Benchmarking of structure refinement methods for protein complex models

Verburgt, Jacob

doi:10.1002/prot.26188

Cited by 8 publications

(6 citation statements)

References 42 publications

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“…Computational docking methods can be generally divided into classical docking software and deep learning-based methods (Biesiada et al, 2011; Torchala et al, 2013; Vakser, 2014; Schindler et al, 2017; Weng et al, 2019; Yan et al, 2020; Sunny and Jayaraj, 2021; Christoffer et al, 2021). Specifically, classical docking software is typically very slow and follows a three-step framework of candidate sampling, ranking (Launay et al, 2020; Eismann et al, 2021), and refinement (Verburgt and Kihara, 2022). Since deep learning has made a big impact on structural biology (Laine et al, 2021), AlphaFold2 (Jumper et al, 2021) and RoseTTAFold (Baek et al, 2021) have also been utilized to predict protein complex structures by adding a sequence linker between individual protein sequences.…”

Section: Backgrounds and Related Workmentioning

confidence: 99%

xTrimoDock: Rigid Protein Docking via Cross-Modal Representation Learning and Spectral Algorithm

Luo¹,

Li²,

Sun³

et al. 2023

Preprint

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Protein-protein interactions are the basis for the formation of protein complexes which are essential for almost all cellular processes. Knowledge of the structures of protein complexes is of major importance for understanding the biological function of these protein-protein interactions and designing protein drugs. Here we address the problem of rigid protein docking which assumes no deformation of the involved proteins during interactions. We develop a method called, xTrimoDock, which leverages a cross-modal representation learning to predict the protein distance map, and then uses a spectral initialization and gradient descent to obtain the roto-translation transformation for docking. We show that, on antibody heavy-chain and light-chain docking, and antibody-antigen docking, xTrimoDock consistently outperforms the state-of-the-art such as AlphaFold-Multimer and HDock, and can lead to as much as a 10% improvement in DockQ metric. xTrimoDock has been applied as a useful tool in protein drug design at BioMap.

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Section: Backgrounds and Related Workmentioning

confidence: 99%

xTrimoDock: Rigid Protein Docking via Cross-Modal Representation Learning and Spectral Algorithm

Luo¹,

Li²,

Sun³

et al. 2023

Preprint

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“…The former ones are based on template structures of homologous complexes obtained by searching databases of known structures iteratively. 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].…”

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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“…For protein complexes traditional rigid-body docking does not allow for any conformational changes on binding. Thus, refinement becomes an essential step if the model is to be used for drug design or protein-protein interactions (Verburgt & Kihara, 2022). Several docking programs already include flexible modelling, such as iATTRACT (Schindler et al, 2015) and HADDOCK (De Vries et al, 2010), in an attempt to improve natural contacts at the interface and provide more native shape conformity (Schindler et al, 2015).The Deepmind group designed AF2_Multimer (Evans et al, 2021) specifically to model complex structures following Yoshitaka Moriwaki's work showing that AF2 could be used to model complex protein structures using a linker between two chains.…”

Section: Introductionmentioning

confidence: 99%

Improvement of protein tertiary and quaternary structure predictions using the ReFOLD4 refinement method and the AlphaFold2 recycling process

Adiyaman

Edmunds

Genc

et al. 2022

Preprint

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Motivation: The accuracy gap between predicted and experimental structures has been significantly reduced following the development of AlphaFold2. However, for further studies, such as drug discovery and protein design, AlphaFold2 structures need to be representative of proteins in solution, yet AlphaFold2 was trained to generate only a few structural conformations rather than a conformational landscape. In previous CASP experiments, MD simulation-based methods have been widely used to improve the accuracy of single 3D models. However, these methods are highly computationally intensive and less applicable for practical use in large-scale applications. Despite this, the refinement concept can still provide a better understanding of conformational dynamics and improve the quality of 3D models at a modest computational cost. Here, our ReFOLD4 pipeline was adopted to provide the conformational landscape of AlphaFold2 predictions while maintaining high model accuracy. In addition, the AlphaFold2 recycling process was utilised to improve 3D models by using them as custom template inputs for tertiary and quaternary structure predictions. Results: According to the Molprobity score, 94% of the generated 3D models by ReFOLD4 were improved. As measured by average change in lDDT, AlphaFold2 recycling showed an improvement rate of 87.5% (using MSAs) and 81.25% (using single sequences) for monomeric AF2 models and 100% (MSA) and 97.8% (single sequence) for monomeric non-AF2 models. By the same measure, the recycling of multimeric models showed an improvement rate of as much as 80% for AF2 models and 94% for non-AF2 models. The AlphaFold2 recycling processes and ReFOLD4 method can be combined very efficiently to provide conformational landscapes at the AlphaFold2-accuracy level, while also significantly improving the global quality of 3D models for both tertiary and quaternary structures, with much less computational complexity than traditional refinement methods.

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Benchmarking of structure refinement methods for protein complex models

Cited by 8 publications

References 42 publications

xTrimoDock: Rigid Protein Docking via Cross-Modal Representation Learning and Spectral Algorithm

xTrimoDock: Rigid Protein Docking via Cross-Modal Representation Learning and Spectral Algorithm

A Roadmap for Big Model

Improvement of protein tertiary and quaternary structure predictions using the ReFOLD4 refinement method and the AlphaFold2 recycling process

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