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ABSTRACT Predicting the structure of ligands bound to proteins is a foundational problem in modern biotechnology and drug discovery, yet little is known about how to combine the predictions of protein‐ligand structure (poses) produced by the latest deep learning methods to identify the best poses and how to accurately estimate the binding affinity between a protein target and a list of ligand candidates. Further, a blind benchmarking and assessment of protein‐ligand structure and binding affinity prediction is necessary to ensure it generalizes well to new settings. Towards this end, we introduceMULTICOM_ligand, a deep learning‐based protein‐ligand structure and binding affinity prediction ensemble featuring structural consensus ranking for unsupervised pose ranking and a new deep generative flow matching model for joint structure and binding affinity prediction. Notably,MULTICOM_ligand ranked among the top‐5 ligand prediction methods in both protein‐ligand structure prediction and binding affinity prediction in the 16th Critical Assessment of Techniques for Structure Prediction (CASP16), demonstrating its efficacy and utility for real‐world drug discovery efforts. The source code for MULTICOM_ligand is freely available on GitHub.
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Engineering gain‐of‐function mutants of a WW domain by dynamics and structural analysis
Proteins gain optimal fitness such as foldability and function through evolutionary selection. However, classical studies have found that evolutionarily designed protein sequences alone cannot guarantee foldability, or at least not without considering local contacts associated with the initial folding steps. We previously showed that foldability and function can be restored by removing frustration in the folding energy landscape of a model WW domain protein, CC16, which was designed based on Statistical Coupling Analysis (SCA). Substitutions ensuring the formation of five local contacts identified as “on‐path” were selected using the closest homolog native folded sequence, N21. Surprisingly, the resulting sequence, CC16‐N21, bound to Group I peptides, while N21 did not. Here, we identified single‐point mutations that enable N21 to bind a Group I peptide ligand through structure and dynamic‐based computational design. Comparison of the docked position of the CC16‐N21/ligand complex with the N21 structure showed that residues at positions 9 and 19 are important for peptide binding, whereas the dynamic profiles identified position 10 as allosterically coupled to the binding site and exhibiting different dynamics between N21 and CC16‐N21. We found that swapping these positions in N21 with matched residues from CC16‐N21 recovers nature‐like binding affinity to N21. This study validates the use of dynamic profiles as guiding principles for affecting the binding affinity of small proteins.
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- Award ID(s):
- 1901709
- PAR ID:
- 10483563
- Publisher / Repository:
- Wiley
- Date Published:
- Journal Name:
- Protein Science
- Volume:
- 32
- Issue:
- 9
- ISSN:
- 0961-8368
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.1002/pro.4759
