Using molecular dynamics and enhanced sampling techniques to find cryptic druggable pockets in proteins of pharmaceutical interest

Cryptic pockets are sites on protein targets that are hidden in the unliganded form and only become apparent when drugs bind. These sites provide a promising alternative to classical substrate binding sites for drug development, especially when the latter are not druggable. In this thesis I investigate the nature and dynamical properties of cryptic sites in a number of pharmacologically relevant targets, while comparing the efficacy of various simulation-based approaches in discovering them. I found that the studied cryptic sites do not correspond to local minima in the computed conformational free-energy landscape of the unliganded proteins. They thus promptly close in all of the molecular dynamics simulations performed, irrespective of the force-field used. Temperature-based enhanced sampling approaches, such as parallel tempering, do not improve the situation, as the entropic term does not help in the opening of the sites. The use of fragment probes helps, as in long simulations occasionally it leads to the opening and binding to the cryptic sites. The observed mechanism of cryptic site formation is suggestive of interplay between two classical mechanisms: induced-fit and conformational selection. Employing this insight, I developed a novel Hamiltonian replica exchange-based method SWISH (sampling water interfaces through scaled Hamiltonians), which combined with probes resulted in a promising general approach for cryptic site discovery. In addition, we revisit the rather ill-defined concept of the cryptic pockets in order to propose an alternative measurable interpretation. I outline how the new practical definition can be applied to the ligandable targets reported in the PDB, in order to provide a consistent data-driven view on crypticity and how it may impact the drug discovery. This thesis presents a comprehensive study of the cryptic pocket phenomenon: from understanding the nature of their formation to novel detection methodology, and towards understanding their global significance in drug discovery

Thermodynamics and structure of methionine enkephalin using the statistical temperature molecular dynamics algorithm

Kim, Straub, and Keyes introduced the statistical temperature molecular dynamics (STMD) algorithm to overcome broken ergodicity by sampling a non­-Boltzmann flat energy histogram as noted in Kim, Straub, and Keyes, Phys. Rev. Lett. 97: 050601 (2007). Canonical averages are calculated via reweighting to the desired temperature. While STMD is promising, its application has been almost entirely to simple or model systems. In this dissertation the implementation of STMD into the biosimulation package CHARMM is used to simulate the methionine enkephalin pentamer peptide with a methione terminal cap in a droplet of CHARMM TIP3P water molecules. Chain thermodynamics is analyzed from the novel perspective of the statistical temperature as a function of potential energy, $TS(U), automatically generated by STMD. Both the minimum in the slope of$TS(U), and the peak in the heat capacity as a function of temperature, calculated via reweighting, indicate a collapse transition at Tθ ≈ 253K. Distributions of dihedral angles are obtained as a function of temperature. Rotamer regions found in the literature are reproduced, along with unique regions not found previously, including with advanced algorithms, indicating the power of STMD enhanced sampling

Theoretical Analysis of Biomolecular Systems: Computational Simulations, Core-set Markov State Models, Clustering, Molecular Docking

The analysis of the structural and the dynamical behavior of biomolecules is very important to under- stand their biological function, stability or physico-chemical properties. In this thesis, it is highlighted how different theoretical methods to characterize the aforementioned structural and dynamical properties can be used and combined, to obtain kinetic information or to detect biomolecule-ligand interactions. The basis for most of the analyses, performed in the course of this work, are molecular dynamics sim- ulations sampling the conformational space of the biomolecule of interest. Using molecular dynamics simulations, the remarkable stable water-soluble-binding-protein is examined first. On a theoretical ba- sis, structural modifications that can influence the stability of the protein are discussed. Additionally, by combining the simulations with a QM/MM optimization scheme and quantum chemical calculations, spectroscopical properties can be investigated. Markov State Models are applied frequently to capture the slow dynamics within simulation trajectories. They are based on a discretization of the conformational space. This discretization, however, introduces an error in the outcome of the analysis. The application of a core-set discretization can reduce this error. In this thesis, it is discussed how density-based cluster algorithms can be used to determine these core sets, and the application on linear and cyclic peptides is highlighted. The performance of a promising cluster algorithm is investigated and error sources in the construction of the Markov models are discussed. Finally, it is shown how molecular docking combined with molecular dynamics simulations can be used to determine the binding behavior of ligands towards biomolecules. In this context, the important in- teractions within the active site of an enzyme, and different binding modes of DNA intercalators are identified

RNA Structural Dynamics As Captured by Molecular Simulations: A Comprehensive Overview

With both catalytic and genetic functions, ribonucleic acid (RNA) is perhaps the most pluripotent chemical species in molecular biology, and its functions are intimately linked to its structure and dynamics. Computer simulations, and in particular atomistic molecular dynamics (MD), allow structural dynamics of biomolecular systems to be investigated with unprecedented temporal and spatial resolution. We here provide a comprehensive overview of the fast-developing field of MD simulations of RNA molecules. We begin with an in-depth, evaluatory coverage of the most fundamental methodological challenges that set the basis for the future development of the field, in particular, the current developments and inherent physical limitations of the atomistic force fields and the recent advances in a broad spectrum of enhanced sampling methods. We also survey the closely related field of coarse-grained modeling of RNA systems. After dealing with the methodological aspects, we provide an exhaustive overview of the available RNA simulation literature, ranging from studies of the smallest RNA oligonucleotides to investigations of the entire ribosome. Our review encompasses tetranucleotides, tetraloops, a number of small RNA motifs, A-helix RNA, kissing-loop complexes, the TAR RNA element, the decoding center and other important regions of the ribosome, as well as assorted others systems. Extended sections are devoted to RNA-ion interactions, ribozymes, riboswitches, and protein/RNA complexes. Our overview is written for as broad of an audience as possible, aiming to provide a much-needed interdisciplinary bridge between computation and experiment, together with a perspective on the future of the field

Development & Application of Constant pH Molecular Dynamics (CPHMD ) for Investigating pH-mediated Transient Conformational States and Their Effects on Nucleic Acid & Protein Activity.

pH is a ubiquitous regulator of biological activity, with widespread impact ranging from its role in catalysis to carcinogenesis. Traditionally, a combination of biophysical and computational methods are used to measure pH-dependent activity profiles, and protonation equilibria (i.e. pKa values) of specific residues, and these data are used in conjunction with structural data to provide mechanistic understanding of pH-mediated biological function. More recent developments have also demonstrated the role of transient conformational states in a wide range of biological activities, which naturally leads to the question of how pH affects such transient states, and in turn, their resulting functional activity. In the study of biomolecular transient states, the detection limit is the key limitation of most experimental techniques. To bridge the gap in detection limit, we have developed an appropriate molecular dynamics based computational method, where protonation states are dynamically adjusted as a function of an external pH bath and the local environment surrounding the titrating site. Also known as the explicit solvent constant pH molecular dynamics (CPHMD^MSλD) framework, we use CPHMD^MSλD simulations and enhanced sampling methods to demonstrate the role of pH-regulated transient states in both nucleic acid and protein activity. First, we demonstrate the utility of CPHMD^MSλD simulations in conjunction with NMR experiments to characterize transiently populated Hoogsteen GC+ base pairs in DNA duplexes. The role of pH-dependent transient states is then generalized to RNA activity, including that of the catalytic mechanism of the hairpin ribozyme, where the existence of pH-dependent transient states can be used to reconcile a collection of seemingly inconsistent experimental observations in the literature. In addition, our CPHMD^MSλD simulations of proteins have elucidated the role of pH-dependent transient states in residues that are buried or occluded from solvent, including that of the pH-dependent optical properties of a cyan fluorescent protein mutant, where the existence of pH-dependent transient states can be used to explain its non-monotonic spectroscopic behavior.PHDChemistryUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113562/1/gbgoh_1.pd

Ab initio RNA folding

RNA molecules are essential cellular machines performing a wide variety of functions for which a specific three-dimensional structure is required. Over the last several years, experimental determination of RNA structures through X-ray crystallography and NMR seems to have reached a plateau in the number of structures resolved each year, but as more and more RNA sequences are being discovered, need for structure prediction tools to complement experimental data is strong. Theoretical approaches to RNA folding have been developed since the late nineties when the first algorithms for secondary structure prediction appeared. Over the last 10 years a number of prediction methods for 3D structures have been developed, first based on bioinformatics and data-mining, and more recently based on a coarse-grained physical representation of the systems. In this review we are going to present the challenges of RNA structure prediction and the main ideas behind bioinformatic approaches and physics-based approaches. We will focus on the description of the more recent physics-based phenomenological models and on how they are built to include the specificity of the interactions of RNA bases, whose role is critical in folding. Through examples from different models, we will point out the strengths of physics-based approaches, which are able not only to predict equilibrium structures, but also to investigate dynamical and thermodynamical behavior, and the open challenges to include more key interactions ruling RNA folding.Comment: 28 pages, 18 figure

Monte Carlo simulation studies of DNA hybridization and DNA-directed nanoparticle assembly

A coarse-grained lattice model of DNA oligonucleotides is proposed to investigate how fundamental thermodynamic processes are encoded by the nucleobase sequence at the microscopic level, and to elucidate the general mechanisms by which single-stranded oligonucleotides hybridize to their complements either in solution or when tethered to nanoparticles. Molecular simulations based on a high-coordination cubic lattice are performed using the Monte Carlo method. The dependence of the model's thermal stability on sequence complementarity is shown to be qualitatively consistent with experiment and statistical mechanical models. From the analysis of the statistical distribution of base-paired states and of the associated free-energy landscapes, two general hybridization scenarios are found. For sequences that do not follow a two-state process, hybridization is weakly cooperative and proceeds in multiple sequential steps involving stable intermediates with increasing number of paired bases. In contrast, sequences that conform to two-state thermodynamics exhibit moderately rough landscapes, in which multiple metastable intermediates appear over broad free-energy barriers. These intermediates correspond to duplex species that bridge the configurational and energetic gaps between duplex and denatured states with minimal loss of conformational entropy, and lead to a strongly cooperative hybridization. Remarkably, two-state thermodynamic signatures are generally observed in both scenarios. The role of cooperativity in the assembly of nanoparticles tethered with model DNA oligonucleotides is similarly addressed with the Monte Carlo method, where nanoparticles are represented as finely discretized hard-core spheres on a cubic lattice. The energetic and structural mechanisms of self-assembling are investigated by simulating the aggregation of small "satellite" particles from the bulk onto a large "core" particle. A remarkable enhancement of the system's thermal stability is attained by increasing the number of strands per satellite particle available to hybridize with those on the core particle. This cooperative process is driven by the formation of multiple bridging duplexes under favorable conditions of reduced translational entropy and the resultant energetic compensation; this behavior rapidly weakens above a certain threshold of linker strands per satellite particle. Cooperativity also enhances the structural organization of the assemblies by systematically narrowing the radial distribution of the satellite particles bound the core

Efficient Computer Simulations of Protein-Peptide Binding Using Weighted Ensemble Sampling

Molecular dynamics simulations can, in principle, provide detailed views of protein-protein association processes. However, these processes frequently occur on timescales inaccessible on current computing resources. These are not particularly slow processes, but rather they are rare — fast but infrequent. The weighted ensemble (WE) sampling approach provides a way to exploit this separation of timescales and focus computing power efficiently on rare events. In this work, it is demonstrated that WE sampling can be used to efficiently obtain kinetic rate constants, pathways, and energy landscapes of molecular association processes. Chapter 1 of this dissertation further discusses the need for enhanced sampling techniques like the WE approach. In Chapter 2, WE sampling is used to study the kinetics of association of four model molecular recognition systems (methane/methane, Na+/Cl–, methane/benzene, and K+/18-crown-6 ether) using molecular dynamics (MD) simulations in explicit water. WE sampling reproduces straightforward “brute force” results while increasing the efficiency of sampling by up to three orders of magnitude. Importantly, the efficiency of WE simulation increases with increasing complexity of the systems under consideration. In Chapter 3, weighted ensemble Brownian dynamics (BD) simulations are used to explore the association between a 13-residue fragment of the p53 tumor suppressor and the MDM2 oncoprotein. The association rates obtained compare favorably with experiment. By directly comparing both flexible and pre-organized variants of p53, it is shown that the “fly-casting” effect, by which natively unstructured proteins may increase their association rates, is not significant in MDM2-p53 peptide binding. Including hydrodynamic interactions in the simulation model dramatically alters the association rate, indicating that the detailed motion of solvent may have substantial effects on the kinetics of protein-protein association. In Chapter 4, an all-atom molecular dynamics simulation of p53-MDM2 binding is described. We obtain an association rate that agrees with the experimental value. The free energy landscape of binding is “funnel-like”, downhill after the initial encounter between p53 and MDM2. Together, the studies described here establish that WE sampling is highly effective in simulating rare molecular association events