This review explores the concept of deconvolution in protein engineering, particularly in the context of directed evolution of stereoselective enzymes. The introduction of deconvolution techniques has revealed that mutations can interact cooperatively or antagonistically, rather than acting additively. This phenomenon has been observed in various enzymes, including lipases, esterases, and cytochrome P450 monooxygenases. The review highlights several case studies where partial or complete deconvolution of multi-mutational variants has been performed, leading to the construction of unique fitness pathway landscapes. These landscapes provide insights into the cooperative and antagonistic interactions of mutations, which are not typically observed in traditional fitness landscapes. The review also discusses the theoretical and computational methods used to understand these non-additive effects, such as molecular dynamics (MD) and quantum mechanics/molecular mechanics (QM/MM) computations. The authors emphasize the importance of deconvolution in gaining mechanistic insights and suggest that it can be applied to a broader range of protein engineering applications, including chemoenzymatic approaches and antibody-based biotherapeutics. The review concludes by calling for a more unified understanding of non-additive and epistatic effects in protein engineering.This review explores the concept of deconvolution in protein engineering, particularly in the context of directed evolution of stereoselective enzymes. The introduction of deconvolution techniques has revealed that mutations can interact cooperatively or antagonistically, rather than acting additively. This phenomenon has been observed in various enzymes, including lipases, esterases, and cytochrome P450 monooxygenases. The review highlights several case studies where partial or complete deconvolution of multi-mutational variants has been performed, leading to the construction of unique fitness pathway landscapes. These landscapes provide insights into the cooperative and antagonistic interactions of mutations, which are not typically observed in traditional fitness landscapes. The review also discusses the theoretical and computational methods used to understand these non-additive effects, such as molecular dynamics (MD) and quantum mechanics/molecular mechanics (QM/MM) computations. The authors emphasize the importance of deconvolution in gaining mechanistic insights and suggest that it can be applied to a broader range of protein engineering applications, including chemoenzymatic approaches and antibody-based biotherapeutics. The review concludes by calling for a more unified understanding of non-additive and epistatic effects in protein engineering.