From Static to Dynamic Structures: Improving Binding Affinity Prediction
with a Graph-Based Deep Learning Model
- URL: http://arxiv.org/abs/2208.10230v3
- Date: Sat, 3 Jun 2023 09:58:11 GMT
- Title: From Static to Dynamic Structures: Improving Binding Affinity Prediction
with a Graph-Based Deep Learning Model
- Authors: Yaosen Min, Ye Wei, Peizhuo Wang, Xiaoting Wang, Han Li, Nian Wu,
Stefan Bauer, Shuxin Zheng, Yu Shi, Yingheng Wang, Ji Wu, Dan Zhao and
Jianyang Zeng
- Abstract summary: Accurate prediction of the protein-ligand binding affinities is an essential challenge in the structure-based drug design.
Here, we curated an MD dataset containing 3,218 different protein-ligand complexes, and developed Dynaformer, a graph-based deep learning model.
Dynaformer was able to accurately predict the binding affinities by learning the geometric characteristics of the protein-ligand interactions from the MD trajectories.
- Score: 33.92165575735532
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: Accurate prediction of the protein-ligand binding affinities is an essential
challenge in the structure-based drug design. Despite recent advance in
data-driven methods in affinity prediction, their accuracy is still limited,
partially because they only take advantage of static crystal structures while
the actual binding affinities are generally depicted by the thermodynamic
ensembles between proteins and ligands. One effective way to approximate such a
thermodynamic ensemble is to use molecular dynamics (MD) simulation. Here, we
curated an MD dataset containing 3,218 different protein-ligand complexes, and
further developed Dynaformer, which is a graph-based deep learning model.
Dynaformer was able to accurately predict the binding affinities by learning
the geometric characteristics of the protein-ligand interactions from the MD
trajectories. In silico experiments demonstrated that our model exhibits
state-of-the-art scoring and ranking power on the CASF-2016 benchmark dataset,
outperforming the methods hitherto reported. Moreover, we performed a virtual
screening on the heat shock protein 90 (HSP90) using Dynaformer that identified
20 candidates and further experimentally validated their binding affinities. We
demonstrated that our approach is more efficient, which can identify 12 hit
compounds (two were in the submicromolar range), including several newly
discovered scaffolds. We anticipate this new synergy between large-scale MD
datasets and deep learning models will provide a new route toward accelerating
the early drug discovery process.
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