A Completely Reimplemented MPI Bioinformatics Toolkit with a New HHpred Server at its Core.

By Lukas Zimmermann, Andrew Stephens, Seung-Zin Nam, David Rau, Jonas M. Kübler, Marko Lozajic, Felix Gabler, Johannes Söding, Andrei N. Lupas, Vikram Alva. 2017

2,250 citations recorded by the source corpus.

The MPI Bioinformatics Toolkit (https://toolkit.tuebingen.mpg.de) is a free, one-stop web service for protein bioinformatic analysis. It currently offers 34 interconnected external and in-house tools, whose functionality covers sequence similarity searching, alignment construction, detection of sequence features, structure prediction, and sequence classification. This breadth has made the Toolkit an important resource for experimental biology and for teaching bioinformatic inquiry. Recently, we replaced the first version of the Toolkit, which was released in 2005 and had served around 2.5 million queries, with an entirely new version, focusing on improved features for the comprehensive analysis of proteins, as well as on promoting teaching. For instance, our popular remote homology detection server, HHpred, now allows pairwise comparison of two sequences or alignments and offers additional profile HMMs for several model organisms and domain databases. Here, we introduce the new version of our Toolkit and its application to the analysis of proteins.

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

In Computational Biology & Bioinformatics

Citations in the corpus

Improved prediction of protein-protein interactions using AlphaFold2 (2021, methodology): “In addition, we extracted eight novel protein complexes deposited in PDB after 15 June 2021, which produced no results for at least one chain in each complex when submitted to the HHPRED web server (version 01-09-2021) 28,29 , see Table S1.”
Protein Sequence Analysis Using the MPI Bioinformatics Toolkit (2020, methodology): “The Toolkit has been in continuous operation ever since, and we replaced the first version with an entirely new one built using more scalable and robust web technologies in 2017 (Alva, Nam, Soding, & Lupas, 2016; Zimmermann et al., 2018).”
Blind prediction of homo- and hetero-protein complexes: The CASP13-CAPRI experiment (2019, neutral): “A target was considered a tractable modeling problem even when it was a dimer, or higher order assembly, for which only distantly related templates could be identified, for at least a portion of the components of the target complex, using available tools such as HHpred.(45,46) But targets where even”
A general-purpose protein design framework based on mining sequence–structure relationships in known protein structures (2018, methodology): “Further, submitting the designed sequence to HHpred, a powerful structureprediction method that relies on the ability to identify remote “homologies” between the modeled sequence and a protein of known structure (60, 61), reveals PDB entry 5UP5 as the closest match (with a probability of over 97% an”
Deep learning extends de novo protein modelling coverage of genomes using iteratively predicted structural constraints (2018, background): “The set of dark families available for modelling was taken to be the set of 8,700 lacking an annotated structure minus those families with likely templates not annotated in Pfam - these were found by running HHsearch [41] of the Pfam seed”
Using evolutionary data to make sense of macromolecules with a “face‐lifted” ConSurf (2023, methodology): “In addition, ConSurf offers structure prediction using the HHpred (Söding et al. 2005; Zimmermann et al. 2018) and MODELER (Sali et al. 1995; Sali & Blundell 1993; Webb & Sali 2016) computational tools.”
A structural inventory of native ribosomal ABCE1‐43S pre‐initiation complexes (2020, positive): “Particle images were extracted in RELION 3.0 (Zivanov et al , 2018) and subjected to reference-free 2D classification.”
Transmembrane Membrane Readers form a Novel Class of Proteins That Include Peripheral Phosphoinositide Recognition Domains and Viral Spikes (2022, methodology): “Coiled coils, which mediate oligomerization of proteins via two or more α helices, were predicted using Marcoil [32] in order to assess their presence in TMMRs as they are able to increase binding avidity for membranes.”
The 2019‐new coronavirus epidemic: Evidence for virus evolution (2020, methodology): “Structural templates have been searched and validated using the software available within the SwissModel environment and HHPred.(16) Homology models have been validated using the QMEAN tool.”
Assessment of protein assembly prediction in CASP13 (2019, positive): “the PDB foundwith HHpred [19].”