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

Abstract

Calculating protein stability and predicting stabilizing mutations remain exceedingly difficult tasks, largely due to the inadequacy of potential functions, the difficulty of modeling entropy and the unfolded state, and challenges of sampling, particularly of backbone conformations. Yet, computational design has produced some remarkably stable proteins in recent years, apparently owing to near ideality in structure and sequence features. With caveats, computational prediction of stability can be used to guide mutation, and mutations derived from consensus sequence analysis, especially improved by recent co-variation filters, are very likely to stabilize without sacrificing function. The combination of computational and statistical approaches with library approaches, including new technologies such as deep sequencing and high throughput stability measurements, point to a very exciting near term future for stability engineering, even with difficult computational issues remaining.

Experimental and Computational Analysis of Protein Stabilization by Gly-to-d-Ala Substitution: A Convolution of Native State and Unfolded State Effects

“…The analysis presented here demonstrates that the replacement of such glycines is expected to be stabilizing 95% of the cases and to be significantly stabilizing 80% of the cases. This expected success rate is considerably better than has been observed with consensus method based on multiple sequence alignment and is comparable to the most successful consensus method which take into account co-variation, suggesting that rational protein design is possible in the absence of structural information 49–50 .…”

Prediction of quaternary structure by analysis of hot spot residues in protein‐protein interfaces: the case of anthranilate phosphoribosyltransferases

“…Feature‐based prediction approaches use a variety of different chemical and physical characteristics of interface residues, such as solvent accessible surface area, protrusion index, residue conservation, or B factors. However, predictive performances of different methods vary with data sets, and experimental validations of those computational predictions are scarce …”

Assessment of methods for predicting the effects of PTEN and TPMT protein variants

“…A key advantage of VSP assays is that they are applicable to a wide range of proteins and can measure effects of all possible mutations at all positions. They particularly help overcome issues of overrepresentation of to-alanine (Magliery, 2015) and destabilizing mutations (Pucci, Bernaerts, Kwasigroch, & Rooman, 2018), the underrepresentation of "inverse" mutations (Thiltgen & Goldstein, 2012), and general errors in curation (Yang et al, 2018). Thus, VSP assays are an attractive alternative to existing data sources for the development and validation of computational methods.…”