HH-suite

Last updated
HH-suite
Developer(s) Johannes Söding, Michael Remmert, Andreas Biegert, Andreas Hauser, Markus Meier, Martin Steinegger
Stable release
3.3.0 / 25 August 2020 (2020-08-25)
Repository
Written in C++
Operating system Unix-like; Debian package available [1]
Available in English
Type Bioinformatics tool
License GPL v3
Website https://github.com/soedinglab/hh-suite

The HH-suite is an open-source software package for sensitive protein sequence searching. It contains programs that can search for similar protein sequences in protein sequence databases. Sequence searches are a standard tool in modern biology with which the function of unknown proteins can be inferred from the functions of proteins with similar sequences. HHsearch and HHblits are two main programs in the package and the entry point to its search function, the latter being a faster iteration. [2] [3] HHpred is an online server for protein structure prediction that uses homology information from HH-suite. [4]

Contents

The HH-suite searches for sequences using hidden Markov models (HMMs). The name comes from the fact that it performs HMM-HMM alignments. Among the most popular methods for protein sequence matching, the programs have been cited more than 5000 times total according to Google Scholar. [5]

Background

Proteins are central players in all of life's processes. Understanding them is central to understanding molecular processes in cells. This is particularly important in order to understand the origin of diseases. But for a large fraction of the approximately 20 000 human proteins the structures and functions remain unknown. Many proteins have been investigated in model organisms such as many bacteria, baker's yeast, fruit flies, zebra fish or mice, for which experiments can be often done more easily than with human cells. To predict the function, structure, or other properties of a protein for which only its sequence of amino acids is known, the protein sequence is compared to the sequences of other proteins in public databases. If a protein with sufficiently similar sequence is found, the two proteins are likely to be evolutionarily related ("homologous"). In that case, they are likely to share similar structures and functions. Therefore, if a protein with a sufficiently similar sequence and with known functions and/or structure can be found by the sequence search, the unknown protein's functions, structure, and domain composition can be predicted. Such predictions greatly facilitate the determination of the function or structure by targeted validation experiments.

Sequence searches are frequently performed by biologists to infer the function of an unknown protein from its sequence. For this purpose, the protein's sequence is compared to the sequences of other proteins in public databases and its function is deduced from those of the most similar sequences. Often, no sequences with annotated functions can be found in such a search. In this case, more sensitive methods are required to identify more remotely related proteins or protein families. From these relationships, hypotheses about the protein's functions, structure, and domain composition can be inferred. HHsearch performs searches with a protein sequence through databases. The HHpred server and the HH-suite software package offer many popular, regularly updated databases, such as the Protein Data Bank, as well as the InterPro, Pfam, COG, and SCOP databases.

Algorithm

Iterative sequence search scheme of HHblits HHblits-Schematic.png
Iterative sequence search scheme of HHblits

Modern sensitive methods for protein search utilize sequence profiles. They may be used to compare a sequence to a profile, or in more advanced cases such as HH-suite, to match among profiles. [2] [6] [7] [8] Profiles and alignments are themselves derived from matches, using for example PSI-BLAST or HHblits. A position-specific scoring matrix (PSSM) profile contains for each position in the query sequence the similarity score for the 20 amino acids. The profiles are derived from multiple sequence alignments (MSAs), in which related proteins are written together (aligned), such that the frequencies of amino acids in each position can be interpreted as probabilities for amino acids in new related proteins, and be used to derive the "similarity scores". Because profiles contain much more information than a single sequence (e.g. the position-specific degree of conservation), profile-profile comparison methods are much more powerful than sequence-sequence comparison methods like BLAST or profile-sequence comparison methods like PSI-BLAST. [6]

HHpred and HHsearch represent query and database proteins by profile hidden Markov models (HMMs), an extension of PSSM sequence profiles that also records position-specific amino acid insertion and deletion frequencies. HHsearch searches a database of HMMs with a query HMM. Before starting the search through the actual database of HMMs, HHsearch/HHpred builds a multiple sequence alignment of sequences related to the query sequence/MSA using the HHblits program. From this alignment, a profile HMM is calculated. The databases contain HMMs that are precalculated in the same fashion using PSI-BLAST. The output of HHpred and HHsearch is a ranked list of database matches (including E-values and probabilities for a true relationship) and the pairwise query-database sequence alignments.

HHblits, a part of the HH-suite since 2001, builds high-quality multiple sequence alignments (MSAs) starting from a single query sequence or a MSA. As in PSI-BLAST, it works iteratively, repeatedly constructing new query profiles by adding the results found in the previous round. It matches against a pre-built HMM databases derived from protein sequence databases, each representing a "cluster" of related proteins. In the case of HHblits, such matches are done on the level of HMM-HMM profiles, which grants additional sensitivity. Its prefiltering reduces the tens of millions HMMs to match against to a few thousands of them, thus speeding up the slow HMM-HMM comparison process. [3]

The HH-suite comes with a number of pre-built profile HMMs that can be searched using HHblits and HHsearch, among them a clustered version of the UniProt database, of the Protein Data Bank of proteins with known structures, of Pfam protein family alignments, of SCOP structural protein domains, and many more. [9]

Applications

Applications of HHpred and HHsearch include protein structure prediction, complex structure prediction, function prediction, domain prediction, domain boundary prediction, and evolutionary classification of proteins. [10]

HHsearch is often used for homology modeling, that is, to build a model of the structure of a query protein for which only the sequence is known: For that purpose, a database of proteins with known structures such as the protein data bank is searched for "template" proteins similar to the query protein. If such a template protein is found, the structure of the protein of interest can be predicted based on a pairwise sequence alignment of the query with the template protein sequence. For example, a search through the PDB database of proteins with solved 3D structure takes a few minutes. If a significant match with a protein of known structure (a "template") is found in the PDB database, HHpred allows the user to build a homology model using the MODELLER software, starting from the pairwise query-template alignment.

HHpred servers have been ranked among the best servers during CASP7, 8, and 9, for blind protein structure prediction experiments. In CASP9, HHpredA, B, and C were ranked 1st, 2nd, and 3rd out of 81 participating automatic structure prediction servers in template-based modeling [11] and 6th, 7th, 8th on all 147 targets, while being much faster than the best 20 servers. [12] In CASP8, HHpred was ranked 7th on all targets and 2nd on the subset of single domain proteins, while still being more than 50 times faster than the top-ranked servers. [4]

Contents

In addition to HHsearch and HHblits, the HH-suite contains programs and perl scripts for format conversion, filtering of MSAs, generation of profile HMMs, the addition of secondary structure predictions to MSAs, the extraction of alignments from program output, and the generation of customized databases.

hhblits(Iteratively) search an HHblits database with a query sequence or MSA
hhsearchSearch an HHsearch database of HMMs with a query MSA or HMM
hhmakeBuild an HMM from an input MSA
hhfilterFilter an MSA by maximum sequence identity, coverage, and other criteria
hhalignCalculate pairwise alignments, dot plots etc. for two HMMs/MSAs
reformat.plReformat one or many MSAs
addss.plAdd Psipred predicted secondary structure to an MSA or HHM file
hhmakemodel.plGenerate MSAs or coarse 3D models from HHsearch or HHblits results
hhblitsdb.plBuild HHblits database with prefiltering, packed MSA/HMM, and index files
multithread.plRun a command for many files in parallel using multiple threads
splitfasta.plSplit a multiple-sequence FASTA file into multiple single-sequence files
renumberpdb.plGenerate PDB file with indices renumbered to match input sequence indices

The HMM-HMM alignment algorithm of HHblits and HHsearch was significantly accelerated using vector instructions in version 3 of the HH-suite. [13]

See also

Related Research Articles

<span class="mw-page-title-main">Sequence alignment</span> Process in bioinformatics that identifies equivalent sites within molecular sequences

In bioinformatics, a sequence alignment is a way of arranging the sequences of DNA, RNA, or protein to identify regions of similarity that may be a consequence of functional, structural, or evolutionary relationships between the sequences. Aligned sequences of nucleotide or amino acid residues are typically represented as rows within a matrix. Gaps are inserted between the residues so that identical or similar characters are aligned in successive columns. Sequence alignments are also used for non-biological sequences such as calculating the distance cost between strings in a natural language, or to display financial data.

Protein engineering is the process of developing useful or valuable proteins through the design and production of unnatural polypeptides, often by altering amino acid sequences found in nature. It is a young discipline, with much research taking place into the understanding of protein folding and recognition for protein design principles. It has been used to improve the function of many enzymes for industrial catalysis. It is also a product and services market, with an estimated value of $168 billion by 2017.

<span class="mw-page-title-main">Protein structure prediction</span> Type of biological prediction

Protein structure prediction is the inference of the three-dimensional structure of a protein from its amino acid sequence—that is, the prediction of its secondary and tertiary structure from primary structure. Structure prediction is different from the inverse problem of protein design. Protein structure prediction is one of the most important goals pursued by computational biology; it is important in medicine and biotechnology.

<span class="mw-page-title-main">CASP</span> Protein structure prediction challenge

Critical Assessment of Structure Prediction (CASP), sometimes called Critical Assessment of Protein Structure Prediction, is a community-wide, worldwide experiment for protein structure prediction taking place every two years since 1994. CASP provides research groups with an opportunity to objectively test their structure prediction methods and delivers an independent assessment of the state of the art in protein structure modeling to the research community and software users. Even though the primary goal of CASP is to help advance the methods of identifying protein three-dimensional structure from its amino acid sequence many view the experiment more as a “world championship” in this field of science. More than 100 research groups from all over the world participate in CASP on a regular basis and it is not uncommon for entire groups to suspend their other research for months while they focus on getting their servers ready for the experiment and on performing the detailed predictions.

<span class="mw-page-title-main">Protein family</span> Group of evolutionarily-related proteins

A protein family is a group of evolutionarily related proteins. In many cases, a protein family has a corresponding gene family, in which each gene encodes a corresponding protein with a 1:1 relationship. The term "protein family" should not be confused with family as it is used in taxonomy.

The European Bioinformatics Institute (EMBL-EBI) is an intergovernmental organization (IGO) which, as part of the European Molecular Biology Laboratory (EMBL) family, focuses on research and services in bioinformatics. It is located on the Wellcome Genome Campus in Hinxton near Cambridge, and employs over 600 full-time equivalent (FTE) staff. Institute leaders such as Rolf Apweiler, Alex Bateman, Ewan Birney, and Guy Cochrane, an adviser on the National Genomics Data Center Scientific Advisory Board, serve as part of the international research network of the BIG Data Center at the Beijing Institute of Genomics.

In molecular biology, protein threading, also known as fold recognition, is a method of protein modeling which is used to model those proteins which have the same fold as proteins of known structures, but do not have homologous proteins with known structure. It differs from the homology modeling method of structure prediction as it is used for proteins which do not have their homologous protein structures deposited in the Protein Data Bank (PDB), whereas homology modeling is used for those proteins which do. Threading works by using statistical knowledge of the relationship between the structures deposited in the PDB and the sequence of the protein which one wishes to model.

<span class="mw-page-title-main">Multiple sequence alignment</span> Alignment of more than two molecular sequences

Multiple sequence alignment (MSA) is the process or the result of sequence alignment of three or more biological sequences, generally protein, DNA, or RNA. These alignments are used to infer evolutionary relationships via phylogenetic analysis and can highlight homologous features between sequences. Alignments highlight mutation events such as point mutations, insertion mutations and deletion mutations, and alignments are used to assess sequence conservation and infer the presence and activity of protein domains, tertiary structures, secondary structures, and individual amino acids or nucleotides.

InterPro is a database of protein families, protein domains and functional sites in which identifiable features found in known proteins can be applied to new protein sequences in order to functionally characterise them.

<span class="mw-page-title-main">Homology modeling</span> Method of protein structure prediction using other known proteins

Homology modeling, also known as comparative modeling of protein, refers to constructing an atomic-resolution model of the "target" protein from its amino acid sequence and an experimental three-dimensional structure of a related homologous protein. Homology modeling relies on the identification of one or more known protein structures likely to resemble the structure of the query sequence, and on the production of an alignment that maps residues in the query sequence to residues in the template sequence. It has been seen that protein structures are more conserved than protein sequences amongst homologues, but sequences falling below a 20% sequence identity can have very different structure.

Loop modeling is a problem in protein structure prediction requiring the prediction of the conformations of loop regions in proteins with or without the use of a structural template. Computer programs that solve these problems have been used to research a broad range of scientific topics from ADP to breast cancer. Because protein function is determined by its shape and the physiochemical properties of its exposed surface, it is important to create an accurate model for protein/ligand interaction studies. The problem arises often in homology modeling, where the tertiary structure of an amino acid sequence is predicted based on a sequence alignment to a template, or a second sequence whose structure is known. Because loops have highly variable sequences even within a given structural motif or protein fold, they often correspond to unaligned regions in sequence alignments; they also tend to be located at the solvent-exposed surface of globular proteins and thus are more conformationally flexible. Consequently, they often cannot be modeled using standard homology modeling techniques. More constrained versions of loop modeling are also used in the data fitting stages of solving a protein structure by X-ray crystallography, because loops can correspond to regions of low electron density and are therefore difficult to resolve.

<span class="mw-page-title-main">HMMER</span> Software package for sequence analysis

HMMER is a free and commonly used software package for sequence analysis written by Sean Eddy. Its general usage is to identify homologous protein or nucleotide sequences, and to perform sequence alignments. It detects homology by comparing a profile-HMM to either a single sequence or a database of sequences. Sequences that score significantly better to the profile-HMM compared to a null model are considered to be homologous to the sequences that were used to construct the profile-HMM. Profile-HMMs are constructed from a multiple sequence alignment in the HMMER package using the hmmbuild program. The profile-HMM implementation used in the HMMER software was based on the work of Krogh and colleagues. HMMER is a console utility ported to every major operating system, including different versions of Linux, Windows, and macOS.

RAPTOR is protein threading software used for protein structure prediction. It has been replaced by RaptorX, which is much more accurate than RAPTOR.

CS-BLAST (Context-Specific BLAST) is a tool that searches a protein sequence that extends BLAST, using context-specific mutation probabilities. More specifically, CS-BLAST derives context-specific amino-acid similarities on each query sequence from short windows on the query sequences. Using CS-BLAST doubles sensitivity and significantly improves alignment quality without a loss of speed in comparison to BLAST. CSI-BLAST is the context-specific analog of PSI-BLAST, which computes the mutation profile with substitution probabilities and mixes it with the query profile. CSI-BLAST is the context specific analog of PSI-BLAST. Both of these programs are available as web-server and are available for free download.

Phyre and Phyre2 are free web-based services for protein structure prediction. Phyre is among the most popular methods for protein structure prediction having been cited over 1500 times. Like other remote homology recognition techniques, it is able to regularly generate reliable protein models when other widely used methods such as PSI-BLAST cannot. Phyre2 has been designed to ensure a user-friendly interface for users inexpert in protein structure prediction methods. Its development is funded by the Biotechnology and Biological Sciences Research Council.

RaptorX is a software and web server for protein structure and function prediction that is free for non-commercial use. RaptorX is among the most popular methods for protein structure prediction. Like other remote homology recognition/protein threading techniques, RaptorX is able to regularly generate reliable protein models when the widely used PSI-BLAST cannot. However, RaptorX is also significantly different from those profile-based methods in that RaptorX excels at modeling of protein sequences without a large number of sequence homologs by exploiting structure information. RaptorX Server has been designed to ensure a user-friendly interface for users inexpert in protein structure prediction methods.

SWISS-MODEL is a structural bioinformatics web-server dedicated to homology modeling of 3D protein structures. Homology modeling is currently the most accurate method to generate reliable three-dimensional protein structure models and is routinely used in many practical applications. Homology modelling methods make use of experimental protein structures ("templates") to build models for evolutionary related proteins ("targets").

PredictProtein (PP) is an automatic service that searches up-to-date public sequence databases, creates alignments, and predicts aspects of protein structure and function. Users send a protein sequence and receive a single file with results from database comparisons and prediction methods. PP went online in 1992 at the European Molecular Biology Laboratory; since 1999 it has operated from Columbia University and in 2009 it moved to the Technische Universität München. Although many servers have implemented particular aspects, PP remains the most widely used public server for structure prediction: over 1.5 million requests from users in 104 countries have been handled; over 13000 users submitted 10 or more different queries. PP web pages are mirrored in 17 countries on 4 continents. The system is optimized to meet the demands of experimentalists not experienced in bioinformatics. This implied that we focused on incorporating only high-quality methods, and tried to collate results omitting less reliable or less important ones.

<span class="mw-page-title-main">CS23D</span>

CS23D is a web server to generate 3D structural models from NMR chemical shifts. CS23D combines maximal fragment assembly with chemical shift threading, de novo structure generation, chemical shift-based torsion angle prediction, and chemical shift refinement. CS23D makes use of RefDB and ShiftX.

<span class="mw-page-title-main">I-TASSER</span>

I-TASSER is a bioinformatics method for predicting three-dimensional structure model of protein molecules from amino acid sequences. It detects structure templates from the Protein Data Bank by a technique called fold recognition. The full-length structure models are constructed by reassembling structural fragments from threading templates using replica exchange Monte Carlo simulations. I-TASSER is one of the most successful protein structure prediction methods in the community-wide CASP experiments.

References

  1. ↑ Debian hhsuite package
  2. 1 2 Söding J (2005). "Protein homology detection by HMM-HMM comparison". Bioinformatics. 21 (7): 951–960. doi: 10.1093/bioinformatics/bti125 . hdl: 11858/00-001M-0000-0017-EC7A-F . PMID   15531603.
  3. 1 2 Remmert M, Biegert A, Hauser A, Söding J (2011). "HHblits: Lightning-fast iterative protein sequence searching by HMM-HMM alignment" (PDF). Nat. Methods. 9 (2): 173–175. doi:10.1038/NMETH.1818. hdl: 11858/00-001M-0000-0015-8D56-A . PMID   22198341. S2CID   205420247.
  4. 1 2 Söding J, Biegert A, Lupas AN (2005). "The HHpred interactive server for protein homology detection and structure prediction". Nucleic Acids Research. 33 (Web Server issue): W244–248. doi:10.1093/nar/gki408. PMC   1160169 . PMID   15980461.
  5. ↑ Citations to HHpred, to HHsearch, to HHblits
  6. 1 2 Jaroszewski L, Rychlewski L, Godzik A (2000). "Improving the quality of twilight-zone alignments". Protein Science. 9 (8): 1487–1496. doi:10.1110/ps.9.8.1487. PMC   2144727 . PMID   10975570.
  7. ↑ Sadreyev RI, Baker D, Grishin NV (2003). "Profile–profile comparisons by COMPASS predict intricate homologies between protein families". Protein Science. 12 (10): 2262–2272. doi:10.1110/ps.03197403. PMC   2366929 . PMID   14500884.
  8. ↑ Dunbrack RL Jr (2006). "Sequence comparison and protein structure prediction". Current Opinion in Structural Biology. 16 (3): 374–384. doi:10.1016/j.sbi.2006.05.006. PMID   16713709.
  9. ↑ Li, Zhaoyu. "Some Notes about HHSuite". Archived from the original on 3 April 2019. Retrieved 3 April 2019.
  10. ↑ Guerler A, Govindarajoo B, Zhang Y (2013). "Mapping Monomeric Threading to Protein–Protein Structure Prediction". Journal of Chemical Information and Modeling. 53 (3): 717–25. doi:10.1021/ci300579r. PMC   4076494 . PMID   23413988.
  11. ↑ Official CASP9 results for the template-based modeling category (121 targets)
  12. ↑ Official CASP9 results for all 147 targets
  13. ↑ Steinegger M, Meier M, Mirdita M, Vöhringer H, Haunsberger S, Söding J (2019). "HH-suite3 for fast remote homology detection and deep protein annotation". BMC Bioinformatics. 20 (1): 473. doi: 10.1186/s12859-019-3019-7 . PMC   6744700 . PMID   31521110.
This page is based on this Wikipedia article
Text is available under the CC BY-SA 4.0 license; additional terms may apply.
Images, videos and audio are available under their respective licenses.