Frontiers in Microbiology | www.frontiersin.org 1 April 2022 | Volume 13 | Article 812903
ORIGINAL RESEARCH
published: 15 April 2022
doi: 10.3389/fmicb.2022.812903
Edited by:
Octavio Luiz Franco,
Catholic University of Brasilia (UCB),
Brazil
Reviewed by:
Abhijit Mishra,
Indian Institute of Technology
Gandhinagar, India
Viorica Patrulea,
University of Oxford, UnitedKingdom
*Correspondence:
Sascha Jung
†ORCID:
Claudia Feurstein
orcid.org/0000-0001-7046-4183
Vera Meyer
orcid.org/0000-0002-2298-2258
Sascha Jung
orcid.org/0000-0003-4970-6904
Specialty section:
This article was submitted to
Antimicrobials, Resistance and
Chemotherapy,
a section of the journal
Frontiers in Microbiology
Received: 10 November 2021
Accepted: 23 March 2022
Published: 15 April 2022
Citation:
Feurstein C, Meyer V and
Jung S (2022) Structure–Activity
Predictions From Computational
Mining of Protein Databases to Assist
Modular Design of Antimicrobial
Peptides.
Front. Microbiol. 13:812903.
doi: 10.3389/fmicb.2022.812903
Structure–Activity Predictions From
Computational Mining of Protein
Databases to Assist Modular Design
of Antimicrobial Peptides
ClaudiaFeurstein
†, VeraMeyer
† and SaschaJung *†
Chair of Applied and Molecular Microbiology, Institute of Biotechnology, Technische Universität Berlin, Berlin, Germany
Antimicrobial peptides (AMPs) are naturally produced by pro- and eukaryotes and are
promising alternatives to antibiotics to fight multidrug-resistant microorganisms. However,
despite thousands of AMP entries in respective databases, predictions about their
structure–activity relationships are still limited. Similarly, common or dissimilar properties
of AMPs that have evolved in different taxonomic groups are nearly unknown. Weleveraged
data entries for 10,987 peptides currently listed in the three antimicrobial peptide databases
APD, DRAMP and DBAASP to aid structure–activity predictions. However, this number
reduced to 3,828 AMPs that wecould use for computational analyses, due to our stringent
quality control criteria. The analysis uncovered a strong bias towards AMPs isolated from
amphibians (1,391), whereas only 35 AMPs originate from fungi (0.9%), hindering
evolutionary analyses on the origin and phylogenetic relationship of AMPs. The majority
(62%) of the 3,828 AMPs consists of less than 40 amino acids but with a molecular weight
higher than 2.5 kDa, has a net positive charge and shares a hydrophobic character. They
are enriched in glycine, lysine and cysteine but are depleted in glutamate, aspartate and
methionine when compared with a peptide set of the same size randomly selected from
the UniProt database. The AMPs that deviate from this pattern (38%) can befound in
different taxonomic groups, in particular in Gram-negative bacteria. Remarkably, the γ-core
motif claimed so far as a unifying structural signature in cysteine-stabilised AMPs is absent
in nearly 90% of the peptides, questioning its relevance as a prerequisite for antimicrobial
activity. The disclosure of AMPs pattern and their variation in producing organism groups
extends our knowledge of the structural diversity of AMPs and will assist future peptide
screens in unexplored microorganisms. Structural design of peptide antibiotic drugs will
benefit using natural AMPs as lead compounds. However, a reliable and statistically
balanced database is missing which leads to a large knowledge gap in the AMP field.
Thus, thorough evaluation of the available data, mitigation of biases and standardised
experimental setups need to beimplemented to leverage the full potential of AMPs for
drug development programmes in the clinics and agriculture.
Keywords: antimicrobial peptide, antifungal peptide, data mining, data analysis, peptide database, AMP,
gamma-core motif
Frontiers in Microbiology | www.frontiersin.org 2 April 2022 | Volume 13 | Article 812903
Feurstein et al. Computational Analyses of AMPs
INTRODUCTION
A well-established approach for the development of medical or
agricultural antimicrobials is the use of natural peptides as scaffold
to design novel synthetic derivatives. These peptides can beboth
of ribosomal and non-ribosomal origin and are found in pro-
and eukaryotes as part of the first line defence against invading
microorganisms and have various structural features (Fleming,
1929; Yeaman and Yount, 2007; Richter etal., 2014). The review
of Koo and Seo (2019) lists 36 peptide antibiotics. Twenty-seven
of them are in clinical phases I to III and nine are preclinical.
Among them are also AMPs of ribosomal origin, as pexiganan
(phase III), omiganan (phase III) and arenicin (preclinical). So
far, the only FDA (food and drug association) approved peptide
antibiotics are either of non-ribosomal origin or synthetic. The
statement in the review by Zhang et al. (2021), where human
lactoferrin peptide 1–11 (hLF1-11) is listed as being approved
by the FDA could not beverified in our literature search. However,
among the FDA approved synthetic peptides, there are a few
representatives which could also besynthesised ribosomally and
do not show specific modifications or special amino acid derivatives.
This displays that AMPs (of ribosomal origin) are generally
appropriate to reach late clinical trials and become approved.
Hence, it might be only a matter of time but also of extended
AMP basic research before ribosomally derived AMPs or their
derivatives will pass phase III clinical studies, finally.
Generally, non-ribosomal antimicrobial peptides, like penicillin
or vancomycin, are small, and their structures can be linear,
cyclic or branched (Walsh, 2004; Tajbakhsh et al., 2017). Their
characteristics include but are not limited to the usage of
non-proteinogenic and D-amino acids, and posttranslational
modification such as glycosylation, acetylation and methylation
(Tajbakhsh et al., 2017). A paradigm for a non-ribosomal
antimicrobial peptide is penicillin, which was discovered by
Alexander Fleming in 1928 (Fleming, 1929). For many years,
it was the lead compound to treat bacterial infections (Aminov,
2010). However, due to extended drug use, resistance mechanisms
evolved in bacteria (Miller, 2002). Semisynthetic derivatives of
penicillin were developed by chemical modification while
maintaining the penicillin’s core region, a β-lactam thiazolidine
ring system (Miller, 2002; Rolinson and Geddes, 2007).
Nevertheless, bacteria rapidly evolved resistance to those derivatives
as well. The history of penicillin explains the steady increase
in multi-resistant bacteria and the decrease of available antibiotics.
Ribosomal antimicrobial peptides (AMPs), like AFP from
Aspergillus giganteus or the human cathelicidin LL-37, are generally
defined by a mean size of 20–40 amino acids, a net positive
charge, and an amphipathic character (Yeaman and Yount, 2007;
Bondaryk et al., 2017; Huan et al., 2020). Further classification
of AMPs can be made regarding the accumulation of certain
amino acid residues in their primary structure, e.g. glycine,
proline, arginine, tryptophan or histidine, which can determine
the AMPs’ modes of action (Huan et al., 2020). AMPs can
also be classified based on their secondary structures which
can include α-helices, β-sheets, linear extension or both α-helices
and β-sheets (Reddy etal., 2004; Huan etal., 2020). Furthermore,
primary- and secondary-structural features can form structural
and/or functional motifs, for example the γ-core motif.
The γ-core motif was postulated as a unifying structural
signature present in all cysteine-stabilised AMPs (Yount and
Yeaman, 2004). AFP from A. giganteus, PAF from P. chrysogenum,
HNP-3 from H. sapiens and Drosomycin from D. melagonaster
are some examples for γ-core AMPs. It is a three-dimensional
structural component in disulphide-stabilised AMPs, which has
a positive net charge and an amphipathic character (Yount
and Yeaman, 2004). This motif consists of two beta strands
and a specific primary structure of one glycine and two cysteines
appearing in three consensus pattern isoforms named dextromeric
or D-isoform (NH2…[X1–3]-[GXC]-[X3–9]-[C]…COOH),
levomeric 1 or L1-isoform (NH2…[C]-[X3–9]-[CXG]-[X1–3]…
COOH) and levomeric 2 or L2-isoform (NH2…[C]-[X3–9]-
[GXC]-[X1–3]…COOH; Yount and Yeaman, 2004; Yeaman and
Yount, 2007). The γ-core motif was recently described to bean
important structural component upon peptide-membrane
interaction of AMPs due to its net positive charge and
amphipathic surface properties (Yeaman and Yount, 2007;
Tajbakhsh et al., 2017; Utesch et al., 2018).
Most structure–activity studies of AMPs focus on
understanding the interaction of AMPs with molecular
components of the target organisms of interest, including cell
wall and plasma membrane, membrane and cytoplasmatic
proteins, lipid biosynthetic pathways as well as DNA and RNA
(Reddy et al., 2004; Gogoladze et al., 2014; Tajbakhsh et al.,
2017). However, the correlation between an AMP structure
and its target organism, potentially determining specificity
towards target organisms, appears to be neglected.
We hypothesise that AMP producing hosts have developed
specific patterns for their AMPs to interact with surrounding
microorganisms. Thereby, AMPs can either display a broad-
spectrum activity or are specifically active against selected target
organisms supposedly present in the same natural niche as
the producing organism.
Broad-spectrum AMPs, such as human cathelicidin LL-37
(a pore-forming peptide) or pig protegrin-1, are not only active
against microorganisms but also against cancer cells and can
exert immunoregulatory functions (Scheenstra et al., 2019).
LL-37 adopts an alpha-helical structure and has a net positive
charge (+6) (Scheenstra etal., 2019). In contrast, the antifungal
peptide AFP produced by the filamentous fungus Aspergillus
giganteus is specifically active against various filamentous fungi,
whereas yeast or bacteria, plant or mammalian cells are not
affected (Meyer, 2008). AFP is the founder molecule of the
AFP family of peptides which are small, amphipathic and
cationic, adopt a beta-barrel structure consisting of five beta
strands and which contain a γ-core motif (Meyer, 2008; Meyer
and Jung, 2018; Paege et al., 2019). It interacts with lipids of
the plasma membrane but also binds to the cell wall compound
chitin (Hagen et al., 2007; Utesch et al., 2018). Furthermore,
it was shown to inhibit the enzymatic activity of chitin synthases
(Hagen et al., 2007). In contrast to LL-37, pore formation or
Abbreviations: AMPs, Antimicrobial peptides.; APD Antimicrobial peptide database.;
DBAASP, Database of antimicrobial activity and structure of peptides.; DRAMP,
Data repository of antimicrobial peptides.
Feurstein et al. Computational Analyses of AMPs
Frontiers in Microbiology | www.frontiersin.org 3 April 2022 | Volume 13 | Article 812903
additional functions of AFP, e.g. immunoregulation, are not
reported. Both peptides, LL-37 and AFP, have different structural
properties which exert different biological activities. Since this
was only one example among the variety of AMPs, we aimed
to identify basic structural properties of AMPs relevant for
their antimicrobial activities. Hence, the three databases,
Antimicrobial Peptide Database (APD), Data Repository of
Antimicrobial Peptides (DRAMP) and Database of Antimicrobial
Activity and Structure of Peptides (DBAASP), were mined to
investigate three questions: (i) Do the structural properties of
an AMP depend on its producing organism? (ii) Does the
choice of the target organisms tested depend on the AMP
producing organism? (iii) Does the activity of an AMP against
a taxonomic group correlates with its structural properties?
To address the first question, wespecified 13 taxonomic groups
known to produce AMPs, i.e. Gram-positive bacteria, Gram-
negative bacteria, fungi, plants, arachnids, insects, crustaceans,
molluscs, fish, amphibians, mammals (including AMPs of human
origin), birds and reptiles. For the second and third question,
wefocused on filamentous fungi, yeast, Gram-positive bacteria,
Gram-negative bacteria, virus, mammalian cells and mammalian
cancer cells as target organisms.
MATERIALS AND METHODS
AMP Database Mining and Data
Processing
Data mining was performed on the three publicly accessible
online platforms APD, DRAMP and DBAASP
(Supplementary Additional Files 1–4; Gogoladze etal., 2014;
Wang et al., 2016; Kang et al., 2019). Resulting peptide lists
included website ID, peptide sequence, sequence length, peptide
name, producer organism, target organisms, references and if
available gene and PDB ID. After combining the data, further
processing involved excluding peptides which (i) were listed
twice based on the amino acid sequence (3,856 entries), (ii)
synthetic (788 entries), (iii) containing non-canonical amino
acids (283 entries), (iv) are not published in a peer-reviewed
publication (282 entries) or (v) did not have a producer organism
recorded (32 entries). AMPs showing identical amino acid
sequences were further distinguished if they had a modification
on the C- and/or N-terminus. In total, 161 peptides were
modified at their termini, leading to 201 modified peptides.
Highest modification rate was five per sequence.
Producer organisms were grouped in archaea, gram-positive
and negative bacteria, fungi, plants, arachnids, insects,
crustaceans, molluscs, fish, amphibians, mammals (including
AMPs of human origin), birds, reptiles and other using the
NCBI taxonomy browser (Schoch etal., 2020). Target organisms
were assigned into the groups: filamentous fungi, yeasts, Gram-
positive and negative bacteria, viruses, mammal cancer and
non-cancer cells as well as other. The producer and target
groups ‘other’ include organisms that could not be allocated
to the other groups. Multiple AMPs of the three databases
contained overlapping entries. Thus, if an organism on species
level was tested more than once per peptide it was only counted
once to avoid double count. The final processing step excluded
peptides without a target given, the categories ‘other’ of both
producer and target organisms and the peptides of the producer
group archaea, since the number of peptides in those groups
were not statistically evaluable. All data were processed according
to their appearance on the databases and have not been inspected
regarding completeness and correctness. A more detailed
description of the data mining and processing procedure is
available in Supplementary Additional File 1. The final list
of AMPs and their primary structure characteristics is available
in Supplementary Additional Files 5 and 6, respectively. Date
of last access: October 2020.
All three databases were investigated for new entries in
September 2021. APD showed 23 new entries, DBAASP showed
76 new entries and DRAMP showed 633 new entries. After
automated application of exclusion criteria in the first round,
solely 63 entries from DBAASP remained. This number of
entries is expected to decrease further when additional manual
editing of data is performed. In conclusion, the number of
evaluable AMPs increased for merely 2% or even less within
the period of 1 year which emphasises the validity of this in
silico study.
UniProt Data Mining and Processing
To put the statistical findings of the producer-structure relation
in context, additional data were retrieved from UniProt (The
UniProt Consortium, 2019). On the website reviewed, peptides
of the Protein knowledgebase (UniProtKB) were retrieved using
the taxonomy view. Here, taxonomy groups belonging to the
AMP producer groups were chosen, peptides longer than 190
amino acids excluded, since this corresponds to the longest
peptide of the analysed AMPs. Random peptides were chosen
(i) in the same amount as the test producer organism groups
and (ii) for each producer group 250 peptides. Afterwards,
the corresponding peptides were analysed regarding their
structural characteristics. A more detailed description of the
data mining and processing procedure is available in
Supplementary Additional File 2. The final list of UniProt
peptides and their structural characteristics is available in
Supplementary Additional Files 7–9.
Data Analysis
Peptide evaluation included the following characteristics: charge
at physiological pH (pH 7.4), the molecular weight, the length,
the amino acid composition, the hydropathy (GRAVY: grand
average of hydropathy, Scale as described in Eisenberg et al.,
1984, column ‘Normalized consensus’) and the γ-core motif
(Yount and Yeaman, 2004). The checked postulated characteristics
of the γ-core motif include its primary structure, a positive
charge at pH 7.4, and if it is hydrophobic (Yount and Yeaman,
2004). Those properties were correlated with the producer
group and specific target organisms. Latter organisms include
the filamentous fungi Aspergillus spp. and Fusarium spp., the
yeasts Candida spp. and Cryptococcus spp. Additionally, the
ESKAPE organisms (which is an acronym formed by the first
letter of the six following genera) were examined, including
Feurstein et al. Computational Analyses of AMPs
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the Gram-positive bacteria Enterococcus faecium and
Staphylococcus aureus and Gram-negative bacteria Klebsiella
pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa
and Enterobacter spp. Given, AMP concentrations were not
evaluated, due to large variations regarding cultivation conditions
and the reporting of the inhibiting concentration. Additionally,
producer organism and target organism groups have been
correlated by calculating (i) the mean of species tested per
peptide of each target group, (ii) the mean of species tested
per peptide and producer organism group of each target group
and (iii) the maximum and minimum of species tested per
producer organism group of each target group. Data were
statistically assessed applying the Χ2 test [chi (capital Greek
letter) square] of independence and the effect size measurement
Cohen’s ω to generate the contingency table (Cohen, 1988;
McHugh, 2013). The effect size according to Cohen can besmall
(0.1–0.29), medium (0.3–0.49) and large (≥0.5). Statistical
evaluation could not have been applied if in the Χ2 test for
more than 20% of relations tested the expected sample size
was less than 5 (Cohen, 1988; McHugh, 2013). To normalise
differences to the peptide amount of each producer group or
target organism, percentages were used to generate heat maps.
Non-ribosomal and ribosomal peptides were not distinguished
since this information was only given by one of three databases.
RESULTS
The Molecular Patterns of AMPs Are
Associated With the Producing Organism
Groups
The three antimicrobial peptide databases APD, DRAMP and
DBAASP (Gogoladze et al., 2014; Wang et al., 2016; Kang
et al., 2019) were mined to investigate main structural
characteristics of AMPs. In total, 10,987 entries for AMPs were
obtained, semi-automatically processed and due to stringent
exclusion criteria (e.g. incomplete entries and double entries,
see Materials and Methods) reduced to a final number of
3,828 AMPs (Figure 1). Since only the DBAASP database
reports if a peptide is of ribosomal or non-ribosomal origin,
wedid not make a distinction in that point. However, at least
62 AMPs are of non-ribosomal origin, which represents 1.6%
of the total analysed AMPs. Thus, the vast majority of analysed
peptides is of ribosomal origin. Additionally, inhibiting
concentrations are reported in publications and thus in databases
in multiple ways [e.g. minimal inhibitory concentration (MIC),
inhibitory concentration at 50% (IC50) and hazardous
concentration at 50% (HC50)]. As these values depend on the
test and cultivation conditions applied,1 total values of inhibitory
concentrations were not considered in our study.
Remaining peptide entries were examined with respect to
their structural properties and the corresponding groups of
producing as well as target organisms. With 1,391 AMPs, the
highest number is reported from amphibians (36.3%), whereas
only 35 AMPs (0.9%) are reported from fungal origin (Figure2).
1
www.eucast.org, Accessed: November 9th, 2021.
Likewise, AMPs from Gram-negative bacteria (42), molluscs
(58), birds (51) and reptiles (78) are only lowly represented
in the final AMP protein set, clearly demonstrating a large
bias towards amphibian AMP resources.
To test the statistical significance of the structural properties
of AMPs associated with the producing organism and to
determine the effect size of this association, the Χ2 test of
independence (significance) and the Cohen’s ω (effect size)
were calculated according to McHugh (2013) and Cohen (1988).
Except for the occurrence of the γ-core motif, all structural
characteristics could be proven to be associated with the
producer organisms (p-values< 0.01). The dataset for a statistical
analysis of the γ-core motif did not match the mathematical
constraints. Data analysis unveiled and confirmed distinctive
structural features of AMPs in general but also showed a
dependence of structural features on their producing taxonomic
group. The investigated AMP characteristics included the
molecular weight, the charge at physiological pH (pH 7.4),
the length, the hydropathy (GRAVY value), the occurrence of
a γ-core motif and the amino acid composition. At least 62%
of the investigated 3,828 AMPs showed a molecular weight
higher than 2,500 Da, a net positive charge between 0 and + 5,
an overall length below 40 amino acids and shared a hydrophobic
character (Figure 2) and thus define a common theme.
Furthermore, the amino acid composition of the AMPs is
enriched in lysine, cysteine and glycine, but depleted in aspartic
acid, glutamic acid and methionine when compared to a
randomly chosen peptide set from the UniProt database of
the same size (Figure 2, compare total values in percent).
These findings are statistically significant in all cases
(p-values< 0.01) and confirm the common description of typical
AMP properties reported in most studies (Bondaryk et al.,
2017; Huan et al., 2020).
Interestingly, the γ-core motif is present in less than 13%
of the 3,828 AMPs, which is still more than double the amount
compared to randomly chosen peptides (5.5%; Figure2, compare
total values in percent). Those AMPs having a potential γ-core
mainly contain the levomeric L2-isoform (50.1%) or the
dextromeric D-isoform (36.1%), whereas the levomeric
L1-isoform is less frequent (13.7%, Figure2). Hence, the γ-core
which is described as unifying structural signature present in
all cysteine-stabilised AMPs is less abundant than presumed,
if considered as fundamental contributor of AMP’s activity.
Thus, the activity of the vast majority of AMPs does not depend
on the presence of the γ-core motif. However, 9 out of 13
taxonomic groups that produce AMPs show a higher abundance
of the γ-core motif in their AMPs compared to the random
UniProt set of peptides, whereas four taxonomic groups (Gram-
positive, Gram-negative, fish and amphibians) show an equal
or even lower abundance. Consequently, these results further
corroborate the γ-core motif as characteristic part of several
AMPs but also mitigate its importance of being a unifying
structural element in this group.
Interestingly, the molecular pattern of AMPs from several
AMP producers deviates from the common description
summarised above, e.g. from Gram-negative bacteria. This
group mainly encodes AMPs with a molecular weight below
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2.5 kDa, a net negative charge, the second lowest abundance
of a γ-core motif and an increased amount of alanine and
serine in their amino acid sequence (Figure2 and Table 1).
Additionally, amphibians also appear to produce rather small
AMPs with a molecular weight below 2.5 kDa and possess
a higher amount of alanine (Table 1). Further AMPs that
deviate from the typical AMP pattern are found in mammals,
birds and reptiles. These groups produce AMPs with a net
positive charge higher than +5 and with a hydrophilic
character (Table 1). Moreover, AMPs from mammals and
birds show high amounts of arginine in their amino acid
sequences, whereas lysine is below average. Regarding those
AMPs containing a γ-core motif, birds (51%) and fungi
(48.6%) clearly show much increased presence of this
characteristic in their AMPs. Thus, the natural AMP reservoir
is more versatile than expected.
X2 analysis demonstrated that the analysed AMP properties
are statistically significantly related to the producing organism
group. Wetherefore performed Cohen’s ω calculations to evaluate
differences in the effect size of these associations (see methods).
This led to a clear ranking in the structural characteristics of
AMPs (Figure2). Whereas all structural features show at least
FIGURE1 | Schematic of data mining, processing and analysis process. Data were retrieved from the three databases APD, DRAMP and DBASSP and
subsequently filtered after predefined criteria (no double sequences, only peer-reviewed entries, no synthetic peptides, etc.). This semi-automated editing of the
entries improved the data for automated processing. The producer organism group ‘Archaea’ and ‘Other’ as well as the target organism group ‘Other’ were
excluded since the number of peptides was not statistically evaluable. This led to total of 3,828 peptides to beanalysed. The same amount was retrieved from
UniProt to compare the AMP structural features to randomly selected peptides. The peptides were examined regarding (i) the relationship of the producing organism
group and the AMP structure, (ii) the dependence of the investigated target organism and the AMP producing group and (iii) the influence of the AMP structure on
the target organism. For further details, see text.
Feurstein et al. Computational Analyses of AMPs
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FIGURE2 | Distribution of AMP properties in different producing organism groups. Data were mined from the APD, DRAMP and DBAASP databases and
subsequently processed. Percentage values were used to generate heat maps with blue colour for low and red colour for high values. The margins for the
corresponding tables are given directly below the tables. The row ‘Total’ describes the summary of the data of the producer organism rows. The values in the row
named ‘Total UniProt’ are from randomly chosen peptides of the peptide database UniProt. The row ‘Total UniProt 250’ displays the results, if the randomly chosen
peptides have a total number of 250 for each producing organism group. The association and its magnitude were determined by the p-value and the Cohen’s ω,
respectively. Asterisks indicate highest changes for increased (green) and decreased (violet) numbers of amino acid residues compared to randomly chosen
peptides from UniProt.
Feurstein et al. Computational Analyses of AMPs
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small Cohens ω-values (>0.1), these values were high in case
of charge (0.57) and hydropathy (0.58). Peptides of the same
producing organism group randomly chosen from UniProt only
showed a medium effect size (0.22) for the relation of producer
and charge and a low effect size for the relation to hydropathy
(0.11). Moreover, the effect size for the amino acid composition
of randomly selected peptides (0.16) is less than half than of
AMPs (0.35). These findings for randomly chosen proteins
from UniProt also did not significantly change when an equal
amount of 250 peptides was chosen for each producing organism
group (Figure 2). This indicates a high-ranking importance
of the charge, hydropathy and amino acid composition as
determinants of the molecular patterns of AMPs. Even though
the previously used statistics could not be applied to the
occurrence of a γ-core motif, its sequence was found 2.3-fold
more often in AMPs (12.9%) than in the randomly selected
peptide control group (5.5%). In contrast, the molecular weight
and length of AMPs are less important when compared to
randomly selected peptides within a producing organism group.
Taken together, these data demonstrate tendencies of
structural properties for each producer group even though
uniform specific molecular characteristics can befound for
the majority (62%) of AMPs.
Our in silico analyses did not unveil a phylogenetic connection
between the producing organism group and the occurrence
of specific AMP properties (Figure 3), suggesting high and
independent adaptive variation of AMPs throughout evolution
with their functional properties being pronounced to a greater
or lesser extent.
AMP Activity Tests Have a Strong Bias
Towards Specific Target Organisms
Next, we calculated for each target organism group the
mean, the maximum and the minimum of tested species
for all 3,828 AMPs. Subsequently, these values were used
to determine if target organisms were tested to a greater
or lesser extent per producing organism group than the
average of all AMPs of each target organism group. This
approach uncovered that bacteria were tested for the highest
number of AMPs (Gram-positive: 3,209 peptides, Gram-
negative: 3,080 peptides), followed by yeast (1,708 peptides)
and mammalian cells (1,449 peptides). Filamentous fungi
(705 peptides), mammalian cancer cells (465 peptides) and
viruses (209 peptides) were the least tested target organism
groups (Figure 4).
The average amount of species tested per AMP was the
highest for Gram-positive bacteria (3.2 target organisms per
AMP) and filamentous fungi (3.1 target organisms per AMP),
followed by Gram-negative bacteria (2.8 target organisms
per AMP) and mammalian cancer cells (2.6 target organisms
per AMP, Figure4). Hence, independent of the AMP source
activity testing against bacteria comprises a broader set of
tested bacterial species than filamentous fungi, yeast, virus
or cancer cells. In contrast, yeast, virus and mammalian
cells were tested with 2–3 times lesser extent (1.3–1.5 target
organisms per AMP). Assessing the single producing organism
groups, peptides from fungi are tested above average regarding
their activity towards filamentous fungi, yeast and Gram-
positive bacteria (Figure 4). However, AMPs produced by
plants are most often tested towards their activity against
filamentous fungi and yeast, but below average against Gram-
positive, Gram-negative and viruses. Activity testing of AMPs
from amphibians and mammals is less often performed
against filamentous fungi, Gram-positive and Gram-
negative bacteria.
These results show an association (p-value <0.01) with a
high effect size (Cohen’s ω = 0.53), clearly showing a biased
choice of tested target organisms that depends on the AMP
producer organism group. Hence, the evaluation of the
TABLE1 | Maximum and minimum values of AMP properties in association with the producing organism group.
Molecular
weight Charge pH 7.4 Length GRAVY γ-core motif
Amino acid
Highest Lowest
Producer
organism
groups
Bacteria Gram-positive 2,500 < MW Positive 20 < L ≤ 40 Hydrophobic L1 G/A/K M/H/E
Gram-negative MW ≤ 2,500 Negative L ≤ 20 Hydrophobic D G/A/S H/M/W
Fungi 2,500 < MW Positive 20 < L ≤ 40 Hydrophilic L2 G/C/K M/W/Q
Plants 2,500 < MW Positive 20 < L ≤ 40 Hydrophilic D/L2 C/G/R M/W/H
Invertebrates Arachnids 2,500 < MW Positive 20 < L ≤ 40 Hydrophobic L2 G/K/L M/W/H
Insects 2,500 < MW Positive L ≤ 20 Hydrophobic L2 G/K/A M/W/Y
Crustaceans 2,500 < MW Highly positive 60 < L Hydrophilic D G/P/R M/W/H
Molluscs 2,500 < MW Positive 20 < L ≤ 40 Hydrophilic L1 C/G/R M/W/H
Vertebrates Fish 2,500 < MW Positive 20 < L ≤ 40 Hydrophobic D G/K/R M/W/Y
Amphibians MW ≤ 2,500 Positive 20 < L ≤ 40 Hydrophobic L2 L/K/G Y/W/H
Mammals 2,500 < MW Highly positive 20 < L ≤ 40 Hydrophilic L2 R/K/G M/W/H
Birds 2,500 < MW Highly positive 20 < L ≤ 40 Hydrophilic D R/C/G M/E/D
Reptiles 2,500 < MW Highly positive 20 < L ≤ 40 Hydrophilic D K/R/G M/W/H
Total 2,500 < MW Positive 20 < L ≤ 40 Hydrophobic L2 G/K/L H/M/W
p-value (total) <0.01 <0.01 <0.01 <0.01 <0.01
Cohen’s ω (total) 0.30 0.57 0.48 0.58 0.35
Feurstein et al. Computational Analyses of AMPs
Frontiers in Microbiology | www.frontiersin.org 8 April 2022 | Volume 13 | Article 812903
relationship between the AMPs’ structural characteristics and
a target organism group is likely compromised by the bias in
AMP activity testing.
AMPs Directed Towards Specific Human
Pathogens Show Distinct Structural
Properties
Although a bias was detected regarding the producer organism
group of AMPs and the target organisms tested, two fungal
species and ESKAPE bacteria were examined to evaluate possible
structure-target organism relationships. Again, the investigated
AMP molecular characteristics included the molecular weight,
the charge at physiological pH (pH 7.4), the peptide length,
the hydropathy (GRAVY), the occurrence of a γ-core motif
and the amino acid composition.
All analysed 3,123 AMP structural characteristics were
statistically significantly associated with the investigated target
organisms (p < 0.01). However, the strength of the relationship,
expressed as Cohen’s ω, was low for the charge (0.10), the
length (0.17) and the γ-core motif (0.12) and very low for
the molecular weight (0.08) and the amino acid composition
FIGURE3 | Phylogenetic representation of the most abundant AMP properties per producing organism group. The most abundant amino acids (glycine, lysine and
leucine) as well as the different γ-core motifs are given as proportions with boxes representing 100%. Gram + represents Gram-positive and gram – represents
Gram-negative bacteria. Distribution of the taxonomic groups is as labelled in the top panel. Each phylogenetic tree has its own colour legend and is related to one
of the following AMP properties (as stated in the figure): molecular weight, GRAVY, length, charge, amino acids and γ-core motif.
Feurstein et al. Computational Analyses of AMPs
Frontiers in Microbiology | www.frontiersin.org 9 April 2022 | Volume 13 | Article 812903
A
B
FIGURE4 | Bias of AMP activity testing. Data were mined from the APD, DRAMP and DBAASP databases and subsequently processed. The average of tested
species for each target group was determined (red dotted line). Additionally, the mean (blue mark), the maximum (right grey mark) and the minimum (left grey mark)
of tested species for each target organism and producing organism group were calculated. For better assessment of the displayed data, the diagrams of (A) were
magnified and are displayed in (B). The association and its magnitude were determined by the p-value and the Cohen’s ω, respectively.
Feurstein et al. Computational Analyses of AMPs
Frontiers in Microbiology | www.frontiersin.org 10 April 2022 | Volume 13 | Article 812903
(0.09, Figure 3). AMPs examined against the chosen target
organisms are generally bigger than 2.5 kDa, have a charge
between 0 and +5, a length between 20 and 40 amino acids
are hydrophobic and possess the levomeric L2-isoform of the
γ-core motif (Figure5). This pattern is equivalent to the overall
molecular pattern of characteristics of all examined AMPs
(Figures2, 5). However, AMPs with concurrent activities against
fungi, Gram-positive and Gram-negative bacteria had varying
strength of the occurrence of these characteristics. Those 127
AMPs showed a larger molecular weight (83.5%) and length,
a more diffuse net charge, are more often hydrophilic (63.8%),
have a higher content of the γ-core motif (24.4%), and showed
deviations in their amino acid sequence with more arginine
and histidine and less serine and leucine compared to the
average AMP molecular pattern. Interestingly, at least 1.5-fold
more AMPs with activities against Aspergillus spp., Fusarium
spp. and Cryptococcus spp. Contained the γ-core motif (16.9–
33.1%) compared to AMPs with activities against the ESKAPE
organisms (4.1–11%). Additionally, AMPs with activities against
Fusarium spp. contained a comparatively high amount of cysteine
residues in parallel to the γ-core motif with a higher abundance,
and they were more often hydrophilic than the other AMPs.
Notably, 2,616 of all investigated AMPs (3,828) were tested
against the organisms Staphylococcus aureus (68.3%), followed
by 1,601 AMPs against Candida sp. (41.8%) and 1,274 AMPs
against Pseudomonas aeruginosa (33.3%). This further
corroborates the observation of a bias in AMP activity testing.
Taken together, these results show a clear variation of the
AMPs structural properties related to their activities against
specific (Fusarium spp. Or Cryptococcus spp.) or a broader
range of pathogens (filamentous fungi, yeast and bacteria)
compared to AMPs which do not show a corresponding activity.
However, the data can only deliver a limited insight regarding
the relation between the AMPs properties and their effects on
target organisms due to the bias in activity testing.
DISCUSSION
The natural AMP reservoir is more diverse than anticipated –
the results of this study clearly show specific structural properties
of AMPs that are distinct from randomly chosen peptides.
The most common description of AMPs includes a molecular
weight above 2.5 kDa, a length below 40 amino acid residues
and a hydrophobic character with a net positive charge between
0 and + 5 (Figure2). Although these AMPs properties are true
for at least 62% of all AMPs analysed in this study, up to
38% of the remaining AMPs differ from this generalisation.
The latter AMP group comprises a hydrophilic character, a
negative or strongly increased net positive charge above +5
or a length of more than 40 amino acid residues.
An additional important feature is the specific amino acid
profile of AMPs including increased amounts of cysteines,
lysines and glycines, whereas amounts of methionine, aspartate
and glutamate residues are decreased compared to randomly
selected peptides from UniProt. However, AMPs from some
producer organism groups differ from this description, e.g.
Gram-negative bacteria have less cysteines and more alanine
in comparison with AMPs or randomly selected peptides.
Consequently, search strategies using generalised or specific
AMP molecular patterns will miss potential AMP candidates
if applied to other taxonomic groups. Therefore, future AMP
screening programmes mainly based on in silico data must
consider these two limitations.
The natural AMP reservoir remains largely unexploited –
our survey uncovered a large bias in AMP research. More
than one-third of analysed AMPs are derived from amphibians,
whereas fungi, Gram-negative bacteria, molluscs, birds and
reptiles show the lowest numbers of investigated AMPs. They
display together less than 7% of all AMPs analysed in this
study. Hence, the choice of a target organism to be tested
needs to be uncoupled from the AMP producing organism
and, ideally, a broader set of target organisms needs to betested
by default. The high clinical relevance of bacterial pathogens
is one a reason why research is focussed on antibacterial
compounds and the neglection of other pathogens (fungi, yeast
and virus) or diseases (cancer cells). However, fungal infections
annually kill more patients than tuberculosis or malaria, a
severe threat that is not well communicated in public debates
compared to bacterial infections (Bongomin etal., 2017). Crucial
for future AMP screening programmes are thus the
implementation of standardised tests of AMPs against a broad
set of microorganisms, insects, parasites and mammalian/cancer
cells to leverage the natural AMP reservoir and aid new drug
templates in both the clinics and agriculture.
Modular design of AMPs – various AMPs evolved in
organisms from different habitats, leading to a wide variety
of AMP structures and antimicrobial activities. Using the
obtained data of the three databases, wecould associate multiple
structural characteristics of AMPs to their producing organism
group. Interestingly, only the charge, the hydropathy (GRAVY),
and the amino acid fingerprint of AMPs were found to
be stronger related to the peptide producing group than in
randomly chosen control peptides of the same producing group.
Furthermore, the amino acid composition differs greatly among
the producing organism group although they follow a general
AMP structural pattern. In contrast, the molecular size given
in length or weight appears to beof less importance for AMPs’
structure definition and activity.
Could this knowledge already beused to aid AMP design?
For example, AMPs targeting Fusarium spp. or Cryptococcus
spp. show an accumulation of the γ-core motif are rather
hydrophilic than hydrophobic and include rather arginine than
lysine residues (Figure5). Additionally, they have high amounts
of cysteine residues, in the case of AMPs tested against Fusarium
spp. (Figure 5). In contrast, AMPs targeting the ESKAPE
pathogen Enterococcus faecium appear to berather hydrophobic
than hydrophilic, include rather lysine than arginine residues,
contain increased amounts of alanine residues and show less
accumulation of the γ-core (Figure 5). However, can AMPs
with broad-spectrum antimicrobial activities towards fungi,
yeast and bacteria bedesigned based on their structural deviations
from the generalised structural pattern? Figure 6 highlights
the concept of a building block approach that envisions a
Feurstein et al. Computational Analyses of AMPs
Frontiers in Microbiology | www.frontiersin.org 11 April 2022 | Volume 13 | Article 812903
future modular design of (semi)synthetic AMPs to combat
microbial infections and diseases.
Still, the data input for this concept is very limited and
biased and thus need to be applied cautiously. For example,
although AMPs with an activity towards Fusaria or Cryptococci
show the γ-core as structural property, this does not imply
that AMPs having a γ-core are automatically active against
these target organisms.
FIGURE5 | Distribution of AMP properties in different target organism groups. Data were mined from the APD, DRAMP and DBAASP databases and subsequently
processed. Percentage values were used to generate heat maps. The row ‘Total’ describes the summary of the data of the target organism rows. The values in the
row named ‘Exclusive F/Y/G+/G-’ summarise the properties from AMPs, which encompass in parallel activities against filamentous fungi (F), yeast (Y), Gram-positive
(G+) and Gram-negative (G-) bacteria. The row ‘Total (all AMPs)’ displays the results when all AMPs are considered (for comparison, see Figure2). The association
and its magnitude were determined by the p-value and the Cohen’s ω, respectively.
Feurstein et al. Computational Analyses of AMPs
Frontiers in Microbiology | www.frontiersin.org 12 April 2022 | Volume 13 | Article 812903
CONCLUSION
Due to inconsistent entries in the antimicrobial peptide databases
APD, DRAMP and DBAASP, 3,828 AMPs out of 10,987 AMPs
were available for the current study. We could show that a
correlation between the producer organism of an AMP, its
structural molecular pattern and the target organism exists.
The presented data support the general assumption that natural
AMPs can provide scaffolds for the development of novel
antimicrobial compounds, which might beapplied in the clinics
and agriculture. To fully exploit that natural potential, a
standardised testing and reporting method regarding the AMPs
activity towards target organism and target molecular structures
needs to be established or at least followed. The European
Committee on Antimicrobial Susceptibility Testing (EUCAST)
and the US Clinical and Laboratory Standards Institute (CLSI)
provide corresponding standard protocols2,3, which represent
a gold standard, ensuring the avoidance of research bias and
an acceleration of the development of novel antimicrobial agents.
DATA AVAILABILITY STATEMENT
The original contributions presented in the study are included
in the article/Supplementary Material; further inquiries can
be directed to the corresponding author.
AUTHOR CONTRIBUTIONS
CF performed the data mining, the processing and statistical
analysis of the retrieved data. CF and SJ developed the study
design and coordinated the project. CF, SJ, and
2
www.eucast.org, Accessed: November 9th, 2021.
3
www.clsi.org, Accessed: November 9th, 2021.
VM performed the data interpretation and wrote the manuscript.
All authors have read and approved the final manuscript.
FUNDING
The work was funded by the Deutsche Forschungsgemeinschaft
(DFG, GRK2473 ‘Bioactive Peptides’—project number 392923329).
We acknowledge support by the Deutsche Forschungsgemeinschaft,
the Open Access Publication Funds of TU Berlin, and the German
DEAL consortium for open access funding.
SUPPLEMENTARY MATERIAL
The Supplementary Material for this article can befound online
at https://www.frontiersin.org/articles/10.3389/fmicb.2022.812903/
full#supplementary-material
Supplementary Additional File 1 | Detailed description of the data mining and
processing process.
Supplementary Additional File 2 | Mined data from the APD website.
Supplementary Additional File 3 | Mined data from the DRAMP website.
Supplementary Additional File 4 | Mined data from the DBAASP website.
Supplementary Additional File 5 | Final processed AMP data.
Supplementary Additional File 6 | Primary structure characteristics of the
peptides in Additional File 5.
Supplementary Additional File 7 | Final UniProt peptides.
Supplementary Additional File 8 | Primary structure characteristics of the
control peptides in Additional File 7 with an equal amount as test
producer groups.
Supplementary Additional File 9 | Primary structure characteristics of the
control peptides in Additional File 7 with 250 peptides per producer groups.
FIGURE6 | Modular AMP design approach. Relevant building blocks determining the structural properties of AMPs are given on the right. The molecular weight/
length is indicated by the overall height of the building block stacks on the left which can beeither higher or lower 2.5 kDa/20 residues. Amino acids are indicated by
one-letter code. ‘General AMP’ displays the average structure retrieved from all analysed AMPs. ‘AMP from gram-’displays the average structure of all AMPs
produced exclusively by Gram-negative bacteria. ‘AMP vs. F/Y/G+/G-’shows the main structural properties of AMPs with concurrent activities against filamentous
fungi (F), yeast (Y), Gram-positive (G+) and Gram-negative (G-) bacteria.
Feurstein et al. Computational Analyses of AMPs
Frontiers in Microbiology | www.frontiersin.org 13 April 2022 | Volume 13 | Article 812903
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