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Online Journal of Bioinformatics©
Volume 6 : 42-50, 2005.
Substitution of polymorphic amino acids in proteins:Predicting functional alterations using amino acid properties.
Baksi K, Bhattacharyya NP.
Crystallography and Molecular Biology Division, Saha Institute of Nuclear Physics, Kolkata 700 064, India.
Baksi K., Bhattacharyya NP. Substitution of polymorphic amino acid in proteins:Predicting functional alterations using amino acid properties. Onl J Bioinform., 6:42-50, 2005. Polymorphic amino acid substitutions (AAS) among non-synonymous single nucleotide polymorphisms (nsSNPs), likely to alter the function of proteins were prioritized. Calculated changes in molecular volume (δV), molecular mass (δM), molecular surface (δS) and polarity (δP) of amino acids in 1018 evolutionary neutral (set I) AASs and 566 AASs that are known to alter the function (set II) (training sets) were calculated. Mean values of δV, δM, δS and δP were (28.5 ± 21.8) Å3, (23.6 ± 18.1) D, (34.4 ±25.2) Å2 and (1.3 ± 1.4) units respectively for set I. For set II, these values were (48.4 ± 35.0) Å3, (36.8 ± 24.0) D, (55.3 ± 35.7) Å2 and (2.9 ± 2.0) units respectively which were higher than that obtained in the neutral data set I (for all of the parameters p < 0.0001, t test). Linear Discriminant Analysis (LDA) revealed that both sets had significant differences between their centroid positions for all four parameters taken together as variables (Wilks’ Lambda=0.756, p<0.001). The findings showed that depending on the changes in the above properties of the amino acids, the training sets could be differentiated. On the basis of this observation, we proposed that changes in any one of the parameters beyond δV > 50Å3, δM > 40D, δS> 60 Å2 and δP > 3 units were likely to alter the function of the proteins. Using this prediction rule, we analyzed 756 AAS in an independent evolutionary data set (IA), 15,066 AAS from Human Mutation Database (HGMD, set III) and 3,747 validated nsSNPs AAS (set IV). We predicted that 30% of AAS in set IA, 69% AAS in set III and 44% AAS of set IV were likely to alter the function of the proteins by such substitutions. False positive error rate for this prediction was thus 30%. Subtracting the possible false prediction data (30%), about 14% of the validated nsSNPs is likely to be deleterious. We compared this prediction rule with BLOSUM62 and SIFT, and obtained comparable results. The findings suggest that the simple amino acid based prediction (SABP) rule, based on the intrinsic properties of the amino acids only, could be used to prioritize nsSNPs and used for association studies in complex genetic diseases.
KEYWORDS: Amino Acid, protein, prediction