Kermut：用于蛋白质变异效应预测的复合核回归方法
Kermut: Composite kernel regression for protein variant effects
AI总结：
针对蛋白质变异效应预测中不确定性估计缺失的问题，提出带新型复合核的高斯过程回归模型Kermut，实现有监督预测的SOTA性能并提供不确定性估计，同时验证了整体校准效果及实例级校准的挑战。
摘要：
可靠的蛋白质变异效应预测对蛋白质优化和推动生物学认知都至关重要。在蛋白质工程的实际应用中，为预测结果提供可靠的不确定性估计同样重要，但尽管近年来预测精度取得了长足进步，不确定性指标却鲜有报道。本文提出一种高斯过程回归模型Kermut，该模型采用新型复合核来建模突变相似性，在有监督蛋白质变异效应预测任务上取得了当前最优性能，同时还能通过其后验分布提供不确定性估计。对不确定性估计质量的分析表明，本模型的整体校准达到了有意义的水平，但实例级的不确定性校准仍更具挑战性。
英文摘要：
Reliable prediction of protein variant effects is crucial for both protein optimization and for advancing biological understanding. For practical use in protein engineering, it is important that we can also provide reliable uncertainty estimates for our predictions, and while prediction accuracy has seen much progress in recent years, uncertainty metrics are rarely reported. We here provide a Gaussian process regression model, Kermut, with a novel composite kernel for modeling mutation similarity, which obtains state-of-the-art performance for supervised protein variant effect prediction while also offering estimates of uncertainty through its posterior. An analysis of the quality of the uncertainty estimates demonstrates that our model provides meaningful levels of overall calibration, but that instance-specific uncertainty calibration remains more challenging.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
