PepQSAR: A Comprehensive Data Source and Information Platform for Peptide Quantitative Structure–Activity Relationships

doi:10.21203/rs.3.rs-2004780/v1

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Research Article

PepQSAR: A Comprehensive Data Source and Information Platform for Peptide Quantitative Structure–Activity Relationships

https://doi.org/10.21203/rs.3.rs-2004780/v1

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Peptide quantitative structure–activity relationships (pQSARs) have been widely applied to the statistical modeling and extrapolative prediction of peptide activity, property and feature. In the procedure, the peptide structure is characterized at sequence level using amino acid descriptors (AADs) and then correlated with observations by machine learning methods (MLMs), consequently resulting in a variety of quantitative regression models used to explain the structural factors that govern peptide activities, to generalize peptide properties of unknown from known samples, and to design new peptides with desired features. In this study, we developed a comprehensive platform, termed PepQSAR database, for pQSARs, which is a systematic collection and decomposition of various data sources and abundant information regarding the pQSARs, including AADs, MLMs, data sets, peptide sequences, measured activities, model statistics, literatures, etc. The database also provides a comparison function for the various previously built pQSAR models reported by different groups via distinct approaches. The structured and searchable PepQSAR database is expected to provide a useful resource and powerful tool for the computational peptidology community, which is freely available at http://i.uestc.edu.cn/PQsarDB.

PepQSAR database

bioactive peptide

quantitative structure–activity relationship

statistical modeling

computational peptidology

Bioactive peptides possess various metabolic and physiological regulatory functions in human body and have been implicated in the therapeutic purpose of various diseases and disorders [1]. They are easily digested and absorbed, and can generate considerable benefits in the design and development of new peptidic drugs. In addition, various protein–protein interactions (PPIs) in the human interactome are directly or indirectly mediated by short peptide segments, namely peptide-mediated PPIs (PMIs), which are normally weak and transient, and thus ideal for controlling fast biological events, and easy to be formed and disrupted [2]. Over the past decades, peptide quantitative structure–activity relationships (pQSARs) have been widely used to model and predict the diverse in vitro and in vivo behaviors of peptides in a statistical point of view, including inhibitory activity, binding affinity, physiochemical property and biological function. The pioneering work of pQSARs was done by Sneath et al. in 1960s [3], who characterized peptide sequences by using semi-quantitative experimental parameters of 20 natural amino acids; this was the first attempt of applying amino acid descriptors (AADs) to parameterize peptide’s structural feature at sequence level. Over the past decades, pQSARs have been widely spread in the biology and medicine areas, which are becoming as an important branch of the computational peptidology [4] and have been successfully employed to, for example, develop protein–peptide affinity scoring predictor [5, 6], guide rational peptide library evolution [7, 8], and analyze functional peptide property [9].

Peptides can be characterized with respect to their primary sequence and three-dimensional structure; the former is usually fulfilled with AADs (local descriptors), which are the few informative latent components extracting from a large set of amino acid properties, while the latter regards a peptide as a stereochemical molecule and employs conventional molecular descriptors (global descriptors) to parameterize the peptide [10]. The AADs are standard pQSAR characterization strategy, which are normally derived from a variety of original amino acid properties such as topological, physicochemical, structural and quantum-chemical parameters by using multivariate statistical techniques such as principal components analysis (PCA) and factor analysis (FA) [11]. The characterization was then correlated with observed activities over a panel of peptide samples by using machine learning methods to generate a number of statistical regression models and then validated by internal cross-validation and external blind test. Previously, we successfully developed several AADs, including T-scale [12], DPPS [13, 14] and VSW [15], which have been diversely applied to the statistical modeling, activity prediction, property analysis and rational design of a variety of bioactive peptides such as SH3-binding peptides [16], antioxidant peptides [17], antihypertensive peptides [18] and food peptides [19]. Recently, we also performed systematic comparison and comprehensive evaluation of 80 AADs in pQSAR modeling and concluded that since there are various AADs available to date and they already cover numerous amino acid properties, further development of new AADs is not an essential choice to improve peptide QSAR modeling; the traditional AAD methodology is believed to almost reach its upper limit nowadays [20].

The peptide databases and online servers are increasing rapidly over the past decades, such as the BIOPEP [21], EROP [22] and SwePep [23] that focus on endogenous peptides, and the BIOPEP that deposits peptides of food origin. The first 3 use resources mainly from the area of life sciences; the fourth uses predominantly literature from the area of food science. SwePep contains the most advanced tools for identification of peptides. BIOPEP and PepBank may allow constructing the biological activity profile of peptide fragments. PepBank can be screened by BLAST or SSEARCH using protein sequence as a query [24]. The other peptide sequence databases are focused on antimicrobial and allergenic peptides, such as APD [25] and ASDP [26]. Previously, our group addressed considerable works on the development and releasing of manually enriched display peptide databases. The BDB was special for biopanning data, which incorporated biopanning data from 195 screening experiments [27]. Since the database has not been upgraded since 2002, we implemented and described the MimoDB database to collect these valuable data together [28]. The updated 2.0 version includes biopanning data isolated from random peptide libraries constructed by diverse display technologies [29]. However, it is a remarkable fact that all existing databases are exclusive of NGPD data. Computational tools for analyzing biopanning data are continuously emerging. However, none of them can be utilized to perform TUP analysis within big biopanning data. Therefore, we curated NGPD data from peer-reviewed publications and integrated standalone data analysis tools to achieve a significant upgrade to the BDB database in 2018. We have also developed several bioinformatic tools for peptide identification and analysis, such as NIEluter [30], SABinder [31], PSBinder [32], NeuroPP [33], SAROTUP [34], etc. In the current study, we established a new database of Peptide Quantitative Structure–Activity Relationship (PepQSAR) as a comprehensive data source and information platform for pQSAR studies. The database consists of three blocks, namely AADs, peptide samples and published pQSAR models, representing a basic and useful resource in the QSAR community.

2.1. Data source

PepQSAR contains data for 189 published pQSAR models built by 80 AADs and 34 bioactive peptide sample panels, supported by 337 experimental evidences resulting from more than 1,000 literatures (summarized in Tables 1 and 2, Fig. 1). Data in PepQSAR is freely accessible without limitations and can be downloaded in format as tab delimited flat files. As for modeling methods, multiple linear regression, principal component regression and PLS can be employed for linear modeling while quadratic PLS and neural network are fit for nonlinear modeling. The leave-one-out cross-test correlation coefficient (q²) and root mean square error (RMSCV) are usually treated as a criterion for evaluating the stability and predictive power of the model. For the three statistical parameters of the fitted correlation coefficient r², cross-validation q² and external prediction q_ext², the former is in turn a sufficient condition for the latter, i.e. the QSAM model must have high self-stability (q²) in order to have excellent predictive power (q_ext²), and high self-stability requires strong fitting ability (r²) as a guarantee. An excellent model should have both good fitting ability for internal samples and powerful generalization ability for any external sample [35].

Table 1

Collection and classification of reported AAD descriptors
Type	Descriptor
Physiochemical	φ-vector, z-scale, z’-scale, VHSE, t-scale, zz-scale, CP-score, FF-factor, E-scale, PCPS, DPPS, V-scale, CVH-scale, P-scale, G-scale, HESHB, VHESH, HESH, PP-score, SC-parameter, PC-component, PSR, VVV, SVHEHS
3D-structural	MS-WHIM (extended), MS-WHIM (rotameric), ECI-ISA, t-score, 3Dfield (individual), 3Dfield (combined), SSIA-AM1, SSIA-PM3, SSIA-HF, SSIA-DFT, SVGER, SVGT, VSW, SVWG, SVWGM, SVRG, SVMW, SPCR, CoMFA-score, CoMSIA-score, PDAR
Mixed	FASGAI, ProtFP, BLOSUM62-scale, VARIMAX-scale, VARIMAX-scale (weighted), GSPC, T-descriptor, SZOTT, NNAAIndex, PRIN, WS-descriptor, I-variable
Quantum	CBFQ, CDFQ, CUFQ, ADFQ, ATSQ, MolSurf, QCD, DFTF
Topological	T-scale, ST-scale, SATD, VSTPV, VTSA, GTP
Orthogonal Hydrophobic Electronic Lipophilic/Hydrophilic Conformational Constitutional Hydrophilic Statistical	Additive GH-scale SVEEVA HMLP CP-parameter, Chou-Fasman t-Rig/t-Flx SFED Frequency

Table 2

Collection and annotation of pQSAR-based bioactive peptide sample panels
Datasets	Activity indictor
ACE inhibitory dipeptides	pIC₅₀ (logarithm of inhibitory concentration lowering the ACE activity by 50%)
ACE inhibitory tripeptides	IC₅₀ (concentration of the peptide to inhibit the ACE activity by 50%)
ACE inhibitory tetrapeptides	IC₅₀ (concentration of the peptide to inhibit the ACE activity by 50%)
Bitter tasting thresholds	T (The threshold concentration giving rise to a bitter taste)
Bactericidal peptides	MIC (minimum inhibitory concentration)
Oxytocin analogues	OA_calc (calculate oxytocic activity from the loadings for that amino acid that is in position 3)
Thromboplastin inhibitor sequences	IC₅₀ (activity with respect to two responses: activated partial thromboplastine time and thromboplastine time)
GPCR derived peptides	K_i (an affinity narrowed down to only include annotations with protein confidence score)
Cationic AMP peptides	MIC (minimal inhibitory concentrations for the series of 101 CAMEL-s against the listed microorganisms have been previously averaged to produce the mean antibiotic potency parameters)
Antimicrobial peptides	N (the number of Staphylococcus aureus killed within 2 h)
Elastase substrates	log (K_cat/K_m) (K_m was calculated from the Lineweaver-Burk plot obtained with substrate concentrations in the range 0.01–2.5 mM, K_cat = v/[E])
Bovine lactoferricin-(17–31)-Pentadecapeptide	MIC (minimal growth inhibitory concentration against S.aureus)
Tachykinins	RVD, RPA and HT (the isolated rat vas deferens, the rabbit pulmonary artery, and the hamster isolated trachea)
Bradykinin potentiating pentapeptides	logRAI (the logarithm of a relative potentiating activity index)
Neurotensin analogues	pK_d (binding potency at the human neurotensin receptor and the rat neurotensin receptor were evaluated as equilibrium dissociation constants from radioligand binding assays)
Pseudopeptides	LD₅₀ (lethal median doses, Oncostatic activity on L1210 leukemia and acute toxicity)
Pepstatin analogues	Ki (the inhibition constants of porcine pepsin)
SH3 binding peptide	BLU (the Boehringer light unit derived from previous arbitrary light intensity assays following SPOT peptide synthesis)
HLA-A*0201-restricted CTL epitopes	pBL₅₀ (the half-maximal binding level which is the peptide concentration yielding the half-maximal FI of the reference peptide in each assay)
Peptide analogues of PG-1	log(10³/MIC) (the logarithm of minimum inhibitory concentration)
Non-nucleotide reverse transcriptase inhibitors	EC₅₀ (activity against 14 HIV mutants and HIV replication was quantified by measuring the EGFP fluorescence)
Protease inhibitors	Relative pIC₅₀ (difference between mutant pIC₅₀ and wild-type pIC₅₀)
Inorganic-binding peptides	Binding (%, binding affinity to mica surface)
Periplasmic chaperone (FIMC dataset)	EC₅₀ (ability to prevent FimC/FimH complexation was expressed as inhibition at 50 µM concentration)

3.1. Architecture analysis

B/S architecture is an application structure for distributed processing of information, and its business logic is on the server side. B/S architecture is implemented by a browser. It can be used on any operating system provided that the computer has Internet access, and it can be used by simply installing a browser or other software that can access the Internet.

3.2. MySQL

Relational databases such as MySQL facilitate the handling of large amounts of data that need to be related in a system because they are supported by mathematical relationships. In contrast, flat databases such as Excel are not suitable for handling multiple tables that are related. PepQSAR is based on MySQL, which has separate clients that allow users to interact directly with the MySQL database using SQL. MySQL is the preferred database choice for small-scale web development because of its quick start and ease of use. It also offers high performance at a low cost, and is more suitable for small to medium sized websites, although it is a little less expensive than other expensive and large database systems.

3.3. PHP

PHP's unique syntax is a mix of C, Java, Perl, and PHP's own new syntax. PHP can also execute compiled code, which can be encrypted and optimized to run faster than CGI. Its advantages also include open-source code, fast program development, and cross-platform, can run under UNIX, LINUX, WINDOWS.

3.4 Web technology

Bootstrap. Bootstrap is based on HTML, CSS, Javascript for general use, simple and beautiful responsive front-end development UI library, also known as a framework. The emergence of this framework gives front-end developers a faster development tool that greatly improves productivity.

JQuery. JQuery is a free and open-source library of Javascript that simplifies HTML DOM manipulation, event handling, and Ajax processing in CSS. Approximately 73% of the 10 million popular websites use JQuery technology till 2019. The benefits of JQuery include isolating HTML from the Javascript text, simplicity, and a high degree of extensibility.

HTML5. HTML is a text markup language that can be used to display structured content on websites. It is the last major version of HTML recommended by the World Wide Web Consortium.

CSS. CSS (Cascading Style Sheets) can be used to render the presentation of HTML documents. CSS is intended to act on the separation of content and presentation, including colors, fonts, and layout. This separation improves the readability of content and can provide greater flexibility and control in the specification of representation features. Pages on a website that require the same style can have the same format by being written in the same CSS file, and the complexity and repetition of overall website authoring can be well simplified.

Javscript. Javascript is a computer programming language that complies with the ECMAScript standard and programming specifications. It can be used to embed dynamic text in HTML pages, respond to browser-generated events, and validate a portion of the data submitted to the backend server.

Apache tomcat. Apache HTTP Server (Apache) is an open-source web server that can be run by any computer. Apache Tomcat (Tomcat) is a free open-source lightweight web server that is characterized by its high availability without affecting the live environment.

Developing a web software based on B/S structure requires a combination of technologies. The client side is generally written using HTML, CSS, Java Script, and other technologies, and uses a browser to interpret the graphical interface and provide the user with the ability to browse or operate. The server side uses a web server to receive requests from the client, respond to the client with the results of the requests, use a database management system to store and manage the data required for business processing in the website and respond to the results in a timely manner through the server scripting language, and dynamically generate the content of the pages by accessing the database. Although there are various versions of web development technology components for developers to choose from, the open-source LAMP has received attention from the entire IT community for its high compatibility, low investment cost, and stable operation. Incorporating some of the best features of modern programming languages, combination of PHP, Apache and MySQL has become a standard for web servers.

PepQSAR was developed using a MySQL database, HTML (for GUI), CSS (for style sheets), PHP (server-side scripting language) and Javascript (for displaying alert messages or data validation) in a manner that is transparent to the end user. The details of field designing for amino acid descriptor data sheet are listed in Table 3.

Table 3

Design table in MySQL
Database field	Description	Data type	Length	Constraint
Name	Name of descriptors	varchar	50	Primary key
Style	Type of descriptors	varchar	50
Brief introduction	Physicochemical property of amino acids	char	50
Number of factors	Factors in descriptors	Int	20
Method	Methods to select factors	char	50
Number of variables	Variables selected in descriptors	Int	20
Application	Peptide Characterization	char	50
DOI	Digital Object Unique Identifier	varchar	255
References	The source documents	varchar	20	Foreign key

3.5 Functional requirements analysis

Functional requirements are the most basic requirements in web development, and they are the basic expectations of the users (Fig. 2). Functional requirement analysis is very critical and it changes from time to time because of the continuous development of the Internet. This website is developed using the B/S architecture, so that users can use this website as long as they access the Internet on their own computers. Based on the overall demand analysis of the system, the requirements of the system are two main modules:

(1) Web front-end module:

I. About: Users can see the database details in this module.

II. Amino acid descriptor: Users can see the table data and detailed information of amino acid descriptors.

III. Bioactive peptides: Users can see the activity data and modeling results of active peptides.

IV.QSAR models: Users can see the modeling data of the active peptide characterized by descriptors in this module.

V. Latest information: Users can see the latest literature about descriptors and active peptides updated by the administrator.

VI. Contact information: The email and contact address of the administrator are provided at the bottom of the page.

(2) Backend function module:

The administrator organizes the database data and updates the related latest information, mainly by adding and deleting data in a timely manner.

On the top left of the home page is the logo of the site, below the logo of the home page is the navigation bar of the site, click Home to return to the home page. Clicking About will jump to the brief introduction page of the database. Click Amino Acid Descriptor and it will appear a small drop-down menu, users click Introduction to jump to the brief introduction and summary of the descriptor. Besides, users click Search will switch to the search page of the descriptor. There is also a small drop-down menu for bioactive peptide, users click Introduction to jump to the brief introduction of the active peptide, and click Search page to switch to the search page of the peptide. At the bottom of the home page there is a detailed way to contact the webmaster.

3.6. Database block

The organization and architecture of PepQSAR database consists of three modules as shown in Fig. 3.

(1) Descriptor block:

Above the search box is to choose the search method, for retrieving entries of interest, PepQSAR provides search tools of keyword searches, users can enter the name in the search box to switch to the search result page. If the user selects type search, there is a drop-down selection box for the user to click. PepQSAR will return search results as a sorted table according to the user input keywords, including Name, Style, Brief introduction. If users click Name, it will jump to the detailed information of the descriptor, including Name, Style, Brief introduction, Number of factors, Method, Number of variables, Method, Descriptor table, Application, QSAR result, DOI and References. Descriptor table and QSAR modeling data of the descriptor, users can see and download the detailed information by clicking on View.

(2) Peptide block:

Enter the active peptide name in the search box to switch to the search result page. The search result will display the active peptide information in the form of a list, which includes Name and Introduction. Click on Name to jump to the detailed information of the peptide, including name, brief introduction, method, modeling results and references. QSAR results and peptide activity values can be viewed and downloaded.

(3) QSAR model block:

Descriptors and peptides corresponding to each model are shown in this page. You can get relevant information by simply entering names of active peptides or descriptors in the search box. The search result will display the information in the form of a list, which includes Descriptor, Peptide, QSAR Result and References.

3.7. Software testing

Software testing is an indispensable step after design and development of every program, because it means that the functions of the website need to be more precise.

Software testing is a systematic project for developers to test the reasonableness and safety of the software according to the requirements. After completing the software testing, the developers also have to reprogram the software and modify the modules that are different from the intended logic and functions within a certain scope. Black-box testing is that without knowing how the program works, the developers enter some commands according to the requirement specification, and then sees whether the program receives the parameters and outputs the corresponding results in order. White-box testing is defined as testing each program interface after getting the code, based on the design and the requirements analysis given by the requirements person, including static and dynamic analysis methods, etc.

Based on a comprehensive understanding of the site's functionality, we performed a long period of testing for content accuracy, image and click accuracy. PepQSAR also received better feedback on the implementation effect, button accuracy and content accuracy after alpha test. The second test did not find any serious errors, and the bugs found had been modified and improved, so PepQSAR could be launched as scheduled.

The literature is constantly being added to, so a strategy to maintain PepQSAR database is a necessity. At present we have settled on manual, periodic literature searches to identify new publications that we can mine for QSAR data. It would be advantageous if journals that publish significant numbers of peptide QSAR studies could be persuaded to require submission to PepQSAR a requirement as is done for GenBank. We hope that as PepQSAR becomes more widely used, that journals will require or at least strongly encourage authors to submit their research data directly to us.

In this fashion, researchers should be able to dispense with lengthy material searching and associated data collecting. This should significantly speed up the new research course. Meanwhile, we will keep trying to implement the more advanced features and rendering effects of the database.

The PepQSAR database is a comprehensive data repository of amino acid descriptors and peptide QSARs with a web-based user interface. The built-in tools for browses, searches and interactive displays of study data make PepQSAR a useful resource for further identifying potential amino descriptor and developing computational tools for predicting activity of peptides. In addition, we provide free download of the data and categorized files for all the education and research users.

Availability and Requirements

Database is available freely at http://i.uestc.edu.cn/PQsarDB. The website has been tested with browsers of Safari, Opera, Internet Explorer 7 or later, Firefox and Google Chrome.

Acknowledgments

This work was supported by the Medicine-Engineering Interdisciplinary Foundation of UESTC/SPPH (No. ZYGX2021YGLH209) and the National Natural Science Foundation of China (No. 31671361).

Statement of Informed Consent

Not applicable.

Statement of Human and Animal Rights

Not applicable.

Conflict of interest

None.

Compliance with Ethical Standards.

Author Contributions Statement

J.L. and L.W. developed the database; Y.Z. and S.W. provided technical supports for the database development; H.Y., J.L. and J.S. collected data for the database; J.L. and P.Z. wrote the main manuscript text; J.H. and P.Z. proposed and supervised the researches; all authors reviewed the manuscript.

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No competing interests reported.

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Editorial decision: Major revision
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19 Sep, 2022
Reviewers agreed at journal
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PepQSAR: A Comprehensive Data Source and Information Platform for Peptide Quantitative Structure–Activity Relationships

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Abstract

Figures

1. Introduction

2. Construction And Content

2.1. Data source

3. Utility And Discussion

3.1. Architecture analysis

3.2. MySQL

3.3. PHP

3.4 Web technology

3.5 Functional requirements analysis

3.6. Database block

3.7. Software testing

4. Conclusions

Declarations

References

Additional Declarations

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