Zschach, H. | bioRxiv Science

bioRxiv2022

Accurate protein stability predictions from homology models

Calculating changes in protein stability ({Delta}{Delta}G) has been shown to be central for predicting the consequences of single amino acid substitutions in protein engineering as well as interpretation of genomic variants for disease risk. Structure-based calculations are considered most accurate, however the tools used to calculate {Delta}{Delta}Gs have been developed on experimentally resolved structures. Extending those calculations to homology models based on related proteins would greatly extend their applicability as large parts of e.g. the human proteome are not structurally resolved. In this study we aim to investigate the accuracy of {Delta}{Delta}G values predicted on homology models compared to crystal structures. Specifically, we identified four proteins with a large number of experimentally tested {Delta}{Delta}Gs and templates for homology modeling across a broad range of sequence identities, and selected three methods for {Delta}{Delta}G calculations to test. We find that {Delta}{Delta}G-values predicted from homology models compare equally well to experimental {Delta}{Delta}Gs as those predicted on experimentally established crystal structures, as long as the sequence identity of the model template to the target protein is at least 40%. In particular, the Rosetta cartesian_ddg protocol is robust against the small perturbations in the structure which homology modeling introduces. In an independent assessment, we observe a similar trend when using {Delta}{Delta}Gs to categorize variants as low or wild-type-like abundance. Overall, our results show that stability calculations performed on homology models can substitute for those on crystal structures with acceptable accuracy as long as the model is built on a template with sequence identity of at least 40% to the target protein.

biophysics↗

bioRxiv2022

Interpreting the molecular mechanisms of disease variants in human membrane proteins

Next-generation sequencing of human genomes reveals millions of missense variants, some of which may lead to loss of protein function and ultimately disease. We here investigate missense variants in membrane proteins -- key drivers in cell signaling and recognition. We find enrichment of pathogenic variants in the transmembrane region across 19,000 functionally classified variants in human membrane proteins. To accurately predict variant consequences, one fundamentally needs to understand the reasons for pathogenicity. A key mechanism underlying pathogenicity in missense variants of soluble proteins has been shown to be loss of stability. Membrane proteins though are widely understudied. We here interpret for the first time on a larger scale variant effects by performing structure-based estimations of changes in thermodynamic stability under the usage of a membrane-specific force-field and evolutionary conservation analyses of 15 transmembrane proteins. We find evidence for loss of stability being the cause of pathogenicity in more than half of the pathogenic variants, indicating that this is a driving factor also in membrane-protein-associated diseases. Our findings show how computational tools aid in gaining mechanistic insights into variant consequences for membrane proteins. To enable broader analyses of disease-related and population variants, we include variant mappings for the entire human proteome. SIGNIFICANCEGenome sequencing is revealing thousands of variants in each individual, some of which may increase disease risks. In soluble proteins, stability calculations have successfully been used to identify variants that are likely pathogenic due to loss of protein stability and subsequent degradation. This knowledge opens up potential treatment avenues. Membrane proteins form about 25% of the human proteome and are key to cellular function, however calculations for disease-associated variants have not systematically been tested on them. Here we present a new protocol for stability calculations on membrane proteins under the usage of a membrane specific force-field and its proof-of-principle application on 15 proteins with disease-associated variants. We integrate stability calculations with evolutionary sequence analysis, allowing us to separate variants where loss of stability is the most likely mechanism from those where other protein properties such as ligand binding are affected.

biophysics↗