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The Global Epistatic Model for predicting Mutational Effects (GEMME) is a computational method that reconstructs protein mutational landscapes using sequence data alone. In this chapter, we delve into the broader biological questions that GEMME can address beyond mere landscape reconstruction. We provide several examples to guide users in maximizing the utility of GEMME. Additionally, we discuss recent advancements that enhance GEMME’s predictive accuracy and extend its capabilities by integrating sequence data with structural information and allele frequency data, when available. These innovations enable GEMME to answer new biological questions and offer deeper insights into protein evolution and function.

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Unlocking Protein Evolution Insights: Efficient and Interpretable Mutational Effect Predictions with GEMME

  • 实验方案

  • 发表日期：

    2026年

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  • A. Carbone,
  • E. Laine,
  • G. Lombardi

摘要

The Global Epistatic Model for predicting Mutational Effects (GEMME) is a computational method that reconstructs protein mutational landscapes using sequence data alone. In this chapter, we delve into the broader biological questions that GEMME can address beyond mere landscape reconstruction. We provide several examples to guide users in maximizing the utility of GEMME. Additionally, we discuss recent advancements that enhance GEMME’s predictive accuracy and extend its capabilities by integrating sequence data with structural information and allele frequency data, when available. These innovations enable GEMME to answer new biological questions and offer deeper insights into protein evolution and function.