Single-sequence protein structure prediction using a language model and deep learning.

Ratul Chowdhury¹, Nazim Bouatta², Surojit Biswas^3,4, Christina Floristean⁵, Anant Kharkar⁵, Koushik Roy⁵, Charlotte Rochereau⁶, Gustaf Ahdritz⁷, Joanna Zhang⁵, George M Church^1,3, Peter K Sorger^8,9, Mohammed AlQuraishi^10,11

¹Laboratory of Systems Pharmacology, Program in Therapeutic Science, Harvard Medical School, Boston, MA, USA.

²Laboratory of Systems Pharmacology, Program in Therapeutic Science, Harvard Medical School, Boston, MA, USA. nazim_bouatta@hms.harvard.edu.

³Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.

⁴Nabla Bio, Inc., Boston, MA, USA.

⁵Department of Computer Science, Columbia University, New York, NY, USA.

⁶Integrated Program in Cellular, Molecular, and Biomedical Studies, Columbia University, New York, NY, USA.

⁷Department of Systems Biology, Columbia University, New York, NY, USA.

⁸Laboratory of Systems Pharmacology, Program in Therapeutic Science, Harvard Medical School, Boston, MA, USA. peter_sorger@hms.harvard.edu.

⁹Department of Systems Biology, Harvard Medical School, Boston, MA, USA. peter_sorger@hms.harvard.edu.

¹⁰Department of Computer Science, Columbia University, New York, NY, USA. ma4129@cumc.columbia.edu.

¹¹Department of Systems Biology, Columbia University, New York, NY, USA. ma4129@cumc.columbia.edu.

PubMed PMC DOI PDF

Abstract

AlphaFold2 and related computational systems predict protein structure using deep learning and co-evolutionary relationships encoded in multiple sequence alignments (MSAs). Despite high prediction accuracy achieved by these systems, challenges remain in (1) prediction of orphan and rapidly evolving proteins for which an MSA cannot be generated; (2) rapid exploration of designed structures; and (3)…

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