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Important amino acid properties for determining the transition state structures of two‐state protein mutants
Author(s) -
Gromiha M.Michael,
Selvaraj S
Publication year - 2002
Publication title -
febs letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.593
H-Index - 257
eISSN - 1873-3468
pISSN - 0014-5793
DOI - 10.1016/s0014-5793(02)03122-8
Subject(s) - phi value analysis , protein folding , enthalpy , entropy (arrow of time) , chemistry , accessible surface area , folding (dsp implementation) , mutant , native state , molecular dynamics , protein secondary structure , sh3 domain , thermodynamics , crystallography , biochemistry , computational chemistry , physics , gene , signal transduction , electrical engineering , engineering , proto oncogene tyrosine protein kinase src
Understanding the mechanism in the folding pathways of proteins is an important problem in molecular biology. The Φ ‐value analysis provides insight into the transition state structures during protein folding. In this work, we have analyzed the relationship between the observed Φ values upon mutations in two‐state proteins (FK506 binding protein, chymotrypsin inhibitor and src SH3 domain) and the changes in 48 various physico‐chemical, energetic and conformational properties. We found that the classification of mutations based on solvent accessibility improved the correlation significantly. The relationship between conformational properties and Φ values determines the presence/absence of secondary structures in the transition state. In buried mutations, the physical properties volume, shape and flexibility, and the thermodynamic properties enthalpy, entropy and free‐energy change have significant correlation with Φ . The short and medium‐range non‐bonded energy in partially buried mutations and average long‐range contacts in exposed mutations showed a strong correlation with Φ values. Multiple regression analysis incorporating combinations of three properties from among all possible combinations of the 48 properties increased the correlation coefficient up to 0.99, by an average rise of 20% for all the data sets. Information about local sequence and structure is more important in surface mutations than those in buried mutations for explaining the transition state structures of two‐state proteins. Further, the implications of our results for understanding the process of protein folding have been discussed.