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The history of the CATH structural classification of protein domains

BIOCHIMIE 2015年 119卷 209-217页

作者： Sillitoe, Ian Dawson, Natalie Thornton, Janet Orengo, Christine UCL London WC1E 6BT England

This article presents a historical review of the protein structure classification database CATH. Together with the SCOP database, CATH remains comprehensive and reasonably up-to-date with the now more than 100,000 protein structures in the PDB. We review the expansion of the CATH and SCOP resources to capture predicted domain structures in the genome sequence data and to provide information on the likely functions of proteins mediated by their constituent domains. The establishment of comprehensive function annotation resources has also meant that domain families can be functionally annotated allowing insights into functional divergence and evolution within protein families. (C) 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://***/licenses/by/4.0/).

关键词： Protein structure structure classification

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Robust structural identification via polyhedral template matching

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MODELLING AND SIMULATION IN MATERIALS SCIENCE AND ENGINEERING 2016年第5期24卷 055007-055007页

作者： Larsen, Peter Mahler Schmidt, Soren Schiotz, Jakob Tech Univ Denmark Dept Phys DK-2800 Lyngby Denmark

Successful scientific applications of large-scale molecular dynamics often rely on automated methods for identifying the local crystalline structure of condensed phases. Many existing methods for structural identification, such as common neighbour analysis, rely on interatomic distances (or thresholds thereof) to classify atomic structure. As a consequence they are sensitive to strain and thermal displacements, and preprocessing such as quenching or temporal averaging of the atomic positions is necessary to provide reliable identifications. We propose a new method, polyhedral template matching (PTM), which classifies structures according to the topology of the local atomic environment, without any ambiguity in the classification, and with greater reliability than e.g. common neighbour analysis in the presence of thermal fluctuations. We demonstrate that the method can reliably be used to identify structures even in simulations near the melting point, and that it can identify the most common ordered alloy structures as well. In addition, the method makes it easy to identify the local lattice orientation in polycrystalline samples, and to calculate the local strain tensor. An implementation is made available under a Free and Open Source Software license.

关键词： structure classification atomic-scale simulations crystal structure atomic systems visualization analysis of molecular dynamics neighbor analysis alloy classification

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Efficient Non-Local Kernel Regression With Structural classification For Multiview Image Denoising 35

Efficient Non-Local Kernel Regression With Structural Classi...

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IEEE Region 10 Conference

作者： Lin, Zhou-Chi Wu, Jia-Fei Chan, Shing-Chow Univ Hong Kong Dept Elect & Elect Engn Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781479986415

This paper presents an effective image structure classification method, which was recently proposed for selecting the key parameter of non-local kernel regression (NLKR) namely the kernel bandwidth. Meanwhile, to overcome the problem of intensive computation cost of the non-local patch searching in NLKR, a fast patch searching strategy is proposed according to the classified structure regions. The proposed structure-aware NLKR (SA-NLKR) is applied to the multiview image denoising problem and the effectiveness of the proposed algorithm is illustrated by experimental results.

关键词： Non-local Kernel Regression structure classification Multiview Image Denoising

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Topological framework for local structure analysis in condensed matter

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PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA 2015年第43期112卷 E5769-E5776页

作者： Lazar, Emanuel A. Han, Jian Srolovitz, David J. Univ Penn Dept Mat Sci & Engn Philadelphia PA 19104 USA Univ Penn Dept Mech Engn & Appl Mech Philadelphia PA 19104 USA

Physical systems are frequently modeled as sets of points in space, each representing the position of an atom, molecule, or mesoscale particle. As many properties of such systems depend on the underlying ordering of their constituent particles, understanding that structure is a primary objective of condensed matter research. Although perfect crystals are fully described by a set of translation and basis vectors, real-world materials are never perfect, as thermal vibrations and defects introduce significant deviation from ideal order. Meanwhile, liquids and glasses present yet more complexity. A complete understanding of structure thus remains a central, open problem. Here we propose a unified mathematical framework, based on the topology of the Voronoi cell of a particle, for classifying local structure in ordered and disordered systems that is powerful and practical. We explain the underlying reason why this topological description of local structure is better suited for structural analysis than continuous descriptions. We demonstrate the connection of this approach to the behavior of physical systems and explore how crystalline structure is compromised at elevated temperatures. We also illustrate potential applications to identifying defects in plastically deformed polycrystals at high temperatures, automating analysis of complex structures, and characterizing general disordered systems.

关键词： structure classification Voronoi topology atomic systems visualization

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A pre-classification strategy for identification of compounds in traditional Chinese medicine analogous formulas by high-performance liquid chromatography-Mass spectrometry

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JOURNAL OF PHARMACEUTICAL AND BIOMEDICAL ANALYSIS 2014年第0期92卷 82-89页

作者： Xiao, Shun Luo, Kedi Wen, Xuexun Fan, Xiaohui Cheng, Yiyu Zhejiang Univ Coll Pharmaceut Sci Pharmaceut Informat Inst Hangzhou 310058 Zhejiang Peoples R China

Compound identification is the essential step in the mechanistic research of traditional Chinese medicine (TCM). As the most common mode of practice in the clinic, TCM formula (Fangji or FJ in Chinese) is often utilized to treat diseases. With the proved therapeutic efficacies by century-long clinical applications, these TCM formulas have been the valuable resource of drug discovery and it is important to understand their mechanisms of action systemically in order to broaden their utilizations in modern system of medicine. structure elucidation of compounds in FJs has been a very difficult and time-consuming task due to the extremely complex composition. In this work we developed a pre-classification strategy on multiple spectrums (high-performance liquid chromatography-mass spectrometry) of TCM analogous formulas to assist compound identification from FJs. Here Ma Huang decoction group (Herba Ephedrae, MHs, widely used in respiratory diseases) were analyzed as an example of TCM analogous formulas to demonstrate the accuracy and speed of our method. The results showed this strategy is very effective and powerful for this kind of complex sample analysis. Additionally, we carried out the chemical differentiation study of this formula group, which was reported for the first time and would be useful to further pharmacological studies. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Traditional Chinese Medicine Analogous formulas structure classification Chemical differentiation HPLC-MS

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Protein Structural Information Derived from NMR Chemical Shift with the Neural Network Program

Protein Structural Information Derived from NMR Chemical Shi...

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作者： Yang Shen Ad Bax

Chemical shifts are obtained at the first stage of any protein structural study by NMR spectroscopy. Chemical shifts are known to be impacted by a wide range of structural factors, and the artificial neural network based TALOS-N program has been trained to extract backbone and side-chain torsion angles from 1H, 15N, and 13C shifts. The program is quite robust and typically yields backbone torsion angles for more than 90 % of the residues and side-chain χ1 rotamer information for about half of these, in addition to reliably predicting secondary structure. The use of TALOS-N is illustrated for the protein DinI, and torsion angles obtained by TALOS-N analysis from the measured chemical shifts of its backbone and 13Cβ nuclei are compared to those seen in a prior, experimentally determined structure. The program is also particularly useful for generating torsion angle restraints, which then can be used during standard NMR protein structure calculations. less

关键词： NMR Protein structure Analysis Neural Network Model Database Search structure Prediction structure classification Secondary structure Chemical shifts Side-chain conformation Backbone torsion angle

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A Geometric Clustering Algorithm and Its Applications to Structural Data

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18th Annual International Conference on Research in Computational Molecular Biology (RECOMB)

作者： Xu, Shutan Zou, Shuxue Wang, Lincong Jilin Univ Coll Comp Sci & Technol Changchun 130023 Jilin Peoples R China

ISBN: (纸本)9783319052694;9783319052687

An important feature of structural data especially those from structural determination and protein-ligand docking programs is that their distribution could be both uniform and non-uniform. Traditional clustering algorithms developed specifically for non-uniformly distributed data may not be adequate for their classification. Here we present a geometric partitional algorithm that could be applied to both uniformly and non-uniformly distributed data. The algorithm is a top-down approach that recursively selects the outliers as the seeds to form new clusters until all the structures within a cluster satisfy certain requirements. The applications of the algorithm to a diverse set of data from NMR structure determination, protein-ligand docking and simulation show that it is superior to the previous clustering algorithms for the identification of the correct but minor clusters. The algorithm should be useful for the identification of correct docking poses and for speeding up an iterative process widely used in NMR structure determination.

关键词： clustering algorithm structure determination protein-ligand docking structure classification

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Supervised machine learning algorithms for protein structure classification

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COMPUTATIONAL BIOLOGY AND CHEMISTRY 2009年第3期33卷 216-223页

作者： Jain, Pooja Garibaldi, Jonathan M. Hirst, Jonathan D. Univ Nottingham Sch Chem Nottingham NG7 2RD England Univ Nottingham Sch Comp Sci & IT Nottingham NG8 1BB England

We explore automation of protein structural classification using supervised machine learning methods on a set of 11,360 pairs of protein domains (up to 35% sequence identity) consisting of three secondary structure elements. Fifteen algorithms from five categories of supervised algorithms are evaluated for their ability to learn for a pair of protein domains, the deepest common structural level within the SCOP hierarchy, given a one-dimensional representation of the domain structures. This representation encapsulates evolutionary information in terms of sequence identity and structural information characterising the secondary structure elements and lengths of the respective domains. The evaluation is performed in two steps, first selecting the best performing base learners and subsequently evaluating boosted and bagged meta learners. The boosted random forest, a collection of decision trees, is found to be the most accurate, with a cross-validated accuracy of 97.0% and F-measures of 0.97, 0.85, 0.93 and 0.98 for classification of proteins to the Class, Fold, Super-Family and Family levels in the SCOP hierarchy. The meta learning regime, especially boosting, improved performance by more accurately classifying the instances from less populated classes. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： Supervised learning structure classification Random forest

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Structural Alphabets for Protein structure classification: A Comparison Study

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JOURNAL OF MOLECULAR BIOLOGY 2009年第2期387卷 431-450页

作者： Le, Quan Pollastri, Gianluca Koehl, Patrice Univ Calif Davis Dept Comp Sci Davis CA 95616 USA Univ Calif Davis Genome Ctr Davis CA 95616 USA Univ Coll Dublin Sch Informat & Comp Sci Complex & Adapt Syst Lab Dublin 2 Ireland

Finding structural similarities between proteins often helps reveal shared functionality, which otherwise might not be detected by native sequence information alone. Such similarity is usually detected and quantified by protein structure alignment. Determining the optimal alignment between two protein structures, however, remains a hard problem. An alternative approach is to approximate each three-dimensional protein structure using a sequence of motifs derived from a structural alphabet. Using this approach, structure comparison is performed by comparing the corresponding motif sequences or structural sequences. In this article, we measure the performance of such alphabets in the context of the protein structure classification problem. We consider both local and global structural sequences. Each letter of a local structural sequence corresponds to the best matching fragment to the corresponding local segment of the protein structure. The global structural sequence is designed to generate the best possible complete chain that matches the full protein structure. We use an alphabet of 20 letters, corresponding to a library of 20 motifs or protein fragments having four residues. We show that the global structural sequences approximate well the native structures of proteins, with an average coordinate root mean square of 0.69 angstrom over 2225 test proteins. The approximation is best for all a-proteins, while relatively poorer for all beta-proteins. We then test the performance of four different sequence representations of proteins (their native sequence, the sequence of their secondary-structure elements, and the local and global structural sequences based on our fragment library) with different classifiers in their ability to classify proteins that belong to five distinct folds of CATH. Without surprise, the primary sequence alone performs poorly as a structure classifier. We show that addition of either secondary-structure information or local information from the s

关键词： protein structure structural alphabet structure classification protein sequence comparison sequence feature space

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A Modular Perspective of Protein structures: Application to Fragment Based Loop Modeling

A Modular Perspective of Protein Structures: Application to ...

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作者： Narcis Fernandez-Fuentes Andras Fiser

Proteins can be decomposed into supersecondary structure modules. We used a generic definition of supersecondary structure elements, so-called Smotifs, which are composed of two flanking regular secondary structures connected by a loop, to explore the evolution and current variety of structure building blocks. Here, we discuss recent observations about the saturation of Smotif geometries in protein structures and how it opens new avenues in protein structure modeling and design. As a first application of these observations we describe our loop conformation modeling algorithm, ArchPred that takes advantage of Smotifs classification. In this application, instead of focusing on specific loop properties the method narrows down possible template conformations in other, often not homologous structures, by identifying the most likely supersecondary structure environment that cradles the loop. Beyond identifying the correct starting supersecondary structure geometry, it takes into account information of fit of anchor residues, sterical clashes, match of predicted and observed dihedral angle preferences, and local sequence signal. less

关键词： Loop Modeling Protein structure Analysis structure Prediction Database Search structure classification Secondary structure Supersecondary structure Smotif

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