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

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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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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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Hydrophobic pulses predict transmembrane helix irregularities and channel transmembrane units

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BMC BIOINFORMATICS 2011年第1期12卷 1-10页

作者： Paulet, Damien Claustres, Mireille Beroud, Christophe Univ Montpellier I Montpellier France INSERM U827 Montpellier France Hop Arnaud de Villeneuve Lab Genet Mol CHU Montpellier Montpellier France

Background: Few high-resolution structures of integral membranes proteins are available, as crystallization of such proteins needs yet to overcome too many technical limitations. Nevertheless, prediction of their transmembrane (TM) structure by bioinformatics tools provides interesting insights on the topology of these proteins. Methods: We describe here how to extract new information from the analysis of hydrophobicity variations or hydrophobic pulses (HPulses) in the sequence of integral membrane proteins using the Hydrophobic Pulse Predictor, a new tool we developed for this purpose. To analyze the primary sequence of 70 integral membrane proteins we defined two levels of analysis: G1-HPulses for sliding windows of n = 2 to 6 and G2-HPulses for sliding windows of n = 12 to 16. Results: The G2-HPulse analysis of 541 transmembrane helices allowed the definition of the new concept of transmembrane unit (TMU) that groups together transmembrane helices and segments with potential adjacent structures. In addition, the G1-HPulse analysis identified helix irregularities that corresponded to kinks, partial helices or unannotated structural events. These irregularities could represent key dynamic elements that are alternatively activated depending on the channel status as illustrated by the crystal structures of the lactose permease in different conformations. Conclusions: Our results open a new way in the understanding of transmembrane secondary structures: hydrophobicity through hydrophobic pulses strongly impacts on such embedded structures and is not confined to define the transmembrane status of amino acids.

关键词： Integral Membrane protein Transmembrane Helix protein data bank file Structural Irregularity Lactose Permease

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Predicting DNA-binding locations and orientation on proteins using knowledge-based learning of geometric properties

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PROTEOME SCIENCE 2011年第1-Sup期9卷 S11-S11页

作者： Wang, Chien-Chih Chen, Chien-Yu Natl Taiwan Univ Dept Bioind Mechatron Engn Taipei 106 Taiwan Natl Taiwan Univ Dept Comp Sci & Informat Engn Taipei 106 Taiwan

Background: DNA-binding proteins perform their functions through specific or non-specific sequence recognition. Although many sequence-or structure-based approaches have been proposed to identify DNA-binding residues on proteins or protein-binding sites on DNA sequences with satisfied performance, it remains a challenging task to unveil the exact mechanism of protein-DNA interactions without crystal complex structures. Without information from complexes, the linkages between DNA-binding proteins and their binding sites on DNA are still missing. Methods: While it is still difficult to acquire co-crystallized structures in an efficient way, this study proposes a knowledge-based learning method to effectively predict DNA orientation and base locations around the protein's DNA-binding sites when given a protein structure. First, the functionally important residues of a query protein are predicted by a sequential pattern mining tool. After that, surface residues falling in the predicted functional regions are determined based on the given structure. These residues are then clustered based on their spatial coordinates and the resultant clusters are ranked by a proposed DNA-binding propensity function. Clusters with high DNA-binding propensities are treated as DNA-binding units (DBUs) and each DBU is analyzed by principal component analysis (PCA) to predict potential orientation of DNA grooves. More specifically, the proposed method is developed to predict the direction of the tangent line to the helix curve of the DNA groove where a DBU is going to bind. Results: This paper proposes a knowledge-based learning procedure to determine the spatial location of the DNA groove with respect to the query protein structure by considering geometric propensity between protein side chains and DNA bases. The 11 test cases used in this study reveal that the location and orientation of the DNA groove around a selected DBU can be predicted with satisfied errors. Conclusions: This study pr

关键词： Root Mean Square Deviation Query protein Amino Acid Type Sequential Pattern Mining 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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Potentials 'R'Us web-server for protein energy estimations with coarse-grained knowledge-based potentials

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BMC BIOINFORMATICS 2010年第1期11卷 92-92页

作者： Feng, Yaping Kloczkowski, Andrzej Jernigan, Robert L. Iowa State Univ Dept Biochem Biophys & Mol Biol Ames IA 50011 USA Iowa State Univ LH Baker Ctr Bioinformat & Biol Stat Ames IA 50011 USA

Background: Knowledge-based potentials have been widely used in the last 20 years for fold recognition, protein structure prediction from amino acid sequence, ligand binding, protein design, and many other purposes. However generally these are not readily accessible online. Results: Our new knowledge-based potential server makes available many of these potentials for easy use to automatically compute the energies of protein structures or models supplied. Our web server for protein energy estimation uses four-body potentials, short-range potentials, and 23 different two-body potentials. Users can select potentials according to their needs and preferences. files containing the coordinates of protein atoms in the PDB format can be uploaded as input. The results will be returned to the user's email address. Conclusions: Our Potentials 'R'Us server is an easily accessible, freely available tool with a web interface that collects all existing and future protein coarse-grained potentials and computes energies of multiple structural models.

关键词： protein data bank protein Structure Prediction Fold Recognition Nuclear Magnetic Resonance Structure protein data bank file

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The protein-DNA Interface database

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BMC BIOINFORMATICS 2010年第1期11卷 262-262页

作者： Norambuena, Tomas Melo, Francisco Pontificia Univ Catolica Chile Fac Ciencias Biol Dept Mol Genet & Microbiol Santiago Chile

The protein-DNA Interface database (PDIdb) is a repository containing relevant structural information of protein-DNA complexes solved by X-ray crystallography and available at the protein data bank. The database includes a simple functional classification of the protein-DNA complexes that consists of three hierarchical levels: Class, Type and Subtype. This classification has been defined and manually curated by humans based on the information gathered from several sources that include PDB, PubMed, CATH, SCOP and COPS. The current version of the database contains only structures with resolution of 2.5 angstrom or higher, accounting for a total of 922 entries. The major aim of this database is to contribute to the understanding of the main rules that underlie the molecular recognition process between DNA and proteins. To this end, the database is focused on each specific atomic interface rather than on the separated binding partners. Therefore, each entry in this database consists of a single and independent protein-DNA interface. We hope that PDIdb will be useful to many researchers working in fields such as the prediction of transcription factor binding sites in DNA, the study of specificity determinants that mediate enzyme recognition events, engineering and design of new DNA binding proteins with distinct binding specificity and affinity, among others. Finally, due to its friendly and easy-to-use web interface, we hope that PDIdb will also serve educational and teaching purposes.

关键词： protein data bank protein data bank Code Interaction Class Biological Unit protein data bank file

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