In silico optimization of zinc binding proteins for biosensor applications

Currently there are many studies in process aiming to provide proteins a new function and improve their existing functions. Proteins are potential sensor molecules which are binding to metal ions with wide range of binding affinities and performing diverse functions. There are variety of techniques to optimize the metal binding affinities. One of the conventional methods is "site directed mutagenesis", with which it is hard and time consuming to optimize metal binding affinities at the desired level. Another disadvantage of this technique is inclusion body formation during expression. Also, the tag sequences which are being added in the process of purification might cause non specific interactions in the FRET system. All these information suggest that the sensor molecule should be stable and small. We foresee that improved protein biosensors can be discovered faster by employing a computational approach. Therefore, we aimed optimizing the zinc binding affinity of small peptides with known three dimensional structures computationally. Our study is comprised of mutating amino acids in vicinity of zinc binding region of peptides that are shorter than 70 amino acids. Before and after each mutation, zinc binding affinity is calculated by FoldX software. Autodock Vina software was used to validate our results for few proteins by redocking method. We identified several mutants which have increased zinc binding affinity and greater stability