A Flexible Volumetric Comparison of Protein Cavities can Reveal Patterns in Ligand Binding Specificity

Publication Type:

Conference Paper


Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine, ACM, p.445-454 (2014)


Conformational flexibility is an underlying cause of error in all comparisons of protein structure. Using flexible representations, some comparison algorithms can identify subtle functional similarities among distantly related proteins even when they exhibit different backbone conformations. The same techniques are not designed to identify subtle variations among closely related proteins that might cause differences in specificity. In such cases, molecular flexibility obscures structural details that influence the specific recognition of similar but non-identical ligands. To enhance the analysis of ligand binding specificity, this paper presents FAVA (Flexible Aggregate Volumetric Analysis), a conformationally robust tool for comparing similar binding cavities with different binding preferences. FAVA examines a large number of conformational samples to characterize local flexibility using Constructive Solid Geometry. Using molecular dynamics simulations as a source for conformational samples, we used FAVA to analyze a nonredundant sample of serine protease and enolase structures. Different snapshots from the same proteins exhibited significant variations in binding cavity shape. Nonetheless, analysis with FAVA revealed subfamilies with different binding preferences. FAVA also identified amino acids associated with differences in binding preferences, predicting established experimental results. These results illustrate a new approach to flexible comparison that uses sampled conformational data. It reveals that detailed comparisons of very similar proteins, such as those within small ligand binding cavities, are possible even in the presence of conformational flexibility. Identifying influences on specificity in this manner points to new applications of protein engineering and drug design.