By continuing you agree to the use of cookies. Article Contact Potential for Structure Prediction of Proteins and Protein Complexes from Potts Model Ivan Anishchenko,1 Petras J. Kundrotas,1,* and Ilya A. Vakser1,* 1Computational Biology Program and Department of Molecular Biosciences, The University of Kansas, Lawrence, Kansas ABSTRACT The energy function is the key component of protein modeling methodology. These models are powerful because they capture higher order correlations induced by mutations evolving under constraints and help quantify the connections between protein sequence, structure, and function maintained through evolution. We review recent work with Potts models to predict protein structure and sequence-dependent conformational free energy landscapes, to survey protein fitness landscapes and to explore the effects of epistasis on fitness. Potts energies predict sequence-dependent structure and free energy landscapes. We review inference techniques and their use in particular Potts model applications. Potts Models: Theory Review of Potts Model Results Protein Families and their Evolution – A Structural perspective. It consists of several discrete variables that … biology: from protein sequence space to fitness of organisms and populations COSB 2017 Potts Hamiltonian models of protein sequence co-variation are statistical models constructed from the pair correlations observed in a multiple sequence alignment (MSA) of a protein family. 10/31/2006 14 Applications of the Potts Model (about 1,000,000 Google hits…) Liquid-gas transitions Foam behaviors Protein Folds Biological Membranes Social Behavior Separation in binary alloys Spin glasses Neural Networks Flocking birds Beating heart cells … Annual Review of Biochemistry 2005 Bridging the physical scales in ev. We also comment on the numerical methods used to infer these models for each application. Potts models in which the Potts Hamiltonian is used to probe protein fitness. The Potts model was initially used to describe interacting spins on a crystalline lattice. One of the prime motivations for developing Potts models of protein sequence space has been to disentangle the direct from the indirect correlations as part of a procedure known as ‘direct coupling analysis’ (DCA), which improves on traditional covariance methods by providing a mapping between strongly directly interacting residues inferred from the model and contacts in 3D protein … The Potts Hamiltonian is a mapping of sequences to statistical scores in which sequences lower Potts statistical energy are more probable, generating a land-scape termed the ‘prevalence landscape’ [40,41,42 , 43 ].Recentstudies have demonstrated thatexperimen- Potts energies serve as a proxy for protein fitness, incorporating epistasis. DCA [31–36,55] models are based on the Potts model for reducing transitive noise, which is a generalization of the Ising model in statistical mechanics. Copyright © 2020 Elsevier B.V. or its licensors or contributors. ScienceDirect ® is a registered trademark of Elsevier B.V. ScienceDirect ® is a registered trademark of Elsevier B.V. Potts Hamiltonian models of protein co-variation, free energy landscapes, and evolutionary fitness, https://doi.org/10.1016/j.sbi.2016.11.004. Improved contact prediction in proteins: Using pseudo-likelihoods to infer Potts models Magnus Ekeberg1, Cecilia L ovkvist3,Yueheng Lan4, Martin Weigt5, Erik Aurell2 ;3 y (Dated: October 17, 2012) Spatially proximate amino acid in a protein tend to co-evolve. We use cookies to help provide and enhance our service and tailor content and ads. A Potts Hamiltonian can be inferred from a protein multiple sequence alignment.

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