Predicting the effect of mutations in proteins is one of the most critical challenges in protein engineering; by knowing the effect a substitution of one (or several) residues in the protein's sequence has on its overall properties, could design a variant with a desirable function. New strategies and methodologies to create predictive models are continually being developed. However, those that claim to be general often do not reach adequate performance, and those that aim to a particular task improve their predictive performance at the cost of the method's generality. Moreover, these approaches typically require a particular decision to encode the amino acidic sequence, without an explicit methodological agreement in such endeavor. To address these issues, in this work, we applied clustering, embedding, and dimensionality reduction techniques to the AAIndex database to select meaningful combinations of physicochemical properties for the encoding stage. We then used the chosen set of properties to obtain several encodings of the same sequence, to subsequently apply the Fast Fourier Transform (FFT) on them. We perform an exploratory stage of Machine-Learning models in the frequency space, using different algorithms and hyperparameters. Finally, we select the best performing predictive models in each set of properties and create an assembled model. We extensively tested the proposed methodology on different datasets and demonstrated that the generated assembled model achieved notably better performance metrics than those models based on a single encoding and, in most cases, better than those previously reported. The proposed method is available as a Python library for non-commercial use under the GNU General Public License (GPLv3) license.

中文翻译：

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数字信号处理和组装预测模型的结合促进了蛋白质的合理设计

预测蛋白质突变的影响是蛋白质工程中最关键的挑战之一。通过了解蛋白质序列中一个（或几个）残基的替换对其整体特性的影响，可以设计出具有所需功能的变体。正在不断开发用于创建预测模型的新策略和方法。然而，那些声称具有通用性的方法往往无法达到足够的性能，而那些针对特定任务的方法则以牺牲方法的通用性为代价来提高其预测性能。此外，这些方法通常需要一个特定的决定来编码氨基酸序列，在这种努力中没有明确的方法学协议。为了解决这些问题，在这项工作中，我们应用了聚类、嵌入、和降维技术到 AAIndex 数据库，以便为编码阶段选择有意义的物理化学特性组合。然后我们使用选定的属性集来获得相同序列的多个编码，随后对它们应用快速傅立叶变换 (FFT)。我们使用不同的算法和超参数在频率空间中执行机器学习模型的探索阶段。最后，我们在每组属性中选择性能最佳的预测模型并创建一个组装模型。我们在不同的数据集上广泛测试了所提出的方法，并证明生成的组装模型比基于单一编码的模型获得了明显更好的性能指标，并且在大多数情况下，比以前报告的更好。