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Multimodal assessment of protein functional deficiency supports pathogenicity of BRCA1 p.V1688del.

ABSTRACT: Unequivocal discrimination between neutral variants and deleterious mutations is crucial for appropriate counseling of individuals with a BRCA1 or BRCA2 sequence change. An increasing number of variants of uncertain significance (VUS) are being identified, the unclassified biological effect of which poses clinical concerns. A multifactorial likelihood-based approach recently suggested disease causality for BRCA1 p.V1688del, a VUS recurrent in Italian breast/ovarian cancer families. Whether and how this single amino acid deletion in the BRCA1 COOH terminus (BRCT) domain affects the function of the mutant protein (DeltaValBRCA1) has not been elucidated. We undertook comprehensive functional characterization of DeltaValBRCA1, comprising comparative structural modeling, analysis of protein stability and associations, and analysis of DNA repair function. Our model predicted BRCT domain destabilization and folding disruption caused by BRCA1 p.V1688del. Consistently, the recombinant DeltaValBRCA1 was less stable than wild-type BRCA1 and, unlike the latter, failed to associate with BRIP1, CtIP, and Rap80 and to relocalize to sites of DNA damage. Yeast two-hybrid analysis revealed a compromised interaction with FHL2 and KPNA2, which is likely responsible for improper subcellular localization of DeltaValBRCA1. In addition, we found four new breast/ovarian cancer families of Italian ancestry who carried this sequence alteration. These results provide the first evidence of the effect of BRCA1 p.V1688del on protein stability and function, supporting the view that it is a deleterious mutation. Multimodal analyses like ours could advance understanding of tumor suppression by BRCA1 and ultimately contribute to developing efficient strategies for screening and characterization of VUS.

SUBMITTER: De Nicolo A

PROVIDER: S-EPMC2748232 | biostudies-literature | 2009 Sep

REPOSITORIES: biostudies-literature

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Multimodal assessment of protein functional deficiency supports pathogenicity of BRCA1 p.V1688del.

De Nicolo Arcangela A Parisini Emilio E Zhong Quan Q Dalla Palma Maurizia M Stoeckert Kathryn A KA Domchek Susan M SM Nathanson Katherine L KL Caligo Maria A MA Vidal Marc M Cusick Michael E ME Garber Judy E JE

Cancer research 20090825 17

Unequivocal discrimination between neutral variants and deleterious mutations is crucial for appropriate counseling of individuals with a BRCA1 or BRCA2 sequence change. An increasing number of variants of uncertain significance (VUS) are being identified, the unclassified biological effect of which poses clinical concerns. A multifactorial likelihood-based approach recently suggested disease causality for BRCA1 p.V1688del, a VUS recurrent in Italian breast/ovarian cancer families. Whether and h ...[more]

PMID: 19706752

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Project description:With the surge of the high-throughput sequencing technologies, many genetic variants have been identified in the past decade. The vast majority of these variants are defined as variants of uncertain significance (VUS), as their significance to the function or health of an organism is not known. It is urgently needed to develop intelligent models for the clinical interpretation of VUS. State-of-the-art artificial intelligence (AI)-based variant effect predictors only learn features from primary amino acid sequences, leaving out information about the most important three-dimensional structure that is more related to its function. Methods: We proposed a deep convolutional neural network model named variant effect recognition network for BRCA1 (vERnet-B) to recognize the clinical pathogenicity of missense single-nucleotide variants in the BRCT domain of BRCA1. vERnet-B learned features associated with the pathogenicity from the tertiary protein structures of variants predicted by AlphaFold2. Results: After performing a series of validation and analyses on vERnet-B, we discovered that it exhibited significant advances over previous works. Recognizing the phenotypic consequences of VUS is one of the most daunting challenges in genetic informatics; however, we achieved 85% accuracy in recognizing disease BRCA1 variants with an ideal balance of false-positive and true-positive detection rates. vERnet-B correctly recognized the pathogenicity of variant A1708E, which was poorly predicted by AlphaFold2 as previously described. The vERnet-B web server is freely available from URL: http://ai-lab.bjrz.org.cn/vERnet. Conclusions: We applied protein tertiary structures to successfully recognize the pathogenic missense SNVs, which were difficult to be addressed by classical approaches based on sequences. Our work demonstrated that AlphaFold2-predicted structures were expected to be used for rich feature learning and revealed unique insights into the clinical interpretation of VUS in disease-related genes, using vERnet-B as a discovery tool.

Dataset Information

Multimodal assessment of protein functional deficiency supports pathogenicity of BRCA1 p.V1688del.

Publications

Multimodal assessment of protein functional deficiency supports pathogenicity of BRCA1 p.V1688del.

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