京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第20002号 / 工博第4246号 / 新制||工||1657(附属図書館) / 33098 / 京都大学大学院工学研究科合成・生物化学専攻 / (主査)教授 濵地 格, 教授 森 泰生, 教授 跡見 晴幸 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

Atomistic modeling and simulation methods facilitate biomedical research from many respects, including structure-based drug design. The ability of these methods to address biologically relevant problems is largely determined by the accuracy of the treatment of complex solvation effects in target biomolecules surrounded by water. The implicit solvent model – which treats solvent as a continuum with the dielectric and non-polar properties of water – offers a good balance between accuracy and speed. Simple and efficient, generalized Born (GB) model has become a widely used implicit solvent responsible for the estimation of key electrostatic interactions. The main goal of this research is to improve the accuracy of protein-ligand binding calculations in the implicit solvent framework. To address the problem (1) GBNSR6, an accurate yet efficient flavor of GB, has been thoroughly explored in the context of protein-ligand binding, (2) a global multidimensional optimization pipeline is developed to find the optimal dielectric boundary made of atomic and water probe radii specifically for protein-ligand binding calculations using GBNSR6. The pipeline includes (3) two novel post-processing steps for optimum robustness analysis and optimization landscape visualization. In the final step of this research, (4) accuracy gain the optimal dielectric boundary can bring in practice is explored on binding benchmarks, including the SARS-CoV-2 spike receptor-binding domain and the human ACE2 receptor. / Doctor of Philosophy / Drug discovery is one of the most challenging tasks in biological sciences as it takes about 10-15 years and $1.5-2 billion on average to discover a new drug. Therefore, efforts to speed up this process or lower its costs are highly valuable. Computer-aided drug design (CADD) plays a crucial role in the early stage of drug discovery. In CADD, computational approaches are used in order to discover, develop, and analyze drugs and similar biologically active molecules, such as proteins. Proteins are an important class of biological macromolecules that perform their functionality mainly through interactions with other molecules, for example, binding to small molecules so-called ligands. Thorough understanding of protein-ligand interactions is central to comprehending biology at the molecular level. In this study, we introduce and analyze a computational model used for protein-ligand binding free energy calculations. A global multidimensional optimization pipeline is developed to find the optimal parameters of the model,˘aparticularly˘athose parameters involved in the dielectric boundary. In order to examine the robustness of the optimal model to unavoidable perturbations and uncertainties, virtually inevitable in any complex system being optimized, a novel robustness metric is introduced. Finally, the robust optimal model is tested on protein-ligand benchmarks, including a complex related to the novel coronavirus. Results demonstrate relatively higher accuracy in terms of binding free energy calculations compared to reference models.

<p dir="ltr">In computer-aided drug discovery, methods that are approximate, but computationally inexpensive play an essential role during the initial phase of the discovery process. Although often inaccurate, they enable the screening of vast drug libraries to identify potential inhibitors with favorable activities, before large amounts of computational resources could be dedicated to studying these individual molecules. This thesis presents<b> </b>such an approach, based on the concept of hydrogen bond wrapping, to study protein-ligand interactions in the context of drug discovery. The ‘wrapping’ refers to the tendency of hydrophobic groups to surround a hydrogen bond in water, leading to its desolvation, thereby stabilizing it.</p><p dir="ltr">Herein, a molecular descriptor was employed, which quantifies the extent of hydrophobic wrapping around a protein’s backbone hydrogen bonds (BHBs) and could help speed up the discovery process by providing cues for the design or optimization of inhibitors. Additionally, these insights could help tailor not just the binding affinity of inhibitors, but also their specificity toward an intended target protein. The human transmembrane protease serine 2 (TMPRSS2) was used as an illustrative target protein due to the pressing need for COVID-19 therapeutics, and since the current understanding of the binding mechanisms of known TMPRSS2 inhibitors is limited.</p><p dir="ltr">Molecular docking with a Generalized Born - surface area (GBSA) scoring function was first performed to virtually screen for TMPRSS2 inhibitors. The molecular descriptor was then used to analyze the change in wrapping groups of TMPRSS2 BHBs due to docked ligands, with the aim of identifying BHBs with a high propensity for desolvation. The BHBs involving residues Cys437, Gln438, Asp440, and Ser441 of TMPRSS2 were seen to have some of the largest average increases in wrapping. These general results were also compared to results from docking of the known TMPRSS2 inhibitors, camostat, and nafamostat.</p><p dir="ltr">The data generated from docking were then used to examine potential applications of the wrapping molecular descriptor using machine learning techniques: (i) for prediction of the solvent-accessible surface area term ΔG_sa of the GBSA score using regression and (ii) for classifying the solvent interactions of a TMPRSS2-inhibitor complex as favorable or unfavorable. The descriptor was seen to be only weakly related to ΔG_sa; the best-performing regression model had a Pearson correlation coefficient of 0.76 between the predictions and the actual values. The ability of the descriptor to classify solvent interactions was more satisfactory, with a highest value for area under the receiver operating characteristic curve of 0.75.</p><p dir="ltr">The descriptor was then used to analyze the effect of inhibitor binding on the dynamics of TMPRSS2 BHBs. For this, molecular dynamics simulation was carried out for the uncomplexed TMPRSS2, as well as its complex with known inhibitors and hit molecules from docking. The binding of these ligands was seen to improve the stability of TMPRSS2; certain BHBs which were unstable or not formed in the uncomplexed case, showed increased stability. These prominently included a couple of BHBs identified from docking as having gained a large increase in wrapping. The improved stability coincided with an increase in wrapping groups in several cases. The descriptor also successfully rationalized the desolvation of a few BHBs due to inhibitor binding.</p><p dir="ltr">This work demonstrates the potential application of the concept of hydrogen bond wrapping in understanding the mechanism of inhibitor binding and the resultant desolvation effects on a protein’s BHBs, without computationally expensive calculations. While the analysis methods require further improvement, the wrapping descriptor shows promising results and could be developed into a simple, yet powerful tool for drug discovery.</p>

Drug discovery has witnessed an increase in the application of in silico methods to complement existing in vitro and in vivo experiments, in an attempt to 'fail fast' and reduce the high attrition rates of clinical phases. Computer algorithms have been successfully employed for many tasks including biological target selection, hit identification, lead optimization, binding affinity determination, ADME and toxicity prediction, side-effect prediction, drug repurposing, and, in general, to direct experimental work. This thesis describes a multifaceted approach to virtual screening, to computationally identify small-molecule inhibitors against a biological target of interest. Conformer generation is a critical step in all virtual screening methods that make use of atomic 3D data. We therefore analysed the ability of computational tools to reproduce high quality, experimentally resolved conformations of organic small-molecules. We selected the best performing method (RDKit), and developed a protocol that generates a non-redundant conformer ensemble which tends to contain low-energy structures close to those experimentally observed. We then outline the steps we took to build a multi-million, small-molecule database (including molecule standardization and efficient exact, substructure and similarity searching capabilities), for use in our virtual screening experiments. We generated conformers and descriptors for the molecules in the database. We tagged a subset of the database as `drug-like' and clustered this to provide a reduced, diverse set of molecules for use in more computationally-intensive virtual screening protocols. We next describe a novel virtual screening method we developed, called Ligity, that makes use of known protein-ligand holo structures as queries to search the small-molecule database for putative actives. Ligity has been validated against targets from the DUD-E dataset, and has shown, on average, better performance than other 3D methods. We also show that performance improved when we fused the results from multiple input structures. This bodes well for Ligity's future use, especially when considering that protein structure databases such as the Protein Data Bank are growing exponentially every year. Lastly, we describe the fruitful application of structure-based and ligand-based virtual screening methods to Plasmodium falciparum Subtilisin-like Protease 1 (PfSUB1), an important drug target in the human stages of the life-cycle of the malaria parasite. Our ligand-based virtual screening study resulted in the discovery of novel PfSUB1 inhibitors. Further lead optimization of these compounds, to improve binding affinity in the nanomolar range, may promote them as drug candidates. In this thesis we postulate that the accuracy of computational tools in drug discovery may be enhanced to take advantage of the exponential increase of experimental data and the availability of cheaper computational power such as cloud computing.

Developing a new drug costs up to US$2.6B and 13.5 years. To save money and time, we have developed a toolset for computer-aided drug discovery, and utilized our toolset to discover drugs for the treatment of cancers and influenza. / We first implemented a fast protein-ligand docking tool called idock, and obtained a substantial speedup over a popular counterpart. To facilitate the large-scale use of idock, we designed a heterogeneous web platform called istar, and collected a huge database of more than 23 million small molecules. To elucidate molecular interactions in web, we developed an interactive visualizer called iview. To synthesize novel compounds, we developed a fragment-based drug design tool called iSyn. To improve the predictive accuracy of binding affinity, we exploited the machine learning technique random forest to re-score both crystal and docked poses. To identify structurally similar compounds, we ported the ultrafast shape recognition algorithms to istar. All these tools are free and open source. / We applied our novel toolset to real world drug discovery. We repurposed anti-acne drug adapalene for the treatment of human colon cancer, and identified potential inhibitors of influenza viral proteins. Such new findings could hopefully save human lives. / 開發一種新藥需要多至26億美元和13年半的時間。為節省金錢和時間，我們開發了一套計算機輔助藥物研發工具集，並運用該工具集尋找藥物治療癌症和流感。 / 我們首先實現了一個快速的蛋白與配體對接工具idock，相比一個同類流行軟件獲得了顯著的速度提升。為輔助idock 的大規模使用，我們設計了一個異構網站平台istar，收集了多達兩千三百萬個小分子的大型數據庫。為在網頁展示分子間相互作用，我們開發了一個交互式可視化軟件iview。為生成全新的化合物，我們開發了一個基於分子片段的藥物設計工具iSyn。為改進結合強度預測的精度，我們利用了機器學習技術隨機森林去重新打分晶體及預測構象。為尋找結構相似的化合物，我們移植了超快形狀識別算法至istar。所有這些工俱全是免費和開源。 / 我們應用了此創新工具集至現實世界藥物尋找中。我們發現抗痤瘡藥阿達帕林可用於治療人類結腸癌，亦篩選出流感病毒蛋白的潛在抑制物。這些新發現可望拯救人類生命。 / Li, Hongjian. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2015. / Includes bibliographical references (leaves 340-394). / Abstracts also in Chinese. / Title from PDF title page (viewed on 15, September, 2016). / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only.

Staphyloccocus aureus is an opportunisitic pathogen that causes soft skin and tissue infections (SSTI) such as endocarditis, osteomyelitis and meningitis. In recent years, the re-emergence of antibiotic-resistant S. aureus such as MRSA presents a formidable challenge for infection management worldwide. Amidst this global epidemic of antimicrobial resistance, several research efforts have turned their focus towards exploiting the cell-wall biosynthesis pathway for novel anti-bacterial targets. Recently, the lipoteichoic acid (LTA) biosynthesis pathway has emerged as a potential anti-bacterial target. LTA is an anionic polymer found on the cell envelope of Gram-positive bacteria. It comprises of repeating units of glycerol-phosphate (GroP) and is important for bacterial cell physiology and virulence. For example, it is critically involved in regulating ion homeostasis, cell division, host colonization and immune system invasion. Several reports showed that bacteria lacking LTA are unable to grow. At the same time, they suffer from severe cell division defects and also exhibit aberrant cell morphologies. The key protein involved in the LTA biosynthesis pathway is the Lipoteichoic acid synthase (LtaS). LtaS is located on the cell membrane of Gram-positive bacteria and can be divided into two parts: a transmembrane domain and an extra-cellular domain responsible for its enzymatic activity (annotated eLtaS). Given that LtaS is important for bacterial survival and there are no known eLtaS homologues in eukaryotic cells, this protein is an attractive antibacterial target. In 2013, a small molecule eLtaS inhibitor (termed 1771) was discovered. Although 1771 was able to deplete LTA production, the binding mechanism of 1771 to eLtaS remains unknown. Additionally, 1771 could only prolong the survival of infected mice temporarily because of its in vivo instability. Therefore, the need for finding more potent and metabolically stable inhibitors of eLtaS still remains. Computational-aided drug design (CADD) is a cost-effective and useful approach that has been widely integrated into the drug discovery process. The protein eLtaS lends itself to be a good target for CADD since its crystal structure and a known inhibitor (with limited structure-activity data) is available. In this work, I have targeted eLtaS using CADD methodology followed by prospective validation using various biophysical, biochemical and microbiological assays. My project can broadly be sub-divided into three phases: (a) identification of small molecule binding “hot spots”, (b) optimization of existing inhibitor and (c) discovery of new hits. Through a systematic use of different computational approaches, I modelled a plausible 1771-bound eLtaS complex and used the structural insights to generate new inhibitors against eLtaS. To this end, I discovered EN-19, which is a more potent inhibitor of eLtaS. Additionally, by targeting transient cryptic pockets predicted by Molecular Dynamic simulations, I have discovered a new inhibitor chemotype that seems to exhibit a different mode of action against eLtaS. Taken together, my work presents a computational platform for future drug design against eLtaS and reinforces the notion that targeting eLtaS is a viable strategy to combat Gram-positive infections.

Tuberculosis is a disease that kills more people every year than any other infectious disease and is caused by the human pathogen, Mycobacterium tuberculosis (Mtb). This disease can be treated by a standard six month course of four antimicrobial drugs that have been in use since the 1960s. However, the rise of multi-drug resistant and extensively drug-resistant strains of TB has complicated the efforts to eradicate the disease. Therefore, there is a critical need for the development of new anti-TB drugs with a novel mechanism of action that can speed up treatment duration and help avoid resistance. The discovery of twenty genes encoding cytochrome P450 enzymes in the Mtb H37Rv genome sequence has pointed to the significance of these enzymes in the physiology and pathogenicity of this bacterium. Consequently, the characterisation of these Mtb P450 enzymes may define their physiological roles of which can be a novel anti-tubercular drug target. To date, the characterisations of selected Mtb P450 enzymes have highlighted their diverse and unexpected roles in the metabolism of cholesterol and lipids and the production of secondary metabolites. Biochemical and biophysical studies of these enzymes provided knowledge of their active site properties that may be exploited for drug discovery. Therefore, with the prospect of defining novel functions and identifying novel drug targets, characterisations of the remaining orphan Mtb P450s is of interest. M. tuberculosis CYP141A1 and CYP143A1 are orphan enzymes with unknown physiological function in Mtb which is characterised in this study through use of various spectroscopic and biophysical techniques. Interestingly, CYP141A1 can be expressed in form of which 54 amino acids (Del54CYP141A1) are deleted from the N-terminus. Although Del54CYP141A1 still retain spectroscopic characteristics, this form of P450 cannot be crystallized. Optimisation of full-length CYP141A1 buffer composition resulted to the formation of reproducible crystals and determination of CYP141A1 structure. Spectroscopic and structural characterisations presented in this thesis revealed many characteristics of CYP141A1 and CYP143A1 are comparable to previous Mtb P450s reported to date. CYP141A1 and CYP143A1 active site consist of b-type heme iron ligated by cysteine residue and a water molecule at its proximal and distal face, respectively. Both enzymes bind tightly to azole antifungal drugs highlighting their potential as a drug target. In addition, fragment-based screening applied to CYP141A1 and CYP143A1 provided the starting point for the development of potent, isoform-specific inhibitors for both orphan Mtb P450 enzymes. The first crystal structure of CYP141A1 and identification of new fragment binders of CYP141A1 and CYP143A1 are presented in this thesis. Overall, this research remains significant in providing new knowledge on the spectroscopic and structural properties of the M. tuberculosis P450s CYP141A1 and CYP143A1.

Kynurenine 3-monooxygenase (KMO) is an enzyme central to the kynurenine pathway of tryptophan degradation. KMO is emerging as an increasingly important target for drug development. The enzyme is implicated in the development and progression of several neurodegenerative disorders, in the regulation of the immune response and in sterile systemic inflammation. Production of recombinant human enzyme is challenging due to the presence of transmembrane domains, which localise KMO to the outer mitochondrial membrane and render KMO insoluble in many in vitro expression systems. Although several in vitro KMO assay techniques have been reported in the literature these methods are typically insensitive or require purified protein for use in high-throughput screening assays of human KMO enzyme. The first report of bacterial expression of soluble active human KMO enzyme is described here. Fusion protein tags were used to optimise soluble expression and enable characterisation and partial purification of the active protein constructs. Functional enzyme was used to develop several novel high-throughput drug screening techniques for the discovery of inhibitors specifically targeting human KMO. These screening techniques were fully characterised and validated using known KMO inhibitors from the patent literature. One of the novel KMO assay techniques was implemented for compound screening and several hit compounds were identified, validated and their in vitro DMPK characteristics determined. In addition to assay development, KMO was characterised at the cellular level when overexpressed in HEK293 cells. These experiments indicated that KMO overexpressing cells undergo bidirectional adaptation via alteration of kynurenine pathway homeostasis. As a result, these cells are protected from cytotoxicity mediated by 3-hydroxykynurenine (3-HK), the toxic product of KMO catalysis. The development of novel high throughput screening techniques targeting KMO has enabled screening of potential new inhibitors specifically targeting the human enzyme. Implementation of these screening assays will allow accelerated and improved discovery and development of novel KMO inhibitors for the potential treatment of numerous disease states.

Research has revealed that current antidepressant treatment is less than adequate at alleviating behavioral endophenotypes associated with major depressive disorder (MDD) and there is a need for appropriate animal models to validate novel antidepressant pharmacological targets. In the present study, we wished to establish an ethologically relevant social defeat stress model in combination with a chronic unpredictable stress model, to more accurately mimic severe stress that is common in MDD. Before each day of the introduction of the stressor, animals were given saline or a 40 mg/kg dose of 3-aminobenzamide (3-AB), a poly ADP-ribose (PARP) inhibitor. PARP is a DNA repair enzyme that is increased in activity in response to DNA oxidation, which is elevated in the prefrontal cortical white matter in MDD post-mortem donors. One stressed group was given the common antidepressant fluoxetine (10mg/kg) to serve as a positive control. Results of this study demonstrated that 3-AB alleviated decreases in sucrose preference, a natural reward, along with avoidance on a social interaction test given at the end of social defeat. Preliminary telemetry readings indicated 3-AB was able to significantly decrease heart rate and blood pressure in response to SDS as compared to saline treated rats. Therefore, it appears that PARP inhibition alleviated behavioral endophenotypes associated with stress and represents a new pharmacological treatment for MDD in humans.

Multi-drug resistance in bacterial pathogens has become a global health crisis. Each year, millions of people worldwide are infected with bacterial strains that are resistant to currently available antibiotics. Diseases such as tuberculosis, pneumonia, and gonorrhea have become increasingly more difficult to treat. It is essential that novel drugs and cellular targets be identified in order to treat this resistance. Bacterial topoisomerase IA is a novel drug target that is essential for cellular growth. As it has never been targeted by existing antibiotics, it is an attractive target. Topoisomerase IA is responsible for relieving torsional strain on DNA by relaxing supercoiled DNA following processes such as replication and transcription. The aim of this study is to find novel compounds that can be developed as leads for antibiotics targeting bacterial type IA topoisomerase. Various approaches were used in order to screen thousands of compounds against bacterial type IA topoisomerases, including mixture-based screening and virtual screening. In the mixture-based screen, scaffold mixtures were tested against the M. tuberculosis topoisomerase I enzyme and subsequently optimized for maximum potency and selectivity. The optimized compounds were effective at inhibiting the enzyme at low micromolar concentrations, as well as killing the tuberculosis bacteria. In a virtual screen, libraries with hundreds of thousands of compounds were screened against the E. coli and M. tuberculosis topoisomerase I crystal structures in order to find new classes of drugs. The top hits were effective at inhibiting the enzymes, as well as preventing the growth of M. smegmatis cells in the presence of efflux pump inhibitors. Organometallic complexes containing Cu(II) or Co(III) were tested as well against various topoisomerases in order to determine their selectivity. We discovered a poison for human type II topoisomerase that has utility as an anticancer agent, as it killed even very resistant cell lines of breast and colon cancer. The Co(III) complexes were found to inhibit the bacterial topoisomerase I very selectively over other topoisomerases. The various methods of drug discovery utilized here have been successful at identifying new classes of compounds that may be further developed into antibiotic drugs that specifically target bacterial type IA topoisomerases.