Identifying Pathogenic Amino Acid Substitutions in Human Proteins Using Deep Learning

• Main Author: Kvist, Alexander
• Format: Others
• Language: English
• Published: KTH, Skolan för kemi, bioteknologi och hälsa (CBH) 2018
• Subjects: Bioinformatics; deep learning; proteomics; mutations; single nucleotide polymorphism; SNP; convolutional neural network; variant effect prediction; Medical Engineering; Medicinteknik
• Online Access: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-233513

Many diseases of genetic origin originate from non-synonymous single nucleotide polymorphisms (nsSNPs). These cause changes in the final protein product encoded by a gene. Through large scale sequencing and population studies, there is growing availability of information of which variations are tolerated and which are not. Variant effect predictors use a wide range of information about such variations to predict their effect, often focusing on evolutionary information. Here, a novel amino acid substitution variant effect predictor is developed. The predictor is a deep convolutional neural network incorporating evolutionary information, sequence information, as well as structural information, to predict both the pathogenicity as well as the severity of amino acid substitutions. The model achieves state-of-the-art performance on benchmark datasets.