Improved the Protein Complex Prediction with Protein Language Models

Chen, Bo; Xie, Ziwei; Xu, Jinbo; Qiu, Jiezhong; Ye, Zhaofeng; Tang, Jie

doi:10.1101/2022.09.15.508065

Cited by 7 publications

(8 citation statements)

References 64 publications

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“…Can DiffPALM improve complex structure prediction by AFM? To address this question, we consider 15 complexes, listed in Table S1, whose structures are not included in the training set of the AFM release we used unless specified otherwise (v2, see “Supplementary material, General points on AFM”), and for which the default AFM complex prediction was previously reported to perform poorly [7, 18] (see “Supplementary material, Eukaryotic complexes”).…”

Section: Resultsmentioning

confidence: 99%

“…Recent work [18] also used MSA Transformer for paralog matching, in a method called ESM-Pair. It relies on column attention matrices and compares them across the MSAs of interacting partners.…”

Section: Discussionmentioning

confidence: 99%

“…ESMPair may be more closely related to phylogeny- or orthology-based pairing methods, since column attention encodes phylogenetic relationships [52]. 13 out of the 15 eukaryotic protein complexes we considered were also studied in [18], but no substantial improvement (and often a degradation of performance) was reported for those when using ESMPair instead of the default AFM pairing, except for 7BQU. By contrast, DiffPALM yields strong improvements for 6L5K and 6FYH, and no significant performance degradation when using orthologs as positive examples.…”

Section: Discussionmentioning

confidence: 99%

“…We considered targets whose structures are not in the training set of AFM with v2 weights, and where default AFM predictions are poor. Specifically, we started from those eukaryotic targets from Table A1 of [18] and from the “Benchmark 2” dataset in [7] whose PDB structures were released after the training cutoff for the AFM v2 weights (April 30, 2018). Among those, we focused on multimers with no more than 2 different types of monomers, where both monomers come from the same species, and with paired sequences not longer than 500 amino acids, due to GPU memory constraints.…”

Section: Supplementary Materialsmentioning

confidence: 99%

“…However, constructing paired MSAs poses the challenge of properly pairing sequences. Accordingly, the quality of pairings strongly impacts the accuracy of heteromer structure prediction [9, 17, 18]. Pairing interaction partners is difficult because many protein families contain several paralogous proteins encoded within the same genome.…”

Section: Introductionmentioning

confidence: 99%

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Pairing interacting protein sequences using masked language modeling

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Bitbol

2023

Preprint

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Predicting which proteins interact together from amino-acid sequences is an important task. We develop a method to pair interacting protein sequences which leverages the power of protein language models trained on multiple sequence alignments, such as MSA Transformer and the EvoFormer module of AlphaFold. We formulate the problem of pairing interacting partners among the paralogs of two protein families in a differentiable way. We introduce a method called DiffPALM that solves it by exploiting the ability of MSA Transformer to fill in masked amino acids in multiple sequence alignments using the surrounding context. MSA Transformer encodes coevolution between functionally or structurally coupled amino acids. We show that it captures inter-chain coevolution, while it was trained on single-chain data, which means that it can be used out-of-distribution. Relying on MSA Transformer without fine-tuning, DiffPALM outperforms existing coevolution-based pairing methods on difficult benchmarks of shallow multiple sequence alignments extracted from ubiquitous prokaryotic protein datasets. It also outperforms an alternative method based on a state-of-the-art protein language model trained on single sequences. Paired alignments of interacting protein sequences are a crucial ingredient of supervised deep learning methods to predict the three-dimensional structure of protein complexes. DiffPALM substantially improves the structure prediction of some eukaryotic protein complexes by AlphaFold-Multimer, without significantly deteriorating any of those we tested. It also achieves competitive performance with using orthology-based pairing.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Supplementary Materialsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Pairing interacting protein sequences using masked language modeling

Lupo

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Bitbol

2023

Preprint

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Evaluation ofAlphaFoldantibody–antigen modeling with implications for improving predictive accuracy

Yin,

Pierce

2023

Protein Science

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High resolution antibody–antigen structures provide critical insights into immune recognition and can inform therapeutic design. The challenges of experimental structural determination and the diversity of the immune repertoire underscore the necessity of accurate computational tools for modeling antibody–antigen complexes. Initial benchmarking showed that despite overall success in modeling protein–protein complexes, AlphaFold and AlphaFold‐Multimer have limited success in modeling antibody–antigen interactions. In this study, we performed a thorough analysis of AlphaFold's antibody–antigen modeling performance on 427 nonredundant antibody–antigen complex structures, identifying useful confidence metrics for predicting model quality, and features of complexes associated with improved modeling success. Notably, we found that the latest version of AlphaFold improves near‐native modeling success to over 30%, versus approximately 20% for a previous version, while increased AlphaFold sampling gives approximately 50% success. With this improved success, AlphaFold can generate accurate antibody–antigen models in many cases, while additional training or other optimization may further improve performance.

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Recent Advances and Challenges in Protein Structure Prediction

Peng,

Liang,

Xia

et al. 2023

J. Chem. Inf. Model.

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Artificial intelligence has made significant advances in the field of protein structure prediction in recent years. In particular, DeepMind's end-to-end model, AlphaFold2, has demonstrated the capability to predict three-dimensional structures of numerous unknown proteins with accuracy levels comparable to those of experimental methods. This breakthrough has opened up new possibilities for understanding protein structure and function as well as accelerating drug discovery and other applications in the field of biology and medicine. Despite the remarkable achievements of artificial intelligence in the field, there are still some challenges and limitations. In this Review, we discuss the recent progress and some of the challenges in protein structure prediction. These challenges include predicting multidomain protein structures, protein complex structures, multiple conformational states of proteins, and protein folding pathways. Furthermore, we highlight directions in which further improvements can be conducted.

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Improved the Protein Complex Prediction with Protein Language Models

Cited by 7 publications

References 64 publications

Pairing interacting protein sequences using masked language modeling

Pairing interacting protein sequences using masked language modeling

Evaluation ofAlphaFoldantibody–antigen modeling with implications for improving predictive accuracy

Recent Advances and Challenges in Protein Structure Prediction

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