Improving AlphaFold2-based Protein Tertiary Structure Prediction with MULTICOM in CASP15

Liu, Jian; Guo, Zhiye; Wu, Tai Tsun; Roy, Raj; Chen, Chen; Cheng, Jianlin

doi:10.1101/2023.05.01.538929

Cited by 4 publications

(2 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Notably, the most successful prediction methods in both regular and multimeric target categories have leveraged AlphaFold2 as their foundation. These methods include MULTICOM, 137 MultiFOLD, 138 Wallner_TS, 139 Yang‐Multimer, and MEGA‐Protein, all incorporating enhancements in the various steps of the underlying workflow: from improving multiple sequence alignment (MSA) input to rescoring and refining the output models.…”

Section: Discussionmentioning

confidence: 99%

Protein target highlights in CASP15: Analysis of models by structure providers

Alexander,

Durairaj,

Kryshtafovych

et al. 2023

Proteins

View full text Add to dashboard Cite

We present an in‐depth analysis of selected CASP15 targets, focusing on their biological and functional significance. The authors of the structures identify and discuss key protein features and evaluate how effectively these aspects were captured in the submitted predictions. While the overall ability to predict three‐dimensional protein structures continues to impress, reproducing uncommon features not previously observed in experimental structures is still a challenge. Furthermore, instances with conformational flexibility and large multimeric complexes highlight the need for novel scoring strategies to better emphasize biologically relevant structural regions. Looking ahead, closer integration of computational and experimental techniques will play a key role in determining the next challenges to be unraveled in the field of structural molecular biology.

show abstract

Section: Discussionmentioning

confidence: 99%

Protein target highlights in CASP15: Analysis of models by structure providers

Alexander,

Durairaj,

Kryshtafovych

et al. 2023

Proteins

View full text Add to dashboard Cite

show abstract

“…Currently, MD simulations remain the only proven way of generating multiple structures for protein binding site prediction but their usefulness is limited because these simulations are computationally costly. Computationally cheaper generation of multiple protein conformations is an area of much interest [47,48,49,50,51], however, it is not yet clear if these methods are able to replace effectively information gained from MD simulations.…”

Section: Protein Dynamics and Binding Sitesmentioning

confidence: 99%

Learnt representations of proteins can be used for accurate prediction of small molecule binding sites on experimentally determined and predicted protein structures

Carbery,

Buttenschoen,

Skyner

et al. 2023

Preprint

View full text Add to dashboard Cite

Protein-ligand binding site prediction is a useful tool for understanding the functional behaviour and potential drug-target interactions of a novel protein of interest. However, most binding site prediction methods are tested by providing crystallised ligand-bound (holo) structures as input. This testing regime is insufficient to understand the performance on novel protein targets where experimental structures are not available. An alternative option is to provide computationally predicted protein structures, but this is not commonly tested. However, due to the training data used, computationally-predicted protein structures tend to be extremely accurate, and are often biased toward a holo conformation. In this study we describe and benchmark IF-SitePred, a protein-ligand binding site prediction method which is based on the labelling of ESM-IF1 protein language model embeddings combined with point cloud annotation and clustering. We show that not only is IF-SitePred competitive with state-of-the-art methods when predicting binding sites on experimental structures, but it performs better on proxies for novel proteins where low accuracy has been simulated by molecular dynamics. Finally, IF-SitePred outperforms other methods if ensembles of predicted protein structures are generated.

show abstract

Enhancing AlphaFold-Multimer-based Protein Complex Structure Prediction with MULTICOM in CASP15

Liu

Guo

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

AlphaFold-Multimer has emerged as the state-of-the-art tool for predicting the quaternary structure of protein complexes (assemblies or multimers) since its release in 2021. To further enhance the AlphaFold-Multimer-based complex structure prediction, we developed a new quaternary structure prediction system (MULTICOM) to improve the input fed to AlphaFold-Multimer and evaluate and refine the outputs generated by AlphaFold2-Multimer. Specifically, MULTICOM samples diverse multiple sequence alignments (MSAs) and templates for AlphaFold-Multimer to generate structural models by using both traditional sequence alignments and new Foldseek-based structure alignments, ranks structural models through multiple complementary metrics, and refines the structural models via a Foldseek structure alignment-based refinement method. The MULTICOM system with different implementations was blindly tested in the assembly structure prediction in the 15th Critical Assessment of Techniques for Protein Structure Prediction (CASP15) in 2022 as both server and human predictors. Our server (MULTICOM_qa) ranked 3rd among 26 CASP15 server predictors and our human predictor (MULTICOM_human) ranked 7th among 87 CASP15 server and human predictors. The average TM-score of the first models predicted by MULTICOM_qa for CASP15 assembly targets is ~0.76, 5.3% higher than ~0.72 of the standard AlphaFold-Multimer. The average TM-score of the best of top 5 models predicted by MULTICOM_qa is ~0.80, about 8% higher than ~0.74 of the standard AlphaFold-Multimer. Moreover, the novel Foldseek Structure Alignment-based Model Generation (FSAMG) method based on AlphaFold-Multimer outperforms the widely used sequence alignment-based model generation. The source code of MULTICOM is available at: https://github.com/BioinfoMachineLearning/MULTICOM3.

show abstract

Improving AlphaFold2-based Protein Tertiary Structure Prediction with MULTICOM in CASP15

Cited by 4 publications

References 36 publications

Protein target highlights in CASP15: Analysis of models by structure providers

Protein target highlights in CASP15: Analysis of models by structure providers

Learnt representations of proteins can be used for accurate prediction of small molecule binding sites on experimentally determined and predicted protein structures

Enhancing AlphaFold-Multimer-based Protein Complex Structure Prediction with MULTICOM in CASP15

Contact Info

Product

Resources

About