Activity of diverse chalcones against several targets: statistical analysis of a high-throughput virtual screen of a custom chalcone library
- Authors: Sarron, Arthur F D
- Date: 2020
- Subjects: Acetophenone , Benzaldehyde , Ketones , Pyruvate kinase , Drug development , Aromatic compounds , Heat shock proteins
- Language: English
- Type: text , Thesis , Doctoral , PhD
- Identifier: http://hdl.handle.net/10962/116028 , vital:34291
- Description: Chalcone family molecules are well known to have therapeutic proprieties (anti-inflammatory, anti-microbial or anti-cancer, etc). However the mechanism of action in some cases is not well known. A virtual library of this family of compounds was constructed using custom scripts, based on the aldol condensation, and this library was modified further to analogues by expansion of the α,β-unsaturated ketone linker. Acetophenone and benzaldehyde derivatives which are available and purchasable were used as a base to design the chalcone virtual library. 8063 chalcones were constructed and geometrically optimized with Gaussian 09. Their physicochemical characteristics linked to the Lipinski rules were analyzed with Knime and CDK. The entire library was after docked against several targets including HIV-1 integrase, MRSA pyruvate kinase, HSP90, COX-1, COX-2, ALR2, MAOA, MAOB, acetylcholinesterase, butyrylcholinesterase and PLA2. With the exception of MAOA, which does not have a crystal structure ligand, all dockings were validated by redocking the original ligand provided by the literature. These targets are known in the literature to be inhibited by chalcone-derivatives. However, specificity of the particular known chalcone inhibitors to the particular targets is not known. To this end the performance of the generated chalcone library against the list of targets was of interest. The binding energy of ligand-protein complexes was generally good across the library. Statistical analysis including principal component analysis and hierarchical clustering analysis were made in order to investigate for any physical/chemical characteristics which might explain what chalcone features affect the binding energy of the ligand-protein complexes. The spherical polar coordinates defining the orientation of the binding poses were also calculated and used in the statistical analysis. The statistical analysis has allowed us to hypothesize the importance of these radial distances and the polar angles of key atoms in the chalcones in binding to the pyruvate kinase crystal structure. This was validated by the docking of another small library of compound models in which the α,β-unsaturated ketone chain of the chalcone was replaced by incrementally longer conjugated chains. Further studies on the chalcones themselves reveal rotameric systems in both cis and trans-configurations (which may impact binding), and also studied was the effect of Topliss-based modification and its impact of binding to HSP90. Molecular dynamics confirmed good binding of identified chalcone hits.
- Full Text:
- Authors: Sarron, Arthur F D
- Date: 2020
- Subjects: Acetophenone , Benzaldehyde , Ketones , Pyruvate kinase , Drug development , Aromatic compounds , Heat shock proteins
- Language: English
- Type: text , Thesis , Doctoral , PhD
- Identifier: http://hdl.handle.net/10962/116028 , vital:34291
- Description: Chalcone family molecules are well known to have therapeutic proprieties (anti-inflammatory, anti-microbial or anti-cancer, etc). However the mechanism of action in some cases is not well known. A virtual library of this family of compounds was constructed using custom scripts, based on the aldol condensation, and this library was modified further to analogues by expansion of the α,β-unsaturated ketone linker. Acetophenone and benzaldehyde derivatives which are available and purchasable were used as a base to design the chalcone virtual library. 8063 chalcones were constructed and geometrically optimized with Gaussian 09. Their physicochemical characteristics linked to the Lipinski rules were analyzed with Knime and CDK. The entire library was after docked against several targets including HIV-1 integrase, MRSA pyruvate kinase, HSP90, COX-1, COX-2, ALR2, MAOA, MAOB, acetylcholinesterase, butyrylcholinesterase and PLA2. With the exception of MAOA, which does not have a crystal structure ligand, all dockings were validated by redocking the original ligand provided by the literature. These targets are known in the literature to be inhibited by chalcone-derivatives. However, specificity of the particular known chalcone inhibitors to the particular targets is not known. To this end the performance of the generated chalcone library against the list of targets was of interest. The binding energy of ligand-protein complexes was generally good across the library. Statistical analysis including principal component analysis and hierarchical clustering analysis were made in order to investigate for any physical/chemical characteristics which might explain what chalcone features affect the binding energy of the ligand-protein complexes. The spherical polar coordinates defining the orientation of the binding poses were also calculated and used in the statistical analysis. The statistical analysis has allowed us to hypothesize the importance of these radial distances and the polar angles of key atoms in the chalcones in binding to the pyruvate kinase crystal structure. This was validated by the docking of another small library of compound models in which the α,β-unsaturated ketone chain of the chalcone was replaced by incrementally longer conjugated chains. Further studies on the chalcones themselves reveal rotameric systems in both cis and trans-configurations (which may impact binding), and also studied was the effect of Topliss-based modification and its impact of binding to HSP90. Molecular dynamics confirmed good binding of identified chalcone hits.
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Enumeration, conformation sampling and population of libraries of peptide macrocycles for the search of chemotherapeutic cardioprotection agents
- Authors: Sigauke, Lester Takunda
- Date: 2019
- Subjects: Peptides -- Synthesis , Macrocyclic compounds , Drug development , Drug discovery , Cardiovascular system -- Diseases -- Prevention , Proteins -- Synthesis
- Language: English
- Type: text , Thesis , Doctoral , PhD
- Identifier: http://hdl.handle.net/10962/116056 , vital:34293
- Description: Peptides are uniquely endowed with features that allow them to perturb previously difficult to drug biomolecular targets. Peptide macrocycles in particular have seen a flurry of recent interest due to their enhanced bioavailability, tunability and specificity. Although these properties make them attractive hit-candidates in early stage drug discovery, knowing which peptides to pursue is non‐trivial due to the magnitude of the peptide sequence space. Computational screening approaches show promise in their ability to address the size of this search space but suffer from their inability to accurately interrogate the conformational landscape of peptide macrocycles. We developed an in‐silico compound enumerator that was tasked with populating a conformationally laden peptide virtual library. This library was then used in the search for cardio‐protective agents (that may be administered, reducing tissue damage during reperfusion after ischemia (heart attacks)). Our enumerator successfully generated a library of 15.2 billion compounds, requiring the use of compression algorithms, conformational sampling protocols and management of aggregated compute resources in the context of a local cluster. In the absence of experimental biophysical data, we performed biased sampling during alchemical molecular dynamics simulations in order to observe cyclophilin‐D perturbation by cyclosporine A and its mitochondrial targeted analogue. Reliable intermediate state averaging through a WHAM analysis of the biased dynamic pulling simulations confirmed that the cardio‐protective activity of cyclosporine A was due to its mitochondrial targeting. Paralleltempered solution molecular dynamics in combination with efficient clustering isolated the essential dynamics of a cyclic peptide scaffold. The rapid enumeration of skeletons from these essential dynamics gave rise to a conformation laden virtual library of all the 15.2 Billion unique cyclic peptides (given the limits on peptide sequence imposed). Analysis of this library showed the exact extent of physicochemical properties covered, relative to the bare scaffold precursor. Molecular docking of a subset of the virtual library against cyclophilin‐D showed significant improvements in affinity to the target (relative to cyclosporine A). The conformation laden virtual library, accessed by our methodology, provided derivatives that were able to make many interactions per peptide with the cyclophilin‐D target. Machine learning methods showed promise in the training of Support Vector Machines for synthetic feasibility prediction for this library. The synergy between enumeration and conformational sampling greatly improves the performance of this library during virtual screening, even when only a subset is used.
- Full Text:
- Authors: Sigauke, Lester Takunda
- Date: 2019
- Subjects: Peptides -- Synthesis , Macrocyclic compounds , Drug development , Drug discovery , Cardiovascular system -- Diseases -- Prevention , Proteins -- Synthesis
- Language: English
- Type: text , Thesis , Doctoral , PhD
- Identifier: http://hdl.handle.net/10962/116056 , vital:34293
- Description: Peptides are uniquely endowed with features that allow them to perturb previously difficult to drug biomolecular targets. Peptide macrocycles in particular have seen a flurry of recent interest due to their enhanced bioavailability, tunability and specificity. Although these properties make them attractive hit-candidates in early stage drug discovery, knowing which peptides to pursue is non‐trivial due to the magnitude of the peptide sequence space. Computational screening approaches show promise in their ability to address the size of this search space but suffer from their inability to accurately interrogate the conformational landscape of peptide macrocycles. We developed an in‐silico compound enumerator that was tasked with populating a conformationally laden peptide virtual library. This library was then used in the search for cardio‐protective agents (that may be administered, reducing tissue damage during reperfusion after ischemia (heart attacks)). Our enumerator successfully generated a library of 15.2 billion compounds, requiring the use of compression algorithms, conformational sampling protocols and management of aggregated compute resources in the context of a local cluster. In the absence of experimental biophysical data, we performed biased sampling during alchemical molecular dynamics simulations in order to observe cyclophilin‐D perturbation by cyclosporine A and its mitochondrial targeted analogue. Reliable intermediate state averaging through a WHAM analysis of the biased dynamic pulling simulations confirmed that the cardio‐protective activity of cyclosporine A was due to its mitochondrial targeting. Paralleltempered solution molecular dynamics in combination with efficient clustering isolated the essential dynamics of a cyclic peptide scaffold. The rapid enumeration of skeletons from these essential dynamics gave rise to a conformation laden virtual library of all the 15.2 Billion unique cyclic peptides (given the limits on peptide sequence imposed). Analysis of this library showed the exact extent of physicochemical properties covered, relative to the bare scaffold precursor. Molecular docking of a subset of the virtual library against cyclophilin‐D showed significant improvements in affinity to the target (relative to cyclosporine A). The conformation laden virtual library, accessed by our methodology, provided derivatives that were able to make many interactions per peptide with the cyclophilin‐D target. Machine learning methods showed promise in the training of Support Vector Machines for synthetic feasibility prediction for this library. The synergy between enumeration and conformational sampling greatly improves the performance of this library during virtual screening, even when only a subset is used.
- Full Text:
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