Debora Marks (Harvard Medical School)
Harvard Medical School
I am a computational biologist interested in how to read the genome and interpret its variation. Recently, we have used evolutionary couplings determined from genomic sequencing to accurately protein 3D structure from sequences alone, including the experimentally challenging transmembrane proteins. Continuing from this my lab aims to predict alternative conformations and plasticity of proteins, and the consequences of protein genetic variation on pharmacological intervention. In a complementary approach, we are examining on the effect of drugs on patients and cell lines by bringing together large bodies of data from multiple perturbations and thousands of cancer patient tissues.
Protein structure and function from sequences
Amino acid covariation in proteins, extracted from the evolutionary sequence record, can be used to fold proteins, including transmembrane proteins. Addressing a fundamental challenge in computational molecular biology, a new prediction method (EVfold) applies a maximum entropy approach to infer evolutionary couplings between sequence positions from correlated mutations in the multiple sequence alignment of a protein family. When translated to distance constraints, such residue-residue couplings are sufficient to generate good all-atom models of proteins from different fold classes, ranging in size from 50 to more than 500 residues. We use the technique to predict previously unknown 3D structures of large transmembrane proteins of biomedical interest, from their sequences alone. We show how the method can also plausibly predict oligomerization, functional sites, and conformational changes.
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