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Background: Many stresses trigger cellular senescence, including telomere shortening, oncogene activation, DNA-replication stress, oxidative stress, and resistance to apoptosis. Forkhead box protein O4 (FOXO4) plays a pivotal role in cellular senescence, by binding to and inactivating p53, thereby leading to cellular senescence and inhibiting p53 mediated apoptosis. As a result, misregulation of FOXO4-p53 is linked to numerous diseases including cancers and aging-related diseases, pulmonary fibrosis and neurodegenerative diseases. Targeting of the FOXO4-p53 interface has been accomplished using a synthetic D retro inverse peptide (DRI), however recognition of the key molecular drivers of the interactions remains unknown. Understanding these interactions on a molecular level could lead to the development of small compounds, with enhanced pharmacological qualities, such as high stability, membrane permeability, and oral bioavailability, that mimics the activity of the DRI peptide. Methods and Results: Using an in silico model of the FOXO4-p53 interaction, we designed shorter peptides within the DRI to identify the key interactions. A biophysical assay demonstrates these shorter peptides retain strong binding affinity to p53, suggesting that the DRI's hot spot is located within these peptides. Conclusions: Taken together, this data provides information at the molecular level that is key to understanding the interplay of two proteins responsible for cellular senescence and provides the basis for the design of small molecules to inhibit this interaction.

The manuscript discusses recent advances on computer-aided drug discovery (CADD) with focus on data-dependent drug discovery. Herein, we do not intend to review the many CADD methodologies comprehensively. Instead, the review discusses progress on selected concepts, methodologies, resources, and applications that are part of multidisciplinary efforts: the manuscript covers advances in artificial intelligence, machine learning, virtual screening, and chemical space including the concept of chemical multiverses, and novel extended similarity methods for chemical space exploration. Throughout the review, we emphasize public resources and open-source code available to the scientific community working in academia and non-profit institutions.

Macrocycles target proteins that are otherwise considered undruggable because of a lack of hydrophobic cavities and the presence of extended featureless surfaces. Increasing efforts by computational chemists have developed effective software to overcome the restrictions of torsional and conformational freedom that arise as a consequence of macrocyclization. Moloc is an efficient algorithm, with an emphasis on high interactivity, and has been constantly updated since 1986 by drug designers and crystallographers of the Roche biostructural community. In this work, we have benchmarked the shape-guided algorithm using a dataset of 208 macrocycles, carefully selected on the basis of structural complexity. We have quantified the accuracy, diversity, speed, exhaustiveness, and sampling efficiency in an automated fashion and we compared them with four commercial (Prime, MacroModel, molecular operating environment, and molecular dynamics) and four open-access (experimental-torsion distance geometry with additional "basic knowledge" alone and with Merck molecular force field minimization or universal force field minimization, Cambridge Crystallographic Data Centre conformer generator, and conformator) packages. With three-quarters of the database processed below the threshold of high ring accuracy, Moloc was identified as having the highest sampling efficiency and exhaustiveness without producing thousands of conformations, random ring splitting into two half-loops, and possibility to interactively produce globular or flat conformations with diversity similar to Prime, MacroModel, and molecular dynamics. The algorithm and the Python scripts for full automatization of these parameters are freely available for academic use.

Introduction Cancer stem cells (CSCs) drive the initiation, maintenance, and therapy response of breast tumors. CD49f is expressed in breast CSCs and functions in the maintenance of stemness. Thus, blockade of CD49f is a potential therapeutic approach for targeting breast CSCs. In the present study, we aimed to repurpose drugs as CD49f antagonists. Materials and methods We performed consensus molecular docking using a subdomain of CD49f that is critical for heterodimerization and a collection of pharmochemicals clinically tested. Molecular dynamics simulations were employed to further characterize drug-target binding. Using MDA-MB-231 cells, we evaluated the effects of potential CD49f antagonists on 1) cell adhesion to laminin; 2) mammosphere formation; and 3) cell viability. We analyzed the effects of the drug with better CSC-selectivity on the activation of CD49f-downstream signaling by Western blot (WB) and co-immunoprecipitation. Expressions of the stem cell markers CD44 and SOX2 were analyzed by flow cytometry and WB, respectively. Transactivation of SOX2 promoter was evaluated by luciferase reporter assays. Changes in the number of CSCs were assessed by limiting-dilution xenotransplantation. Results Pranlukast, a drug used to treat asthma, bound to CD49f in silico and inhibited the adhesion of CD49f+ MDA-MB-231 cells to laminin, indicating that it antagonizes CD49f-containing integrins. Molecular dynamics analysis showed that pranlukast binding induces conformational changes in CD49f that affect its interaction with β1-integrin subunit and constrained the conformational dynamics of the heterodimer. Pranlukast decreased the clonogenicity of breast cancer cells on mammosphere formation assay but had no impact on the viability of bulk tumor cells. Brief exposure of MDA-MB-231 cells to pranlukast altered CD49f-dependent signaling, reducing focal adhesion kinase (FAK) and phosphatidylinositol 3-kinase (PI3K) activation. Further, pranlukast-treated cells showed decreased CD44 and SOX2 expression, SOX2 promoter transactivation, and in vivo tumorigenicity, supporting that this drug reduces the frequency of CSC. Conclusion Our results support the function of pranlukast as a CD49f antagonist that reduces the CSC population in triple-negative breast cancer cells. The pharmacokinetics and toxicology of this drug have already been established, rendering a potential adjuvant therapy for breast cancer patients.

The SARS-CoV-2 viral spike protein S receptor-binding domain (S-RBD) binds ACE2 on host cells to initiate molecular events, resulting in intracellular release of the viral genome. Therefore, antagonists of this interaction could allow a modality for therapeutic intervention. Peptides can inhibit the S-RBD:ACE2 interaction by interacting with the protein-protein interface. In this study, protein contact atlas data and molecular dynamics simulations were used to locate interaction hotspots on the secondary structure elements α1, α2, α3, β3, and β4 of ACE2. We designed a library of discontinuous peptides based upon a combination of the hotspot interactions, which were synthesized and screened in a bioluminescence-based assay. The peptides demonstrated high efficacy in antagonizing the SARS-CoV-2 S-RBD:ACE2 interaction and were validated by microscale thermophoresis which demonstrated strong binding affinity (∼10 nM) of these peptides to S-RBD. We anticipate that such discontinuous peptides may hold the potential for an efficient therapeutic treatment for COVID-19.

CD44 promotes metastasis, chemoresistance, and stemness in different types of cancer and is a target for the development of new anti-cancer therapies. All CD44 isoforms share a common N-terminal domain that binds to hyaluronic acid (HA). Herein, we used a computational approach to design new potential CD44 antagonists and evaluate their target-binding ability. By analyzing 30 crystal structures of the HA-binding domain (CD44HAbd), we characterized a subdomain that binds to 1,2,3,4-tetrahydroisoquinoline (THQ)-containing compounds and is adjacent to residues essential for HA interaction. By computational combinatorial chemistry (CCC), we designed 168,190 molecules and compared their conformers to a pharmacophore containing the key features of the crystallographic THQ binding mode. Approximately 0.01% of the compounds matched the pharmacophore and were analyzed by computational docking and molecular dynamics (MD). We identified two compounds, Can125 and Can159, that bound to human CD44HAbd (hCD44HAbd) in explicit-solvent MD simulations and therefore may elicit CD44 blockage. These compounds can be easily synthesized by multicomponent reactions for activity testing and their binding mode, reported here, could be helpful in the design of more potent CD44 antagonists.

Background: Human cancer cell lines are valuable models for anti-cancer drug development. Although all cancer cells share common biological features, each cancer cell line has unique genotypic/ phenotypic characteristics that affect drug response. Thus, the information obtained with a specific cancer cell line cannot be easily extrapolated to other cancer cells. Consequently, cell line selection during experimental design is critical for providing proper and clinically relevant structure-activity analysis. Methods: Herein, we critically review the use of cancer cell lines as tools for activity analysis by comparing two different scenarios: i) the use of multiple cancer cell lines, with the NCI-60 Program as the most representative example; and, ii) the selection of a single cell line with specific biological characteristics that match the rationale of compound design. Results: Considering that most laboratories evaluate the activity of new compounds using few cell lines, we provide a systematic strategy for selection based on the expression levels and genetic status of the target and the effectiveness of target inhibition or silencing. We exemplify the use of public databases for data retrieval and analysis as well as the critical comparison of such information with published results. Conclusion: This approach refines cell line selection, avoiding the perpetuation of published poor selection and enhancing the relevance of the results.

Bile acids (BAs) facilitate intestinal fat absorption and act as important signaling molecules in host-gut microbiota crosstalk. BA-metabolizing pathways in the microbial community have been identified, but it remains largely unknown how the highly variable genomes of gut bacteria interact with host BA metabolism. We characterized 8,282 structural variants (SVs) of 55 bacterial species in the gut microbiomes of 1,437 individuals from two cohorts and performed a systematic association study with 39 plasma BA parameters. Both variations in SV-based continuous genetic makeup and discrete clusters showed correlations with BA metabolism. Metagenome-wide association analysis identified 809 replicable associations between bacterial SVs and BAs and SV regulators that mediate the effects of lifestyle factors on BA metabolism. This is the largest microbial genetic association analysis to demonstrate the impact of bacterial SVs on human BA composition, and it highlights the potential of targeting gut microbiota to regulate BA metabolism through lifestyle intervention.

Physicochemical property switching of chemical space is of great importance for optimization of compounds, for example, for biological activity. Cyclization is a key method to control 3D and other properties. A two-step approach, which involves a multicomponent reaction followed by cyclization, is reported to achieve the transition from basic moieties to charge neutral cyclic derivatives. A series of multisubstituted oxazolidinones, oxazinanones, and oxazepanones as well as their thio and sulfur derivatives are synthesized from readily available building blocks with mild conditions and high yields. Like a few other methods, MCR and cyclization allow for the collective transformation of a large chemical space into a related one with different properties.

RSC medicinal chemistry 12(3), 370 - 379 (2020). doi:10.1039/D0MD00367K, The rapid growth of COVID-19 cases is causing an increasing death toll and also paralyzing the world economy. De novo drug discovery takes years to move from idea and/or pre-clinic to market, and it is not a short-term solution for the current SARS-CoV-2 pandemic. Drug repurposing is perhaps the only short-term solution, while vaccination is a middle-term solution. Here, we describe the discovery path of the HCV NS3-4A protease inhibitors boceprevir and telaprevir as SARS-CoV-2 main protease (3CLpro) inhibitors. Based on our hypothesis that α-ketoamide drugs can covalently bind to the active site cysteine of the SARS-CoV-2 3CLpro, we performed docking studies, enzyme inhibition and co-crystal structure analyses and finally established that boceprevir, but not telaprevir, inhibits replication of SARS-CoV-2 and mouse hepatitis virus (MHV), another coronavirus, in cell culture. Based on our studies, the HCV drug boceprevir deserves further attention as a repurposed drug for COVID-19 and potentially other coronaviral infections as well., Published by Royal Society of Chemistry, Cambridge

Macrocycles target proteins that are otherwise considered undruggable because of a lack of hydrophobic cavities and the presence of extended featureless surfaces. Increasing efforts by computational chemists have developed effective software to overcome the restrictions of torsional and conformational freedom that arise as a consequence of macrocyclization. Moloc is an efficient algorithm, with an emphasis on high interactivity, and has been constantly updated since 1986 by drug designers and crystallographers of the Roche biostructural community. In this work, we have benchmarked the shape-guided algorithm using a dataset of 208 macrocycles, carefully selected on the basis of structural complexity. We have quantified the accuracy, diversity, speed, exhaustiveness, and sampling efficiency in an automated fashion and we compared them with four commercial (Prime, MacroModel, molecular operating environment, and molecular dynamics) and four open-access (experimental-torsion distance geometry with additional "basic knowledge" alone and with Merck molecular force field minimization or universal force field minimization, Cambridge Crystallographic Data Centre conformer generator, and conformator) packages. With three-quarters of the database processed below the threshold of high ring accuracy, Moloc was identified as having the highest sampling efficiency and exhaustiveness without producing thousands of conformations, random ring splitting into two half-loops, and possibility to interactively produce globular or flat conformations with diversity similar to Prime, MacroModel, and molecular dynamics. The algorithm and the Python scripts for full automatization of these parameters are freely available for academic use.

Breast cancer is the second cause of cancer death in women and the large majority of those deaths are due to drug resistance and/or recurrence. The cancer stem cell (CSC) model suggests that tumor stem-like cells play key roles in resistance and recurrence in breast cancer patients. Thus, CSCs have been pointed as targets for new anti-cancer therapies. CD49f is an integrin subunit that participates in the CSC-niche interaction. Knock-down of CD49f in breast cancer cells impairs mammosphere formation in vitro and tumorigenesis in vivo, suggesting that this protein is needed for maintenance of stemness. Aiming to target breast CSC, we used consensus docking to select potential CD49f antagonists from a collection of 13,000+ drugs with previous clinical evaluations. Seven compounds with the lowest consensus Z score were selected for in vitro biological validation. Cell adhesion assays showed that four of the selected drugs (5193, 1382, 7631, and 12723) decrease the binding of CD49f+ MDA-MB-231 breast cancer cells to laminin, suggesting that those compounds antagonize the receptor. Mammosphere formation assays showed that compounds 5193, 1382, and 12723 limit the clonogenic capability of CD49f+ breast cancer cells in vitro. Compound 5193 was highly toxic in 2D cell viability assays. On the other hand, the clonogenic impairment produced by compounds 1382 and 12723 is independent of bulk cell line cytotoxicity, suggesting a direct impact on the CSC pool. The analyses of the effect of these drugs on the expression of breast CSC markers and CD49f-activated pathways are on their way. Thus, we have identified some drugs that might be repurposed to target breast CSC. Since the pharmacokinetics and toxicology of those drugs is known, they could easily be ready for clinical trials. We demonstrated that in silico screening is useful to identify antagonists of receptors with relevant roles in breast CSC biology. This work was supported by CONACYT 221105, PAPIIT-UNAM IN228616, and Red temática de células troncales y medicina regenerativa. Citation Format: Velasco-Velázquez M, Velázquez-Quesada I, Aguirre-Alvarado C, Guerrero-Rodríguez S, Ruiz-Moreno A, Ramirez-Salinas G, Segura-Cabrera A, Pérez-Tapia M. Selection of presumed CD49f antagonists and their biological evaluation in breast cancer cells [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr P3-06-09.

CD44 is a receptor for hyaluronan (HA) that promotes epithelial-to-mesenchymal transition (EMT), induces cancer stem cell (CSC) expansion, and favors metastasis. Thus, CD44 is a target for the development of antineoplastic agents. In order to repurpose drugs as CD44 antagonists, we performed consensus-docking studies using the HA-binding domain of CD44 and 11,421 molecules. Drugs that performed best in docking were examined in molecular dynamics simulations, identifying etoposide as a potential CD44 antagonist. Ligand competition and cell adhesion assays in MDA-MB-231 cells demonstrated that etoposide decreased cell binding to HA as effectively as a blocking antibody. Etoposide-treated MDA-MB-231 cells developed an epithelial morphology; increased their expression of E-cadherin; and reduced their levels of EMT-associated genes and cell migration. By gene expression analysis, etoposide reverted an EMT signature similarly to CD44 knockdown, whereas other topoisomerase II (TOP2) inhibitors did not. Moreover, etoposide decreased the proportion of CD44+/CD24− cells, lowered chemoresistance, and blocked mammosphere formation. Our data indicate that etoposide blocks CD44 activation, impairing key cellular functions that drive malignancy, thus rendering it a candidate for further translational studies and a potential lead compound in the development of new CD44 antagonists.