# Hydrophobic cluster analysis at protein and proteome scales

Isabelle Callebaut, Jean-Paul Mornon

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Isabelle Callebaut, Jean-Paul Mornon. Hydrophobic cluster analysis at protein and proteome scales. Biochimie, 2025, 10.1016/j.biochi.2025.07.013. hal-05345260

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Mini-review

Hydrophobic cluster analysis at protein and proteome scales

Isabelle Callebaut *, Jean-Paul Mornon

Sorbonne Université, Muséum National d'Histoire Naturelle, UMR CNRS 7590, Institut de Minéralogie, de Physique des Matériaux et de Cosmochimie, IMPMC, 75005, Paris, France

ARTICLE INFO

Article history:

Received 10 June 2025

Received in revised form

2 July 2025

Accepted 9 July 2025

Available online xxx

Handling Editor: Dr B Friguet

Keywords:

Secondary structures

Domains

Foldability

Disorder

Conditional order

A B S T R A C T

Hydrophobic Cluster Analysis (HCA) adds secondary structure information to the analysis of protein amino acid sequence. Focusing on the elementary building blocks of protein folds, this approach has proved to be a powerful tool for detecting distant (hidden) relationships between proteins. At a time when huge masses of data are now available, both in terms of protein sequences and models of three-dimensional structures, it still constitutes a relevant tool for analyzing structural features at the scale of whole proteomes, enabling, among other things, to characterize the continuum between disorder and order and to explore the characteristics of protein dark matter. The aim of this mini-review is to provide a brief overview of this approach, describing its principles and achievements, recent developments and future prospects.

© 2025 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

More than fifty years ago, the resolution of the first crystallo-
graphic structures of proteins, as well as their sequencing, paved
the way for the birth of bioinformatics, which took offs with the
development of molecular biology and computer science, leading
to a massive influx of data [1]. In line with this, several approaches
were developed at the end of the 1980s to describe, analyze and
compare amino acid sequences by incorporating structural di-
mensions. Some rely on the use of structural alphabets, first
introduced by Unger in 1989 [2] and further developed in several
outstanding pioneering works in the lab of Serge Hazout (see the
introduction to this special issue by A.G. de Brevern). Following
this concept, protein 3D structures are represented as a series of
overlapping fragments (each labeled with a symbol), capturing
local conformations. Since, structural alphabets have been largely
used for multiple purposes, among which local structure predic-
tion, analysis and prediction of protein flexibility and disorder, as
well as comparison of protein 3D structures (reviewed in the
introduction to this special issue by A.G. de Brevern). A recent
extension of this concept to a 3D interaction alphabet (3Di) is the
basis of the Foldseek tool [3,4], enabling structure similarity

searches to become as widespread as sequence similarity searches,
with an obvious gain in sensitivity to detect distant relationships.

The purpose of this mini-review is to provide a brief overview of the principles and achievements, recent developments and future prospects of another structure-driven approach, first described in 1987 and known as Hydrophobic Cluster Analysis (HCA) [5-7]. HCA was conceived to reveal information specific to the regular secondary structures of a protein, based solely on knowledge of its amino acid sequence. It makes direct use of the hydrophobic/hydrophilic dichotomy within a two-dimensional representation of the amino acid sequence [5-7]. In particular, HCA gives access, in an intuitive way, to the amino acids that make up the core of globular domains and participate in regular secondary structures, intrinsically (without learning) and from the analysis of a single sequence (without referring to homologous sequences) (Fig. 1-A). This was of great interest when dealing with orphan sequences, with no known homolog. This fundamental notion of partitioning between essentially hydrophobic or hydrophilic entities, is a simple design engine but has proved effective in deciphering many facets of protein behavior and at the basis of *de novo* protein design ([8-10] and references therein).

Several fundamental studies were first carried out to support
the reliability of the HCA approach as a tool for understanding and
comparing protein folds, in particular thanks to the work of several
master's and doctoral students, who attended the DEA AGM2

<footnote>This article is part of a special issue entitled: Protein structure: bioinformatics insights published in Biochimie.</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: isabelle.callebaut@sorbonne-universite.fr (I. Callebaut).</footnote>
<figure><img src="image_3.png" /><figcaption>**Fig. 1.** Delineation of foldable/non foldable segments using HCA - the example of Wiskott-Aldrich Syndrome protein (WASp, UniProt P42768), also known as the actin nucleation-promoting protein.</figcaption></figure>

A) The foldable regions, as predicted using SEG-HCA/pyHCA [19,21], are boxed (blue) on the HCA plot (bottom, http://osbornite.impmc.upmc.fr/hca/hca-form.html) and compared to the AF2 3D structure prediction (top left, AF-P42768-F1-model_v4 [43], ribbon colored according to the pLDDT values). Bars colored according to the averaged pLDDT values are reported above/below the HCA plot and illustrate the overall agreement of AF2 and HCA on the limits of the foldable/non foldable regions, while AF2-predicted regular secondary structures, reported below the HCA plot, illustrate the correspondence with the hydrophobic clusters. Of note is that two additional secondary structures (labeled 1 and 2-red) are predicted by AF2 with good confidence in the N-terminal part of the **WH1 (A) domain** (corresponding to the two first hydrophobic clusters), while absent from experimental 3D structures (pdb 1MKE, N-WASP in complex with WIP, not shown). An as yet undescribed elongation of the WH1 C-terminal helix (labeled 3-red) is also proposed by AF2. The 3D structure model of the **C domain (GBD)** (GTPase-Binding Domain), which folds in contact with the **E helix** (included in the C-terminal VCA (Verprolin homology, Cofilin homology and Acidic) region) refers to the protein's auto-inhibited state of the protein (pdb 1EJ5), while GDB binding to Cdc42 causes a conformational change (pdb 1CEE - secondary structures are shown in grey, below those of AF2/pdb 1EJ5), which results in the release of the C terminus, enabling its interaction with the actin regulatory machinery. The HCA plot of the **C region** illustrates the secondary structure content of its different conformations. We also illustrate a 3D structure model built by AlphaFold3 (AF3) [44] of the same protein (top right), which is similar for the folded regions to the AF2 prediction in every respect, with the exception of the α-helix associated with the **WH2 (D) motif**, which interacts with the WH1 domain, in the vicinity but not overlapping the WIP binding site (the WIP peptide is shown with green balls after superimposition the experimental 3D structure of the N-WASP-WIP complex (pdb 1MKE) with the AF3 WH1 domain). The foldable **B region**, predicted as disordered by AF2 and AF3, is a good candidate for folding upon binding an as yet undescribed partner. **B) pyHCA-disorder prediction integrated in the CAID prediction portal** (https://caid.idpcentral.org/.[25,26]) on the human WASp sequence, reported together with that of a few other predictors (flDPnn and IUPred3 (disorder) and ANCHOR2 and MoRFchibi (binding), as described in Ref. [45]). This example illustrates the complexity in predicting conditional order in the WASp C-terminal part by current prediction tools, in particular considering a binary assignment (boxes) rather than raw values (lines). It highlights the value of considering, in a complementary way, the delineation of foldable segments by SEG-HCA/pyHCA and the analysis of HCA plots, independently of other criteria.

course created by Serge Hazout (see the introduction to this spe-
cial issue by A.G. de Brevern). The very first studies demonstrated,

from the analysis of experimental 3D structure databases, the
correspondence between hydrophobic clusters and regular
secondary structures, as well as the superiority of an α-helical support, leading to maximum compaction of hydrophobic information, as well as that of a seven-letter hydrophobic alphabet (V, I, L, M, F, Y, W) to optimize this correspondence [5,11]. HCA binary patterns are constrained by a connectivity distance specific to the use of this two-dimensional support, allowing protein words (regular secondary structures) to be separated from each other in the manner of spaces in a text. So, unlike simple binary patterns, hydrophobic clusters cannot overlap, nor can they be included within each other. This property enables them to carry much more differentiated and relevant structural information, and thus permits enhanced prediction of secondary structures, in particular β-strands [10,12]. The hydrophobic components of hydrophobic clusters mainly correspond to the inner face of the regular secondary structures, oriented towards the hydrophobic core in soluble domains. Work by Poupon and Mornon [13] has shown that only half the total number of hydrophobic amino acids of soluble domains are conserved in terms of hydrophobic character among homologous domains and that these positions are less dispersed and more buried than the non-conserved ones, thus making up the inner core of the protein domains. These analyses suggest that these positions, which constitute fold signatures and are connected to folding nucleus [14], are likely to be critical for protein stability. A study of the occurrence of hydrophobic clusters in real sequences compared with random sequences has also enabled to characterize the building blocks used by nature to construct 3D protein folds and to assess their robustness with respect to evolution. This information can be used to identify coding phases at DNA level, characterize the different types of domains, compare their sequences and study their evolution. An HCA dictionary summarizes the association preferences of the most frequently observed clusters within protein 3D structures (~300 distinct binary codes) to secondary structures [15,16]. Two-thirds of hydrophobic clusters have strong preferences for either α-helices and β-strands, in line with the hydrophobic amino acid periodicity typical of these structures [16]. The propensities of different amino acids for different secondary structure states at each of the hydrophobic cluster positions (major and minor states) make it possible to refine secondary structure predictions from single protein sequence analysis, and can also be considered as a basis to predict regions of interaction with their partners [16]. Hydrophobic clusters with high affinity for α-helices and β-strands often constitute fold signatures, which are much more conserved than sequences within families of homologous sequences [16] and have been largely considered to detect remote relationships, especially leading to the identification of novel families of domains (see Ref. [17] for a typical example and [18] for a review).

SEG-HCA [19], integrated into the subsequent, more advanced pyHCA tool [20,21], was conceived as a rapid tool of simple design (measuring the density of hydrophobic clusters over a sliding window of fixed length), for cutting protein sequences into foldable segments. These foldable segments are defined as regions with high densities in hydrophobic clusters (above a minimum threshold of 10 %), whose boundaries are specified using a decision tree [19]. An exhaustive inventory of all the foldable segments present in several reference proteomes has been compiled, showing that they possessed characteristics of globular domains, as stored in domain databases and in the Protein Data Bank (PDB) [19]. For instance, the vast majority of sequences matching PDB (filtered relative to MobiDB to exclude disordered regions, 97.8 %) and SCOP A to F classes (97.5 %) are included in foldable segments, while the few remaining ones correspond to folds devoid of hydrophobic cores [19]. This property makes it possible to discriminate foldable from non-foldable (disordered) segments (Fig. 1-A). Moreover, foldable regions have been shown to contain a specific

class of conditional order, capable of undergoing disorder to order transitions, and generally possessing specific features in terms of their hydrophobic cluster content [18] (exemplified by regions B to E in Fig. 1-A). Such HCA-based analysis has been described in the context of disorder predictors [22] and has been integrated into the FELLS server aiming at visualizing structural features, in particular related to conditional folding [23] (http://old.protein.bio.unipd.it/fells/). In addition to the SEG-HCA tool, a foldability index (or HCA score) was implemented in the pyHCA tool, making it possible to account for the density of hydrophobic clusters within the boundaries of the regions studied and thus obtain a range of reference values for folded domains. A first version of this metric was optimized by maximizing the separation between DisProt and PDB-derived sequences and was associated with p-values in reference to an empirical distribution of scores computed from DisProt sequences [20]. This metric has been integrated in the ODINPRED disorder prediction tool [24], while a pyHCA-disorder predictor, taking into account HCA-derived and physicochemical features and trained on MobiDB sequences, has been integrated into the CAID prediction server [25,26] (Fig. 1-B). SEG-HCA and this first pyHCA foldability metric have also been used to identify and/or characterize orphan genes and noncanonical ORFs, including genes that have emerged de novo from non-genic regions (Drosophila orphan domains [27], intergenic ORFs [28], alternative reading frames of existing ORFs [29]) and stable non-canonical peptides [30]). The HCA score was afterwards further optimized by maximizing the separation between foldable segments delineated in databases of disorder (DisProt) and order (SCOPE [soluble domains] and OPM [transmembrane domains]) [21]. Logically, foldable segments cover more than 90 % of sequences from SCOPE and OPM, whereas they represent only 50 % of sequences from DisProt. In this way, it is possible to estimate the folding potential of an isolated sequence and describe the order/disorder continuum, which covers different states ranging from extended conformations to compact 3D structures corresponding to soluble and membrane domains, via different states of conditional order observed in intrinsically disordered regions.

Proteome-wise analyses by SEG-HCA have shown that a significant proportion of foldable segments, particularly small ones, do not yet belong to any already known family (orphan domains) [19,31]. These tools therefore make it possible to target and quantify the foldable "dark proteome", which represented, at the time they were developed, a significant proportion of foldable region sequences in eukaryotic proteomes (around 40 % and up to 60 % of Apicomplexa proteomes) ([31], also see Refs. [32,33]). Despite the progress made in recent years in the prediction of 3D structures thanks to the development of machine learning and artificial intelligence, which have led to the resounding success of methods such as AlphaFold2 (AF2) [34], part of the foldable "dark proteome" still escapes reliable prediction, as illustrated for the human proteome [35]. Recent studies associating large-scale clustering of sequences and structures with sophisticated classification procedures have led to a comprehensive description of the protein universe, revealing the part of the dark side that can be now illuminated by considering metagenomic data [36] or 3D structure predictions [37–39]. These studies also highlighted the proportion of still unannotated sequences (without homolog and/or without predicted structure, i.e. AF2 predictions of low/very low confidence, whether unstructured or ordered, the latter category corresponding to 7 % of the domains included in The Encyclopedia of Domains [39] and containing some cases of hallucinated predictions, common when faced with unknown). It is therefore important to pursue methodological developments to characterize these sequences in terms of foldability and structural characteristics, in order to continue deciphering the unknown. In this context, the pyHCA tool [20] was
<figure><img src="image_4.png" /></figure>

<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /><figcaption>**Fig. 2.** **Two examples of foldable regions with HCA scores typical of soluble, folded domains but with AF2 very low confidence predictions.** These examples were chosen in the database reported in Ref. [40]. Foldable segments are boxed on the HCA plots (with the N- and C-terminal limits indicated with balls on the AF2 3D structure models). The secondary structure affinities of hydrophobic clusters, as extracted from HCDB [16], are indicated below the HCA plots when non ambiguous (E/e: extended (beta-strand), H/h: helix; lower and upper cases correspond to high and low affinities, respectively). A) AF2 predictions including regular secondary structures. Of note here is that the hydrophobic clusters correspond to β-strands or extended structures on the 3D structure model, suggesting that secondary structures are well predicted while their assembly not. B) AF2 predictions with no regular secondary structure. Of note is that the hydrophobic clusters have here globally less pronounced affinity for regular secondary structures, suggesting a less stable fold.</figcaption></figure>

applied to the sequence set of 21 reference proteomes included in the first version of the AlphaFold database (AFBD v1). A large fraction of the amino acids in the AF2 models associated with a very low confidence index are included in non-foldable segments, supporting the quality of AF2 as a predictor of disorder (Fig. 1-A). However, in every proteome, low-confidence AF2 predictions also feature soluble and folded domains, showing that low pLDDT values are not necessarily synonymous with disorder [20]. An analysis of AF2 predictions made on long foldable segments (>30 amino acids) displaying soluble domain characteristics, as estimated by HCA scores, and showing very low pLDDT values along their entire length, led to the identification of cases of conditional order (foldable sequences predicted as disordered), as well as sequences predicted as non-disordered but which escape AF2 prediction due to shallow multiple sequence alignments and/or as yet undocumented structure or fold [40] (Fig. 2). AlphaFold and pyHCA can therefore be advantageously combined to discover cryptic structural features in whole proteomes and to refine predictions for different types of disorder.

At a time when millions of sequences and 3D structures are available, HCA thus remains an invaluable tool for probing the characteristics of protein matter. The huge amount of confident structural information provided by AF2 (AFDB) can be considered when combining HCA with profile-based similarity searches and clustering, for instance to illustrate the structure/fold of novel domains [41] or to specifically retrieve members of novel families within large superfamilies, by adding unique structural constraints [42]. This huge mass of structural data can also be used to determine, for predictive purposes, the co-occurrences and interaction topologies of hydrophobic clusters within protein domains, studies hitherto limited by the too small number of representatives in the datasets. With this in mind, the HCA's lexical character (words corresponding to hydrophobic clusters) appears also suited to a linguistic approach, in which the meaning of a sentence can be deduced from the study of words distribution. Hence, protein language models might be used to understand grammar of the protein

structures, i.e. the word (clusters) association rules that generate protein domain structures.

### CRediT authorship contribution statement

**Isabelle Callebaut:** Writing – review & editing, Writing – original draft, Conceptualization.
**Jean-Paul Mornon:** Writing – review & editing, Conceptualization.

### Declaration of competing interest

We have no conflict of interest to declare.

### Acknowledgments

First of all, we would like to thank Alexandre G. de Brevern for his initiative and invitation to take part in this special issue in memory of Serge Hazout, who was a key player in the development of structural bioinformatics in France. We thank Christine Gaboriaud, Guilhem Faure and Tristan Bitard-Feildel, for their outstanding contributions to the development of HCA, SEG-HCA and pyHCA, respectively, later followed by the work of Apolline Bruley and Elodie Duprat. Thanks also to all the DEA/M2 and PhD students, as well as post-docs and researchers, who worked on the development of HCA (in the alphabetic order, with apologies for any omissions): Thierry Benchetrit, Véronique Bissery, Guy Bourat, Anne Broudiscou, Luc Canard, Jacques Chomilier, Nathalie Colloc'h, Fabienne Dulin, Patrick Durand, Frédérique Ella Zogo, Richard Eudes, Guillaume Fourty, Nicolas Francillonne, Jean-Christophe Gelly, Jérôme Hennetin, Bernard Henrissat, Gilles Labesse, Alexis Lamiable, Vincent Leduc, Laurence Lemesle-Varloot, Khan Le Tuan, Emilie Levivier, David Marchand, Anne Morgat, Anne Poupon, Karine Prat-Albeau, Flavien Quintus, Joseph Rebehmed, Lucas Rouaud, Françoise Schoentgen, Michel T. Semertzidis, Alain Soyer, Etienne Thoreau, Thomas Vincent, Steeve Woodcock.

HCA methodological developments have been supported by the
I. Callebaut and J.-P. Mornon

Biochimie xxx (xxxx) xxx

French Ministry of Higher Education and Research, Sorbonne University and the National Centre for Scientific Research (CNRS). We also acknowledge the support of the French National Research Agency (ANR Blanc-SVSE-8-2011, ANR-14-CE10-0021, ANR-14-CE10-0006-03, ANR-17-CE12-0016, ANR-19-CE01-0005, ANR-19-CE44-0017-01, ANR-21-CE12-0021) and the Institut National Du Cancer (INCa 2011-PL BIO-10, 2014-1-PL BIO-09 and 2016-PL BIO-11).

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