Dingfeng Wu

Combining genomic and network characteristics for extended capability in predicting synergistic drugs for cancer

The identification of synergistic chemotherapeutic agents from a large pool of candidates is highly challenging. Here, we present a Ranking-system of Anti-Cancer Synergy (RACS) that combines features of targeting networks and transcriptomic profiles, and validate it on three types of cancer. Using data on human β-cell lymphoma from the Dialogue for Reverse Engineering Assessments and Methods consortium we show a probability concordance of 0.78 compared with 0.61 obtained with the previous best algorithm. We confirm 63.6% of our breast cancer predictions through experiment and literature, including four strong synergistic pairs. Further in vivo screening in a zebrafish MCF7 xenograft model confirms one prediction with strong synergy and low toxicity. Validation using A549 lung cancer cells shows similar results. Thus, RACS can significantly improve drug synergy prediction and markedly reduce the experimental prescreening of existing drugs for repurposing to cancer treatment, although the molecular mechanism underlying particular interactions remains unknown.

SEPPA 2.0—more refined server to predict spatial epitope considering species of immune host and subcellular localization of protein antigen

Spatial Epitope Prediction server for Protein Antigens (SEPPA) has received lots of feedback since being published in 2009. In this improved version, relative ASA preference of unit patch and consolidated amino acid index were added as further classification parameters in addition to unit-triangle propensity and clustering coefficient which were previously reported. Then logistic regression model was adopted instead of the previous simple additive one. Most importantly, subcellular localization of protein antigen and species of immune host were fully taken account to improve prediction. The result shows that AUC of 0.745 (5-fold cross-validation) is almost the baseline performance with no differentiation like all the other tools. Specifying subcellular localization of protein antigen and species of immune host will generally push the AUC up. Secretory protein immunized to mouse can push AUC to 0.823. In this version, the false positive rate has been largely decreased as well. As the first method which has considered the subcellular localization of protein antigen and species of immune host, SEPPA 2.0 shows obvious advantages over the other popular servers like SEPPA, PEPITO, DiscoTope-2, B-pred, Bpredictor and Epitopia in supporting more specific biological needs. SEPPA 2.0 can be accessed at http://badd.tongji.edu.cn/seppa/. Batch query is also supported.

Screening of selective histone deacetylase inhibitors by proteochemometric modeling

BackgroundHistone deacetylase (HDAC) is a novel target for the treatment of cancer and it can be classified into three classes, i.e., classes I, II, and IV. The inhibitors selectively targeting individual HDAC have been proved to be the better candidate antitumor drugs. To screen selective HDAC inhibitors, several proteochemometric (PCM) models based on different combinations of three kinds of protein descriptors, two kinds of ligand descriptors and multiplication cross-terms were constructed in our study.ResultsThe results show that structure similarity descriptors are better than sequence similarity descriptors and geometry descriptors in the leftacterization of HDACs. Furthermore, the predictive ability was not improved by introducing the cross-terms in our models. Finally, a best PCM model based on protein structure similarity descriptors and 32-dimensional general descriptors was derived (R2 = 0.9897, Qtest2 = 0.7542), which shows a powerful ability to screen selective HDAC inhibitors.ConclusionsOur best model not only predict the activities of inhibitors for each HDAC isoform, but also screen and distinguish class-selective inhibitors and even more isoform-selective inhibitors, thus it provides a potential way to discover or design novel candidate antitumor drugs with reduced side effect.

Systematic transcriptome analysis reveals elevated expression of alcohol-metabolizing genes in NAFLD livers

Obese animals and non‐alcoholic fatty liver disease (NAFLD) patients exhibit elevated blood alcohol, suggesting potential contributions of alcohol metabolism to the development of NAFLD. Liver gene expression in patients with biopsy‐proven mild (N = 40) and severe (N = 32) NAFLD were compared to that in healthy liver donors (N = 7) and alcoholic hepatitis (AH; N = 15) using microarrays. Principal components analyses (PCA) revealed similar gene expression patterns between mild and severe NAFLD which clustered with those of AH but were distinct from those of healthy livers. Differential gene expression between NAFLD and healthy livers was consistent with established NAFLD‐associated genes and NAFLD pathophysiology. Alcohol‐metabolizing enzymes including ADH, ALDH, CYP2E1, and CAT were up‐regulated in NAFLD livers. The expression level of alcohol‐metabolizing genes in severe NAFLD was similar to that in AH. The NAFLD gene expression profiles provide new directions for future investigations to identify disease markers and targets for prevention and treatment, as well as to foster our understanding of NAFLD pathogenesis and pathophysiology. Particularly, increased expression of alcohol‐metabolizing genes in NAFLD livers supports a role for endogenous alcohol metabolism in NAFLD pathology and provides further support for gut microbiome therapy in NAFLD management. Copyright

Study on human GPCR–inhibitor interactions by proteochemometric modeling

G protein-coupled receptors (GPCRs) are the most frequently addressed drug targets in the pharmaceutical industry. However, achieving highly safety and efficacy in designing of GPCR drugs is quite challenging since their primary amino acid sequences show fairly high homology. Systematic study on the interaction spectra of inhibitors with multiple human GPCRs will shed light on how to design the inhibitors for different diseases which are related to GPCRs. To reach this goal, several proteochemometric models were constructed based on different combinations of two protein descriptors, two ligand descriptors and one ligand-receptor cross-term by two kinds of statistical learning techniques. Our results show that support vector regression (SVR) performs better than Gaussian processes (GP) for most combinations of descriptors. The transmembrane (TM) identity descriptors have more powerful ability than the z-scale descriptors in the characterization of GPCRs. Furthermore, the performance of our PCM models was not improved by introducing the cross-terms. Finally, based on the TM Identity descriptors and 28-dimensional drug-like index, two best PCM models with GP and SVR (GP-S-DLI: R(2)=0.9345, Q(2)test=0.7441; SVR-S-DLI: R(2)=1.0000, Q(2)test=0.7423) were derived respectively. The area of ROC curve was 0.8940 when an external test set was used, which indicates that our PCM model obtained a powerful capability for predicting new interactions between GPCRs and ligands. Our results indicate that the derived best model has a high predictive ability for human GPCR-inhibitor interactions. It can be potentially used to discover novel multi-target or specific inhibitors of GPCRs with higher efficacy and fewer side effects.

The recent progress in proteochemometric modelling: focusing on target descriptors, cross-term descriptors and application scope

Abstract As an extension of the conventional quantitative structure activity relationship models, proteochemometric (PCM) modelling is a computational method that can predict the bioactivity relations between multiple ligands and multiple targets. Traditional PCM modelling includes three essential elements: descriptors (including target descriptors, ligand descriptors and cross‐term descriptors), bioactivity data and appropriate learning functions that link the descriptors to the bioactivity data. Since its appearance, PCM modelling has developed rapidly over the past decade by taking advantage of the progress of different descriptors and machine learning techniques, along with the increasing amounts of available bioactivity data. Specifically, the new emerging target descriptors and cross‐term descriptors not only significantly increased the performance of PCM modelling but also expanded its application scope from traditional protein‐ligand interaction to more abundant interactions, including protein‐peptide, protein‐DNA and even protein‐protein interactions. In this review, target descriptors and cross‐term descriptors, as well as the corresponding application scope, are intensively summarized. Additionally, we look forward to seeing PCM modelling extend into new application scopes, such as Target‐Catalyst‐Ligand systems, with the further development of descriptors, machine learning techniques and increasing amounts of available bioactivity data.

Proteochemometric Modeling of the Antigen-Antibody Interaction: New Fingerprints for Antigen, Antibody and Epitope-Paratope Interaction

Despite the high specificity between antigen and antibody binding, similar epitopes can be recognized or cross-neutralized by paratopes of antibody with different binding affinities. How to accurately characterize this slight variation which may or may not change the antigen-antibody binding affinity is a key issue in this area. In this report, by combining cylinder model with shell structure model, a new fingerprint was introduced to describe both the structural and physical-chemical features of the antigen and antibody protein. Furthermore, beside the description of individual protein, the specific epitope-paratope interaction fingerprint (EPIF) was developed to reflect the bond and the environment of the antigen-antibody interface. Finally, Proteochemometric Modeling of the antigen-antibody interaction was established and evaluated on 429 antigen-antibody complexes. By using only protein descriptors, our model achieved the best performance (R2=0.91,Qtest2=0.68) among peers. Further, together with EPIF as a new cross-term, our model (R2=0.92,Qtest2=0.74) can significantly outperform peers with multiplication of ligand and protein descriptors as a cross-term (R2≤0.81,Qtest2≤0.44). Results illustrated that: 1) our newly designed protein fingerprints and EPIF can better describe the antigen-antibody interaction; 2) EPIF is a better and specific cross-term in Proteochemometric Modeling for antigen-antibody interaction. The fingerprints designed in this study will provide assistance to the description of antigen-antibody binding, and in future, it may be valuable help for the high-throughput antibody screening. The algorithm is freely available on request.

Systematic toxicity mechanism analysis of proton pump inhibitors: an in silico study.

Proton pump inhibitors (PPIs) are extensively used for the treatment of gastric acid-related disorders. PPIs appear to be well tolerated and almost have no short-term side effects. However, the clinical adverse reactions of long-term PPI usage are increasingly reported in recent years. So far, there is no study that elucidates the side effect mechanisms of PPIs comprehensively and systematically. In this study, a well-defined small molecule perturbed microarray data set of 344 compounds and 1695 samples was analyzed. With this high-throughput data set, a new index (Identity, I) was designed to identify PPI-specific differentially expressed genes. Results indicated that (1) up-regulated genes, such as RETSAT, CYP1A1, CYP1A2, and UGT, enhanced vitamin As metabolism processes in the cellular retinol metabolism pathway; and that (2) down-regulated genes, such as C1QA, C1QC, C4BPA, C4BPB, CFI, and SERPING1, enriched in the complement and coagulation cascades pathway. In addition, strong association was observed between these PPI-specific differentially expressed genes and the reported side effects of PPIs by the gene-disease association network analysis. One potential toxicity mechanism of PPIs as suggested from this systematic PPI-specific gene expression analysis is that PPIs are enriched in acidic organelles where they are activated and inhibit V-ATPases and acid hydrolases, and consequently block the pathways of antigen presentation, the synthesis and secretion of cytokines, and complement component proteins and coagulation factors. The strategies developed in this work could be extended to studies on other drugs.

Incorporating structure context of HA protein to improve antigenicity calculation for influenza virus A/H3N2.

The rapid and consistent mutation of influenza requires frequent evaluation of antigenicity variation among newly emerged strains, during which several in-silico methods have been reported to facilitate the assays. In this paper, we designed a structure-based antigenicity scoring model instead of those sequence-based previously published. Protein structural context was adopted to derive the antigenicity-dominant positions, as well as the physic-chemical change of local micro-environment in correlation with antigenicity change. Then a position specific scoring matrix (PSSM) profile and local environmental change over above positions were integrated to predict the antigenicity variance. Independent testing showed a high accuracy of 0.875, and sensitivity of 0.986, with a significant ability to discover antigenic-escaping strains. When applying this model to the historical data, global and regional antigenic drift events can be successfully detected. Furthermore, two well-known vaccine failure events were clearly suggested. Therefore, this structure-context model may be particularly useful to identify those to-be-failed vaccine strains, in addition to suggest potential new vaccine strains.

CE-BLAST makes it possible to compute antigenic similarity for newly emerging pathogens

Major challenges in vaccine development include rapidly selecting or designing immunogens for raising cross-protective immunity against different intra- or inter-subtypic pathogens, especially for the newly emerging varieties. Here we propose a computational method, Conformational Epitope (CE)-BLAST, for calculating the antigenic similarity among different pathogens with stable and high performance, which is independent of the prior binding-assay information, unlike the currently available models that heavily rely on the historical experimental data. Tool validation incorporates influenza-related experimental data sufficient for stability and reliability determination. Application to dengue-related data demonstrates high harmonization between the computed clusters and the experimental serological data, undetectable by classical grouping. CE-BLAST identifies the potential cross-reactive epitope between the recent zika pathogen and the dengue virus, precisely corroborated by experimental data. The high performance of the pathogens without the experimental binding data suggests the potential utility of CE-BLAST to rapidly design cross-protective vaccines or promptly determine the efficacy of the currently marketed vaccine against emerging pathogens, which are the critical factors for containing emerging disease outbreaks.Sparse immune-binding data for emerging pathogens limits the ability of existing in silico antigenicity prediction methods to aid vaccine design. Here, the authors introduce a computational method that estimates antigenic pathogen similarity based on epitope structure.