Dynamics in Natural and Designed Elastins and their Relation to Elastic Fiber Structure and Recoil

Abstract

biochemistry is computationally predictable. Partial Order Optimum Likelihood (POOL) is a machine learning method developed by us to predict residues important for function, using the 3D structure of the query protein. The input features to POOL are based on computed electrostatic and chemical properties from THEMATICS. These input features are effectively measures of the strength of coupling between protonation events. POOL is used to predict the residues important for catalysis and ligand binding. Typical predicted catalytic sites are characterized by networks of strongly coupled protonation states; these networks impart the necessary electrostatic and proton-transfer properties to the active residues in the first layer around the reacting substrate molecule(s). Most often these networks include first-, second-, and sometimes third-layer residues. POOL-predicted, multi-layer active sites with significant participation by distal residues have been verified experimentally by single-point sitedirected mutagenesis and kinetics assays for multiple examples, including human phosphoglucose isomerase, human PARK2 (an E3 ubiquitin ligase), and E. coli ornithine transcarbamoylase. Mechanisms for the effects of diseaseassociated mutations, and implications for personalized medicine, are discussed.