An integrated quantitative structure and mechanism of action-activity relationship model of human serum albumin binding

Serra, Angela; Önlü, Serli; Coretto, Pietro; Greco, Dario

doi:10.1186/s13321-019-0359-2

ScienceOpen: research and publishing network

For Publishers

For Researchers

Blog
About

Search
Advanced search

views

recommends

Record: found
Abstract: found
Article: found

Is Open Access

An integrated quantitative structure and mechanism of action-activity relationship model of human serum albumin binding

research-article

Author(s): Angela Serra ¹ , Serli Önlü ¹ ^, ⁴ , Pietro Coretto ² , Dario Greco ¹ ^, ³ ^, ⁵ ^,

Publication date (Electronic): 6 June 2019

Journal: Journal of Cheminformatics

Publisher: Springer International Publishing

Keywords: QSAR, MOA, QSMARt, Molecular descriptors, Human serum albumin binding, Integrative analysis, Safe-by-design, Lasso, Regression

Read this article at

ScienceOpen Publisher PMC

Bookmark

There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

Abstract

Background

Traditional quantitative structure-activity relationship models usually neglect the molecular alterations happening in the exposed systems (the mechanism of action, MOA), that mediate between structural properties of compounds and phenotypic effects of an exposure.

Results

Here, we propose a computational strategy that integrates molecular descriptors and MOA information to better explain the mechanisms underlying biological endpoints of interest. By applying our methodology, we obtained a statistically robust and validated model to predict the binding affinity to human serum albumin. Our model is also able to provide new venues for the interpretation of the chemical-biological interactions.

Conclusion

Our observations suggest that integrated quantitative models of structural and MOA-activity relationships are promising complementary tools in the arsenal of strategies aiming at developing new safe- and useful-by-design compounds.

Electronic supplementary material

The online version of this article (10.1186/s13321-019-0359-2) contains supplementary material, which is available to authorized users.

Related collections

Most cited references 24

Record: found
Abstract: found
Article: not found

Summaries of Affymetrix GeneChip probe level data.

R. A. Irizarry (2003)

High density oligonucleotide array technology is widely used in many areas of biomedical research for quantitative and highly parallel measurements of gene expression. Affymetrix GeneChip arrays are the most popular. In this technology each gene is typically represented by a set of 11-20 pairs of probes. In order to obtain expression measures it is necessary to summarize the probe level data. Using two extensive spike-in studies and a dilution study, we developed a set of tools for assessing the effectiveness of expression measures. We found that the performance of the current version of the default expression measure provided by Affymetrix Microarray Suite can be significantly improved by the use of probe level summaries derived from empirically motivated statistical models. In particular, improvements in the ability to detect differentially expressed genes are demonstrated.

0 comments Cited 251 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

Comments on the definition of the Q2 parameter for QSAR validation.

Davide Ballabio, Viviana Consonni, Roberto Todeschini (2009)

This paper deals with the problem of evaluating the predictive ability of QSAR models and continues the discussion about proper estimates of the predictive ability from an external evaluation set reported in Schüürmann G., Ebert R.-U., et al. External Validation and Prediction Employing the Predictive Squared Correlation Coefficient--Test Set Activity Mean vs Training Set Activity Mean. J. Chem. Inf. Model. 2008, 48, 2140-2145 . The two formulas for calculating the predictive squared correlation coefficient Q2 previously discussed by Schüürmann et al. are one that adopted by the current OECD guidelines about QSAR validation and based on SS (sum of squares) of the external test set referring to the training set response mean and the other based on SS of the external test set referring to the test set response mean. In addition to these two formulas, another formula is evaluated here, based on SS referring to mean deviations of observed values from the training set mean over the training set instead of the external evaluation set.

0 comments Cited 89 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

External validation and prediction employing the predictive squared correlation coefficient test set activity mean vs training set activity mean.

Xue-bin Wang, Jingwen Chen, Ralf-Uwe Ebert … (2008)

The external prediction capability of quantitative structure-activity relationship (QSAR) models is often quantified using the predictive squared correlation coefficient, q (2). This index relates the predictive residual sum of squares, PRESS, to the activity sum of squares, SS, without postprocessing of the model output, the latter of which is automatically done when calculating the conventional squared correlation coefficient, r (2). According to the current OECD guidelines, q (2) for external validation should be calculated with SS referring to the training set activity mean. Our present findings including a mathematical proof demonstrate that this approach yields a systematic overestimation of the prediction capability that is triggered by the difference between the training and test set activity means. Example calculations with three regression models and data sets taken from literature show further that for external test sets, q (2) based on the training set activity mean may become even larger than r (2). As a consequence, we suggest to always use the test set activity mean when quantifying the external prediction capability through q (2) and to revise the respective OECD guidance document accordingly. The discussion includes a comparison between r (2) and q (2) value ranges and the q (2) statistics for cross-validation.

0 comments Cited 73 times – based on 0 reviews      Review now

Bookmark

All references

Author and article information

Contributors

Angela Serra: angela.serra@tuni.fi

Serli Önlü: serlionlu@gmail.com

Pietro Coretto: pcoretto@unisa.it

Dario Greco:

ORCID: http://orcid.org/0000-0001-9195-9003

dario.greco@tuni.fi

Journal

Journal ID (nlm-ta): J Cheminform

Journal ID (iso-abbrev): J Cheminform

Title: Journal of Cheminformatics

Publisher: Springer International Publishing (Cham )

ISSN (Electronic): 1758-2946

Publication date (Electronic): 6 June 2019

Publication date PMC-release: 6 June 2019

Publication date Collection: 2019

Volume: 11

Electronic Location Identifier: 38

Affiliations

[1 ]ISNI 0000 0001 2314 6254, GRID grid.502801.e, Faculty of Medicine and Health Technology, , Tampere University, ; Arvo Ylpön katu 34, Tampere, Finland

[2 ]ISNI 0000 0004 1937 0335, GRID grid.11780.3f, DISES, STATLAB, , University of Salerno, ; Giovanni Paolo II 132, Fisciano, Italy

[3 ]ISNI 0000 0004 0410 2071, GRID grid.7737.4, Institute of Biotechnology, , University of Helsinki, Finland, ; Helsinki, Finland

[4 ]Present Address: Corporate Product Safety/Henkel AG & Co. KGaA, Düsseldorf, Germany

[5 ]ISNI 0000 0001 2314 6254, GRID grid.502801.e, BioMediTech institute, , Tampere University, ; Tampere, Finland

Author information

Dario Greco http://orcid.org/0000-0001-9195-9003

Article

Publisher ID: 359

DOI: 10.1186/s13321-019-0359-2

PMC ID: 6551915

PubMed ID: 31172382

SO-VID: 33003af2-a6f0-4c8e-80eb-500ad8a9a818

License:

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

History

Date received : 16 January 2019

Date accepted : 22 May 2019

Funding

Funded by: FundRef http://dx.doi.org/10.13039/501100005878, Terveyden Tutkimuksen Toimikunta;

Award ID: 275151

Award ID: 292307

Award Recipient : Dario Greco

Custom metadata

ScienceOpen disciplines: Chemoinformatics

Keywords: qsar,moa,qsmart,molecular descriptors,human serum albumin binding,integrative analysis,safe-by-design,lasso,regression

Data availability:

ScienceOpen disciplines: Chemoinformatics

Keywords: qsar, moa, qsmart, molecular descriptors, human serum albumin binding, integrative analysis, safe-by-design, lasso, regression

An integrated quantitative structure and mechanism of action-activity relationship model of human serum albumin binding

Read this article at

Abstract

Background

Results

Conclusion

Electronic supplementary material

Related collections

EPA CompTox Chemicals Dashboard

Most cited references 24

Summaries of Affymetrix GeneChip probe level data.

Comments on the definition of the Q2 parameter for QSAR validation.

External validation and prediction employing the predictive squared correlation coefficient test set activity mean vs training set activity mean.

Author and article information

Contributors

Journal

Affiliations

Author information

Article

History

Funding

Categories

Custom metadata

Comments

Comment on this article

Similar content 32

Cited by 12

Most referenced authors 1,003