Abstract
BackgroundHH-suite is a widely used open source software suite for sensitive sequence similarity searches and protein fold recognition. It is based on pairwise alignment of profile Hidden Markov models (HMMs), which represent multiple sequence alignments of homologous proteins.ResultsWe developed a single-instruction multiple-data (SIMD) vectorized implementation of the Viterbi algorithm for profile HMM alignment and introduced various other speed-ups. These accelerated the search methods HHsearch by a factor 4 and HHblits by a factor 2 over the previous version 2.0.16. HHblits3 is ∼10× faster than PSI-BLAST and ∼20× faster than HMMER3. Jobs to perform HHsearch and HHblits searches with many query profile HMMs can be parallelized over cores and over cluster servers using OpenMP and message passing interface (MPI). The free, open-source, GPLv3-licensed software is available at https://github.com/soedinglab/hh-suite.ConclusionThe added functionalities and increased speed of HHsearch and HHblits should facilitate their use in large-scale protein structure and function prediction, e.g. in metagenomics and genomics projects.
Highlights
A sizeable fraction of proteins in genomics and metagenomics projects remain without annotation due to the lack of an identifiable, annotated homologous protein [1]
The added information improves the sensitivity of profile Hidden Markov Model (HMM)-based methods like HHblits or HMMER3 over ones based on sequence profiles, such as PSI-BLAST [10]
Memory reduction We reduced the memory required during Viterbi HMMHMM alignment by a factor of 1.5 for Streaming SIMD Extension 2 (SSE2) and implemented Advanced Vector Extension 2 (AVX2) with only a 1.3 times increase, despite the need to keep scores for 4 (SSE2) or 8 (AVX2) target profile HMMs in memory instead of just one
Summary
A sizeable fraction of proteins in genomics and metagenomics projects remain without annotation due to the lack of an identifiable, annotated homologous protein [1]. To find template proteins for comparative protein structure modeling and for deep functional annotation, the most sensitive search tools such as HMMER [3, 4] and HHblits [5] are often used [6,7,8,9] These tools can improve homology detection by aligning single sequences against other sequences, but using more information in form of multiple sequence alignments (MSAs) containing many homologous sequences. A profile Hidden Markov Model (HMM) extends sequence profiles by augmenting the position-specific amino acid substitution scores with position-specific penalties for insertions and deletions These can be estimated from the frequencies of insertions and deletions in the MSA. HH-suite is a widely used open source software suite for sensitive sequence similarity searches and protein fold recognition It is based on pairwise alignment of profile Hidden Markov models (HMMs), which represent multiple sequence alignments of homologous proteins
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