Development Of Database And Computational Methods For Disease Detection And Drug Discovery

Ph.DDOCTOR OF PHILOSOPH

Genome wide exploration of the origin and evolution of amino acids

Background: Even after years of exploration, the terrestrial origin of bio-molecules remains unsolved and controversial. Today, observation of amino acid composition in proteins has become an alternative way for a global understanding of the mystery encoded in whole genomes and seeking clues for the origin of amino acids. Results: In this study, we statistically monitored the frequencies of 20 alpha-amino acids in 549 taxa from three kingdoms of life: archaebacteria, eubacteria, and eukaryotes. We found that the amino acids evolved independently in these three kingdoms; but, conserved linkages were observed in two groups of amino acids, (A, G, H, L, P, Q, R, and W) and (F, I, K, N, S, and Y). Moreover, the amino acids encoded by GC-poor codons (F, Y, N, K, I, and M) were found to "lose" their usage in the development from single cell eukaryotic organisms like S. cerevisiae to H. sapiens, while the amino acids encoded by GC-rich codons (P, A, G, and W) were found to gain usage. These findings further support the co-evolution hypothesis of amino acids and genetic codes. Conclusion: We proposed a new chronological order of the appearance of amino acids (L, A, V/E/G, S, I, K, T, R/D, P, N, F, Q, Y, M, H, W, C). Two conserved evolutionary paths of amino acids were also suggested: A -> G -> R -> P and K -> Y.National Natural Science Foundation of China [20572061, 20732004]; Program for New Century Excellent Talents in University (NCET) of MO

Update of TTD: Therapeutic Target Database

Increasing numbers of proteins, nucleic acids and other molecular entities have been explored as therapeutic targets, hundreds of which are targets of approved and clinical trial drugs. Knowledge of these targets and corresponding drugs, particularly those in clinical uses and trials, is highly useful for facilitating drug discovery. Therapeutic Target Database (TTD) has been developed to provide information about therapeutic targets and corresponding drugs. In order to accommodate increasing demand for comprehensive knowledge about the primary targets of the approved, clinical trial and experimental drugs, numerous improvements and updates have been made to TTD. These updates include information about 348 successful, 292 clinical trial and 1254 research targets, 1514 approved, 1212 clinical trial and 2302 experimental drugs linked to their primary targets (3382 small molecule and 649 antisense drugs with available structure and sequence), new ways to access data by drug mode of action, recursive search of related targets or drugs, similarity target and drug searching, customized and whole data download, standardized target ID, and significant increase of data (1894 targets, 560 diseases and 5028 drugs compared with the 433 targets, 125 diseases and 809 drugs in the original release described in previous paper). This database can be accessed at http://bidd.nus.edu.sg/group/cjttd/TTD.asp

A two-step target binding and selectivity support vector machines approach for virtual screening of dopamine receptor subtype-selective ligands

10.1371/journal.pone.0039076PLoS ONE76

The performance of our new method 2SBR-SVM and that of previously used methods Combi-SVM, ML-kNN and RAkEL-DT in predicting dopamine receptor multi-subtype ligands as non-selective ligands.

<p>The performance of our new method 2SBR-SVM and that of previously used methods Combi-SVM, ML-kNN and RAkEL-DT in predicting dopamine receptor multi-subtype ligands as non-selective ligands.</p

Top-ranked molecular descriptors for distinguishing dopamine receptor subtype D1, D2, D3 or D4 selective ligands selected by RFE feature selection method.

<p>Top-ranked molecular descriptors for distinguishing dopamine receptor subtype D1, D2, D3 or D4 selective ligands selected by RFE feature selection method.</p

Datasets of our collected dopamine receptor D1, D2, D3 and D4 selective ligands against another subtype.

<p>The binding affinity ratio is the experimentally measured binding affinity to the second subtype divided by that to the first subtype: (Ki of the second subtype / Ki of the first subtype). This dataset was used as positive samples for testing subtype selectivity of our developed virtual screening models.</p

Datasets of our collected dopamine receptor D1, D2, D3 and D4 ligands, non-ligands and putative non-ligands.

<p>Dopamine receptor D1, D2, D3 and D4 ligands (Ki <1 μM) and non-ligands (ki >10 μM) were collected as described in method section, and putative non-ligands were generated from representative compounds of compound families with no known ligand. These datasets were used for training and testing the multi-label machine learning models.</p