HCAtk and pyHCA: A Toolkit and Python API for the Hydrophobic Cluster Analysis of Protein Sequences

Bitard‐Feildel, Tristan; Callebaut, Isabelle

doi:10.1101/249995

Cited by 14 publications

(12 citation statements)

References 30 publications

(22 reference statements)

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“…It has been proposed that sidechains of isoleucine (ILE), leucine (LEU) and valine (VAL) residues often form hydrophobic or so-called (ILV)-clusters that prevent the intrusion of water molecules and serve as cores of stability in high-energy partially folded states ( 23 ). Various tools for the analysis of hydrophobic clusters solely from protein sequences have been developed ( 27 ) and recently made available as a Python package ( 28 ). Another possibility is to identify hydrophobic clusters directly in a protein structure.…”

Section: Methodsmentioning

confidence: 99%

ProteinTools: a toolkit to analyze protein structures

Ferruz

Schmidt

Höcker

2021

Nucleic Acids Research

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The experimental characterization and computational prediction of protein structures has become increasingly rapid and precise. However, the analysis of protein structures often requires researchers to use several software packages or web servers, which complicates matters. To provide long-established structural analyses in a modern, easy-to-use interface, we implemented ProteinTools, a web server toolkit for protein structure analysis. ProteinTools gathers four applications so far, namely the identification of hydrophobic clusters, hydrogen bond networks, salt bridges, and contact maps. In all cases, the input data is a PDB identifier or an uploaded structure, whereas the output is an interactive dynamic web interface. Thanks to the modular nature of ProteinTools, the addition of new applications will become an easy task. Given the current need to have these tools in a single, fast, and interpretable interface, we believe that ProteinTools will become an essential toolkit for the wider protein research community. The web server is available at https://proteintools.uni-bayreuth.de.

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Section: Methodsmentioning

confidence: 99%

ProteinTools: a toolkit to analyze protein structures

Ferruz

Schmidt

Höcker

2021

Nucleic Acids Research

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“…Hydrophobic clusters were identified using the HCA algorithm 122 (Hydrophobic Cluster Analysis). Domains were inferred using the Seg-HCA 123 procedure implemented in pyHCA 124 . This procedure derives a score, S, and a p-value (p) related to foldability for each domain.…”

Section: Log(c S )mentioning

confidence: 99%

ODiNPred: comprehensive prediction of protein order and disorder

Dass

Mulder

Nielsen

2020

Sci Rep

162

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Structural disorder is widespread in eukaryotic proteins and is vital for their function in diverse biological processes. It is therefore highly desirable to be able to predict the degree of order and disorder from amino acid sequence. It is, however, notoriously difficult to predict the degree of local flexibility within structured domains and the presence and nuances of localized rigidity within intrinsically disordered regions. To identify such instances, we used the CheZOD database, which encompasses accurate, balanced, and continuous-valued quantification of protein (dis)order at amino acid resolution based on NMR chemical shifts. To computationally forecast the spectrum of protein disorder in the most comprehensive manner possible, we constructed the sequence-based protein order/disorder predictor ODiNPred, trained on an expanded version of CheZOD. ODiNPred applies a deep neural network comprising 157 unique sequence features to 1325 protein sequences together with the experimental NMR chemical shift data. Cross-validation for 117 protein sequences shows that ODiNPred better predicts the continuous variation in order along the protein sequence, suggesting that contemporary predictors are limited by the quality of training data. The inclusion of evolutionary features reduces the performance gap between ODiNPred and its peers, but analysis shows that it retains greater accuracy for the more challenging prediction of intermediate disorder.

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“…The secondary structures of the PD1, PD-L1, and PD-L2 proteins were predicted by the CFSSP program [54,88]. Hydrophobic cluster analysis (HCA) was performed using the HCA 1.0.2 program [89] via the Mobyle@RPBS web portal and framework [90].…”

Section: Bioinformatics Analysis Of Protein Sequencesmentioning

confidence: 99%

Adaptive selection in the evolution of programmed cell death-1 and its ligands in vertebrates

Ahmad¹,

Zhou²,

Ahmad

et al. 2020

Aging

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Programmed cell death-1 (PD-1) and its ligands, particularly PD-L1 and PD-L2, are the most important proteins responsible for signaling T-cell inhibition and arbitrating immune homeostasis and tolerance mechanisms. However, the adaptive evolution of these genes is poorly understood. In this study, we aligned protein-coding genes from vertebrate species to evaluate positive selection constraints and evolution in the PD1, PD-L1 and PD-L2 genes conserved across up to 166 vertebrate species, with an average of 55 species per gene. We determined that although the positive selection was obvious, an average of 5.3% of codons underwent positive selection in the three genes across vertebrate lineages, and increased positive selection pressure was detected in both the Ig-like domains and transmembrane domains of the proteins. Moreover, the PD1, PD-L1 and PD-L2 genes were highly expressed in almost all tissues of the selected species indicating a distinct expression pattern in different tissues among most species. Our study reveals that adaptive selection plays a key role in the evolution of PD1 and its ligands in the majority of vertebrate species, which is in agreement with the contribution of these residues to the mechanisms of pathogen identification and coevolution in the complexity and novelties of vertebrate immune systems.

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HCAtk and pyHCA: A Toolkit and Python API for the Hydrophobic Cluster Analysis of Protein Sequences

Abstract: Motivation: Detecting protein domains sharing no similarity to known domains, as stored in domain

Cited by 14 publications

References 30 publications

ProteinTools: a toolkit to analyze protein structures

ProteinTools: a toolkit to analyze protein structures

ODiNPred: comprehensive prediction of protein order and disorder

Adaptive selection in the evolution of programmed cell death-1 and its ligands in vertebrates

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