Biallelic Variant in the AGXT Gene in a Family Segregating Primary Hyperoxaluria; Accurate Genetic Diagnosis and Carrier Detection

Nephrology, Journal Year: 2025, Volume and Issue: 30(1)

Published: Jan. 1, 2025

ABSTRACT Aim Autosomal recessive primary hyperoxalurias (PH) are genetic disorders characterised by elevated oxalate production. Mutations in genes involved glycoxylate metabolism the underlying cause of PH. Type 1 PH (PH1) results malfunctioning alanine‐glyoxylate aminotransferase enzymes liver due to a change sequence ( AGXT ) gene. We encountered large family segregating disease high kidney stones. A analysis was carried out with aim identify defect. Methods multiple affected individuals recruited for this study. An extensive clinical evaluation, followed analysis, out. Due heterogeneous nature disease, two members having symptoms were subjected whole exome sequencing (WES). Variants annotated, filtered, and prioritised using various bioinformatic tools detect associated defects. Results Unbiased hypothesis‐free WES data performed. Raw reads (fastq files) mapped reference genome duplicates removed. prioritised. low‐frequency missense variant (c. 1049G>A) gene considered candidate variant. This replaces highly conserved glycine amino acid aspartate (p.Gly350Asp). The is destabilising protein–protein interaction based on predicted binding free energy (ΔΔG). All phenotype found homozygous mutation. Both parents unaffected heterozygous Conclusion Identification pathogenic gene, family, provides genotype–phenotype correlation permits accurate diagnosis as well carrier detection. Moreover, extends mutation spectrum different population highlights significance diagnostic relevance

Language: Английский

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Predicting Antibody Affinity Changes upon Mutation Based on Unbound Protein Structures

International Journal of Molecular Sciences, Journal Year: 2025, Volume and Issue: 26(3), P. 1343 - 1343

Published: Feb. 5, 2025

Antibodies are key proteins in the immune system that can reversibly and non-covalently bind specifically to their corresponding antigens, forming antigen–antibody complexes. They play a crucial role recognizing foreign or self-antigens during adaptive response. Monoclonal antibodies have emerged as promising class of biological macromolecule therapeutics with broad market prospects. In process antibody drug development, engineering challenge is improve affinity candidate antibodies, without experimentally resolved structures complexes input for computer-aided predictive methods. this work, we present an approach predicting effect residue mutations on The method involves graph representation utilizes pre-trained encoder. encoder captures residue-level microenvironment target along antigen context pre- post-mutation. inherently possesses potential identify paratope residues. addition, curated benchmark dataset antibody. Compared baseline methods based complex sequences, our achieves superior comparable average accuracy datasets. Additionally, validate its advantage not requiring effects against SARS-CoV-2, influenza, human cytomegalovirus. Our shows identifying practical applications.

Language: Английский

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Decoding the effects of mutation on protein interactions using machine learning

Biophysics Reviews, Journal Year: 2025, Volume and Issue: 6(1)

Published: Feb. 21, 2025

Accurately predicting mutation-caused binding free energy changes (ΔΔGs) on protein interactions is crucial for understanding how genetic variations affect between proteins and other biomolecules, such as proteins, DNA/RNA, ligands, which are vital regulating numerous biological processes. Developing computational approaches with high accuracy efficiency critical elucidating the mechanisms underlying various diseases, identifying potential biomarkers early diagnosis, developing targeted therapies. This review provides a comprehensive overview of recent advancements in impact mutations across different interaction types, central to processes disease mechanisms, including cancer. We summarize progress predictive approaches, physicochemical-based, machine learning, deep learning methods, evaluating strengths limitations each. Additionally, we discuss challenges related mutational data, biases, data quality, dataset size, explore difficulties accurate prediction tools mutation-induced effects interactions. Finally, future directions advancing these tools, highlighting capabilities technologies, artificial intelligence drive significant improvements prediction.

Language: Английский

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SFM-Net: Selective Fusion of Multiway Protein Feature Network for Predicting Binding Affinity Changes upon Mutations

Journal of Chemical Information and Modeling, Journal Year: 2025, Volume and Issue: unknown

Published: March 20, 2025

Accurately predicting the effect of mutations on protein-protein interactions (PPIs) is essential for understanding protein structure and function, as well providing insights into disease-causing mechanisms. Many recent popular approaches based three-dimensional proteins have been proposed to predict changes in binding affinity caused by mutations, i.e. ΔΔG. However, how effectively use structural information comprehensively exploit complex within integrate multisource features remains a significant challenge. In this study, we propose SFM-Net, powerful deep learning model constructed with GNN-based multiway feature extractors new context-aware selective fusion module that jointly leverages sequence, structural, evolutionary information. Such design enables SFM-Net selectively from different sources facilitate change prediction. Benchmarking experiments targeted ablation studies illustrate effectiveness robustness our method improving

Language: Английский

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VarEPS-MPXV: A risk evaluation system for observed and virtual variations in mpox virus genomes

hLife, Journal Year: 2025, Volume and Issue: unknown

Published: April 1, 2025

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SAAMBE-SEQ: a sequence-based method for predicting mutation effect on protein–protein binding affinity

Bioinformatics, Journal Year: 2020, Volume and Issue: 37(7), P. 992 - 999

Published: Aug. 24, 2020

Vast majority of human genetic disorders are associated with mutations that affect protein-protein interactions by altering wild-type binding affinity. Therefore, it is extremely important to assess the effect on free energy assist development therapeutic solutions. Currently, most popular approaches use structural information deliver predictions, which precludes them be applicable genome-scale investigations. Indeed, progress genomic sequencing, researchers frequently dealing assessing for there no structure available.Here, we report a Gradient Boosting Decision Tree machine learning algorithm, SAAMBE-SEQ, completely sequence-based and does not require at all. SAAMBE-SEQ utilizes 80 features representing evolutionary information, change physical properties upon mutation site. The approach shown achieve Pearson correlation coefficient (PCC) 0.83 in 5-fold cross validation benchmarking test against experimentally determined (ΔΔG). Further, blind (no-STRUC) compiled collecting experimental ΔΔG protein complexes available used benchmark resulting PCC range 0.37-0.46. accuracy method found either better or comparable advanced structure-based methods. very fast, as webserver stand-alone code, indeed only sequence thus investigations study interactions.SAAMBE-SEQ http://compbio.clemson.edu/saambe_webserver/indexSEQ.php#started.Supplementary data Bioinformatics online.

Language: Английский

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VarEPS: an evaluation and prewarning system of known and virtual variations of SARS-CoV-2 genomes

Nucleic Acids Research, Journal Year: 2021, Volume and Issue: 50(D1), P. D888 - D897

Published: Sept. 30, 2021

The genomic variations of SARS-CoV-2 continue to emerge and spread worldwide. Some mutant strains show increased transmissibility virulence, which may cause reduced protection provided by vaccines. Thus, it is necessary continuously monitor analyze the SARS-COV-2 genomes. We established an evaluation prewarning system, system (VarEPS), including known virtual mutations genomes achieve rapid risks posed strains. From perspective genomics structural biology, database comprehensively analyzes effects on physicochemical properties, translation efficiency, secondary structure, binding capacity ACE2 neutralizing antibodies. An AI-based algorithm was used verify effectiveness these biology characteristic quantities for risk prediction. This classifier could be further group viral their affinity unique resource makes possible quickly evaluate variation key sites, guide research development vaccines drugs. freely accessible at www.nmdc.cn/ncovn.

Language: Английский

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The Impact of Mutation Sets in Receptor-Binding Domain of SARS-CoV-2 Variants on Stability of the RBD–ACE2 Complex

Future Virology, Journal Year: 2023, Volume and Issue: 18(4), P. 225 - 242

Published: March 1, 2023

Aim: Bioinformatic analysis of mutation sets in receptor-binding domain (RBD) currently and previously circulating SARS-CoV-2 variants concern (VOCs) interest (VOIs) to assess their ability bind the ACE2 receptor. Methods:In silico sequence structure-oriented approaches were used evaluate impact single multiple mutations. Results: Mutations detected VOCs VOIs led reduction binding free energy RBD-ACE2 complex, forming additional chemical bonds with ACE2, an increase complex stability. Conclusion: Mutation characteristic have effects on affinity associated amino acid interactions at sites, as well acquisition other viral adaptive advantages.The infectious potential (Alpha, Beta, Gamma, Delta, Omicron, etc.) that causes COVID-19 is mainly due virus Particularly important for development disease interaction coronavirus spike protein a receptor surface human cell, result which penetrates cell. Angiotensin-binding enzyme (ACE2) such humans, there protein. In this study, using bioinformatic methods, mutations RBD was carried out find influence functionality interact high stability, ultimately leads infection. A number virus. More recent more than one RBD, so are complex. It constantly evolving, increasing receptor, avoiding immune response. The Omicron variant, has least 15 most successful these directions.

Language: Английский

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Predicting the Effect of Single Mutations on Protein Stability and Binding with Respect to Types of Mutations

International Journal of Molecular Sciences, Journal Year: 2023, Volume and Issue: 24(15), P. 12073 - 12073

Published: July 28, 2023

The development of methods and algorithms to predict the effect mutations on protein stability, protein-protein interaction, protein-DNA/RNA binding is necessitated by needs engineering for understanding molecular mechanism disease-causing variants. vast majority leading require a database experimentally measured folding free energy changes training. These databases are collections experimental data taken from scientific investigations typically aimed at probing role particular residues above-mentioned thermodynamic characteristics, i.e., not introduced random do necessarily represent originating single nucleotide variants (SNV). Thus, reported performance assessed these or other limited cases may be applicable predicting SNVs seen in human population. Indeed, we demonstrate that non-SNVs equally presented corresponding databases, distribution same. It shown Pearson correlation coefficients (PCCs) obtained involving smaller than non-SNVs, indicating caution should used applying them reveal SNVs. Furthermore, it demonstrated some sensitive chemical nature mutations, resulting PCCs differ factor four across chemically different mutations. All found underestimate roughly 2.

Language: Английский

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MuToN Quantifies Binding Affinity Changes upon Protein Mutations by Geometric Deep Learning

Advanced Science, Journal Year: 2024, Volume and Issue: unknown

Published: July 12, 2024

Abstract Assessing changes in protein–protein binding affinity due to mutations helps understanding a wide range of crucial biological processes within cells. Despite significant efforts create accurate computational models, predicting how affect remains challenging the complexity mechanisms involved. In present work, geometric deep learning framework called MuToN is introduced for quantifying protein change upon residue mutations. The method, designed with attention networks, mechanism‐aware. It captures interfaces mutated complexes and assesses allosteric effects amino acids. Experimental results highlight MuToN's superiority compared existing methods. Additionally, flexibility effectiveness are illustrated by its precise predictions between SARS‐CoV‐2 variants ACE2 complex.

Language: Английский

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