Cheminformatics Modeling of Gene Silencing for Both Natural and Chemically Modified siRNAs.

In designing effective siRNAs for a specific mRNA target, it is critically important to have predictive models for the potency of siRNAs. None of the published methods characterized the chemical structures of individual nucleotides constituting a siRNA molecule; therefore, they cannot predict the potency of gene silencing by chemically modified siRNAs (cm-siRNA). We propose a new approach that can predict the potency of gene silencing by cm-siRNAs, which characterizes each nucleotide (NT) using 12 BCUT cheminformatics descriptors describing its charge distribution, hydrophobic and polar properties. Thus, a 21-NT siRNA molecule is described by 252 descriptors resulting from concatenating all the BCUT values of its composing nucleotides. Partial Least Square is employed to develop statistical models. The Huesken data (2431 natural siRNA molecules) were used to perform model building and evaluation for natural siRNAs. Our results were comparable with or superior to those from Huesken's algorithm. The Bramsen dataset (48 cm-siRNAs) was used to build and test the models for cm-siRNAs. The predictive r2 of the resulting models reached 0.65 (or Pearson r values of 0.82). Thus, this new method can be used to successfully model gene silencing potency by both natural and chemically modified siRNA molecules.

Learning Drug-Disease-Target Embedding (DDTE) from knowledge graphs to inform drug repurposing hypotheses.

We aimed to develop and validate a new graph embedding algorithm for embedding drug-disease-target networks to generate novel drug repurposing hypotheses. Our model denotes drugs, diseases and targets as subjects, predicates and objects, respectively. Each entity is represented by a multidimensional vector and the predicate is regarded as a translation vector from a subject to an object vectors. These vectors are optimized so that when a subject-predicate-object triple represents a known drug-disease-target relationship, the summed vector between the subject and the predicate is to be close to that of the object; otherwise, the summed vector is distant from the object. The DTINet dataset was utilized to test this algorithm and discover unknown links between drugs and diseases. In cross-validation experiments, this new algorithm outperformed the original DTINet model. The MRR (Mean Reciprocal Rank) values of our models were around 0.80 while those of the original model were about 0.70. In addition, we have identified and verified several pairs of new therapeutic relations as well as adverse effect relations that were not recorded in the original DTINet dataset. This approach showed excellent performance, and the predicted drug-disease and drug-side-effect relationships were found to be consistent with literature reports. This novel method can be used to analyze diverse types of emerging biomedical and healthcare-related knowledge graphs (KG).

A semantic similarity based methodology for predicting protein-protein interactions: Evaluation with P53-interacting kinases.

Biomedical literature contains unstructured, rich information regarding proteins, ligands, diseases as well as biological pathways in which they are involved. Systematically analyzing such textual corpus has the potential for biomedical discovery of new protein-protein interactions and hidden drug indications. For this purpose, we have investigated a methodology that is based on a well-established text mining tool, Word2Vec, for the analysis of PubMed full text articles to derive word embeddings, and the use of a simple semantic similarity comparison either by itself or in conjunction with k-Nearest Neighbor (kNN) technique for the prediction of new relationships. To test this methodology, three lines of retrospective analyses of a dataset with known P53-interacting proteins have been conducted. First, we demonstrated that Word2Vec semantic similarity can infer functional relatedness among all kinases known to interact with P53. Second, in a series of time-split experiments, we demonstrated that both a simple similarity comparison and kNN models built with papers published up to a certain year were able to discover P53 interactors described in later publications. Third, in a different scenario of time-split experiments, we examined the predictions of P53-interacting proteins based on the kNN models built on data prior to a certain split year for different time ranges past that year, and found that the cumulative number of correct predictions was indeed increasing with time. We conclude that text mining of research papers in the PubMed literature based on Word2Vec analysis followed by a simple similarity comparison or kNN modeling affords excellent predictions of protein-protein interactions between P53 and kinases, and should have wide applications in translational biomedical studies such as repurposing of existing drugs, drug-drug interaction, and elucidation of mechanisms of action for drugs.

Literature-Wide Association Studies (LWAS) for a Rare Disease: Drug Repurposing for Inflammatory Breast Cancer.

Drug repurposing is an effective means for rapid drug discovery. The aim of this study was to develop and validate a computational methodology based on Literature-Wide Association Studies (LWAS) of PubMed to repurpose existing drugs for a rare inflammatory breast cancer (IBC). We have developed a methodology that conducted LWAS based on the text mining technology Word2Vec. 3.80 million "cancer"-related PubMed abstracts were processed as the corpus for Word2Vec to derive vector representation of biological concepts. These vectors for drugs and diseases served as the foundation for creating similarity maps of drugs and diseases, respectively, which were then employed to find potential therapy for IBC. Three hundred and thirty-six (336) known drugs and three hundred and seventy (370) diseases were expressed as vectors in this study. Nine hundred and seventy (970) previously known drug-disease association pairs among these drugs and diseases were used as the reference set. Based on the hypothesis that similar drugs can be used against similar diseases, we have identified 18 diseases similar to IBC, with 24 corresponding known drugs proposed to be the repurposing therapy for IBC. The literature search confirmed most known drugs tested for IBC, with four of them being novel candidates. We conclude that LWAS based on the Word2Vec technology is a novel approach to drug repurposing especially useful for rare diseases.

TNFAIP8 regulates autophagy, cell steatosis, and promotes hepatocellular carcinoma cell proliferation.

Tumor necrosis factor-α-induced protein 8 (TNFAIP8) expression has been linked to tumor progression in various cancer types, but the detailed mechanisms of TNFAIP8 are not fully elucidated. Here we define the role of TNFAIP8 in early events associated with development of hepatocellular carcinoma (HCC). Increased TNFAIP8 levels in HCC cells enhanced cell survival by blocking apoptosis, rendering HCC cells more resistant to the anticancer drugs, sorafenib and regorafenib. TNFAIP8 also induced autophagy and steatosis in liver cancer cells. Consistent with these observations, TNFAIP8 blocked AKT/mTOR signaling and showed direct interaction with ATG3-ATG7 proteins. TNFAIP8 also exhibited binding with fatty acids and modulated expression of lipid/fatty-acid metabolizing enzymes. Chronic feeding of mice with alcohol increased hepatic levels of TNFAIP8, autophagy, and steatosis but not in high-fat-fed obese mice. Similarly, higher TNFAIP8 expression was associated with steatotic livers of human patients with a history of alcohol use but not in steatotic patients with no history of alcohol use. Our data indicate a novel role of TNFAIP8 in modulation of drug resistance, autophagy, and hepatic steatosis, all key early events in HCC progression.

Oncogenic Role of Tumor Necrosis Factor α-Induced Protein 8 (TNFAIP8).

Tumor necrosis factor (TNF)-α-induced protein 8 (TNFAIP8) is a founding member of the TIPE family, which also includes TNFAIP8-like 1 (TIPE1), TNFAIP8-like 2 (TIPE2), and TNFAIP8-like 3 (TIPE3) proteins. Expression of TNFAIP8 is strongly associated with the development of various cancers including cancer of the prostate, liver, lung, breast, colon, esophagus, ovary, cervix, pancreas, and others. In human cancers, TNFAIP8 promotes cell proliferation, invasion, metastasis, drug resistance, autophagy, and tumorigenesis by inhibition of cell apoptosis. In order to better understand the molecular aspects, biological functions, and potential roles of TNFAIP8 in carcinogenesis, in this review, we focused on the expression, regulation, structural aspects, modifications/interactions, and oncogenic role of TNFAIP8 proteins in human cancers.

Receptor-based pharmacophore and pharmacophore key descriptors for virtual screening and QSAR modeling.

The intuitive nature of the pharmacophore concept has made it widely accepted by the medicinal chemistry community, evidenced by the past 3 decades of development and application of computerized pharmacophore modeling tools. On the other hand, shape complementarity has been recognized as a critical factor in molecular recognition between drugs and their receptors. Recent development of fast and accurate shape comparison tools has facilitated the wide spread use of shape matching technologies in drug discovery. However, pharmacophore and shape technologies, if used separately, often lead to high false positive rate. Thus, integrating pharmacophore matching and shape matching technologies into one program has the potential to reduce the false positive rates in virtual screening. Other issues of current pharmacophore technologies include sometimes high false negative rate and non-quantitative prediction. In this article, we first focus on a recently implemented method (Shape4) that combines receptor based shape matching and pharmacophore comparison in a single algorithm to create shape pharmacophore models for virtual screening. We also examine a recent example that utilizes multi-complex information to develop receptor-based pharmacophore models that promises to reduce false negative rate. Finally, we review several methods that employ receptor-based pharmacophore map and pharmacophore key descriptors for QSAR modeling. We conclude by emphasizing the concept of receptor-based shape pharmacophore and its roles in future drug discovery.

Structure-based quantitative structure--activity relationship modeling of estrogen receptor ß-ligands.

BACKGROUND: A variety of chemotypes have been studied as estrogen receptor (ER) ß-selective ligands for potential drugs against various indications, including neurodegenerative diseases. Their structure--activity relationship data and the x-ray structures of the ERß ligand-binding domain bound with different ligands have become available. Thus, it is vitally important for future development of ERß-selective ligands that robust quantitative structure-activity relationship (QSAR) models be built. METHODS/RESULTS: We employed a newly developed structure--based QSAR method (structure-based pharmacophore keys QSAR) that utilizes both the structure--activity relationship data and the 3D structural information of ERß, as well as a robust QSAR workflow to analyze 37 ligands. Four sets of QSAR models were obtained, among which approximately 30 models afforded high (>0.60) training-r(2) and test set-R(2) statistics. CONCLUSION: We have obtained an ensemble of predictive models of ERß ligands that will be useful in the future discovery of novel ERß-selective molecules.

A novel structure-based multimode QSAR method affords predictive models for phosphodiesterase inhibitors.

Quantitative structure-activity relationship (QSAR) methods aim to build quantitatively predictive models for the discovery of new molecules. It has been widely used in medicinal chemistry for drug discovery. Many QSAR techniques have been developed since Hansch's seminal work, and more are still being developed. Motivated by Hopfinger's receptor-dependent QSAR (RD-QSAR) formalism and the Lukacova-Balaz scheme to treat multimode issues, we have initiated studies that focus on a structure-based multimode QSAR (SBMM QSAR) method, where the structure of the target protein is used in characterizing the ligand, and the multimode issue of ligand binding is systematically treated with a modified Lukacova-Balaz scheme. All ligand molecules are first docked to the target binding pocket to obtain a set of aligned ligand poses. A structure-based pharmacophore concept is adopted to characterize the binding pocket. Specifically, we represent the binding pocket as a geometric grid labeled by pharmacophoric features. Each pose of the ligand is also represented as a labeled grid, where each grid point is labeled according to the atom types of nearby ligand atoms. These labeled grids or three-dimensional (3D) maps (both the receptor map (R-map) and the ligand map (L-map)) are compared to each other to derive descriptors for each pose of the ligand, resulting in a multimode structure-activity relationship (SAR) table. Iterative partial least-squares (PLS) is employed to build the QSAR models. When we applied this method to analyze PDE-4 inhibitors, predictive models have been developed, obtaining models with excellent training correlation (r(2) = 0.65-0.66), as well as test correlation (R(2) = 0.64-0.65). A comparative analysis with 4 other QSAR techniques demonstrates that this new method affords better models, in terms of the prediction power for the test set.

Structure-based shape pharmacophore modeling for the discovery of novel anesthetic compounds.

Current anesthetics, especially the inhaled ones, have troublesome side effects and may be associated with durable changes in cognition. It is therefore highly desirable to develop novel chemical entities that reduce these effects while preserving or enhancing anesthetic potency. In spite of progress toward identifying protein targets involved in anesthesia, we still do not have the necessary atomic level structural information to delineate their interactions with anesthetic molecules. Recently, we have described a protein target, apoferritin, to which several anesthetics bind specifically and in a pharmacodynamically relevant manner. Further, we have reported the high resolution X-ray structure of two anesthetic/apoferritin complexes (Liu, R.; Loll, P. J.; Eckenhoff, R. G. FASEB J. 2005, 19, 567). Thus, we describe in this paper a structure-based approach to establish validated shape pharmacophore models for future application to virtual and high throughput screening of anesthetic compounds. We use the 3D structure of apoferritin as the basis for the development of several shape pharmacophore models. To validate these models, we demonstrate that (1) they can be used to effectively recover known anesthetic agents from a diverse database of compounds; (2) the shape pharmacophore scores afford a significant linear correlation with the measured binding energetics of several known anesthetic compounds to the apoferritin site; and (3) the computed scores based on the shape pharmacophore models also predict the trend of the EC(50) values of a set of anesthetics. Therefore, we have now obtained a set of structure-based shape pharmacophore models, using ferritin as the surrogate target, which may afford a new way to rationally discover novel anesthetic agents in the future.

Development of improved models for phosphodiesterase-4 inhibitors with a multi-conformational structure-based QSAR method.

Phosphodiesterase-4 (PDE-4) is an important drug target for several diseases, including COPD (chronic obstructive pulmonary disorder) and neurodegenerative diseases. In this paper, we describe the development of improved QSAR (quantitative structure-activity relationship) models using a novel multi-conformational structure-based pharmacophore key (MC-SBPPK) method. Similar to our previous work, this method calculates molecular descriptors based on the matching of a molecule's pharmacophore features with those of the target binding pocket. Therefore, these descriptors are PDE4-specific, and most relevant to the problem under study. Furthermore, this work expands our previous SBPPK QSAR method by explicitly including multiple conformations of the PDE-4 inhibitors in the regression analysis, and thus addresses the issue of molecular flexibility. The nonlinear regression problem resulted from including multiple conformations has been transformed into a linear equation and solved by an iterative partial least square (iPLS) procedure, according to the Lukacova-Balaz scheme. 35 PDE-4 inhibitors have been analyzed with this new method, and predictive models have been developed. Based on the prediction statistics for both the training set and the test set, these new models are more robust and predictive than those obtained by traditional ligand-based QSAR techniques as well as that obtained with the SBPPK method reported in our previous work. As a result, multiple predictive models have been added to the collection of QSAR models for PDE4 inhibitors. Collectively, these models will be useful for the discovery of new drug candidates targeting the PDE-4 enzyme.

A new structure-based QSAR method affords both descriptive and predictive models for phosphodiesterase-4 inhibitors.

We describe the application of a new QSAR (quantitative structure-activity relationship) formalism to the analysis and modeling of PDE-4 inhibitors. This new method takes advantage of the X-ray structural information of the PDE-4 enzyme to characterize the small molecule inhibitors. It calculates molecular descriptors based on the matching of their pharmacophore feature pairs with those (the reference) of the target binding pocket. Since the reference is derived from the X-ray crystal structures of the target under study, these descriptors are target-specific and easy to interpret. We have analyzed 35 indole derivative-based PDE-4 inhibitors where Partial Least Square (PLS) analysis has been employed to obtain the predictive models. Compared to traditional QSAR methods such as CoMFA and CoMSIA, our models are more robust and predictive measured by statistics for both the training and test sets of molecules. Our method can also identify critical pharmacophore features that are responsible for the inhibitory potency of the small molecules. Thus, this structure-based QSAR method affords both descriptive and predictive models for phosphodiesterase-4 inhibitors. The success of this study has also laid a solid foundation for systematic QSAR modeling of the PDE family of enzymes, which will ultimately contribute to chemical genomics research and drug discovery targeting the PDE enzymes.

Ab initio study of the role of entropy in the kinetics of acetylene production in filament-assisted diamond growth environments.

We present a new theoretical strategy, ab initio rate constants plus integration of rate equations, that is used to characterize the role of entropy in driving high-temperature/low-pressure hydrocarbon chemical kinetics typical of filament-assisted diamond growth environments. Twelve elementary processes were analyzed that produce a viable pathway for converting methane in a feed gas to acetylene. These calculations clearly relate the kinetics of this conversion to the properties of individual species, demonstrating that (1) loss of translational entropy restricts addition of hydrogen (and other radical species) to unsaturated carbon-carbon bonds, (2) rotational entropy determines the direction of the rate-limiting abstraction reactions, and (3) the overall pathway is enhanced by high beta-scission reaction rates driven by translational entropy. These results suggest that the proposed strategy is likely applicable to understand gas-phase chemistry occurring in the systems of combustion and other chemical vapor depositions.