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Lecture

Protein Folding: From DCA to AlphaFold

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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 delves into the evolution of protein folding techniques, starting from DCA to the groundbreaking AlphaFold. The instructor explains the concept of pseudo-likelihood and its application in predicting amino acid sequences. The lecture explores the transition from physics-based potentials to co-evolutionary restraints, leading to the development of AlphaFold. It discusses the workflow of direct coupling analysis, the significance of multiple sequence alignment, and the role of deep learning in protein structure prediction. The instructor highlights the improvements brought by AlphaFold, its training process, and the utilization of attention mechanisms and transformers. The lecture concludes by emphasizing the importance of simple methods like DCA in exploring datasets and understanding the evolution of protein folding algorithms.

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Instructors (10)

Paolo De Los Rios

Paolo De Los Rios earned his master in Electronic Engineering at the Turin Institute of Technology (Politecnico di Torino) in May 1993. In November 1993 he moved to Trieste, Italy, to enter the PhD program in Theoretical Condensed Matter Theory at the International School for Advanced Studies (SISSA/ISAS) where he obtained the PhD degree in October 1996 for his work on the statistical physics of disordered systems. After a one year postdoc at the Max-Planck Institute for the Physics of Complex Systems in Dresden, Germany, in November 1997 he moved to the University of Fribourg, Switzerland, to join the group of Prof. Yi-Cheng Zhang. There he has worked on various applications of statistical physics to complex systems. In September 2000 he has been appointed Assistant Professor in Statistical Physics of Living Matter and Complex Systems at the Institute of Theoretical Physics of the University of Lausanne, Switzerland. Since April 2010 he is Associate Professor at the Institute of Theoretical Physics of the Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland.

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.

Anne-Florence Raphaëlle Bitbol

Lenka Zdeborová

Lenka Zdeborová is a Professor of Physics and of Computer Science in École Polytechnique Fédérale de Lausanne where she leads the Statistical Physics of Computation Laboratory. She received a PhD in physics from University Paris-Sud and from Charles University in Prague in 2008. She spent two years in the Los Alamos National Laboratory as the Director's Postdoctoral Fellow. Between 2010 and 2020 she was a researcher at CNRS working in the Institute of Theoretical Physics in CEA Saclay, France. In 2014, she was awarded the CNRS bronze medal, in 2016 Philippe Meyer prize in theoretical physics and an ERC Starting Grant, in 2018 the Irène Joliot-Curie prize, in 2021 the Gibbs lectureship of AMS. She is an editorial board member for Journal of Physics A, Physical Review E, Physical Review X, SIMODS, Machine Learning: Science and Technology, and Information and Inference. Lenka's expertise is in applications of concepts from statistical physics, such as advanced mean field methods, replica method and related message-passing algorithms, to problems in machine learning, signal processing, inference and optimization. She enjoys erasing the boundaries between theoretical physics, mathematics and computer science.

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Related concepts (19)

Protein folding

Protein folding is the physical process where a protein chain is translated into its native three-dimensional structure, typically a "folded" conformation, by which the protein becomes biologically functional. Via an expeditious and reproducible process, a polypeptide folds into its characteristic three-dimensional structure from a random coil. Each protein exists first as an unfolded polypeptide or random coil after being translated from a sequence of mRNA into a linear chain of amino acids.

Protein structure prediction

Protein structure prediction is the inference of the three-dimensional structure of a protein from its amino acid sequence—that is, the prediction of its secondary and tertiary structure from primary structure. Structure prediction is different from the inverse problem of protein design. Protein structure prediction is one of the most important goals pursued by computational biology; and it is important in medicine (for example, in drug design) and biotechnology (for example, in the design of novel enzymes).

Protein fold class

In molecular biology, protein fold classes are broad categories of protein tertiary structure topology. They describe groups of proteins that share similar amino acid and secondary structure proportions. Each class contains multiple, independent protein superfamilies (i.e. are not necessarily evolutionarily related to one another). Four large classes of protein that are generally agreed upon by the two main structure classification databases (SCOP and CATH).

Evolutionary algorithm

In computational intelligence (CI), an evolutionary algorithm (EA) is a subset of evolutionary computation, a generic population-based metaheuristic optimization algorithm. An EA uses mechanisms inspired by biological evolution, such as reproduction, mutation, recombination, and selection. Candidate solutions to the optimization problem play the role of individuals in a population, and the fitness function determines the quality of the solutions (see also loss function).

Genetic algorithm

In computer science and operations research, a genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on biologically inspired operators such as mutation, crossover and selection. Some examples of GA applications include optimizing decision trees for better performance, solving sudoku puzzles, hyperparameter optimization, causal inference, etc.

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