In silico approach for the prediction of protein functional effects from the detection of sequence variants of DNA

Abstract

The development of technologies of mass sequencing of coding regions of DNA in recent years has had an impact in the field of molecular pathology. However, the results of this process have surpassed the capabilities for the prediction and association of DNA sequence variants with type pathological effects of alterations in proteins linked to physiological, anatomical and metabolic processes. To alleviate this effect, a series of computational tools have been developed to translate the results of sequence variants in functional effects that explain the etiology of many diseases classified as Mendelian. In this paper we evaluate two of these tools, Sift and PolyPhen-2. For this evaluation, exomic sequencing data of a group of 2 patients diagnosed with any of the diseases classified in the group of mucopolysaccharidosis, from southwestern Colombia were used. In the study a total of 60,000 sequence variants per patient were recorded on average, at least half of it classified as not registered in the literature. From the analysis of these data, it were recorded at least 3 variants whose effect would explain the pathological phenomenon of patients. The use of this type of computational tools is then proposed to improve the interpretation of the results of exome sequencing. Therefore, this improving would allow a better therapeutic process, including counseling on patients that suffer from Mendelian diseases and also enable implementation of new strategies for these diseases as is the case of enzyme replacement therapy.