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Numerous machine learning (ML) models employed in protein function andstructure prediction depend on evolutionary information, which is capturedthrough multiple-sequence alignments (MSA) or position-specific scoringmatrices (PSSM) as generated by PSI-BLAST. Consequently, these predictivemethods are burdened by substantial computational demands and prolongedcomputing time requirements. The principal challenge stems from the necessityimposed on the PSI-BLAST software to load large sequence databases sequentiallyin batches and then search for sequence alignments akin to a given querysequence. In the case of batch queries, the runtime scales even linearly. Thepredicament at hand is becoming more challenging as the size of bio-sequencedata repositories experiences exponential growth over time and as aconsequence, this upward trend exerts a proportional strain on the runtime ofPSI-BLAST. To address this issue, an eminent resolution lies in leveraging theMMseqs2 method, capable of expediting the search process by a magnitude of 100.However, MMseqs2 cannot be directly employed to generate the final output inthe desired format of PSI-BLAST alignments and PSSM profiles. In this researchwork, I developed a comprehensive pipeline that synergistically integrates bothMMseqs2 and PSI-BLAST, resulting in the creation of a robust, optimized, andhighly efficient hybrid alignment pipeline. Notably, the hybrid tool exhibits asignificant speed improvement, surpassing the runtime performance of PSI-BLASTin generating sequence alignment profiles by a factor of two orders ofmagnitude. It is implemented in C++ and is freely available under the MITlicense at https://github.com/issararab/EPSAPG.

EPSAPG: A Pipeline Combining MMseqs2 and PSI-BLAST to Quickly Generate Extensive Protein Sequence Alignment Profiles