Proteasome inhibitors with pyrazole scaffolds from structure-based virtual screening

A Shortcut from Genome to Drug: The Employment of Bioinformatic Tools to Find New Targets for Gastric Cancer Treatment

Gastric cancer (GC) is a highly heterogeneous, complex disease and the fifth most common cancer worldwide (about 1 million cases and 784,000 deaths worldwide in 2018). GC has a poor prognosis (the 5-year survival rate is less than 20%), but there is an effort to find genes highly expressed during tumor establishment and use the related proteins as targets to find new anticancer molecules. Data were collected from the Gene Expression Omnibus (GEO) bank to obtain three dataset matrices analyzing gastric tumor tissue versus normal gastric tissue and involving microarray analysis performed using the GPL570 platform and different sources. The data were analyzed using the GEPIA tool for differential expression and KMPlot for survival analysis. For more robustness, GC data from the TCGA database were used to corroborate the analysis of data from GEO. The genes found in in silico analysis in both GEO and TCGA were confirmed in several lines of GC cells by RT-qPCR. The AlphaFold Protein Structure Database was used to find the corresponding proteins. Then, a structure-based virtual screening was performed to find molecules, and docking analysis was performed using the DockThor server. Our in silico and RT-qPCR analysis results confirmed the high expression of the AJUBA, CD80 and NOLC1 genes in GC lines. Thus, the corresponding proteins were used in SBVS analysis. There were three molecules, one molecule for each target, MCULE-2386589557-0-6, MCULE-9178344200-0-1 and MCULE-5881513100-0-29. All molecules had favorable pharmacokinetic, pharmacodynamic and toxicological properties. Molecular docking analysis revealed that the molecules interact with proteins in critical sites for their activity. Using a virtual screening approach, a molecular docking study was performed for proteins encoded by genes that play important roles in cellular functions for carcinogenesis. Combining a systematic collection of public microarray data with a comparative meta-profiling, RT-qPCR, SBVS and molecular docking analysis provided a suitable approach for finding genes involved in GC and working with the corresponding proteins to search for new molecules with anticancer properties.

Attempting to Increase the Effectiveness of the Antidepressant Trazodone Hydrochloride Drug Using π-Acceptors

Major depressive disorder is a prevalent mood illness that is mildly heritable. Cases with the highest familial risk had recurrence and onset at a young age. Trazodone hydrochloride is an antidepressant medicine that affects the chemical messengers in the brain known as neurotransmitters, which include acetylcholine, norepinephrine, dopamine, and serotonin. In the present research, in solid and liquid phases, the 1:1 charge-transfer complexes between trazodone hydrochloride (TZD) and six different π-acceptors were synthesized and investigated using different microscopic techniques. The relation of dative ion pairs [TZD+, A-], where A is the acceptor, was inferred via intermolecular charge-transfer complexes. Additionally, a molecular docking examination was utilized to compare the interactions of protein receptors (serotonin-6BQH) with the TZD alone or in combination with the six distinct acceptor charge-transfer complexes. To refine the docking results acquired from AutoDock Vina and to better examine the molecular mechanisms of receptor-ligand interactions, a 100 ns run of molecular dynamics simulation was used. All the results obtained in this study prove that the 2,6-dichloroquinone-4-chloroimide (DCQ)/TZD complex interacts with serotonin receptors more efficiently than reactant donor TZD only and that [(TZD)(DCQ)]-serotonin has the highest binding energy value of all π-acceptor complexes.

Developing New Treatments for COVID-19 through Dual-Action Antiviral/Anti-Inflammatory Small Molecules and Physiologically Based Pharmacokinetic Modeling

Broad-spectrum antiviral agents that are effective against many viruses are difficult to develop, as the key molecules, as well as the biochemical pathways by which they cause infection, differ largely from one virus to another. This was more strongly highlighted by the COVID-19 pandemic, which found health systems all over the world largely unprepared and proved that the existing armamentarium of antiviral agents is not sufficient to address viral threats with pandemic potential. The clinical protocols for the treatment of COVID-19 are currently based on the use of inhibitors of the inflammatory cascade (dexamethasone, baricitinib), or inhibitors of the cytopathic effect of the virus (monoclonal antibodies, molnupiravir or nirmatrelvir/ritonavir), using different agents. There is a critical need for an expanded armamentarium of orally bioavailable small-molecular medicinal agents, including those that possess dual antiviral and anti-inflammatory (AAI) activity that would be readily available for the early treatment of mild to moderate COVID-19 in high-risk patients. A multidisciplinary approach that involves the use of in silico screening tools to identify potential drug targets of an emerging pathogen, as well as in vitro and in vivo models for the determination of a candidate drug’s efficacy and safety, are necessary for the rapid and successful development of antiviral agents with potentially dual AAI activity. Characterization of candidate AAI molecules with physiologically based pharmacokinetics (PBPK) modeling would provide critical data for the accurate dosing of new therapeutic agents against COVID-19. This review analyzes the dual mechanisms of AAI agents with potential anti-SARS-CoV-2 activity and discusses the principles of PBPK modeling as a conceptual guide to develop new pharmacological modalities for the treatment of COVID-19.

Drug Discovery for Mycobacterium tuberculosis Using Structure-Based Computer-Aided Drug Design Approach

Developing new, more effective antibiotics against resistant Mycobacterium tuberculosis that inhibit its essential proteins is an appealing strategy for combating the global tuberculosis (TB) epidemic. Finding a compound that can target a particular cavity in a protein and interrupt its enzymatic activity is the crucial objective of drug design and discovery. Such a compound is then subjected to different tests, including clinical trials, to study its effectiveness against the pathogen in the host. In recent times, new techniques, which involve computational and analytical methods, enhanced the chances of drug development, as opposed to traditional drug design methods, which are laborious and time-consuming. The computational techniques in drug design have been improved with a new generation of software used to develop and optimize active compounds that can be used in future chemotherapeutic development to combat global tuberculosis resistance. This review provides an overview of the evolution of tuberculosis resistance, existing drug management, and the design of new anti-tuberculosis drugs developed based on the contributions of computational techniques. Also, we show an appraisal of available software and databases on computational drug design with an insight into the application of this software and databases in the development of anti-tubercular drugs. The review features a perspective involving machine learning, artificial intelligence, quantum computing, and CRISPR combination with available computational techniques as a prospective pathway to design new anti-tubercular drugs to combat resistant tuberculosis.

Bioactive recombinant human oncostatin M for NMR-based screening in drug discovery

Oncostatin M (OSM) is a pleiotropic, interleukin-6 family inflammatory cytokine that plays an important role in inflammatory diseases, including inflammatory bowel disease, rheumatoid arthritis, and cancer progression and metastasis. Recently, elevated OSM levels have been found in the serum of COVID-19 patients in intensive care units. Multiple anti-OSM therapeutics have been investigated, but to date no OSM small molecule inhibitors are clinically available. To pursue a high-throughput screening and structure-based drug discovery strategy to design a small molecule inhibitor of OSM, milligram quantities of highly pure, bioactive OSM are required. Here, we developed a reliable protocol to produce highly pure unlabeled and isotope enriched OSM from E. coli for biochemical and NMR studies. High yields (ca. 10 mg/L culture) were obtained in rich and minimal defined media cultures. Purified OSM was characterized by mass spectrometry and circular dichroism. The bioactivity was confirmed by induction of OSM/OSM receptor signaling through STAT3 phosphorylation in human breast cancer cells. Optimized buffer conditions yielded ¹H, ¹⁵N HSQC NMR spectra with intense, well-dispersed peaks. Titration of ¹⁵N OSM with a small molecule inhibitor showed chemical shift perturbations for several key residues with a binding affinity of 12.2 ± 3.9 μM. These results demonstrate the value of bioactive recombinant human OSM for NMR-based small molecule screening.

An Updated Review of Computer-Aided Drug Design and Its Application to COVID-19

The recent outbreak of the deadly coronavirus disease 19 (COVID-19) pandemic poses serious health concerns around the world. The lack of approved drugs or vaccines continues to be a challenge and further necessitates the discovery of new therapeutic molecules. Computer-aided drug design has helped to expedite the drug discovery and development process by minimizing the cost and time. In this review article, we highlight two important categories of computer-aided drug design (CADD), viz., the ligand-based as well as structured-based drug discovery. Various molecular modeling techniques involved in structure-based drug design are molecular docking and molecular dynamic simulation, whereas ligand-based drug design includes pharmacophore modeling, quantitative structure-activity relationship (QSARs), and artificial intelligence (AI). We have briefly discussed the significance of computer-aided drug design in the context of COVID-19 and how the researchers continue to rely on these computational techniques in the rapid identification of promising drug candidate molecules against various drug targets implicated in the pathogenesis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The structural elucidation of pharmacological drug targets and the discovery of preclinical drug candidate molecules have accelerated both structure-based as well as ligand-based drug design. This review article will help the clinicians and researchers to exploit the immense potential of computer-aided drug design in designing and identification of drug molecules and thereby helping in the management of fatal disease.

Ubiquitination Regulators Discovered by Virtual Screening for the Treatment of Cancer

The ubiquitin-proteasome system oversees cellular protein degradation in order to regulate various critical processes, such as cell cycle control and DNA repair. Ubiquitination can serve as a marker for mutation, chemical damage, transcriptional or translational errors, and heat-induced denaturation. However, aberrant ubiquitination and degradation of tumor suppressor proteins may result in the growth and metastasis of cancer. Hence, targeting the ubiquitination cascade reaction has become a potential strategy for treating malignant diseases. Meanwhile, computer-aided methods have become widely accepted as fast and efficient techniques for early stage drug discovery. This review summarizes ubiquitination regulators that have been discovered via virtual screening and their applications for cancer treatment.

Discovery of novel tripeptide propylene oxide proteasome inhibitors for the treatment of multiple myeloma

The ubiquitin proteasome pathway (UPP) plays a critical role in the maintenance of cell homeostasis and the development of diseases, such as cancer and neurodegenerative disease. A series of novel tripeptide propylene oxide compounds as proteasome inhibitors were designed, synthesized and biologically investigated in this manuscript. The enzymatic activities of final compounds against 20S human proteasome were investigated and structure-activity relationship (SAR) was summarized. Some potent compounds were further evaluated to inhibit the proliferation of multiple myeloma (MM) cancer cell lines RPMI8226 and U266B. The results showed that some compounds were active against MM cancer cell lines with IC₅₀ values of less than 50 nM. The microsomal metabolic stabilities in human, rat and mice species were carried out and the results showed that compounds 30 and 31 were stable enough to be in vivo investigated. The in vivo pharmacokinetic results showed that compounds 30 and 31 had acceptable biological parameters for both ig and iv administrations. In vivo antitumor activities of compounds 30 and 31 with the doses of 100 mg/kg and 50 mg/kg BIW were performed by using RPMI8226 xenograft nude mouse model. Toxicities of compounds 30 and 31 were not observed during the experiment and dose dependent effect was obvious and the tumor volume was greatly inhibited.

Novel Compounds Identified by Structure-Based Prion Disease Drug Discovery Using In Silico Screening Delay the Progression of an Illness in Prion-Infected Mice

The accumulation of abnormal prion protein (PrPSc) produced by the structure conversion of PrP (PrPC) in the brain induces prion disease. Although the conversion process of the protein is still not fully elucidated, it has been known that the intramolecular chemical bridging in the most fragile pocket of PrP, known as the "hot spot," stabilizes the structure of PrPC and inhibits the conversion process. Using our original structure-based drug discovery algorithm, we identified the low molecular weight compounds that predicted binding to the hot spot. NPR-130 and NPR-162 strongly bound to recombinant PrP in vitro, and fragment molecular orbital (FMO) analysis indicated that the high affinity of those candidates to the PrP is largely dependent on nonpolar interactions, such as van der Waals interactions. Those NPRs showed not only significant reduction of the PrPSc levels but also remarkable decrease of the number of aggresomes in persistently prion-infected cells. Intriguingly, treatment with those candidate compounds significantly prolonged the survival period of prion-infected mice and suppressed prion disease-specific pathological damage, such as vacuole degeneration, PrPSc accumulation, microgliosis, and astrogliosis in the brain, suggesting their possible clinical use. Our results indicate that in silico drug discovery using NUDE/DEGIMA may be widely useful to identify candidate compounds that effectively stabilize the protein.

A Structure-Based Drug Discovery Paradigm

Structure-based drug design is becoming an essential tool for faster and more cost-efficient lead discovery relative to the traditional method. Genomic, proteomic, and structural studies have provided hundreds of new targets and opportunities for future drug discovery. This situation poses a major problem: the necessity to handle the “big data” generated by combinatorial chemistry. Artificial intelligence (AI) and deep learning play a pivotal role in the analysis and systemization of larger data sets by statistical machine learning methods. Advanced AI-based sophisticated machine learning tools have a significant impact on the drug discovery process including medicinal chemistry. In this review, we focus on the currently available methods and algorithms for structure-based drug design including virtual screening and de novo drug design, with a special emphasis on AI- and deep-learning-based methods used for drug discovery.

Turning to Computer-aided Drug Design in the Treatment of Diffuse Large B-cell Lymphoma: Has it been Helpful?

INTRODUCTION

Amidst the numerous effective therapeutic options available for the treatment of Diffuse Large B-cell Lymphoma (DLBCL), about 30-40% of patients treated with first-line chemoimmunotherapy still experience a relapse or refractory DLBCL. This has necessitated a continuous search for new therapeutic agents to augment the existing therapeutic arsenal.

METHODS

The dawn of Computer-Aided Drug Design (CADD) in the drug discovery process has accounted for persistency in the application of computational approaches either alone or in combinatorial strategies with experimental methods towards the identification of potential hit compounds with high therapeutic efficacy in abrogating DLBCL.

RESULTS

This review showcases the interventions of structure-based and ligand-based computational approaches which have led to the identification of numerous small molecule inhibitors against implicated targets in DLBCL therapy, even though many of these potential inhibitors are piled-up awaiting further experimental validation and exploration.

CONCLUSION

We conclude that a successful and a conscious amalgamation of CADD and experimental approaches could pave the way for the discovery of the next generation potential leads in DLBCL therapy with improved activities and minimal toxicities.

Discovery of a novel dipeptidyl boronic acid proteasome inhibitor for the treatment of multiple myeloma and triple-negative breast cancer

A series of novel dipeptidyl boronic acid compounds were designed, synthesized and biologically investigated for the inhibition of the β5 subunit of 20S proteasome and several compounds showed high activities with IC50 values of less than 10 nM. Some of these compounds potently inhibited the multiple myeloma (MM) cancer cell lines with IC50 values of less than 10 nM. It was reported that the inhibition of both β2 and β5 subunits strongly increased the cytotoxicity of proteasome inhibitors in solid tumor cells, so some of the compounds were evaluated for the inhibition of the β2 subunit and the solid tumor triple-negative breast cancer cell line MDA-MB-231. The results showed that three compounds were active for both the β2 subunit and the triple-negative breast cancer cell line MDA-MB-231. The in vivo pharmacokinetic results showed that compound 8t had good biological parameters for both ig and iv administrations. An in vivo pharmacodynamic experiment showed that compound 8t inhibited the β5 subunit in whole blood more greatly than the marketed MLN9708 with the same dose at different time periods. A pathological analysis indicated that the injection of compound 8t in the tumor of a triple-negative breast cancer xenograft mice model led to tumor cell necrosis, nucleus condensation, deep staining, cell fragmentation, dissolution and neutrophil infiltration compared with the control group. The data in hand showed that compound 8t might be an effective candidate for the treatment of both MM and triple-negative breast cancer.

Design, synthesis, and biological evaluation of novel phenol ether derivatives as non-covalent proteasome inhibitors

A series of novel phenol ether derivatives were designed, synthesized, and evaluated as non-covalent proteasome inhibitors. Most compounds exhibited moderate to excellent proteasome inhibitory activity. In particular, compound 18x proved to be the most potent compound (chymotrypsin-like: IC₅₀ = 49 nM), exhibiting a 2-fold higher potency compared to the reported PI-1840. Besides, compound 18x exhibited excellent metabolic stability and selective anti-proliferative activity against solid cancer cell lines including HepG2 and HGC27, providing incentive for the further development as a potential anticancer agent against solid cancers.

Pharmacophore-based virtual screening for identifying β5 subunit inhibitor of 20S proteasome

Proteasomal system plays an important role in maintaining cell homeostatis. Overexpression of proteasomes leads to several major diseases, such as cancer and autoimmune disorder. The β5 subunit of proteasome is a crucial active site in proteolysis, and targeting proteasome β5 subunit is essential for proteasome inhibition. In the present study, a pharmacophore-based virtual screening and molecular docking were employed to identify ligands as inhibitors of β5 subunit of proteasome. The pharmacophore features were built with one hydrogen bond donor, two hydrogen bond acceptors, and one hydrophobic feature using native ligand of proteasome (HU10), which was then used to screen ZINC database using ZINCPharmer. The retrieved virtual hits were subjected to molecular docking analysis using iDock. The best six hits were subjected to molecular dynamics (MD) simulation and each complex was stable during 40 ns MD simulation as indicated by root-mean-square-deviation (RMSD) and root-mean-square-fluctuation (RMSF) values. The current study identifies 5 best hits having better binding potentials than HU10 as predicted by molecular mechanics Poisson-Boltzmann Surface Area (MM-PBSA) method, i.e. Lig1540/ZINC33356240, Lig1546/ZINC33356235, Lig1522/ZINC20854878, Lig980/ZINC12391945, and Lig1119/ZINC19865241, which can be used in the development of new proteasome inhibitors.

Next-generation proteasome inhibitors for cancer therapy

Over 2 decades ago, the proteasome was considered a risky or even untenable therapeutic target. Today, proteasome inhibitors are a mainstay in the treatment of multiple myeloma (MM) and have sales in excess of 3 billion US dollars annually. More importantly, the availability of proteasome inhibitors has greatly improved the survival and quality of life for patients with MM. Despite the remarkable success of proteasome inhibitor therapies to date, the potential for improvement remains, and the development and optimal use of proteasome inhibitors as anticancer agents continues to be an active area of research. In this review, we briefly discuss the features and limitations of the 3 proteasome inhibitor drugs currently used in the clinic and provide an update on current efforts to develop next-generation proteasome inhibitors with the potential to overcome the limitations of existing proteasome inhibitor drugs.

Design, synthesis, in vitro and in vivo evaluation, and structure-activity relationship (SAR) discussion of novel dipeptidyl boronic acid proteasome inhibitors as orally available anti-cancer agents for the treatment of multiple myeloma and mechanism studies

A series of novel dipeptidyl boronic acid inhibitors of 20S proteasome were designed and synthesized. Aliphatic groups at R¹ position were designed for the first time to fully understand the SAR (structure-activity relationship). Among the screened compounds, novel inhibitor 5c inhibited the CT-L (chymotrypsin-like) activity with IC₅₀ of 8.21 nM and the MM (multiple myeloma) cells RPMI8226, U266B and ARH77 proliferations with the IC₅₀ of 8.99, 6.75 and 9.10 nM, respectively, which showed similar in vitro activities compared with the compound MLN2238 (biologically active form of marketed MLN9708). To investigate the oral availability, compound 5c was esterified to its prodrug 6a with the enzymatic IC₅₀ of 6.74 nM and RPMI8226, U266B and ARH77 cell proliferations IC₅₀ of 2.59, 4.32 and 3.68 nM, respectively. Furthermore, prodrug 6a exhibited good pharmacokinetic properties with oral bioavailability of 24.9%, similar with MLN9708 (27.8%). Moreover, compound 6a showed good microsomal stabilities and displayed stronger in vivo anticancer efficacy than MLN9708 in the human ARH77 xenograft mouse model. Finally, cell cycle results showed that compound 6a had a significant inhibitory effect on CT-L and inhibited cell cycle progression at the G2M stage.

Blockade of the malignant phenotype by β-subunit selective noncovalent inhibition of immuno- and constitutive proteasomes

A structure-based virtual screening of over 400,000 small molecules against the constitutive proteasome activity followed by in vitro assays led to the discovery of a family of proteasome inhibitors with a sulfonyl piperazine scaffold. Some members of this family of small non-peptidic inhibitors were found to act selectively on the β2 trypsin-like catalytic site with a preference for the immunoproteasome β2i over the constitutive proteasome β2c, while some act on the β5 site and post-acid site β1 of both, the immunoproteasome and the constitutive proteasome. Anti-proliferative and anti-invasive effects on tumor cells were investigated and observed for two compounds. We report novel chemical inhibitors able to interfere with the three types of active centers of both, the immuno- and constitutive proteasomes. Identifying and analyzing a novel scaffold with decorations able to shift the binders’ active site selectivity is essential to design a future generation of proteasome inhibitors able to distinguish the immunoproteasome from the constitutive proteasome.

Structure-based design of human immuno- and constitutive proteasomes inhibitors

Starting from the X-ray structure of our previous tripeptidic linear mimics of TMC-95A in complex with yeast 20S proteasome, we introduced new structural features to induce a differential inhibition between human constitutive and immunoproteasome 20S particles. Libraries of 24 tripeptidic and 6 dipeptidic derivatives were synthesized. The optimized preparation of 3-hydroxyoxindolyl alanine residues from tryptophan and their incorporation in peptides were described. Several potent inhibitors of human constitutive proteasome and immunoproteasome acting at the nanomolar level (IC₅₀ = 7.1 nM against the chymotrypsin-like activity for the best inhibitor) were obtained. A cytotoxic effect at the submicromolar level was observed against 6 human cancer cell lines.

Design, synthesis and biological evaluation of novel non-peptide boronic acid derivatives as proteasome inhibitors

A novel series of non-peptide proteasome inhibitors bearing the 1, 4-naphthoquinone scaffold and boronic acid warhead was developed. In the biological evaluation on the chymotrypsin-like activity of human 20S proteasome, five compounds showed IC₅₀ values in the nanomolar range. Docking experiments into the yeast 20S proteasome rationalized their biological activities and allowed further optimization of this interesting class of inhibitors. Within the cellular proliferation inhibition assay and western blot analysis, compound 3e demonstrated excellent anti-proliferative activity against solid tumor cells and clear accumulation of ubiquitinated cellular proteins. Furthermore, in the microsomal stability assay compound 3e demonstrated much improved metabolic stability compared to bortezomib, emerging as a promising lead compound for further design of non-peptide proteasome inhibitors.

Computational Approaches for the Discovery of Human Proteasome Inhibitors: An Overview

Proteasome emerged as an important target in recent pharmacological research due to its pivotal role in degrading proteins in the cytoplasm and nucleus of eukaryotic cells, regulating a wide variety of cellular pathways, including cell growth and proliferation, apoptosis, DNA repair, transcription, immune response, and signaling processes. The last two decades witnessed intensive efforts to discover 20S proteasome inhibitors with significant chemical diversity and efficacy. To date, the US FDA approved to market three proteasome inhibitors: bortezomib, carfilzomib, and ixazomib. However new, safer and more efficient drugs are still required. Computer-aided drug discovery has long being used in drug discovery campaigns targeting the human proteasome. The aim of this review is to illustrate selected in silico methods like homology modeling, molecular docking, pharmacophore modeling, virtual screening, and combined methods that have been used in proteasome inhibitors discovery. Applications of these methods to proteasome inhibitors discovery will also be presented and discussed to raise improvements in this particular field.

3D-QSAR-aided design, synthesis, in vitro and in vivo evaluation of dipeptidyl boronic acid proteasome inhibitors and mechanism studies

Proteasome had been clinically validated as an effective target for the treatment of cancers. Up to now, many structurally diverse proteasome inhibitors were discovered. And two of them were launched to treat multiple myeloma (MM) and mantle cell lymphoma (MCL). Based on our previous biological results of dipeptidyl boronic acid proteasome inhibitors, robust 3D-QSAR models were developed and structure-activity relationship (SAR) was summarized. Several structurally novel compounds were designed based on the theoretical models and finally synthesized. Biological results showed that compound 12e was as active as the standard bortezomib in enzymatic and cellular activities. In vivo pharmacokinetic profiles suggested compound 12e showed a long half-life, which indicated that it could be administered intravenously. Cell cycle analysis indicated that compound 12e inhibited cell cycle progression at the G2M stage.

Development of novel proteasome inhibitors based on phthalazinone scaffold

In this study we designed a series of proteasome inhibitors using pyridazinone as initial scaffold, and extended the structure with rational design by computer aided drug design (CADD). Two different synthetic routes were explored and the biological evaluation of the phthalazinone derivatives was investigated. Most importantly, electron positive triphenylphosphine group was first introduced in the structure of proteasome inhibitors and potent inhibition was achieved. As 6c was the most potent inhibitor of proteasome, we examined the structure-activity relationship (SAR) of 6c analogs.

Fundamental reaction pathway and free energy profile of proteasome inhibition by syringolin A (SylA)

In this study, molecular dynamics (MD) simulations and first-principles quantum mechanical/molecular mechanical free energy (QM/MM-FE) calculations have been performed to uncover the fundamental reaction pathway of proteasome with a representative inhibitor syringolin A (SylA). The calculated results reveal that the reaction process consists of three steps. The first step is a proton transfer process, activating Thr1-O(γ) directly by Thr1-N(z) to form a zwitterionic intermediate. The next step is a nucleophilic attack on the olefin carbon of SylA by the negatively charged Thr1-O(γ) atom. The last step is a proton transfer from Thr1-N(z) to another olefin carbon of SylA to complete the inhibition reaction process. The calculated free energy profile demonstrates that the second step should be the rate-determining step and has the highest free energy barrier of 24.6 kcal mol(-1), which is reasonably close to the activation free energy (∼22.4-23.0 kcal mol(-1)) derived from the available experimental kinetic data. In addition, our computational results indicate that no water molecule can assist the rate-determining step, since the second step is not involved in a proton transfer process. The obtained mechanistic insights should be valuable for understanding the inhibition process of proteasome by SylA and structurally related inhibitors at a molecular level, and thus provide a solid mechanistic base and valuable clues for future rational design of novel, more potent inhibitors of proteasome.

Synthesis of Pyrano[3,2-c]pyrazol-7(1H)-one Derivatives by Tandem Cyclization of 2-Diazo-3,5-dioxo-6-ynoates (Ynones)

The construct of the core of pyrano[3,2-c]pyrazol-7(1H)-one derivatives is realized. The key step includes a tandem cyclization, namely, a metal-free cascade 6-π electrocyclic ring closure-Michael reaction of 2-diazo-3,5-dioxo-6-ynoates (ynones). The cascade reaction cleanly generated the desired products in excellent yields under mild conditions.

Molecular docking and structure-based drug design strategies

Pharmaceutical research has successfully incorporated a wealth of molecular modeling methods, within a variety of drug discovery programs, to study complex biological and chemical systems. The integration of computational and experimental strategies has been of great value in the identification and development of novel promising compounds. Broadly used in modern drug design, molecular docking methods explore the ligand conformations adopted within the binding sites of macromolecular targets. This approach also estimates the ligand-receptor binding free energy by evaluating critical phenomena involved in the intermolecular recognition process. Today, as a variety of docking algorithms are available, an understanding of the advantages and limitations of each method is of fundamental importance in the development of effective strategies and the generation of relevant results. The purpose of this review is to examine current molecular docking strategies used in drug discovery and medicinal chemistry, exploring the advances in the field and the role played by the integration of structure- and ligand-based methods.