An end-to-end framework for the prediction of protein structure and fitness from single sequence

Chen, Yinghui; Xu, Yunxin; Liu, Di; Xing, Yaoguang; Gong, Haipeng

doi:10.1038/s41467-024-51776-x

Cited by 3 publications

References 61 publications

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GDFold2: a fast and parallelizable protein folding environment with freely defined objective functions

Mi,

Xiao,

Gong

2024

Preprint

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An important step of mainstream protein structure prediction is to model the 3D protein structure based on the predicted 2D inter-residue geometric information. This folding step has been integrated into a unified neural network to allow end-to-end training in state-of-the-art methods like AlphaFold2, but is separately implemented using the Rosetta folding environment in some traditional methods like trRosetta. Despite the inferiority in prediction accuracy, the traditional approach allows sampling various protein conformations compatible with the predicted geometric constraints, partially capturing the dynamical information. Here, we propose GDFold2, a novel protein folding environment, to address the limitations of Rosetta. On the one hand, GDFold2 is highly computationally efficient, capable of accomplishing multiple folding processes in parallel within the time scale of minutes for generic proteins. On the other hand, GDFold2 supports freely defined objective functions in order to fulfill diversified optimization requirement. Moreover, we propose a quality assessment (QA) model to provide reliable prediction on the quality of protein structures folded by GDFold2, thus substantially simplifying the selection of structural models.

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GDFold2: a fast and parallelizable protein folding environment with freely defined objective functions

Mi,

Xiao,

Gong

2024

Preprint

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Towards Robust Evaluation of Protein Generative Models: A Systematic Analysis of Metrics

Strashnov,

Shevtsov,

Meshchaninov

et al. 2024

Preprint

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The rapid advancement of protein generative models necessitates robust and principled methods for their evaluation and comparison. As new models of increasing complexity continue to emerge, it is crucial to ensure that the metrics used for assessment are well-understood and reliable. In this work, we conduct a systematic investigation of commonly used metrics for evaluating protein generative models, focusing on quality, diversity, and distributional similarity. We examine the behavior of these metrics under various conditions, including synthetic perturbations and real-world generative models. Our analysis explores different design choices, parameters, and underlying representation models, revealing how these factors influence metric performance. We identify several challenges in applying these metrics, such as sample size dependencies, sensitivity to data distribution shifts, and computational efficiency trade-offs. By testing metrics on both synthetic datasets with controlled properties and outputs from state-of-the-art protein generators, we provide insights into each metric's strengths, limitations, and practical applicability. Based on our findings, we offer a set of practical recommendations for researchers to consider when evaluating protein generative models, aiming to contribute to the development of more robust and meaningful evaluation practices in the field of protein design.

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Review of major scientific research achievements on the 40th Anniversary of the Re-establishment of Department of Biology at Tsinghua University

Chen,

Huang

et al. 2024

Chin. Sci. Bull.

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An end-to-end framework for the prediction of protein structure and fitness from single sequence

Cited by 3 publications

References 61 publications

GDFold2: a fast and parallelizable protein folding environment with freely defined objective functions

GDFold2: a fast and parallelizable protein folding environment with freely defined objective functions

Towards Robust Evaluation of Protein Generative Models: A Systematic Analysis of Metrics

Review of major scientific research achievements on the 40th Anniversary of the Re-establishment of Department of Biology at Tsinghua University

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