Structure-based ab initio prediction of transcription factor-binding sites.
We present an all-atom molecular modeling method that can predict the binding specificity of a transcription factor based on its 3D structure, with no further information required. We use molecular dynamics and free energy calculations to compute the relative binding free energies for a transcription factor with multiple possible DNA sequences. These sequences are then used to construct a position weight matrix to represent the transcription factor-binding sites. Free energy differences are calculated by morphing one base pair into another using a multi-copy representation in which multiple base pairs are superimposed at a single DNA position. Water-mediated hydrogen bonds between transcription factor side chains and DNA bases are known to contribute to binding specificity for certain transcription factors. To account for this important effect, the simulation protocol includes an explicit molecular water solvent and counter-ions. For computational efficiency, we use a standard additive approximation for the contribution of each DNA base pair to the total binding free energy. The additive approximation is not strictly necessary, and more detailed computations could be used to investigate non-additive effects.
Liu
L Angela
LA
Department of Biomedical Engineering and Institute for Multiscale Modeling of Biological Interactions, John Hopkins University, Baltimore, MD, USA.
Bader
Joel S
JS
D000465:Algorithms; D000818:Animals; D001665:Binding Sites; D019295:Computational Biology / Q000191:economics / Q000379:methods*; D003198:Computer Simulation; D004247:DNA / Q000737:chemistry / Q000378:metabolism*; D005544:Forecasting; D006801:Humans; D008954:Models, Biological; D008958:Models, Molecular; D011487:Protein Conformation; D013379:Substrate Specificity; D014157:Transcription Factors / Q000737:chemistry* / Q000378:metabolism*
D014157:Transcription Factors; D004247:DNA