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Dynamic-Backbone Protein-Ligand Structure Prediction with Multiscale Generative Diffusion Models

Qiao, Zhuoran and Nie, Weili and Vahdat, Arash and Miller, Thomas F. and Anandkumar, Anima (2022) Dynamic-Backbone Protein-Ligand Structure Prediction with Multiscale Generative Diffusion Models. . (Unpublished)

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Molecular complexes formed by proteins and small-molecule ligands are ubiquitous, and predicting their 3D structures can facilitate both biological discoveries and the design of novel enzymes or drug molecules. Here we propose NeuralPLexer, a deep generative model framework to rapidly predict protein-ligand complex structures and their fluctuations using protein backbone template and molecular graph inputs. NeuralPLexer jointly samples protein and small-molecule 3D coordinates at an atomistic resolution through a generative model that incorporates biophysical constraints and inferred proximity information into a time-truncated diffusion process. The reverse-time generative diffusion process is learned by a novel stereochemistry-aware equivariant graph transformer that enables efficient, concurrent gradient field prediction for all heavy atoms in the protein-ligand complex. NeuralPLexer outperforms existing physics-based and learning-based methods on benchmarking problems including fixed-backbone blind protein-ligand docking and ligand-coupled binding site repacking. Moreover, we identify preliminary evidence that NeuralPLexer enriches bound-state-like protein structures when applied to systems where protein folding landscapes are significantly altered by the presence of ligands. Our results reveal that a data-driven approach can capture the structural cooperativity among protein and small-molecule entities, showing promise for the computational identification of novel drug targets and the end-to-end differentiable design of functional small-molecules and ligand-binding proteins.

Item Type:Report or Paper (Discussion Paper)
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URLURL TypeDescription Paper
Qiao, Zhuoran0000-0002-5704-7331
Miller, Thomas F.0000-0002-1882-5380
Anandkumar, Anima0000-0002-6974-6797
Additional Information:Z.Q. acknowledges graduate research funding from Caltech and partial support from the Amazon–Caltech AI4Science fellowship. T.M. acknowledge partial support from the Caltech DeLogi fund, and A.A. acknowledges support from a Caltech Bren professorship.
Funding AgencyGrant Number
Amazon AI4Science FellowshipUNSPECIFIED
Caltech De Logi FundUNSPECIFIED
Bren Professor of Computing and Mathematical SciencesUNSPECIFIED
Record Number:CaltechAUTHORS:20221221-004659742
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:118548
Deposited By: George Porter
Deposited On:22 Dec 2022 18:45
Last Modified:22 Dec 2022 18:45

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