Date/Time:08 February 2017, Physics Colloquium Wednesday@10.30AM
Speaker: Prof. Martin Weigt, Statistical Genomics and Biological Physics, Universite Pierre et Marie Curie, Paris
Title: Statistical modelling of protein sequences: Inferring structure,
interactions, and mutational landscapes
Venue: NISER, Jatni, SPS Seminar Room
Abstract:
Over the last years, biological research has been revolutionised by experimental high-throughput techniques. Unprecedented amounts of data are accumulating, causing an urgent need to develop computational modelling approaches to unveil information
hidden in raw data, and to help to increase our understanding of complex biological systems. Approaches based on statistical physics and inference have played an important role across diverse systems ranging from proteins over neural networks to the collective behaviour of animal groups.
To give a specific example, proteins show a remarkable degree of structural and functional conservation in the course of evolution, despite a large variability in amino-acid sequences.
Thanks to modern sequencing techniques, this amino-acid variability is easily observable, contrary to time- and labour-intensive experiments determining, e.g., the three-dimensional fold of a protein. I will present recent developments around the so-called Direct-Coupling Analysis [1,2], a statistical-mechanics inspired inference approach, which links sequence variability to protein structure and function. I will show that this methodology can be used
to (i) to infer contacts between residues and thus to guide 3D structure prediction of proteins and their complexes [3], (ii) to infer conserved protein-protein interaction networks [4], and (iii) to reconstruct mutational landscapes and thus to predict the effect of mutations [5]. Beyond a direct bioinformatic interest of such findings, they provide us also insight into underlying principles connecting protein evolution, structure and function.
Негізгі бет Prof Martin Weigt - SPS Physics Colloquium at NISER
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