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The Classification of Local Structures for Modeling Protein Structure Geometric Constraints

Kentaro Onizuka, Kiyoshi Asai, Stophen T. C. Wong
1992 Genome Informatics Series  
In this paper, we describe a new method to classify three-dimensional local structures of protein and to model the geometric constraints of the protein tertiary structures.  ...  These constraints would allow us to predict tertiary structure more accurately than existing techniques. 2  ...  This method can he used to classify local structures of various sizes, and to model the geometric constraints of protein structure. We have illustrated its practicality with experimental results.  ... 
doi:10.11234/gi1990.3.113 fatcat:jkvflo553vf33kpkr2zemk7tnq

Application of Bioinformatics on Protein Structure Prediction

2017 DEStech Transactions on Computer Science and Engineering  
This paper describes the establishment and development of common database of protein, protein structure analysis, protein secondary and tertiary structure prediction; The application of bioinformatics  ...  in protein structure analysis is comprehensively summarized, which provides theoretical basis for the wide application of bioinformatics in protein engineering.  ...  relationships. which is obtained by manual classification, in which the proteins of known structure are hierarchical classified (Murzin et This is a three-dimensional structure database used by the  ... 
doi:10.12783/dtcse/cib2015/16173 fatcat:zywns4pb5bezfc6dw77y5rfbly

An Empirical Evaluation of the Effectiveness of Different Types of Predictor Attributes in Protein Function Prediction [chapter]

Fernando Otero, Marc Segond, Alex A. Freitas, Colin G. Johnson, Denis Robilliard, Cyril Fonlupt
2009 Studies in Computational Intelligence  
Many classification schemes for defining protein functions, such as Gene Ontology (GO), are organised in a hierarchical structure.  ...  In a data mining perspective, hierarchical structures present a more challenging problem, since the relationship between nodes need to be considered.  ...  In this chapter, we apply data mining methods to induce a classification model which can be used to predict the function of uncharacterised proteins using the Gene Ontology functional classification scheme  ... 
doi:10.1007/978-3-642-01536-6_13 fatcat:whuxereiwjhhpnhqanxjlbgi2y

Opportunities and Challenges in RNA Structural Modeling and Design

Tamar Schlick, Anna Marie Pyle
2017 Biophysical Journal  
Topics include fundamental processes of RNA, such as structural assemblies (hierarchical folding, multiple conformational states and their clustering), RNA motifs, and chemical reactivity of RNA, as used  ...  models Complementary to these atomic-level simulations and modeling studies, various coarse-grained representations of RNA (e.g., (33-37)) have been shown to be effective in many applications, including  ...  ACKNOWLEDGMENTS We thank all meeting participants for their generous time in contributing to the exciting presentations and discussions related to the work discussed here.  ... 
doi:10.1016/j.bpj.2016.12.037 pmid:28162235 pmcid:PMC5529161 fatcat:i55b65jtybhfrhxnskde3umbvm

Boosting Methods for Protein Fold Recognition: An Empirical Comparison

Yazhene Krishnaraj, Chandan K. Reddy
2008 2008 IEEE International Conference on Bioinformatics and Biomedicine  
Boosting algorithms have the potential to build efficient classification models in a very fast manner.  ...  Protein fold recognition is the prediction of protein's tertiary structure (Fold) given the protein's sequence without relying on sequence similarity.  ...  There are four distinct aspects of a protein's structure: Primary structure, Secondary structure, Tertiary structure and Quaternary structure.  ... 
doi:10.1109/bibm.2008.83 dblp:conf/bibm/KrishnarajR08 fatcat:tdsku2sefzabncrysmjcwvryki

Sequence-Based Prediction of Protein Folding Rates Using Contacts, Secondary Structures and Support Vector Machines

Guan Ning Lin, Zheng Wang, Dong Xu, Jianlin Cheng
2009 2009 IEEE International Conference on Bioinformatics and Biomedicine  
Here we developed a method, SeqRate, to predict both protein folding kinetic type (two-state versus multi-state) and real-value folding rate using features extracted from only protein sequence with support  ...  Predicting protein folding rate is useful for understanding protein folding process and guiding protein design.  ...  These methods require the tertiary structure topologies of a protein as input to predict its folding rate.  ... 
doi:10.1109/bibm.2009.21 dblp:conf/bibm/LinWXC09 fatcat:cmw2trfpbzattgbee45hfbrlly

SeqRate: sequence-based protein folding type classification and rates prediction

Guan Lin, Zheng Wang, Dong Xu, Jianlin Cheng
2010 BMC Bioinformatics  
Most previous methods of predicting protein folding rate require the tertiary structure of a protein as an input.  ...  Here we developed a method, SeqRate, to predict both protein folding kinetic type (two-state versus multi-state) and real-value folding rate using sequence length, amino acid composition, contact order  ...  These methods require the tertiary structure of a protein as input to predict its folding rate.  ... 
doi:10.1186/1471-2105-11-s3-s1 pmid:20438647 pmcid:PMC2863059 fatcat:qrxnubtetfakrjqnz7jmkcrt6q

Triage protein fold prediction

Hongxian He, Gregory McAllister, Temple F. Smith
2002 Proteins: Structure, Function, and Bioinformatics  
In the first step of the triage method, the most probable structural class is predicted using a set of manually constructed, high-level, generalized structural HMMs that represent seven general protein  ...  In the second step, only those fold models belonging to the determined structural class are selected for the final fold prediction.  ...  ACKNOWLEDGMENTS We thank Jadwiga Bienkowska for her helpful discussions about the model construction and Robert Rogers, Jr., for setting up and maintaining the web server.  ... 
doi:10.1002/prot.10194 pmid:12211033 fatcat:ubkspdktzneqvawwv55kyqjaje

Hierarchical Classification of Protein Folds Using a Novel Ensemble Classifier

Chen Lin, Ying Zou, Ji Qin, Xiangrong Liu, Yi Jiang, Caihuan Ke, Quan Zou, Andrew R. Dalby
2013 PLoS ONE  
Unfortunately, the prediction of protein fold patterns is challenging due to the existence of compound protein structures.  ...  Here, we processed the latest release of the Structural Classification of Proteins (SCOP, version 1.75) database and exploited novel techniques to impressively increase the accuracy of protein fold classification  ...  Fold pattern prediction, which represents a deeper level of analysis than protein structural classification [4] , lies between trapped secondary structure prediction and the partially effective tertiary  ... 
doi:10.1371/journal.pone.0056499 pmid:23437146 pmcid:PMC3577917 fatcat:mlhdl7rd25hy5dtkymfoxcnnjm

Computational RNA Structure Prediction

Marc Marti-Renom, Emidio Capriotti
2008 Current Bioinformatics  
In this review, we outline the general principles that govern RNA structure and describe the databases and algorithms for analyzing and predicting RNA secondary and tertiary structure.  ...  Finally, we assess the impact of the current coverage of the RNA structural space on comparative modeling RNA structures.  ...  The SCOR database organizes RNA motifs in a hierarchical classification system similar to the SCOP database for protein domains [43] .  ... 
doi:10.2174/157489308783329823 fatcat:rpo3hjt2nzgkzdgkhumwz7kmou

Introduction to informatics approaches in structural genomics: modeling and representation of function from macromolecular structure

Patricia C Babbitt, Philip E Bourne, Sean D Mooney
2005 Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing  
Leveraging of genomic context and evolutionary information to improve classification and predictive power is a second prominent theme in the papers represented here.  ...  This section describes work relevant to this problem from several perspectives, including new approaches that take advantage of combined structure and sequence-based classification.  ...  Tang, et al. provide an analysis of clusters of short protein segments. They find that their model improves both tertiary and secondary structure prediction methods.  ... 
pmid:15759637 fatcat:n6dx3qovxre5lfk2dcm3wqnmwm

Variable-Length Protein Sequence Motif Extraction Using Hierarchically-Clustered Hidden Markov Models

Cody Hudson, Bernard Chen
2013 2013 12th International Conference on Machine Learning and Applications  
This work would propose the Hierarchically Clustered-Hidden Markov Model approach, which represents the behavior and structure of proteins in terms of a Hidden Markov Model chain and hierarchically clusters  ...  Primary sequence motif extraction from protein amino sequences is a field of growing importance in bioinformatics due to its relevance to both sequential and structural analysis.  ...  Another prominent extension to this methodology is to extend its utility to include tertiary structure prediction.  ... 
doi:10.1109/icmla.2013.37 dblp:conf/icmla/HudsonC13 fatcat:lurhorjzdfbstjbk3jyt33niq4

PvaxDB: a comprehensive structural repository of Plasmodium vivax proteome

Ankita Singh, Rahul Kaushik, Himani Kuntal, B Jayaram
2018 Database: The Journal of Biological Databases and Curation  
This is also the first attempt to create a reliable comprehensive computational structural repository of all the soluble proteins of P. vivax.  ...  P.vivax comprises 5392 proteins mostly predicted, out of which 4211 are soluble proteins and 2205 of these belong to blood and liver stages of malarial cycle.  ...  protein sequence analysis and classification using prediction models or signatures compiled from different databases and LocTree3 (65) , which is a support vector machine learning-based hierarchical system  ... 
doi:10.1093/database/bay021 pmid:29688373 pmcid:PMC5852996 fatcat:zpogs4zborh7tf574z5f7yj75i

A New Toolkit for Modeling RNA from a Pseudo-Torsional Space

Namhee Kim, Tamar Schlick
2012 Journal of Molecular Biology  
Such cataloguing of RNA topologies has also led to RNA design. [19] [20] [21] [22] [23] In parallel, RNA structures have been predicted by assembling fragments from the coarse-grained libraries using programs  ...  using a virtual bond system that reduces the backbone's seven torsion degrees to two pseudo-torsional angles.  ...  Acknowledgements This work is supported by the National Science Foundation (DMS-0201160 and CCF-0727001) and the National Institutes of Health (GM100469 and GM081410) awards to T.S.  ... 
doi:10.1016/j.jmb.2012.05.027 pmid:22634179 fatcat:sbvgllohk5g2vah3kvtmkyyutq

Sample Reduction Strategies for Protein Secondary Structure Prediction

Sema Atasever, Zafer Aydın, Hasan Erbay, Mostafa Sabzekar
2019 Applied Sciences  
As new genes and proteins are discovered, the large size of the protein databases and datasets that can be used for training prediction models grows considerably.  ...  A two-stage hybrid classifier, which employs dynamic Bayesian networks and a support vector machine (SVM) has been shown to provide state-of-the-art prediction accuracy for protein secondary structure  ...  The tertiary structure is the global three-dimensional structure of an amino acid chain or a domain within a protein.  ... 
doi:10.3390/app9204429 fatcat:s7yhy7wcajdypgv2g2nsgeknlm
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