Mining structural signatures in proteins using intrachain interactions.

In this work we use a data mining approach to analyze similarity of proteins and to extract conserved information on dissimilar sequences of proteins of the same family.X-meeting 2007

Mining protein database using machine learning techniques

With a large amount of information relating to proteins accumulating in databases widely available online, it is of interest to apply machine learning techniques that, by extracting underlying statistical regularities in the data, make predictions about the functional and evolutionary characteristics of unseen proteins. Such predictions can help in achieving a reduction in the space over which experiment designers need to search in order to improve our understanding of the biochemical properties. Previously it has been suggested that an integration of features computable by comparing a pair of proteins can be achieved by an artificial neural network, hence predicting the degree to which they may be evolutionary related and homologous. We compiled two datasets of pairs of proteins, each pair being characterised by seven distinct features. We performed an exhaustive search through all possible combinations of features, for the problem of separating remote homologous from analogous pairs, we note that significant performance gain was obtained by the inclusion of sequence and structure information. We find that the use of a linear classifier was enough to discriminate a protein pair at the family level. However, at the superfamily level, to detect remote homologous pairs was a relatively harder problem. We find that the use of nonlinear classifiers achieve significantly higher accuracies. In this paper, we compare three different pattern classification methods on two problems formulated as detecting evolutionary and functional relationships between pairs of proteins, and from extensive cross validation and feature selection based studies quantify the average limits and uncertainties with which such predictions may be made. Feature selection points to a "knowledge gap" in currently available functional annotations. We demonstrate how the scheme may be employed in a framework to associate an individual protein with an existing family of evolutionarily related proteins

SPACE: a suite of tools for protein structure prediction and analysis based on complementarity and environment

We describe a suite of SPACE tools for analysis and prediction of structures of biomolecules and their complexes. LPC/CSU software provides a common definition of inter-atomic contacts and complementarity of contacting surfaces to analyze protein structure and complexes. In the current version of LPC/CSU, analyses of water molecules and nucleic acids have been added, together with improved and expanded visualization options using Chime or Java based Jmol. The SPACE suite includes servers and programs for: structural analysis of point mutations (MutaProt); side chain modeling based on surface complementarity (SCCOMP); building a crystal environment and analysis of crystal contacts (CryCo); construction and analysis of protein contact maps (CMA) and molecular docking software (LIGIN). The SPACE suite is accessed at

PDB-Ligand: a ligand database based on PDB for the automated and customized classification of ligand-binding structures

PDB-Ligand (http://www.idrtech.com/PDB-Ligand/) is a three-dimensional structure database of small molecular ligands that are bound to larger biomolecules deposited in the Protein Data Bank (PDB). It is also a database tool that allows one to browse, classify, superimpose and visualize these structures. As of May 2004, there are about 4870 types of small molecular ligands, experimentally determined as a complex with protein or DNA in the PDB. The proteins that a given ligand binds are often homologous and present the same binding structure to the ligand. However, there are also many instances wherein a given ligand binds to two or more unrelated proteins, or to the same or homologous protein in different binding environments. PDB-Ligand serves as an interactive structural analysis and clustering tool for all the ligand-binding structures in the PDB. PDB-Ligand also provides an easier way to obtain a number of different structure alignments of many related ligand-binding structures based on a simple and flexible ligand clustering method. PDB-Ligand will be a good resource for both a better interpretation of ligand-binding structures and the development of better scoring functions to be used in many drug discovery applications

Setting up a large set of protein-ligand PDB complexes for the development and validation of knowledge-based docking algorithms

<p>Abstract</p> <p>Background</p> <p>The number of algorithms available to predict ligand-protein interactions is large and ever-increasing. The number of test cases used to validate these methods is usually small and problem dependent. Recently, several databases have been released for further understanding of protein-ligand interactions, having the Protein Data Bank as backend support. Nevertheless, it appears to be difficult to test docking methods on a large variety of complexes. In this paper we report the development of a new database of protein-ligand complexes tailored for testing of docking algorithms.</p> <p>Methods</p> <p>Using a new definition of molecular contact, small ligands contained in the 2005 PDB edition were identified and processed. The database was enriched in molecular properties. In particular, an automated typing of ligand atoms was performed. A filtering procedure was applied to select a non-redundant dataset of complexes. Data mining was performed to obtain information on the frequencies of different types of atomic contacts. Docking simulations were run with the program DOCK.</p> <p>Results</p> <p>We compiled a large database of small ligand-protein complexes, enriched with different calculated properties, that currently contains more than 6000 non-redundant structures. As an example to demonstrate the value of the new database, we derived a new set of chemical matching rules to be used in the context of the program DOCK, based on contact frequencies between ligand atoms and points representing the protein surface, and proved their enhanced efficiency with respect to the default set of rules included in that program.</p> <p>Conclusion</p> <p>The new database constitutes a valuable resource for the development of knowledge-based docking algorithms and for testing docking programs on large sets of protein-ligand complexes. The new chemical matching rules proposed in this work significantly increase the success rate in DOCKing simulations. The database developed in this work is available at <url>http://cimlcsext.cim.sld.cu:8080/screeningbrowser/</url>.</p

ccPDB: compilation and creation of data sets from Protein Data Bank

ccPDB (http://crdd.osdd.net/raghava/ccpdb/) is a database of data sets compiled from the literature and Protein Data Bank (PDB). First, we collected and compiled data sets from the literature used for developing bioinformatics methods to annotate the structure and function of proteins. Second, data sets were derived from the latest release of PDB using standard protocols. Third, we developed a powerful module for creating a wide range of customized data sets from the current release of PDB. This is a flexible module that allows users to create data sets using a simple six step procedure. In addition, a number of web services have been integrated in ccPDB, which include submission of jobs on PDB-based servers, annotation of protein structures and generation of patterns. This database maintains >30 types of data sets such as secondary structure, tight-turns, nucleotide interacting residues, metals interacting residues, DNA/RNA binding residues and so on

Apresentação gráfica de parâmetros protéicos utilizando o Java Protein Dossier.

Parâmetros apresentados pelo JPD. Sequência de resíduos. Contatos. Contatos internos. Contatos na interface. Estrutura secundária. Dupla ocupância. Fator de temperatura. Entropia relativa. Confiabilidade. Acessibilidade de resíduos. Ângulos de torsão. Potencial eletrostático. Curvatura na superfície. Hidrofobicidade. Analisando com maior detalhes os parâmetros apresentados.bitstream/CNPTIA/9899/1/comuntec40.pdfAcesso em: 30 maio 2008

CAPS-DB: a structural classification of helix-capping motifs

The regions of the polypeptide chain immediately preceding or following an α-helix are known as Nt- and Ct cappings, respectively. Cappings play a central role stabilizing α-helices due to lack of intrahelical hydrogen bonds in the first and last turn. Sequence patterns of amino acid type preferences have been derived for cappings but the structural motifs associated to them are still unclassified. CAPS-DB is a database of clusters of structural patterns of different capping types. The clustering algorithm is based in the geometry and the (ϕ–ψ)-space conformation of these regions. CAPS-DB is a relational database that allows the user to search, browse, inspect and retrieve structural data associated to cappings. The contents of CAPS-DB might be of interest to a wide range of scientist covering different areas such as protein design and engineering, structural biology and bioinformatics. The database is accessible at: http://www.bioinsilico.org/CAPSDB

CAMPO, SCR_FIND and CHC_FIND: a suite of web tools for computational structural biology

The identification of evolutionarily conserved features of protein structures can provide insights into their functional and structural properties. Three methods have been developed and implemented as WWW tools, CAMPO, SCR_FIND and CHC_FIND, to analyze evolutionarily conserved residues (ECRs), structurally conserved regions (SCRs) and conserved hydrophobic contacts (CHCs) in protein families and superfamilies, on the basis of their 3D structures and the homologous sequences available. The programs identify protein segments that conserve a similar main-chain conformation, compute residue-to-residue hydrophobic contacts involving only apolar atoms common to all the 3D structures analyzed and allow the identification of conserved amino-acid sites among protein structures and their homologous sequences. The programs also allow the visualization of SCRs, CHCs and ECRs directly on the superposed structures and their multiple structural and sequence alignments. Tools and tutorials explaining their usage are available at , and

Prediction of FAD interacting residues in a protein from its primary sequence using evolutionary information

Background: Flavin binding proteins (FBP) plays a critical role in several biological functions such as electron transport system (ETS). These flavoproteins contain very tightly bound, sometimes covalently, flavin adenine dinucleotide (FAD) or flavin mono nucleotide (FMN). The interaction between flavin nucleotide and amino acids of flavoprotein is essential for their functionality. Thus identification of FAD interacting residues in a FBP is an important step for understanding their function and mechanism. Results: In this study, we describe models developed for predicting FAD interacting residues using 15, 17 and 19 window pattern. Support vector machine (SVM) based models have been developed using binary pattern of amino acid sequence of protein and achieved maximum accuracy 69.65% with Mathew's Correlation Coefficient (MCC) 0.39 and Area Under Curve (AUC) 0.773. The performance of these models have been improved significantly from 69.65% to 82.86% with MCC 0.66 and AUC 0.904, when evolutionary information is used as input in SVM. The evolutionary information was generated in form of position specific score matrix (PSSM) profile by using PSI-BLAST at e-value 0.001. All models were developed on 198 non-redundant FAD binding protein chains containing 5172 FAD interacting residues and evaluated using fivefold cross-validation technique. Conclusion: This study suggests that evolutionary information of 17 amino acid patterns perform best for FAD interacting residues prediction. We also developed a web server which predicts FAD interacting residues in a protein which is freely available for academics