De novo design of drug-binding proteins with predictable binding energy and specificity

The de novo design of small molecule-binding proteins has seen exciting recent progress; however, high-affinity binding and tunable specificity typically require laborious screening and optimization after computational design. We developed a computational procedure to design a protein that recognizes a common pharmacophore in a series of poly(ADP-ribose) polymerase-1 inhibitors. One of three designed proteins bound different inhibitors with affinities ranging from <5 nM to low micromolar. X-ray crystal structures confirmed the accuracy of the designed protein-drug interactions. Molecular dynamics simulations informed the role of water in binding. Binding free energy calculations performed directly on the designed models were in excellent agreement with the experimentally measured affinities. We conclude that de novo design of high-affinity small molecule-binding proteins with tuned interaction energies is feasible entirely from computation.

Porin-independent accumulation in Pseudomonas enables antibiotic discovery

Gram-negative antibiotic development has been hindered by a poor understanding of the types of compounds that can accumulate within these bacteria1,2. The presence of efflux pumps and substrate-specific outer-membrane porins in Pseudomonas aeruginosa renders this pathogen particularly challenging3. As a result, there are few antibiotic options for P. aeruginosa infections4 and its many porins have made the prospect of discovering general accumulation guidelines seem unlikely5. Here we assess the whole-cell accumulation of 345 diverse compounds in P. aeruginosa and Escherichia coli. Although certain positively charged compounds permeate both bacterial species, P. aeruginosa is more restrictive compared to E. coli. Computational analysis identified distinct physicochemical properties of small molecules that specifically correlate with P. aeruginosa accumulation, such as formal charge, positive polar surface area and hydrogen bond donor surface area. Mode of uptake studies revealed that most small molecules permeate P. aeruginosa using a porin-independent pathway, thus enabling discovery of general P. aeruginosa accumulation trends with important implications for future antibiotic development. Retrospective antibiotic examples confirmed these trends and these discoveries were then applied to expand the spectrum of activity of a gram-positive-only antibiotic, fusidic acid, into a version that demonstrates a dramatic improvement in antibacterial activity against P. aeruginosa. We anticipate that these discoveries will facilitate the design and development of high-permeating antipseudomonals.

Omicron: a drug developer's perspective

We performed an annotation of 35 mutations in the spike protein of the SARS-CoV-2 Omicron variant. Our analysis of the mutations indicates that Omicron has gained prominent immune evasion and potential for enhanced transmissibility. Previous modeling study has revealed that continued evolution in both immune evasion and enhanced transmissibility by SARS-CoV-2 would compromise vaccines as tools for the pandemic control. To combat the future variants of SARS-CoV-2, the world needs novel antiviral drugs that are effective at curb viral spreading without introducing additional selective pressure towards resistant variants.

Software-designed miniproteins could create new class of drugs

Small versions of antibodies bind to virtually any target protein.

Design and Activity of Novel Oxadiazole Based Compounds That Target Poly(ADP-ribose) Polymerase

Novel PARP inhibitors with selective mode-of-action have been approved for clinical use. Herein, oxadiazole based ligands that are predicted to target PARP-1 have been synthesized and screened for the loss of cell viability in mammary carcinoma cells, wherein seven compounds were observed to possess significant IC₅₀ values in the range of 1.4 to 25 µM. Furthermore, compound 5u, inhibited the viability of MCF-7 cells with an IC₅₀ value of 1.4µM, when compared to Olaparib (IC₅₀ = 3.2 µM). Compound 5s also decreased cell viability in MCF-7 and MDA-MB-231 cells with IC₅₀ values of 15.3 and 19.2 µM, respectively. Treatment of MCF-7 cells with compounds 5u and 5s produced PARP cleavage, H2AX phosphorylation and CASPASE-3 activation comparable to that observed with Olaparib. Compounds 5u and 5s also decreased foci-formation and 3D Matrigel growth of MCF-7 cells equivalent to or greater than that observed with Olaparib. Finally, in silico analysis demonstrated binding of compound 5s towardsthe catalytic site of PARP-1, indicating that these novel oxadiazoles synthesized herein may serve as exemplars for the development of new therapeutics in cancer.

Aromatic Sulfonamides including a Sulfonic Acid Tail: New Membrane Impermeant Carbonic Anhydrase Inhibitors for Targeting Selectively the Cancer-Associated Isoforms

We report here a new drug design strategy for producing membrane-impermeant carbonic anhydrase (CA; EC 4.2.1.1) inhibitors selectively targeting the tumor-associated, membrane-bound human CAs IX and XII over off-target cytosolic isoforms. To date, this approach has only been pursued by including permanent positively charged pyridinium type or highly hydrophilic glycosidic moieties into the structure of aromatic sulfonamide CA inhibitors (CAIs). Aliphatic (propyl and butyl) sulfonic acid tails, deprotonated at physiological pH, were thus incorporated onto a benzenesulfonamide scaffold by a common 1,2,3-triazole linker and different types of spacers. Twenty such derivatives were synthesized and tested for their inhibition of target (hCAs IV, IX, and XII) and off-target CAs (hCAs I and II). Most sulfonate CAIs induced a potent inhibition of hCAs II, IX, and XII up to a low nanomolar K_I range (0.9–459.4 nM) with a limited target/off-target CA selectivity of action. According to the drug design schedule, a subset of representative derivatives was assessed for their cell membrane permeability using Caco-2 cells and a developed FIA-MS/MS method. The complete membrane impermeability of the sulfonate tailed CAIs (≥98%) validated these negatively charged moieties as being suitable for achieving, in vivo, the selective targeting of the tumor-associated CAs over off-target ones.

Commercial SARS-CoV-2 Targeted, Protease Inhibitor Focused and Protein-Protein Interaction Inhibitor Focused Molecular Libraries for Virtual Screening and Drug Design

Since December 2019, the new SARS-CoV-2-related COVID-19 disease has caused a global pandemic and shut down the public life worldwide. Several proteins have emerged as potential therapeutic targets for drug development, and we sought out to review the commercially available and marketed SARS-CoV-2-targeted libraries ready for high-throughput virtual screening (HTVS). We evaluated the SARS-CoV-2-targeted, protease-inhibitor-focused and protein-protein-interaction-inhibitor-focused libraries to gain a better understanding of how these libraries were designed. The most common were ligand- and structure-based approaches, along with various filtering steps, using molecular descriptors. Often, these methods were combined to obtain the final library. We recognized the abundance of targeted libraries offered and complimented by the inclusion of analytical data; however, serious concerns had to be raised. Namely, vendors lack the information on the library design and the references to the primary literature. Few references to active compounds were also provided when using the ligand-based design and usually only protein classes or a general panel of targets were listed, along with a general reference to the methods, such as molecular docking for the structure-based design. No receptor data, docking protocols or even references to the applied molecular docking software (or other HTVS software), and no pharmacophore or filter design details were given. No detailed functional group or chemical space analyses were reported, and no specific orientation of the libraries toward the design of covalent or noncovalent inhibitors could be observed. All libraries contained pan-assay interference compounds (PAINS), rapid elimination of swill compounds (REOS) and aggregators, as well as focused on the drug-like model, with the majority of compounds possessing their molecular mass around 500 g/mol. These facts do not bode well for the use of the reviewed libraries in drug design and lend themselves to commercial drug companies to focus on and improve.

Dynophore-Based Approach in Virtual Screening: A Case of Human DNA Topoisomerase IIα

In this study, we utilized human DNA topoisomerase IIα as a model target to outline a dynophore-based approach to catalytic inhibitor design. Based on MD simulations of a known catalytic inhibitor and the native ATP ligand analog, AMP-PNP, we derived a joint dynophore model that supplements the static structure-based-pharmacophore information with a dynamic component. Subsequently, derived pharmacophore models were employed in a virtual screening campaign of a library of natural compounds. Experimental evaluation identified flavonoid compounds with promising topoisomerase IIα catalytic inhibition and binding studies confirmed interaction with the ATPase domain. We constructed a binding model through docking and extensively investigated it with molecular dynamics MD simulations, essential dynamics, and MM-GBSA free energy calculations, thus reconnecting the new results to the initial dynophore-based screening model. We not only demonstrate a new design strategy that incorporates a dynamic component of molecular recognition, but also highlight new derivates in the established flavonoid class of topoisomerase II inhibitors.

Development of Computational Approaches with a Fragment-Based Drug Design Strategy: In Silico Hsp90 Inhibitors Discovery

A semi-exhaustive approach and a heuristic search algorithm use a fragment-based drug design (FBDD) strategy for designing new inhibitors in an in silico process. A deconstruction reconstruction process uses a set of known Hsp90 ligands for generating new ones. The deconstruction process consists of cutting off a known ligand in fragments. The reconstruction process consists of coupling fragments to develop a new set of ligands. For evaluating the approaches, we compare the binding energy of the new ligands with the known ligands.

Virtual Screening with Gnina 1.0

Virtual screening-predicting which compounds within a specified compound library bind to a target molecule, typically a protein-is a fundamental task in the field of drug discovery. Doing virtual screening well provides tangible practical benefits, including reduced drug development costs, faster time to therapeutic viability, and fewer unforeseen side effects. As with most applied computational tasks, the algorithms currently used to perform virtual screening feature inherent tradeoffs between speed and accuracy. Furthermore, even theoretically rigorous, computationally intensive methods may fail to account for important effects relevant to whether a given compound will ultimately be usable as a drug. Here we investigate the virtual screening performance of the recently released Gnina molecular docking software, which uses deep convolutional networks to score protein-ligand structures. We find, on average, that Gnina outperforms conventional empirical scoring. The default scoring in Gnina outperforms the empirical AutoDock Vina scoring function on 89 of the 117 targets of the DUD-E and LIT-PCBA virtual screening benchmarks with a median 1% early enrichment factor that is more than twice that of Vina. However, we also find that issues of bias linger in these sets, even when not used directly to train models, and this bias obfuscates to what extent machine learning models are achieving their performance through a sophisticated interpretation of molecular interactions versus fitting to non-informative simplistic property distributions.

Semi-continuous Propagation of Influenza A Virus and Its Defective Interfering Particles: Analyzing the Dynamic Competition To Select Candidates for Antiviral Therapy

Defective interfering particles (DIPs) of influenza A virus (IAV) are naturally occurring mutants that have an internal deletion in one of their eight viral RNA (vRNA) segments, rendering them propagation-incompetent. Upon coinfection with infectious standard virus (STV), DIPs interfere with STV replication through competitive inhibition. Thus, DIPs are proposed as potent antivirals for treatment of the influenza disease. To select corresponding candidates, we studied de novo generation of DIPs and propagation competition between different defective interfering (DI) vRNAs in an STV coinfection scenario in cell culture. A small-scale two-stage cultivation system that allows long-term semi-continuous propagation of IAV and its DIPs was used. Strong periodic oscillations in virus titers were observed due to the dynamic interaction of DIPs and STVs. Using next-generation sequencing, we detected a predominant formation and accumulation of DI vRNAs on the polymerase-encoding segments. Short DI vRNAs accumulated to higher fractions than longer ones, indicating a replication advantage, yet an optimum fragment length was observed. Some DI vRNAs showed breaking points in a specific part of their bundling signal (belonging to the packaging signal), suggesting its dispensability for DI vRNA propagation. Over a total cultivation time of 21 days, several individual DI vRNAs accumulated to high fractions, while others decreased. Using reverse genetics for IAV, purely clonal DIPs derived from highly replicating DI vRNAs were generated. We confirm that these DIPs exhibit a superior in vitro interfering efficacy compared to DIPs derived from lowly accumulated DI vRNAs and suggest promising candidates for efficacious antiviral treatment. IMPORTANCE Defective interfering particles (DIPs) emerge naturally during viral infection and typically show an internal deletion in the viral genome. Thus, DIPs are propagation-incompetent. Previous research suggests DIPs as potent antiviral compounds for many different virus families due to their ability to interfere with virus replication by competitive inhibition. For instance, the administration of influenza A virus (IAV) DIPs resulted in a rescue of mice from an otherwise lethal IAV dose. Moreover, no apparent toxic effects were observed when only DIPs were administered to mice and ferrets. IAV DIPs show antiviral activity against many different IAV strains, including pandemic and highly pathogenic avian strains, and even against nonhomologous viruses, such as SARS-CoV-2, by stimulation of innate immunity. Here, we used a cultivation/infection system, which exerted selection pressure toward accumulation of highly competitive IAV DIPs. These DIPs showed a superior interfering efficacy in vitro, and we suggest them for effective antiviral therapy.

Dissimilar Ligands Bind in a Similar Fashion: A Guide to Ligand Binding-Mode Prediction with Application to CELPP Studies

The molecular similarity principle has achieved great successes in the field of drug design/discovery. Existing studies have focused on similar ligands, while the behaviors of dissimilar ligands remain unknown. In this study, we developed an intercomparison strategy in order to compare the binding modes of ligands with different molecular structures. A systematic analysis of a newly constructed protein–ligand complex structure dataset showed that ligands with similar structures tended to share a similar binding mode, which is consistent with the Molecular Similarity Principle. More importantly, the results revealed that dissimilar ligands can also bind in a similar fashion. This finding may open another avenue for drug discovery. Furthermore, a template-guiding method was introduced for predicting protein–ligand complex structures. With the use of dissimilar ligands as templates, our method significantly outperformed the traditional molecular docking methods. The newly developed template-guiding method was further applied to recent CELPP studies.

The transcriptome of Balamuthia mandrillaris trophozoites for structure-guided drug design

Balamuthia mandrillaris, a pathogenic free-living amoeba, causes cutaneous skin lesions as well as granulomatous amoebic encephalitis, a 'brain-eating' disease. As with the other known pathogenic free-living amoebas (Naegleria fowleri and Acanthamoeba species), drug discovery efforts to combat Balamuthia infections of the central nervous system are sparse; few targets have been validated or characterized at the molecular level, and little is known about the biochemical pathways necessary for parasite survival. Current treatments of encephalitis due to B. mandrillaris lack efficacy, leading to case fatality rates above 90%. Using our recently published methodology to discover potential drugs against pathogenic amoebas, we screened a collection of 85 compounds with known antiparasitic activity and identified 59 compounds that impacted the growth of Balamuthia trophozoites at concentrations below 220 µM. Since there is no fully annotated genome or proteome of B. mandrillaris, we sequenced and assembled its transcriptome from a high-throughput RNA-sequencing (RNA-Seq) experiment and located the coding sequences of the genes potentially targeted by the growth inhibitors from our compound screens. We determined the sequence of 17 of these target genes and obtained expression clones for 15 that we validated by direct sequencing. These will be used in the future in combination with the identified hits in structure guided drug discovery campaigns to develop new approaches for the treatment of Balamuthia infections.

Lead optimization of novel quinolone chalcone compounds by a structure-activity relationship (SAR) study to increase efficacy and metabolic stability

Many agents targeting the colchicine binding site in tubulin have been developed as potential anticancer agents. However, none has successfully made it to the clinic, due mainly to dose limiting toxicities and the emergence of multi-drug resistance. Chalcones targeting tubulin have been proposed as a safe and effective alternative. We have shown previously that quinolone chalcones target tubulin and maintain potent anti-proliferative activity vis-à-vis colchicine, while also having high tolerability and low toxicity in mouse models of cancer and refractivity to multi-drug resistance mechanisms. To identify the most effective anticancer chalcone compound, we synthesized 17 quinolone-chalcone derivatives based on our previously published CTR-17 and CTR-20, and then carried out a structure-activity relationship study. We identified two compounds, CTR-21 [((E)-8-Methoxy-3-(3-(2-methoxyphenyl)-3-oxoprop-1-enyl) quinolin-2(1H)-one)] and CTR-32 [((E)-3-(3-(2-ethoxyphenyl)-3-oxoprop-1-enyl) quinolin-2(1H)-one)] as potential leads, which contain independent moieties that play a significant role in their enhanced activities. At the nM range, CTR-21 and CTR-32 effectively kill a panel of different cancer cells originated from a variety of different tissues including breast and skin. Both compounds also effectively kill multi-drug resistant cancer cells. Most importantly, CTR-21 and CTR-32 show a high degree of selectivity against cancer cells. In silico, both of them dock near the colchicine-binding site with similar energies. Whereas both CTR-21 and CTR-32 effectively prevents tubulin polymerization, leading to the cell cycle arrest at G2/M, CTR-21 has more favorable metabolic properties. Perhaps not surprisingly, the combination of CTR-21 and ABT-737, a Bcl-2 inhibitor, showed synergistic effect in killing cancer cells, since we previously found the "parental" CTR-20 also exhibited synergism. Taken together, CTR-21 can potentially be a highly effective and relatively safe anticancer drug.

Short Peptides Make a Big Difference: The Role of Botany-Derived AMPs in Disease Control and Protection of Human Health

Botany-derived antimicrobial peptides (BAMPs), a class of small, cysteine-rich peptides produced in plants, are an important component of the plant immune system. Both in vivo and in vitro experiments have demonstrated their powerful antimicrobial activity. Besides in plants, BAMPs have cross-kingdom applications in human health, with toxic and/or inhibitory effects against a variety of tumor cells and viruses. With their diverse molecular structures, broad-spectrum antimicrobial activity, multiple mechanisms of action, and low cytotoxicity, BAMPs provide ideal backbones for drug design, and are potential candidates for plant protection and disease treatment. Lots of original research has elucidated the properties and antimicrobial mechanisms of BAMPs, and characterized their surface receptors and in vivo targets in pathogens. In this paper, we review and introduce five kinds of representative BAMPs belonging to the pathogenesis-related protein family, dissect their antifungal, antiviral, and anticancer mechanisms, and forecast their prospects in agriculture and global human health. Through the deeper understanding of BAMPs, we provide novel insights for their applications in broad-spectrum and durable plant disease prevention and control, and an outlook on the use of BAMPs in anticancer and antiviral drug design.

Structural principles of insulin formulation and analog design: A century of innovation

BACKGROUND

The discovery of insulin in 1921 and its near-immediate clinical use initiated a century of innovation. Advances extended across a broad front, from the stabilization of animal insulin formulations to the frontiers of synthetic peptide chemistry, and in turn, from the advent of recombinant DNA manufacturing to structure-based protein analog design. In each case, a creative interplay was observed between pharmaceutical applications and then-emerging principles of protein science; indeed, translational objectives contributed to a growing molecular understanding of protein structure, aggregation and misfolding.

SCOPE OF REVIEW

Pioneering crystallographic analyses-beginning with Hodgkin's solving of the 2-Zn insulin hexamer-elucidated general features of protein self-assembly, including zinc coordination and the allosteric transmission of conformational change. Crystallization of insulin was exploited both as a step in manufacturing and as a means of obtaining protracted action. Forty years ago, the confluence of recombinant human insulin with techniques for site-directed mutagenesis initiated the present era of insulin analogs. Variant or modified insulins were developed that exhibit improved prandial or basal pharmacokinetic (PK) properties. Encouraged by clinical trials demonstrating the long-term importance of glycemic control, regimens based on such analogs sought to resemble daily patterns of endogenous β-cell secretion more closely, ideally with reduced risk of hypoglycemia.

MAJOR CONCLUSIONS

Next-generation insulin analog design seeks to explore new frontiers, including glucose-responsive insulins, organ-selective analogs and biased agonists tailored to address yet-unmet clinical needs. In the coming decade, we envision ever more powerful scientific synergies at the interface of structural biology, molecular physiology and therapeutics.

Opportunities for overcoming tuberculosis: Emerging targets and their inhibitors

Tuberculosis (TB), an airborne infectious disease mainly caused by Mycobacterium tuberculosis (Mtb), remains a leading cause of human morbidity and mortality worldwide. Given the alarming rise of resistance to anti-TB drugs and latent TB infection (LTBI), new targets and novel bioactive compounds are urgently needed for the treatment of this disease. We provide an overview of the recent advances in anti-TB drug discovery, emphasizing several newly validated targets for which an inhibitor has been reported in the past five years. Our review presents several attractive directions that have potential for the development of next-generation therapies.

Accelerating antibiotic discovery through artificial intelligence

By targeting invasive organisms, antibiotics insert themselves into the ancient struggle of the host-pathogen evolutionary arms race. As pathogens evolve tactics for evading antibiotics, therapies decline in efficacy and must be replaced, distinguishing antibiotics from most other forms of drug development. Together with a slow and expensive antibiotic development pipeline, the proliferation of drug-resistant pathogens drives urgent interest in computational methods that promise to expedite candidate discovery. Strides in artificial intelligence (AI) have encouraged its application to multiple dimensions of computer-aided drug design, with increasing application to antibiotic discovery. This review describes AI-facilitated advances in the discovery of both small molecule antibiotics and antimicrobial peptides. Beyond the essential prediction of antimicrobial activity, emphasis is also given to antimicrobial compound representation, determination of drug-likeness traits, antimicrobial resistance, and de novo molecular design. Given the urgency of the antimicrobial resistance crisis, we analyze uptake of open science best practices in AI-driven antibiotic discovery and argue for openness and reproducibility as a means of accelerating preclinical research. Finally, trends in the literature and areas for future inquiry are discussed, as artificially intelligent enhancements to drug discovery at large offer many opportunities for future applications in antibiotic development.

Dual-target inhibitors of bromodomain-containing protein 4 (BRD4) in cancer therapy: Current situation and future directions

Bromodomain-containing protein 4 (BRD4) is emerging as a therapeutic target that acts synergistically with other targets of small-molecule drugs in cancer. Therefore, the discovery of potential new dual-target inhibitors of BRD4 may be a promising strategy for cancer therapy. In this review, we highlight a series of strategies to design therapeutic dual-target inhibitors of BRD4 that focus on the synergistic functions of this protein. Drug combinations that exploit synthetic lethality, protein-protein interactions, functional complementarity, and blocking of resistance mechanisms could ultimately overcome the barriers inherent to the development of BRD4 inhibitors as future cancer drugs.

Learning from Nature: From a Marine Natural Product to Synthetic Cyclooxygenase-1 Inhibitors by Automated De Novo Design

The repertoire of natural products offers tremendous opportunities for chemical biology and drug discovery. Natural product-inspired synthetic molecules represent an ecologically and economically sustainable alternative to the direct utilization of natural products. De novo design with machine intelligence bridges the gap between the worlds of bioactive natural products and synthetic molecules. On employing the compound Marinopyrrole A from marine Streptomyces as a design template, the algorithm constructs innovative small molecules that can be synthesized in three steps, following the computationally suggested synthesis route. Computational activity prediction reveals cyclooxygenase (COX) as a putative target of both Marinopyrrole A and the de novo designs. The molecular designs are experimentally confirmed as selective COX-1 inhibitors with nanomolar potency. X-ray structure analysis reveals the binding of the most selective compound to COX-1. This molecular design approach provides a blueprint for natural product-inspired hit and lead identification for drug discovery with machine intelligence.

E3 Ligase Ligands for PROTACs: How They Were Found and How to Discover New Ones

Bifunctional degrader molecules, also called proteolysis-targeting chimeras (PROTACs), are a new modality of chemical tools and potential therapeutics to understand and treat human disease. A required PROTAC component is a ligand binding to an E3 ubiquitin ligase, which is then joined to another ligand binding to a protein to be degraded via the ubiquitin-proteasome system. The advent of nonpeptidic small-molecule E3 ligase ligands, notably for von Hippel-Lindau (VHL) and cereblon (CRBN), revolutionized the field and ushered in the design of drug-like PROTACs with potent and selective degradation activity. A first wave of PROTAC drugs are now undergoing clinical development in cancer, and the field is seeking to extend the repertoire of chemistries that allow hijacking new E3 ligases to improve the scope of targeted protein degradation.Here, we briefly review how traditional E3 ligase ligands were discovered, and then outline approaches and ligands that have been recently used to discover new E3 ligases for PROTACs. We will then take an outlook at current and future strategies undertaken that invoke either target-based screening or phenotypic-based approaches, including the use of DNA-encoded libraries (DELs), display technologies and cyclic peptides, smaller molecular glue degraders, and covalent warhead ligands. These approaches are ripe for expanding the chemical space of PROTACs and usher in the advent of other emerging bifunctional modalities of proximity-based pharmacology.

In Silico Drug-designing Studies on Sulforaphane Analogues: Pharmacophore Mapping, Molecular Docking and QSAR Modeling

AIMS

In the presented work we successfully discovered several novel NQO1 inducers using the computational approaches.

BACKGROUND

The phytochemical sulforaphane (SFN) is a potent inducer of carcinogen detoxication enzymes like NAD(P)H:quinone oxidoreductase 1 (NQO1) through the Kelch-like erythroid cellderived protein with CNC homology[ECH]-associated protein 1 (Keap1)-[NF-E2]-related factor 2 (Nrf2) signaling pathway.

OBJECTIVE

In this paper, we report the first QSAR and pharmacophore modeling study of sulforaphane analogues as NQO1 inducers. The pharmacophore model and understanding the relationships between the structures and activities of the known inducers will give useful information on the structural basis for NQO1 enzymatic activity and lead optimization for future rational design of new sulforaphane analogues as potent NQO1 inducers.

METHODS

In this study, a combination of QSAR modeling, pharmacophore generation, virtual screening and molecular docking was performed on a series of sulforaphane analogues as NQO1 inducers.

RESULTS

In deriving the QSAR model, the stepwise multiple linear regression established a reliable model with the training set (N: 43, R: 0.971, RMSE: 0.216) and test set (N: 14, R: 0.870, RMSE: 0.324, Q2: 0.80) molecules. The best ligand-based pharmacophore model comprised two hydrophobic (HY), one ring aromatic (RA) and three hydrogen bond acceptor (HBA) sites. The model was validated by a testing set and the decoys set, Güner-Henry (GH) scoring methods, etc. The enrichment of model was assessed by the sensitivity (0.92) and specificity (0.95). Moreover, the values of enrichment factor (EF) and the area under the receiver operating characteristics curve (AUC) were 12 and 0.94, respectively. This well-validated model was applied to screen two Asinex libraries for the novel NQO1 inducers. The hits were subsequently subjected to molecular docking after being filtering by Lipinski's, MDDR-like, and Veber rules as well as evaluating their interaction with three major drugmetabolizing P450 enzymes, CYP2C9, CYP2D6 and CYP3A4. Ultimately, 12 hits filtered by molecular docking were subjected to validated QSAR model for calculating their inducer potencies and were introduced as potential NQO1 inducers for further investing action.

CONCLUSION

Conclusively, the validated QSAR model was applied on the hits to calculate their inducer potencies and these 12 hits were introduced as potential NQO1 inducers for further investigations.

Novel approaches for the treatment of methicillin-resistant Staphylococcus aureus: Using nanoparticles to overcome multidrug resistance

Methicillin-resistant Staphylococcus aureus (MRSA) causes serious infections in both community and hospital settings, with high mortality rates. Treatment of MRSA infections is challenging because of the rapidly evolving resistance mechanisms combined with the protective biofilms of S. aureus. Together, these characteristic resistance mechanisms continue to render conventional treatment modalities ineffective. The use of nanoformulations with unique modes of transport across bacterial membranes could be a useful strategy for disease-specific delivery. In this review, we summarize treatment approaches for MRSA, including novel techniques in nanoparticulate designing for better therapeutic outcomes; and facilitate an understanding that nanoparticulate delivery systems could be a robust approach in the successful treatment of MRSA.

CaverDock: A Novel Method for the Fast Analysis of Ligand Transport

Here we present a novel method for the analysis of transport processes in proteins and its implementation called CaverDock. Our method is based on a modified molecular docking algorithm. It iteratively places the ligand along the access tunnel in such a way that the ligand movement is contiguous and the energy is minimized. The result of CaverDock calculation is a ligand trajectory and an energy profile of transport process. CaverDock uses the modified docking program Autodock Vina for molecular docking and implements a parallel heuristic algorithm for searching the space of possible trajectories. Our method lies in between the geometrical approaches and molecular dynamics simulations. Contrary to the geometrical methods, it provides an evaluation of chemical forces. However, it is far less computationally demanding and easier to set up compared to molecular dynamics simulations. CaverDock will find a broad use in the fields of computational enzymology, drug design, and protein engineering. The software is available free of charge to the academic users at https://loschmidt.chemi.muni.cz/caverdock/.