Abstract
The evolutionary trajectories of biological sequences are propelled by mutation and whittled away by selection to maintain function. In this talk I will computational methods that, when combined with recent growth in sequence databases, quantify evolutionary constraints in terms of evolutionary couplings between residues. We develop methods that not only predict accurate 3D structures of proteins, RNA and complexes but also their conformational plasticity including alternative 3D states, and structural information on ‘disordered’ proteins, propensity to form fibrils and polymerization I will introduce challenges and opportunities for extending these methods, including deep learning, to applications in molecular design and disease prediction.
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