Extensive efforts have been directed at the discovery, investigation and clinical monitoring of targeted therapeutics. These efforts may be facilitated by the convenient access of the genetic, proteomic, interactive and other aspects of the therapeutic targets. Here, we describe an update of the Therapeutic target database (TTD) previously featured in NAR. This update includes: (i) 2000 drug resistance mutations in 83 targets and 104 target/drug regulatory genes, which are resistant to 228 drugs targeting 63 diseases (49 targets of 61 drugs with patient prevalence data); (ii) differential expression profiles of 758 targets in the disease-relevant drug-targeted tissue of 12 615 patients of 70 diseases; (iii) expression profiles of 629 targets in the non-targeted tissues of 2565 healthy individuals; (iv) 1008 target combinations of 1764 drugs and the 1604 target combination of 664 multi-target drugs; (v) additional 48 successful, 398 clinical trial and 21 research targets, 473 approved, 812 clinical trial and 1120 experimental drugs, and (vi) ICD-10-CM and ICD-9-CM codes for additional 482 targets and 262 drugs against 98 disease conditions. This update makes TTD more useful for facilitating the patient focused research, discovery and clinical investigations of the targeted therapeutics. TTD is accessible at http://bidd.nus.edu.sg/group/ttd/ttd.asp.

There has been renewed interests in the exploration of natural products (NPs) for drug discovery, and continuous investigations of the therapeutic claims and mechanisms of traditional and herbal medicines. In-silico methods have been employed for facilitating these studies. These studies and the optimization of in-silico algorithms for NP applications can be facilitated by the quantitative activity and species source data of the NPs. A number of databases collectively provide the structural and other information of ∼470 000 NPs, including qualitative activity information for many NPs, but only ∼4000 NPs are with the experimental activity values. There is a need for the activity and species source data of more NPs. We therefore developed a new database, NPASS (Natural Product Activity and Species Source) to complement other databases by providing the experimental activity values and species sources of 35 032 NPs from 25 041 species targeting 5863 targets (2946 proteins, 1352 microbial species and 1227 cell-lines). NPASS contains 446 552 quantitative activity records (e.g. IC50, Ki, EC50, GI50 or MIC mainly in units of nM) of 222 092 NP-target pairs and 288 002 NP-species pairs. NPASS, http://bidd2.nus.edu.sg/NPASS/, is freely accessible with its contents searchable by keywords, physicochemical property range, structural similarity, species and target search facilities.

Adenosine triphosphate (ATP), commonly produced in mitochondria, is required by almost all the living organisms; thus fluorescent probes for monitoring mitochondrial ATP levels fluctuation are essential and highly desired. Herein, we report a multisite-binding switchable fluorescent probe, ATP-Red 1, which selectively and rapidly responds to intracellular concentrations of ATP. Live-cell imaging indicated that ATP-Red 1 mainly localized to mitochondria with good biocompatibility and membrane penetration. In particular, with the help of ATP-Red 1, we successfully observed not only the decreased mitochondrial ATP levels in the presence of KCN and starvation state, but also the increased mitochondrial ATP levels in the early stage of cell apoptosis. These results indicate that ATP-Red 1 is a useful tool for investigating ATP-relevant biological processes.

Knowledge of protein function is important for biological, medical and therapeutic studies, but many proteins are still unknown in function. There is a need for more improved functional prediction methods. Our SVM-Prot web-server employed a machine learning method for predicting protein functional families from protein sequences irrespective of similarity, which complemented those similarity-based and other methods in predicting diverse classes of proteins including the distantly-related proteins and homologous proteins of different functions. Since its publication in 2003, we made major improvements to SVM-Prot with (1) expanded coverage from 54 to 192 functional families, (2) more diverse protein descriptors protein representation, (3) improved predictive performances due to the use of more enriched training datasets and more variety of protein descriptors, (4) newly integrated BLAST analysis option for assessing proteins in the SVM-Prot predicted functional families that were similar in sequence to a query protein, and (5) newly added batch submission option for supporting the classification of multiple proteins. Moreover, 2 more machine learning approaches, K nearest neighbor and probabilistic neural networks, were added for facilitating collective assessment of protein functions by multiple methods. SVM-Prot can be accessed at http://bidd2.nus.edu.sg/cgi-bin/svmprot/svmprot.cgi.

Aqueous solubility is an important physicochemical property of compounds in anti-cancer drug discovery. Artificial intelligence solubility prediction tools have scored impressive performances by employing regression, machine learning, and deep learning methods. The reported performances vary significantly partly because of the different datasets used. Solubility prediction on novel compounds needs to be improved, which may be achieved by going deeper with deep learning. We constructed deeper-net models of ∼20-layer modified ResNet convolutional neural network architecture, which were trained and tested with 9,943 compounds encoded by molecular fingerprints. Retrospectively tested by 62 recently-published novel compounds, one deeper-net model outperformed four established tools, shallow-net models, and four human experts. Deeper-net models also outperformed others in predicting the solubility values of a series of novel compounds newly-synthesized for anti-cancer drug discovery. Solubility prediction may be improved by going deeper with deep learning. Our deeper-net models are accessible at http://www.npbdb.net/solubility/index.jsp.

The antitumor enzyme asparaginase, which targets essential amino acid L-asparagine and catalyzes it to L-aspartic acid and ammonia, has been used for years in the treatment of acute lymphoblastic leukemia (ALL), subtypes of myeloid leukemia and T-cell lymphomas, whereas the anti-chronic myeloid leukemia (CML) effect of asparaginase and its underlying mechanism has not been completely elucidated. We have shown here that asparaginase induced significant growth inhibition and apoptosis in K562 and KU812 cells. Apart from induction of apoptosis, we reported for the first time that asparaginase induced autophagic response in K562 and KU812 cells as evidenced by the formation of autophagosome, microtubule-associated protein light chain 3 (LC3)-positive autophagy-like vacuoles, and the upregulation of LC3-II. Further study suggested that the Akt/mTOR (mammalian target of rapamycin) and Erk (extracellular signal-regulated kinase) signaling pathway were involved in asparaginase-induced autophagy in K562 cells. Moreover, blocking autophagy using pharmacological inhibitors LY294002, chloroquine (CQ) and quinacrine (QN) enhanced asparaginase-induced cell death and apoptosis, indicating the cytoprotective role of autophagy in asparaginase-treated K562 and KU812 cells. Together, these findings provide a rationale that combination of asparaginase anticancer activity and autophagic inhibition might be a promising new therapeutic strategy for CML.

The frontline tyrosine kinase inhibitor (TKI) imatinib has revolutionized the treatment of patients with chronic myeloid leukemia (CML). However, drug resistance is the major clinical challenge in the treatment of CML. The Hedgehog (Hh) signaling pathway and autophagy are both related to tumorigenesis, cancer therapy, and drug resistance. This study was conducted to explore whether the Hh pathway could regulate autophagy in CML cells and whether simultaneously regulating the Hh pathway and autophagy could induce cell death of drug-sensitive or -resistant BCR-ABL(+) CML cells. Our results indicated that pharmacological or genetic inhibition of Hh pathway could markedly induce autophagy in BCR-ABL(+) CML cells. Autophagic inhibitors or ATG5 and ATG7 silencing could significantly enhance CML cell death induced by Hh pathway suppression. Based on the above findings, our study demonstrated that simultaneously inhibiting the Hh pathway and autophagy could markedly reduce cell viability and induce apoptosis of imatinib-sensitive or -resistant BCR-ABL(+) cells. Moreover, this combination had little cytotoxicity in human peripheral blood mononuclear cells (PBMCs). Furthermore, this combined strategy was related to PARP cleavage, CASP3 and CASP9 cleavage, and inhibition of the BCR-ABL oncoprotein. In conclusion, this study indicated that simultaneously inhibiting the Hh pathway and autophagy could potently kill imatinib-sensitive or -resistant BCR-ABL(+) cells, providing a novel concept that simultaneously inhibiting the Hh pathway and autophagy might be a potent new strategy to overcome CML drug resistance.

Hedgehog (Hh) pathway controls complex developmental processes in vertebrates. Abnormal activation of Hh pathway is responsible for tumorigenesis and maintenance of multiple cancers, and thus addressing this represents promising therapeutic opportunities. In recent years, two Hh inhibitors have been approved for basal cell carcinoma (BCC) treatment and show extraordinary clinical outcomes. Meanwhile, a series of novel agents are being developed for the treatment of several cancers, including lung cancer, leukemia, and pancreatic cancer. Unfortunately, Hh inhibition fails to show satisfactory benefits in these cancer types compared with the success stories in BCC, highlighting the need for better understanding of Hh signaling in cancer. Autophagy, a conserved biological process for cellular component elimination, plays critical roles in the initiation, progression, and drug resistance of cancer, and therefore, implied potential to be targeted. Recent evidence demonstrated that Hh signaling interplays with autophagy in multiple cancers. Importantly, modulating this crosstalk exhibited noteworthy capability to sensitize primary and drug-resistant cancer cells to Hh inhibitors, representing an emerging opportunity to reboot the efficacy of Hh inhibition in those insensitive tumors, and to tackle drug resistance challenges. This review will highlight recent advances of Hh pathway and autophagy in cancers, and focus on their crosstalk and the implied therapeutic opportunities.