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Prediction of RNA-binding amino acids from protein and RNA sequences

BMC BIOINFORMATICS 2011年第s13期12卷 1-12页

作者： Choi, Sungwook Han, Kyungsook Inha Univ Sch Comp Sci & Engn Inchon 402751 South Korea

Background: Many learning approaches to predicting RNA-binding residues in a protein sequence construct a non-redundant training dataset based on the sequence similarity. The sequence similarity-based method either takes a whole sequence or discards it for a training dataset. However, similar sequences or even identical sequences can have different interaction sites depending on their interaction partners, and this information is lost when the sequences are removed. Furthermore, a training dataset constructed by the sequence similarity-based method may contain redundant data when the remaining sequence contains similar subsequences within the sequence. In addition to the problem with the training dataset, most approaches do not consider the interacting partner (i.e., RNA) of a protein when they predict RNA-binding amino acids. Thus, they always predict the same RNA-binding sites for a given protein sequence even if the protein binds to different RNA molecules. Results: We developed a feature vector-based method that removes data redundancy for a non-redundant training dataset. The feature vector-based method constructed a larger training dataset than the standard sequence similarity-based method, yet the dataset contained no redundant data. We identified effective features of protein and RNA (the interaction propensity of amino acid triplets, global features of the protein sequence, and RNA feature) for predicting RNA-binding residues. Using the method and features, we built a support vector machine (SVM) model that predicted RNA-binding residues in a protein sequence. Our SVM model showed an accuracy of 84.2%, an F-measure of 76.1%, and a correlation coefficient of 0.41 with 5-fold cross validation on a non-redundant dataset from 3,149 protein-RNA interacting pairs. In an independent test dataset that does not include the 3,149 pairs and were not used in training the SVM model, it achieved an accuracy of 90.3%, an F-measure of 72.8%, and a correlation coefficient o

关键词： Feature Vector Training dataset Support Vector Machine Model protein data bank file Redundancy Reduction

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A spatial simulation approach to account for protein structure when identifying non-random somatic mutations

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BMC BIOINFORMATICS 2014年第1期15卷 1-13页

作者： Ryslik, Gregory A. Cheng, Yuwei Cheung, Kei-Hoi Bjornson, Robert D. Zelterman, Daniel Modis, Yorgo Zhao, Hongyu Yale Sch Publ Hlth Dept Biostat New Haven CT 06520 USA Yale Univ Program Computat Biol & Bioinformat New Haven CT 06520 USA Yale Sch Med Yale Ctr Med Informat New Haven CT USA Yale Univ Dept Comp Sci New Haven CT 06520 USA Yale Univ Dept Mol Biophys & Biochem New Haven CT 06520 USA

Background: Current research suggests that a small set of "driver" mutations are responsible for tumorigenesis while a larger body of "passenger" mutations occur in the tumor but do not progress the disease. Due to recent pharmacological successes in treating cancers caused by driver mutations, a variety of methodologies that attempt to identify such mutations have been developed. Based on the hypothesis that driver mutations tend to cluster in key regions of the protein, the development of cluster identification algorithms has become critical. Results: We have developed a novel methodology, SpacePAC (Spatial protein Amino acid Clustering), that identifies mutational clustering by considering the protein tertiary structure directly in 3D space. By combining the mutational data in the Catalogue of Somatic Mutations in Cancer (COSMIC) and the spatial information in the protein data bank (PDB), SpacePAC is able to identify novel mutation clusters in many proteins such as FGFR3 and CHRM2. In addition, SpacePAC is better able to localize the most significant mutational hotspots as demonstrated in the cases of BRAF and ALK. The R package is available on Bioconductor at: http://***/packages/release/bioc/html/***. Conclusion: SpacePAC adds a valuable tool to the identification of mutational clusters while considering protein tertiary structure.

关键词： protein data bank Anaplastic Lymphoma Kinase Driver Mutation protein Tertiary Structure protein data bank file

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SSMap: A new UniProt-PDB mapping resource for the curation of structural-related information in the UniProt/Swiss-Prot Knowledgebase

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BMC BIOINFORMATICS 2008年第1期9卷 1-12页

作者： David, Fabrice P. A. Yip, Yum L. Swiss Inst Bioinformat Swiss Prot Grp CH-1211 Geneva Switzerland Univ Geneva Dept Struct Biol & Bioinformat CH-1211 Geneva Switzerland

Background: Sequences and structures provide valuable complementary information on protein features and functions. However, it is not always straightforward for users to gather information concurrently from the sequence and structure levels. The UniProt knowledgebase (UniProtKB) strives to help users on this undertaking by providing complete cross-references to protein data bank (PDB) as well as coherent feature annotation using available structural information. In this study, SSMap - a new UniProt-PDB residue-residue level mapping - was generated. The primary objective of this mapping is not only to facilitate the two tasks mentioned above, but also to palliate a number of shortcomings of existent mappings. SSMap is the first isoform sequence-specific mapping resource and is up-to-date for UniProtKB annotation tasks. The method employed by SSMap differs from the other mapping resources in that it stresses on the correct reconstruction of the PDB sequence from structures, and on the correct attribution of a UniProtKB entry to each PDB chain by using a series of post-processing steps. Results: SSMap was compared to other existing mapping resources in terms of the correctness of the attribution of PDB chains to UniProtKB entries, and of the quality of the pairwise alignments supporting the residue-residue mapping. It was found that SSMap shared about 80% of the mappings with other mapping sources. New and alternative mappings proposed by SSMap were mostly good as assessed by manual verification of data subsets. As for local pairwise alignments, it was shown that major discrepancies (both in terms of alignment lengths and boundaries), when present, were often due to differences in methodologies used for the mappings. Conclusion: SSMap provides an independent, good quality UniProt-PDB mapping. The systematic comparison conducted in this study allows the further identification of general problems in UniProt-PDB mappings so that both the coverage and the quality of the ma

关键词： protein data bank Alternative Mapping Mapping Resource protein data bank file protein data bank Entry

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PDB4DNA: Implementation of DNA geometry from the protein data bank (PDB) description for Geant4-DNA Monte-Carlo simulations

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COMPUTER PHYSICS COMMUNICATIONS 2015年 192卷 282-288页

作者： Delage, E. Pham, Q. T. Karamitros, M. Payno, H. Stepan, V. Incerti, S. Maigne, L. Perrot, Y. Clermont Univ CNRS IN2P3 Lab Phys Corpusculaire Clermont FerrandLPC F-63171 Aubiere France Univ Bordeaux CENBG Ctr Etudes Nucl Bordeaux Gradignan CNRSIN2P3 F-33175 Gradignan France

This paper describes PDB4DNA, a new Geant4 user application, based on an independent, cross-platform, free and open source C++ library, so-called PDBlib, which enables use of atomic level description of DNA molecule in Geant4 Monte Carlo particle transport simulations. For the evaluation of direct damage induced on the DNA molecule by ionizing particles, the application makes use of an algorithm able to determine the closest atom in the DNA molecule to energy depositions. Both the PDB4DNA application and the PDBlib library are available as free and open source under the Geant4 license. Program summary Program title: PDB4DNA Catalogue identifier: AEWB_v1_0 Program summary URL: http://.***/summaries/AEWB_v1_*** Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: Geant4 license, http://***/geant4/license/*** No. of lines in distributed program, including test data, etc.: 34714 No. of bytes in distributed program, including test data, etc.: 515359 Distribution format: *** Programming language: C++. Computer: All. Operating system: Cross-Platform. Classification: 3. External routines: Geant4 10.0 or later Nature of problem: To evaluate strand breaks induced on the DNA molecule by ionizing particles using Monte Carlo method. It requires the implementation of DNA geometry, physical processes adapted to nanodosimetry simulations and an algorithm to determine the closest atom in the DNA molecule to energy depositions. Solution method: PDB4DNA is a Geant4 user application that simulates energy deposition in a target volume generated from a protein data bank file representing the geometry of DNA molecule. Energy depositions are allocated to DNA geometry in order to compute DNA strand breaks. Restrictions: The algorithm for finding the closest atom to energy depositions has been optimized for DNA molecule and is not proposed for other stereochemistry conformation. Running time: De

关键词： Biology and molecular biology DNA geometry protein data bank file Monte Carlo simulations

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Proteopedia -: a scientific 'wiki' bridging the rift between three-dimensional structure and function of biomacromolecules

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GENOME BIOLOGY 2008年第8期9卷 R121-R121页

作者： Hodis, Eran Prilusky, Jaime Martz, Eric Silman, Israel Moult, John Sussman, Joel L. Weizmann Inst Sci Israel Struct Prote Ctr IL-76100 Rehovot Israel Weizmann Inst Sci Biol Serv Unit IL-76100 Rehovot Israel Univ Massachusetts Dept Microbiol Amherst MA 01003 USA Weizmann Inst Sci Dept Neurobiol IL-76100 Rehovot Israel Univ Maryland Inst Biotechnol Ctr Adv Res Biotechnol Rockville MD 20850 USA Weizmann Inst Sci Dept Biol Struct IL-76100 Rehovot Israel

Many scientists lack the background to fully utilize the wealth of solved three-dimensional biomacromolecule structures. Thus, a resource is needed to present structure/function information in a user-friendly manner to a broad scientific audience. Proteopedia http://*** is an interactive, wiki web-resource whose pages have embedded threedimensional structures surrounded by descriptive text containing hyperlinks that change the appearance (view, representations, colors, labels) of the adjacent three-dimensional structure to reflect the concept explained in the text.

关键词： protein data bank Structural Biology Authoring Tool Myristoyl protein data bank file

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Functionally important segments in proteins dissected using Gene Ontology and geometric clustering of peptide fragments

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GENOME BIOLOGY 2008年第3期9卷 R52-R52页

作者： Karuppasamy, Manikandan Pal, Debnath Suryanarayanarao, Ramakumar Brener, Nathan E. Iyengar, Sitharama S. Seetharaman, Guna Indian Inst Sci Bioinformat Ctr Bangalore 560012 Karnataka India Indian Inst Sci Dept Phys Bangalore 560012 Karnataka India Indian Inst Sci Supercomp Educ & Res Ctr Bangalore 560012 Karnataka India Louisiana State Univ Dept Comp Sci Baton Rouge LA 70803 USA

We have developed a geometric clustering algorithm using backbone phi,psi angles to group conformationally similar peptide fragments of any length. By labelling each fragment in the cluster with level-specific Gene Ontology molecular function (MF) term of its protein, we are able to compute statistics for MF-propensity and p-value of individual fragments in the cluster. Clustering-cum-statistical analysis for 8-length-trans-peptide fragments shows MF-propensities >= 20 and p-values <= 0.05 can dissect fragments within a protein linked to the MF.

关键词： Gene Ontology protein data bank Additional data file protein data bank file protein data bank Entry

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MUFOLD-DB: a processed protein structure database for protein structure prediction and analysis

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BMC GENOMICS 2014年第11-Sup期15卷 1-7页

作者： He, Zhiquan Zhang, Chao Xu, Yang Zeng, Shuai Zhang, Jingfen Xu, Dong Univ Missouri Dept Comp Sci Columbia MO 65211 USA Univ Missouri CS Bond Life Sci Ctr Columbia MO 65211 USA

Background: protein structure data in protein data bank (PDB) are widely used in studies of protein function and evolution and in protein structure prediction. However, there are two main barriers in large-scale usage of PDB data: 1) PDB data are highly redundant in terms of sequence and structure similarity;and 2) many PDB files have issues due to inconsistency of data and standards as well as missing residues, so that automated retrieval and analysis are often difficult. Description: To address these issues, we have created MUFOLD-DB http://***/***, a web-based database, to collect and process the weekly PDB files thereby providing users with non-redundant, cleaned and partially-predicted structure data. For each of the non-redundant sequences, we annotate the SCOP domain classification and predict structures of missing regions by loop modelling. In addition, evolutional information, secondary structure, disorder region, and processed three-dimensional structure are computed and visualized to help users better understand the protein. Conclusions: MUFOLD-DB integrates processed PDB sequence and structure data and multiple computational results, provides a friendly interface for users to retrieve, browse and download these data, and offers several useful functionalities to facilitate users' data operation.

关键词： protein data bank Loop Modelling protein Structure Prediction protein data bank file Relative Solvent Accessibility

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BUDDY-system: A web site for constructing a dataset of protein pairs between ligand-bound and unbound states

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BMC Research Notes 2011年第1期4卷 1-4页

作者： Morita, Mizuki Terada, Tohru Nakamura, Shugo Shimizu, Kentaro Department of Fundamental Research National Institute of Biomedical Innovation (NIBIO) Ibaraki Osaka 567-0085 7-6-8 Saito Asagi Japan Institute for Bioinformatics Research and Development (BIRD) Japan Science and Technology Agency (JST) Kawaguchi Saitama 332-0012 4-1-8 Honcho Japan Agricultural Bioinformatics Research Unit University of Tokyo Bunkyo Tokyo 113-8657 1-1-1 Yayoi Japan Department of Biotechnology University of Tokyo Bunkyo Tokyo 113-8657 1-1-1 Yayoi Japan

Background: Elucidating molecular recognition by proteins, such as in enzyme-substrate and receptor-ligand interactions, is a key to understanding biological phenomena. To delineate these protein interactions, it is important to perform structural bioinformatics studies relevant to molecular recognition. Such studies require a dataset of protein structure pairs between ligand-bound and unbound states. In many studies, the same well-designed and high-quality dataset has been used repeatedly, which has spurred the development of subsequent relevant research. Using previously constructed datasets, researchers are able to fairly compare obtained results with those of other studies;in addition, much effort and time is saved. Therefore, it is important to construct a refined dataset that will appeal to many researchers. However, constructing such datasets is not a trivial task. Findings. We have developed the BUDDY-system, a web site designed to support the building of a dataset comprising pairs of protein structures between ligand-bound and unbound states, which are widely used in various areas associated with molecular recognition. In addition to constructing a dataset, the BUDDY-system also allows the user to search for ligand-bound protein structures by its unbound state or by its ligand;and to search for ligands by a particular receptor protein. Conclusions: The BUDDY-system receives input from the user as a single entry or a dataset consisting of a list of ligand-bound state protein structures, unbound state protein structures, or ligands and returns to the user a list of protein structure pairs between the ligand-bound and the corresponding unbound states. This web site is designed for researchers who are involved not only in structural bioinformatics but also in experimental studies. The BUDDY-system is freely available on the web. © 2011 Morita et al;licensee BioMed Central Ltd.

关键词： protein data bank Unbind State Initial dataset protein data bank file protein data bank Entry

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ProDaMa: An open source Python library to generate protein structure datasets

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BMC Research Notes 2009年第1期2卷 1-4页

作者： Armano, Giuliano Manconi, Andrea Department of Electrical and Electronic Engineering University of Cagliari 09123 Cagliari P.zza D'Armi Italy

Background. The huge difference between the number of known sequences and known tertiary structures has justified the use of automated methods for protein analysis. Although a general methodology to solve these problems has not been yet devised, researchers are engaged in developing more accurate techniques and algorithms whose training plays a relevant role in determining their performance. From this perspective, particular importance is given to the training data used in experiments, and researchers are often engaged in the generation of specialized datasets that meet their requirements. Findings. To facilitate the task of generating specialized datasets we devised and implemented ProDaMa, an open source Python library than provides classes for retrieving, organizing, updating, analyzing, and filtering protein data. Conclusion. ProDaMa has been used to generate specialized datasets useful for secondary structure prediction and to develop a collaborative web application aimed at generating and sharing protein structure datasets. The library, the related database, and the documentation are freely available at the URL http://***/prodama. © 2009 Manconi et al.

关键词： protein data bank Local database Filter Operator Specialized dataset protein data bank file

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EasyModeller: A graphical interface to MODELLER

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BMC Research Notes 2010年第1期3卷 1-5页

作者： Kuntal, Bhusan K Aparoy, Polamarasetty Reddanna, Pallu School of Life Sciences University of Hyderabad Hyderabad 500 046 India

Background. MODELLER is a program for automated protein Homology Modeling. It is one of the most widely used tool for homology or comparative modeling of protein three-dimensional structures, but most users find it a bit difficult to start with MODELLER as it is command line based and requires knowledge of basic Python scripting to use it efficiently. Findings. The study was designed with an aim to develop of "EasyModeller" tool as a frontend graphical interface to MODELLER using Perl/Tk, which can be used as a standalone tool in windows platform with MODELLER and Python preinstalled. It helps inexperienced users to perform modeling, assessment, visualization, and optimization of protein models in a simple and straightforward way. Conclusion. EasyModeller provides a graphical straight forward interface and functions as a stand-alone tool which can be used in a standard personal computer with Microsoft Windows as the operating system. © 2010 Reddanna et al;licensee BioMed Central Ltd.

关键词： protein data bank Template Structure Loop Modeling protein data bank file Chain Information

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