Therapeutic target database update 2014: a resource for targeted therapeutics.

Here we describe an update of the Therapeutic Target Database (http://bidd.nus.edu.sg/group/ttd/ttd.asp) for better serving the bench-to-clinic communities and for enabling more convenient data access, processing and exchange. Extensive efforts from the research, industry, clinical, regulatory and management communities have been collectively directed at the discovery, investigation, application, monitoring and management of targeted therapeutics. Increasing efforts have been directed at the development of stratified and personalized medicines. These efforts may be facilitated by the knowledge of the efficacy targets and biomarkers of targeted therapeutics. Therefore, we added search tools for using the International Classification of Disease ICD-10-CM and ICD-9-CM codes to retrieve the target, biomarker and drug information (currently enabling the search of almost 900 targets, 1800 biomarkers and 6000 drugs related to 900 disease conditions). We added information of almost 1800 biomarkers for 300 disease conditions and 200 drug scaffolds for 700 drugs. We significantly expanded Therapeutic Target Database data contents to cover >2300 targets (388 successful and 461 clinical trial targets), 20 600 drugs (2003 approved and 3147 clinical trial drugs), 20,000 multitarget agents against almost 400 target-pairs and the activity data of 1400 agents against 300 cell lines.

Design, synthesis, and biological evaluation of curcumin analogues as multifunctional agents for the treatment of Alzheimer's disease.

A series of novel curcumin analogues were designed, synthesized, and evaluated as potential multifunctional agents for the treatment of AD. The in vitro studies showed that these compounds had better inhibitory properties against Aß aggregation than curcumin. Superior anti-oxidant properties (better than the reference compound Trolox) of these compounds were observed by the oxygen radical absorbance capacity (ORAC) method and a cell-based assay using DCFH-DA as a probe. In addition they were able to chelate metals such as iron and copper and decrease metal-induced Aß aggregation. The structure-activity relationships were discussed. The results suggested that our curcumin analogues could be selected as multifunctional agents for further investigation of AD treatment.

SVM-Prot 2016: A Web-Server for Machine Learning Prediction of Protein Functional Families from Sequence Irrespective of Similarity.

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.

The Assessment of the Readiness of Molecular Biomarker-Based Mobile Health Technologies for Healthcare Applications.

Mobile health technologies to detect physiological and simple-analyte biomarkers have been explored for the improvement and cost-reduction of healthcare services, some of which have been endorsed by the US FDA. Advancements in the investigations of non-invasive and minimally-invasive molecular biomarkers and biomarker candidates and the development of portable biomarker detection technologies have fuelled great interests in these new technologies for mhealth applications. But apart from the development of more portable biomarker detection technologies, key questions need to be answered and resolved regarding to the relevance, coverage, and performance of these technologies and the big data management issues arising from their wide spread applications. In this work, we analyzed the newly emerging portable biomarker detection technologies, the 664 non-invasive molecular biomarkers and the 592 potential minimally-invasive blood molecular biomarkers, focusing on their detection capability, affordability, relevance, and coverage. Our analysis suggests that a substantial percentage of these biomarkers together with the new technologies can be potentially used for a variety of disease conditions in mhealth applications. We further propose a new strategy for reducing the workload in the processing and analysis of the big data arising from widespread use of mhealth products, and discuss potential issues of implementing this strategy.