Virtual compound screening and SAR analysis: method development and practical applications in the design of new serine and cysteine protease inhibitors

Virtual screening is an important tool in drug discovery that uses different computational methods to screen chemical databases for the identification of possible drug candidates. Most virtual screening methodologies are knowledge driven where the availability of information on either the nature of the target binding pocket or the type of ligand that is expect to bind is essential. In this regard, the information contained in X-ray crystal structures of protein-ligand complexes provides a detailed insight into the interactions between the protein and the ligand and opens the opportunity for further understanding of drug action and structure activity relationships at molecular level. Protein-ligand interaction information can be utilized to introduce target-specific interaction-based constraints in the design of focused combinatorial libraries. It can also be directly transformed into structural interaction fingerprints and can be applied in virtual screening to analyze docking studies or filter compounds. However, the integration of protein-ligand interaction information into two-dimensional compound similarity searching is not fully explored. Therefore, novel methods are still required to efficiently utilize protein-ligand interaction information in two-dimensional ligand similarity searching. Furthermore, application of protein-ligand interaction information in the interpretation of SARs at the ligand level needs further exploration. Thus, utilization of three-dimensional protein ligand interaction information in virtual screening and SAR analysis was the major aim of this thesis. The thesis is presented in two major parts. In the first part, utilization of three-dimensional protein-ligand interaction information for the development of a new hybrid virtual screening method and analysis of the nature of SARs in analog series at molecular level is presented. The second part of the thesis is focused on the application of different virtual screening methods for the identification of new cysteine and membrane-bound serine proteases inhibitors. In addition, molecular modeling studies were also applied to analyze the binding mode of structurally complex cyclic peptide inhibitors

Pyrrolidine in Drug Discovery: A Versatile Scaffold for Novel Biologically Active Compounds

The five-membered pyrrolidine ring is one of the nitrogen heterocycles used widely by medicinal chemists to obtain compounds for the treatment of human diseases. The great interest in this saturated scaffold is enhanced by (1) the possibility to efficiently explore the pharmacophore space due to sp3-hybridization, (2) the contribution to the stereochemistry of the molecule, (3) and the increased three-dimensional (3D) coverage due to the non-planarity of the ring\u2014a phenomenon called \u201cpseudorotation\u201d. In this review, we report bioactive molecules with target selectivity characterized by the pyrrolidine ring and its derivatives, including pyrrolizines, pyrrolidine-2-one, pyrrolidine-2,5-diones and prolinol described in the literature from 2015 to date. After a comparison of the physicochemical parameters of pyrrolidine with the parent aromatic pyrrole and cyclopentane, we investigate the influence of steric factors on biological activity, also describing the structure\u2013activity relationship (SAR) of the studied compounds. To aid the reader\u2019s approach to reading the manuscript, we have planned the review on the basis of the synthetic strategies used: (1) ring construction from different cyclic or acyclic precursors, reporting the synthesis and the reaction conditions, or (2) functionalization of preformed pyrrolidine rings, e.g., proline derivatives. Since one of the most significant features of the pyrrolidine ring is the stereogenicity of carbons, we highlight how the different stereoisomers and the spatial orientation of substituents can lead to a different biological profile of drug candidates, due to the different binding mode to enantioselective proteins. We believe that this work can guide medicinal chemists to the best approach in the design of new pyrrolidine compounds with different biological profiles

Discovery of Novel Glycogen Synthase Kinase-3beta Inhibitors: Molecular Modeling, Virtual Screening, and Biological Evaluation

Glycogen synthase kinase-3 (GSK-3) is a multifunctional serine/threonine protein kinase which is engaged in a variety of signaling pathways, regulating a wide range of cellular processes. Due to its distinct regulation mechanism and unique substrate specificity in the molecular pathogenesis of human diseases, GSK-3 is one of the most attractive therapeutic targets for the unmet treatment of pathologies, including type-II diabetes, cancers, inflammation, and neurodegenerative disease. Recent advances in drug discovery targeting GSK-3 involved extensive computational modeling techniques. Both ligand/structure-based approaches have been well explored to design ATP-competitive inhibitors. Molecular modeling plus dynamics simulations can provide insight into the protein-substrate and protein-protein interactions at substrate binding pocket and C-lobe hydrophobic groove, which will benefit the discovery of non-ATP-competitive inhibitors. To identify structurally novel and diverse compounds that effectively inhibit GSK-3â, we performed virtual screening by implementing a mixed ligand/structure-based approach, which included pharmacophore modeling, diversity analysis, and ensemble docking. The sensitivities of different docking protocols to the induced-fit effects at the ATP-competitive binding pocket of GSK-3â have been explored. An enrichment study was employed to verify the robustness of ensemble docking compared to individual docking in terms of retrieving active compounds from a decoy dataset. A total of 24 structurally diverse compounds obtained from the virtual screening experiment underwent biological validation. The bioassay results shothat 15 out of the 24 hit compounds are indeed GSK-3â inhibitors, and among them, one compound exhibiting sub-micromolar inhibitory activity is a reasonable starting point for further optimization. To further identify structurally novel GSK-3â inhibitors, we performed virtual screening by implementing another mixed ligand-based/structure-based approach, which included quantitative structure-activity relationship (QSAR) analysis and docking prediction. To integrate and analyze complex data sets from multiple experimental sources, we drafted and validated hierarchical QSAR, which adopts a multi-level structure to take data heterogeneity into account. A collection of 728 GSK-3 inhibitors with diverse structural scaffolds were obtained from published papers of 7 research groups based on different experimental protocols. Support vector machines and random forests were implemented with wrapper-based feature selection algorithms in order to construct predictive learning models. The best models for each single group of compounds were then selected, based on both internal and external validation, and used to build the final hierarchical QSAR model. The predictive performance of the hierarchical QSAR model can be demonstrated by an overall R2 of 0.752 for the 141 compounds in the test set. The compounds obtained from the virtual screening experiment underwent biological validation. The bioassay results confirmed that 2 hit compounds are indeed GSK-3â inhibitors exhibiting sub-micromolar inhibitory activity, and therefore validated hierarchical QSAR as an effective approach to be used in virtual screening experiments. We have successfully implemented a variant of supervised learning algorithm, named multiple-instance learning, in order to predict bioactive conformers of a given molecule which are responsible for the observed biological activity. The implementation requires instance-based embedding, and joint feature selection and classification. The goal of the present project is to implement multiple-instance learning in drug activity prediction, and subsequently to identify the bioactive conformers for each molecule. The proposed approach was proven not to suffer from overfitting and to be highly competitive with classical predictive models, so it is very powerful for drug activity prediction. The approach was also validated as a useful method for pursuit of bioactive conformers

In Silico Design and Selection of CD44 Antagonists:implementation of computational methodologies in drug discovery and design

Drug discovery (DD) is a process that aims to identify drug candidates through a thorough evaluation of the biological activity of small molecules or biomolecules. Computational strategies (CS) are now necessary tools for speeding up DD. Chapter 1 describes the use of CS throughout the DD process, from the early stages of drug design to the use of artificial intelligence for the de novo design of therapeutic molecules. Chapter 2 describes an in-silico workflow for identifying potential high-affinity CD44 antagonists, ranging from structural analysis of the target to the analysis of ligand-protein interactions and molecular dynamics (MD). In Chapter 3, we tested the shape-guided algorithm on a dataset of macrocycles, identifying the characteristics that need to be improved for the development of new tools for macrocycle sampling and design. In Chapter 4, we describe a detailed reverse docking protocol for identifying potential 4-hydroxycoumarin (4-HC) targets. The strategy described in this chapter is easily transferable to other compounds and protein datasets for overcoming bottlenecks in molecular docking protocols, particularly reverse docking approaches. Finally, Chapter 5 shows how computational methods and experimental results can be used to repurpose compounds as potential COVID-19 treatments. According to our findings, the HCV drug boceprevir could be clinically tested or used as a lead molecule to develop compounds that target COVID-19 or other coronaviral infections. These chapters, in summary, demonstrate the importance, application, limitations, and future of computational methods in the state-of-the-art drug design process

Technological developments in Virtual Screening for the discovery of small molecules with novel mechanisms of action

Programa de Doctorat en Recerca, Desenvolupament i Control de Medicaments[eng] Advances in structural and molecular biology have favoured the rational development of novel drugs thru structure-based drug design (SBDD). Particularly, computational tools have proven to be rapid and efficient tools for hit discovery and optimization. The main motivation of this thesis is to improve and develop new methods in the area of computer-based drug discovery in order to study challenging targets. Specifically, this thesis is focused on docking and Virtual Screening (VS) methodologies to be able to exploit non-standard sites, like protein-protein interfaces or allosteric sites, and discover bioactive molecules with novel mechanisms of action. First, I developed an automatic pipeline for binding mode prediction that applies knowledge- based restraints and validated the approach by participating in the CELPP Challenge, a blind pose prediction challenge. The aim of the first VS in this thesis is to find small molecules able to not only disrupt the RANK-RANKL interaction but also inhibit the constitutive activation of the receptor. With a combination of computational, biophysical, and cell-based assays we were able to identify the first small molecule binders for RANK that could be used as a treatment for Triple Negative Breast Cancer. When working with challenging targets, or with non-standard mechanisms of action, the relationship between binding and the biological response is unpredictable, because the biological response (if any) will depend on the biological function of the particular allosteric site, which is generally unknown. For this reason, we then tested the applicability of the combination of ultrahigh-throughput VS with low-throughput high content assay. This allowed us to characterize a novel allosteric pocket in PTEN and also describe the first allosteric modulators for this protein. Finally, as the accessible Chemical Space grows at a rapid pace, we developed an algorithm to efficiently explore ultra-large Chemical Collections using a Bottom-up approach. We prospectively validated the approach in BRD4 and identified novel BRD4 inhibitors with an affinity comparable to advanced drug candidates for this target.[spa] Els avenços en biologia estructural i molecular han afavorit el desenvolupament racional de nous fàrmacs a través del disseny de fàrmacs basat en l'estructura (SBDD). En particular, les eines computacionals han demostrat ser ràpides i eficients per al descobriment i l'optimització de fàrmacs. La principal motivació d'aquesta tesi és millorar i desenvolupar nous mètodes en l'àrea del descobriment de fàrmacs per ordinador per tal d'estudiar proteïnes complexes. Concretament, aquesta tesi se centra en les metodologies d'acoblament i de cribratge virtual (CV) per poder explotar llocs no estàndard, com interfícies proteïna-proteïna o llocs al·lostèrics, i descobrir molècules bioactives amb nous mecanismes d'acció. En primer lloc, vaig desenvolupar un protocol automàtic per a la predicció del mode d’unió aplicant restriccions basades en el coneixement i vaig validar l'enfocament participant en el repte CELPP, un repte de predicció del mode d’unió a cegues. L'objectiu del primer CV d'aquesta tesi és trobar petites molècules capaces no només d'interrompre la interacció RANK-RANKL sinó també d'inhibir l'activació constitutiva del receptor. Amb una combinació d'assajos computacionals, biofísics i basats en cèl·lules, vam poder identificar les primeres molècules petites per a RANK que es podrien utilitzar com a tractament per al càncer de mama triple negatiu. Quan es treballa amb proteïnes complexes, o amb mecanismes d'acció no estàndard, la relació entre la unió i la resposta biològica és impredictible, perquè la resposta biològica (si n'hi ha) dependrà de la funció biològica del lloc al·lostèric particular, que generalment és desconeguda. Per aquest motiu, després vam provar l'aplicabilitat de la combinació de CV d'alt rendiment amb assaig de contingut alt de baix rendiment. Això ens va permetre caracteritzar un nou lloc d’unió al·lostèric en PTEN i també descriure els primers moduladors al·lostèrics d'aquesta proteïna. Finalment, a mesura que l'espai químic accessible creix a un ritme ràpid, hem desenvolupat un algorisme per explorar de manera eficient col·leccions de productes químics molt grans mitjançant un enfocament de baix a dalt. Vam validar aquest enfocament amb BRD4 i vam identificar nous inhibidors de BRD4 amb una afinitat comparable als candidats a fàrmacs més avançats per a aquesta proteïna

Technological developments in Virtual Screening for the discovery of small molecules with novel mechanisms of action

[eng] Advances in structural and molecular biology have favoured the rational development of novel drugs thru structure-based drug design (SBDD). Particularly, computational tools have proven to be rapid and efficient tools for hit discovery and optimization. The main motivation of this thesis is to improve and develop new methods in the area of computer-based drug discovery in order to study challenging targets. Specifically, this thesis is focused on docking and Virtual Screening (VS) methodologies to be able to exploit non-standard sites, like protein-protein interfaces or allosteric sites, and discover bioactive molecules with novel mechanisms of action. First, I developed an automatic pipeline for binding mode prediction that applies knowledge- based restraints and validated the approach by participating in the CELPP Challenge, a blind pose prediction challenge. The aim of the first VS in this thesis is to find small molecules able to not only disrupt the RANK-RANKL interaction but also inhibit the constitutive activation of the receptor. With a combination of computational, biophysical, and cell-based assays we were able to identify the first small molecule binders for RANK that could be used as a treatment for Triple Negative Breast Cancer. When working with challenging targets, or with non-standard mechanisms of action, the relationship between binding and the biological response is unpredictable, because the biological response (if any) will depend on the biological function of the particular allosteric site, which is generally unknown. For this reason, we then tested the applicability of the combination of ultrahigh-throughput VS with low-throughput high content assay. This allowed us to characterize a novel allosteric pocket in PTEN and also describe the first allosteric modulators for this protein. Finally, as the accessible Chemical Space grows at a rapid pace, we developed an algorithm to efficiently explore ultra-large Chemical Collections using a Bottom-up approach. We prospectively validated the approach in BRD4 and identified novel BRD4 inhibitors with an affinity comparable to advanced drug candidates for this target.[cat] Els avenços en biologia estructural i molecular han afavorit el desenvolupament racional de nous fàrmacs a través del disseny de fàrmacs basat en l'estructura (SBDD). En particular, les eines computacionals han demostrat ser ràpides i eficients per al descobriment i l'optimització de fàrmacs. La principal motivació d'aquesta tesi és millorar i desenvolupar nous mètodes en l'àrea del descobriment de fàrmacs per ordinador per tal d'estudiar proteïnes complexes. Concretament, aquesta tesi se centra en les metodologies d'acoblament i de cribratge virtual (CV) per poder explotar llocs no estàndard, com interfícies proteïna-proteïna o llocs al·lostèrics, i descobrir molècules bioactives amb nous mecanismes d'acció. En primer lloc, vaig desenvolupar un protocol automàtic per a la predicció del mode d’unió aplicant restriccions basades en el coneixement i vaig validar l'enfocament participant en el repte CELPP, un repte de predicció del mode d’unió a cegues. L'objectiu del primer CV d'aquesta tesi és trobar petites molècules capaces no només d'interrompre la interacció RANK-RANKL sinó també d'inhibir l'activació constitutiva del receptor. Amb una combinació d'assajos computacionals, biofísics i basats en cèl·lules, vam poder identificar les primeres molècules petites per a RANK que es podrien utilitzar com a tractament per al càncer de mama triple negatiu. Quan es treballa amb proteïnes complexes, o amb mecanismes d'acció no estàndard, la relació entre la unió i la resposta biològica és impredictible, perquè la resposta biològica (si n'hi ha) dependrà de la funció biològica del lloc al·lostèric particular, que generalment és desconeguda. Per aquest motiu, després vam provar l'aplicabilitat de la combinació de CV d'alt rendiment amb assaig de contingut alt de baix rendiment. Això ens va permetre caracteritzar un nou lloc d’unió al·lostèric en PTEN i també descriure els primers moduladors al·lostèrics d'aquesta proteïna. Finalment, a mesura que l'espai químic accessible creix a un ritme ràpid, hem desenvolupat un algorisme per explorar de manera eficient col·leccions de productes químics molt grans mitjançant un enfocament de baix a dalt. Vam validar aquest enfocament amb BRD4 i vam identificar nous inhibidors de BRD4 amb una afinitat comparable als candidats a fàrmacs més avançats per a aquesta proteïna