Machine learning methods for predicting protein structure from single sequences
Shaun M. Kandathil, Andy M. Lau, David T. Jones
Abstract
Recent breakthroughs in protein structure prediction have increasingly relied on the use of deep neural networks. These recent methods are notable in that they produce 3-D atomic coordinates as a direct output of the networks, a feature which presents many advantages. Although most techniques of this type make use of multiple sequence alignments as their primary input, a new wave of methods have attempted to use just single sequences as the input. We discuss the make-up and operating principles of these models, and highlight new developments in these areas, as well as areas for future development.
Topics & Concepts
Computer scienceFeature (linguistics)Artificial intelligenceArtificial neural networkProtein structure predictionMachine learningSequence (biology)Deep learningProtein structureBiologyGeneticsLinguisticsBiochemistryPhilosophyProtein Structure and DynamicsEnzyme Structure and FunctionRNA and protein synthesis mechanisms