50 years of amino acid hydrophobicity scales: revisiting the capacity for peptide classification
© The Author(s) 2016
Received: 14 January 2016
Accepted: 17 June 2016
Published: 4 July 2016
Physicochemical properties are frequently analyzed to characterize protein-sequences of known and unknown function. Especially the hydrophobicity of amino acids is often used for structural prediction or for the detection of membrane associated or embedded β-sheets and α-helices. For this purpose many scales classifying amino acids according to their physicochemical properties have been defined over the past decades. In parallel, several hydrophobicity parameters have been defined for calculation of peptide properties. We analyzed the performance of separating sequence pools using 98 hydrophobicity scales and five different hydrophobicity parameters, namely the overall hydrophobicity, the hydrophobic moment for detection of the α-helical and β-sheet membrane segments, the alternating hydrophobicity and the exact ß-strand score.
Most of the scales are capable of discriminating between transmembrane α-helices and transmembrane β-sheets, but assignment of peptides to pools of soluble peptides of different secondary structures is not achieved at the same quality. The separation capacity as measure of the discrimination between different structural elements is best by using the five different hydrophobicity parameters, but addition of the alternating hydrophobicity does not provide a large benefit. An in silico evolutionary approach shows that scales have limitation in separation capacity with a maximal threshold of 0.6 in general. We observed that scales derived from the evolutionary approach performed best in separating the different peptide pools when values for arginine and tyrosine were largely distinct from the value of glutamate. Finally, the separation of secondary structure pools via hydrophobicity can be supported by specific detectable patterns of four amino acids.
It could be assumed that the quality of separation capacity of a certain scale depends on the spacing of the hydrophobicity value of certain amino acids. Irrespective of the wealth of hydrophobicity scales a scale separating all different kinds of secondary structures or between soluble and transmembrane peptides does not exist reflecting that properties other than hydrophobicity affect secondary structure formation as well. Nevertheless, application of hydrophobicity scales allows distinguishing between peptides with transmembrane α-helices and β-sheets. Furthermore, the overall separation capacity score of 0.6 using different hydrophobicity parameters could be assisted by pattern search on the protein sequence level for specific peptides with a length of four amino acids.
KeywordsHydrophobicity scale Transmembrane sheets Transmembrane helix Beta-sheet Amino acid pattern Alternate hydrophobicity
Hydrophobicity as a physicochemical property is frequently used to characterize secondary structures of proteins. Early on it was noted that this property of amino acids dominates the initial interactions during protein folding [1, 2]. In addition, the physicochemical properties of secondary structures depend on the properties of their amino acids and differ in relation to the native environment of the secondary structure, e.g., in solution or in membranes [3–5]. Considering this, it is not of surprise that the classification and characterization of amino acids according to their hydrophobicity attracted much attention.
In 1962 the first hydrophobicity scale of amino acids was formulated . In addition, a first model to calculate the difference in free energy for the unfolded and native form of the protein catalase in solution was established . Ever since many “hydrophobicity scales” were published. However, not all of these scales focus exclusively on hydrophobicity, but we will continue using this term. The information about hydrophobicity for the amino acids were extracted from biochemical experiments , distributions of amino acids in different protein classes [8, 9], the capacity of amino acids to participate in hydrophobic or hydrophilic milieu [10, 11] or from in silico calculations . Today, about 98 “hydrophobicity scales” exist which contain a defined hydrophobicity value for each of the 20 amino acids. A high variance between these scales can be expected due to the variance of the underlying experimental approaches.
At the same time many hydrophobicity parameters for peptide classification have been developed for specific purposes. The overall hydrophobicity was introduced to globally classify peptides . In addition, a hydrophobic moment for detection of the helical membrane segments , the alternating hydrophobicity for detection of membrane embedded ß-sheets [14, 15] or exact ß-strand score (EBSS) considering the frequency of amino acids pointing inward or outward of a ß-barrel  has been defined.
In parallel many alternative algorithms and methods have been developed to predict protein properties based on hydrophobicity scales and classify them concerning environment (soluble, transmembrane) or function. Among them are routines for the prediction of transmembrane regions [17–20] or protein folding [21–25]. Even today, the hydrophobicity scales are often used to define properties of peptides within proteins [26–29]. However, the wealth of hydrophobicity scales complicates the process of scale selection and of the parameters to be calculated.
Thus, 50 years after formulation of the first scale we analyzed 98 different hydrophobicity scales present in the literature [22, 30, 31]. We used the overall hydrophobicity, the hydrophobic moment for detection of α-helical and β-sheet transmembrane elements, the alternating hydrophobicity and the EBSS as parameters to evaluate their influences on the separation on different secondary structure pools. For the analysis of the different scales and parameters we developed a five dimensional consensus approach to define the quality of the combinatory usage. Finally, we clustered the hydrophobicity scales to classify their performance for general separation capacity of secondary structures, environmental specifications or subsets thereof. We found that the overall performance of the hydrophobicity scales is rather comparable irrespective of the strategy of generation. However, the application of more than one hydrophobicity parameter enhances the capacity of the pool separation, but the alternating hydrophobicity has the lowest impact on the separation capacity when compared with the other four parameters. In general hydrophobicity is suitable to classify transmembrane α-helices and β-sheets better than peptides with other secondary structures. However, specific pattern of four or five amino acids were identified in the different peptide pools analyzed.
Results and discussion
Sequence pools, hydrophobicity scales and parameter selection
Sequence pools based on secondary structure dissection
Non-transmembrane random coils
Sequence pools based in silico K/R-digestion
random coil; continuous
random coil; discontinuous
No dominating SSE
β-sheets and random coils
α-helices and random coils
β-sheets and α-helices
β-sheets, a-helices, and random coils
TM α-helices with additional AA at N- or C-terminus
TM β-sheets with additional AA at N- or C-terminus
Improved, calculated and inverted hydrophobicity scales
Tanford and Nozaki
Kyte and Doolittle
Sweet and Eisenberg
Sweet and Eisenberg
Cohen and Kuntz
Sweet and Eisenberg
Sweet and Eisenberg
Rao and Argos
Rao and Argos
Bull and Breese
Wimley and White
Experimental hydrophobicity scales
Bull and Breese
Manavalan and Ponnuswamy
Heijne and Bloomberg
Wolfenden and Cullis
Fauchere and Pliska
Fauchere and Pliska
Miyazawa and Jerningen
Jacobs and White
Cowan and Whittacker
Cowan and Whittacker
Black and Mould
Cassari and Sippl
Ponnuswamy and Gromiha
Ponnuswamy and Gromiha
Ponnuswamy and Gromiha
Ponnuswamy and Gromiha
Wimley and White
Max. exact β-strand score (EBSS)
Parameter to score the probability of a sequence with ≥10 AA to be a TM β-sheet 
Min. exact β-strand score (EBSS)
Max. alternating hydrophobicity
Min. alternating hydrophobicity
Max. hydrophobicity-moment α
Analyzing the distribution of hydrophobicity considering the amino acid distribution in α-helices with an angle between amino acids of 100° to probe for potential to form a TM α-helix 
Min. hydrophobicity-moment α
Max. hydrophobicity-moment β
Analyzing the distribution of hydrophobicity considering the amino acid distribution of β-sheets with an angle between amino acids of 180° to probe for potential to form a TM β-sheet 
Min. hydrophobicity-Moment β
Max. average hydrophobicity
Average hydrophobicity of the peptide 
Min. average hydrophobicity
The relation of the hydrophobicity scales
As expected, the hydrophobicity scales generated by inverting the amino acid values cluster with the original scales (Table 3). However, not all hydrophobicity scales that have been created by the same experimental approach or by the same author cluster together (Table 4). Most prominent examples are (i) the scales generated by Jones (scales 29, 63; JONES, JOND750101; cluster K)  which adjusted the scale of Zimmerman (scale 69; ZIMJ680101; cluster J)  by considering experimental derived values (scale 54, Tanford; cluster A) ; or (ii) the scales proposed by Zviling (scales 89-91; SET1-3; cluster I)  that have been based on the scales of Kyte and Doolittle (cluster B)  and Engelman (cluster A) .
Calculation of separation capacity of hydrophobicity scales
Next we analyzed the capacity of the 98 hydrophobicity scales to separate the 17 defined sequence pools. The initial analysis was based on all five hydrophobicity parameters. For each parameter the maximal and minimal value for each peptide was calculated (Table 5). However, we realized that the simultaneous application of the minimal and maximal value of the same parameter does not increase the separation performance. By this we limit the parameter selection too either the minimal or the maximum value. We calculated the 32 parameter combinations (five parameter and alternating minimal and maximal value) for each peptide for all 98 hydrophobicity scales. The resulting five dimensional vectors for each peptide and each hydrophobicity scale were used to define five dimensional clouds for each pool and each specific hydrophobicity scale.
Next, we defined a separation capacity score (Formula 1) to rank all scenarios. Sv is a score based on the volume of the overlap in relation to the volumes of the two clouds. For Sp we counted all peptides in the overlap volume of two sequence pools and set them in relation to all peptides of the two clouds. The score S is scaled between zero (both clouds totally overlap) and one (no peptides in overlap volume) and gives the quality of a certain hydrophobicity scale for the separation of two defined pools.
Here, P1 and P2 are the total numbers of peptides of pool 1 and 2, P ov is the number of all sequences in the overlap volume, V1 and V2 are the volumes defined by the sequence pools 1 and 2, and V ov is the overlapping volume of both pools. The number of Vi and Pi was always i = 2 because two pools were analyzed in parallel.
In parallel, the average S value for the clusters of hydrophobicity scales (Fig. 1) defined according to the UPGMA-tree was calculated (Fig. 3b). The average S values observed for the peptide pools obtained by in silico tryptic digest (Fig. 3b, blue) or by the combination of peptide pools generated by the two strategies (Fig. 3b green) do not show a dependence on the selected cluster. Only for the secondary structure peptide pools the observed average S values differ between 0.28 for the cluster B and 0.13 for cluster X. Moreover, after sorting the clusters according to the average S values for the secondary structure peptide pools the order of clusters does not follow the order in the UPGMA tree (Fig. 3b, orange).
Separation of specific structure pools via hydrophobicity
In detail, the three pools with transmembrane α-helix (krtm-helix), with transmembrane β-sheet (krtm-sheet) or without random coil content (no-random) generated by digestion have the largest S value while analyzing the overlap with other sequence pools, irrespective whether the best scale (Fig. 4a) or the best value (Fig. 4b) is considered. In contrast, the secondary structure transmembrane pools (tm-sheet, tm-helix) show low S values while analyzing the overlap with other pools. Nevertheless, the S values of the secondary structure transmembrane pools are larger than the S values found while analyzing the overlap of the remaining sequence pools (Fig. 4b).
Remarkably, high S values were found when the overlap between the two secondary structural transmembrane pools (tm-sheet, tm-helix) and the three pools with transmembrane α-helix (krtm-helix), with transmembrane β-sheet (krtm-sheet) or without random coil content (no-random) generated by digestion was calculated. This might suggest that the regions flanking the transmembrane domain present in the sequences of the peptide pools generated by digestion provide additional information. This information in combination with the hydrophobicity might give an additional signature for such domains. Hence, in silico digestion with subsequent analysis by the described parameters using e.g. the hydrophobicity scale 14 can be used to detect transmembrane helices and sheets.
With respect to the remaining pools we observed that the S value obtained while analyzing the overlap of the secondary structure pools (s-sheet, s-helix and random) is higher when compared to the pools containing sequences with mixed structures (Fig. 4a, b). This result is expected, as the chosen parameters detect the individual elements and a mixture thereof yields mixed information.
Benefit of amino acid pattern to separate specific structure pools
An amino acid based approach for the different structure pools was subsequently considered in addition to the hydrophobicity based separation. At first the amino acid composition of the different pools was analyzed, which did not yield a significant difference between the individual pools (Additional file 4: Table S3).
Patterns of four amino acids
FO in pool
Average FO in remaining set
Average FO in remaining set GBSS
Amino acid patterns of five amino acids
FO in pool
FO in remaining set
FO in remaining set GBSS
Factors influencing pool separation
After six rounds of in silico evolution the created random hydrophobicity scales reached a separation threshold of 0.6, which is comparable to the separation potential of the best performing hydrophobicity scale. This suggests that a limit of the potential of amino acid scales for the separation of structural sequence pools exists by 0.6. Furthermore, we realized during the evolution of the hydrophobicity scales that the value of some amino acids had greater positive or negative influence on the separation capacity like others.
After establishing the evolutionary scale, we aimed at an understanding which property of a scale has an impact on its separation capacity. At first, we tested whether the general order of amino acids with respect to their hydrophobicity value is important. We realized that it is not the overall order of the amino acid hydrophobicity values that influences the performance of the hydrophobicity scale (Additional file 7: Fig. S2). At second we analyzed whether the value of specific amino acids dominate the separation capacity of a scale. We realized high S values for hydrophobicity scales sharing rather comparable hydrophobicity values for Gln, His, Gly, Ser or Arg to the evolved scale or for scales with hydrophobicity values for Cys, Met, Lys, Val or Ile distinct from the evolved scale (Additional file 8: Fig. S3). Thus, the hydrophobicity value of some amino acids like Gln, His, Gly, Ser or Arg might be more important for the separation capacity of the scales than others.
Inspecting the information we realized that a large difference of the hydrophobicity values for glutamate and arginine to each other exists. In addition, the hydrophobicity value of glutamate is most distant to the hydrophobicity values of tyrosine, tryptophan, leucine and isoleucine, respectively Fig. 8a, blue frame). The hydrophobicity value of arginine is distant to the value of phenylalanine and methionine (Fig. 8a, blue frame). In turn, three distinct clusters of amino acids with comparable hydrophobicity values become obvious (Fig. 8a, orange frames). Considering all pairs one can draw relations of the hydrophobicity values within these clusters. Interestingly, the hydrophobicity values of cluster three are most distant form arginine (Fig. 8b), while the hydrophobicity values of cluster one are most distant to glutamate. However, these clusters do not correlate with the amino acid pattern detected for the specific sequence pools (Tables 6, 7) and moreover, they do not necessarily represent the physicochemical properties of the amino acids.
We demonstrate that most of the hydrophobicity scales reach the same level of peptide separation capacity (Figs. 3, 4) and thereby, the method by which the scale was generated has no direct influence on clustering or separation capacity (Figs. 1, 3). Nevertheless, if at all we realized that the scale 14 defined by Naderi-Manesh developed in 2001  performs somewhat better than the other hydrophobicity scales. We propose a rule of thumb for experimentalists that aim to use a hydrophobicity scale for identification of peptides with transmembrane segments from a pool of peptides. The hydrophobicity value of arginine and tyrosine should be most distant from the value of glutamate, while the hydrophobicity values of Asn, Asp, His, Lys should be in the center of the scale (Fig. 8c). We further observed that separation of sequence pools defined by known secondary structures is more likely than separation of sequence pools with a combination of secondary structures derived from in silico digestion (Figs. 3, 4), but the tryptic digested sequence pools with helical and strand content or transmembrane ß-strand or α-helix content are best separable from the other pools (Fig. 4). Nevertheless, we realized a threshold of S = 0.6, irrespective of the nature of the scale, which is supported by an in silico approach to optimize the scale (Fig. 7). In turn, the separation capacity depends on the number of parameter calculated (Additional file 3: Fig. S1), although we realized that the alternating hydrophobicity has the lowest capacity for sequence pool separation (Fig. 5). Remarkably, we observed that detection of ß-strands in peptides can be supported by the detection of penta-peptides (Fig. 6) because such peptides have been detected in the structural pool and in the pools generated by simulated tryptic digest (Table 7). Similarly, amino acid patterns specific for the transmembrane ß-strands (Tables 6, 7) or largely random content (Table 7) have been observed. In turn, for pools mainly consisting of helical structures only one specific penta-peptide for soluble (s-helix) and transmembrane (krtm-helix) α-helices could be detected (Table 7). Summarizing, the quality of separation of sequence pools depends rather on the parameter used for calculation than on the scale used and can be supported by the search for specific amino acid pattern.
98 hydrophobicity scales (Tables 3, 4)—16 are only reversed algebraic figures of other scales in the set were extracted from three different sources (http://www.genome.jp/aaindex/ ; http://split4.pmfst.hr/split/scales.html ; http://web.expasy.org/protscale/ ). The path of hydrophobicity scales development is given in Additional file 10: Fig. S4.
Five different hydrophobicity parameters (Table 5) were used to analyze their influence on the separation capacity. For all of those hydrophobicity parameters we used contrary pairs (max. and min.) of the parameters to look for potential differences. The EBBS  should be able to detect β-sheets, whereas the alternating hydrophobicity [14, 15] should be more specific to detect transmembrane β-sheets. The hydrophobicity moment α and β  were used to identify α-helices and β-sheet in general. The last parameter was the average hydrophobicity, which had no preferentially detectable secondary structure so far.
The known secondary structure pools (Tables 1, 2) were extracted from the ASTRAL40 database  and differed in random coil (random), cytosolic β-sheets (s-sheet), cytosolic α-helix (s-helix), transmembrane β-sheet (tm-sheet) and transmembrane α-helix (tm-helix). Further, we implemented an in silico tryptic digest approach to split sequences after Lysine (K) and Arginine (R) of the whole ASTRAL database and classified the peptide fragments concerning their secondary structures. These were divided in fragments containing a (i) continuous dominating SSE (dc), (ii) discontinuous dominating SSE (dd), (iii) no dominating SSE but only two different structures (no-), (iv) all three secondary structures (all) or (v) transmembrane sheet or helix fragments (krtm-).
Pool separation via hydrophobicity scales and parameter
The initial cloud is calculated based on a randomly chosen as subset of ~30 points (peptides defined by vectors). Then, the cloud is expanded until each point is considered. In general, the algorithm calculates all distances and directions within the n-dimensional space between all given points (peptides) and tests if these sites are valid. A site is valid if all points of the entire cloud follow the direction of the hypersurface. By this it is determined if an added point lies within the so far calculated cloud.
The existing cloud is expanded point by point to determine the cloud by a set of sites between points. After each point the set of sites is updated by the procedure (i) and all remaining points are tested if these points are inner points or putative boundary points.
The final cloud volume is calculated based on the outer sites between the boundary points in the n dimensional space that form a convex envelope. All points are placed inside the cloud (inner points) or on the convex envelope (boundary points).
All peptides of two structure pools in a given scenario were used to calculate the heuristic hypervolumes of each pool, respectively, defined by the hypersurface via a pipeline. A scenario is defined by the used number of dimensions represented by the selected hydrophobicity parameters, the selection of the hydrophobicity scale and which two structure pools are used for comparison. The number of the points (peptides) positioned by the according vector within the clouds was counted as well as the points within the overlap of both cloud volumes.
We remove the boundary points (petides) of both structure pools building the convex envelope to avoid big volumes of the clouds based on outliers. We analyzed the volume and number of peptides per structure pool for all combinations with n = 5 dimensions and calculate the loss of peptides and loss of volume in percentage. Due to the high amount of combinations per structure pool (defined by number of hydrophobicity parameter and number of hydrophobicity scales) we calculate the minimum, maximum and average of volume and peptide reduction removing the boundary points (Additional file 11: Table S7). In average, this procedure leads to an elimination of 6.8 % peptide sequences of the structure pool, but a decrease of the according volume of 44.6 %. By that, removal of putative outlier cause on average a sevenfold increase of the volume per peptide. For pools with low amount of peptides (krtm-sheet, krtm-helix, no-random) the increase of the volume per peptide is lower, namely in the order of twofold. Nevertheless, this increase of density justifies the procedure.
Hydrophobicity scale clustering
For the hydrophobicity scale clustering the dissimilarity of the different pairs of hydrophobicity values for each amino acid was calculated. This was done by using autocorrelation between all pairs of the 98 different hydrophobicity scales. Afterwards, the Pearson correlation values were normalized to get the dissimilarity and used by MEGA6  to create an UPGMA tree of the dissimilarity. The clustering of the hydrophobicity scales was done by determining a threshold of 0.05 (5 %) for dissimilarity to split the tree in groups.
Amino acid pattern search
For the amino acid pattern search the different structure pools were used. First, the peptide fragments were analyzed for all occurring amino acid patterns of a specific length based on a Markov chain algorithm of the MEME and MAST suite package (fasta-get-markov) . The algorithm estimates a Markov model from a FASTA file of sequences with previous filtering of ambiguous characters. For example a peptide of four amino acids in length has a conditional probability that one amino acid follows the other amino acid given a specific pool of peptide sequences. So the Markov chain allows the calculation of the transition probability from one state to another state and by this determines the probability of an amino acid occurring in an amino acid peptide of a certain length of a specific pool of peptides. In this approach all possible patterns were detected in the peptides starting from a pattern length of one and incrementing by all different 20 possibilities for each amino acid. The occurrence of the different pattern was normalized to one and compared to the occurrence of the other structure pools to determine the pairwise difference between the pools to detect pool specific pattern of specific length. Furthermore, we performed multiple testing with our identified pattern of length four and five amino acids. We used the Fisher exact test to calculate p values examining the significance of the contingency between occurrences of a specific pattern in relation to a specific structure pool. As reference we pooled all 17 structure pools together. To overcome artificial errors using multiple times the fisher exact test we used as post hoc test Benjamini/Hochberg false discovery rate (fdr) multiple test correction to adjust our p values (Additional file 5: Table S4, Additional file 6: Table S5, p values). All amino acid pattern of length four (Table 6) and five (Table 7) with an adjusted p value below α = 0.05 were marked in bold.
In silico creation of random hydrophobicity scales
The generation of in silico hydrophobicity scales is based on the minimum and maximum hydrophobicity values extracted out of the 98 analyzed hydrophobicity scales, which were determined as borders for the interval. We used five structure pools to calculate the separation capacity score (dd-sheet, dd-helix, dd-random, krtm-sheet, krtm-helix). Two hundred random hydrophobicity scales were created. Based on the best in silico random hydrophobicity scale of the previous steps 2000 scales were created; 100 per amino acid. Half of the hydrophobicity scales per amino acid changed the hydrophobicity value of the single amino acid in the positive [0.001:5] and negative [−0.001:−5] interval (evo1 and evo2). In the following in silico evolution steps (evo3 to evo5) the top 100 newly generated hydrophobicity scales with best performance were analyzed to filter for amino acids which have an influence on the separation capsacity. Only these amino acids were changed in the evo steps evo3 to evo5 to analyze their influence. For evo3 100 hydrophobicity scales per amino acid were created changing within the interval [0.001:10] for E and Y and [−0.001:−10] for A, H, F and L. For evo4 200 hydrophobicity scales per amino acid were created changing within the interval [0.001:20] for E and [−0.001:−20] for A and H. In evo5 400 hydrophobicity scales were created changing within the interval [0.001:40] for E. Finally, in evo6 1000 random hydrophobicity scales based on the best scale of evo5 were created. For each amino acid 25 hydrophobicity scales were created changing within the positive [0.001:5] and 25 scales were created changing within the negative [−0.001:−5] interval.
ES and SS conceived and designed the experiments. OM searched the databses for hydrophobicity scales and parameter and used the cloud algorithm. JE created the structure pools via in silico digestion, performed analysis of pool separation and created random hydrophobicity scales. SS performed hydrophobicity scale clustering and amino acid pattern search. ES, SS and JE contribute to write the manuscript. All authors read and approved the final manuscript.
The work was supported by grants from the Deutsche Forschungsgemeinschaft SFB807-P17 to ES. We thank Nikolaos Konstantinidis, Mario Keller, Katharina Wiesemann and Benjamin Weis for discussion and Johannes Uhlmann for implementing the tool pipeline for heuristic hyper volume calculation.
The authors declare that they have no competing interests.
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