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OJBâ

Online Journal of Bioinformaticsâ

Volume 1: 83-95, 2002

In Silico markovian bioinformatics for predicting 1Ha-NMR chemical shifts in mouse epidermis growth factor (mEGF)

 

Díaz HG1-2, De Armas RR2, Uriarte E3

 

1 Chemical Bio-actives Center, 2 Department of Chemistry, Central University of "Las Villas" 54830, Cuba. 3Department of Organic Chemistry, Faculty of Pharmacy, University of Santiago de Compostela, Spain.

 

ABSTRACT

 

Díaz HG, De Armas RR, Uriarte E.,  In Silico markovian bioinformatics for predicting 1Ha-NMR chemical shifts in mouse epidermis growth factor (mEGF)., Online J Bioinformatics 1:83-95, 2002.  A Markovian chemical In silico design (MARCH-INSIDE) method produced piecewise linear regression (r = 0.93) between protein backbone descriptors with (1Ha- NMR) values for mouse mEGF protein. The method extends preliminary findings on electron intra-molecular Markovian delocalization to polymer chains. Original results were supported by cross validation procedures excluding each fourth amino acid from processing resulting in r = Q1 = 0.93, Q2 = 0.94, Q3 = 0.94 and Q4 = 0.96 with a break-point value of 4.42 ppm characterizing 2 amino acid clusters.

 

KEYWORDS: Markov chains, Proteins, mouse Epidermis Growth Factor, NMR, piecewise regression, Bioinformatics.

 

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