Document Detail

Amino Acid Sequence Autocorrelation vectors and ensembles of Bayesian-Regularized Genetic Neural Networks for prediction of conformational stability of human lysozyme mutants.
MedLine Citation:
PMID:  16711745     Owner:  NLM     Status:  MEDLINE    
Development of novel computational approaches for modeling protein properties from their primary structure is a main goal in applied proteomics. In this work, we reported the extension of the autocorrelation vector formalism to amino acid sequences for encoding protein structural information with modeling purposes. Amino Acid Sequence Autocorrelation (AASA) vectors were calculated by measuring the autocorrelations at sequence lags ranging from 1 to 15 on the protein primary structure of 48 amino acid/residue properties selected from the AAindex database. A total of 720 AASA descriptors were tested for building predictive models of the thermal unfolding Gibbs free energy change of human lysozyme mutants. In this sense, ensembles of Bayesian-Regularized Genetic Neural Networks (BRGNNs) were used for obtaining an optimum nonlinear model for the conformational stability. The ensemble predictor described about 88% and 68% variance of the data in training and test sets, respectively. Furthermore, the optimum AASA vector subset was shown not only to successfully model unfolding thermal stability but also to distribute wild-type and mutant lysozymes on a stability Self-organized Map (SOM) when used for unsupervised training of competitive neurons.
Julio Caballero; Leyden Fernández; José Ignacio Abreu; Michael Fernández
Related Documents :
23665035 - Utility of coi, cad and morphological data for resolving relationships within the genus...
10977075 - Prediction of the number of residue contacts in proteins.
16201915 - Bayesian models and markov chain monte carlo methods for protein motifs with the second...
25302315 - A design of irregular grid map for large-scale wi-fi lan fingerprint positioning systems.
12813725 - Analysis of multilocus models of association.
24303075 - Discriminating between medium-sized tridactyl trackmakers: tracking ornithopod tracks i...
Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  Journal of chemical information and modeling     Volume:  46     ISSN:  1549-9596     ISO Abbreviation:  -     Publication Date:    2006 May-Jun
Date Detail:
Created Date:  2006-05-22     Completed Date:  2006-06-28     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  101230060     Medline TA:  J Chem Inf Model     Country:  United States    
Other Details:
Languages:  eng     Pagination:  1255-68     Citation Subset:  IM    
Molecular Modeling Group, Center for Biotechnological Studies, Faculty of Agronomy, and Artificial Intelligence Lab, Faculty of Informatics, University of Matanzas, 44740 Matanzas, Cuba.
Export Citation:
APA/MLA Format     Download EndNote     Download BibTex
MeSH Terms
Bayes Theorem*
Muramidase / chemistry*,  genetics
Neural Networks (Computer)*
Protein Conformation
Reg. No./Substance:

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine

Previous Document:  Combinatorial QSAR modeling of P-glycoprotein substrates.
Next Document:  A quantum and deductive chemical study for all congeners of polybromo/chlorodibenzo-p-dioxin and pol...