Human Genetics最新文献

{"title":"Healthcare professionals' perspectives on and experiences with non-invasive prenatal testing: a systematic review.","authors":"Chanelle Warton, Danya F Vears","doi":"10.1007/s00439-025-02736-y","DOIUrl":"https://doi.org/10.1007/s00439-025-02736-y","url":null,"abstract":"<p><strong>Background: </strong>The increasing integration of non-invasive prenatal testing (NIPT) into antenatal practice and public healthcare systems globally raises both significant challenges in standardising service delivery and important ethical questions around routinisation and reproductive autonomy. This systematic review aims to synthesise existing primary empirical research on healthcare professionals' views on and experiences with NIPT.</p><p><strong>Methods: </strong>A systematic search was conducted across four major databases in September 2023 and repeated in December 2024. Studies that reported findings from primary empirical research, including quantitative, qualitative and mixed methods research were included.</p><p><strong>Results: </strong>Searches returned 65 eligible articles, spanning 38 countries and 1 special administrative region and at least 12 professions. Views on who NIPT should have access to and which conditions should be screened for were influenced by perceived clinical utility. While healthcare professionals acknowledged NIPT as beneficial for supporting reproductive autonomy, concerns were raised about the amount and complexity of information to be conveyed during prenatal counseling and potential pressure to test. Cost was also identified as a significant barrier. Challenges reported during post-test counseling included communicating test failures and gaining information from laboratories. Views on the implications of NIPT for decision-making around abortion and for people with disabilities varied.</p><p><strong>Conclusions: </strong>Healthcare professionals play a critical role in facilitating the access to and decisions by pregnant people around prenatal genetic testing. Addressing barriers in clinical practice and increasing consistency across and access to clinical guidelines and education resources may support healthcare professionals in supporting reproductive autonomy.</p>","PeriodicalId":13175,"journal":{"name":"Human Genetics","volume":"144 4","pages":"343-374"},"PeriodicalIF":3.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12003526/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143999424","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"QAFI: a novel method for quantitative estimation of missense variant impact using protein-specific predictors and ensemble learning.","authors":"Selen Ozkan, Natàlia Padilla, Xavier de la Cruz","doi":"10.1007/s00439-024-02692-z","DOIUrl":"10.1007/s00439-024-02692-z","url":null,"abstract":"<p><p>Next-generation sequencing (NGS) has revolutionized genetic diagnostics, yet its application in precision medicine remains incomplete, despite significant advances in computational tools for variant annotation. Many variants remain unannotated, and existing tools often fail to accurately predict the range of impacts that variants have on protein function. This limitation restricts their utility in relevant applications such as predicting disease severity and onset age. In response to these challenges, a new generation of computational models is emerging, aimed at producing quantitative predictions of genetic variant impacts. However, the field is still in its early stages, and several issues need to be addressed, including improved performance and better interpretability. This study introduces QAFI, a novel methodology that integrates protein-specific regression models within an ensemble learning framework, utilizing conservation-based and structure-related features derived from AlphaFold models. Our findings indicate that QAFI significantly enhances the accuracy of quantitative predictions across various proteins. The approach has been rigorously validated through its application in the CAGI6 contest, focusing on ARSA protein variants, and further tested on a comprehensive set of clinically labeled variants, demonstrating its generalizability and robust predictive power. The straightforward nature of our models may also contribute to better interpretability of the results.</p>","PeriodicalId":13175,"journal":{"name":"Human Genetics","volume":" ","pages":"191-208"},"PeriodicalIF":3.8,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11976337/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141758427","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing predictions on fitness effects of missense variants in HMBS in CAGI6.","authors":"Jing Zhang, Lisa Kinch, Panagiotis Katsonis, Olivier Lichtarge, Milind Jagota, Yun S Song, Yuanfei Sun, Yang Shen, Nurdan Kuru, Onur Dereli, Ogun Adebali, Muttaqi Ahmad Alladin, Debnath Pal, Emidio Capriotti, Maria Paola Turina, Castrense Savojardo, Pier Luigi Martelli, Giulia Babbi, Rita Casadio, Fabrizio Pucci, Marianne Rooman, Gabriel Cia, Matsvei Tsishyn, Alexey Strokach, Zhiqiang Hu, Warren van Loggerenberg, Frederick P Roth, Predrag Radivojac, Steven E Brenner, Qian Cong, Nick V Grishin","doi":"10.1007/s00439-024-02680-3","DOIUrl":"10.1007/s00439-024-02680-3","url":null,"abstract":"<p><p>This paper presents an evaluation of predictions submitted for the \"HMBS\" challenge, a component of the sixth round of the Critical Assessment of Genome Interpretation held in 2021. The challenge required participants to predict the effects of missense variants of the human HMBS gene on yeast growth. The HMBS enzyme, critical for the biosynthesis of heme in eukaryotic cells, is highly conserved among eukaryotes. Despite the application of a variety of algorithms and methods, the performance of predictors was relatively similar, with Kendall's tau correlation coefficients between predictions and experimental scores around 0.3 for a majority of submissions. Notably, the median correlation (≥ 0.34) observed among these predictors, especially the top predictions from different groups, was greater than the correlation observed between their predictions and the actual experimental results. Most predictors were moderately successful in distinguishing between deleterious and benign variants, as evidenced by an area under the receiver operating characteristic (ROC) curve (AUC) of approximately 0.7 respectively. Compared with the recent two rounds of CAGI competitions, we noticed more predictors outperformed the baseline predictor, which is solely based on the amino acid frequencies. Nevertheless, the overall accuracy of predictions is still far short of positive control, which is derived from experimental scores, indicating the necessity for considerable improvements in the field. The most inaccurately predicted variants in this round were associated with the insertion loop, which is absent in many orthologs, suggesting the predictors still heavily rely on the information from multiple sequence alignment.</p>","PeriodicalId":13175,"journal":{"name":"Human Genetics","volume":" ","pages":"173-189"},"PeriodicalIF":3.8,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12085147/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141897332","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CAGI6 ID panel challenge: assessment of phenotype and variant predictions in 415 children with neurodevelopmental disorders (NDDs).","authors":"Maria Cristina Aspromonte, Alessio Del Conte, Shaowen Zhu, Wuwei Tan, Yang Shen, Yexian Zhang, Qi Li, Maggie Haitian Wang, Giulia Babbi, Samuele Bovo, Pier Luigi Martelli, Rita Casadio, Azza Althagafi, Sumyyah Toonsi, Maxat Kulmanov, Robert Hoehndorf, Panagiotis Katsonis, Amanda Williams, Olivier Lichtarge, Su Xian, Wesley Surento, Vikas Pejaver, Sean D Mooney, Uma Sunderam, Rajgopal Srinivasan, Alessandra Murgia, Damiano Piovesan, Silvio C E Tosatto, Emanuela Leonardi","doi":"10.1007/s00439-024-02722-w","DOIUrl":"10.1007/s00439-024-02722-w","url":null,"abstract":"<p><p>The Genetics of Neurodevelopmental Disorders Lab in Padua provided a new intellectual disability (ID) Panel challenge for computational methods to predict patient phenotypes and their causal variants in the context of the Critical Assessment of the Genome Interpretation, 6th edition (CAGI6). Eight research teams submitted a total of 30 models to predict phenotypes based on the sequences of 74 genes (VCF format) in 415 pediatric patients affected by Neurodevelopmental Disorders (NDDs). NDDs are clinically and genetically heterogeneous conditions, with onset in infant age. Here, we assess the ability and accuracy of computational methods to predict comorbid phenotypes based on clinical features described in each patient and their causal variants. We also evaluated predictions for possible genetic causes in patients without a clear genetic diagnosis. Like the previous ID Panel challenge in CAGI5, seven clinical features (ID, ASD, ataxia, epilepsy, microcephaly, macrocephaly, hypotonia), and variants (Pathogenic/Likely Pathogenic, Variants of Uncertain Significance and Risk Factors) were provided. The phenotypic traits and variant data of 150 patients from the CAGI5 ID Panel Challenge were provided as training set for predictors. The CAGI6 challenge confirms CAGI5 results that predicting phenotypes from gene panel data is highly challenging, with AUC values close to random, and no method able to predict relevant variants with both high accuracy and precision. However, a significant improvement is noted for the best method, with recall increasing from 66% to 82%. Several groups also successfully predicted difficult-to-detect variants, emphasizing the importance of variants initially excluded by the Padua NDD Lab.</p>","PeriodicalId":13175,"journal":{"name":"Human Genetics","volume":" ","pages":"227-242"},"PeriodicalIF":3.8,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11976362/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142947999","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing the predicted impact of single amino acid substitutions in MAPK proteins for CAGI6 challenges.","authors":"Paola Turina, Maria Petrosino, Carlos A Enriquez Sandoval, Leonore Novak, Alessandra Pasquo, Emil Alexov, Muttaqi Ahmad Alladin, David B Ascher, Giulia Babbi, Constantina Bakolitsa, Rita Casadio, Jianlin Cheng, Piero Fariselli, Lukas Folkman, Akash Kamandula, Panagiotis Katsonis, Minghui Li, Dong Li, Olivier Lichtarge, Sajid Mahmud, Pier Luigi Martelli, Debnath Pal, Shailesh Kumar Panday, Douglas E V Pires, Stephanie Portelli, Fabrizio Pucci, Carlos H M Rodrigues, Marianne Rooman, Castrense Savojardo, Martin Schwersensky, Yang Shen, Alexey V Strokach, Yuanfei Sun, Junwoo Woo, Predrag Radivojac, Steven E Brenner, Roberta Chiaraluce, Valerio Consalvi, Emidio Capriotti","doi":"10.1007/s00439-024-02724-8","DOIUrl":"10.1007/s00439-024-02724-8","url":null,"abstract":"<p><p>New thermodynamic and functional studies have been recently conducted to evaluate the impact of amino acid substitutions on the Mitogen Activated Protein Kinases 1 and 3 (MAPK1/3). The Critical Assessment of Genome Interpretation (CAGI) data provider, at Sapienza University of Rome, measured the unfolding free energy and the enzymatic activity of a set of variants (MAPK challenge dataset). Thermodynamic measurements for the denaturant-induced equilibrium unfolding of the phosphorylated and unphosphorylated forms of the MAPKs were obtained by monitoring the far-UV circular dichroism and intrinsic fluorescence changes as a function of denaturant concentration. These values have been used to calculate the change in unfolding free energy between the variant and wild-type proteins at zero concentration of denaturant ( <math><mrow><mi>Δ</mi> <mi>Δ</mi> <msup><mi>G</mi> <mrow><msub><mtext>H</mtext> <mn>2</mn></msub> <mtext>O</mtext></mrow> </msup> </mrow> </math> ). The enzymatic activity of the phosphorylated MAPKs variants was also measured using Chelation-Enhanced Fluorescence to monitor the phosphorylation of a peptide substrate. The MAPK challenge dataset, composed of a total of 23 single amino acid substitutions (11 and 12 for MAPK1 and MAPK3, respectively), was used to assess the effectiveness of the computational methods in predicting the <math><mrow><mi>Δ</mi> <mi>Δ</mi> <msup><mi>G</mi> <mrow><msub><mtext>H</mtext> <mn>2</mn></msub> <mtext>O</mtext></mrow> </msup> </mrow> </math> values, associated with the variants, and categorize them as destabilizing and not destabilizing. The data on the enzymatic activity of the MAPKs mutants were used to assess the performance of the methods for predicting the functional impact of the variants. For the sixth edition of CAGI, thirteen independent research groups from four continents (Asia, Australia, Europe and North America) submitted > 80 sets of predictions, obtained from different approaches. In this manuscript, we summarized the results of our assessment to highlight the possible limitations of the available algorithms.</p>","PeriodicalId":13175,"journal":{"name":"Human Genetics","volume":" ","pages":"265-280"},"PeriodicalIF":3.8,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11975483/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143457846","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Critical assessment of missense variant effect predictors on disease-relevant variant data.","authors":"Ruchir Rastogi, Ryan Chung, Sindy Li, Chang Li, Kyoungyeul Lee, Junwoo Woo, Dong-Wook Kim, Changwon Keum, Giulia Babbi, Pier Luigi Martelli, Castrense Savojardo, Rita Casadio, Kirsley Chennen, Thomas Weber, Olivier Poch, François Ancien, Gabriel Cia, Fabrizio Pucci, Daniele Raimondi, Wim Vranken, Marianne Rooman, Céline Marquet, Tobias Olenyi, Burkhard Rost, Gaia Andreoletti, Akash Kamandula, Yisu Peng, Constantina Bakolitsa, Matthew Mort, David N Cooper, Timothy Bergquist, Vikas Pejaver, Xiaoming Liu, Predrag Radivojac, Steven E Brenner, Nilah M Ioannidis","doi":"10.1007/s00439-025-02732-2","DOIUrl":"10.1007/s00439-025-02732-2","url":null,"abstract":"<p><p>Regular, systematic, and independent assessments of computational tools that are used to predict the pathogenicity of missense variants are necessary to evaluate their clinical and research utility and guide future improvements. The Critical Assessment of Genome Interpretation (CAGI) conducts the ongoing Annotate-All-Missense (Missense Marathon) challenge, in which missense variant effect predictors (also called variant impact predictors) are evaluated on missense variants added to disease-relevant databases following the prediction submission deadline. Here we assess predictors submitted to the CAGI 6 Annotate-All-Missense challenge, predictors commonly used in clinical genetics, and recently developed deep learning methods. We examine performance across a range of settings relevant for clinical and research applications, focusing on different subsets of the evaluation data as well as high-specificity and high-sensitivity regimes. Our evaluations reveal notable advances in current methods relative to older, well-cited tools in the field. While meta-predictors tend to outperform their constituent individual predictors, several newer individual predictors perform comparably to commonly used meta-predictors. Predictor performance varies between high-specificity and high-sensitivity regimes, highlighting that different methods may be optimal for different use cases. We also characterize two potential sources of bias. Predictors that incorporate allele frequency as a predictive feature tend to have reduced performance when distinguishing pathogenic variants from very rare benign variants, and predictors trained on pathogenicity labels from curated variant databases often inherit gene-level label imbalances. Our findings help illuminate the clinical and research utility of modern missense variant effect predictors and identify potential areas for future development.</p>","PeriodicalId":13175,"journal":{"name":"Human Genetics","volume":" ","pages":"281-293"},"PeriodicalIF":3.8,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11976771/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143669683","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An AI-based approach driven by genotypes and phenotypes to uplift the diagnostic yield of genetic diseases.","authors":"S Zucca, G Nicora, F De Paoli, M G Carta, R Bellazzi, P Magni, E Rizzo, I Limongelli","doi":"10.1007/s00439-023-02638-x","DOIUrl":"10.1007/s00439-023-02638-x","url":null,"abstract":"<p><p>Identifying disease-causing variants in Rare Disease patients' genome is a challenging problem. To accomplish this task, we describe a machine learning framework, that we called \"Suggested Diagnosis\", whose aim is to prioritize genetic variants in an exome/genome based on the probability of being disease-causing. To do so, our method leverages standard guidelines for germline variant interpretation as defined by the American College of Human Genomics (ACMG) and the Association for Molecular Pathology (AMP), inheritance information, phenotypic similarity, and variant quality. Starting from (1) the VCF file containing proband's variants, (2) the list of proband's phenotypes encoded in Human Phenotype Ontology terms, and optionally (3) the information about family members (if available), the \"Suggested Diagnosis\" ranks all the variants according to their machine learning prediction. This method significantly reduces the number of variants that need to be evaluated by geneticists by pinpointing causative variants in the very first positions of the prioritized list. Most importantly, our approach proved to be among the top performers within the CAGI6 Rare Genome Project Challenge, where it was able to rank the true causative variant among the first positions and, uniquely among all the challenge participants, increased the diagnostic yield of 12.5% by solving 2 undiagnosed cases.</p>","PeriodicalId":13175,"journal":{"name":"Human Genetics","volume":" ","pages":"159-171"},"PeriodicalIF":3.8,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11976766/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140193639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Genetic variants and phenotypic data curated for the CAGI6 intellectual disability panel challenge.","authors":"Maria Cristina Aspromonte, Alessio Del Conte, Roberta Polli, Demetrio Baldo, Francesco Benedicenti, Elisa Bettella, Stefania Bigoni, Stefania Boni, Claudia Ciaccio, Stefano D'Arrigo, Ilaria Donati, Elisa Granocchio, Isabella Mammi, Donatella Milani, Susanna Negrin, Margherita Nosadini, Fiorenza Soli, Franco Stanzial, Licia Turolla, Damiano Piovesan, Silvio C E Tosatto, Alessandra Murgia, Emanuela Leonardi","doi":"10.1007/s00439-025-02733-1","DOIUrl":"10.1007/s00439-025-02733-1","url":null,"abstract":"<p><p>Neurodevelopmental disorders (NDDs) are common conditions including clinically diverse and genetically heterogeneous diseases, such as intellectual disability, autism spectrum disorders, and epilepsy. The intricate genetic underpinnings of NDDs pose a formidable challenge, given their multifaceted genetic architecture and heterogeneous clinical presentations. This work delves into the intricate interplay between genetic variants and phenotypic manifestations in neurodevelopmental disorders, presenting a dataset curated for the Critical Assessment of Genome Interpretation (CAGI6) ID Panel Challenge. The CAGI6 competition serves as a platform for evaluating the efficacy of computational methods in predicting phenotypic outcomes from genetic data. In this study, a targeted gene panel sequencing has been used to investigate the genetic causes of NDDs in a cohort of 415 paediatric patients. We identified 60 pathogenic and 49 likely pathogenic variants in 102 individuals that accounted for 25% of NDD cases in the cohort. The most mutated genes were ANKRD11, MECP2, ARID1B, ASH1L, CHD8, KDM5C, MED12 and PTCHD1 The majority of pathogenic variants were de novo, with some inherited from mildly affected parents. Loss-of-function variants were the most common type of pathogenic variant. In silico analysis tools were used to assess the potential impact of variants on splicing and structural/functional effects of missense variants. The study highlights the challenges in variant interpretation especially in cases with atypical phenotypic manifestations. Overall, this study provides valuable insights into the genetic causes of NDDs and emphasises the importance of understanding the underlying genetic factors for accurate diagnosis, and intervention development in neurodevelopmental conditions.</p>","PeriodicalId":13175,"journal":{"name":"Human Genetics","volume":" ","pages":"309-326"},"PeriodicalIF":3.8,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11976335/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143523342","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of calmodulin missense variants associated with congenital arrhythmia on the thermal stability and the degree of unfolding.","authors":"Giuditta Dal Cortivo, Valerio Marino, Davide Zamboni, Daniele Dell'Orco","doi":"10.1007/s00439-023-02629-y","DOIUrl":"10.1007/s00439-023-02629-y","url":null,"abstract":"<p><p>Thermal denaturation profiles of proteins that bind several ligands may deviate from the single transition, making their thermodynamic description challenging. We report an empirical method that estimates melting temperatures (T_m) from multi-transition thermal denaturation profiles of 16 variants of calmodulin (CaM) associated with congenital arrhythmia. Differences in T_m estimated by empirical fitting correlate (for apo CaM variants) with those obtained by thermodynamic models. Most CaM variants were more stable than the wild type (WT) in the absence of Ca²⁺, but less stable in the presence of Ca²⁺, and displayed either WT-like or higher unfolding percentages in their apo-form, as evaluated by circular dichroism spectroscopy.</p>","PeriodicalId":13175,"journal":{"name":"Human Genetics","volume":" ","pages":"337-341"},"PeriodicalIF":3.8,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12163107/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139048655","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring the effects of missense mutations on protein thermodynamics through structure-based approaches: findings from the CAGI6 challenges.","authors":"Carlos H M Rodrigues, Stephanie Portelli, David B Ascher","doi":"10.1007/s00439-023-02623-4","DOIUrl":"10.1007/s00439-023-02623-4","url":null,"abstract":"<p><p>Missense mutations are known contributors to diverse genetic disorders, due to their subtle, single amino acid changes imparted on the resultant protein. Because of this, understanding the impact of these mutations on protein stability and function is crucial for unravelling disease mechanisms and developing targeted therapies. The Critical Assessment of Genome Interpretation (CAGI) provides a valuable platform for benchmarking state-of-the-art computational methods in predicting the impact of disease-related mutations on protein thermodynamics. Here we report the performance of our comprehensive platform of structure-based computational approaches to evaluate mutations impacting protein structure and function on 3 challenges from CAGI6: Calmodulin, MAPK1 and MAPK3. Our stability predictors have achieved correlations of up to 0.74 and AUCs of 1 when predicting changes in ΔΔG for MAPK1 and MAPK3, respectively, and AUC of up to 0.75 in the Calmodulin challenge. Overall, our study highlights the importance of structure-based approaches in understanding the effects of missense mutations on protein thermodynamics. The results obtained from the CAGI6 challenges contribute to the ongoing efforts to enhance our understanding of disease mechanisms and facilitate the development of personalised medicine approaches.</p>","PeriodicalId":13175,"journal":{"name":"Human Genetics","volume":" ","pages":"327-335"},"PeriodicalIF":3.8,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11976750/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139472312","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}