Dataset Information

Cross-modality and self-supervised protein embedding for compound-protein affinity and contact prediction.

ABSTRACT: Computational methods for compound-protein affinity and contact (CPAC) prediction aim at facilitating rational drug discovery by simultaneous prediction of the strength and the pattern of compound-protein interactions. Although the desired outputs are highly structure-dependent, the lack of protein structures often makes structure-free methods rely on protein sequence inputs alone. The scarcity of compound-protein pairs with affinity and contact labels further limits the accuracy and the generalizability of CPAC models. To overcome the aforementioned challenges of structure naivety and labeled-data scarcity, we introduce cross-modality and self-supervised learning, respectively, for structure-aware and task-relevant protein embedding. Specifically, protein data are available in both modalities of 1D amino-acid sequences and predicted 2D contact maps that are separately embedded with recurrent and graph neural networks, respectively, as well as jointly embedded with two cross-modality schemes. Furthermore, both protein modalities are pre-trained under various self-supervised learning strategies, by leveraging massive amount of unlabeled protein data. Our results indicate that individual protein modalities differ in their strengths of predicting affinities or contacts. Proper cross-modality protein embedding combined with self-supervised learning improves model generalizability when predicting both affinities and contacts for unseen proteins. Data and source codes are available at https://github.com/Shen-Lab/CPAC. Supplementary data are available at Bioinformatics online.

SUBMITTER: You Y

PROVIDER: S-EPMC9486597 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature

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Cross-modality and self-supervised protein embedding for compound-protein affinity and contact prediction.

You Yuning Y Shen Yang Y

Bioinformatics (Oxford, England) 20220901 Suppl_2

<h4>Motivation</h4>Computational methods for compound-protein affinity and contact (CPAC) prediction aim at facilitating rational drug discovery by simultaneous prediction of the strength and the pattern of compound-protein interactions. Although the desired outputs are highly structure-dependent, the lack of protein structures often makes structure-free methods rely on protein sequence inputs alone. The scarcity of compound-protein pairs with affinity and contact labels further limits the accur ...[more]

PMID: 36124802

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Dataset Information

Cross-modality and self-supervised protein embedding for compound-protein affinity and contact prediction.

Publications

Cross-modality and self-supervised protein embedding for compound-protein affinity and contact prediction.

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