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Learning functional properties of proteins with language models

Nature Machine Intelligence volume 4, pages 227–245 (2022)Cite this article

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A preprint version of the article is available at bioRxiv.

Abstract

Data-centric approaches have been used to develop predictive methods for elucidating uncharacterized properties of proteins; however, studies indicate that these methods should be further improved to effectively solve critical problems in biomedicine and biotechnology, which can be achieved by better representing the data at hand. Novel data representation approaches mostly take inspiration from language models that have yielded ground-breaking improvements in natural language processing. Lately, these approaches have been applied to the field of protein science and have displayed highly promising results in terms of extracting complex sequence–structure–function relationships. In this study we conducted a detailed investigation over protein representation learning by first categorizing/explaining each approach, subsequently benchmarking their performances on predicting: (1) semantic similarities between proteins, (2) ontology-based protein functions, (3) drug target protein families and (4) protein–protein binding affinity changes following mutations. We evaluate and discuss the advantages and disadvantages of each method over the benchmark results, source datasets and algorithms used, in comparison with classical model-driven approaches. Finally, we discuss current challenges and suggest future directions. We believe that the conclusions of this study will help researchers to apply machine/deep learning-based representation techniques to protein data for various predictive tasks, and inspire the development of novel methods.

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Fig. 1: Schematic representation of the study.
Fig. 2: Protein semantic similarity inference benchmark results.
Fig. 3: Ontology-based protein function prediction benchmark results.
Fig. 4: Drug target protein family classification benchmark results.
Fig. 5: Protein–protein binding affinity estimation benchmark results.

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Data availability

All of the datasets and results of this study are available for download at https://github.com/kansil/PROBE. Protein representation and MSA files are available via Zenodo at https://doi.org/10.5281/zenodo.5795850 (ref. 116).

Code availability

The source code of this study is available for download at https://github.com/kansil/PROBE. A ready-to-use web-tool containing all models of four benchmarks, to reproduce the results and to test new representation methods on the same predictive tasks are available on the CodeOcean platform, which is reachable from https://PROBE.kansil.org (ref. 117).

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Acknowledgements

This work was supported by TUBITAK (project no. 318S218). We thank G. Tatar (faculty member, KTU, Turkey) for reading and commenting on the manuscript, and to G.M.Ç. Şılbır (PhD Candidate, KTU, Turkey) for contributing to the drawing of figures.

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Authors and Affiliations

  1. Cancer Systems Biology Laboratory (KanSiL), Graduate School of Informatics, Middle East Technical University, Ankara, Turkey

    Serbulent Unsal, Heval Atas, Aybar C. Acar & Tunca Doğan

  2. Department of Biostatistics and Medical Informatics, Karadeniz Technical University, Trabzon, Turkey

    Serbulent Unsal, Muammer Albayrak & Kemal Turhan

  3. Department of Computer Engineering, Hacettepe University, Ankara, Turkey

    Tunca Doğan

  4. Institute of Informatics, Hacettepe University, Ankara, Turkey

    Tunca Doğan

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Contributions

T.D., S.U. and A.C.A. conceived the idea and planned the work. S.U. evaluated the literature, constructed representation vectors and prepared the datasets and carried out the analysis for the semantic similarity inference and protein–protein binding affinity estimation benchmarks. H.A. and S.U. prepared the datasets and carried out the analysis for the ontology-based protein family classification benchmark. M.A. and S.U. prepared the datasets and carried out the analysis for the drug target protein family classification benchmark. S.U., A.C.A. and T.D. have written the manuscript. T.D., A.C.A and K.T. supervised the overall study. All authors approved the manuscript.

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Correspondence to Tunca Doğan.

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Nature Machine Intelligence thanks Christian Dallago, Céline Marquet and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary information

Supplementary Information

1. Different approaches for representing proteins. 2. Classical protein representations. 3. Protein representation learning. 4. Protein representation methods benchmarked in this study. 5. Objective-based classification of a comprehensive list of protein representations. 6. Traits of successful protein representations. 7. Performance evaluation metrics. 8. Extended results. 9. Extended discussion. Supplementary Figs. 1–15 and Tables 1–11.

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Unsal, S., Atas, H., Albayrak, M. et al. Learning functional properties of proteins with language models. Nat Mach Intell 4, 227–245 (2022). https://doi.org/10.1038/s42256-022-00457-9

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