Institute of Information Science, Academia Sinica

Predicting binding sites of a transcription factor (TF) in the genome is an important but challenging issue in studying gene regulation. To further utilize the available structural models in PDB, we developed a web server, PiDNA, that aims at first constructing reliable position weight matrices (PWMs) by applying an atomic-level knowledge-based scoring function on numerous in silico mutated complex structures, and then using the PWM constructed by the structure models with small energy changes to predict the interaction between proteins and DNA sequences. Three types of information can be downloaded after prediction: (a) the ranked list of mutated sequences; (b) the PWM constructed by the favourable mutated structures; and (c) any mutated protein-DNA complex structure models specified by the user. This talk will first show that PiDNA delivers reliable PWMs. Second, the prediction accuracy of PiDNA in detecting relatively high-specificity sites is evaluated by comparing the ranked lists against in vitro experiments from protein binding microarrays (PBMs). Finally, PiDNA is shown to be able to select the known binding sites from random ones with high accuracy. With PiDNA, the users can design biological experiments based on the predicted specificity and/or request mutated structure models for further protein design.