AESOP: A Python Library for Investigating Electrostatics in Protein Interactions

• Published in: Biophysical journal Vol. 112; no. 9; pp. 1761 - 1766
• Main Authors: Harrison, Reed E.S., Mohan, Rohith R., Gorham, Ronald D., Kieslich, Chris A., Morikis, Dimitrios
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 09.05.2017; Biophysical Society; The Biophysical Society
• Subjects: Alanine - chemistry; Alanine - metabolism; Algorithms; Amino acids; Computation; Computational Tools; Computer applications; Diffusion; Diffusion rate; Electric fields; Electrostatic properties; Electrostatics; Enzymes; Internet; Mutants; Mutation; Protein families; Protein interaction; Protein structure; Proteins; Proteins - chemistry; Proteins - genetics; Proteins - metabolism; Python; Software; Static Electricity; Thermodynamics
• ISSN: 0006-3495; 1542-0086
• DOI: 10.1016/j.bpj.2017.04.005

Electric fields often play a role in guiding the association of protein complexes. Such interactions can be further engineered to accelerate complex association, resulting in protein systems with increased productivity. This is especially true for enzymes where reaction rates are typically diffusion limited. To facilitate quantitative comparisons of electrostatics in protein families and to describe electrostatic contributions of individual amino acids, we previously developed a computational framework called AESOP. We now implement this computational tool in Python with increased usability and the capability of performing calculations in parallel. AESOP utilizes PDB2PQR and Adaptive Poisson-Boltzmann Solver to generate grid-based electrostatic potential files for protein structures provided by the end user. There are methods within AESOP for quantitatively comparing sets of grid-based electrostatic potentials in terms of similarity or generating ensembles of electrostatic potential files for a library of mutants to quantify the effects of perturbations in protein structure and protein-protein association.