There has been a rapid development of genome-wide techniques in the last decade along with significant lowering of the cost of gene sequencing, which generated widely available genomic data. However, the interpretation of genomic data and prediction of the association of genetic variations with diseases and phenotypes still require significant improvement. Missense mutations can render proteins nonfunctional and may be responsible for many diseases. The effects caused by missense mutations can be pinpointed by in silico modeling that makes it more feasible to find a treatment and reverse the effect. Specific human phenotype is largely determined by stability, activity, and interactions between proteins and with other biomolecules which work together to provide specific cellular functions. Therefore, the analysis of the effects of missense mutations on proteins and their complexes would give us important clues for identifying functionally important missense mutations, understanding the molecular mechanisms of diseases and facilitating their treatment and prevention.Herein,wedevelopedpowerfulcomputational methods and tools to estimate the effects of missense mutations on proteinstability, binding affinity and function, and provide a molecular understanding of the mutational impacts. By investigating all cancer-related missense mutations on Casitas B-lineage lymphoma gene that is found in manytumors, wefoundthatprotein stability changes and binding affinity changes are highly correlated with the function of mutant proteins and can successfully predict the functional consequences of cancer mutations on proteinactivity.