Computational tools occupy the prime position in the analysis of large volume of post-genomic data. These tools have advantage over the wet lab experiments in terms of high coverage, cost and time. Breast cancer is the most common cancer in females worldwide. It is a genetically heterogeneous disorder and many genes are involved in the pathway of the disease. Mutations in metastasis suppressor gene are the major cause of the disease. In this study, the effects of mutations in breast cancer metastasis suppressor 1gene upon protein structure and function were examined by means of computational tools and information from databases.This study can be useful to predict the potential effect of every allelic variant, devise new biological experiments and to interpret and predict the patho-physiological impact of new mutations or non-synonymous polymorphisms. Background: In now a days, computers are as likely to be used by biologists as by any other highlytrained professionals, more specifically in field of bioinformatics; which is focused onmaking predictions about biological systems and to analyze biological data related to different diseases like cancer [1]. As in computational biology tools are used to predict if two proteins interact or not, if prediction is accurate then computational biology could further be used to analyze biological data obtained from a wet lab experiment. This field can be further broken down into molecular modeling and bioinformatics [2]. Several bioinformatics methods are applied for the different mutational disease analysis [3]. Many of them are based on protein sequence, but several are structure-based, as the latter are more reliable and provide more information. In this work, we have built a homology model of mutated BRMS1 gene applying the most updated available methods of Homology modelling through MODELLER, [4] and have investigated the effects of mutations of BRMS1 using different software, including SNAP it is a neural-network based method which evaluates the single amino acid substitution effects on protein functions [5]. I-Mutant2.0 is a web server used for the prediction of protein stability changes upon single-site mutations. It works on Support Vector Machine based method [6]. PolyPhen2.0.9 it uses structural and comparative evolutionary considerations andpredicts the impacts of amino acid substitutions on the stability and function of human proteins [7]. IUPredpredicts the disorder tendency of particular amino acid [8], PrDOSgives the information about the disorder region of particular protein [9] and HNB servers is a protein function annotation tool having consortium of three different tools (SMART, miniPEDANT and STRING) that collectively involves in functional domain prediction along with the alignment to different protein databases [10].