Hybrid approaches for the detection of networks of critical residues involved in functional motions in protein families

Abstract

Background Currently there is great interest in identifying critical residues in proteins, to improve our understanding and allow for the engineering of protein families. Diverse approaches combine sequence information, structural data, dynamics analysis and functional description to determine the importance of amino acids with regards to protein function. In this work, we propose a hybrid approach for the identification of critical residues in proteins, combining the use of evolutionary information (co-evolution), cross-correlation of atomic fluctuations derived from Anisotropic Normal Mode Analysis simu-lations [1] (ANMA) and network analysis. Subsequently we have compared this method to existing approaches. Results By combining the information of the covariance matrix derived from Statistical Coupling Analysis (SCA) [2] and the cross-correlation matrix of atomic fluctuations derived from ANMA, it was possible to identify a network of evo-lutionarily coupled residues involved in relevant motions in protein families. The outstanding sites revealed by our hybrid approach (ANMA.SCA) showed a high correspon-dence with experimental data, confirming the critical role of these sites in the functional mobility of proteins. In addition, our approach was found to be complementary to previous approaches. It maintained a good correspondence with approaches derived from extensive molecular dynamics, while being faster and less expensive in terms of computational resources [3]. Conclusions The hybrid approach ANMA.SCA opens a wide range of possibilities in the study of functional motion within protein families. By means of detecting networks of cri-tical sites and their topology it is able to reveal the hid-den aspects of protein dynamics. Acknowledgements DA-M acknowledges the PhD fellowship (35083) from CONACYT and (IN-204714) DGAPA. EP-R was supported by a grant (IN-204714) from DGAPA and (155116) from CONACYT.