Computational biochemistry: predictive modelling for biology and medicine

Lead Research Organisation: University of Bristol
Department Name: Chemistry

Abstract

All of biology - life itself - depends on enzymes. Enzymes are large, natural molecules that allow specific biochemical reactions to take place quickly. As yet we do not understand what it is that makes them such good natural catalysts. There are many reasons for studying enzymes and the reactions they catalyse: many drugs are enzyme inhibitors (they stop specific enzymes from working), so better understanding of enzymes will help in the design of new drugs. It should also help understand and predict the effects of genetic variation, for example in understanding why some people may benefit from a particular drug, or may be at risk from a disease. Enzymes are also very good and environmentally friendly catalysts - knowing how they function should help in the design and development of new 'green' catalysts for industrial applications. Enzymes also show great promise as 'molecular machines' in the emerging field of nanotechnology. We will develop and apply advanced computer modelling methods , in collaboration with experimental biochemistry, to analyse in detail how enzymes work. We will study enzymes that are targets for designing drugs for the treatment of pain and anxiety, and study how drugs are broken down by enzymes in the body. We will develop new modelling methods, capable of dealing accurately with these large and complex systems, and the chemical reactions they catalyse. We will bring together state-of-the-art computer software and hardware, and new theoretical methods, to achieve unprecedented accuracy for modelling enzymes. These modelling methods promise to add an extra dimension to studying enzyme reactions - e.g. making molecular 'movies' of how enzymes work. We will also use the methods we develop to predict how strongly potential drugs bind to their protein targets. The methods we will develop and use are based on fundamental quantum mechanics, so will be better than current approximate techniques. Current methods for predicting how strongly different drugs bind to proteins are efficient, but lack reliability because they fail to capture the essential physics. Quantum mechanics provides a physically accurate representation of the interactions, but until now these methods have been too computationally intensive for practical use. We will base our developments on methods that can accurately model chemical reactions of small molecules, combined with techniques for modelling protein structure and dynamics, and extend these to study enzymes and their reactions. We will make use of the great power provided by the latest 'multi-core' computer chips. Altogether, this will require several ground-breaking developments, which we are well placed to carry out. We will develop and apply new methods that can calculate how reactions happen in enzymes, describing the energies of breaking and forming chemical bonds accurately and analyse how reaction is affected by protein dynamics. This work will be carried out in collaboration with experiments, with project partners in academia and industry, in the UK and abroad. We will make predictions and compare with experiments on the same enzymes to test our theoretical methods. This will involve the transfer and exchange of methods, data, ideas and researchers between experimental and modelling groups, in new and existing collaborations. The methods we develop and the results we obtain will be made widely available (e.g. via the web), and should be very useful to biologists, biochemists, drug designers and other researchers working on enzymes. We will extend these high-level methods to new areas of biology, to provide new tools for studying protein structure. The results should provide new and exciting insight into how enzymes function, and promise to make a major contribution to the development of new drugs.

Publications

10 25 50
publication icon
Bathelt CM (2008) QM/MM modeling of benzene hydroxylation in human cytochrome P450 2C9. in The journal of physical chemistry. A

publication icon
Byrne MJ (2016) The Catalytic Mechanism of a Natural Diels-Alderase Revealed in Molecular Detail. in Journal of the American Chemical Society

publication icon
Chudyk EI (2014) QM/MM simulations as an assay for carbapenemase activity in class A ß-lactamases. in Chemical communications (Cambridge, England)

 
Description The overall aim of my Leadership Fellowship was the development and application of computational molecular simulation methods for biological problems. Combined quantum mechanics/molecular mechanics (QM/MM) methods are a particular focus. Aims include modelling mechanisms of enzyme catalysis and studies of ligand binding. The Fellowship has been a step change in my career. It facilitated my promotion to a Chair. It has significantly broadened the range, scope and impact of my work. Method development, and particularly testing of simulations against experiment, has helped to define reliable and generally useful protocols and parameters which will be widely applicable. In addition to the need for accurate methods, the findings emphasise the importance of accounting for the dynamic and conformational complexity of biomolecules. These methods are finding increasingly wide application in biology, expanding beyond the 'traditional' investigations of enzyme biochemistry into areas such as drug design. The objectives were in four interweaved strands; progress in each area is summarized below. These included development of methods for modelling biomolecular mechanisms; modelling mechanisms of drug metabolism; methods for protein-ligand binding; and new areas of application of QM/MM methods. Throughout, comparison with experiment has been important (examples include fluorocitrate synthesis, alkene oxidation, epoxide ring opening, enzyme-inhibitor complex structures, kinetic isotope effects (KIEs), spectroscopic properties and covalent inhibitor reactivity). We have shown that useful modelling relies both on accurate methods and also, crucially, on taking into account protein dynamics. This work has included high level QM/MM (e.g. the first applications of SCS-MP2 QM/MM methods), in combination with molecular dynamics and Monte Carlo simulations. A broad aim is to disseminate QM/MM methods, and develop collaborations particularly with experimental groups. Another aim has been to use advanced computational technology. While the collaboration with Clearspeed unfortunately ceased because of that company's financial situation, I have continued software development efforts (e.g. in a separate current EPSRC project, EP/I030395/1) for efficient use of HPC resources, multicore processors and graphics processing units (GPUs), which offer tremendous potential increases in speed and thus the scale of problems that can be tackled.
1. Biomolecular mechanisms: Analysis of stereospecificity determinants in 'lethal synthesis' of fluorocitrate by ab initio QM/MM (van der Kamp et al. Angewandte VIP 2011); antibiotic breakdown in a Class A ß-lactamase (Hermann et al. JPCA 2009); carboxypeptidase A; chitinase (Jitonnom et al. Biochemistry 2011); analysis of catalysis in chorismate mutase (Ranaghan et al. OBC 2011); implementation of the nudged elastic band method for reaction pathways and testing QM/MM methods (e.g. van der Kamp et al. JPCB 2010); GPUs for protein molecular dynamics simulations (Woods et al. Biochemistry, submitted), and enhanced sampling in QM/MM (Shaw et al. JACS submitted); development of a model for KIEs in enzymes involving quantum tunnelling (Glowacki et al. Nature Chem. 2012). 2. Mechanisms of drug metabolism: e.g., QM/MM studies of cytochrome P450 enzymes including human isoforms 2C9, 3A4, 2B4 and complexes with warfarin (Lonsdale et al JACS 2011); analysis of determinants of metabolism of dextromethorphan in 2D6 (Olah et al. PNAS 2011); modelling reactions of epoxide hydrolase, involved in Phase 2 drug metabolism (Lonsdale et al. Biochemistry 2012), identifying origins of observed specificity. 3. QM/MM free energy calculations and new methods for protein-ligand binding: ab initio QM/MM thermodynamic integration free energy calculations (e.g. binding in neuraminidase complexes, Shaw et al. JACS, submitted); testing QM/MM models by free energy calculations (Shaw et al. JPCL 2010); development of a new 'water-swap' reaction coordinate for calculation of absolute protein-ligand binding free energies (Woods et al. J. Chem. Phys. 2011); importance of dispersion effects and dynamics in alkene binding and reactivity in P450s (Lonsdale et al JPCL 2010, JPCB 2010); modelling of inhibitor complexes of the drug target fatty acid amide hydrolase (FAAH). 4. New applications of QM/MM methods e.g. QM/MM calculations combined with optimal control theory to design laser pulses for specific excitation of substrate vibrational quantum states relevant to reaction in aromatic amine dehydrogenase (AADH) (Ren et al. Chem. Phys. Lett. 2010); development of parameters for QM/MM modelling of nucleic acids (Pentikainen et al JCTC 2009); QM/MM modelling of protein splicing (Mujika et al. JPCB 2009, OBC 2012); Our QM/MM prediction of the structure of FAAH complexes was verified by crystallography (highlighted in RSC Chemistry World 2010); analysis of factors responsible for the observed pharmacokinetics of FAAH carbamate inhibitors (Lodola et al. Chem Comm 2011); multivariate analysis of reaction in FAAH (Lodola et al. JCTC 2010). I have published 27 peer-reviewed papers and 5 book chapters during my Fellowship, and organized conferences such as Computational Molecular Science 2010. I am Chair of the new EPSRC supported collaborative computational project in biomolecular simulation, CCP-BioSim. The Flagship Project is on developing multiscale modelling methods. I have collaborated with groups as planned e.g. in Parma (on FAAH) and Bristol (on ß-lactamase structure and dynamics) and established new collaborations with experimental groups e.g. in Kansas, Leicester, Leeds, UIUC, Michigan, and Chulalongkorn, with funding from NIH(US), Thai Government and EPSRC, including in HPC software development. Industrial links include a studentship with Neusentis, building on work with Pfizer, and paid consultancy with GSK and Shire Pharmaceuticals. Dissemination to experimentalists has involved practical guides (e.g. Lonsdale et al. Chem. Soc. Rev. 2012) and workshops e.g. on QM/MM via CCP-BioSim, and teaching on FEBS and Biochemical Society courses, and numerous conference presentations and research seminars.
Exploitation Route CCP-BioSim will provide an excellent avenue for disseminating the methods developed here to ensure their uptake and use by the wider research community. See www.ccpbiosim.ac.uk and HECBioSim.ac.uk Software tools will be useful to researchers in industry and academia. Scientists from the Universities of Bristol and Parma, Italy, have used molecular simulations to understand resistance to osimertinib - an anticancer drug used to treat types of lung cancer.

Osimertinib binds tightly to a protein, epidermal growth factor receptor (EGFR), which is overexpressed in many tumours.

EGFR is involved in a pathway that signals for cell proliferation, and so is a target for drugs. Blocking the action of EGFR (inhibiting it) can switch it off, and so is a good way to treat the disease.

Osimertinib is an effective anticancer drug that works in this way. It is used to treat non-small-cell lung cancer (NSCLC), in cases where the cancer cells have a particular (T790M) mutant form of EGFR.

It is a so-called 'third-generation' EGFR inhibitor, which was approved as a cancer treatment in 2017. Osimertinib is a covalent inhibitor: as such, it binds irreversibly to EGFR by forming a chemical bond with it.

Although patients generally respond well to osimertinib, most acquire drug resistance within one year of treatment, so the drug stops working.

Drug resistance arises because the EGFR protein mutates, so that the drug binds less tightly.

One such mutation, called L718Q, was recently discovered in patients in the clinic by the Medical Oncology Unit of the University Hospital of Parma.

In this drug resistant mutant, a single amino acid is changed. Unlike other drug resistant mutants, it was not at all clear how this change stops the drug from binding effectively, information potentially crucial in developing new drugs to overcome resistance.

Now, a collaboration between medicinal and computational chemists and clinical oncologists has revealed exactly how subtle changes in the protein target cause drug resistance.

Using a range of advanced molecular simulation techniques, scientists from the Universities of Bristol and Parma, Italy, showed that the structure of the mutant protein changes in a way that stops the drug reacting and binding to it.

Adrian Mulholland, Professor of Chemistry at the University of Bristol, said: "This work shows how molecular simulations can reveal mechanisms of drug resistance, which can be subtle and non-obvious.

"In particular, here we've used combined quantum mechanics/molecular mechanics (QM/MM) methods, which allow us to study chemical reactions in proteins.

"This is crucial in investigating covalent inhibitors, which react with their biological targets, and are the focus of growing interest in the pharmaceutical industry."

His collaborators, Professor Alessio Lodola and Professor Marco Mor of the Drug Design and Discovery group at the University of Parma, added: "It was an exciting experience to work closely with clinical colleagues who identified the mutant, and to help analyse its effects.

"Now the challenge is to exploit this discovery in the development of novel drugs targeting EGFR mutants for cancer treatment in future."

This research is a collaboration between The Drug Design and Discovery group of the Department of Food and Drug at the University of Parma; the Medical Oncology Unit, University Hospital of Parma and the Centre for Computational Chemistry, in the School of Chemistry of the University of Bristol.

This research was funded with support from the Engineering and Physical Sciences Research Council (EPSRC) partly through the CCP-BioSim project (www.ccpbiosim.ac.uk) and the Biotechnology and Biological Sciences Research Council (BBSRC), and used BlueCrystal, the University of Bristol high performance computer (www.acrc.bris.ac.uk)
Sectors Chemicals,Digital/Communication/Information Technologies (including Software),Pharmaceuticals and Medical Biotechnology

URL https://mulhollandgroup.wordpress.com
 
Description The overall aim of my Fellowship was the development and application of computational molecular simulation methods for biological problems. Combined quantum mechanics/molecular mechanics (QM/MM) methods are a particular focus. Aims include modelling mechanisms of enzyme catalysis and studies of ligand binding. The Fellowship has been a step change in my career. It facilitated my promotion to a Chair. It has significantly broadened the range, scope and impact of my work. Method development, and particularly testing of simulations against experiment, has helped to define reliable and generally useful protocols and parameters which will be widely applicable. In addition to the need for accurate methods, the findings emphasise the importance of accounting for the dynamic and conformational complexity of biomolecules. These methods are finding increasingly wide application in biology, expanding beyond the 'traditional' investigations of enzyme biochemistry into areas such as drug design. The objectives were in four interweaved strands; progress in each area is summarized below. These included development of methods for modelling biomolecular mechanisms; modelling mechanisms of drug metabolism; methods for protein-ligand binding; and new areas of application of QM/MM methods. Throughout, comparison with experiment has been important (examples include fluorocitrate synthesis, alkene oxidation, epoxide ring opening, enzyme-inhibitor complex structures, kinetic isotope effects (KIEs), spectroscopic properties and covalent inhibitor reactivity). We have shown that useful modelling relies both on accurate methods and also, crucially, on taking into account protein dynamics. This work has included high level QM/MM (e.g. the first applications of SCS-MP2 QM/MM methods), in combination with molecular dynamics and Monte Carlo simulations. A broad aim is to disseminate QM/MM methods, and develop collaborations particularly with experimental groups. Another aim has been to use advanced computational technology. While the collaboration with Clearspeed unfortunately ceased because of that company's financial situation, I have continued software development efforts (e.g. in a separate current EPSRC project, EP/I030395/1) for efficient use of HPC resources, multicore processors and graphics processing units (GPUs), which offer tremendous potential increases in speed and thus the scale of problems that can be tackled. 1. Biomolecular mechanisms: Analysis of stereospecificity determinants in 'lethal synthesis' of fluorocitrate by ab initio QM/MM (van der Kamp et al. Angewandte VIP 2011); antibiotic breakdown in a Class A ß-lactamase (Hermann et al. JPCA 2009); carboxypeptidase A; chitinase (Jitonnom et al. Biochemistry 2011); analysis of catalysis in chorismate mutase (Ranaghan et al. OBC 2011); implementation of the nudged elastic band method for reaction pathways and testing QM/MM methods (e.g. van der Kamp et al. JPCB 2010); GPUs for protein molecular dynamics simulations (Woods et al. Biochemistry, submitted), and enhanced sampling in QM/MM (Shaw et al. JACS submitted); development of a model for KIEs in enzymes involving quantum tunnelling (Glowacki et al. Nature Chem. 2012). 2. Mechanisms of drug metabolism: e.g., QM/MM studies of cytochrome P450 enzymes including human isoforms 2C9, 3A4, 2B4 and complexes with warfarin (Lonsdale et al JACS 2011); analysis of determinants of metabolism of dextromethorphan in 2D6 (Olah et al. PNAS 2011); modelling reactions of epoxide hydrolase, involved in Phase 2 drug metabolism (Lonsdale et al. Biochemistry 2012), identifying origins of observed specificity. 3. QM/MM free energy calculations and new methods for protein-ligand binding: ab initio QM/MM thermodynamic integration free energy calculations (e.g. binding in neuraminidase complexes, Shaw et al. JACS, submitted); testing QM/MM models by free energy calculations (Shaw et al. JPCL 2010); development of a new 'water-swap' reaction coordinate for calculation of absolute protein-ligand binding free energies (Woods et al. J. Chem. Phys. 2011); importance of dispersion effects and dynamics in alkene binding and reactivity in P450s (Lonsdale et al JPCL 2010, JPCB 2010); modelling of inhibitor complexes of the drug target fatty acid amide hydrolase (FAAH). 4. New applications of QM/MM methods e.g. QM/MM calculations combined with optimal control theory to design laser pulses for specific excitation of substrate vibrational quantum states relevant to reaction in aromatic amine dehydrogenase (AADH) (Ren et al. Chem. Phys. Lett. 2010); development of parameters for QM/MM modelling of nucleic acids (Pentikainen et al JCTC 2009); QM/MM modelling of protein splicing (Mujika et al. JPCB 2009, OBC 2012); Our QM/MM prediction of the structure of FAAH complexes was verified by crystallography (highlighted in RSC Chemistry World 2010); analysis of factors responsible for the observed pharmacokinetics of FAAH carbamate inhibitors (Lodola et al. Chem Comm 2011); multivariate analysis of reaction in FAAH (Lodola et al. JCTC 2010). I have published 27 peer-reviewed papers and 5 book chapters during my Fellowship, and organized conferences such as Computational Molecular Science 2010. I am Chair of the new EPSRC supported collaborative computational project in biomolecular simulation, CCP-BioSim. The Flagship Project is on developing multiscale modelling methods. I have collaborated with groups as planned e.g. in Parma (on FAAH) and Bristol (on ß-lactamase structure and dynamics) and established new collaborations with experimental groups e.g. in Kansas, Leicester, Leeds, UIUC, Michigan, and Chulalongkorn, with funding from NIH(US), Thai Government and EPSRC, including in HPC software development. Industrial links include a studentship with Neusentis, building on work with Pfizer, and paid consultancy with GSK and Shire Pharmaceuticals. Dissemination to experimentalists has involved practical guides (e.g. Lonsdale et al. Chem. Soc. Rev. 2012) and workshops e.g. on QM/MM via CCP-BioSim, and teaching on FEBS and Biochemical Society courses, and numerous conference presentations and research seminars. Lung cancer drug resistance explained by computer simulations Molecular models of the lung cancer drug osimertinib bound to its protein target, EGFR and to drug-resistant EGFR (L718Q mutant EGFR). The drug works by binding to a particular group in the protein, Cys797. In the drug resistant mutant, the structure of the protein changes, preventing the drug from reacting with it. Press release issued: 12 February 2018 Scientists from the Universities of Bristol and Parma, Italy, have used molecular simulations to understand resistance to osimertinib - an anticancer drug used to treat types of lung cancer. Osimertinib binds tightly to a protein, epidermal growth factor receptor (EGFR), which is overexpressed in many tumours. EGFR is involved in a pathway that signals for cell proliferation, and so is a target for drugs. Blocking the action of EGFR (inhibiting it) can switch it off, and so is a good way to treat the disease. Osimertinib is an effective anticancer drug that works in this way. It is used to treat non-small-cell lung cancer (NSCLC), in cases where the cancer cells have a particular (T790M) mutant form of EGFR. It is a so-called 'third-generation' EGFR inhibitor, which was approved as a cancer treatment in 2017. Osimertinib is a covalent inhibitor: as such, it binds irreversibly to EGFR by forming a chemical bond with it. Although patients generally respond well to osimertinib, most acquire drug resistance within one year of treatment, so the drug stops working. Drug resistance arises because the EGFR protein mutates, so that the drug binds less tightly. One such mutation, called L718Q, was recently discovered in patients in the clinic by the Medical Oncology Unit of the University Hospital of Parma. In this drug resistant mutant, a single amino acid is changed. Unlike other drug resistant mutants, it was not at all clear how this change stops the drug from binding effectively, information potentially crucial in developing new drugs to overcome resistance. Now, a collaboration between medicinal and computational chemists and clinical oncologists has revealed exactly how subtle changes in the protein target cause drug resistance. Using a range of advanced molecular simulation techniques, scientists from the Universities of Bristol and Parma, Italy, showed that the structure of the mutant protein changes in a way that stops the drug reacting and binding to it. Adrian Mulholland, Professor of Chemistry at the University of Bristol, said: "This work shows how molecular simulations can reveal mechanisms of drug resistance, which can be subtle and non-obvious. "In particular, here we've used combined quantum mechanics/molecular mechanics (QM/MM) methods, which allow us to study chemical reactions in proteins. "This is crucial in investigating covalent inhibitors, which react with their biological targets, and are the focus of growing interest in the pharmaceutical industry." His collaborators, Professor Alessio Lodola and Professor Marco Mor of the Drug Design and Discovery group at the University of Parma, added: "It was an exciting experience to work closely with clinical colleagues who identified the mutant, and to help analyse its effects. "Now the challenge is to exploit this discovery in the development of novel drugs targeting EGFR mutants for cancer treatment in future." This research is a collaboration between The Drug Design and Discovery group of the Department of Food and Drug at the University of Parma; the Medical Oncology Unit, University Hospital of Parma and the Centre for Computational Chemistry, in the School of Chemistry of the University of Bristol. This research was funded with support from the Engineering and Physical Sciences Research Council (EPSRC) partly through the CCP-BioSim project (www.ccpbiosim.ac.uk) and the Biotechnology and Biological Sciences Research Council (BBSRC), and used BlueCrystal, the University of Bristol high performance computer (www.acrc.bris.ac.uk) Further information Paper: 'L718Q mutant EGFR escapes covalent inhibition by stabilizing a non-reactive conformation of the lung cancer drug osimertinib' by D. Callegari, K. Ranaghan, C. Woods, R. Minari, M. Tiseo, M. Mor, A. Mulholland and A. Lodola in Chemical Science.
First Year Of Impact 2008
Sector Chemicals,Digital/Communication/Information Technologies (including Software),Education,Healthcare,Manufacturing, including Industrial Biotechology,Pharmaceuticals and Medical Biotechnology
Impact Types Economic

 
Description BBSRC Tools and Techniques: Computational tools for enzyme engineering: bridging the gap between enzymologists and expert simulation
Amount £146,027 (GBP)
Funding ID BB/L018756/1 
Organisation Biotechnology and Biological Sciences Research Council (BBSRC) 
Sector Public
Country United Kingdom
Start 07/2014 
End 01/2016
 
Description BBSRC sLoLa: Innovative Routes to Monoterpene Hydrocarbons and Their High Value Derivatives
Amount £3,038,984 (GBP)
Funding ID BB/M000354/1 
Organisation Biotechnology and Biological Sciences Research Council (BBSRC) 
Sector Public
Country United Kingdom
Start 10/2010 
End 09/2019
 
Description EPSRC
Amount £188,950 (GBP)
Funding ID E/EP/G007705/1 
Organisation Engineering and Physical Sciences Research Council (EPSRC) 
Sector Academic/University
Country United Kingdom
Start 10/2013 
End 03/2014
 
Description Synthetic Biology Research Centre. BrisSynBio: Bristol Centre for Synthetic Biology
Amount £13,528,180 (GBP)
Funding ID BB/L01386X/1 
Organisation Biotechnology and Biological Sciences Research Council (BBSRC) 
Sector Public
Country United Kingdom
Start 07/2014 
End 07/2019
 
Description Catalysis Hub 
Organisation Research Complex at Harwell
Department UK Catalysis Hub
Country United Kingdom 
Sector Public 
PI Contribution Modelling and simulation of enzyme mechanisms for applications in biocatalysts via the Catalysis Hub
Collaborator Contribution Modelling and simulation of enzyme mechanisms for applications in biocatalysts via the Catalysis Hub and training of Hub PDRAs.
Impact Catalysis is a core area of contemporary science posing major fundamental and conceptual challenges, while being at the heart of the chemical industry - an immensely successful and important part of the overall UK economy (generating in excess of £50 billion per annum). UK catalytic science currently has a strong presence, but there is intense competition in both academic and industrial sectors, and a need for UK industrial activity to shift towards new innovative areas posing major challenges for the future. In light of these challenges the UK Catalysis Hub endeavours to become a leading institution, both nationally and internationally, in the field and acts to coordinate, promote and advance the UK catalysis research portfolio. With a strong emphasis on effective use of the world-leading facilities on the RAL campus. Structure The project has four mature themes and a fifth theme starting in 2015 , each with a lead investigator as PI - Catalysis by Design (Catlow); Energy (Hardacre); Environment (Hutchings); Chemical Transformations (Davidson) and the new Biocatalysis and Biotransformations (Nick Turner Manchester) - with the design theme based in the Harwell hub. Each theme is supported by £3 - 3.5M EPSRC funding over 5 years and within each theme there are typically six to eight sub-projects funded initially for 2 years, involving collaborative teams working at a variety of sites throughout the UK. Professor Hutchings acts as director of the whole national programme for the first three year period and chairs the management group, which is supported by a steering group and an industrial advisory panel. We note that engagement with industry is one of the key aims of the catalysis hub project. As well as hosting the design theme, the centre within the Research Complex at Harwell (RCaH) will coordinate the programme, be a base for national and international visitors and provide both training and outreach activities.
Start Year 2015
 
Description Modelling of enzyme catalysed reaction mechanisms relevant to pharmaceutical development 
Organisation Pfizer Ltd
Country United Kingdom 
Sector Private 
PI Contribution Confidential
Collaborator Contribution Confidential
Impact Confidential
Start Year 2011
 
Title FESetup 
Description FESetup FESetup is a tool to automate the setup of (relative) alchemical free energy simulations like thermodynamic integration (TI) and free energy perturbation (FEP) as well as post-processing methods like MM-PBSA and LIE. FESetup can also be used for general simulation setup ("equilibration") through an abstract MD engine. The latest releases are available from the project web page. 
Type Of Technology Software 
Year Produced 2017 
Impact FESetup FESetup is a tool to automate the setup of (relative) alchemical free energy simulations like thermodynamic integration (TI) and free energy perturbation (FEP) as well as post-processing methods like MM-PBSA and LIE. FESetup can also be used for general simulation setup ("equilibration") through an abstract MD engine. The latest releases are available from the project web page. 
 
Title Sire 2010.1 
Description 2010.1 release of Sire molecular simulation framework. Main enhancement was the development and inclusion of code to perform waterswap absolute binding free energy calculations. This was the first release of software capable of performing these kinds of calculation. 
Type Of Technology Software 
Year Produced 2010 
Open Source License? Yes  
Impact This version of the code was used to run the simulations in "A water-swap reaction coordinate for the calculation of absolute protein-ligand binding free energies", Woods, CJ, Malaisree, M, Hannongbua, S & Mulholland, AJ (2011) Journal of Chemical Physics, vol 134, no. 5, 054114 DOI:10.1063/1.3519057, the first application of the WaterSwap method. The accurate prediction of absolute protein-ligand binding free energies is one of the grand challenge problems of computational science. Binding free energy measures the strength of binding between a ligand and a protein, and an algorithm that would allow its accurate prediction would be a powerful tool for rational drug design. Here we present the development of a new method that allows for the absolute binding free energy of a protein-ligand complex to be calculated from first principles, using a single simulation. Our method involves the use of a novel reaction coordinate that swaps a ligand bound to a protein with an equivalent volume of bulk water. This water-swap reaction coordinate is built using an identity constraint, which identifies a cluster of water molecules from bulk water that occupies the same volume as the ligand in the protein active site. A dual topology algorithm is then used to swap the ligand from the active site with the identified water cluster from bulk water. The free energy is then calculated using replica exchange thermodynamic integration. This returns the free energy change of simultaneously transferring the ligand to bulk water, as an equivalent volume of bulk water is transferred back to the protein active site. This, directly, is the absolute binding free energy. It should be noted that while this reaction coordinate models the binding process directly, an accurate force field and sufficient sampling are still required to allow for the binding free energy to be predicted correctly. In this paper we present the details and development of this method, and demonstrate how the potential of mean force along the water-swap coordinate can be improved by calibrating the soft-core Coulomb and Lennard-Jones parameters used for the dual topology calculation. The optimal parameters were applied to calculations of protein-ligand binding free energies of a neuraminidase inhibitor (oseltamivir), with these results compared to experiment. These results demonstrate that the water-swap coordinate provides a viable and potentially powerful new route for the prediction of protein-ligand binding free energies. 
URL http://www.siremol.org/Sire/Home.html