京都大学 / 新制・課程博士 / 博士(工学) / 甲第24581号 / 工博第5087号 / 新制||工||1974(附属図書館) / 京都大学大学院工学研究科材料工学専攻 / (主査)教授 田中 功, 教授 安田 秀幸, 教授 中村 裕之 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

Detection and analysis of protein-ligand binding sites is an important area of research in drug discovery. The FTMap web server is an established computational method for detection of binding hot spots, or regions on the protein surface that contribute disproportionately to the ligand binding free energy. This body of work primarily focuses on the utilization and advancement of FTMap for the study of protein-ligand interactions and their applications to drug discovery. First, the driving forces behind why some proteins require compounds beyond Lipinski’s rule-of-five (bRo5) guidelines are evaluated for 37 protein targets. Three types of proteins are identified on the basis of their binding hot spots, described by FTMap, and their ligand binding affinity profiles. We describe the multifaceted motivations for bRo5 drug discovery for each group of targets, including increased binding affinity, improved selectivity, decreased toxicity, and decreased off-target effects. Second, the conservation of surface binding properties in protein models is evaluated, with particular emphasis on their utility in drug discovery. Here, the probe-binding locations determined by FTMap are used to generate a binding fingerprint, and the Pearson correlation between the binding fingerprint of an experimental structure and a predicted model indicates the level of surface property conservation, without any knowledge of the protein function a priori. This analysis was performed on the protein models submitted to the Critical Assessment of Techniques for Protein Structure Prediction (CASP) rounds 12 and 14, and results were correlated with well-established structure quality metrics. Third, development of the publicly-available FTMove web server (https://ftmove.bu.edu) is described for detection of binding sites and their respective strengths across multiple different conformations of a protein. FTMove was tested on 22 proteins with known allosteric binding sites, and reliably identified both the orthosteric and allosteric binding sites as highly ranked binding sites. The results yield important insight into the dynamics and druggability of such binding sites. Finally, high throughput affinity purified, mass spectrometry data is evaluated for identification of protein-metabolite interactions (PMIs) in Escherichia coli. A detailed search for known PMIs in both the Protein Data Bank and KEGG database is described, and the resulting curated sets of 21 recovered and 37 potentially novel PMIs in E. Coli are presented. Finally, high confidence novel PMIs were evaluated with the template-based small molecule docking program, LigTBM. / 2023-01-26T00:00:00Z

A protein's properties are influenced by both its amino-acid sequence and its three-dimensional conformation. Ascertaining a protein's sequence is relatively easy using modern techniques, but determining its conformation requires much more expensive and time-consuming techniques. Consequently, it would be useful to identify a method that can accurately predict a protein's secondary-structure conformation using only the protein's sequence data. This problem is not trivial, however, because identical amino-acid subsequences in different contexts sometimes have disparate secondary structures, while highly dissimilar amino-acid subsequences sometimes have identical secondary structures. We propose (1) to develop a set of metrics that facilitates better comparisons between dissimilar subsequences and (2) to design a custom set of inputs for machine-learning models that can harness contextual dependence information between the secondary structures of successive amino acids in order to achieve better secondary-structure prediction accuracy.

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No / A crystal structure prediction (CSP) study of three small, rigid and structurally related organic compounds (differing only in the position and number of methyl groups) is presented. A tailor-made force field (TMFF; a non-transferable force field specific for each molecule) was constructed with the aid of a dispersion-corrected density functional theory method (the hybrid method). Parameters for all energy terms in each TMFF were fitted to reference data generated by the hybrid method. Each force field was then employed during structure generation. The experimentally observed crystal structures of two of the three molecules were found as the most stable crystal packings in the lists of their force-field-optimised structures. A number of the most stable crystal structures were re-optimised with the hybrid method. One experimental crystal structure was still calculated to be the most stable structure, whereas for another compound the experimental structure became the third most stable structure according to the hybrid method. For the third molecule, the experimentally observed polymorph, which was found to be the fourth most stable form using its TMFF, became the second most stable form. Good geometrical agreements were observed between the experimental structures and those calculated by both methods. The average structural deviation achieved by the TMFFs was almost twice that obtained with the hybrid method. The TMFF approach was extended by exploring the accuracy of a more general TMFF (GTMFF), which involved fitting the force-field parameters to the reference data for all three molecules simultaneously. This GTMFF was slightly less accurate than the individual TMFFs but still of sufficient accuracy to be used in CSP. A study of the isostructural relationships between these molecules and their crystal lattices revealed a potential polymorph of one of the compounds that has not been observed experimentally and that may be accessible in a thorough polymorph screen, through seeding, or through the use of a suitable tailor-made additive.

No / A crystal ball? A new method for crystal structure prediction combines a tailor-made force field with a density functional theory method incorporating a van der Waals correction for dispersive interactions. In a blind test, the method predicts the correct crystal structure for all four compounds, one of which is a cocrystal. The picture shows the predicted structure of one of the compounds in green and the experimental structure in blue.

Yes / The creation of co-crystals as a route to creating new pharmaceutical phases with modified or defined physicochemical properties is an area of intense research. Much of the current research has focused on creating new phases for numerous active pharmaceutical ingredients (APIs) to alter physical properties such as low solubilities, enhancing processability or stability. Such studies have identified suitable co-formers and common bonding motifs to aid with the design of new co-crystals but understanding how the changes in the molecular structure of the components are reflected in the packing and resulting properties is still lacking. This lack of insight means that the design and growth of new co-crystals is still a largely empirical process with co-formers selected and then attempts to grow the different materials undertaken to evaluate the resulting properties. This work will report on the results of a combination of crystal structure database analysis with computational chemistry studies to identify what structural features are retained across a selection of families of co-crystals with common components. The competition between different potential hydrogen bonding motifs was evaluated using ab initio quantum mechanical calculations and this was related to the commonality in the packing motifs when observed. It is found while the stronger local bonding motifs are often retained within systems, the balance of weaker long-range packing forces gives rise to many subtle shifts in packing leading to greater challenges in the prediction of final crystal structures.

Background: RNA secondary structure prediction by energy minimization is the central computational tool for the analysis of structural non-coding RNAs and their interactions. Sparsification has been successfully applied to improve the time efficiency of various structure prediction algorithms while guaranteeing the same result; however, for many such folding problems, space efficiency is of even greater concern, particularly for long RNA sequences. So far, spaceefficient sparsified RNA folding with fold reconstruction was solved only for simple base-pair-based pseudo-energy models. Results: Here, we revisit the problem of space-efficient free energy minimization. Whereas the space-efficient minimization of the free energy has been sketched before, the reconstruction of the optimum structure has not even been discussed. We show that this reconstruction is not possible in trivial extension of the method for simple energy models. Then, we present the time- and space-efficient sparsified free energy minimization algorithm SparseMFEFold that guarantees MFE structure prediction. In particular, this novel algorithm provides efficient fold reconstruction based on dynamically garbage-collected trace arrows. The complexity of our algorithm depends on two parameters, the number of candidates Z and the number of trace arrows T; both are bounded by n2, but are typically much smaller. The time complexity of RNA folding is reduced from O(n3) to O(n2 + nZ); the space complexity, from O(n2) to O(n + T + Z). Our empirical results show more than 80 % space savings over RNAfold [Vienna RNA package] on the long RNAs from the RNA STRAND database (≥2500 bases). Conclusions: The presented technique is intentionally generalizable to complex prediction algorithms; due to their high space demands, algorithms like pseudoknot prediction and RNA–RNA-interaction prediction are expected to profit even stronger than \"standard\" MFE folding. SparseMFEFold is free software, available at http://www.bioinf.unileipzig. de/~will/Software/SparseMFEFold.