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A Mechanistic Variant Effect Predictor for disentangling the biophysical mechanisms driving disease

CORDIS · observation · Publication date unknown

bution of sequence variation across organisms, have emerged as promising tools for quantifying the clinical pathogenicity of variants, rivaling the accuracy of experimental approaches. However, these machine learning predictors fall short in discerning the effects induced by pathogenic mutations that impact on organismal fitness and drive disease. I propose to address this knowledge gap by combining the evolutionary-based variant tolerance predictors with protein folding models derived from energy landscape theory. In order to fold in biological timescales, an amino acid chain must minimize energy conflicts within it. In natural proteins, remaining conflicts can be measured with a transferable energy function, pointing out regions that did not evolve maintaining foldability, as active or binding sites. Incorporating this approach, I will focus on disentangling the biophysical ambiguity between the alteration of specific functions and local stability changes along proteins, firstly for single variants from which thermodynamic data is available for testing. Furthermore, I will integrat

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recordType

award

status

SIGNED

region

EU

value

209914.56

unit

EUR