Protein oligomer modeling guided by predicted interchain contacts in <scp>CASP14</scp>

Baek, Minkyung; Anishchenko, Ivan; Park, Hahnbeom; Humphreys, Ian R.; Baker, David

doi:10.1002/prot.26197

Cited by 20 publications

(21 citation statements)

References 41 publications

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“…Free docking approaches start from individual subunits of known structure and attempt to select the most plausible interfaces from thousands of sampled orientations. The top 3 entrants of CASP14 Multimers — Baker-experimental [19], Venclovas [20] and Takeda-Shitaka lab [21] — all used methodologies based on these approaches. Takeda-Shitaka used a purely template-based approach, whilst Baker-experimental and Venclovas used a combination of template-based and free-docking.…”

Section: Related Workmentioning

confidence: 99%

Protein complex prediction with AlphaFold-Multimer

Evans

O’Neill

Pritzel

et al. 2021

Preprint

2,513

2,557

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While the vast majority of well-structured single protein chains can now be predicted to high accuracy due to the recent AlphaFold [1] model, the prediction of multi-chain protein complexes remains a challenge in many cases. In this work, we demonstrate that an AlphaFold model trained specifically for multimeric inputs of known stoichiometry, which we call AlphaFold-Multimer, significantly increases accuracy of predicted multimeric interfaces over input-adapted single-chain AlphaFold while maintaining high intra-chain accuracy. On a benchmark dataset of 17 heterodimer proteins without templates (introduced in [2]) we achieve at least medium accuracy (DockQ [3]≥0.49) on 14 targets and high accuracy (DockQ≥0.8) on 6 targets, compared to 9 targets of at least medium accuracy and 4 of high accuracy for the previous state of the art system (an AlphaFold-based system from [2]). We also predict structures for a large dataset of 4,433 recent protein complexes, from which we score all non-redundant interfaces with low template identity. For heteromeric interfaces we successfully predict the interface (DockQ≥0.23) in 67% of cases, and produce high accuracy predictions (DockQ≥0.8) in 23% of cases, an improvement of +25 and +11 percentage points over the flexible linker modification of AlphaFold [4] respectively. For homomeric interfaces we successfully predict the interface in 69% of cases, and produce high accuracy predictions in 34% of cases, an improvement of +5 percentage points in both instances.

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Section: Related Workmentioning

confidence: 99%

Protein complex prediction with AlphaFold-Multimer

Evans

O’Neill

Pritzel

et al. 2021

Preprint

2,513

2,557

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“…For heterodimers and homodimers, the interface was successfully predicted in 67% and 69% of the cases, respectively, and high accuracy models were produced in 23% and 34% of the cases, respectively. Baker's group also developed an oligomer structure generation program based on the accurate prediction of interchain contacts [5]. Despite some success by both algorithms in predicting heterodimers and homodimers, both need to be improved, as well as extended to handle higher-order assemblies.…”

Section: Introduction 1alphafold and Rosettafoldmentioning

confidence: 99%

Prediction of the Iron–Sulfur Binding Sites in Proteins Using the Highly Accurate Three-Dimensional Models Calculated by AlphaFold and RoseTTAFold

Golinelli‐Pimpaneau¹

2021

Inorganics

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AlphaFold and RoseTTAFold are deep learning-based approaches that predict the structure of proteins from their amino acid sequences. Remarkable success has recently been achieved in the prediction accuracy of not only the fold of the target protein but also the position of its amino acid side chains. In this article, I question the accuracy of these methods to predict iron–sulfur binding sites. I analyze three-dimensional models calculated by AlphaFold and RoseTTAFold of Fe–S–dependent enzymes, for which no structure of a homologous protein has been solved experimentally. In all cases, the amino acids that presumably coordinate the cluster were gathered together and facing each other, which led to a quite accurate model of the Fe–S cluster binding site. Yet, cysteine candidates were often involved in intramolecular disulfide bonds, and the number and identity of the protein amino acids that should ligate the cluster were not always clear. The experimental structure determination of the protein with its Fe–S cluster and in complex with substrate/inhibitor/product is still needed to unambiguously visualize the coordination state of the cluster and understand the conformational changes occurring during catalysis.

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“…The latter ones typically follow three steps: 1) randomly sample a large number of orientations, 2) employ a scoring function to rank all generated candidates [1588,1589,1590,1591], and 3) refine the top complexes according to an energy model [1592]. Recent efforts have been devoted to using a hybrid of template-based and free docking and building deep learning based systems to get more accurate scoring functions [1593,1594]. The performance of these methods is usually not satisfying either due to missing good structure templates or inaccurate scoring function for ranking different orientations.…”

Section: Protein Structure Predictionmentioning

confidence: 99%

A Roadmap for Big Model

Yuan¹,

Zhao²,

Jiahong³

et al. 2022

Preprint

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domains indexed by Google News. It contains 31 million documents with an average length of 793 BPE tokens. Like C4, it excludes examples with duplicate URLs. News dumps from December 2016 through March 2019 were used as training data, articles published in April 2019 from the April 2019 dump were used for evaluation. OpenWebText2(OWT2). OWT2 is an enhanced version of the original OpenWebTextCorpus, including content from multiple languages, document metadata, multiple dataset versions, and open source replication code, covering all Reddit submissions from 2005 up until April 2020. PubMed Central(PMC). PMC is a free full-text archive of biomedical and life sciences journal literature from the U.S. National Institutes of Health's National Library of Medicine (NIH/NLM). The dataset is updated daily. In addition to full-text articles, they contain corrections, retractions, and expressions of concern, as well as file lists that include metadata for articles in each dataset.PMC obtained by open registration in Amazon Web Services (AWS) includes The PMC Open Access Subset and The Author Manuscript Dataset. The PMC Open Access Subset includes all articles and preprints in PMC with a machine-readable Creative Commons license that allows reuse. The Author Manuscript Dataset includes accepted author manuscripts collected under a funder policy in PMC and made available in machine-readable formats for text mining. ArXiv. ArXiv is a repository of 1.7 million articles, with relevant features such as article titles, authors, categories, abstracts, full text PDFs, and more. It provides open access to academic articles, covering many subdisciplines from vast branches of physics to computer science to everything in between, including math, statistics, electrical engineering, quantitative biology, and economics, which is helpful to the potential downstream applications of the research field. In addition, the writing language of LaTeX also contributes to the study of language models. Colossal Clean Crawled Corpus(C4). C4 is a colossal, cleaned version of Common Crawl's web crawl corpus. It is based on Common Crawl dataset and was used to train the T5 text-to-text Transformer models. The cleaned English version of C4 has 364,868,901 training examples and 364,608 validation examples, while the uncleaned English version has 1,063,805,324 training examples and 1,065,029 validation examples; the realnewslike version has 13,799,838 training examples and 13,863 validation examples, while the webtextlike version has 4,500,788 training examples and 4,493 validation examples. Wiki-40B. Wikipedia (Wiki-40B) is a clean-up text collection containing more than 40 Wikipedia language editions of pages corresponding to entities. The dataset is split into train/validation/test sets for each language. The training set has 2,926,536 examples, the validation set has 163,597 examples, and the test set has 162,274 examples. Wiki-40B is cleaned by a page filter to remove ambiguous, redirected, deleted, and non-physical pages. CLUECorpus2020. CLUECorpus2020 ...

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Protein oligomer modeling guided by predicted interchain contacts in CASP14

Cited by 20 publications

References 41 publications

Protein complex prediction with AlphaFold-Multimer

Protein complex prediction with AlphaFold-Multimer

Prediction of the Iron–Sulfur Binding Sites in Proteins Using the Highly Accurate Three-Dimensional Models Calculated by AlphaFold and RoseTTAFold

A Roadmap for Big Model

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