Novel Enhanced Sampling Methods in Multiscale Modeling
Lead Research Organisation:
King's College London
Department Name: Chemistry
Abstract
Computer-based technologies are becoming one of the most promising novel approaches due to continuously accelerated growth of both hardware processing power and software algorithm efficiency. One recent example includes machine learning algorithms that revolutionised data analysis in computer science, and lead to new computer games, visual recognition, and other applications that overtake human performance in many cases.
Here, we propose to perform atomistic molecular simulations using novel enhanced sampling algorithms. Most biologically important processes take place on significantly longer timescales than those accessible to current computer simulations. Therefore, to obtain meaningful and accurate results regarding the kinetics and conformational dynamics of complex molecular systems, we use algorithms that enhance the sampling using parallel calculations with different biases. Developing more optimal biasing algorithms will allow us to model faster and more accurately the key biological processes of interest, including ligand binding, protein conformations, etc.
Here we aim to use statistical algorithms inspired by machine learning to develop novel enhanced sampling methods for molecular simulations. Novel algorithms can be applied to a wide range of molecular modeling problems. We will focus on phosphate catalytic enzymes, and study key DNA processing enzymes to reveal the catalytic mechanism in these systems.
Due to the essential nature of phosphate catalytic enzymes in most biological processes, a large number of drugs in current clinical practice also target phosphate-processing enzymes treating a wide range of diseases. Examples include reverse transcriptase and integrase inhibitors used against HIV and hepatitis B, proton pump inhibitors used in gastric diseases, kinase, PARP and topoisomerase inhibitors used against a large number of cancers. Studying phosphate catalytic systems with modern molecular modeling methods will enable fundamental advances in our current knowledge of the molecular basis of life. It will also create opportunities for rational development of better drugs to fight diseases.
Here, we propose to perform atomistic molecular simulations using novel enhanced sampling algorithms. Most biologically important processes take place on significantly longer timescales than those accessible to current computer simulations. Therefore, to obtain meaningful and accurate results regarding the kinetics and conformational dynamics of complex molecular systems, we use algorithms that enhance the sampling using parallel calculations with different biases. Developing more optimal biasing algorithms will allow us to model faster and more accurately the key biological processes of interest, including ligand binding, protein conformations, etc.
Here we aim to use statistical algorithms inspired by machine learning to develop novel enhanced sampling methods for molecular simulations. Novel algorithms can be applied to a wide range of molecular modeling problems. We will focus on phosphate catalytic enzymes, and study key DNA processing enzymes to reveal the catalytic mechanism in these systems.
Due to the essential nature of phosphate catalytic enzymes in most biological processes, a large number of drugs in current clinical practice also target phosphate-processing enzymes treating a wide range of diseases. Examples include reverse transcriptase and integrase inhibitors used against HIV and hepatitis B, proton pump inhibitors used in gastric diseases, kinase, PARP and topoisomerase inhibitors used against a large number of cancers. Studying phosphate catalytic systems with modern molecular modeling methods will enable fundamental advances in our current knowledge of the molecular basis of life. It will also create opportunities for rational development of better drugs to fight diseases.
Planned Impact
Academia
Our results are shared via workshops, tutorials, conferences and seminars. Computational groups will be able to download our newly developed programs from my research group website (www.rostaresearch.com), or from CCPBioSim workshop/tutorial webpages (http://www.ccpbiosim.ac.uk/). Our experimental collaborators will benefit from the more accurate and efficient algorithms that we can subsequently apply to design novel ligands, mutants, and test experimental hypothesis. We will work closely with experimental and theoretical collaborator groups in the UK, and overseas.
Public Sector, Business, Industry
On long term, health-related public sectors will benefit from basic research on structure and mechanism of phosphate processing enzymes. Our methods can be used and may be inspirational to a large number of projects studying ligand binding kinetics, or the dynamics of phosphate-processing enzymes that are relevant to many diseases. Phosphate processing enzymes are validated targets of a large number of drugs used in current clinical practices treating a wide range of diseases. These include reverse transcriptase and integrase inhibitors used against HIV and hepatitis B, proton pump inhibitors used in gastric diseases, kinase and topoisomerase inhibitors used in chemotherapy to treat cancers.
Our basic research results are therefore also relevant to UK charities such as Cancer Research UK. In addition to drug design, insights related to controlling enzyme activity is also relevant for biotechnology industries, e.g., businesses developing industrial enzymes such as Novozymes.
General Public, Education
The general public, high school and university students will benefit from new basic research developments in general, by public lectures in the UK and world-wide (e.g., via STEM seminars, such as the one I presented in a London-area Girls' School), or by the Open Days at King's. My lab also hosted 10 high school students to date since 2013, who were introduced to on-going research in my lab via the In2Science and Nuffield Research Placement programs.
Our results are shared via workshops, tutorials, conferences and seminars. Computational groups will be able to download our newly developed programs from my research group website (www.rostaresearch.com), or from CCPBioSim workshop/tutorial webpages (http://www.ccpbiosim.ac.uk/). Our experimental collaborators will benefit from the more accurate and efficient algorithms that we can subsequently apply to design novel ligands, mutants, and test experimental hypothesis. We will work closely with experimental and theoretical collaborator groups in the UK, and overseas.
Public Sector, Business, Industry
On long term, health-related public sectors will benefit from basic research on structure and mechanism of phosphate processing enzymes. Our methods can be used and may be inspirational to a large number of projects studying ligand binding kinetics, or the dynamics of phosphate-processing enzymes that are relevant to many diseases. Phosphate processing enzymes are validated targets of a large number of drugs used in current clinical practices treating a wide range of diseases. These include reverse transcriptase and integrase inhibitors used against HIV and hepatitis B, proton pump inhibitors used in gastric diseases, kinase and topoisomerase inhibitors used in chemotherapy to treat cancers.
Our basic research results are therefore also relevant to UK charities such as Cancer Research UK. In addition to drug design, insights related to controlling enzyme activity is also relevant for biotechnology industries, e.g., businesses developing industrial enzymes such as Novozymes.
General Public, Education
The general public, high school and university students will benefit from new basic research developments in general, by public lectures in the UK and world-wide (e.g., via STEM seminars, such as the one I presented in a London-area Girls' School), or by the Open Days at King's. My lab also hosted 10 high school students to date since 2013, who were introduced to on-going research in my lab via the In2Science and Nuffield Research Placement programs.
Publications
Pan X
(2018)
Representation of the QM Subsystem for Long-Range Electrostatic Interaction in Non-Periodic Ab Initio QM/MM Calculations.
in Molecules (Basel, Switzerland)
Huggins D
(2018)
Biomolecular simulations: From dynamics and mechanisms to computational assays of biological activity
in WIREs Computational Molecular Science
Olesinska M
(2019)
Modular supramolecular dimerization of optically tunable extended aryl viologens.
in Chemical science
Noé F
(2019)
Special Topic: Markov Models of Molecular Kinetics
Readman C
(2019)
Anomalously Large Spectral Shifts near the Quantum Tunnelling Limit in Plasmonic Rulers with Subatomic Resolution.
in Nano letters
Pereverzev A
(2019)
Spectroscopic Evidence for Peptide-Bond-Selective Ultraviolet Photodissociation
in The Journal of Physical Chemistry Letters
Kells A
(2019)
Mean first passage times in variational coarse graining using Markov state models
in The Journal of Chemical Physics
Pereverzev AY
(2019)
Gas-phase structures reflect the pain-relief potency of enkephalin peptides.
in Physical chemistry chemical physics : PCCP
Wu G
(2019)
Controlling the structure and photophysics of fluorophore dimers using multiple cucurbit[8]uril clampings.
in Chemical science
Noé F
(2019)
Markov Models of Molecular Kinetics.
in The Journal of chemical physics
Wu G
(2019)
Cucurbit[8]uril-mediated pseudo[2,3]rotaxanes.
in Chemical communications (Cambridge, England)
Faulkner M
(2020)
Molecular simulations unravel the molecular principles that mediate selective permeability of carboxysome shell protein
in Scientific Reports
Faizi F
(2020)
Efficient Irreversible Monte Carlo Samplers.
in Journal of chemical theory and computation
Berta D
(2020)
Toward Understanding CB[7]-Based Supramolecular Diels-Alder Catalysis.
in Frontiers in chemistry
Ojambati O
(2020)
Breaking the Selection Rules of Spin-Forbidden Molecular Absorption in Plasmonic Nanocavities
in ACS Photonics
Faizi F
(2020)
Simulated tempering with irreversible Gibbs sampling techniques.
in The Journal of chemical physics
Cook N
(2020)
Structural basis of second-generation HIV integrase inhibitor action and viral resistance
in Science
Jenkins K
(2020)
Combining data integration and molecular dynamics for target identification in a-Synuclein-aggregating neurodegenerative diseases: Structural insights on Synaptojanin-1 (Synj1).
in Computational and structural biotechnology journal
Fanelli R
(2020)
Organocatalytic Access to a cis-Cyclopentyl-?-amino Acid: An Intriguing Model of Selectivity and Formation of a Stable 10/12-Helix from the Corresponding ?/a-Peptide.
in Journal of the American Chemical Society
Berta D
(2020)
Cations in motion: QM/MM studies of the dynamic and electrostatic roles of H+ and Mg2+ ions in enzyme reactions.
in Current opinion in structural biology
Kells A
(2020)
Correlation functions, mean first passage times, and the Kemeny constant.
in The Journal of chemical physics
Wagner A
(2020)
Host-Guest Chemistry Meets Electrocatalysis: Cucurbit[6]uril on a Au Surface as a Hybrid System in CO2 Reduction.
in ACS catalysis
Berta D
(2021)
Modelling the active SARS-CoV-2 helicase complex as a basis for structure-based inhibitor design
in Chemical Science
Griffiths J
(2021)
Resolving sub-angstrom ambient motion through reconstruction from vibrational spectra.
in Nature communications
Dürr S
(2021)
The Role of Conserved Residues in the DEDDh Motif: the Proton-Transfer Mechanism of HIV-1 RNase H
in ACS Catalysis
Pan X
(2021)
A simplified charge projection scheme for long-range electrostatics in ab initio QM/MM calculations.
in The Journal of chemical physics
Sicard F
(2021)
Position-Dependent Diffusion from Biased Simulations and Markov State Model Analysis.
in Journal of chemical theory and computation
Wright D
(2021)
Mechanistic study of an immobilized molecular electrocatalyst by in situ gap-plasmon-assisted spectro-electrochemistry
in Nature Catalysis
Koczor-Benda Z
(2021)
Molecular Screening for Terahertz Detection with Machine-Learning-Based Methods
in Physical Review X
Xomalis A
(2021)
Detecting mid-infrared light by molecular frequency upconversion in dual-wavelength nanoantennas
in Science
Peng J
(2022)
In-Situ Spectro-Electrochemistry of Conductive Polymers Using Plasmonics to Reveal Doping Mechanisms.
in ACS nano
Lin Q
(2022)
Optical suppression of energy barriers in single molecule-metal binding.
in Science advances
Badaoui M
(2022)
Combined Free-Energy Calculation and Machine Learning Methods for Understanding Ligand Unbinding Kinetics.
in Journal of chemical theory and computation
Koczor-Benda Z
(2022)
Molecular Vibration Explorer: an Online Database and Toolbox for Surface-Enhanced Frequency Conversion and Infrared and Raman Spectroscopy.
in The journal of physical chemistry. A
Koczor-Benda Z
(2023)
Direct Calculation of Electron Transfer Rates with the Binless Dynamic Histogram Analysis Method.
in The journal of physical chemistry letters
Hu S
(2023)
Full Control of Plasmonic Nanocavities Using Gold Decahedra-on-Mirror Constructs with Monodisperse Facets.
in Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Berta D
(2023)
Mechanism-Based Redesign of GAP to Activate Oncogenic Ras.
in Journal of the American Chemical Society
Buigues P
(2023)
Investigating the Unbinding of Muscarinic Antagonists from the Muscarinic 3 Receptor
in Journal of Chemical Theory and Computation
Koskin V
(2023)
Variational kinetic clustering of complex networks.
in The Journal of chemical physics
Description | collaboration with Novartis |
Organisation | Novartis |
Department | Drug Discovery & Development |
Country | United States |
Sector | Private |
PI Contribution | We have shared data and software/home-made code to derive kinetic rates from umbrella sampling simulations. We also developed methods to be used for calculating residence times frlom atomistic simulations. |
Collaborator Contribution | Shared data with us of atomistic simulations for drug molecules crossing the membrane. |
Impact | We have joint publications. |
Start Year | 2018 |