Numerous machine learning (ML) models employed in protein function and structure prediction depend on evolutionary information, which is captured through multiple-sequence alignments (MSA) or position-specific scoring matrices (PSSM) as generated by PSI-BLAST. Consequently, these predictive methods are burdened by substantial computational demands and prolonged computing time requirements. The principal challenge stems from the necessity imposed on the PSI-BLAST software to load large sequence databases sequentially in batches and then search for sequence alignments akin to a given query sequence. In the case of batch queries, the runtime scales even linearly. The predicament at hand is becoming more challenging as the size of bio-sequence data repositories experiences exponential growth over time and as a consequence, this upward trend exerts a proportional strain on the runtime of PSI-BLAST. To address this issue, an eminent resolution lies in leveraging the MMseqs2 method, capable of expediting the search process by a magnitude of 100. However, MMseqs2 cannot be directly employed to generate the final output in the desired format of PSI-BLAST alignments and PSSM profiles. In this research work, I developed a comprehensive pipeline that synergistically integrates both MMseqs2 and PSI-BLAST, resulting in the creation of a robust, optimized, and highly efficient hybrid alignment pipeline. Notably, the hybrid tool exhibits a significant speed improvement, surpassing the runtime performance of PSI-BLAST in generating sequence alignment profiles by a factor of two orders of magnitude. It is implemented in C++ and is freely available under the MIT license at https://github.com/issararab/EPSAPG.

翻译：众多用于蛋白质功能与结构预测的机器学习模型依赖于进化信息，这些信息通过PSI-BLAST生成的多序列比对或位置特异性得分矩阵获取。因此，这类预测方法面临计算需求高、计算时间长的挑战。其主要瓶颈在于PSI-BLAST软件必须逐批加载大规模序列数据库，并针对给定查询序列搜索相似序列比对。当进行批量查询时，运行时间甚至呈线性增长。由于生物序列数据库规模随时间呈指数级增长，这一困境日益严峻，且该增长趋势对PSI-BLAST的运行时间施加了成比例的压力。为解决此问题，一种有效方案是利用MMseqs2方法——该技术可将搜索过程加速100倍。然而，MMseqs2无法直接生成PSI-BLAST比对与PSSM谱所需的最终输出格式。在本研究中，我开发了一套完整流水线，通过协同整合MMseqs2与PSI-BLAST，创建了稳健、优化且高效的工具。该混合工具在生成序列比对谱时，其运行速度相较于PSI-BLAST实现了两个数量级的显著提升。该系统采用C++实现，并以MIT许可证在https://github.com/issararab/EPSAPG上开源提供。