A physics-based energy function allows the computational redesign of a PDZ domain

Abstract

A powerful approach to understand protein structure and evolution is to perform computer simulations that mimic aspects of evolution. In particular, structure-based computational protein design (CPD) can address the inverse folding problem, exploring a large space of amino acid sequences and selecting ones predicted to adopt a given fold. Previously, CPD has been used to entirely redesign several proteins: all or most of the protein sequence was allowed to mutate freely; among sampled sequences, those with low computed folding energy were selected, and a few percent of them did indeed adopt the correct fold. Those studies used an energy function that was partly or largely knowledge-based, with several empirical terms. Here, we show that a PDZ domain can be entirely redesigned using a "physics-based" energy function that combines standard molecular mechanics and a recent, continuum electrostatic solvent model. Many thousands of sequences were generated by Monte Carlo simulation. Among the lowest-energy sequences, three were chosen for experimental testing. All three could be overexpressed and had native-like circular dichroism and 1D-NMR spectra. Two exhibited an increase in their thermal denaturation curves when a peptide ligand was present, indicating binding and suggesting correctly folded proteins. Evidently, the physical principles that govern molecular mechanics and continuum electrostatics are sufficient to perform whole-protein redesign. This is encouraging, since these methods provide physical insights, can be systematically improved, and are transferable to other biopolymers and ligands of medical or technological interest.2

De novo protein design by an energy function based on series expansion in distance and orientation dependence

“…The low success rate described above is largely due to the lack of an accurate energy function (Li, et al, 2013). This happened despite that many knowledge-based, physical-based and empirical energy functions were developed (Boas and Harbury, 2007;Opuu, et al, 2020;Poole and Ranganathan, 2006;Vizcarra and Mayo, 2005) with newly established terms such as the solvent-exposure dependent potential (DeLuca, et al, 2011;Xiong, et al, 2014) and the structure-derived sequence profile (Dai, et al, 2010;Li, et al, 2014). A lipophilicity-based energy function for membrane-protein modeling and design was also developed (Weinstein, et al, 2019).…”