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. 2021 Dec 13;11:23916. doi: 10.1038/s41598-021-03431-4

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© The Author(s) 2021

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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Embeddings outperformed MSA-based predictions. Comparison of performance between bindPredictML17⁵ using multiple sequence alignments (MSAs) and the method introduced here, bindEmbed21DL, using only embeddings from ProtT5²⁸. We also compare using binding annotations from BioLiP⁹ or the PDB³¹. bindEmbed21DL (embeddings-only) clearly outperformed bindPredictML17 (MSA + BioLiP) by 13 percentage points (F1 = 47 ± 2% vs. F1 = 34 ± 2%). We used annotations from BioLiP⁹ to assess the performance for both methods. Although bindPredictML17 had been trained on annotations from PDB³¹ for enzymes and PDIdb⁵⁷ for DNA-binding proteins, it reached higher performance (lighter shaded colors vs. lightest shaded colors) for BioLiP annotations. Error bars indicate 95% CIs.