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1	Machine Learning-Based Methods for Predicting the Most Stable Conformation and Binding Affinity of Protein-Drug Complexes / 機械学習に基づくタンパク質-薬剤分子の最安定配座および結合親和性の予測手法 Koji, Shiota 24 September 2024 (has links) 京都大学 / 新制・課程博士 / 博士(情報学) / 甲第25630号 / 情博第886号 / 京都大学大学院情報学研究科知能情報学専攻 / (主査)教授阿久津達也, 教授山本章博, 教授鹿島久嗣 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM Machine learning Virtual screening Moluecular docking Quantum mechanics CASF-2016 007
2	Multipose Binding in Molecular Docking Atkovska, Kalina, Samsonov, Sergey A., Paszkowski-Rogacz, Maciej, Pisabarro, M. Teresa 09 July 2014 (has links) (PDF) Molecular docking has been extensively applied in virtual screening of small molecule libraries for lead identification and optimization. A necessary prerequisite for successful differentiation between active and non-active ligands is the accurate prediction of their binding affinities in the complex by use of docking scoring functions. However, many studies have shown rather poor correlations between docking scores and experimental binding affinities. Our work aimed to improve this correlation by implementing a multipose binding concept in the docking scoring scheme. Multipose binding, i.e., the property of certain protein-ligand complexes to exhibit different ligand binding modes, has been shown to occur in nature for a variety of molecules. We conducted a high-throughput docking study and implemented multipose binding in the scoring procedure by considering multiple docking solutions in binding affinity prediction. In general, improvement of the agreement between docking scores and experimental data was observed, and this was most pronounced in complexes with large and flexible ligands and high binding affinities. Further developments of the selection criteria for docking solutions for each individual complex are still necessary for a general utilization of the multipose binding concept for accurate binding affinity prediction by molecular docking. Docking Molekulares Docking Scoring-Optimierung Bindungsaffinität Vorhersage TU Dresden Publikationsfonds immultipose binding high-throughput docking scoring optization binding affinity prediction Technical University Dresden Publication funds ddc:570 ddc:540 rvk:WA 15000 rvk:VA 1120
3	Multipose Binding in Molecular Docking Atkovska, Kalina, Samsonov, Sergey A., Paszkowski-Rogacz, Maciej, Pisabarro, M. Teresa 09 July 2014 (has links) Molecular docking has been extensively applied in virtual screening of small molecule libraries for lead identification and optimization. A necessary prerequisite for successful differentiation between active and non-active ligands is the accurate prediction of their binding affinities in the complex by use of docking scoring functions. However, many studies have shown rather poor correlations between docking scores and experimental binding affinities. Our work aimed to improve this correlation by implementing a multipose binding concept in the docking scoring scheme. Multipose binding, i.e., the property of certain protein-ligand complexes to exhibit different ligand binding modes, has been shown to occur in nature for a variety of molecules. We conducted a high-throughput docking study and implemented multipose binding in the scoring procedure by considering multiple docking solutions in binding affinity prediction. In general, improvement of the agreement between docking scores and experimental data was observed, and this was most pronounced in complexes with large and flexible ligands and high binding affinities. Further developments of the selection criteria for docking solutions for each individual complex are still necessary for a general utilization of the multipose binding concept for accurate binding affinity prediction by molecular docking. info:eu-repo/classification/ddc/570 ddc:570 info:eu-repo/classification/ddc/540 ddc:540
4	Searching for novel protein-protein specificities using a combined approach of sequence co-evolution and local structural equilibration Nordesjö, Olle January 2016 (has links) Greater understanding of how we can use protein simulations and statistical characteristics of biomolecular interfaces as proxies for biological function will make manifest major advances in protein engineering. Here we show how to use calculated change in binding affinity and coevolutionary scores to predict the functional effect of mutations in the interface between a Histidine Kinase and a Response Regulator. These proteins participate in the Two-Component Regulatory system, a system for intracellular signalling found in bacteria. We find that both scores work as proxies for functional mutants and demonstrate a ~30 fold improvement in initial positive predictive value compared with choosing randomly from a sequence space of 160 000 variants in the top 20 mutants. We also demonstrate qualitative differences in the predictions of the two scores, primarily a tendency for the coevolutionary score to miss out on one class of functional mutants with enriched frequency of the amino acid threonine in one position. Biotechnology Bioinformatics Molecular Structure Protein Conformation Computing Methodologies Protein-protein interaction Binding affinity Mutation analysis Amino acid mutation in-silico binding affinity prediction Two-component regulatory system Histidine Kinase Response Regulator Prediction PhoQ PhoP Phosphatase Bioinformatics and Systems Biology Bioinformatik och systembiologi Structural Biology Strukturbiologi Bioinformatics (Computational Biology) Bioinformatik (beräkningsbiologi)

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