Literature DB >> 31344267

A further leap of improvement in tertiary structure prediction in CASP13 prompts new routes for future assessments.

Luciano A Abriata1,2, Giorgio E Tamò1,2, Matteo Dal Peraro1,2.   

Abstract

We present our assessment of tertiary structure predictions for hard targets in Critical Assessment of Structure Prediction round 13 (CASP13). The analysis includes (a) assignment and discussion of best models through scores-aided visual inspection of models for each evaluation unit (EU); (b) ranking of predictors resulting from this evaluation and from global scores; and (c) evaluation of progress, state of the art, and current limitations of protein structure prediction. We witness a sizable improvement in tertiary structure prediction building on the progress observed from CASP11 to CASP12, with (a) top models reaching backbone RMSD <3 å for several EUs of size <150 residues, contributed by many groups; (b) at least one model that roughly captures global topology for all EUs, probably unprecedented in this track of CASP; and (c) even quite good models for full, unsplit targets. Better structure predictions are brought about mainly by improved residue-residue contact predictions, and since this CASP also by distance predictions, achieved through state-of-the-art machine learning methods which also progressed to work with slightly shallower alignments compared to CASP12. As we reach a new realm of tertiary structure prediction quality, new directions are proposed and explored for future CASPs: (a) dropping splitting into EUs, (b) rethinking difficulty metrics probably in terms of contact and distance predictions, (c) assessing also side chains for models of high backbone accuracy, and (d) assessing residue-wise and possibly residue-residue quality estimates.
© 2019 Wiley Periodicals, Inc.

Entities:  

Keywords:  contact prediction; critical assessment of structure prediction; distance prediction; homology modeling; machine learning; molecular modeling; residue coevolution; sequence alignment; structural bioinformatics; structure prediction

Mesh:

Substances:

Year:  2019        PMID: 31344267     DOI: 10.1002/prot.25787

Source DB:  PubMed          Journal:  Proteins        ISSN: 0887-3585


  23 in total

1.  High-accuracy protein structures by combining machine-learning with physics-based refinement.

Authors:  Lim Heo; Michael Feig
Journal:  Proteins       Date:  2019-11-15

2.  Improved protein structure prediction using predicted interresidue orientations.

Authors:  Jianyi Yang; Ivan Anishchenko; Hahnbeom Park; Zhenling Peng; Sergey Ovchinnikov; David Baker
Journal:  Proc Natl Acad Sci U S A       Date:  2020-01-02       Impact factor: 11.205

3.  Improved protein structure prediction using potentials from deep learning.

Authors:  Andrew W Senior; Richard Evans; John Jumper; James Kirkpatrick; Laurent Sifre; Tim Green; Chongli Qin; Augustin Žídek; Alexander W R Nelson; Alex Bridgland; Hugo Penedones; Stig Petersen; Karen Simonyan; Steve Crossan; Pushmeet Kohli; David T Jones; David Silver; Koray Kavukcuoglu; Demis Hassabis
Journal:  Nature       Date:  2020-01-15       Impact factor: 49.962

4.  Assessment of protein assembly prediction in CASP13.

Authors:  Dmytro Guzenko; Aleix Lafita; Bohdan Monastyrskyy; Andriy Kryshtafovych; Jose M Duarte
Journal:  Proteins       Date:  2019-08-27

5.  Protein inter-residue contact and distance prediction by coupling complementary coevolution features with deep residual networks in CASP14.

Authors:  Yang Li; Chengxin Zhang; Wei Zheng; Xiaogen Zhou; Eric W Bell; Dong-Jun Yu; Yang Zhang
Journal:  Proteins       Date:  2021-08-19

6.  Topology evaluation of models for difficult targets in the 14th round of the critical assessment of protein structure prediction (CASP14).

Authors:  Lisa N Kinch; Jimin Pei; Andriy Kryshtafovych; R Dustin Schaeffer; Nick V Grishin
Journal:  Proteins       Date:  2021-07-23

7.  Study of Real-Valued Distance Prediction for Protein Structure Prediction with Deep Learning.

Authors:  Jin Li; Jinbo Xu
Journal:  Bioinformatics       Date:  2021-05-07       Impact factor: 6.937

8.  3D architecture and structural flexibility revealed in the subfamily of large glutamate dehydrogenases by a mycobacterial enzyme.

Authors:  Melisa Lázaro; Roberto Melero; Charlotte Huet; Jorge P López-Alonso; Sandra Delgado; Alexandra Dodu; Eduardo M Bruch; Luciano A Abriata; Pedro M Alzari; Mikel Valle; María-Natalia Lisa
Journal:  Commun Biol       Date:  2021-06-03

9.  Target classification in the 14th round of the critical assessment of protein structure prediction (CASP14).

Authors:  Lisa N Kinch; R Dustin Schaeffer; Andriy Kryshtafovych; Nick V Grishin
Journal:  Proteins       Date:  2021-08-19

10.  Highly accurate protein structure prediction with AlphaFold.

Authors:  John Jumper; Richard Evans; Alexander Pritzel; Tim Green; Michael Figurnov; Olaf Ronneberger; Kathryn Tunyasuvunakool; Russ Bates; Augustin Žídek; Anna Potapenko; Alex Bridgland; Clemens Meyer; Simon A A Kohl; Andrew J Ballard; Andrew Cowie; Bernardino Romera-Paredes; Stanislav Nikolov; Rishub Jain; Demis Hassabis; Jonas Adler; Trevor Back; Stig Petersen; David Reiman; Ellen Clancy; Michal Zielinski; Martin Steinegger; Michalina Pacholska; Tamas Berghammer; Sebastian Bodenstein; David Silver; Oriol Vinyals; Andrew W Senior; Koray Kavukcuoglu; Pushmeet Kohli
Journal:  Nature       Date:  2021-07-15       Impact factor: 49.962

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