Selective glucocorticoid receptor properties of GSK866 analogs with cysteine reactive warheads

Synthetic glucocorticoids (GC) are the mainstay therapy for treatment of acute and chronic inflammatory disorders. Due to the high adverse effects associated with long-term use, GC pharmacology has focused since the nineties on more selective GC ligand-binding strategies, classified as selective glucocorticoid receptor (GR) agonists (SEGRAs) or selective glucocorticoid receptor modulators (SEGRMs). In the current study, GSK866 analogs with electrophilic covalent-binding warheads were developed with potential SEGRA properties to improve their clinical safety profile for long-lasting topical skin disease applications. Since the off-rate of a covalently binding drug is negligible compared to that of a non-covalent drug, its therapeutic effects can be prolonged and typically, smaller doses of the drug are necessary to reach the same level of therapeutic efficacy, thereby potentially reducing systemic side effects. Different analogs of SEGRA GSK866 coupled to cysteine reactive warheads were characterized for GR potency and selectivity in various biochemical and cellular assays. GR-and NF kappa B dependent reporter gene studies show favorable anti-inflammatory properties with reduced GR transactivation of two non-steroidal GSK866 analogs UAMC-1217 and UAMC-1218, whereas UAMC-1158 and UAMC-1159 compounds failed to modulate cellular GR activity. These results were further supported by GR immuno-localization and S211 phospho-GR western analysis, illustrating significant GR phosphoactivation and nuclear translocation upon treatment of GSK866, UAMC-1217, or UAMC-1218, but not in case of UAMC-1158 or UAMC-1159. Furthermore, mass spectrometry analysis of tryptic peptides of recombinant GR ligand-binding domain (LBD) bound to UAMC-1217 or UAMC-1218 confirmed covalent cysteine-dependent GR binding. Finally, molecular dynamics simulations, as well as glucocorticoid receptor ligand-binding domain (GR-LBD) coregulator interaction profiling of the GR-LBD bound to GSK866 or its covalently binding analogs UAMC-1217 or UAMC-1218 revealed subtle conformational differences that might underlie their SEGRA properties. Altogether, GSK866 analogs UAMC-1217 and UAMC-1218 hold promise as a novel class of covalent-binding SEGRA ligands for the treatment of topical inflammatory skin disorders

A Computational Simulation Study of Benzamidine Derivatives Binding to Arginine-Specific Gingipain (HRgpA) from Periodontopathogen Porphyromonas gingivalis

We have shown that the binding free energy calculation from molecular dynamics can be adapted successfully to cysteine proteinases, such as arginine-specific gingipain (HRgpA) from Porphyromonas gingivalis. The binding free energy obtained is in good agreement with the available experimental data for eight benzamidine derivatives including urea and ether linker. The calculations showed that the electrostatic energies between HRgpA and inhibitors were important in determining the relative affinities of the inhibitors to the HRgpA, with an average binding free energy of about −5 kcal/mol. The average structures of the eight complexes suggest that benzamidine inhibitors interact with Asp387, His435, and Cys468 by hydrogen bonding and with Trp508 by hydrophilic interactions that are essential for the activities of benzamidine inhibitors. It can therefore be expected that the method provides a reliable tool for the investigation of new HRgpA inhibitors. This finding could significantly benefit the future design of HRgpA inhibitors

Enumeration, conformation sampling and population of libraries of peptide macrocycles for the search of chemotherapeutic cardioprotection agents

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

Exploring the Role of Molecular Dynamics Simulations in Most Recent Cancer Research: Insights into Treatment Strategies

Cancer is a complex disease that is characterized by uncontrolled growth and division of cells. It involves a complex interplay between genetic and environmental factors that lead to the initiation and progression of tumors. Recent advances in molecular dynamics simulations have revolutionized our understanding of the molecular mechanisms underlying cancer initiation and progression. Molecular dynamics simulations enable researchers to study the behavior of biomolecules at an atomic level, providing insights into the dynamics and interactions of proteins, nucleic acids, and other molecules involved in cancer development. In this review paper, we provide an overview of the latest advances in molecular dynamics simulations of cancer cells. We will discuss the principles of molecular dynamics simulations and their applications in cancer research. We also explore the role of molecular dynamics simulations in understanding the interactions between cancer cells and their microenvironment, including signaling pathways, proteinprotein interactions, and other molecular processes involved in tumor initiation and progression. In addition, we highlight the current challenges and opportunities in this field and discuss the potential for developing more accurate and personalized simulations. Overall, this review paper aims to provide a comprehensive overview of the current state of molecular dynamics simulations in cancer research, with a focus on the molecular mechanisms underlying cancer initiation and progression.Comment: 49 pages, 2 figure

Discovery of a new generation of angiotensin receptor blocking drugs:Receptor mechanisms and in silico binding to enzymes relevant to SARS-CoV-2

The discovery and facile synthesis of a new class of sartan-like arterial antihypertensive drugs (angiotensin receptor blockers [ARBs]), subsequently referred to as “bisartans” is reported. In vivo results and complementary molecular modelling presented in this communication indicate bisartans may be beneficial for the treatment of not only heart disease, diabetes, renal dysfunction, and related illnesses, but possibly COVID-19. Bisartans are novel bis-alkylated imidazole sartan derivatives bearing dual symmetric anionic biphenyl tetrazole moieties. In silico docking and molecular dynamics studies revealed bisartans exhibited higher binding affinities for the ACE2/spike protein complex (PDB 6LZG) compared to all other known sartans. They also underwent stable docking to the Zn2+ domain of the ACE2 catalytic site as well as the critical interfacial region between ACE2 and the SARS-CoV-2 receptor binding domain. Additionally, semi-stable docking of bisartans at the arginine-rich furin-cleavage site of the SARS-CoV-2 spike protein (residues 681–686) required for virus entry into host cells, suggest bisartans may inhibit furin action thereby retarding viral entry into host cells. Bisartan tetrazole groups surpass nitrile, the pharmacophoric “warhead” of PF-07321332, in its ability to disrupt the cysteine charge relay system of 3CLpro. However, despite the apparent targeting of multifunctional sites, bisartans do not inhibit SARS-CoV-2 infection in bioassays as effectively as PF-07321332 (Paxlovid)

Design and synthesis of engineered peptides to target undruggable PPIs: from in silico to in vitro studies

[eng] Major unsolved diseases such as Cancer, cardiopathies or neurodegenerative disorders are frequently related with the malunction of complex protein networks. These networks are integrated by the interaction of multiple proteins that in case of a misregulation can trigger an undesired effect. Therefore, disruption of protein-protein interactions (PPIs), that are involucrated in a protein signalling cascade which is relevant for a particular diseases, is a hot topic in pharmaceutical industry. Unfortunately, traditional small molecules have been found to not be the most suitable inhibitiors of these therapeutic targets as PPI interfaces are characterized by a flat area with a lack of cavities that can fit a small molecule. In this scenario, peptides has called the attention in the drug discovery field for be a more appropiate candidates to target PPIs. Peptides are found in the chemical space between small molecules and antibodies, usullay contain between 2-50 amino acids and have an approximated weight of 250-10.000 Da. Then, their medium size allows a efficient recognition of the target protein without the need of well formed cavieties on the protein surface. However, peptides are characterized by a poor permeability and low stability in blood stream which had limited their therapeutic application in the past. Opportunately, introduction of non-natural amino acids and D-amino acids, N-alkylations of peptide backbone, cyclization and N-terminal and C-terminal modified cappings improve the biophysical properties along with the affinity for the receptor protein. Then, the use of engineered peptides, so-called peptidomimetics, is a promissing approach to target PPIs that can improve the binding potency of natural peptides and overcoming their major drawbacks at the same time. This thesis was carried out at Iproteos, a biotech company positioned in the use of peptidomimetics to target intracellular PPIs. The company has developed an in-house technology coined IPROTech, which is a platform that applys different in silico tools that are focused in the design of peptidomimetics, which are synthesized manually, quantified and finally evaluated in vitro. The experimental results are reintroduced in the platform and the process is repeated iteratively until achieve a final lead candidate. Hence, in the present work, IPROTech was applied to found de novo peptidomimetic molecules that inhibit the interaction of 4 different PPIs that are considered of therapeutic importance, Talin- Vinculin (Cancer), Rad51-BRCA2 (Cancer), Ras-Effectors (Cancer) and Retromer-L2 (HPVs infection). For each PPI, a collabration project with an academic group expert in field was setted up. Iproteos was in charge of the in silico studies of the target protein in order to design and synthesized a set peptidomimetic sequences that were predicted to disrupt the PPI of interest. On the other hand, the collaborators were responsible of the experimental evualtion of the synthesized compounds. In this terms, at least 1 hit was found for each PPI when evaluated in vitro, demonstrating an outsanding overall succes-rate of 31 % when all synthesized peptidomimetics were evaluated in vitro. Additinoally, the inclusion of new fancy builduing blocks into the compounds sintheysis, N-alkylation of the peptidomimetics backbone, pearmeability across biological barriers or the use of cyclodextrins as solvating excipient were other points studied as well.[cat] Trobar nous fàrmacs capaços de trencar interaccions proteïna-proteïna, que d’alguna manera estan involucrades amb una malaltia, és de gran interès en el camp de la indústria farmacèutica. No obstant això, aquest tipus de diana terapèutica normalment no presenten cap cavitat ben definida a la seva superfície, característica necessària per albergar les tradicionals molècules petites. Per aquest motiu, la utilització de pèptids com a possibles fàrmacs és una aproximació molt prometedora perquè en tenir una mida molecular superior poden establir més interaccions amb la proteïna receptora afavorint així la seva unió. Però, aquesta aproximació està limitada per les propietats bioquímiques dels pèptids, ja que normalment són poc permeables i amb una baixa estabilitat un cop administrats. Afortunadament, els avanços fets en la síntesi de pèptids ha permès afegir modificacions sobre la seqüència dels pèptids per tal de millora les seves propietats, això inclou amino àcids no naturals, N-alquilacions, diferent tipus de N-terminals i C-terminals, entre altres. Els compostos obtinguts quan s’aplica aquesta enginyeria es coneixen com a peptidomimetics. Aquesta tesi es va realitzar a Iproteos. Iproteos és una petita empresa biotecnològica, focalitzada en l’ús de peptidomimetics per tal d’inhibir IPPs relacionades amb alguna malaltia. Per fer possible aquesta tasca, Iproteos ha creat una tecnologia, IPROTech, que agrupa tècniques computacionals per tal de cribrar la proteïna d’interès i genera estructures peptidometiques que més tard són sintetitzades, purificades i quantificades. Per aquesta tesi, es va aplicar la tecnologia IPROTech per trobar peptidomimetics amb la capacitat d’inhibir quatre IPP de rellevància terapèutica, Talina-Vinculina (càncer), Rad51- BRCA2 (càncer), Ras-Effectors (càncer) i Retromer-L2 (HPVs). Per cadascuna d’aquestes dianes, a Iproteos es va realitzar els estudis in silico i la síntesi dels peptidomimetics mentre que l'avaluació experimental la van fer grups acadèmics experts en cada camp. En tots els casos es va trobar almenys un compost capaç de trencar amb la interacció. Addicionalment propietats com la solubilitat, permeabilitat o estabilitat van ser avaluades per aquells compostos actius. Finalment, gràcies a les dades generades, alguns d’aquests compostos es van poder optimitzar obtenint un candidat final més potent encara

Current and emerging opportunities for molecular simulations in structure-based drug design

An overview of the current capabilities and limitations of molecular simulation of biomolecular complexes in the context of computer-aided drug design is provided. Steady improvements in computer hardware coupled with more refined representations of energetics are leading to a new appreciation of the driving forces of molecular recognition. Molecular simulations are poised to more frequently guide the interpretation of biophysical measurements of biomolecular complexes. Ligand design strategies emerge from detailed analyses of computed structural ensembles. The feasibility of routine applications to ligand optimization problems hinges upon successful extensive large scale validation studies and the development of protocols to intelligently automate computations