February 15, 2017
MIA Meeting: https://youtu.be/97q2wtoquQk?t=3100
Debora Marks
Department of Systems Biology, Harvard Medical School
Structure and fitness from genomic sequences
Abstract: The evolutionary trajectories of biological sequences are propelled by mutation and whittled away by selection to maintain and develop function. Present day sequences can therefore be regarded as the outcomes of millions of evolutionary experiments that record functional constraints in the genotype-phenotype map. In this talk I will first recap the primer by John and Adam that describes how a generative model for sequences can quantify evolutionary constraints on biomolecules in terms of couplings between specific residue combinations. I will show how we have applied this model to predict (i) accurate 3D structures of proteins, RNA and complexes, (ii) conformational plasticity of ‘disordered’ proteins, (iii) quantitative effects of mutations on organism fitness, and (iv) designed sequences of proteins with desired properties. These computational approaches address the challenge of inferring causality from correlations in genetic sequences but can be applied more widely to other biological information such as gene expression or dynamics, cellular phenotypes or drug response. I will introduce challenges and opportunities for extending these methods to diverse biomedical and engineering applications.
John Ingraham and Adam Riesselman
Marks Lab, Department of Systems Biology, Harvard Medical School
Primer: Generative models of biological sequence families
Abstract: Modern genome sequencing and synthesis can acquire and generate tremendous molecular diversity in a day, but our ability to navigate and interpret the exponentially large space of potential biological sequences remains limited. Central to this challenge is the lack of a priori knowledge about epistasis, i.e. non-additive interactions between positions in a molecule or genome. We will describe a class of generative models, discrete undirected graphical models, that, when fit to deep evolutionary sequence variation, can reveal both the three dimensional structures and mutational landscapes of proteins and RNAs, described in more detail in the talk by Debora after the break. In this primer, we will review the math and intuition behind these models, how they require approximate methods for scalable inference, and connections to other common methods in quantitative biology such as partial correlations and logistic regression. Lastly, we will outline how to go beyond pairwise and detect higher order epistasis with neural-network-powered generative models.
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