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open access www.bioinformation.net Prediction model Volume 6(1) An ANN model for the identification of deleterious nsSNPs in tumor suppressor genes Vinod Chandra1*, Rejimoan Ramakrishnan2, Shalini Ramanathan2 1Department of Computer Applications, College of Engineering Trivandrum, Kerala, India; 2Department of Computer Science, P.S.G. College of Technology, Coimbatore, Tamil Nadu, India; Vinod Chandra - Email: vinodchandrass@gmail.com; Phone: 91 471 2515531; Fax: 91 471 2598370; *Corresponding author Received February 07, 2011; Accepted February 17, 2011; Published March 02, 2011 Abstract: Human genetic variations primarily result from single nucleotide polymorphisms (SNPs) that occurs approximately every 1000 bases in the overall human population. The non-synonymous SNPs (nsSNPs), lead to amino acid changes in the protein product may account for nearly half of the known genetic variations linked to inherited human diseases and cancer. One of the main problems of medical genetics today is to identify nsSNPs that underlie disease-related phenotypes in humans. An attempt was made to develop a new approach to predict such nsSNPs. This would enhance our understanding of genetic diseases and helps to predict the disease. We detect nsSNPs and all possible and reliable alleles by ANN, a soft computing model using potential SNP information. Reliable nsSNPs are identified, based on the reconstructed alleles and on sequence redundancy. The model gives good results with mean specificity (95.85%), sensitivity (97.40%) and accuracy (96.25%). Our results indicate that ANNs can serve as a useful method to analyze quantitative effect of nsSNPs on protein function and would be useful for large-scale analysis of genomic nsSNP data. Keywords: SNP, nsSNP, ANN, Tumor suppressor genes Availability: http://www.snp.mirworks.in ISSN 0973-2063 (online) 0973-8894 (print) Bioinformation 6(1): 41-44 (2011)   41 © 2011 Biomedical Informatics Background: Single Nucleotide Polymorphism (SNP) represents the most abundant class of genetic variations in the human genome. Non-synonymous SNPs (nsSNPs), which cause the changes of amino acid residues in proteins, account for almost half of all DNA mutations and may be functionally neutral or deleterious [1, 2]. The disease causing variations may cause deleterious effects on proteins. They may inactivate the functional sites or interaction sites of enzymes or impact the folding of proteins and may significantly destabilize the stability of proteins, or change the solubility of proteins [3, 4]. So these variations represent critical molecular markers for dissecting the biological mechanisms underlying complex diseases, as well as for Pharmacogenomic studies. Such markers have become very popular for all kinds of genetic analysis, and disease like cancer. Cancer suppressors and Oncogenes play an important role in the control of the cell cycle, apoptosis, angiogenesis, and development processes that are under pressure of purifying selection [5]. Therefore, protein-damaging mutations in cancer-related genes would be expected to be under the pressure of purifying selection and thus to have a lower population frequency. The relationships between the genotype and phenotype of nsSNPs in tumor suppressor genes have received a plenty of research attentions because of their prevalence in the drug responses and cancer therapy [6, 7]. Tumor suppressor genes are normal genes that slow down cell division, repair DNA mistakes, and apoptosis or programmed cell death. Non synonymous SNPs (nsSNPs) are the main cause of these mutations and to mining nsSNPs from cancer related genes considered as a laborious process and done only by site directed mutagenesis experiments and gene knock out/knock in experiments. Recently, some groups have tried to evaluate the deleterious nsSNPs based on 3-dimensional (3D) structure information of proteins and homology based SIFT (Sorting Intolerant from Tolerant) algorithm (http://sift.jcvi.org/, [1]). Some other methods based on site entropy calculations, relative stability changes were also developed for predicting deleterious nsSNPs [8]. These methods based on protein sequence have been demonstrated that the accuracy is the same as other methods using tertiary structure information. However, the theoretical prediction methods for deleterious nsSNPs are still in its infancy since the 3D structural information of most proteins are still unavailable. To overcome these, our primary challenge is that how to accurately predict those potentially deleterious nsSNPs. Deleterious nsSNPs prediction for the tumor suppressor genes has received great focus from experimental researchers. In this work, we suggest a computational model used to predict deleterious nsSNPs in tumor suppressor genes. Evolutionary conservation features and changes in the physicochemical properties of amino acid are used as parameters for ANN. The method predicts deleterious SNPs from a dbSNP id or a SNP sequence. Both fasta and raw formats are acceptable as input sequence. ANN verification is done for predicted deleterious SNPs. A database search is also included for known deleterious nsSNPs. Methodology: Datasets: Details regarding the genes were collected from NCBI entrez genes (http://www.ncbi.nlm.nih.gov/gene). The protein sequences were obtained from Swiss-Prot database (http://expasy.org/sprot/) and NCBI human genome protein sequence (http://www.ncbi.nlm.nih.gov/). The databases of Swiss-Prot