SAV-Pred: A Freely Available Web Application for the Prediction of Pathogenic Amino Acid Substitutions for Monogenic Hereditary Diseases Studied in Newborn Screening

Next Generation Sequencing (NGS) technologies are rapidly entering clinical practice. A promising area for their use lies in the field of newborn screening. The mass screening of newborns using NGS technology leads to the discovery of a large number of new missense variants that need to be assessed...

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Main Authors: Anton D. Zadorozhny, Anastasia V. Rudik, Dmitry A. Filimonov, Alexey A. Lagunin
Format: Article
Language:English
Published: MDPI AG 2023-01-01
Series:International Journal of Molecular Sciences
Subjects:
Online Access:https://www.mdpi.com/1422-0067/24/3/2463
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author Anton D. Zadorozhny
Anastasia V. Rudik
Dmitry A. Filimonov
Alexey A. Lagunin
author_facet Anton D. Zadorozhny
Anastasia V. Rudik
Dmitry A. Filimonov
Alexey A. Lagunin
author_sort Anton D. Zadorozhny
collection DOAJ
description Next Generation Sequencing (NGS) technologies are rapidly entering clinical practice. A promising area for their use lies in the field of newborn screening. The mass screening of newborns using NGS technology leads to the discovery of a large number of new missense variants that need to be assessed for association with the development of hereditary diseases. Currently, the primary analysis and identification of pathogenic variations is carried out using bioinformatic tools. Although extensive efforts have been made in the computational approach to variant interpretation, there is currently no generally accepted pathogenicity predictor. In this study, we used the sequence–structure–property relationships (SSPR) approach, based on the representation of protein fragments by molecular structural formula. The approach predicts the pathogenic effect of single amino acid substitutions in proteins related with twenty-five monogenic heritable diseases from the Uniform Screening Panel for Major Conditions recommended by the Advisory Committee on Hereditary Disorders in Newborns and Children. In order to create SSPR models of classification, we modified a piece of cheminformatics software, MultiPASS, that was originally developed for the prediction of activity spectra for drug-like substances. The created SSPR models were compared with traditional bioinformatic tools (SIFT 4G, Polyphen-2 HDIV, MutationAssessor, PROVEAN and FATHMM). The average AUC of our approach was 0.804 ± 0.040. Better quality scores were achieved for 15 from 25 proteins with a significantly higher accuracy for some proteins (<i>IVD</i>, <i>HADHB</i>, <i>HBB</i>). The best SSPR models of classification are freely available in the online resource SAV-Pred (Single Amino acid Variants Predictor).
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spelling doaj.art-542f99927c1c44cda54225343754017f2023-11-16T16:57:36ZengMDPI AGInternational Journal of Molecular Sciences1661-65961422-00672023-01-01243246310.3390/ijms24032463SAV-Pred: A Freely Available Web Application for the Prediction of Pathogenic Amino Acid Substitutions for Monogenic Hereditary Diseases Studied in Newborn ScreeningAnton D. Zadorozhny0Anastasia V. Rudik1Dmitry A. Filimonov2Alexey A. Lagunin3Department of Bioinformatics, Pirogov Russian National Research Medical University, 117997 Moscow, RussiaDepartment of Bioinformatics, Institute of Biomedical Chemistry, 119121 Moscow, RussiaDepartment of Bioinformatics, Institute of Biomedical Chemistry, 119121 Moscow, RussiaDepartment of Bioinformatics, Pirogov Russian National Research Medical University, 117997 Moscow, RussiaNext Generation Sequencing (NGS) technologies are rapidly entering clinical practice. A promising area for their use lies in the field of newborn screening. The mass screening of newborns using NGS technology leads to the discovery of a large number of new missense variants that need to be assessed for association with the development of hereditary diseases. Currently, the primary analysis and identification of pathogenic variations is carried out using bioinformatic tools. Although extensive efforts have been made in the computational approach to variant interpretation, there is currently no generally accepted pathogenicity predictor. In this study, we used the sequence–structure–property relationships (SSPR) approach, based on the representation of protein fragments by molecular structural formula. The approach predicts the pathogenic effect of single amino acid substitutions in proteins related with twenty-five monogenic heritable diseases from the Uniform Screening Panel for Major Conditions recommended by the Advisory Committee on Hereditary Disorders in Newborns and Children. In order to create SSPR models of classification, we modified a piece of cheminformatics software, MultiPASS, that was originally developed for the prediction of activity spectra for drug-like substances. The created SSPR models were compared with traditional bioinformatic tools (SIFT 4G, Polyphen-2 HDIV, MutationAssessor, PROVEAN and FATHMM). The average AUC of our approach was 0.804 ± 0.040. Better quality scores were achieved for 15 from 25 proteins with a significantly higher accuracy for some proteins (<i>IVD</i>, <i>HADHB</i>, <i>HBB</i>). The best SSPR models of classification are freely available in the online resource SAV-Pred (Single Amino acid Variants Predictor).https://www.mdpi.com/1422-0067/24/3/2463bioinformaticshuman genetic variationsingle amino acid variant (SAV)variant effect predictionnewborn screeningSAR
spellingShingle Anton D. Zadorozhny
Anastasia V. Rudik
Dmitry A. Filimonov
Alexey A. Lagunin
SAV-Pred: A Freely Available Web Application for the Prediction of Pathogenic Amino Acid Substitutions for Monogenic Hereditary Diseases Studied in Newborn Screening
International Journal of Molecular Sciences
bioinformatics
human genetic variation
single amino acid variant (SAV)
variant effect prediction
newborn screening
SAR
title SAV-Pred: A Freely Available Web Application for the Prediction of Pathogenic Amino Acid Substitutions for Monogenic Hereditary Diseases Studied in Newborn Screening
title_full SAV-Pred: A Freely Available Web Application for the Prediction of Pathogenic Amino Acid Substitutions for Monogenic Hereditary Diseases Studied in Newborn Screening
title_fullStr SAV-Pred: A Freely Available Web Application for the Prediction of Pathogenic Amino Acid Substitutions for Monogenic Hereditary Diseases Studied in Newborn Screening
title_full_unstemmed SAV-Pred: A Freely Available Web Application for the Prediction of Pathogenic Amino Acid Substitutions for Monogenic Hereditary Diseases Studied in Newborn Screening
title_short SAV-Pred: A Freely Available Web Application for the Prediction of Pathogenic Amino Acid Substitutions for Monogenic Hereditary Diseases Studied in Newborn Screening
title_sort sav pred a freely available web application for the prediction of pathogenic amino acid substitutions for monogenic hereditary diseases studied in newborn screening
topic bioinformatics
human genetic variation
single amino acid variant (SAV)
variant effect prediction
newborn screening
SAR
url https://www.mdpi.com/1422-0067/24/3/2463
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