The Use of Dedicated Processors to Accelerate the Identification of Novel Antibacterial Peptides
Pp. 3-26 (24)
Gabriel del Rio, Miguel Arias-Estrada and Carlos Polanco González
In the past decades, the procedure to identify novel antibiotic compounds has
been motivated by the heuristic discovery of the antibiotic penicillin by Fleming in 1929.
Since then, researches have been isolating compounds from very wide range of living
forms with the hope of repeating Fleming’s story. Yet, the rate of discovery of new
pharmaceutical compounds has reached a plateau in the last decade and this has promoted
the use of alternative approaches to identify antibiotic compounds. One of these approaches
uses the accumulated information on pharmaceutical compounds to predict new ones using
high-performance computers. Such approach brings up the possibility to screen for millions
of compounds in computer simulations. The better predictors though use sophisticated
algorithms that take up significant amount of computer time, reducing the number of
compounds to analyze and the likelihood to identify potential antibiotic compounds. At the
same time, the appearance of computer processors that may be tailored to perform specific
tasks by the end of the past century provided a tool to accelerate high-performance
computations. The current review focuses on the use of these dedicated processor devices,
particularly Field Programmable Gate Arrays and Graphic Processing Units, to identify
new antibacterial peptides. For that end, we review some of the common computational
methods used to identify antibacterial peptides and highlight the difficulties and advantages
these algorithms present to be coded into FPGA/GPU computational devices. We discuss
the potential of reaching supercomputing performance on FPGA/GPU, and the approaches
for parallelism on these platforms.
Antibacterial peptides, FPGA, GPU, high-performance computations,
parallelism, QSAR, supercomputing.
Department of Biochemistry and Structural Biology, Instituto de Fisiología Celular, Universidad Nacional Autónoma de México, México DF, México.