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Published on: May 16, 2021
Fragment-based diffusion modeling and molecular dynamics simulation validation for the discovery of PD-L1
Jun Liu1, Yuxing Yi1, Xiaoyan Wu1
1College of Materials and Chemical Engineering, South China Agricultural University, Guangzhou, 510630, China.
Abstract:
The programmed cell death-1/programmed cell death-ligand 1 (PD-1/PD-L1) pathway is a key target in cancer immunotherapy. Although monoclonal antibodies (mAbs) have demonstrated remarkable clinical efficacy, their application is limited by poor tissue penetration, high production costs, and the need for intravenous administration. Small-molecule inhibitors provide a promising complementary strategy, but designing them remains challenging due to the large, relatively flat PD-1/PD-L1 interface. In this study, we integrated fragment-based drug design (FBDD) with conditional diffusion modeling to overcome these obstacles. Core scaffolds consisting of key fragments identified through protein-ligand interaction analysis were used as conditional inputs. Considering the relatively conserved binding mode and limited pocket flexibility of reported PD-L1/small-molecule inhibitor complexes, seven representative co-crystal structures were selected to capture the major binding features and guide molecular generation. Structurally plausible candidate inhibitors were generated using diffusion modeling and screened by molecular docking. After 500 ns molecular dynamics (MD) simulations, we identified four candidates (bo1-bo4), which were selected for MD-based evaluation. The predicted binding free energy (BFE) values of three compounds (bo1, bo2, and bo3) were lower than - 40 kcal/mol, as calculated by the molecular mechanics-Poisson Boltzmann surface area (MM-PBSA) method with interaction entropy (IE) correction, suggesting their potential to stabilize the PD-L1 dimer interface in silico and serve as computationally prioritized candidates for further experimental evaluation of PD-1/PD-L1 blockade. Overall, this work suggests that fragment-based diffusion modeling is an efficient and interpretable strategy for the discovery of computationally prioritized PD-L1 small-molecule candidate inhibitors and offers a promising framework for tackling challenging targets in cancer immunotherapy.
Insights
Fragment-based diffusion modeling efficiently discovers small-molecule inhibitors for the PD-1/PD-L1 pathway, a key target in cancer immunotherapy. This approach overcomes challenges associated with traditional antibody therapies, offering new avenues for drug development.
Area of Science:
- Drug Discovery and Development
- Computational Chemistry
- Cancer Immunotherapy
Background:
- The programmed cell death-1/programmed cell death-ligand 1 (PD-1/PD-L1) pathway is crucial in cancer immunotherapy.
- Monoclonal antibodies targeting PD-1/PD-L1 are effective but have limitations like poor tissue penetration and high costs.
- Small-molecule inhibitors offer a complementary approach, but designing them for the large PD-1/PD-L1 interface is challenging.
Purpose of the Study:
- To develop an efficient strategy for discovering small-molecule inhibitors of the PD-1/PD-L1 pathway.
- To overcome the limitations of traditional antibody-based immunotherapies.
- To computationally prioritize novel drug candidates for experimental validation.
Main Methods:
- Integration of fragment-based drug design (FBDD) with conditional diffusion modeling.
- Utilizing core scaffolds from fragment analysis as conditional inputs for molecular generation.
- Employing molecular docking and molecular dynamics (MD) simulations for screening and evaluation.
- Using MM-PBSA with IE correction to calculate binding free energies.
Main Results:
- Generation and screening of structurally plausible candidate inhibitors using diffusion modeling and docking.
- Identification of four promising candidates (bo1-bo4) after MD simulations.
- Three candidates (bo1, bo2, bo3) exhibited predicted binding free energies below -40 kcal/mol.
- Demonstrated potential of candidates to stabilize the PD-L1 dimer interface in silico.
Conclusions:
- Fragment-based diffusion modeling is an efficient and interpretable strategy for discovering PD-L1 small-molecule inhibitors.
- This approach provides a promising framework for targeting challenging protein-protein interactions in cancer immunotherapy.
- The identified candidates are computationally prioritized for further experimental investigation of PD-1/PD-L1 blockade.
