Discriminating between stabilizing and destabilizing protein design mutations via recombination and simulation

Abstract

Accuracy of current computational protein design (CPD) methods is limited by inherent approximations in energy potentials and sampling. These limitations are often used to qualitatively explain design failures; however, relatively few studies provide specific examples or quantitative details that can be used to improve future CPD methods. Expanding the design method to include a library of sequences provides data that is well suited for discriminating between stabilizing and destabilizing design elements. Using thermophilic endoglucanase E1 from Acidothermus cellulolyticus as a model enzyme, we computationally designed a sequence with 60 mutations. The design sequence was rationally divided into structural blocks and recombined with the wild-type sequence. Resulting chimeras were assessed for activity and thermostability. Surprisingly, unlike previous chimera libraries, regression analysis based on one- and two-body effects was not sufficient for predicting chimera stability. Analysis of molecular dynamics simulations proved helpful in distinguishing stabilizing and destabilizing mutations. Reverting to the wild-type amino acid at destabilized sites partially regained design stability, and introducing predicted stabilizing mutations in wild-type E1 significantly enhanced thermostability. The ability to isolate stabilizing and destabilizing elements in computational design offers an opportunity to interpret previous design failures and improve future CPD methods.

Protein stability: computation, sequence statistics, and new experimental methods

“…The deconvolution uncovered a single mutation that stabilized recombinants by nearly 10 °C. Snow and colleagues recently produced a more cautionary tale on the concerted use of consensus, FoldX, Rosetta, molecular dynamics, and SCHEMA, in which additivity was not observed for schema from endoglucanase E1 [76]. While some stabilizing mutations were identified by multiple methods, one destabilizing mutation looked favorable by FoldX, Rosetta and consensus, raising red flags only in MD simulation.…”

A Computational Study of the Ionic Liquid-Induced Destabilization of the Miniprotein Trp-Cage

“…As several previous studies have demonstrated, RTIL-induced changes in protein structure can be investigated by molecular dynamics (MD) simulations. − Particularly, MD has been used to study the interactions of RTILs with various enzymes, including cellulases, xylanase, lipase, and chymotrypsin. − The protein–cation–solvent interactions have been investigated to determine the mechanism of action of RTILs on proteins. Most of these studies have used classical MD exclusively, or docking protocols coupled with spectroscopy. , However, ionic liquid-induced unfolding is challenging to simulate due to the rapidly increasing solvent viscosity with ion concentration, which results in sluggish dynamics that frustrate sampling. ,, In order to overcome such challenges, Pfaendtner and colleagues employed classical MD in conjunction with the metadynamics family of enhanced sampling methods to study protein stability in ionic liquids. − These studies investigated ionic-liquid-induced stabilization/destabilization mechanisms but did not address the folding routes and thermodynamics of proteins in the conformation-temperature phase space.…”

Destabilization of Human Serum Albumin by Ionic Liquids Studied Using Enhanced Molecular Dynamics Simulations

“…Several enzymes have been shown experimentally to tolerate the presence of ILs in concentrations of up to 20% v/v in water before deactivating; others have been shown to tolerate neat ILs and retain high levels of catalytic activity. The IL tolerance of several different enzymes, including xylanase, lipase, chymotrypsin, and several cellulases, has been studied by MD simulations and with various experimental techniques. − Specific interactions between cations, anions, and the proteins of interest were analyzed to determine why certain ILs stabilize or destabilize these enzymes. Typically, the analysis of IL–protein MD simulations includes many standard measures of protein or solvent structure and dynamics including root-mean-square deviation and fluctuation (RMSD and RMSF) of protein structure, principle component analysis (PCA), interaction lifetimes between ions and catalytic residues, surface residue entropy, structuring of water and ions around the protein, and secondary structure evolution, among others .…”