Evaluating parameterization protocols for hydration free energy calculations with the AMOEBA polarizable force field

Hydration free energy (HFE) calculations are often used to assess the performance of biomolecular force fields and the quality of assigned parameters. The AMOEBA polarizable force field moves beyond traditional pairwise additive models of electrostatics and may be expected to improve upon predictions of thermodynamic quantities such as HFEs over and above fixed point charge models. The recent SAMPL4 challenge evaluated the AMOEBA polarizable force field in this regard, but showed substantially worse results than those using the fixed point charge GAFF model. Starting with a set of automatically generated AMOEBA parameters for the SAMPL4 dataset, we evaluate the cumulative effects of a series of incremental improvements in parameterization protocol, including both solute and solvent model changes. Ultimately the optimized AMOEBA parameters give a set of results that are not statistically significantly different from those of GAFF in terms of signed and unsigned error metrics. This allows us to propose a number of guidelines for new molecule parameter derivation with AMOEBA, which we expect to have benefits for a range of biomolecular simulation applications such as protein ligand binding studie

Development of Methods for the Investigation of RNA-Ligand Interactions.

Three critical features of RNA make it a unique challenge for drug discovery: a) it is highly negatively charged, increasing non-specific binding, b) it can be highly dynamic, adopting different conformations upon binding varying ligands, and c) it has solvent exposed shallow binding pockets. All these properties represent distinct problems in the advancement of RNA-drug discovery. To address this first problem, MATCH was developed to rapidly, accurately, and universally parameterize small molecules for docking. MATCH accomplishes this by deconstructing a force field into a set of fundamental rules which best replicates existing parameters and permits extension to new molecules. MATCH is not only necessary to study RNA-ligand interactions en masse but will also contribute to understanding the charge-charge consequences of ligand binding. To address RNA flexibility, a method to combine NMR chemical shifts and Molecular Dynamics (MD) was developed to generate dynamic ensembles. To benchmark this technique, a set of 26 RNA structures with experimentally determined chemical shift was selected. An ensemble of structures was optimized to match the chemical shifts of each system. These ensembles were also shown to be consistent with of NMR NOE and RDCs constraints. To further demonstrate the utility of this method a large pool of structures (~350,000) was used to generate an ensemble for a prominent RNA target – the ribosomal decoding site. The conformations within this ensemble were found on favorable areas of the free energy landscape, independently indicating the validity of these structures. Finally to address the solvent exposed binding pocket of RNA and its flexible ligands, a new docking approach for RNA was developed, which performs an enhanced sampling technique by fragmenting the ligand and independently optimizing the conformation of each fragment. To properly benchmark this novel algorithm, a large set of 230 nucleic acid-ligand complexes was compiled. Utilizing this large set of this enhanced sampling technique was compared to ICM – a leading docking program. ICM produced native-like conformations 45% of the time, while our approach yields native-like conformations 55% of the time. Demonstrating the effectiveness of this novel sampling procedure.PHDBiophysicsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/102297/1/jyesselm_1.pd

Espaloma-0.3.0: Machine-learned molecular mechanics force field for the simulation of protein-ligand systems and beyond

Molecular mechanics (MM) force fields -- the models that characterize the energy landscape of molecular systems via simple pairwise and polynomial terms -- have traditionally relied on human expert-curated, inflexible, and poorly extensible discrete chemical parameter assignment rules, namely atom or valence types. Recently, there has been significant interest in using graph neural networks to replace this process, while enabling the parametrization scheme to be learned in an end-to-end differentiable manner directly from quantum chemical calculations or condensed-phase data. In this paper, we extend the Espaloma end-to-end differentiable force field construction approach by incorporating both energy and force fitting directly to quantum chemical data into the training process. Building on the OpenMM SPICE dataset, we curate a dataset containing chemical spaces highly relevant to the broad interest of biomolecular modeling, covering small molecules, proteins, and RNA. The resulting force field, espaloma 0.3.0, self-consistently parametrizes these diverse biomolecular species, accurately predicts quantum chemical energies and forces, and maintains stable quantum chemical energy-minimized geometries. Surprisingly, this simple approach produces highly accurate protein-ligand binding free energies when self-consistently parametrizing protein and ligand. This approach -- capable of fitting new force fields to large quantum chemical datasets in one GPU-day -- shows significant promise as a path forward for building systematically more accurate force fields that can be easily extended to new chemical domains of interest

Výpočetní studie krátkých peptidů a miniproteinů a vliv prostředí na jejich konformaci.

Apart from biological functions, peptides are of uttermost importance as models for un- folded, denatured or disordered state of the proteins. Similarly, miniproteins such as Trp-cage have proven their role as simple models of both experimental and theoretical studies of protein folding. Molecular dynamics and computer simulations can provide an unique insight on processes at atomic level. However, simulations of peptides and minipro- teins face two cardinal problems-inaccuracy of force fields and inadequate conformation sampling. Both principal issues were tackled in this theses. Firstly, the differences in several force field for peptides and proteins were questioned. We demonstrated the inability of the used force fields to predict consistently intrinsic conformational preferences of individual amino acids in the form of dipeptides and the source of the discrepancies was traced. In order to shed light on the nature of conformational ensembles under various denatur- ing conditions, we studied host-guest AAXAA peptides. The simulations revealed that thermal and chemical denaturation by urea produces qualitatively different ensembles and shift propensities of individual amino acids to particular conformers. The problem of insufficient conformation sampling was dealt by introducing gyration- and...Peptidy, kromě své biologické funkce, představují take důležité modely nesbalených, de- naturovaných nebo nestrukturovaných proteinů. Pobobně důležitými modely pro exper- imentální i teoretické studium sbalování proteinů jsou miniproteiny, jako např. Trp- cage. Chování peptidů i proteinů lze studovat v počítačových simulacích pomocí metod molekulární dynamiky, které umožnují sledovat děje v atomistickém rozlišení. Tyto metody však čelí však dvěma zásadním problémům - přesnosti používaných energetick- ých funkcí a nedostatečnému vzorkování konformačních stavů. V této disertaci jsem se zabýval oběma okruhy problémů. Vliv rozdílných, běžně používných energetických funkcí ("force fields") byl testován na modelu aminokyselinových dipeptidů. Žádná sada parametrů však nedokázala konzis- tentně reprodukovat konformační preference jednotlivých aminokyselin. Výsledky simu- lací byly mezi sebou srovnány a byly hledány příčiny jejich vzájemných odlišností. Abychom odhalili, jakým způsobem různé podmínky ovlivňují konformační stavy peptidů, zkoumali jsme vlastnosti aminokyselin v AAXAA peptidech. Simulace odhalily zásadní rozdíl ve vlivu tepelné a chemické denaturace (močovinou) na charakter a zastoupení konformací peptidů, stejně jako konformačních preferencí jednotlivých aminokyselin. K problematice vzorkování...Department of Physical and Macromolecular ChemistryKatedra fyzikální a makromol. chemieFaculty of SciencePřírodovědecká fakult

Identifying prospective inhibitors against LdtMt5 from Mycobacterium tuberculosis as a potential drug target.

Masters Degree. University of KwaZulu-Natal, Durban.Tuberculosis (TB) caused by the bacterium, Mycobacterium tuberculosis (M.tb) has resulted in an unprecedented number of deaths over centuries. L,D-transpeptidase enzymes are known to play a crucial role in the biosynthesis of the cell wall, which confers resistance to most antibiotics. These enzymes catalyze the 3→3 peptidoglycan cross-links of the M.tb cell wall. Specific β-lactam antibiotics (carbapenems) have been reported to inhibit cell wall polymerization of M.tb and they inactivate L,D-transpeptidases through acylation. L,Dtranspeptidase 5 (LdtMt5) is a unique paralog and a vital protein in maintaining integrity of the cell wall specifically in peptidoglycan metabolism therefore making it an important protein target. Carbapenems inhibit LdtMt2, but do not show reasonable inhibitory activities against LdtMt5. We therefore sought to perform virtual screening in order to acquire potential inhibitors against LdtMt5 and to investigate the affinity and to calculate the binding free energies between LdtMt5 and potential inhibitors. Furthermore, we sought to investigate the nature of the transition state involved in the catalytic reaction mechanism; to determine the activation free energies of the mechanism using ONIOM through the thermodynamics and energetics of the reaction path and lastly to express, purify and perform inhibition studies on LdtMt5. A total of 12766 compounds were computationally screened from the ZINC database to identify potential leads against LdtMt5. Docking was performed using two different software programs. Molecular dynamics (MD) simulations were subsequently performed on compounds obtained through virtual screening. Density functional theory (DFT) calculations were then carried out to understand the catalytic mechanism of LdtMt5 with respect to β-lactam derivatives using a hybrid ONIOM quantum mechanics/molecular mechanics (QM/MM) method. LdtMt5 complexes with six selected β-lactam compounds were evaluated. Finally, a lyophilised pET28a-LdtMt5 was used to transform E. coli strain BL21 (DE3) and SDS-PAGE was used to verify the purity, molecular weight and protein profile determination. Finally, an in vitro binding thermodynamics analysis using isothermal titration calorimetry (ITC) was later on performed on a single compound (the strongest binder) from the final set, in a bid to further validate the calculated binding energy values. A number of compounds from four different antimicrobial classes (n = 98) were obtained from the virtual screening and those with docking scores ranging from -7.2 to -9.9 kcal mol-1 were considered for MD analysis (n = 37). A final set of 10 compounds which exhibited the greatest affinity, from four antibiotic classes was selected and Molecular Mechanics/Generalized Born iii Surface Area (MM-GBSA) binding free energies (ΔGbind) from the set were characterised. The calculated binding free energies ranged from -30.68 to -48.52 kcal mol-1 . The β-lactam class of compounds demonstrated the highest ΔGbind and also the greatest number of potential inhibitors. The DFT activation energies (∆G # ) obtained for the acylation of LdtMt5 by the six selected β-lactams were calculated as 13.67, 20.90, 22.88, 24.29, 27.86 and 28.26 kcal mol-1 . The ∆G# results from the 6-membered ring transition state (TS) revealed that all selected six βlactams were thermodynamically more favourable than previously calculated activation energy values for imipenem and meropenem complexed with LdtMt5. The results are also comparable to those observed for LdtMt2, however for compound 1 the values are considerably lower than those obtained for meropenem and imipenem in complex with LdtMt2, thus suggesting in theory that compound 1 is a more potent inhibitor of LdtMt5. We also report the successful expression and and purification of LdtMt5, however the molecule selected for the in vitro inhibition study gave a poor result. On further review, we concluded that the main cause of this outcome was due to the relatively low insolubility of the compound. The outcome of this study provides insight into the design of potential novel leads for LdtMt5. Our screening obtained ten novel compounds from four different antimicrobial classes. We suggest that further in vitro binding thermodynamics analysis of the novel compounds from the four classes, including the carbapenems be performed to evaluate inhibition of these compounds on LdtMt5. If the experimental observations suggest binding affinity to the protein, catalytic mechanistic studies can be undertaken. These results will also be used to verify or modify our computational model

ACCELERATED COMPUTING FOR MOLECULAR DYNAMICS SIMULATION

Molecular dynamics (MD) simulation serves as a computational microscope into the behavior of the biological and chemical macromolecules. At its core, MD models the interactions between atoms at various levels – force fields model the higher quantum level interactions using simpler physics-based models of interaction energies, while periodic boundary conditions model the bulk phase using lattice-based periodic copies of the simulation box. One limitation of the finite size of the simulation box seen during the simulation of membrane bilayers is the artifact of a chemical disequilibrium between the two layers as a drug molecule enters into the bilayer. We have tried to solve this problem by using a periodic boundary condition which has a half screw symmetry. Our results show that the method scales similar to the best-known method for the normal periodic boundary conditions. We have migrated CHARMM to an efficient implementation on the GPUs. These architectures provide thousands of cores on the same chip but require different programming model in order to use the underlying architecture. Our results show that the new CHARMM CUDA engine is efficient in time and accurate in precision. We have also participated in blind prediction challenges organized by SAMPL community to have a fair assessment of the computational chemistry tools. We developed a hybrid QM and MM technique to predict the pKa of drug-like molecules. It avoids the implicit solvent model used by quantum mechanical models and uses explicit solvent molecules. Since modeling explicit solvent molecules is difficult at QM level, they are modeled at the MM level instead. Thermodynamic cycle couples the aqueous Gibbs free energy of deprotonation to simpler components which can be modeled with higher accuracy. We also built a deep learning model to predict the logP of a set of drug-like molecules in a blind fashion. The generated model is robust over a large number of molecules, not just the ones that it was tested for in the SAMPL competition. We expect the method to be interesting for the drug design industry since lipophilicity of a molecule is important to be known even before it has been synthesized

Revealing the Mechanism of Thiopeptide Antibiotics at Atomistic Resolution : Implications for Rational Drug Design

For decades drug design has primarily focused on small molecules that bind to well-formed tight binding pockets, such as the catalytic centers of enzymes. Recently, there is increasing interest to design compounds that disrupt or stabilize biomacromolecular interfaces (e.g. protein–protein, protein–DNA, protein–RNA, protein–lipid interfaces). These non-traditional drug targets hold great therapeutic potential as they govern cellular pathways. In contrast to traditional drug targets, where computational methods are now routinely and productively used to complement experiments, the use of computer-based approaches for the study and design of interfacial modulators is still in its infancy. The current thesis is a first detailed study into understanding the effects of modulators of a protein–RNA interface and developing computer-based approaches for their design. This work focuses on the 23S-L11 subunit of the ribosomal GTPase-associated region (GAR), a prototypic protein–RNA interface of high relevance in the development of novel antibacterials. The GAR is the target of naturally occuring thiopeptide antibiotics. These unique molecules are effective inhibitors of bacterial protein synthesis, but are currently unused in human antibacterial therapy due to their low aqueous solubility. Their mechanism of action is explored in the current thesis, enabling the design and proposition of new chemical scaffolds targeting their binding site. The specific challenges associated with the 23-SL11-thiopeptide system, such as the inherent flexibility of the protein–RNA composite environment and the size and structural complexity of the thiopeptide ligands, are addressed by a combination of computational chemistry approaches at different levels of granularity and a steady feedback with experimental data to validate and improve the computational techniques. These approaches range from quantummechanics for deriving optimized intramolecular parameters and partial atomic charges for the thiopeptide compounds, to molecular dynamics simulations accounting for the binding site’s flexibility, to molecular docking studies for predicting the binding modes of different thiopeptides and derivatives. All-atom molecular dynamics simulations were conducted, providing a detailed understanding of the effect of thiopeptide binding at a previously unmet resolution. The findings of this work, coupled with previous experimental knowledge, strongly support the hypothesis that restricting the binding site’s conformational flexibility is an important component of the thiopeptide antibiotics’ mode of action. With the help of an MD-docking-MD workflow and an energy decomposition analysis crucial residues of the binding site and pharmacologically relevant moieties within the ligand structures could be identified. A 4D-pharmacophore model is presented that was derived from a refined 23S-L11-thiopeptide complex and additionally accounts for the dynamic stability of molecular interactions formed between the antibiotic and the ribosomal binding site as the fourth dimension. The results of this thesis revealed, for the first time, a plausable description of the thiopeptide antibiotics’ mode of action, down to the details of their pharmacologically relevant parts and provide a computational framework for the design of new ligands

Estudios computacionales de mecanismos moleculares de la inmunidad innata

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Farmacia, leída el 20-12-2022Antimicrobial Resistance (AMR) is a worldwide health emergency. ESKAPE pathogens include the most relevant AMR bacterial families. In particular, Gram-negative bacteria stand out due to their cell envelope complexity, which exhibits strong resistance to antimicrobials. A key element for AMR is the chemical structure of bacterial lipopolysaccharide (LPS), and the phospholipid composition of the membrane, inflecting the membrane permeability to antibiotics. We have applied coarse-grained molecular dynamics simulations to capture the role of the phospholipid composition and lipid A structure in the membrane properties and morphology of ESKAPE Gram-negative bacterial vesicles. Moreover, the reported antimicrobial peptides Cecropin B1, JB95, and PTCDA1-kf were used to unveil their implications for membrane disruption. This study opens a promising starting point for understanding the molecular keys of bacterial membranes and promoting the discovery of new antimicrobials to overcome AMR...La resistencia a los antimicrobianos (AMR) es una emergencia sanitaria mundial. Los patógenos ESKAPE incluyen las familias bacterianas más resistentes a antibióticos y son altamente virulentas. En particular, las bacterias Gram negativas destacan por la complejidad de su pared celular, que presenta una fuerte resistencia frente a los antibióticos. Un elemento clave para la AMR es la estructura química del lipopolisacárido bacteriano (LPS) y la composición de los fosfolípidos de la membrana bacteriana, que influyen en su permeabilidad a los antibióticos. Se han empleado simulaciones de dinámica molecular de grano grueso para captar el papel de la composición de los fosfolípidos y la estructura del LPS en las propiedades y morfología de modelos de vesículas bacterianas Gram negativas ESKAPE. Además, se han empleado los péptidos antimicrobianos Cecropin B1, JB95 y PTCDA1-kf para desvelar su mecanismo disrupción de la membrana bacteriana. Este estudio abre un prometedor punto de partida para comprender las claves moleculares de la resistencia en membranas bacterianas y acelerar el descubrimiento de nuevos antibióticos para hacer frente a la AMR...Fac. de FarmaciaTRUEunpu