Structure-based design,synthesis of novel inhibitors of Mycobacterium tuberculosis FabH as potential anti-tuberculosis agents

Mycobacterium tuberculosis FabH, an essential enzyme in mycolic acids biosynthetic pathway, is an attractive target for novel anti-tuberculosis agents. Structure-based design, synthesis of novel inhibitors of mtFabH was reported in this paper. A novel scaffold structure was designed, and 12 candidate compounds that displayed favorable binding with the active site were identified and synthesized. 2009 Song Li. Published by Elsevier B.V. on behalf of Chinese Chemical Society. All rights reserved.

Structure-based simulations complemented by conventional all-atom simulations to provide new insights into the folding dynamics of human telomeric G-quadruplex

The hybrid atomistic structure-based model has been validated to be effective in investigation of G-quadruplex folding.In this study,we performed large-scale conventional all-atom simulations to complement the folding mechanism of human telomeric sequence Htel24 revealed by a multi-basin hybrid atomistic structure-based model.Firstly,the real time-scale of folding rate,which cannot be obtained from the structure-based simulations,was estimated directly by constructing a Markov state model.The results show that Htel24 may fold as fast as on the order of milliseconds when only considering the competition between the hybrid-1 and hybrid-2 G-quadruplex conformations.Secondly,in comparison with the results of structure-based simulations,more metastable states were identified to participate in the formation of hybrid-1 and hybrid-2 conformations.These findings suggest that coupling the hybrid atomistic structure-based model and the conventional all-atom model can provide more insights into the folding dynamics of DNA G-quadruplex.As a result,the multiscale computational framework adopted in this study may be useful to study complex processes of biomolecules involving large conformational changes.

Exploring structure-based drug discovery of GPCRs beyond the orthosteric binding

G-protein coupled receptors(GPCRs)are the largest family of druggable targets.In recent years,GPCR structural biology has made great advances,revealing the three-dimensional structures of many GPCRs and their interactions with ligands,proteins,and membrane components,which also have inspired a surge of structure-based drug discovery campaigns.This article provides a comprehensive summary of the currently available structural insights into the allosteric pockets of GPCRs and their regulatory mechanisms governing GPCR conformational changes.Furthermore,this article also presents several structure-inspired studies that utilize both orthosteric and allosteric modulation to discover small molecular modulators targeting GPCRs.The article emphasizes the promising potential of drug discovery targeting GPCR allosteric sites,while acknowledging the challenges arising from the limited structural information regarding the lipids and cholesterols in the membrane.Finally,the article discusses the future prospects of using large-scale or focused compound libraries to discover novel chemotypes,as well as the application of artificial intelligence(AI)in structure-based virtual screening(SBVS)against GPCRs.

Structure-based drug discovery of novel fusedpyrazolone carboxamide derivatives as potent and selective AXL inhibitors

a novel and promising antitumor target,AXL plays an important role in tumor growth,metastasis,immunosuppression and drug resistance of various malignancies,which has attracted extensive research interest in recent years.In this study,by employing the structure-based drug design and bioisosterism strategies,we designed and synthesized in total 54 novel AXL inhibitors featuring a fusedpyrazolone carboxamide scaffold,of which up to 20 compounds exhibited excellent AXL kinase and BaF3/TEL-AXL cell viability inhibitions.Notably,compound 59 showed a desirable AXL kinase inhibitory activity(IC_(50):3.5 nmol/L)as well as good kinase selectivity,and it effectively blocked the cellular AXL signaling.In turn,compound 59 could potently inhibit BaF3/TEL-AXL cell viability(IC_(50):1.5 nmol/L)and significantly suppress GAS6/AXL-mediated cancer cell invasion,migration and wound healing at the nanomolar level.More importantly,compound 59 oral administration showed good pharmacokinetic profile and in vivo antitumor efficiency,in which we observed significant AXL phosphorylation suppression,and its antitumor efficacy at 20 mg/kg(qd)was comparable to that of BGB324 at 50 mg/kg(bid),the most advanced AXL inhibitor.Taken together,this work provided a valuable lead compound as a potential AXL inhibitor for the further antitumor drug development.

Excellent Structure-Based Multifunction of Morpho Butterfly Wings： A Review

Morpho butterfly, famous for its iridescence wing scales, has gradually evolved a diversity of functions and has attracted much attention recently. On the other hand, it is known that the wing surface of Morpho butterfly has some complex and so- phisticated structures. In fact, they are composed of an alternating multilayer film system of chitin and air layers, which have different refractive indexes. More importantly, these structures can interact strongly with visible light because the feature size of the structures is in the same order of magnitude with light wavelength. It is noteworthy that it is these optical architectures that cause the excellent multifunction including structural color, antireflection, thermal response, selective vapour response, direc- tional adhesion, superhydrophobicity and so on. This review mainly covers the excellent multifunctional features of Morpho butterfly wings with representative functional structures of multilayer film system, photonic crystal and ridges. Then, the mechanism of the structure-based optical multifunction of Morpho butterfly is analyzed. In order to facilitate mechanism analysis, the models of bionic functional structures are reported, as well as the interaction process between the multiscale structures and the external media It is concluded that these functions of Morpho butterfly wings have inevitable and corre- sponding regularity connection with the structural parameters and the dielectric coefficient of the filled medium. At last, the future direction and prospects of this field are briefly addressed. It is hoped that this review could be beneficial to provide some innovative insoirations and new ideas to the researchers in the fields of engineering, biomedicine, and materials science.

Structure-based design of hexahydropyrimidin-5-ols as novel non-peptidic β-secretase inhibitors

Based upon the crystal structure of a previously reported fragment hit that binds to Corresponding author. β-secretase, a novel series of non-peptidic small-molecule β-secretase inhibitors, namely hexahydropyrimidin-5-ols, along with two series of their analogues, were rationally designed through structural modification. The CADD study was performed and revealed good expectation. Inhibitory activities of the corresponding structural cores were tested, which provided further support for our design approach.

Structure-based discovery of orally efficient inhibitors via unique interactions with H-pocket of PDE8 for the treatment of vascular dementia

Our previous study demonstrated that phosphodiesterase 8(PDE8)could work as a potential target for vascular dementia(Va D)using a chemical probe 3a.However,compound 3a is a chiral compound which was obtained by chiral resolution on HPLC,restricting its usage in clinic.Herein,a series of non-chiral 9-benzyl-2-chloro-adenine derivatives were discovered as novel PDE8 inhibitors.Lead 15 exhibited potent inhibitory activity against PDE8A(IC_(50)=11 nmol/L),high selectivity over other PDEs,and remarkable drug-like properties(worthy to mention is that its bioavailability was up to 100%).Oral administration of 15 significantly improved the c AMP level of the right brain and exhibited dosedependent effects on cognitive improvement in a Va D mouse model.Notably,the X-ray crystal structure of the PDE8A—15 complex showed that the potent affinity and high selectivity of 15 might come from the distinctive interactions with H-pocket including T-shapedπ—πinteractions with Phe785 as well as a unique H-bond network,which have never been observed in other PDE-inhibitor complex before,providing new strategies for the further rational design of novel selective inhibitors against PDE8.

Structure-based protein-protein interaction networks and drug design

Proteins carry out their functions by interacting with other proteins and small molecules, forming a complex interaction network. In this review, we briefly introduce classical graph theory based protein-protein interaction networks. We also describe the commonly used experimental methods to construct these networks, and the insights that can be gained from these networks. We then discuss the recent transition from graph theory based networks to structure based protein-protein interaction networks and the advantages of the latter over the former, using two networks as examples. We further discuss the usefulness of structure based protein-protein interaction networks for drug discovery, with a special emphasis on drug repositioning.

Numerical analysis of residence time distribution of solids in a bubbling fluidized bed based on the modified structure-based drag model

The residence time distribution （RTD） of solids and the fluidized structure of a bubbling fluidized bed were investigated numerically using computational fluid dynamics simulations coupled with the modified structure-based drag model. A general comparison of the simulated results with theoretical values shows reasonable agreement. As the mean residence time is increased, the RTD initial peak intensity decreases and the RTD curve tail extends farther. Numerous small peaks on the RTD curve are induced by the back- mixing and aggregation of particles, which attests to the non-uniform flow structure of the bubbling fluidized bed. The low value of t50 results in poor contact between phases, and the complete exit age of the overflow particles is much longer for back-mixed solids and those caught in dead regions. The formation of a gulf-stream flow and back-mixing for solids induces an even wider spread of RTD.

Structure-Based Prediction of Transcription Factor Binding Sites

Transcription Factors（TFs） are a very diverse family of DNA-binding proteins that play essential roles in the regulation of gene expression through binding to specific DNA sequences. They are considered as one of the prime drug targets since mutations and aberrant TF-DNA interactions are implicated in many diseases.Identification of TF-binding sites on a genomic scale represents a critical step in delineating transcription regulatory networks and remains a major goal in genomic annotations. Recent development of experimental high-throughput technologies has provided valuable information about TF-binding sites at genome scale under various physiological and developmental conditions. Computational approaches can provide a cost-effective alternative and complement the experimental methods by using the vast quantities of available sequence or structural information. In this review we focus on structure-based prediction of transcription factor binding sites. In addition to its potential in genomescale predictions, structure-based approaches can help us better understand the TF-DNA interaction mechanisms and the evolution of transcription factors and their target binding sites. The success of structure-based methods also bears a translational impact on targeted drug design in medicine and biotechnology.

Structure-based design and biological evaluation of novel mTOR inhibitors as potential anti-cervical agents

The mammalian target of rapamycin(mTOR) is a critical component of the PI3K-AKT signaling pathway. It is highly activated in cervical cancer, which continues to pose an important clinical challenge with an urgent need for new and improved therapeutic approaches. Herein, we describe the structure-based drug discovery and biological evaluation of a series of m TOR kinase inhibitors as potential anti-cervical cancer agents. The results of enzymatic activity assays supported C3 as a potential m TOR inhibitor, which exhibited high inhibitory activity with an IC50 of 1.57 μM. Molecular docking and dynamics simulation were conducted to predict the binding patterns, suggesting relationships between structure and activity. The anti-proliferative assay against diverse cancer cell lines was displayed subsequently, revealing that C3 exhibited significant proliferation inhibition against cervical cancer cell He La(IC50=0.38μM) compared with other cell lines. Moreover, C3 could effectively reduce the expression of phospho-ribosomal S6 protein(p-S6) in He La cells in a dose-dependent manner. Noteworthily, m TOR signaling and other cellular pathways might contribute to the significant effect of C3 against cervical cancer simultaneously. These data indicated that C3 represented a good lead molecule for further development as a therapeutic agent for cervical cancer treatment.

Algorithmic challenges in structure-based drug design and NMR structural biology

The three-dimensional structure of a biomolecule rather than its one-dimensionM sequence determines its biological function. At present, the most accurate structures are derived from experimental data measured mainly by two techniques： X-ray crystallog- raphy and nuclear magnetic resonance （NMR） spec- troscopy. Because neither X-ray crystallography nor NMR spectroscopy could directly measure the positions of atoms in a biomolecule, algorithms must be designed to compute atom coordinates from the data. One salient feature of most NMR structure computation algorithms is their reliance on stochastic search to find the lowest energy conformations that satisfy the experimentally- derived geometric restraints. However, neither the cor- rectness of the stochastic search has been established nor the errors in the output structures could be quantified. Though there exist exact algorithms to compute struc- tures from angular restraints, similar algorithms that use distance restraints remain to be developed. An important application of structures is rational drug design where protein-ligand docking plays a crit- ical role. In fact, various docking programs that place a compound into the binding site of a target protein have been used routinely by medicinal chemists for both lead identification and optimization. Unfortunately, de- spite ongoing methodological advances and some success stories, the performance of current docking algorithms is still data-dependent. These algorithms formulate the docking problem as a match of two sets of feature points. Both the selection of feature points and the search for the best poses with the minimum scores are accomplished through some stochastic search methods. Both the un- certainty in the scoring function and the limited sam- pling space attained by the stochastic search contribute to their failures. Recently, we have developed two novel docking algorithms： a data-driven docking algorithm and a general docking algorithm that does not rely on experimental data. Our algorithms search the pose space exhaustively with the pose space itself being limited to a set of hierarchical manifolds that represent, respectively, surfaces, curves and points with unique geometric and energetic properties. These algorithms promise to be es- pecially valuable for the docking of fragments and small compounds as well as for virtual screening.

Simulation with a structure-based mass-transfer model for turbulent fluidized beds

A structure-based mass-transfer model for turbulent fluidized beds （TFBs） was established according to mass conservation and the balance of mass transfer and reaction. Unlike the traditional method, which assumes a homogeneous structure, this model considered the presence of voids and particle clusters in TFBs and built correlations for each phase. The flow parameters were solved based on a previously proposed structure-based drag model. The catalytic combustion of methane at three temperatures and ozone decomposition at various gas velocities were used to validate the model. The TFB reactions com- prised intrinsic reaction kinetics, internal diffusion, and external diffusion. The simulation results, which compared favorably with experimental data and were better than those based on the average method, demonstrated that methane was primarily consumed at the bottom of the bed and the methane concentration was closely related to the presence of the catalyst. The flow and diffusion had an important effect on the methane concentration. This model also predicted the outlet concentrations for ozone decomposition, which increased with increasing gas velocity, lnterphase mass transfer was presented as the limiting step for this system. This structure-based mass-transfer model is important for the industrial application of TFBs.

BACE1 inhibitors:A promising therapeutic approach for the management of Alzheimer’s disease

Alzheimer’s disease is a neurological disorder marked by the accumulation of amyloid beta(Aβ)aggregates,resulting from mutations in the amyloid precursor protein.The enzymeβ-secretase,also known asβ-site amyloid precursor protein cleaving enzyme 1(BACE1),plays a crucial role in generating Aβpeptides.With no targeted therapy available for Alzheimer’s disease,inhibiting BACE1 aspartic protease has emerged as a primary treatment target.Since 1999,compounds demonstrating potential binding to the BACE1 receptor have advanced to human trials.Structural optimization of synthetically derived compounds,coupled with computational approaches,has offered valuable insights for developing highly selective leads with drug-like properties.This review highlights pivotal studies on the design and development of BACE1 inhibitors as anti-Alzheimer’s disease agents.It summarizes computational methods employed in facilitating drug discovery for potential BACE1 inhibitors and provides an update on their clinical status,indicating future directions for novel BACE1 inhibitors.The promising clinical results of Elenbecestat(E-2609)catalyze the development of effective,selective BACE1 inhibitors in the future.

Applications and prospects of cryo-EM in drug discovery

Drug discovery is a crucial part of human healthcare and has dramatically benefited human lifespan and life quality in recent centuries, however, it is usually time-and effort-consuming. Structural biology has been demonstrated as a powerful tool to accelerate drug development. Among different techniques, cryo-electron microscopy(cryo-EM) is emerging as the mainstream of structure determination of biomacromolecules in the past decade and has received increasing attention from the pharmaceutical industry. Although cryo-EM still has limitations in resolution, speed and throughput, a growing number of innovative drugs are being developed with the help of cryo-EM. Here, we aim to provide an overview of how cryo-EM techniques are applied to facilitate drug discovery. The development and typical workflow of cryo-EM technique will be briefly introduced, followed by its specific applications in structure-based drug design, fragment-based drug discovery, proteolysis targeting chimeras, antibody drug development and drug repurposing. Besides cryo-EM, drug discovery innovation usually involves other state-of-the-art techniques such as artificial intelligence(AI), which is increasingly active in diverse areas. The combination of cryo-EM and AI provides an opportunity to minimize limitations of cryo-EM such as automation, throughput and interpretation of mediumresolution maps, and tends to be the new direction of future development of cryo-EM. The rapid development of cryo-EM will make it as an indispensable part of modern drug discovery.

Construction and Evaluation of Merged Pharmacophore Based on Peroxisome Proliferator Receptor-Alpha Agonists

Pharmacophore is a commonly used method for molecular simulation, including ligand-based pharmacophore （LBP） and structure-based pharmacophore （SBP）. LBP can be utilized to identify active compounds usual with lower accuracy, and SBP is able to use for distin- guishing active compounds from inactive compounds with frequently higher missing rates. Merged pharmacophore （MP） is presented to integrate advantages and avoid shortcomings of LBP and SBP. In this work, LBP and SBP models were constructed for the study of per- oxisome proliferator receptor-alpha （PPARα） agonists. According to the comparison of the two types of pharmacophore models, mainly and secondarily pharmacological features were identified. The weight and tolerance values of these pharmacological features were adjusted to construct MP models by single-factor explorations and orthogonal experimental design based on SBP model. Then, the reliability and screening efficiency of the best MP model were validated by three databases. The best MP model was utilized to compute PPARα activity of compounds from traditional Chinese medicine. The screening efficiency of MP model outperformed individual LBP or SBP model for PPARα agonists, and was similar to combinatorial screening of LBP and SBP. However, MP model might have an advantage over the combination of LBP and SBP in evaluating the activity of compounds and avoiding the inconsistent prediction of LBP and SBP, which would be beneficial to guide drug design and optimization.

Comparison of Ontology-Based Semantic-Similarity Measures in the Biomedical Text

In recent years, there are many types of semantic similarity measures, which are used to measure the similarity between two concepts. It is necessary to define the differences between the measures, performance, and evaluations. The major contribution of this paper is to choose the best measure among different similarity measures that give us good result with less error rate. The experiment was done on a taxonomy built to measure the semantic distance between two concepts in the health domain, which are represented as nodes in the taxonomy. Similarity measures methods were evaluated relative to human experts’ ratings. Our experiment was applied on the ICD10 taxonomy to determine the similarity value between two concepts. The similarity between 30 pairs of the health domains has been evaluated using different types of semantic similarity measures equations. The experimental results discussed in this paper have shown that the Hoa A. Nguyen and Hisham Al-Mubaid measure has achieved high matching score by the expert’s judgment.

Hypothetical inhibition mechanism of novel urea transporter B inhibitor

This study was designed to develop hypothetical inhibition mechanism of novel UT-B inhibitor and exploit novel compounds with UT-B inhibitory activity and to obtain promising lead compounds. We integrated cell based high throughput screening and in silico method to identify an undiscovered UT-B inhibitor binding site and proposed the mechanism of UT-B inhibitor in cross-species. We employed high-throughput screening using an erythrocyte os- motic lysis assay and identified 4 compounds PU21, PU168, PU468 and PU474 with UT-B inhibitory activity in vitro from 2319 hits. 16 compounds with UT-B inhibitory activity were screened by erythrocyte osmotic lysis assay from 60 analogues of PU21. PU14, one of 16 compounds exhibited potential inhibition activity in human, rabbit, rat, mouse in vitro and pharmacological diuresis activity in vivo. Based on the physiological data, we built a compu- tational mode of human UT-B by homology modeling. The putative UT-B binding site was identified by structure- based drug design and validated by ligand-based and QSAR model. Additionally, UT-B structural and functional differences under inhibitors treated and untreated conditions were simulated by Molecular Dynamics （MD）. The UT-B inhibitor binding site analysis and validation provide structure basses for lead identification and optimization.

Molecular Docking Studies on Anticonvulsant Enaminones Inhibiting Voltage-Gated Sodium Channels

Epilepsy is described as the most common chronic brain disorder. A typical symptom of epilepsy results in uncontrolled convulsions caused by temporary excessive neuronal discharges. Although several new anticon-vulsants have been introduced, some types of seizures have still not been adequately controlled with these new and current therapies. There is an urgent need to develop new anticonvulsant drugs to control the many different types of seizures. Many studies have shown that the epilepsies involve more than one mechanism and therefore may be responsible for the various types of observed seizures. Recently reported studies have shown that a group of newly synthesized 6 Hz active anticonvulsant fluorinated N-benzamide enaminones exhibited selective inhibitions of voltage-gated sodium (Nav) channels. Nav channels are responsible for the initial inward currents during the depolarization phases of the action potential in excitable cells. The activation and opening of Nav channels result in the initial phases of action potentials. We hypothesize that there is an essential pharmacophore model for the interactions between these enaminones and the active sites of Nav channels. The research reported here is focused on molecular docking studies of the interactions that occur between the fluorinated N-benzamide enaminones and the Nav channels. These studies may open an avenue for designing anticonvulsant drugs by inhibiting Nav channels.

Discovery of Novel Irreversible HER2 Inhibitors for Breast Cancer Treatment

It has been widely known that human epidermal growth factor receptor 2 (HER2) inhibitors exhibit distinct antitumor responses against HER2-positive breast cancer. To date, Lapatinib (Tykerb®) has been approved by the U.S. Food and Drug Administration (FDA) as a reversible HER2 inhibitor for treating breast cancer. However, HER2 L755S, T798I and T798M mutations confer drug resistance to lapatinib, restricting its efficacy toward HER2-positive breast cancer. Thus, novel therapy toward mutant HER2 is highly desired. Although several irreversible HER2 inhibitors have been developed to overcome these drug resistance problems, most of them were reported to cause severe side effects. In this study, three pharmacophore models based on HER2 L755S, T798I and T798M mutant structures were constructed and then validated through receiver operating characteristic (ROC) curve analysis and Güner-Henry (GH) scoring methods. Subsequently, these well-validated models were utilized as 3D queries to identify novel irreversible HER2 inhibitors from National Cancer Institute (NCI) database. Finally, two potential irreversible HER2 inhibitor candidates, NSC278329 and NSC718305, were identified and validated through molecular docking, molecular dynamics (MD) simulations and ADMET prediction. Furthermore, the analyses of binding modes showed that both NSC278329 and NSC718305 exhibit good binding interactions with HER2 L755S, T798I and T798M mutants. All together, the above results suggest that both NSC278329 and NSC718305 can serve as novel and effective irreversible HER2 inhibitors for treating breast cancers with HER2 L755S, T798I and T798M mutants. In addition, they may act as lead compounds for designing new irreversible HER2 inhibitors by carrying out structural modifications and optimizations in future studies.