Hot Topics in Bionanoscience
Course description: Speakers from all over the world are invited to present pedagogical introductions to their field with an emphasis on basic concepts. Apart from an introductory lecture, the participants of this course will have an additional discussion with the invited speaker. During that extra hour, they will discuss a recent paper and the holy grails of the field.
The first hour is a lecture and open for everyone to attend (in this case this BN seminar at 12:30-13:45h, on "Optimization and historical contingency in protein sequences"). The second hour is reserved as a discussion hour with the lecturer for the registered class of PhD students and postdocs.
Audience: Registered PhD students and postdocs (see registration form below).
Credits: Those participants who attended (pro-actively) two Hot Topics sessions will be awarded 1 Graduate School Credit (GSC). Thus this session will 'count' for 0.5 GSC.
Preparation: PhD students who have registered for the Hot Topics course need to prepare for the session by reading the articles listed below.
Date: Friday 12 May 2023
Speaker: Anne-Florence Bitbol (EPFL, Switzerland).
The Bitbol lab is interested in understanding biological phenomena in a quantitative way, through physical concepts and mathematical and computational tools. In particular, they focus on understanding the sequence-function mapping in proteins, and on building quantitative models of evolution in microbial populations.
Host: Marianne Bauer
Required reading: Participants are required to prepare for this session by reading the following papers (also as download-able files below):
- Impact of phylogeny on structural contact inference from protein sequence data (Dietler, 2023, J. R. Soc. Interface);
- Generative power of a protein language model trained on multiple sequence alignments (Sgarbossa, 2023, eLife);
- Hydrodynamic flow and concentration gradients in the gut enhance neutral bacterial diversity (Labavic, 2022, PNAS).
Abstract of the BN seminar: Protein sequences are shaped by functional optimization on the one hand and by evolutionary history, i.e. phylogeny, on the other hand. A multiple sequence alignment of homologous proteins contains sequences which evolved from the same ancestral sequence and have similar structure and function. In such an alignment, correlations in amino-acid usage at different sites can arise from structural and functional constraints due to coevolution, but also from historical contingency.
Correlations arising from phylogeny often confound coevolution signal from functional or structural optimization, impairing the inference of structural contacts from sequences. However, inferred Potts models are more robust than local statistics to these effects, which may explain their success. Dedicated corrections can further increase this robustness. Moreover, phylogenetic correlations can in fact provide useful information for some inference tasks, especially to infer interaction partners from sequences among the paralogs of two protein families. In this case, signal from phylogeny and signal from constraints combine constructively, and explicitly exploiting both further improves inference performance.
Protein language models have recently been applied to sequence data, greatly advancing structure, function and mutational effect prediction. Language models trained on multiple sequence alignments capture coevolution and structural contacts, but also phylogenetic relationships. They are able to disentangle signal from structural constraints and from phylogeny more efficiently than Potts models, and they have promising generative properties.
Registration
You can register for the course by filling in the form below. Your place at the course will be confirmed via email before the start of the Hot Topics session. In case there are too many registrants, a selection will be made based on first-come-first-served.