Functional annotation of the mesophilic-like character of mutants in a cold-adapted enzyme by self-organising map analysis of their molecular dynamics.

Multiple comparison of the Molecular Dynamics (MD) trajectories of mutants in a cold-adapted α-amylase (AHA) could be used to elucidate functional features required to restore mesophilic-like activity. Unfortunately it is challenging to identify the different dynamic behaviors and correctly relate them to functional activity by routine analysis. We here employed a previously developed and robust two-stage approach that combines Self-Organising Maps (SOMs) and hierarchical clustering to compare conformational ensembles of proteins. Moreover, we designed a novel strategy to identify the specific mutations that more efficiently convert the dynamic signature of the psychrophilic enzyme (AHA) to that of the mesophilic counterpart (PPA). The SOM trained on AHA and its variants was used to classify a PPA MD ensemble and successfully highlighted the relationships between the flexibilities of the target enzyme and of the different mutants. Moreover the local features of the mutants that mostly influence their global flexibility in a mesophilic-like direction were detected. It turns out that mutations of the cold-adapted enzyme to hydrophobic and aromatic residues are the most effective in restoring the PPA dynamic features and could guide the design of more mesophilic-like mutants. In conclusion, our strategy can efficiently extract specific dynamic signatures related to function from multiple comparisons of MD conformational ensembles. Therefore, it can be a promising tool for protein engineering.