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Global mapping of the energetic and allosteric landscapes of protein binding domains

ABSTRACT: Allosteric communication between distant sites in proteins is central to nearly all biological regulation but still poorly characterised for most proteins, limiting conceptual understanding, biological engineering and allosteric drug development. Typically only a few allosteric sites are known in model proteins, but theoretical, evolutionary and some experimental studies suggest they may be much more widely distributed. An important reason why allostery remains poorly characterised is the lack of methods to systematically quantify long-range communication in diverse proteins. Here we address this shortcoming by developing a method that uses deep mutational scanning to comprehensively map the allosteric landscapes of protein interaction domains. The key concept of the approach is the use of ‘multidimensional mutagenesis’: mutational effects are quantified for multiple molecular phenotypes—here binding and protein abundance—and in multiple genetic backgrounds. This is an efficient experimental design that allows the underlying causal biophysical effects of mutations to be accurately inferred en masse by fitting thermodynamic models using neural networks. We apply the approach to two of the most common human protein interaction domains, an SH3 domain and a PDZ domain, to produce the first global atlases of allosteric mutations for any proteins. Allosteric mutations are widely dispersed with extensive long-range tuning of binding affinity and a large mutational target space of network-altering ‘edgetic’ variants. Mutations are more likely to be allosteric closer to binding interfaces, at Glycines in secondary structure elements and at particular sites including a chain of residues connecting to an opposite surface in the PDZ domain. This general approach of quantifying mutational effects for multiple molecular phenotypes and in multiple genetic backgrounds should allow the energetic and allosteric landscapes of many proteins to be rapidly and comprehensively mapped.

ORGANISM(S): Saccharomyces cerevisiae

PROVIDER: GSE184042 | GEO | 2021/09/13

REPOSITORIES: GEO

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Project description:International differences in the incidence of many cancer types indicate the existence of carcinogen exposures that have not been identified by conventional epidemiology yet potentially make a substantial contribution to cancer burden1. This pertains to clear cell renal cell carcinoma (ccRCC), for which obesity, hypertension, and tobacco smoking are risk factors but do not explain its geographical variation in incidence2. Some carcinogens generate somatic mutations and a complementary strategy for detecting past exposures is to sequence the genomes of cancers from populations with different incidence rates and infer underlying causes from differences in patterns of somatic mutations. Here, we sequenced 962 ccRCC from 11 countries of varying incidence. Somatic mutation profiles differed between countries. In Romania, Serbia and Thailand, mutational signatures likely caused by extracts of Aristolochia plants were present in most cases and rare elsewhere. In Japan, a mutational signature of unknown cause was found in >70% cases and <2% elsewhere. A further mutational signature of unknown cause was ubiquitous but exhibited higher mutation loads in countries with higher kidney cancer incidence rates (p-value <6 × 10−18). Known signatures of tobacco smoking correlated with tobacco consumption, but no signature was associated with obesity or hypertension suggesting non-mutagenic mechanisms of action underlying these risk factors. The results indicate the existence of multiple, geographically variable, mutagenic exposures potentially affecting 10s of millions of people and illustrate the opportunities for new insights into cancer causation through large-scale global cancer genomics. Linked cross omic data sets:Geographic variation of mutagenic exposures in kidney cancer genomes – patient metadata files (Mutographs) associated with this study are available in the European Genome-Phenome Archive: https://ega-archive.org/datasets/EGAD00001012223.

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Global mapping of the energetic and allosteric landscapes of protein binding domains

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