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The traditional dogma in the field of enzyme engineering is that thermostable proteins make the best scaffolds. They are said to possess intrinsic resistence to destabilizing effects of mutations. However, very few proteins have been systemically characterized for their ability to retain stability and activity when mutations are made across large portions of the protein. Thus, efforts to add data to the assertion that one should begin design efforts with a thermostable protein are necessary. Previous work provides a mounting body of evidence that the claim that protein design should process from thermostable scaffolds is mounting. The work of Brian Matthews on hundreds of point mutants of T7 lysozyme is a prime example: almost all of the point mutants retained their fold and were crystalized. Conversely, a 1998 paper on the stability of proteins at temperature above 100 C introduced a double mutation that increased thermostability about 8 fold.  In this study, we functionally characterized over 100 single point mutants of β-glucosidase B from P. polymxa at various temperatures ranging from 30 C to 50 C via a colorimetric assay. We found that, contrary to the accepted dogma, point mutants of this enzyme did not differ in Tm from the wild type enzyme by more than X ± Y C. Out of 83 mutants assayed, we found a mean Tm of 40 ± 0.7 C within the range 37.6–41.4 C.  To understand how an enzyme functions it is necessary to tally the contribution of each residue in the sequence to a variety of functional parameters. Questions like, is it true that most residues in the protein do not contribute to catalysis, can only be answered by obtaining data on the functional effects of those mutations. Each residue plays a role in determining the enzyme’s functional parameters, as well as contributing to protein stability. However, experimentally determining all the possible single point mutations alone (over 8880), is an endeavour too costly to undertake for any ordinary enzyme, let alone the hundreds of thousands with known crystal structures for which these determinations would be feasible. Therefore, it will be necessary to build computational models that allow us to predict the effect of enzyme mutations on an enzyme’s binding affinity for a particular ligand as well as its efficiency in transforming that substrate into a product, as well as overall catalytic efficiency (kcat/KM).   Furthermore, a large number of human diseases are caused by missense mutations to crucial metabolic enzymes that result in non-functional protein. The prediciton of the effect of a single point mutation would thus be useful in clinical medicine for the diagnosis of disease.