Dataset Information

The blobulator: a webtool for identification and visual exploration of hydrophobic modularity in protein sequences.

ABSTRACT:

Motivation

Results

Here, we present the blobulator: an interactive and intuitive web interface to detect intrinsic modularity in any protein sequence based on hydrophobicity. We demonstrate three use cases of the blobulator and show how identifying blobs with biologically relevant parameters provides useful information about a globular protein, two orthologous membrane proteins, and an IDP. Other potential applications are discussed, including: predicting protein segments with critical roles in tertiary interactions, providing a definition of local order and disorder with clear edges, and aiding in predicting protein features from sequence.

Availability

The blobulator GUI can be found at www.blobulator.branniganlab.org, and the source code with pip installable command line tool can be found on GitHub at www.GitHub.com/BranniganLab/blobulator.

SUBMITTER: Pitman C

PROVIDER: S-EPMC10827107 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Revealing protein sequence organization via contiguous hydrophobicity with the blobulator toolkit.

Pitman Connor C Santiago-McRae Ezry E Lohia Ruchi R Lamb Ryan R Bassi Kaitlin K Riggs Lindsey L Joseph Thomas T TT Hansen Matthew E B MEB Brannigan Grace G

bioRxiv : the preprint server for biology 20250318

Clusters of hydrophobic residues are known to promote structured protein stability and drive protein aggregation. Recent work has shown that identifying contiguous hydrophobic residue clusters within protein sequences (termed "blobs") has proven useful in both intrinsically disordered protein (IDP) simulation and human genome studies. However, an accessible toolkit was unavailable, and the role that blobs play across the structural context of a variety of protein families remained unclear. Here, ...[more]

PMID: 38293114

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Project description:BackgroundHydrophobic Cluster Analysis (HCA) is an efficient way to compare highly divergent sequences through the implicit secondary structure information directly derived from hydrophobic clusters. However, its efficiency and application are currently limited by the need of user expertise. In order to help the analysis of HCA plots, we report here the structural preferences of hydrophobic cluster species, which are frequently encountered in globular domains of proteins. These species are characterized only by their hydrophobic/non-hydrophobic dichotomy. This analysis has been extended to loop-forming clusters, using an appropriate loop alphabet.ResultsThe structural behavior of hydrophobic cluster species, which are typical of protein globular domains, was investigated within banks of experimental structures, considered at different levels of sequence redundancy. The 294 more frequent hydrophobic cluster species were analyzed with regard to their association with the different secondary structures (frequencies of association with secondary structures and secondary structure propensities). Hydrophobic cluster species are predominantly associated with regular secondary structures, and a large part (60 %) reveals preferences for alpha-helices or beta-strands. Moreover, the analysis of the hydrophobic cluster amino acid composition generally allows for finer prediction of the regular secondary structure associated with the considered cluster within a cluster species. We also investigated the behavior of loop forming clusters, using a "PGDNS" alphabet. These loop clusters do not overlap with hydrophobic clusters and are highly associated with coils. Finally, the structural information contained in the hydrophobic structural words, as deduced from experimental structures, was compared to the PSI-PRED predictions, revealing that beta-strands and especially alpha-helices are generally over-predicted within the limits of typical beta and alpha hydrophobic clusters.ConclusionThe dictionary of hydrophobic clusters described here can help the HCA user to interpret and compare the HCA plots of globular protein sequences, as well as provides an original fundamental insight into the structural bricks of protein folds. Moreover, the novel loop cluster analysis brings additional information for secondary structure prediction on the whole sequence through a generalized cluster analysis (GCA), and not only on regular secondary structures. Such information lays the foundations for developing a new and original tool for secondary structure prediction.