# Peptipedia: a comprehensive database for peptide research supported by
Assembled predictive models and Data Mining approaches

Cristofer Quiroz  Facultad de Ingeniería, Universidad Autonóma de Chile, Cinco
Pte. 1670, Talca, 3467987, Chile.  Yasna Barrera Saavedra  Escuela de
Ingeniería en Bioinformática, Universidad de Talca, Avenida Lircay SN,
3460000, Talca, Chile.  Benjamín Armijo-Galdames  Centre for Biotechnology and
Bioengineering, University of Chile, Beauchef 851, Santiago, 8370456, Chile.
Department of Chemical Engineering, Biotechnology and Materials, University of
Chile, Beauchef 851, Santiago, 8370456, Chile.  Juan Amado-Hinojosa  Centre
for Biotechnology and Bioengineering, University of Chile, Beauchef 851,
Santiago, 8370456, Chile.  Department of Chemical Engineering, Biotechnology
and Materials, University of Chile, Beauchef 851, Santiago, 8370456, Chile.
Álvaro Olivera-Nappa  Centre for Biotechnology and Bioengineering, University
of Chile, Beauchef 851, Santiago, 8370456, Chile.  Department of Chemical
Engineering, Biotechnology and Materials, University of Chile, Beauchef 851,
Santiago, 8370456, Chile.  Anamaria Sanchez-Daza  David Medina-Ortiz  Centre
for Biotechnology and Bioengineering, University of Chile, Beauchef 851,
Santiago, 8370456, Chile.

###### Abstract

Motivation: Peptides have attracted the attention in this century due to their
remarkable therapeutic properties. Computational tools are being developed to
take advantage of existing information, encapsulating knowledge and making it
available in a simple way for general public use. However, these are property-
specific redundant data systems, and usually do not display the data in a
clear way. In some cases, information download is not even possible. This data
needs to be available in a simple form for drug design and other
biotechnological applications.
Results: We developed Peptipedia, a user-friendly database and web application
to search, characterise and analyse peptide sequences. Our tool integrates the
information from thirty previously reported databases, making it the largest
repository of peptides with recorded activities so far. Besides, we
implemented a variety of services to increase our tool’s usability. The
significant differences of our tools with other existing alternatives becomes
a substantial contribution to develop biotechnological and bioengineering
applications for peptides.
Availability: Peptipedia is available for non-commercial use as an open-access
software, licensed under the GNU General Public License, version GPL 3.0. The
web platform is publicly available at pesb2.cl/peptipedia. Both the source
code and sample datasets are available in the GitHub repository
https://github.com/CristoferQ/PeptideDatabase.
Contact<EMAIL_ADDRESS><EMAIL_ADDRESS>
_K_ eywords Protein Engineering - predictive models $\cdot$ peptide databases
$\cdot$ machine-learning algorithms $\cdot$ digital signal processing $\cdot$
assembled models

## INTRODUCTION

Peptides play a crucial role as signaling molecules, encompassing diverse
therapeutic activities like antimicrobial, antitumoral, hormone replacement,
anti-inflammatory and antihypertensive (Lau and Dunn,, 2018; Lien and Lowman,,
2003). Peptides are polymers that can be sought in natural sources or
synthetically obtained; they are constituted of at least 2 amino acids, and
their maximum length is usually set to 50 - 100 amino acids. However, it seems
there is no consensus about the maximum of amino acids in a sequence to
consider it a peptide or a protein. (Morrison and Boyd,, 1973; Latham,, 1999;
Lien and Lowman,, 2003; Uhlig et al.,, 2014).

As therapeutic agents, peptides are especially attractive because they exhibit
high biological activity and specificity, reduced side effects and low
toxicity. Nevertheless, peptides have some disadvantages over other molecules,
such as high synthesis cost and low stability due of the lack of tertiary
structure, making them particularly susceptible to enzymatic degradation and
difficulties in crossing biological membranes due to their high polarity,
molecular weight, and hydrophilicity. (Vlieghe et al.,, 2010; Uhlig et al.,,
2014).

Despite the disadvantages mentioned, peptide researching interest has
increased, resulting in a significant accumulation of new peptide sequences in
conjunction with their related activities and properties. This has brought to
the market over 70 peptides approved in the US, Europe, and Japan as
therapeutic, more than 200 in clinical trials, and more than 600 in pre-
clinical tests (Srivastava,, 2019; Usmani et al.,, 2017; Lau and Dunn,, 2018).

One of the most significant trends in recent times is ”drug discovery” to
identify new drugs or new functionalities for specific targets. In this
context, computational approaches are continually developed as support tools
for biological fields, where methodologies based on Machine Learning and Data
Mining become relevant tools (Wu et al.,, 2019; Basith et al.,, 2020).
However, these techniques require prior knowledge, which can be obtained from
biological databases that accumulate information on molecules and their
characteristics. These data of interest can be collected and processed to
develop a tool for solving a specific problem.

Several dedicated databases have emerged for peptide grouping, mostly,
according to their activities (e.g., antimicrobial: APD3 (Wang et al.,, 2016),
antituberculosis: AntiTBdb (Usmani et al.,, 2018) AntiTbPdb, antihypertensive:
AHTPDB (Kumar et al.,, 2015)) or origin source (e.g., Plant: PlantPepDB (Das
et al.,, 2020), bacterial: BACTIBASE (Hammami et al.,, 2010), anuran: DADP
(Novković et al.,, 2012)). The first web-based databases including peptides
were reported in 1998 by Tossi and Sandri, (2002), followed by SYFPEITHI,
JenPep, FIMM and HIV database (Rammensee et al.,, 1999; Blythe et al.,, 2002;
Schönbach et al.,, 2000; Korber et al.,, 1998). Then in 2003, the
Antimicrobial Peptide Database (APD) appeared and has been continuously
updated, but currently, the link is down (Wang and Wang,, 2004; Wang et al.,,
2016), and since then, around 40 peptide databases have arisen.

Each database is useful in their specific context, but a comprehensive and
integrated database focused on peptides is not available so far. Also, many of
the databases present some issues which hinder their usability. Most of them
do not indicate their last update, and if reviewing, they seem to have not
been updated since their launch, except for DRAMP, AllergenOnline, BactPepDB,
DBAASP, ConoServer and APD. Other sites are not found: PenBase, ANTIMIC.
Almost all databases have redundancy in their sequences (see section 1 of
Supplementary Information). Others require informatic background, being
unfriendly for users with no advanced computational skills. Many others do not
provide a download tool: YADAMP, Quorumpeps database, DADP, BIOPEP, BioDADpep,
Péptaibol; for others, the download tool is not working: PepBank, StraPep,
PeptideDB, BactPepDB, MHCBN, ForPep, CancerPPD.

Peptipedia was developed to fulfill the necessities that each database cannot
solve separately. We have implemented a user-friendly web application with a
new database that encompasses the highest number of peptide sequences with
reported activity, curated from 30 existing peptide databases. Peptipedia
classifies reported activity for each peptide in categories and subcategories
defined according to our analysis and literature (Kastin,, 2013).

Our application is more than a database compilation: it is the most extensive
integrated peptide persistent-storage system to date. This user-friendly
platform also includes useful physicochemical and statistical properties
estimator from peptides, amino acid sequences characterisation, and a tool for
Machine Learning-based activity prediction for a query peptide.

## Methods

### Collection, preprocessing, characterisation, and database generation

We consolidate the information for Peptipedia by integrating the data from
different computational tools and databases previously reported, such as APD
(Wang et al.,, 2016), LAMP (Zhao et al.,, 2013), and Uniprot (Consortium,,
2015), among others (see section 1 in Supplementary Information for more
details). Firstly, we manually downloaded the sequences from each tool and
processed them independently, generating different CSV files to facilitate
their manipulation. We filtered the sequences according to their length,
considering a minimum of 2 residues and a maximum of 150. Secondly, we
generated a single file with all sequences, eliminating redundancy between
them. For each sequence, we searched its activities, using the previous
information in all databases employed to develop our web information system.
It is important to note that taxonomic and structural information, and
specific information for particular activities, such as IC50 measurements,
experiments, among others, were also included in Peptipedia. Furthermore, the
sequences are categorized depending on whether they present modifications or
non-canonical residues. Then, we used ModLamp library (Müller et al.,, 2017)
to characterise the peptides based on physicochemical and thermodynamic
properties. Statistical properties were obtained for each sequence using the
DMAKit-Lib library (Medina-Ortiz et al., 2020b, ). Finally, the amino acid
frequency for each sequence was obtained through scripts implemented in Python
v3. Now, we store the processed information in a NoSQL database, using MongoDB
as a handler due to its manipulation characteristics, information extraction
speed and scaling.

### Strategies for classification systems

Most sequences report a specific activity in terms of their biochemical roles
and/or biological effects, specially in humans. We noted that a significant
number of peptides are used or were designed for therapeutic purposes, but
there were another seven types of peptide activity which cannot be classified
as therapeutic. Consequently, we classify all peptides in eight categories:
(1) ’therapeutic’, (2) ’immunological’, (3) ’sensorial’, (4) ’neurological’,
(5) ’drug delivery vehicle’, (6) ’transit’, (7) ’propeptide’ and (8) ’signal’.
Each category has subclassifications within it. However, there are a small
group of peptides with particular activity, so we categorise them in the
category (9) ’other activity’. All peptides with no activity reported are in
the category (10) ’no activity reported’.

One of the essential services of Peptipedia is the activity classification
system for peptide sequences based on Machine Learning strategies. The
training of models was based on the application of supervised learning
algorithms combined with sequence coding approaches, using physicochemical
properties and Digital Signal Processing, according to the strategies proposed
by Medina-Ortiz et al., 2020a . In this way, we generated assembled binary
models to recognize activities for peptide sequences employing our categories
proposed in this work. The training process was based on developing binary
data sets to evaluate two categories: presents or absence of activity.
Additionally, we generated each data set using the one v/s rest strategy,
keeping class imbalance minimum. Finally, in those models with low
performance, it was used the recursive binary partitions strategies, according
to the method proposed by Medina-Ortiz et al., 2020c  to improve the
performance of the classification assembled models.

### Implementation and Availability

Peptipedia was designed using a Model View Controller (MVC) design pattern.
The view component and the controllers were implemented using JavaScript
programming language through the Express framework. Display components were
optimised using Bootstrap 4. All the model members, including all service
disposed in this work’s proposed tool, were developed using Python v3
programming language, supported by the libraries DMAKit-Lib (Medina-Ortiz et
al., 2020b, ) and Scikit-Learn (Pedregosa et al.,, 2011). Both the proposed
software architecture and implementation features are detailed in section 2 of
the Supplementary Information.

## Results and Discussion

Figure 1: Representative scheme of building and characteristics of Peptipedia.
Peptipedia is a computational tool for peptide sequence analysis. The
information presented by our tool was consolidated from 30 databases,
considering information on the sequence, taxonomy, and different properties of
stored peptides. Searching for sequences and relevant information in our web
application is easy, personalised and intuitive, allowing download the
information in multiple formats. Peptipedia has enabled different tools that
will help characterise and analyse sequences, as well as functionalities
supported by Machine Learning methods that facilitate the development of
predictive models and an activity predictor system.

Peptipedia is a user-friendly web application system to search, analyse,
evaluate and characterise peptide sequences using different strategies,
Machine Learning, and Data Mining techniques. This web tool has a NoSQL
database system with 92055 peptides registered and described, being the most
extensive database of peptide sequences with activities reported to date. This
tool reports different types of information for each sequence, considering
structural, physicochemical, and phylogenetic properties. Additionally, the
various activities previously identified for each peptide are reported and so
are the databases or repositories where they were extracted from. Finally,
statistical properties related to the percentage of residues for each sequence
and the average per category are included in the database, providing
interesting, useful, and easy-to-understand information for scientists and
researchers (see Figure 1).

### Relevant tools and services available in Peptipedia

#### Searches, Visualization and Downloads

Different types of searches can be generated in Peptipedia, either with the
sequence or through information related to its activity, physicochemical
properties, frequency of residues, among other relevant information. Besides,
it is possible to apply different filters to generate a personalised
exploration for the user’s interest.

We develop a general summary for each search, showing statistical descriptions
and various visualizations to display the information. Furthermore, we present
specific details for each peptide, including thermodynamic properties,
taxonomy, phylogeny, activity and sequence descriptors; we also show the
databases where the peptide sequence was previously reported. Remarkably,
Peptipedia offers specific information like IC50, assays information, organism
evaluation and other relevant characteristics for particular activities such
as antihypertensive, anti-HIV, and antiviral subcategories.

Peptipedia has general and specific modules for downloading data, making it
easier to obtain information, facilitating the download in CSV, Fasta, and
JSON formats. Besides, our tool enables the complete database download in
easily manipulable forms, considering both the sequence and its reported
information.

#### Services

Different services were implemented in Peptipedia to facilitate analysis and
characterisation of peptide sequences. We propose various services that allow
characterization through physicochemical and thermodynamic properties, using
the ModLamp (Müller et al.,, 2017) library. We also provide modules that
enable the estimation of statistical properties for peptide sequences.

Bioinformatic tools such as sequence alignments are available in our web tool:
using the Edlib library Sosic and Sikic, (2017), it is possible to align any
sequence against those registered in our database.

Another relevant service is peptide activity classification system supported
by assembled predictive models: the user can upload a list of amino acid
sequences, and our tool classifies them by the categories proposed in this
work, evaluating each of them. Furthermore, a peptide encoding service is
implemented using common strategies such as One Hot Encoder and more
sophisticated ones such as Embedding through the Tape library.

Finally, Peptipedia allows the generation of predictive models for sets of
peptides with specific user requirements through supervised learning
algorithms and cross-validation techniques. Configuration of hyperparameters,
coding strategy and validation method are selectable. The tool reports the
performance of the generated model by the user, allowing the download to use
it locally. Besides, this service enables the interpretation of the results
giving different recommendations about them.

### Peptides registered, categories, and relevant information in Peptipedia

We developed the largest database of peptides with reported activity to date,
with a total of 92,055 records. Considering the information on previously
reported activities and the characteristics of each their specific properties,
we propose a system of ten categories, which present sub-categories according
to the features of the activities that constitute them. Using these
categories, we analyse the peptide sequences, identifying therapeutic
peptides, signal peptides, and sensory activity, representing the highest
prevalence in our records. While immunological, transit, and neurological
activity show the least trend or have fewer records (see Figure 2 A).

It is important to highlight the moonlighting characteristics of peptides.
This feature is the feasibility of a peptide to present different activities
at the same time (Jeffery,, 1999). The main found tendencies of moonlighting
are between the therapeutic and sensorial peptides, and between propeptides
and signal peptides. This last overlapping of activities makes sense because
propeptides generally contain a signal peptide in their sequence (Wang et
al.,, 2018), which they lose once processed. (see Figure 2 B). This type of
properties reflects the potential features of a peptide when acting as a drug
or presenting different biotechnological uses, making them interesting to
study due to their fascinating characteristics. Residue frequency analysis
allows evaluating amino acid trends for particular activities. We compare
trends for the main reported categories, with a clear preference for arginine
residues for drug delivery peptides, which can be explained because this kind
of peptides are usually design to crosss membranes, so they need a chemical
affinity for negatively charged membranes, which is given by the positive
charge of arginine. In contrast, signal, transit and propeptides generally
show similar trends. However, no major visible patterns were identified (see
Section 4 in Supplementary Information).

Figure 2: Visualisation of registered peptides on Peptipedia Representation of
the information contained in Peptipedia. A: distribution of peptides according
to the categories proposed in this work. B: analysis of the relationship of
simultaneous activities for the same type of peptide; the most significant
trends are seen between therapeutics and sensorial, and between propeptides
and signal.

### Binary classification categories supported by Assembled Models

Using coding of physicochemical properties and their representation in
frequency space (Medina-Ortiz et al., 2020a, ) and employing recursive binary
division strategies to optimise performance measures (Medina-Ortiz et al.,
2020c, ) we depeloped 44 assembled binary models for classification of
activity for peptide sequences, considering the categories and subcategories
proposed in this work. We used k-fold cross-validation to avoid model
overfitting. Remarkably, all the models generated presented an accuracy of
over 83% (see Table 1 and section 5 of the Support Information for details).
We previously compared the results obtained by applying this type of
strategies against classical sequence coding methods, demonstrating better
results (Medina-Ortiz et al., 2020a, ). Furthermore, we compare our results
with previously developed classification models for peptide sequences. Xiao et
al., (2013) proposed a classification system for antimicrobial peptides with
86% accuracy; for the same task, our model achieves a performance of 88.7%.
Similarly, Yi et al., (2019) proposed a classification system for anticancer
peptides using Deep Learning Long Short-Term Memory Model strategies,
achieving an accuracy of 81.48%, while our model achieves 83.54%. Another
relevant example is identifying quorum sensing peptides (QSPs): Rajput et al.,
(2015) proposed an identification system for QSPs based on sequence features
in combination with support vector machine algorithms, obtaining 93 %
accuracy; our accuracy is slightly lower for this peptides, reaching an
accuracy of 86.4 %. Even though we present a lower performance in particular
situations than previously developed methods. Nevertheless, the proposed
strategy is generic, could be apply in activity classification of peptides
sequences problems, prediction of properties, and multiple issues in protein
engineering (Medina-Ortiz et al., 2020a, ). Notably, we validated all our
models using statistical methods. Each data set was created by selecting
random samples and repeating this process 100 times, providing statistical
support and demonstrating the robustness of the activity classification models
implemented in Peptipedia.

# | Category |  | Size
---
dataset
| Weighted
---
Performance
1. | Sensorial Peptides | 19982 | 85.27
2. | Drug Delivery | 4912 | 86.02
3. | Therapeutic | 50000 | 87.32
4. | Neurological | 2712 | 89.33
5. | Immunological | 2178 | 86.12
6. | Other Activity | 490 | 82.98
7. | Transit Peptide | 1350 | 88.48
8. | Signal Peptide | 26794 | 86.41
9. | Propeptide | 17768 | 88.63
Table 1: Weighted performance for binary classification models for the nine
main categories proposed in this work.

### Using Peptipedia to develop predictive models

The study of anti-HIV peptides is relevant because their potential therapeutic
applications. They interact with a specific domain of the glycoprotein 41,
which is their pharmacological target for inhibiting the virus fusion and
entry to the host cell. Different efforts have focused on designing new
sequences, either through traditional techniques such as directed evolution or
rational design strategies. Both strategies currently benefit from the
application of Machine Learning since it facilitates the simulation of the
effects of new variants. We implemented an IC50 predictive model for anti-HIV
peptides to demonstrate the usability of Peptipedia, because this is a crucial
parameter for assessing the performance of antimicrobial and antiviral drugs.
First, using the sequence search engine, we identify all the peptides that
have this category. We manually downloaded and filtered those with a
quantitative IC50 measurement, discarding the cases in which it was expressed
in terms of low, medium or high effect, and standardising the measured values
to work with them using the same units. Subsequently, we used the Peptipedia
predictive models training tool, selecting coding by digital signal
processing, using the alpha-structure property as coding strategy, Random
Forest as supervised learning algorithm, and validation strategy $k$-fold with
$k=10$. The tool reported the model’s performance, achieving a Pearson
coefficient of 0.8 (see Figure 3 A). Furthermore, Peptipedia allows us to
analyse the prediction error’s randomness to determine if there are biases in
the generated predictions (see Figure 3 B). In this way, we are able to
predict the therapeutic potency of an new anti-HIV peptide with no need of
performing lab assays, which, combined with the coding module, becomes
powerful support for designing peptides with desirable activities.

Figure 3: Predictive modeling of IC50 for Anti-HIV peptides using Peptipedia
A: Scatter Plot prediction v/s reality, denoting the performance of the
predictive model. In general, there is no tendency to over-adjust or under-
adjust in any particular range, which shows that the cross-validation
strategies were correctly applied. B: histogram of the error distribution. The
probability of error analysis indicates no tendency for significant errors
that adversely alter the model predictions. The errors are mainly concentrated
between -5 and 5, which is quite acceptable considering the nature of the
entered values, where the largest reach 100 and the smallest are close to
zero.

## Conclusions

We designed and implemented Peptipedia, a web application supported by machine
learning algorithms and data mining strategies to characterise and analyse
peptide sequences. Additionally, our tool has the most extensive database of
peptides with activity reported so far, with a total of 92,055 amino acid
sequences integrated from thirty databases or repositories of previously
reported peptides, Peptipedia has enabled different tools that will help
characterising, getting statistical properties and bioinformatics analysis
supported by sequence alignments, as well as services that facilitate the
development of predictive models.

Additionally, the sequence and the reported activity information of the
registered peptides are integrated into a robust binary classification system,
implemented through Machine Learning strategies, allowing to predict putative
peptide activities. These services are useful as a previous approach to
experimental work for performing an activity screening of novel peptides with
unknown activity. Besides, peptide design also gets benefited, since this tool
helps to find residues patterns based on their activity.

Both the usability and the wide range of services available on Peptipedia, as
well as the robustness of the predictive systems implemented, considerably
improve the current state of the art, becoming an attractive alternative to
existing traditional applications and a good support for research in peptide
engineering and its biotechnological applications.

## CODE AVAILABILITY

All code is available at the authors’ GitHub repository
https://github.com/CristoferQ/PeptideDatabase.

## ACKNOWLEDGEMENTS

This work was supported mainly by the Centre for Biotechnology and
Bioengineering - CeBiB (PIA project FB0001, ANID, Chile), Fondecyt 1180882
project, and Universidad de Magallanes for MAG1895 project. DM-O gratefully
acknowledges ANID, Chile, for Ph.D. fellowship 21181435. JA-H gratefully
acknowledges ANID, for Ph.D. fellowship 21182109. AS-D thanks PAI Programme
(I7818010006).

#### Conflict of interest statement.

None declared.

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