The MMseqs2 protein structure prediction tool has been updated to include GPU acceleration, making it faster and more efficient for researchers in the field of computational biology. This enhancement has the potential to revolutionize protein analysis and have a significant impact on various areas of life sciences research.
The use of NVIDIA CUDA technology in MMseqs2-GPU streamlines the multiple sequence alignment (MSA) process, traditionally reliant on CPU-based processing. With the new gapless prefiltering method, MMseqs2-GPU can analyze protein sequences more directly and efficiently than before. The result is a 1788x speedup over standard CPU implementations.
This advancement reduces memory requirements and allows for scalable solutions for large-scale bioinformatics studies, making high-performance bioinformatics tools more accessible to researchers with limited budgets. The MMseqs2-GPU is 22 times faster and 70 times more cost-efficient than previous methods, without sacrificing accuracy.
It has potential applications in drug discovery, vaccine design, and understanding disease variants, as demonstrated in computational pipelines like Colabfold, where it is reported to be 22 times faster and 70 times more cost-efficient than previous methods, without sacrificing accuracy.
