Active site sequence representation of human kinases outperforms full sequence for affinity prediction and inhibitor generation: 3D effects in a 1D model

26 July 2021, Version 3
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

Recent advances in deep learning have enabled the development of large-scale multimodal models for virtual screening and de novo molecular design. The human kinome with its abundant sequence and inhibitor data presents an attractive opportunity to develop proteochemometric models that exploit the size and internal diversity of this family of targets. Here we challenge a standard practice in sequence-based affinity prediction models: instead of leveraging the full primary structure of proteins, each target is represented by a sequence of 29 residues defining the ATP binding site. In kinase-ligand binding affinity prediction, our results show that the reduced active site sequence representation is not only computationally more efficient but consistently yields significantly higher performance than the full primary structure. This trend persists across different models, datasets, performance metrics and holds true when predicting affinity for both unseen ligands and kinases. Our interpretability analysis further demonstrates that, even without supervision, the full sequence model can learn to focus on the active site residues to a higher extent. We then investigate a de novo molecular design task and find that the active site provides benefits in the computational efficiency, but otherwise, both kinase representations yield similar optimized affinities (for both SMILES and SELFIES-based molecular generators). Our work challenges the assumption that full primary structure is indispensable for modelling human kinases. We hope that these results will inspire additional investigation into hybrid mechanistic-DL modeling approaches to support the identification and optimization of kinase inhibitors’ candidates.

Keywords

kinase representation
deep learning
affinity prediction
molecular design
language modeling
generative models
predictive models
SMILES
SELFIES
active site

Supplementary weblinks

Comments

Comments are not moderated before they are posted, but they can be removed by the site moderators if they are found to be in contravention of our Commenting Policy [opens in a new tab] - please read this policy before you post. Comments should be used for scholarly discussion of the content in question. You can find more information about how to use the commenting feature here [opens in a new tab] .
This site is protected by reCAPTCHA and the Google Privacy Policy [opens in a new tab] and Terms of Service [opens in a new tab] apply.