Predicting the Functional Effect of Amino Acid Substitutions and Indels

By Yongwook Choi, Gregory E. Sims, S. Murphy, Jason Rafe Miller, Agnes P. Chan. 2012

2,817 citations recorded by the source corpus.

As next-generation sequencing projects generate massive genome-wide sequence variation data, bioinformatics tools are being developed to provide computational predictions on the functional effects of sequence variations and narrow down the search of casual variants for disease phenotypes. Different classes of sequence variations at the nucleotide level are involved in human diseases, including substitutions, insertions, deletions, frameshifts, and non-sense mutations. Frameshifts and non-sense mutations are likely to cause a negative effect on protein function. Existing prediction tools primarily focus on studying the deleterious effects of single amino acid substitutions through examining amino acid conservation at the position of interest among related sequences, an approach that is not directly applicable to insertions or deletions. Here, we introduce a versatile alignment-based score as a new metric to predict the damaging effects of variations not limited to single amino acid substitutions but also in-frame insertions, deletions, and multiple amino acid substitutions. This alignment-based score measures the change in sequence similarity of a query sequence to a protein sequence homolog before and after the introduction of an amino acid variation to the query sequence. Our results showed that the scoring scheme performs well in separating disease-associated variants (n = 21,662) from common polymorphisms (n = 37,022) for UniProt human protein variations, and also in separating deleterious variants (n = 15,179) from neutral variants (n = 17,891) for UniProt non-human protein variations. In our approach, the area under the receiver operating characteristic curve (AUC) for the human and non-human protein variation datasets is ∼0.85. We also observed that the alignment-based score correlates with the deleteriousness of a sequence variation. In summary, we have developed a new algorithm, PROVEAN (Protein Variation Effect Analyzer), which provides a generalized approach to predict the functional effects of protein sequence variations including single or multiple amino acid substitutions, and in-frame insertions and deletions. The PROVEAN tool is available online at http://provean.jcvi.org.

Subfields: Genetics, Molecular Biology

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Citations in the corpus

CADD: predicting the deleteriousness of variants throughout the human genome (2018, methodology): “All other scores (i.e. FATHMM (4), LRT(5), MutationAssessor (6), MutationTaster (7), Polyphen2, PROVEAN (8), REVEL, SIFT, SiPhy(9), VEST (10)) were annotated via dbNSFP.”
Assessing the Pathogenicity of Insertion and Deletion Variants with the Variant Effect Scoring Tool (VEST‐Indel) (2015, methodology): “, 2015] handle both in-frame and frameshift insertion/deletions and the fourth method PROVEAN [Choi et al., 2012]”
The Human Gene Mutation Database: building a comprehensive mutation repository for clinical and molecular genetics, diagnostic testing and personalized genomic medicine (2013, positive): “…also been used by a number of different groups to aid the development of post-NGS variant interpretation algorithms including MutPred (Li et al. 2009), PROVEAN (Choi et al. 2012), CAROL (Lopes et al. 2012), CRAVAT (Douville et al. 2013), NEST (Carter et al. 2013) and FATHMM (Shihab et al. 2013).”
PROVEAN web server: a tool to predict the functional effect of amino acid substitutions and indels (2015, methodology): “We have developed a new tool Protein Variation Effect Analyzer (PROVEAN), which uses an alignment-based score approach (Choi, 2012; Choi et al., 2012).”
Protein design and variant prediction using autoregressive generative models (2021, methodology): “The model is able to capture the effects of single amino acid deletions on PTEN phosphatase 75 ( ρ = 0.69, N = 340, HMM ρ = 0.75; PROVEAN ρ = 0.7; Fig.”
LIST-S2: taxonomy based sorting of deleterious missense mutations across species (2020, background): “0a (17) (namely: SIFT (1), SIFT4G (18), PROVEAN (2), phyloP (3) X 3, SiPhy (5), phastCons (6) X 3, GERP++ (4), Eigen (10), CADD (9), DANN (11),”
Evaluation of in silico algorithms for use with ACMG/AMP clinical variant interpretation guidelines (2017, background): “We found that Poly-phen [11] and SIFT [12] are cited most frequently, followed by MutationTaster [13], CADD [14], PROVEAN [15], Mutpred [16], and Condel [17].”
The Human Gene Mutation Database: towards a comprehensive repository of inherited mutation data for medical research, genetic diagnosis and next-generation sequencing studies (2017, neutral): “2014), PROVEAN (Choi et al. 2012), CAROL (Lopes et al.”
Development of Bioinformatics Infrastructure for Genomics Research in H3Africa (2017, positive): “Additionally, HUMA includes Vapor, a variant analysis workflow, which combines the predictions of Provean [23], PolyPhen-2 [24], PhD-SNP [25], FATHMM [26], AUTO-MUTE [27], MuPro [28], and I-Mutant 2.”
dbNSFP v4: a comprehensive database of transcript-specific functional predictions and annotations for human nonsynonymous and splice-site SNVs (2020, background): “…of rank scores (upper triangle) and agreement ratio of binary predictions (lower triangle) between pairs of deleteriousness prediction scores or conservation scores LIST-S2, MPC, MutationAssessor [45], MVP, Polyphen2 HDIV and Polyphen2 HVAR [46], PROVEAN [47], SIFT [48], SIFT4G, VEST4 [49]).”