Reconfigurable computing for accelerating protein secondary structure prediction on FPGAs

Protein is an important molecule that performs a wide range of functions in biological systems. The GOR algorithm is one of the most successful computational methods to predict secondary structure from protein sequence. FPGA chips have emerged as one promising application accelerator to accelerate bioinformatics algorithms by exploiting fine-grained custom design. In this paper, we propose a complete fine-grained parallel hardware implementation on FPGA XC5VLX330 chip to accelerate the GOR IV package for protein secondary structure prediction. The experimental results show a speedup factor of more than 430x over the GOR-IV software version running on a PC platform with AMD Phenom 9650 Quad CPU for 2-Dimensional protein structure prediction. However, the power consumption is only about 30% of that of current general-purpose CPUs.