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Information belonging to 3D structures of the proteins, which are the most fundamental macromolecules of life, plays a key role in bioinformatics studies. Protein fold recognition is considered as an important stage to determine 3D structures of the proteins. In this study, subsequence profile map (SPMap) is firstly used in protein fold recognition. The features extracted from each fold class are...
In an attempt to enhance the potential based fold - recognition methods of remote homologs, we propose a new approach “Higher Order Residue Interaction Based ALgorithm for Fold REcognition (HORIBALFRE)”, where we incorporated the potential contributions not just from one-body and two-body terms, but from three body (triplet interactions) and four-body (quadruple interaction) interactions, to implement...
Optimally combining available information is one of the key challenges in knowledge-driven prediction techniques. In this study, we evaluate six Phi and Psi-based backbone alphabets. We show that the addition of predicted backbone conformations to SVM classifiers can improve fold recognition. Our experimental results show that the inclusion of predicted backbone conformations in our feature representation...
Automatic classification of proteins using machine learning is an important problem that has received significant attention in the literature. One feature of this problem is that expert-defined hierarchies of protein classes exist and can potentially be exploited to improve classification performance. In this article, we investigate empirically whether this is the case for two such hierarchies. We...
Remote homology detection and fold recognition are the central problems in protein classification. In real applications, kernel algorithms that are both accurate and efficient are required for classification of large databases. We explore a class of partial profile alignment kernels to be used with support vector machines (SVMs) for remote homology detection and fold recognition. While existing profile-based...
One of the most important research aims is to understand the relationship between structure and function of protein. Inspired by this motivation, automatic classification of protein structure becomes one of major research approaches. However, how to extract compact and effective feature to characterize protein structure is still a challenge to it. In this paper, 3-D tertiary structure of protein fold...
Inter-residue interactions play an important role in governing the folding and stability of protein structures. In this work, we have analyzed the contacts between amino acid residues in different folding types of globular proteins and various ranges of folding rates. Based on amino acid contacts a novel parameter, multiple contact index has been developed for understanding protein folding rates....
Fold recognition is an important issue in protein structure research. The Rossmann-fold protein that has typical structure is a common kind of alpha/beta protein. The training set, selected from 22 families, is constituted of 79 Rossmann-fold proteins which have less than 25% sequence identity with each other. The hierarchical clustering method according to RMSD is applied and a profile-HMM based...
Fold recognition based on sequence-derived features is a complex classification problem and usually sequence-derived features are exploited using proper machine learning techniques. Here we adress the task of fold recognition on a protein similarity network (PSN) basis. We construct a protein sequence similarity network (PSeSN) using a set of 125 sequence-derived features for an available set of 311...
Protein data contain discriminative patterns that can be used in many beneficial applications if they are defined correctly. Protein classification in terms of fold recognition plays an important role in computational protein analysis, since it can contribute to the determination of the function of a protein whose structure is unknown. In this paper, a probabilistic neural network ensemble (PNNE)...
Fold recognition is a challenging field strongly related with function determination which is of high interest for the biologists and the pharmaceutical industry. Hidden Markov Models (HMMs) have been largely applied for this purpose. In this work, the fold recognition accuracy of a recently introduced Hidden Markov Model with a reduced state-space topology is improved. This model employs an efficient...
Protein sequence alignments reveal the evolutionary information between homologous sequences. Traditional sequence alignment methods only use sequence information and the structure information from template is ignored. Recently, Kleinjung et al. developed a contact-based sequence alignment method that used the structural information from side-chain contacts. Alignment scores are provided by the CAO...
Due to the relatively large gap of knowledge between gene identification and gene function, the ability to construct a computational model describing gene function from sequence information has become an important area of research. In order to understand the biological role of a specific gene, we will require knowledge of the corresponding protein's structure and function. We present a support vector...
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