményeket értünk el, részben rendezetlen fehérjék más fehérjékkel illetve nukleinsavakkal való kölcsönhatásainak vizsgálatában is. Ezen a területen is publikáltunk összefoglalókat is. | The work we performed in the frame of this project between April 1, 2008 and December 31, 2011 is part of a long term project, started more than two decades ago, to provide a state of the art description of protein structure organization. New molecular mechanics and molecular dynamics methods were developed. These methods, those which we developed earlier and those available from the literature were applied to study protein structures and functionally relevant interactions. As part of our general protein structure research I was involved in the construction of the EPIC-DB database, which is available in the WWW. We have completed our long term study on the topology of transmembrane proteins. As a final part of this program we have constructed the TOPDOM database, put it onto the WWW and published some reviews on transmembrane proteins. We have started the next chapter of our transmembrane protein studies, focusing the relative positions and interactions of the transmembrane segments i.e. the 3D structure. Most of the work of this project involved partly or completely unstructured proteins. The currently only publicly available server to predict binding regions of unstructured protein segments, the ANCHOR were developed and placed on the WWW. We have published several important results on unstructured proteins and their interactions as well as a few reviews in this fiel

Systematic analysis of somatic mutations driving cancer: Uncovering functional protein regions in disease development

Background: Recent advances in sequencing technologies enable the large-scale identification of genes that are affected by various genetic alterations in cancer. However, understanding tumor development requires insights into how these changes cause altered protein function and impaired network regulation in general and/or in specific cancer types. Results: In this work we present a novel method called iSiMPRe that identifies regions that are significantly enriched in somatic mutations and short in-frame insertions or deletions (indels). Applying this unbiased method to the complete human proteome, by using data enriched through various cancer genome projects, we identified around 500 protein regions which could be linked to one or more of 27 distinct cancer types. These regions covered the majority of known cancer genes, surprisingly even tumor suppressors. Additionally, iSiMPRe also identified novel genes and regions that have not yet been associated with cancer. Conclusions: While local somatic mutations correspond to only a subset of genetic variations that can lead to cancer, our systematic analyses revealed that they represent an accompanying feature of most cancer driver genes regardless of the primary mechanism by which they are perturbed during tumorigenesis. These results indicate that the accumulation of local somatic mutations can be used to pinpoint genes responsible for cancer formation and can also help to understand the effect of cancer mutations at the level of functional modules in a broad range of cancer driver genes. Reviewers: This article was reviewed by Sándor Pongor, Michael Gromiha and Zoltán Gáspári. © 2016 Mészáros et al