Lecture

Protein Contact Prediction: Pseudolikelihoods and Potts Models

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In course

PHYS-754: Lecture series on scientific machine learning

This lecture presents ongoing work on how scientific questions can be tackled using machine learning. Machine learning enables extracting knowledge from data computationally and in an automatized way. We will learn on examples how this is influencing the v ...

Description

This lecture introduces the problem of predicting contacts in proteins based on coevolution of amino acids. It presents a new approach to Direct-Coupling Analysis (DCA) using Potts models. The Potts model is explained, along with the inverse Potts problem and the regularization techniques used to avoid overfitting. The lecture covers the pseudolikelihood method for maximizing the likelihood of the Potts model. Sequence reweighting and interaction scores are discussed, as well as the comparison between mean-field DCA and pseudolikelihood DCA. The lecture concludes with a comparison of different methods for contact prediction in proteins.

Instructors (10)

Anne-Clémence Corminboeuf

Teaching & PhD PhD Students Liam Oliver Marsh, Pratham Pushparaj Deshmukh, Roel Christiaan M Raike, Thanapat Worakul, Osvaldo Hernandez Cuellar, Juliette Veronique Schleicher, Yannick Calvino Alonso, Sara Bassetta, Yuri Cho, Kelian Dietmar Marc Gaedecke, Guoyuan Liu Past EPFL PhD Students Fabrice Avaltroni, Daniel Filipe Jana, Stephan Niklaus Steinmann, Jérôme Florian Gonthier, Riccardo Petraglia, Antonio Prlj, Laurent Alexis Clément Vannay, Alberto Fabrizio, Kun-Han Lin, Boodsarin Sawatlon, Raimon Fabreg

Philippe Schwaller

Michele Ceriotti

Michele Ceriotti received his Ph.D. in Physics from ETH Zürich in 2010. He spent three years in Oxford as a Junior Research Fellow at Merton College. Since 2013 he leads the laboratory for Computational Science and Modeling in the Institute of Materials at EPFL. His research revolves around the atomic-scale modelling of materials, based on the sampling of quantum and thermal fluctuations and on the use of machine learning to predict and rationalize structure-property relations. He has been awarded the IBM Research Forschungspreis in 2010, the Volker Heine Young Investigator Award in 2013, an ERC Starting Grant in 2016, and the IUPAP C10 Young Scientist Prize in 2018.

David Richard Harvey

Expertise CosmologyDark MatterGalaxy ClustersStrong Gravitational LensingWeak Gravitational Lensing I am an observational and theoretical astrophysicist at EPFL. My main area of research is dark matter and trying to understand its properties and dynamics. I love to use deep learning in my work and understand how fondation models and generative AI can help us understand the Universe. I am a member of Euclid and ARRAKIHS and the lead of the self interacting dark matter Key Project in Euclid. Outside of my r

Matthieu Wyart

Teaching & PhD Past EPFL PhD Students Riccardo Ravasio, Mario Geiger, Wencheng Ji, Leonardo Petrini, Umberto Maria Tomasini, Alessandro Favero Courses Systèmes complexes PHYS-343 This courses show how known (entropy, phase transitions) and new (glasses, percolation, avalanche-type response) physical concepts can be used in various fields (including Computer science, neuroscience, social sciences).

Official source

Related concepts (32)

Maximum likelihood estimation

In statistics, maximum likelihood estimation (MLE) is a method of estimating the parameters of an assumed probability distribution, given some observed data. This is achieved by maximizing a likelihood function so that, under the assumed statistical model, the observed data is most probable. The point in the parameter space that maximizes the likelihood function is called the maximum likelihood estimate. The logic of maximum likelihood is both intuitive and flexible, and as such the method has become a dominant means of statistical inference.

Likelihood function

In statistical inference, the likelihood function quantifies the plausibility of parameter values characterizing a statistical model in light of observed data. Its most typical usage is to compare possible parameter values (under a fixed set of observations and a particular model), where higher values of likelihood are preferred because they correspond to more probable parameter values.

Likelihood principle

In statistics, the likelihood principle is the proposition that, given a statistical model, all the evidence in a sample relevant to model parameters is contained in the likelihood function. A likelihood function arises from a probability density function considered as a function of its distributional parameterization argument.

Covariance matrix

In probability theory and statistics, a covariance matrix (also known as auto-covariance matrix, dispersion matrix, variance matrix, or variance–covariance matrix) is a square matrix giving the covariance between each pair of elements of a given random vector. Any covariance matrix is symmetric and positive semi-definite and its main diagonal contains variances (i.e., the covariance of each element with itself). Intuitively, the covariance matrix generalizes the notion of variance to multiple dimensions.

Likelihood-ratio test

In statistics, the likelihood-ratio test assesses the goodness of fit of two competing statistical models, specifically one found by maximization over the entire parameter space and another found after imposing some constraint, based on the ratio of their likelihoods. If the constraint (i.e., the null hypothesis) is supported by the observed data, the two likelihoods should not differ by more than sampling error. Thus the likelihood-ratio test tests whether this ratio is significantly different from one, or equivalently whether its natural logarithm is significantly different from zero.

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