The HHpred interactive server for protein homology detection and structure prediction

By Johannes Söding, Andreas Biegert, Andrei N. Lupas. 2005

3,775 citations recorded by the source corpus.

HHpred is a fast server for remote protein homology detection and structure prediction and is the first to implement pairwise comparison of profile hidden Markov models (HMMs). It allows to search a wide choice of databases, such as the PDB, SCOP, Pfam, SMART, COGs and CDD. It accepts a single query sequence or a multiple alignment as input. Within only a few minutes it returns the search results in a user-friendly format similar to that of PSI-BLAST. Search options include local or global alignment and scoring secondary structure similarity. HHpred can produce pairwise query-template alignments, multiple alignments of the query with a set of templates selected from the search results, as well as 3D structural models that are calculated by the MODELLER software from these alignments. A detailed help facility is available. As a demonstration, we analyze the sequence of SpoVT, a transcriptional regulator from Bacillus subtilis. HHpred can be accessed at .

Subfields: Molecular Biology, Computational Biology & Bioinformatics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

1. Yang Zhang
2. Jinbo Xu
3. Torsten Schwede
4. Sheng Wang
5. Andriy Kryshtafovych
6. David Baker
7. Björn Wallner
8. Johannes Söding

In Computational Biology & Bioinformatics

1. Yang Zhang
2. Jinbo Xu
3. Sheng Wang
4. Torsten Schwede
5. David Baker
6. Björn Wallner
7. Andriy Kryshtafovych
8. Jianyi Yang

Citations in the corpus

• I-TASSER server: new development for protein structure and function predictions (2015, neutral): “The community-wide blind CASP experiments have shown that many automated servers, such as I-TASSER (1,2), Rosetta (3) and HHpred (4), can now generate structural models with accuracy comparable to the best human-expert guided modeling (5–7).”
• Local Error Estimates Dramatically Improve the Utility of Homology Models for Solving Crystal Structures by Molecular Replacement (2015, methodology): “However, for one of these (TS311, Laufer), only three data points were available so this group was removed from the list.”
• Evaluating Protein Transfer Learning with TAPE (2019, background): “We also show that non-deep alignmentbased features [13–16] outperform features learned via self-supervision on secondary structure and contact prediction, while learned features perform significantly better on remote homology detection.”
• Learning protein sequence embeddings using information from structure (2019, methodology): “Furthermore, our full SSA model is much better for retrieving proteins sharing the same fold — the structural level of most interest for finding distant protein homologues — improving the average precision score by 0.28 on the 2.06 test set and 0.10 on the 2.07 test set over HHalign.”
• Distance-based protein folding powered by deep learning (2018, background): “CNFpred is more sensitive than HHpred (39), although both mainly rely on sequence profile similarity.”
• High resolution comparative modeling with RosettaCM (2013, methodology): “…the GDT) between the first models submitted in the CASP10 experiment by the RosettaServer and the state of the art HHpredA (a widely used public server)(Soding et al., 2005) and ZhangServer (the top-performing server in CASP10) as a measure of the extent of convergence on templates and alignments.”
• SWISS-MODEL: modelling protein tertiary and quaternary structure using evolutionary information (2014, methodology): “SWISS-MODEL accuracy is compared to other state-of-the-art protein structure prediction servers (26–32) and to previous version of the server (5).”
• The Protein Model Portal—a comprehensive resource for protein structure and model information (2013, methodology): “The submission to modeling servers currently supports HHPredB (18), I-TASSER (19), ModWeb (15), M4T (21) and SWISS-MODEL Workspace (22, 23) (Table 1).”
• The MPI Bioinformatics Toolkit for protein sequence analysis (2006, background): “From our own experience as users of the toolkit, its main advantages are as follows: In-house tools : Several programs developed in our group are available only through our toolkit, e.g. HHpred (5), HHrep (6), HHsenser (7), REPPER (8), CLANS (9) and Blammer (10) (see Table 1).”
• The MPI bioinformatics Toolkit as an integrative platform for advanced protein sequence and structure analysis (2016, methodology): “Tertiary Structure bFit (54), HHfrag (55), HHpred (5), Modeller (10), SamCC (31)”