Conservation of protein dynamics in biological evolution

S. MAGUID; S. FERNÁNDEZ-ALBERTI; G. PARISI; J. ECHAVE

Montevideo, Uruguay

Congreso; 6th International Conference of Biological Physics (ICBP 2007); 2007

Since the earliest works of Chothia and Lesk,  the relationship between sequence and structure during protein evolution has been largely studied. It is well established that the structure of most protein families remains significantly conserved, even at low sequence identities. Understanding of the evolutionary divergence of any relevant property is useful for homology recognition and comparative modeling. Protein dynamics has special interest because vibrational motions connect different conformational states to perform biological function. Therefore, dynamics is the essential link between structure and function. The goal of this work is to analyze the evolutionary divergence of proteins vibrational dynamics.We have performed a systematical analysis of a large dataset of homologous proteins. We used the HOMSTRAD database of structurally aligned protein families, then grouping families into superfamilies according to the CAMPASS database classification. Proteins vibrational dynamics was determined using normal modes decomposition. Conventional molecular dynamics methods are computationally demanding for studying thousands of proteins. Instead, we considered a coarse-grained approach and calculated normal modes using the Gaussian Network Model (GNM) developed by Bahar et al. This model represents the crystallographic structure as an elastic network of a-carbons linked by springs within a cutoff distance. GNM has been shown to be a simple and efficient method to predict the collective fluctuation dynamics of proteins.All pairs of homologous proteins within the dataset were structurally aligned using MAMMOTH. We performed statistical analysis over all pairs to determine sequential, structural and dynamical similarities and their relationships. We report a slow divergence of low frequency normal modes, being conserved at family and superfamily levels. However, divergence increases very fast for higher modes. Therefore, the number of conserved modes is proposed as a measurement of dynamical similarity. In all cases, we found a higher dynamical conservation at family level, even for different structural similarities.