A computational tool for designing FRET protein biosensors by rigid-body sampling of their conformational space.

Biosensors relying on the fluorescence resonance energy transfer (FRET) between fluorescent proteins have been used for live-cell imaging of cellular events including Ca(2+) signaling. The efficiency of energy transfer between the donor and acceptor fluorescent proteins depends on the relative distance and orientation between them, which become altered by conformational changes of a fused sensory protein caused by a cellular event. In this way, changes in FRET efficiency of Ca(2+) biosensors can be correlated with Ca(2+) concentrations. The design of these FRET biosensors can be improved by modeling conformational changes before and after a cellular event. Hence, a computational tool called FPMOD was developed to predict FRET efficiency changes by constructing FRET biosensors and sampling their conformational space through rigid-body rotation. We showed with FPMOD that our computational modeling approach can qualitatively predict the FRET efficiencies of a range of biosensors, which had strong agreement with experimental results.

160-fold acceleration of the Smith-Waterman algorithm using a field programmable gate array (FPGA).

BACKGROUND: To infer homology and subsequently gene function, the Smith-Waterman (SW) algorithm is used to find the optimal local alignment between two sequences. When searching sequence databases that may contain hundreds of millions of sequences, this algorithm becomes computationally expensive. RESULTS: In this paper, we focused on accelerating the Smith-Waterman algorithm by using FPGA-based hardware that implemented a module for computing the score of a single cell of the SW matrix. Then using a grid of this module, the entire SW matrix was computed at the speed of field propagation through the FPGA circuit. These modifications dramatically accelerated the algorithm's computation time by up to 160 folds compared to a pure software implementation running on the same FPGA with an Altera Nios II softprocessor. CONCLUSION: This design of FPGA accelerated hardware offers a new promising direction to seeking computation improvement of genomic database searching.