TY - BOOK

T1 - Hybrid Methods and Atomistic Models to Explore Free Energies, Rates and Pathways of Protein Shape Changes

AU - Wang, Yong

PY - 2016

Y1 - 2016

N2 - When I just joined the Lindor-Larsen group as a fresh PhD student, theNobel Prize in Chemistry that year was awarded for the development ofmultiscale models for complex chemical systems" to prize the pioneeringworks of Martin Karplus, Michael Levitt and Arieh Warshel. As a computationalbiologist, I was proud and excited for the breaking news as thisprize is not only to them, but also to the whole community of computationalbiology. There has been progress in the modeling of protein dynamics in recentyears and it has also started to be clear that computer simulations playan irreplaceable role rather than supporting role of wet-lab experiments, toobtain a complete understanding of complex biomolecules. Some of theprogress in the eld has been introduced in the rst Chapter of this thesis.Despite its enormous success, this eld has not yet been fully developed.In some respects, for example, accurately quantifying the free energy differencesand transition times of protein conformational exchanges and theirdependence on sequence modications, we are still at the early stages.In this dissertation, I present a number of new methodological improvementsand applications for protein folding, conformational exchange andbinding with ligands at long time scales. In Chapter 2, we benchmarkedhow well the current force elds and molecular dynamics (MD) simulationscould model changes in structure, dynamics, free energy and kinetics foran extensively studied protein called T4 lysozyme (T4L), whose conformationaldynamics however is still not fully understood. We found modernsimulation methods and force elds are able to capture key aspects of howthis protein changes its shape, paving the way for future studies for systemsthat are dicult to study experimentally. In Chapter 3, we revisitedthe problem of accurately quantifying the thermodynamics and kinetics, byfollowing a novel route. In this route both of the forward and backwardrates are calculated directly from MD simulations using a recently developedenhanced sampling method, called \infrequent metadynamics", andsubsequently used to estimate the free energy dierences based on a twostateassumption. To show its practical utility, we applied this approachby taking T4L-benzene system as the model system in which binding freeenergies from kinetics, free energy perturbation and experiments are all ingood agreement. Indeed, this route has also been applied to calculate thekinetics and thermodynamics of the conformational exchange of T4L (asshown in Chapter 2). In Chapter 4, we designed a novel method, called\pace-adaptive metadynamics", in which the frequency of bias deposition isadjusted at the course of simulations. By testing in a simple model systemand applying in a case of T4L binding/unbinding with two dierent ligands,we showed that the pace adaptive scheme can improve the reliabilityand accuracy of kinetics estimation, importantly without the need of extracomputational resources. So this strategy allows us to utilize the limitedcomputational resources in a more reasonable way. In Chapter 5, we furtherillustrated the possibility to combine the free energy ooding potential obtained from the variational method with infrequent metadynamics to calculatethe long timescale rate. This hybrid method was tested again in thecalculation of the unbinding time of T4L-benzene. The results suggest thishybrid method can obtain similar results as infrequent metadynamics butwith less computational resources. Thus it is promising to apply this hybridmethod to calculate kinetics of escaping from a deep free energy well, e.g.the drug residence time. In Chapter 6, we developed an atomistic hybridmodel by integration of physics-based and structure-based potentials in thecontext of Monte Carlo software packages. We showed the ability of ourmodels to distinguish the folding mechanisms of four topologically similarproteins.

AB - When I just joined the Lindor-Larsen group as a fresh PhD student, theNobel Prize in Chemistry that year was awarded for the development ofmultiscale models for complex chemical systems" to prize the pioneeringworks of Martin Karplus, Michael Levitt and Arieh Warshel. As a computationalbiologist, I was proud and excited for the breaking news as thisprize is not only to them, but also to the whole community of computationalbiology. There has been progress in the modeling of protein dynamics in recentyears and it has also started to be clear that computer simulations playan irreplaceable role rather than supporting role of wet-lab experiments, toobtain a complete understanding of complex biomolecules. Some of theprogress in the eld has been introduced in the rst Chapter of this thesis.Despite its enormous success, this eld has not yet been fully developed.In some respects, for example, accurately quantifying the free energy differencesand transition times of protein conformational exchanges and theirdependence on sequence modications, we are still at the early stages.In this dissertation, I present a number of new methodological improvementsand applications for protein folding, conformational exchange andbinding with ligands at long time scales. In Chapter 2, we benchmarkedhow well the current force elds and molecular dynamics (MD) simulationscould model changes in structure, dynamics, free energy and kinetics foran extensively studied protein called T4 lysozyme (T4L), whose conformationaldynamics however is still not fully understood. We found modernsimulation methods and force elds are able to capture key aspects of howthis protein changes its shape, paving the way for future studies for systemsthat are dicult to study experimentally. In Chapter 3, we revisitedthe problem of accurately quantifying the thermodynamics and kinetics, byfollowing a novel route. In this route both of the forward and backwardrates are calculated directly from MD simulations using a recently developedenhanced sampling method, called \infrequent metadynamics", andsubsequently used to estimate the free energy dierences based on a twostateassumption. To show its practical utility, we applied this approachby taking T4L-benzene system as the model system in which binding freeenergies from kinetics, free energy perturbation and experiments are all ingood agreement. Indeed, this route has also been applied to calculate thekinetics and thermodynamics of the conformational exchange of T4L (asshown in Chapter 2). In Chapter 4, we designed a novel method, called\pace-adaptive metadynamics", in which the frequency of bias deposition isadjusted at the course of simulations. By testing in a simple model systemand applying in a case of T4L binding/unbinding with two dierent ligands,we showed that the pace adaptive scheme can improve the reliabilityand accuracy of kinetics estimation, importantly without the need of extracomputational resources. So this strategy allows us to utilize the limitedcomputational resources in a more reasonable way. In Chapter 5, we furtherillustrated the possibility to combine the free energy ooding potential obtained from the variational method with infrequent metadynamics to calculatethe long timescale rate. This hybrid method was tested again in thecalculation of the unbinding time of T4L-benzene. The results suggest thishybrid method can obtain similar results as infrequent metadynamics butwith less computational resources. Thus it is promising to apply this hybridmethod to calculate kinetics of escaping from a deep free energy well, e.g.the drug residence time. In Chapter 6, we developed an atomistic hybridmodel by integration of physics-based and structure-based potentials in thecontext of Monte Carlo software packages. We showed the ability of ourmodels to distinguish the folding mechanisms of four topologically similarproteins.

UR - https://soeg.kb.dk/permalink/45KBDK_KGL/fbp0ps/alma99122559514405763

M3 - Ph.D. thesis

BT - Hybrid Methods and Atomistic Models to Explore Free Energies, Rates and Pathways of Protein Shape Changes

PB - Department of Biology, Faculty of Science, University of Copenhagen

ER -