Dataset Information

Accurate prediction of protein structures and interactions using a three-track neural network.

ABSTRACT: DeepMind presented notably accurate predictions at the recent 14th Critical Assessment of Structure Prediction (CASP14) conference. We explored network architectures that incorporate related ideas and obtained the best performance with a three-track network in which information at the one-dimensional (1D) sequence level, the 2D distance map level, and the 3D coordinate level is successively transformed and integrated. The three-track network produces structure predictions with accuracies approaching those of DeepMind in CASP14, enables the rapid solution of challenging x-ray crystallography and cryo-electron microscopy structure modeling problems, and provides insights into the functions of proteins of currently unknown structure. The network also enables rapid generation of accurate protein-protein complex models from sequence information alone, short-circuiting traditional approaches that require modeling of individual subunits followed by docking. We make the method available to the scientific community to speed biological research.

SUBMITTER: Baek M

PROVIDER: S-EPMC7612213 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature

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Publications

Accurate prediction of protein structures and interactions using a three-track neural network.

Baek Minkyung M DiMaio Frank F Anishchenko Ivan I Dauparas Justas J Ovchinnikov Sergey S Lee Gyu Rie GR Wang Jue J Cong Qian Q Kinch Lisa N LN Schaeffer R Dustin RD Millán Claudia C Park Hahnbeom H Adams Carson C Glassman Caleb R CR DeGiovanni Andy A Pereira Jose H JH Rodrigues Andria V AV van Dijk Alberdina A AA Ebrecht Ana C AC Opperman Diederik J DJ Sagmeister Theo T Buhlheller Christoph C Pavkov-Keller Tea T Rathinaswamy Manoj K MK Dalwadi Udit U Yip Calvin K CK Burke John E JE Garcia K Christopher KC Grishin Nick V NV Adams Paul D PD Read Randy J RJ Baker David D

Science (New York, N.Y.) 20210715 6557

DeepMind presented notably accurate predictions at the recent 14th Critical Assessment of Structure Prediction (CASP14) conference. We explored network architectures that incorporate related ideas and obtained the best performance with a three-track network in which information at the one-dimensional (1D) sequence level, the 2D distance map level, and the 3D coordinate level is successively transformed and integrated. The three-track network produces structure predictions with accuracies approac ...[more]

PMID: 34282049

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Project description:MotivationThe correct localization of proteins in cell compartments is a key issue for their function. Particularly, mitochondrial proteins are physiologically active in different compartments and their aberrant localization contributes to the pathogenesis of human mitochondrial pathologies. Many computational methods exist to assign protein sequences to subcellular compartments such as nucleus, cytoplasm and organelles. However, a substantial lack of experimental evidence in public sequence databases hampered so far a finer grain discrimination, including also intra-organelle compartments.ResultsWe describe DeepMito, a novel method for predicting protein sub-mitochondrial cellular localization. Taking advantage of powerful deep-learning approaches, such as convolutional neural networks, our method is able to achieve very high prediction performances when discriminating among four different mitochondrial compartments (matrix, outer, inner and intermembrane regions). The method is trained and tested in cross-validation on a newly generated, high-quality dataset comprising 424 mitochondrial proteins with experimental evidence for sub-organelle localizations. We benchmark DeepMito towards the only one recent approach developed for the same task. Results indicate that DeepMito performances are superior. Finally, genomic-scale prediction on a highly-curated dataset of human mitochondrial proteins further confirms the effectiveness of our approach and suggests that DeepMito is a good candidate for genome-scale annotation of mitochondrial protein subcellular localization.Availability and implementationThe DeepMito web server as well as all datasets used in this study are available at http://busca.biocomp.unibo.it/deepmito. A standalone version of DeepMito is available on DockerHub at https://hub.docker.com/r/bolognabiocomp/deepmito. DeepMito source code is available on GitHub at https://github.com/BolognaBiocomp/deepmito.Supplementary informationSupplementary data are available at Bioinformatics online.

Dataset Information

Accurate prediction of protein structures and interactions using a three-track neural network.

Publications

Accurate prediction of protein structures and interactions using a three-track neural network.

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