Dataset Information

Identification of structurally conserved residues of proteins in absence of structural homologs using neural network ensemble.

ABSTRACT: So far various bioinformatics and machine learning techniques applied for identification of sequence and functionally conserved residues in proteins. Although few computational methods are available for the prediction of structurally conserved residues from protein structure, almost all methods require homologous structural information and structure-based alignments, which still prove to be a bottleneck in protein structure comparison studies. In this work, we developed a neural network approach for identification of structurally important residues from a single protein structure without using homologous structural information and structural alignment.A neural network ensemble (NNE) method that utilizes negative correlation learning (NCL) approach was developed for identification of structurally conserved residues (SCRs) in proteins using features that represent amino acid conservation and composition, physico-chemical properties and structural properties. The NCL-NNE method was applied to 6042 SCRs that have been extracted from 496 protein domains. This method obtained high prediction sensitivity (92.8%) and quality (Matthew's correlation coefficient is 0.852) in identification of SCRs. Further benchmarking using 60 protein domains containing 1657 SCRs that were not part of the training and testing datasets shows that the NCL-NNE can correctly predict SCRs with approximately 90% sensitivity. These results suggest the usefulness of NCL-NNE for facilitating the identification of SCRs utilizing information derived from a single protein structure. Therefore, this method could be extremely effective in large-scale benchmarking studies where reliable structural homologs and alignments are limited.

SUBMITTER: Pugalenthi G

PROVIDER: S-EPMC2638999 | biostudies-literature | 2009 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Identification of structurally conserved residues of proteins in absence of structural homologs using neural network ensemble.

Pugalenthi Ganesan G Tang Ke K Suganthan P N PN Chakrabarti Saikat S

Bioinformatics (Oxford, England) 20081127 2

<h4>Motivation</h4>So far various bioinformatics and machine learning techniques applied for identification of sequence and functionally conserved residues in proteins. Although few computational methods are available for the prediction of structurally conserved residues from protein structure, almost all methods require homologous structural information and structure-based alignments, which still prove to be a bottleneck in protein structure comparison studies. In this work, we developed a neur ...[more]

PMID: 19038986

Similar Datasets

Project description:BACKGROUND:Single-molecule microscopic experiments can measure the mechanical response of proteins to pulling forces applied externally along different directions (inducing different residue pairs in the proteins by uniaxial tension). This response to external forces away from equilibrium should in principle, correlate with the flexibility or stiffness of proteins in their folded states. Here, a simple topology-based atomistic anisotropic network model (ANM) is shown which captures the protein flexibility as a fundamental property that determines the collective dynamics and hence, the protein conformations in native state. METHODS:An all-atom ANM is used to define two measures of protein flexibility in the native state. One measure quantifies overall stiffness of the protein and the other one quantifies protein stiffness along a particular direction which is effectively the mechanical resistance of the protein towards external pulling force exerted along that direction. These measures are sensitive to the protein sequence and yields reliable values through computations of normal modes of the protein. RESULTS:ANM at an atomistic level (heavy atoms) explains the experimental (atomic force microscopy) observations viz., different mechanical stability of structurally similar but sequentially distinct proteins which, otherwise were implied to possess similar mechanical properties from analytical/theoretical coarse-grained (backbone only) models. The results are exclusively demonstrated for human fibronectin (FN) protein domains. CONCLUSIONS:The topology of interatomic contacts in the folded states of proteins essentially determines the native flexibility. The mechanical differences of topologically similar proteins are captured from a high-resolution (atomic level) ANM at a low computational cost. The relative trend in flexibility of such proteins is reflected in their stability differences that they exhibit while unfolding in atomic force microscopic (AFM) experiments.

Dataset Information

Identification of structurally conserved residues of proteins in absence of structural homologs using neural network ensemble.

Publications

Identification of structurally conserved residues of proteins in absence of structural homologs using neural network ensemble.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets