We derive a linear minimum mean square error estimator for sparse vector estimation from an underdetermined set of linear equations. The derivation of the estimator uses a prior distribution conditioned on the support set of the underlying sparse vector. The estimator is used in the architecture of the standard orthogonal matching pursuit algorithm to achieve a better performance.