PremPS: Predicting the impact of missense mutations on protein stability

Abstract

Computational methods that predict protein stability changes induced by missense mutations have made a lot of progress over the past decades. Most of the available methods however have very limited accuracy in predicting stabilizing mutations because existing experimental sets are dominated by mutations reducing protein stability. Moreover, few approaches could consistently perform well across different test cases. To address these issues, we developed a new computational method PremPS to more accurately evaluate the effects of missense mutations on protein stability. The PremPS method is composed of only ten evolutionary- and structure-based features and parameterized on a balanced dataset with an equal number of stabilizing and destabilizing mutations. A comprehensive comparison of the predictive performance of PremPS with other available methods on nine benchmark datasets confirms that our approach consistently outperforms other methods and shows considerable improvement in estimating the impacts of stabilizing mutations. A protein could have multiple structures available, and if another structure of the same protein is used, the predicted change in stability for structure-based methods might be different. Thus, we further estimated the impact of using different structures on prediction accuracy, and demonstrate that our method performs well across different types of structures except for low-resolution structures and models built based on templates with low sequence identity. PremPS can be used for finding functionally important variants, revealing the molecular mechanisms of functional influences and protein design. PremPS is freely available at https://lilab.jysw.suda.edu.cn/research/PremPS/, which allows to do large-scale mutational scanning and takes about four minutes to perform calculations for a single mutation per protein with ~ 300 residues and requires ~ 0.4 seconds for each additional mutation.

Prediction of residue-specific contributions to binding and thermal stability using yeast surface display

“…It has been shown that in vitro protein thermal stability and free energy of unfolding are correlated [23,34,35]. We therefore predicted the free energy of unfolding for CcdB mutants using SDM [36], mCSM [37], PoPMuSiC [38], DynaMut [39], DUET [40], MAESTROweb [41], DeepDDG [42], CUPSAT [43], PremPS [44] and INPS-MD [45]. We found moderate correlations, with DeepDDG performing the best (r=0.59), but still poorer compared to our prediction from YSD data (r=0.83).…”

Regulatory approved monoclonal antibodies contain framework mutations predicted from human antibody repertoires

“…To understand the mechanistic basis for S54A selection, we analyzed the four FDA-approved mAbs listed above with this S54A mutation. To test for increased global stability of the mAbs, we utilized the online protein stability prediction algorithm PremPS (50) to determine the impact of reverting the alanine at position 54 in each of the four mAbs back to its identity of a serine in the respective germline sequences ( Figure 7B ). The A54S reversion mutation was predicted by PremPS to decrease mAb stability (calculated as ΔΔG) for all four mAbs, indicating the forward mutation (S54A) in the mature FDA-approved sequences was predicted to be stabilizing.…”

Informing shigellosis prevention and control through pathogen genomics

“…Full details of the modelling and ranking are shown in table 7. The effect of point mutations on the stability of the antigen candidates was assessed using PremPS, and the default criterion of (ΔΔG > 1 kcal mol -1 ) used to defining highly destabilising mutations (79).…”