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.

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 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.

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.

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.

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.

BEAR, a Molecular Docking Refinement and Rescoring Method

BEAR (Binding Estimation After Refinement) is a computational method for structure-based virtual screening. It was set up as a post-docking processing tool for the refinement of ligand binding modes predicted by molecular docking programs and the accurate evaluation of free energies of binding. BEAR has been validated in a number of computational drug discovery applications. It performed well in discriminating active ligands with respect to molecular decoys of biological targets belonging to different protein families as well as in discovering biologically active hits. Recently, it has also been validated in the emerging field of G-protein coupled receptors structure based virtual screening.

Prospective Development of Small Molecule Targets to Oncogenic Ras Proteins

Abnormal expression or mutations in Ras proteins has been found in up to 30% of cancer cell types, making them excellent protein models to probe structure-function relationships of cell-signaling processes that mediate cell transformtion. Yet, there has been very little development of therapies to help tackle Ras-related diseased states. The development of small molecules to target Ras proteins to potentially inhibit abnormal Ras-stimulated cell signaling has been conceptualized and some progress has been made over the last 16 or so years. Here, we briefly review studies characterizing Ras protein-small molecule interactions to show the importance and potential that these small molecules may have for Ras-related drug discovery. We summarize recent results, highlighting small molecules that can be directly targeted to Ras using Structure-Based Drug Design (SBDD) and Fragment-Based Lead Discovery (FBLD) methods. The inactivation of Ras oncogenic signaling in vitro by small molecules is currently an attractive hurdle to try to and leap over in order to attack the oncogenic state. In this regard, important features of previously characterized properties of small molecule Ras targets, as well as a current understanding of conformational and dynamics changes seen for Ras-related mutants, relative to wild type, must be taken into account as newer small molecule design strategies towards Ras are developed.

A New Matchmaking Algorithm Based on Multi-Level Matching Mechanism Combined with Fuzzy Set

The lack of semantic parts, increasing the number of Web services in the Web, and syntactic-based search operation are the main problems of current Web service technologies, these factors make difficult for clients to find a required web service. This paper shows a matchmaking algorithm to discover Semantic Web Services that are satisfying client requirements. It depends on two factors that distinguish it from any conventional Web service discovery algorithm;the first one is using semantic matching technique to overcome shortcoming of keyword matching techniques, the second one is tying Quality of Service (QoS) metrics of Web Service (WS) with fuzzy words that are used in user’s request. At least fifty percent average gain in search relevancy is obtained when our matchmaking algorithm is applied to WSs that are actually matching the chosen fuzzy semantic theme.

Design,synthesis,and biological evaluation of 1,2,4-triazole derivatives as potent antitubercular agents

Inhibition of mycobacterial membrane protein large 3(MmpL3)thereby affecting the mycolic acid biosynthetic pathway has been proven to be an effective strategy for developing antitubercular drugs.Based on the X-ray crystal structure of MmpL3 inhibitor complexes,a series of novel 1,2,4-triazole derivatives were designed,synthesized and evaluated antitubercular activity against Mtb strain H37Rv.Comprehensive structure–activity relationship exploration resulted in the identification of compounds 21 and 28,which possess potent antitubercular activity against Mtb strain H37Rv[minimum inhibitory concentration(MIC)=0.03–0.13μg/mL]and the clinical isolates of multidrug resistance(MDR)and extensive drug resistance(XDR)tuberculosis(MIC=0.06–1.0μg/mL).Moreover,compounds 21 and 28 showed neglectable cytotoxicity(IC_(50)≥32μg/mL)to the mammalian Vero cells and favorable physicochemical and pharmacokinetic properties according to the in silico absorption,distribution,metabolism and excretion(ADME)prediction.Finally,the potential target of representative 1,2,4-triazole 28 was identified to be MmpL3 using a microscale thermophoresis(MST)assay.

Identification of a novel PAK1 inhibitor to treat pancreatic cancer

Pancreatic cancer is one of the most aggressive cancers with poor prognosis and a low 5-year survival rate.The family of P21-activated kinases(PAKs)appears to modulate many signaling pathways that contribute to pancreatic carcinogenesis.In this work,we demonstrated that PAK1 is a critical regulator in pancreatic cancer cell growth.PAK1-targeted inhibition is therefore a new potential therapeutic strategy for pancreatic cancer.Our small molecule screening identified a relatively specific PAK1-targeted inhibitor,CP734.Pharmacological and biochemical studies indicated that CP734 targets residue V342 of PAK1 to inhibit its ATPase activity.Further in vitro and in vivo studies elucidated that CP734 suppresses pancreatic tumor growth through depleting PAK1 kinase activity and its downstream signaling pathways.Little toxicity of CP734 was observed in murine models.Combined with gemcitabine or 5-fluorouracil,CP734 also showed synergistic effects on the anti-proliferation of pancreatic cancer cells.All these favorable results indicated that CP734 is a new potential therapeutic candidate for pancreatic cancer.

Development of non-nucleoside reverse transcriptase inhibitors(NNRTIs):our past twenty years

Human immunodeficiency virus(HIV)is the primary infectious agent of acquired immunodeficiency syndrome(AIDS),and non-nucleoside reverse transcriptase inhibitors(NNRTIs)are the cornerstone of HIV treatment.In the last 20 years,our medicinal chemistry group has made great strides in developing several distinct novel NNRTIs,including 1-[(2-hydroxyethoxy)methyl]-6-(phenylthio)thymine(HEPT),thio-dihydro-alkoxy-benzyl-oxopyrimidine(S-DABO),diaryltriazine(DATA),diarylpyrimidine(DAPY)analogues,and their hybrid derivatives.Application of integrated modern medicinal strategies,including structure-based drug design,fragment-based optimization,scaffold/fragment hopping,molecular/fragment hybridization,and bioisosterism,led to the development of several highly potent analogues for further evaluations.In this paper,we review the development of NNRTIs in the last two decades using the above optimization strategies,including their structure-activity relationships,molecular modeling,and their binding modes with HIV-1 reverse transcriptase(RT).Future directions and perspectives on the design and associated challenges are also discussed.