Métodos computacionais são usados para gerar estruturas de complexo proteína-ligante e estimar suas afinidades. Esse trabalho investigou como as diferentes representações da flexibilidade proteica afetam as poses obtidas por ancoragem molecular e as afinidades atribuídas a essas poses. Os mutantes L99A e L99A/M102Q da lisozima T4 foram escolhidos como sistemas modelo. Um descritor para predição de afinidades baseado na aproximação de energia de interação linear (LIE) foi parametrizado especificamente para ligantes da lisozima e foi usado para estimar as afinidades. A proteína foi representada como um grupo de estruturas cristalográficas ou de estruturas de trajetória de dinâmica molecular. O campo de força OPLS-AA para modelar a proteína e os ligantes e a aproximação de Born generalizada para modelar o solvente foram empregados. O descritor de afinidades parametrizado resultou em desvios médios entre afinidades experimentais e calculadas de 1,8 kcal/mol para um conjunto de testes. O descritor teve desempenho satisfatório na separação entre poses cristalográficas e poses falso-positivo e na identificação de poses falso-positivo. Experimentos de agrupamento de complexos realizados com o objetivo de reduzir o custo computacional para estimar afinidades apresentaram resultados insatisfatórios. As melhores aproximações da teoria do ligante implícito propostas aqui para estimar afinidades consideram conjuntos de estruturas de receptor com o mesmo peso. Configurações de ligante também apresentam o mesmo peso ou são dominadas por uma única configuração. A representação da flexibilidade requer um tratamento estatístico adequado para estimativa de afinidades. Aqui, a associação entre LIE e a teoria do ligante implícito mostrou-se frutífera. / Computational methods are used to generate protein-ligand complex structures and estimate their binding affinities. This work investigated how different representations of protein flexibility affect poses obtained by molecular docking and the affinities attributed to these poses. T4 lysozyme mutants L99A and L99A/M102Q were chosen as model systems. A descriptor for prediction of affinities based on linear interaction energy (LIE) approximation was parametrized specifically to lysozyme ligands and was used to estimate affinities. The protein was represented as a group of crystal structures or as structures from a molecular dynamics trajectory. OPLS-AA force field was used to model protein and ligands and the Generalized Born approximation was used to model solvent. The parametrized affinity descriptor resulted in average deviations between experimental and calculated affinities of 1.8 kcal/mol for a test set. Descriptor performance was satisfactory in the separation between crystal poses and false-positive ones and in the identification of false-positive poses. Clustering of complexes was tried out to reduce computational cost to estimate affinities, but results were poor. The best approximations to the implicit ligand theory proposed here in order to estimate affinities consider groups of receptor structures with the same weight. Ligand configurations also have the same weight or are dominated by only one configuration. The representation of protein flexibility requires an adequate statistical treatment when used to estimate affinities. Here, the linking between LIE and the implicit ligand theory proved itself useful.

Accurate predictions of binding free energies from computer simulations are an invaluable resource for understanding biochemical processes and drug action. The primary aim of the work described in the thesis was to predict and understand ligand binding to several proteins of major pharmaceutical importance using computational methods. We report a computational strategy to quantitatively predict the effects of alanine scanning and ligand modifications based on molecular dynamics free energy simulations. A smooth stepwise scheme for free energy perturbation calculations is derived and applied to a series of thirteen alanine mutations of the human neuropeptide Y1 G-protein coupled receptor and a series of eight analogous antagonists. The robustness and accuracy of the method enables univocal interpretation of existing mutagenesis and binding data. We show how these calculations can be used to validate structural models and demonstrate their ability to discriminate against suboptimal ones. Site-directed mutagenesis, homology modelling and docking were further used to characterize agonist binding to the human neuropeptide Y2 receptor, which is important in feeding behavior and an obesity drug target.  In a separate project, homology modelling was also used for rationalization of mutagenesis data for an integron integrase involved in antibiotic resistance. Blockade of the hERG potassium channel by various drug-like compounds, potentially causing serious cardiac side effects, is a major problem in drug development. We have used a homology model of hERG to conduct molecular docking experiments with a series of channel blockers, followed by molecular dynamics simulations of the complexes and evaluation of binding free energies with the linear interaction energy method. The calculations are in good agreement with experimental binding affinities and allow for a rationalization of three-dimensional structure-activity relationships with implications for design of new compounds. Docking, scoring, molecular dynamics, and the linear interaction energy method were also used to predict binding modes and affinities for a large set of inhibitors to HIV-1 reverse transcriptase. Good agreement with experiment was found and the work provides a validation of the methodology as a powerful tool in structure-based drug design. It is also easily scalable for higher throughput of compounds.

<p>The ability to accurately predict binding free energies from computer simulations is an invaluable resource in understanding biochemical processes and drug action. Several methods based on microscopic molecular dynamics simulations exist, and in this thesis the validation, application, and development of the linear interaction energy (LIE) method is presented.</p><p>For a test case of several hydrophobic ligands binding to P450cam it is found that the LIE parameters do not change when simulations are performed with three different force fields. The nonpolar contribution to binding of these ligands is best reproduced with a constant offset and a previously determined scaling of the van der Waals interactions.</p><p>A new methodology for prediction of binding free energies of protein-protein complexes is investigated and found to give excellent agreement with experimental results. In order to reproduce the nonpolar contribution to binding, a different scaling of the van der Waals interactions is neccesary (compared to small ligand binding) and found to be, in part, due to an electrostatic preorganization effect not present when binding small ligands.</p><p>A new treatment of the electrostatic contribution to binding is also proposed. In this new scheme, the chemical makeup of the ligand determines the scaling of the electrostatic ligand interaction energies. These scaling factors are calibrated using the electrostatic contribution to hydration free energies and proposed to be applicable to ligand binding.</p><p>The issue of codon-anticodon recognition on the ribosome is adressed using LIE. The calculated binding free energies are in excellent agreement with experimental results, and further predict that the Leu2 anticodon stem loop is about 10 times more stable than the Ser stem loop in complex with a ribosome loaded with the Phe UUU codon. The simulations also support the previously suggested roles of A1492, A1493, and G530 in the codon-anticodon recognition process.</p>

The ability to accurately predict binding free energies from computer simulations is an invaluable resource in understanding biochemical processes and drug action. Several methods based on microscopic molecular dynamics simulations exist, and in this thesis the validation, application, and development of the linear interaction energy (LIE) method is presented. For a test case of several hydrophobic ligands binding to P450cam it is found that the LIE parameters do not change when simulations are performed with three different force fields. The nonpolar contribution to binding of these ligands is best reproduced with a constant offset and a previously determined scaling of the van der Waals interactions. A new methodology for prediction of binding free energies of protein-protein complexes is investigated and found to give excellent agreement with experimental results. In order to reproduce the nonpolar contribution to binding, a different scaling of the van der Waals interactions is neccesary (compared to small ligand binding) and found to be, in part, due to an electrostatic preorganization effect not present when binding small ligands. A new treatment of the electrostatic contribution to binding is also proposed. In this new scheme, the chemical makeup of the ligand determines the scaling of the electrostatic ligand interaction energies. These scaling factors are calibrated using the electrostatic contribution to hydration free energies and proposed to be applicable to ligand binding. The issue of codon-anticodon recognition on the ribosome is adressed using LIE. The calculated binding free energies are in excellent agreement with experimental results, and further predict that the Leu2 anticodon stem loop is about 10 times more stable than the Ser stem loop in complex with a ribosome loaded with the Phe UUU codon. The simulations also support the previously suggested roles of A1492, A1493, and G530 in the codon-anticodon recognition process.

Over the last few decades, computer simulation techniques have been established as an essential tool for understanding biochemical processes. This thesis deals mainly with the application of free energy calculations to ribosomal complexes and a cardiac ion channel. The linear interaction energy (LIE) method is used to explore the energetic properties of the essential process of codon–anticodon recognition on the ribosome. The calculations show the structural and energetic consequences and effects of first, second, and third position mismatches in the ribosomal decoding center. Recognition of stop codons by ribosomal termination complexes is fundamentally different from sense codon recognition. Free energy perturbation simulations are used to study the detailed energetics of stop codon recognition by the bacterial ribosomal release factors RF1 and RF2. The calculations explain the vastly different responses to third codon position A to G substitutions by RF1 and RF2. Also, previously unknown highly specific water interactions are identified. The GGQ loop of ribosomal RFs is essential for its hydrolytic activity and contains a universally methylated glutamine residue. The structural effect of this methylation is investigated. The results strongly suggest that the methylation has no effect on the intrinsic conformation of the GGQ loop, and, thus, that its sole purpose is to enhance interactions in the ribosomal termination complex. A first microscopic, atomic level, analysis of blocker binding to the pharmaceutically interesting potassium ion channel Kv1.5 is presented. A previously unknown uniform binding mode is identified, and experimental binding data is accurately reproduced. Furthermore, problems associated with pharmacophore models based on minimized gas phase ligand conformations are highlighted. Generalized Born and Poisson–Boltzmann continuum models are incorporated into the LIE method to enable implicit treatment of solvent, in an effort to improve speed and convergence. The methods are evaluated and validated using a set of plasmepsin II inhibitors.

Enolate formation by proton abstraction from an sp3-hybridized carbon atom situated next to a carbonyl or carboxylate group is an abundant process in nature. Since the corresponding nonenzymatic process in water is slow and unfavorable due to high intrinsic free energy barriers and high substrate pKa s, enzymes catalyzing such reaction steps must overcome both kinetic and thermodynamic obstacles. Computer simulations were used to study enolate formation catalyzed by glyoxalase I (GlxI) and 3-oxo-Δ5-steroid isomerase (KSI). The results, which reproduce experimental kinetic data, indicate that for both enzymes the free energy barrier reduction originates mainly from the balancing of substrate and catalytic base pKas. This was found to be accomplished primarily by electrostatic interactions. The results also suggest that the remaining barrier reduction can be explained by the lower reorganization energy in the preorganized enzyme compared to the solution reaction. Moreover, it seems that quantum effects, arising from zero-point vibrations and proton tunnelling, do not contribute significantly to the barrier reduction in GlxI. For KSI, the formation of a low-barrier hydrogen bond between the enzyme and the enolate, which is suggested to stabilize the enolate, was investigated and found unlikely. The low pKa of the catalytic base in the nonpolar active site of KSI may possibly be explained by the presence of a water molecule not detected by experiments. The hemoglobin-degrading aspartic proteases plasmepsinI and plasmepsin II from Plasmodium falciparum have emerged as putative drug targets against malaria. A series of C2- symmetric compounds with a 1,2-dihydroxyethylene scaffold were investigated for plasmepsin affinity, using computer simulations and enzyme inhibition assays. The calculations correctly predicted the stereochemical preferences of the scaffold and the effect of chemical modifications. Calculated absolute binding free energies reproduced experimental data well. As these inhibitors have down to subnanomolar inhibition constants of the plasmepsins and no measurable affinity to human cathepsin D, they constitute promising lead compounds for further drug development.

Biochemical processes all involve associations and dissociations of chemical entities. Understanding these is of substantial importance for many modern pharmaceutical applications. In this thesis, longstanding problems with regard to ligand binding are treated with computational methods, applied to proteins of key pharmaceutical importance. Homology modeling, docking, molecular dynamics simulations and free-energy calculations are used here for quantitative characterization of ligand binding to proteins. By combining computational tools, valuable contributions have been made for pharmaceutically relevant areas: a neglected tropical disease, an ion channel anti-drug-target, and GPCR drug-targets. We report three compounds inhibiting cruzain, the main cysteine protease of the protozoa causing Chagas’ disease. The compounds were found through an extensive virtual screening study and validated with experimental enzymatic assays. The compounds inhibit the enzyme in the μM-range and are therefore valuable in further lead optimization studies. A high-resolution crystal structure of the BRICHOS domain is reported, together with molecular dynamics simulations and hydrogen-deuterium exchange mass spectrometry studies. This work revealed a plausible mechanism for how the chaperone activity of the domain may operate. Rationalization of structure-activity relationships for a set of analogous blockers of the hERG potassium channel is given. A homology model of the ion channel was used for docking compounds and molecular dynamics simulations together with the linear interaction energy method employed for calculating the binding free-energies. The three-dimensional coordinates of two GPCRs, 5HT1B and 5HT2B, were derived from homology modeling and evaluated in the GPCR Dock 2013 assessment. Our models were in good correlation with the experimental structures and all of them placed among the top quarter of all models assessed.  Finally, a computational method, based on molecular dynamics free-energy calculations, for performing alanine scanning was validated with the A2A adenosine receptor bound to either agonist or antagonist. The calculated binding free-energies were found to be in good agreement with experimental data and the method was subsequently extended to non-alanine mutations. With extensive experimental mutation data, this scheme is a valuable tool for quantitative understanding of ligand binding and can ultimately be used for structure-based drug design.

Antibiotic resistance is a global and accelerating matter. Over time, the bacteria have evolved several defense mechanisms against the antibiotics. One of the defense mechanisms is that the bacteria can produce enzymes with the ability to hydrolyze the characteristic b-lactam ring of the antibiotics. These enzymes are called b-lactamases. There are three different generations of antibiotics clinically available, and b-lactamases have co-evolved with the antibiotics over the generations. The third generation of antibiotics are called the carbapenems and b-lactamases which hydrolyze carbapenems are called carbapenemases. Carbapenemases are promiscuous, which means that they hydrolyze a variety of antibiotics. The b-lactamase IMI-1 is an imipenem-hydrolyzing enzyme and imipenem is a carbapenem, hence IMI-1 is a carbapenemase. In this project, IMI-1 was investigated in complex with the carbapenems imipenem, meropenem and biapenem using computational methods. More specifically, a homology model of IMI-1 was generated and the carbapenems were docked into the model. The system was then used for MD simulations where the important molecular interactions were identified, and the binding free energies were calculated using the LIE method. The results indicate that IMI-1 has flexible loops that enables an open and a closed conformation of IMI- 1. All three carbapenems were docked and simulated in both conformations of IMI-1. The results indicate that open and closed conformations confirms the promiscuity of carbapenemases since the flexibility enables various initial binding mechanisms. in other words, the hydrolysis may occur so quickly that the binding does not have much bearing of the activity of the enzyme. Furthermore, the calculated binding free energies indicate that IMI-1 is optimized for the catalytic process rather than the binding affinity. In conclusion, IMI-1 and similar systems requires further research using computational methods to counteract antibiotic resistance based on knowledge.

The emergence of β-lactamase-mediated antibiotic resistance is one of the biggest threats in modern times. Combined with the discovery void of new forms of antibiotics, this sets the course toward a future where the efficacy of present-day health care will be jeopardized. To hinder the spread of β-lactamase-mediated antibiotic resistance, the development of the drug class β-lactamase inhibitors has been prioritized. The foremost candidate for development of this drug class, that has wide-spectrum inhibition of β-lactamases, is the clinically available avibactam of the diazabicyclooctane-scaffold (i.e., DBO-scaffold). However, the clinical applications of this inhibitor have been limited against one of the more rapidly spreading β-lactamases; OXA-48. In order to bolster the drug development of β-lactamase inhibitors of the DBO-scaffold, with good inhibitory activity toward OXA-48, DBO-ligands with different structure elements were analyzed for stability of the Michaelis Complex in the OXA-48 binding site using molecular dynamic simulations. The results indicate that elongation of the chain to the anionic group of the ligand combined with the addition of a methyl group to the DBO-ring was stabilizing for the productive position between the backbone hydrogens of Y211 and S70. The binding affinity was also estimated using the Linear Interaction Energy method, and an offset parameter of γ ≈ -19 kcal/mol was found and could represent the entropic differences of a flexible ligand-protein system. The results of this study may also indicate that the ligand stability of the Michaelis Complex is of minor consequence to the inhibition mechanism as a whole compared to the reaction rate.