Center for Computational Biology Scholarly Works
https://hdl.handle.net/1808/22109
2024-03-29T01:49:23Z
-
Ligand Gaussian Accelerated Molecular Dynamics 2 (LiGaMD2): Improved Calculations of Ligand Binding Thermodynamics and Kinetics with Closed Protein Pocket
https://hdl.handle.net/1808/34757
Ligand Gaussian Accelerated Molecular Dynamics 2 (LiGaMD2): Improved Calculations of Ligand Binding Thermodynamics and Kinetics with Closed Protein Pocket
Wang, Jinan; Miao, Yinglong
Ligand binding thermodynamics and kinetics are critical parameters for drug design. However, it has proven challenging to efficiently predict ligand binding thermodynamics and kinetics from molecular simulations due to limited simulation timescales. Protein dynamics, especially in the ligand binding pocket, often plays an important role in ligand binding. Based on our previously developed Ligand Gaussian accelerated molecular dynamics (LiGaMD), here we present LiGaMD2 in which a selective boost potential was applied to both the ligand and protein residues in the binding pocket to improve sampling of ligand binding and dissociation. To validate the performance of LiGaMD2, the T4 lysozyme (T4L) mutants with open and closed pockets bound by different ligands were chosen as model systems. LiGaMD2 could efficiently capture repetitive ligand dissociation and binding within microsecond simulations of all T4L systems. The obtained ligand binding kinetic rates and free energies agreed well with available experimental values and previous modeling results. Therefore, LiGaMD2 provides an improved approach to sample opening of closed protein pockets for ligand dissociation and binding, thereby allowing for efficient calculations of ligand binding thermodynamics and kinetics.
This document is the Accepted Manuscript version of a Published Work that appeared in final form in Journal of Chemical Theory and Computation, Copyright © 2023 American Chemical Society after peer review and technical editing by the publisher. To access the final edited and published work see https://doi.org/10.1021/acs.jctc.2c01194.
2023-01-27T00:00:00Z
-
Deep Boosted Molecular Dynamics: Accelerating Molecular Simulations with Gaussian Boost Potentials Generated Using Probabilistic Bayesian Deep Neural Network
https://hdl.handle.net/1808/34748
Deep Boosted Molecular Dynamics: Accelerating Molecular Simulations with Gaussian Boost Potentials Generated Using Probabilistic Bayesian Deep Neural Network
Do, Hung N.; Miao, Yinglong
We have developed a new deep boosted molecular dynamics (DBMD) method. Probabilistic Bayesian neural network models were implemented to construct boost potentials that exhibit Gaussian distribution with minimized anharmonicity, thereby allowing for accurate energetic reweighting and enhanced sampling of molecular simulations. DBMD was demonstrated on model systems of alanine dipeptide and the fast-folding protein and RNA structures. For alanine dipeptide, 30 ns DBMD simulations captured up to 83–125 times more backbone dihedral transitions than 1 μs conventional molecular dynamics (cMD) simulations and were able to accurately reproduce the original free energy profiles. Moreover, DBMD sampled multiple folding and unfolding events within 300 ns simulations of the chignolin model protein and identified low-energy conformational states comparable to previous simulation findings. Finally, DBMD captured a general folding pathway of three hairpin RNAs with the GCAA, GAAA, and UUCG tetraloops. Based on a deep learning neural network, DBMD provides a powerful and generally applicable approach to boosting biomolecular simulations. DBMD is available with open source in OpenMM at https://github.com/MiaoLab20/DBMD/.
This document is the Accepted Manuscript version of a Published Work that appeared in final form in Journal of Physical Chemistry Letters, copyright Copyright © 2023 American Chemical Society after peer review and technical editing by the publisher. To access the final edited and published work see https://doi.org/10.1021/acs.jpclett.3c00926.
2023-05-23T00:00:00Z
-
Critical Non-Covalent Binding Intermediate for an Allosteric Covalent Inhibitor of SUMO E1
https://hdl.handle.net/1808/34747
Critical Non-Covalent Binding Intermediate for an Allosteric Covalent Inhibitor of SUMO E1
Pawnikar, Shristi; Bhattarai, Apurba; Ouyang, S. Xiaohu; Vega, Ramir; Chen, Yuan; Miao, Yinglong
Post-translational modifications by small ubiquitin-like modifiers (SUMOs) are dysregulated in many types of cancers. The SUMO E1 enzyme has recently been suggested as a new immuno-oncology target. COH000 was recently identified as a highly specific allosteric covalent inhibitor of SUMO E1. However, a marked discrepancy was found between the X-ray structure of the covalent COH000-bound SUMO E1 complex and the available structure–activity relationship (SAR) data of inhibitor analogues due to unresolved noncovalent protein–ligand interactions. Here, we have investigated noncovalent interactions between COH000 and SUMO E1 during inhibitor dissociation through novel Ligand Gaussian accelerated molecular dynamics (LiGaMD) simulations. Our simulations have identified a critical low-energy non-covalent binding intermediate conformation of COH000 that agreed excellently with published and new SAR data of the COH000 analogues, which were otherwise inconsistent with the X-ray structure. Altogether, our biochemical experiments and LiGaMD simulations have uncovered a critical non-covalent binding intermediate during allosteric inhibition of the SUMO E1 complex.
This document is the Accepted Manuscript version of a Published Work that appeared in final form in Journal of Physical Chemistry Letters, copyright © Copyright © 2023 American Chemical Society after peer review and technical editing by the publisher. To access the final edited and published work see https://doi.org/10.1021/acs.jpclett.3c00253.
2023-03-10T00:00:00Z
-
Evaluation of AlphaFold-Multimer prediction on multi-chain protein complexes
https://hdl.handle.net/1808/34739
Evaluation of AlphaFold-Multimer prediction on multi-chain protein complexes
Zhu, Wensi; Shenoy, Aditi; Kundrotas, Petras; Elofsson, Arne
Motivation
Despite near-experimental accuracy on single-chain predictions, there is still scope for improvement among multimeric predictions. Methods like AlphaFold-Multimer and FoldDock can accurately model dimers. However, how well these methods fare on larger complexes is still unclear. Further, evaluation methods of the quality of multimeric complexes are not well established.
Results
We analysed the performance of AlphaFold-Multimer on a homology-reduced dataset of homo- and heteromeric protein complexes. We highlight the differences between the pairwise and multi-interface evaluation of chains within a multimer. We describe why certain complexes perform well on one metric (e.g. TM-score) but poorly on another (e.g. DockQ). We propose a new score, Predicted DockQ version 2 (pDockQ2), to estimate the quality of each interface in a multimer. Finally, we modelled protein complexes (from CORUM) and identified two highly confident structures that do not have sequence homology to any existing structures.
Availability and implementation
All scripts, models, and data used to perform the analysis in this study are freely available at https://gitlab.com/ElofssonLab/afm-benchmark.
2023-07-05T00:00:00Z