Search

A continuing challenge in modern medicine is the identification of safer and more efficacious drugs. Precision therapeutics, which have one molecular target, have been long promised to be safer and more effective than traditional therapies. This approach has proven to be challenging for multiple reasons including lack of efficacy, rapidly acquired drug resistance, and narrow patient eligibility criteria. An alternative approach is the development of drugs that address the overall disease network by targeting multiple biological targets ('polypharmacology'). Rational development of these molecules will require improved methods for predicting single chemical structures that target multiple drug targets. To address this need, we developed the Multi-Targeting Drug DREAM Challenge, in which we challenged participants to predict single chemical entities that target pro-targets but avoid anti-targets for two unrelated diseases: RET-based tumors and a common form of inherited Tauopathy. Here, we report the results of this DREAM Challenge and the development of two neural network-based machine learning approaches that were applied to the challenge of rational polypharmacology. Together, these platforms provide a potentially useful first step towards developing lead therapeutic compounds that address disease complexity through rational polypharmacology.

Abstract Background Conformational sampling for small molecules plays an essential role in drug discovery research pipeline. Based on multi-objective evolution algorithm (MOEA), we have developed a conformational generation method called Cyndi in the previous study. In this work, in addition to Tripos force field in the previous version, Cyndi was updated by incorporation of MMFF94 force field to assess the conformational energy more rationally. With two force fields against a larger dataset of 742 bioactive conformations of small ligands extracted from PDB, a comparative analysis was performed between pure force field based method (FFBM) and multiple empirical criteria based method (MECBM) hybrided with different force fields. Results Our analysis reveals that incorporating multiple empirical rules can significantly improve the accuracy of conformational generation. MECBM, which takes both empirical and force field criteria as the objective functions, can reproduce about 54% (within 1Å RMSD) of the bioactive conformations in the 742-molecule testset, much higher than that of pure force field method (FFBM, about 37%). On the other hand, MECBM achieved a more complete and efficient sampling of the conformational space because the average size of unique conformations ensemble per molecule is about 6 times larger than that of FFBM, while the time scale for conformational generation is nearly the same as FFBM. Furthermore, as a complementary comparison study between the methods with and without empirical biases, we also tested the performance of the three conformational generation methods in MacroModel in combination with different force fields. Compared with the methods in MacroModel, MECBM is more competitive in retrieving the bioactive conformations in light of accuracy but has much lower computational cost. Conclusions By incorporating different energy terms with several empirical criteria, the MECBM method can produce more reasonable conformational ensemble with high accuracy but approximately the same computational cost in comparison with FFBM method. Our analysis also reveals that the performance of conformational generation is irrelevant to the types of force field adopted in characterization of conformational accessibility. Moreover, post energy minimization is not necessary and may even undermine the diversity of conformational ensemble. All the results guide us to explore more empirical criteria like geometric restraints during the conformational process, which may improve the performance of conformational generation in combination with energetic accessibility, regardless of force field types adopted.

Abstract Background Genome sequencing and post-genomics projects such as structural genomics are extending the frontier of the study of sequence-structure-function relationship of genes and their products. Although many sequence/structure-based methods have been devised with the aim of deciphering this delicate relationship, there still remain large gaps in this fundamental problem, which continuously drives researchers to develop novel methods to extract relevant information from sequences and structures and to infer the functions of newly identified genes by genomics technology. Results Here we present an ultrafast method, named BSSF(Binding Site Similarity & Function), which enables researchers to conduct similarity searches in a comprehensive three-dimensional binding site database extracted from PDB structures. This method utilizes a fingerprint representation of the binding site and a validated statistical Z-score function scheme to judge the similarity between the query and database items, even if their similarities are only constrained in a sub-pocket. This fingerprint based similarity measurement was also validated on a known binding site dataset by comparing with geometric hashing, which is a standard 3D similarity method. The comparison clearly demonstrated the utility of this ultrafast method. After conducting the database searching, the hit list is further analyzed to provide basic statistical information about the occurrences of Gene Ontology terms and Enzyme Commission numbers, which may benefit researchers by helping them to design further experiments to study the query proteins. Conclusions This ultrafast web-based system will not only help researchers interested in drug design and structural genomics to identify similar binding sites, but also assist them by providing further analysis of hit list from database searching.

Abstract Background The natural product Emodin demonstrates a wide range of pharmacological properties including anticancer, anti-inflammatory, antiproliferation, vasorelaxant and anti-H. pylori activities. Although its H. pylori inhibition was discovered, no acting target information against Emodin has been revealed to date. Results Here we reported that Emodin functioned as a competitive inhibitor against the recombinant β-hydroxyacyl-ACP dehydratase from Helicobacter pylori (HpFabZ), and strongly inhibited the growth of H. pylori strains SS1 and ATCC 43504. Surface plasmon resonance (SPR) and isothermal titration calorimetry (ITC) based assays have suggested the kinetic and thermodynamic features of Emodin/HpFabZ interaction. Additionally, to inspect the binding characters of Emodin against HpFabZ at atomic level, the crystal structure of HpFabZ-Emodin complex was also examined. The results showed that Emodin inhibition against HpFabZ could be implemented either through its occupying the entrance of the tunnel or embedding into the tunnel to prevent the substrate from accessing the active site. Conclusion Our work is expected to provide useful information for illumination of Emodin inhibition mechanism against HpFabZ, while Emodin itself could be used as a potential lead compound for further anti-bacterial drug discovery.

Abstract Background Conformation generation is a ubiquitous problem in molecule modelling. Many applications require sampling the broad molecular conformational space or perceiving the bioactive conformers to ensure success. Numerous in silico methods have been proposed in an attempt to resolve the problem, ranging from deterministic to non-deterministic and systemic to stochastic ones. In this work, we described an efficient conformation sampling method named Cyndi, which is based on multi-objective evolution algorithm. Results The conformational perturbation is subjected to evolutionary operation on the genome encoded with dihedral torsions. Various objectives are designated to render the generated Pareto optimal conformers to be energy-favoured as well as evenly scattered across the conformational space. An optional objective concerning the degree of molecular extension is added to achieve geometrically extended or compact conformations which have been observed to impact the molecular bioactivity (J Comput -Aided Mol Des 2002, 16: 105–112). Testing the performance of Cyndi against a test set consisting of 329 small molecules reveals an average minimum RMSD of 0.864 Å to corresponding bioactive conformations, indicating Cyndi is highly competitive against other conformation generation methods. Meanwhile, the high-speed performance (0.49 ± 0.18 seconds per molecule) renders Cyndi to be a practical toolkit for conformational database preparation and facilitates subsequent pharmacophore mapping or rigid docking. The copy of precompiled executable of Cyndi and the test set molecules in mol2 format are accessible in Additional file 1. Conclusion On the basis of MOEA algorithm, we present a new, highly efficient conformation generation method, Cyndi, and report the results of validation and performance studies comparing with other four methods. The results reveal that Cyndi is capable of generating geometrically diverse conformers and outperforms other four multiple conformer generators in the case of reproducing the bioactive conformations against 329 structures. The speed advantage indicates Cyndi is a powerful alternative method for extensive conformational sampling and large-scale conformer database preparation.

Abstract Background Development of a fast and accurate scoring function in virtual screening remains a hot issue in current computer-aided drug research. Different scoring functions focus on diverse aspects of ligand binding, and no single scoring can satisfy the peculiarities of each target system. Therefore, the idea of a consensus score strategy was put forward. Integrating several scoring functions, consensus score re-assesses the docked conformations using a primary scoring function. However, it is not really robust and efficient from the perspective of optimization. Furthermore, to date, the majority of available methods are still based on single objective optimization design. Results In this paper, two multi-objective optimization methods, called MOSFOM, were developed for virtual screening, which simultaneously consider both the energy score and the contact score. Results suggest that MOSFOM can effectively enhance enrichment and performance compared with a single score. For three different kinds of binding sites, MOSFOM displays an excellent ability to differentiate active compounds through energy and shape complementarity. EFMOGA performed particularly well in the top 2% of database for all three cases, whereas MOEA_Nrg and MOEA_Cnt performed better than the corresponding individual scoring functions if the appropriate type of binding site was selected. Conclusion The multi-objective optimization method was successfully applied in virtual screening with two different scoring functions that can yield reasonable binding poses and can furthermore, be ranked with the potentially compromised conformations of each compound, abandoning those conformations that can not satisfy overall objective functions.

Abstract Background Target identification is important for modern drug discovery. With the advances in the development of molecular docking, potential binding proteins may be discovered by docking a small molecule to a repository of proteins with three-dimensional (3D) structures. To complete this task, a reverse docking program and a drug target database with 3D structures are necessary. To this end, we have developed a web server tool, TarFisDock (Target Fishing Docking) http://www.dddc.ac.cn/tarfisdock, which has been used widely by others. Recently, we have constructed a protein target database, Potential Drug Target Database (PDTD), and have integrated PDTD with TarFisDock. This combination aims to assist target identification and validation. Description PDTD is a web-accessible protein database for in silico target identification. It currently contains >1100 protein entries with 3D structures presented in the Protein Data Bank. The data are extracted from the literatures and several online databases such as TTD, DrugBank and Thomson Pharma. The database covers diverse information of >830 known or potential drug targets, including protein and active sites structures in both PDB and mol2 formats, related diseases, biological functions as well as associated regulating (signaling) pathways. Each target is categorized by both nosology and biochemical function. PDTD supports keyword search function, such as PDB ID, target name, and disease name. Data set generated by PDTD can be viewed with the plug-in of molecular visualization tools and also can be downloaded freely. Remarkably, PDTD is specially designed for target identification. In conjunction with TarFisDock, PDTD can be used to identify binding proteins for small molecules. The results can be downloaded in the form of mol2 file with the binding pose of the probe compound and a list of potential binding targets according to their ranking scores. Conclusion PDTD serves as a comprehensive and unique repository of drug targets. Integrated with TarFisDock, PDTD is a useful resource to identify binding proteins for active compounds or existing drugs. Its potential applications include in silico drug target identification, virtual screening, and the discovery of the secondary effects of an old drug (i.e. new pharmacological usage) or an existing target (i.e. new pharmacological or toxic relevance), thus it may be a valuable platform for the pharmaceutical researchers. PDTD is available online at http://www.dddc.ac.cn/pdtd/.

Abstract Background Coagulase-negative Staphylococcus epidermidis has become a major frequent cause of infections in relation to the use of implanted medical devices. The pathogenicity of S. epidermidis has been attributed to its capacity to form biofilms on surfaces of medical devices, which greatly increases its resistance to many conventional antibiotics and often results in chronic infection. It has an urgent need to design novel antibiotics against staphylococci infections, especially those can kill cells embedded in biofilm. Results In this report, a series of novel inhibitors of the histidine kinase (HK) YycG protein of S. epidermidis were discovered first using structure-based virtual screening (SBVS) from a small molecular lead-compound library, followed by experimental validation. Of the 76 candidates derived by SBVS targeting of the homolog model of the YycG HATPase_c domain of S. epidermidis, seven compounds displayed significant activity in inhibiting S. epidermidis growth. Furthermore, five of them displayed bactericidal effects on both planktonic and biofilm cells of S. epidermidis. Except for one, the compounds were found to bind to the YycG protein and to inhibit its auto-phosphorylation in vitro, indicating that they are potential inhibitors of the YycG/YycF two-component system (TCS), which is essential in S. epidermidis. Importantly, all these compounds did not affect the stability of mammalian cells nor hemolytic activities at the concentrations used in our study. Conclusion These novel inhibitors of YycG histidine kinase thus are of potential value as leads for developing new antibiotics against infecting staphylococci. The structure-based virtual screening (SBVS) technology can be widely used in screening potential inhibitors of other bacterial TCSs, since it is more rapid and efficacious than traditional screening technology.

Background: Despite initial success with FLT3 inhibitors (FLT3is), outcomes for FLT3-ITD acute myeloid leukemia (AML) patients remain unsatisfactory, underscoring the need for more effective treatment options. Epigenetic modifications, such as histone acetylation, contribute to AML's onset and persistence, advocating the potential for epigenetic therapies. However, the poor specificity of pan-histone deacetylase inhibitors (HDACis) leads to undesirable adverse effects, prompting the need for isoform-specific HDACis. This study aims to explore the antileukemic activities and mechanisms of IHCH9033, a novel class I HDACi, alone or combined with FLT3i in FLT3-ITD AML. Methods: The viability of AML cell lines and primary AML cells treated with HDACis alone or in combination with FLT3i was detected by MTT or CCK8 assay. Flow cytometry was utilized to examine cell apoptosis, cell cycle progression and ROS production. RNA sequencing analysis, RT-qPCR, western blotting, and co-immunoprecipitation assays were employed to elucidate the molecule mechanisms. The in vivo anti-leukemia efficacy was tested in xenografted mice models derived from FLT3-ITD cell lines and primary AML patients. Results: Here, we identified IHCH9033, a novel selective class I HDACi, which exhibited an increased antitumor effect in FLT3-ITD AML through effectively eliminating leukemia burden and overcoming resistance to FLT3i. Mechanically, IHCH9033 selectively inhibited DNA repair in FLT3-ITD AML cells, leading to the accumulation of DNA damage that eventually resulted in cell cycle arrest and apoptosis. Additionally, IHCH9033 induced HSP90 acetylation, FLT3 ubiquitination, and proteasomal degradation of FLT3, thereby inhibiting FLT3 downstream signaling. Notably, IHCH9033 maintained its potency in both FLT3i-resistant AML cell lines and primary-resistant patient samples, and exerted strong synergy with the FLT3i quizartinib, leading to tumor regression in FLT3-ITD/TKD AML xenografts. In patient-derived xenografts, the treatment with IHCH9033, both alone and in combination, led to nearly complete eradication of the AML burden, without significant adverse effects. Conclusions: Our study shows that IHCH9033, a novel class I HDACi with a desirable pharmacological profile, is a promising drug candidate for FLT3-ITD AML, and suggests a strategy of combining class I HDACis and FLT3is in AML clinical trials to increase efficacy and overcome resistance, thus potentially providing a curative treatment option. [ABSTRACT FROM AUTHOR]

Reactive oxygen species exacerbate nonalcoholic steatohepatitis (NASH) by oxidizing macromolecules; yet how they promote NASH remains poorly understood. Here, we show that peroxidase activity of global hepatic peroxiredoxin (PRDX) is significantly decreased in NASH, and palmitic acid (PA) binds to PRDX1 and inhibits its peroxidase activity. Using three genetic models, we demonstrate that hepatic PRDX1 protects against NASH in male mice. Mechanistically, PRDX1 suppresses STAT signaling and protects mitochondrial function by scavenging hydrogen peroxide, and mitigating the oxidation of protein tyrosine phosphatases and lipid peroxidation. We further identify rosmarinic acid (RA) as a potent agonist of PRDX1. As revealed by the complex crystal structure, RA binds to PRDX1 and stabilizes its peroxidatic cysteine. RA alleviates NASH through specifically activating PRDX1's peroxidase activity. Thus, beyond revealing the molecular mechanism underlying PA promoting oxidative stress and NASH, our study suggests that boosting PRDX1's peroxidase activity is a promising intervention for treating NASH. Oxidative stress is closely linked with nonalcoholic steatohepatitis (NASH). Here, the authors show that palmitic acid stimulates NASH by inhibiting PRDX1 to increase oxidative stress, while rosmarinic acid improves NASH by activating PRDX1 to reduce oxidative stress. [ABSTRACT FROM AUTHOR]