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<p>Malaria is a widespread disease caused by parasites of the genus <i>Plasmodium</i>. Each year 500 million clinical cases are reported resulting in over one million casualties. The most lethal species, <i>P. falciparum</i>, accounts for ~90% of the fatal cases and has developed resistance to chloroquine. The resistant strains are a major problem and calls for novel drugs.</p><p>In this thesis, the process of computational inhibitor design is illustrated through the development of <i>P. falciparum</i> aspartic protease inhibitors. These proteases, called plasmepsins, are part of the hemoglobin degradation chain. The hemoglobin is degraded during the intraerythrocytic cycle and serves as the major food source. By inhibiting plasmepsins the parasites can be killed by starvation.</p><p>Novel inhibitors with very high affinity were found by using a combination of computational and synthetic chemistry. These inhibitors were selective and did not display any activity on human cathepsin D. The linear interaction energy (LIE) method was utilized in combination with molecular dynamics (MD) simulations to estimate free energies of binding. The MD simulations were also used to characterize the enzyme–inhibitor interactions and explain the binding on a molecular level.</p><p>The influence of the partial charge model on binding free energy calculations with the LIE method was assessed. Two semiempirical and six <i>ab initio</i> quantum chemical charge derivation schemes were evaluated. It was found that the fast semiempirical charge models are equally useful in free energy calculations with the LIE method as the rigorous <i>ab initio</i> charge models.</p>

Malaria is a widespread disease caused by parasites of the genus Plasmodium. Each year 500 million clinical cases are reported resulting in over one million casualties. The most lethal species, P. falciparum, accounts for ~90% of the fatal cases and has developed resistance to chloroquine. The resistant strains are a major problem and calls for novel drugs. In this thesis, the process of computational inhibitor design is illustrated through the development of P. falciparum aspartic protease inhibitors. These proteases, called plasmepsins, are part of the hemoglobin degradation chain. The hemoglobin is degraded during the intraerythrocytic cycle and serves as the major food source. By inhibiting plasmepsins the parasites can be killed by starvation. Novel inhibitors with very high affinity were found by using a combination of computational and synthetic chemistry. These inhibitors were selective and did not display any activity on human cathepsin D. The linear interaction energy (LIE) method was utilized in combination with molecular dynamics (MD) simulations to estimate free energies of binding. The MD simulations were also used to characterize the enzyme–inhibitor interactions and explain the binding on a molecular level. The influence of the partial charge model on binding free energy calculations with the LIE method was assessed. Two semiempirical and six ab initio quantum chemical charge derivation schemes were evaluated. It was found that the fast semiempirical charge models are equally useful in free energy calculations with the LIE method as the rigorous ab initio charge models.

Indications that existing parameter sets of extended Linear Interaction Energy (LIE) models are transferable between lipases from Rhizomucor Miehei and Thermomyces Lanigunosus in complex with a small set of vinyl esters are demonstrated. By calculat- ing energy terms that represents the cost of forming cavities filled by the ligand and the complex we can add them to a LIE model with en established parameter set. The levels of precision attained will be comparable to those of an optimal fit. It is also demonstrated that the Molecular Mechanics/Poisson Boltzmann Surface Area (MM/PBSA) and Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) methods are in- applicable to the problem of calculating absolute binding energies, even when the largest source of variance has been reduced.

<p>Accurate calculations of free energies for molecular association and solvation are important for the understanding of biochemical processes, and are useful in many pharmaceutical applications. In this thesis, molecular dynamics (MD) simulations are used to calculate thermodynamic properties for solvation and ligand binding.</p><p>The thermodynamic integration technique is used to calculate p<i>K</i>_a values for three aspartic acid residues in two different proteins. MD simulations are carried out in explicit and Generalized-Born continuum solvent. The calculated p<i>K</i>_a values are in qualitative agreement with experiment in both cases. A combination of MD simulations and a continuum electrostatics method is applied to examine p<i>K</i>_a shifts in wild-type and mutant epoxide hydrolase. The calculated p<i>K</i>_a values support a model that can explain some of the pH dependent properties of this enzyme.</p><p> Development of the linear interaction energy (LIE) method for calculating solvation and binding free energies is presented. A new model for estimating the electrostatic term in the LIE method is derived and is shown to reproduce experimental free energies of hydration. An LIE method based on a continuum solvent representation is also developed and it is shown to reproduce binding free energies for inhibitors of a malaria enzyme. The possibility of using a combination of docking, MD and the LIE method to predict binding affinities for large datasets of ligands is also investigated. Good agreement with experiment is found for a set of non-nucleoside inhibitors of HIV-1 reverse transcriptase.</p><p>Approaches for decomposing solvation and binding free energies into enthalpic and entropic components are also examined. Methods for calculating the translational and rotational binding entropies for a ligand are presented. The possibility to calculate ion hydration free energies and entropies for alkali metal ions by using rigorous free energy techniques is also investigated and the results agree well with experimental data.</p>

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 calculations of free energies for molecular association and solvation are important for the understanding of biochemical processes, and are useful in many pharmaceutical applications. In this thesis, molecular dynamics (MD) simulations are used to calculate thermodynamic properties for solvation and ligand binding. The thermodynamic integration technique is used to calculate pKa values for three aspartic acid residues in two different proteins. MD simulations are carried out in explicit and Generalized-Born continuum solvent. The calculated pKa values are in qualitative agreement with experiment in both cases. A combination of MD simulations and a continuum electrostatics method is applied to examine pKa shifts in wild-type and mutant epoxide hydrolase. The calculated pKa values support a model that can explain some of the pH dependent properties of this enzyme. Development of the linear interaction energy (LIE) method for calculating solvation and binding free energies is presented. A new model for estimating the electrostatic term in the LIE method is derived and is shown to reproduce experimental free energies of hydration. An LIE method based on a continuum solvent representation is also developed and it is shown to reproduce binding free energies for inhibitors of a malaria enzyme. The possibility of using a combination of docking, MD and the LIE method to predict binding affinities for large datasets of ligands is also investigated. Good agreement with experiment is found for a set of non-nucleoside inhibitors of HIV-1 reverse transcriptase. Approaches for decomposing solvation and binding free energies into enthalpic and entropic components are also examined. Methods for calculating the translational and rotational binding entropies for a ligand are presented. The possibility to calculate ion hydration free energies and entropies for alkali metal ions by using rigorous free energy techniques is also investigated and the results agree well with experimental data.

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>