Systematically predicting the effects of mutations on protein fitness is essential for the understanding of genetic diseases. Indeed, predictions complement experimental efforts in analyzing how variants lead to dysfunctional proteins that in turn can cause diseases. Here we present our new fitness predictor, FiTMuSiC, which leverages structural, evolutionary and coevolutionary information. We show that FiTMuSiC predicts fitness with high accuracy despite the simplicity of its underlying model: it was among the top predictors on the hydroxymethylbilane synthase (HMBS) target of the sixth round of the Critical Assessment of Genome Interpretation challenge (CAGI6) and performs as well as much more complex deep learning models such as AlphaMissense. To further demonstrate FiTMuSiC’s robustness, we compared its predictions with in vitro activity data on HMBS, variant fitness data on human glucokinase (GCK), and variant deleteriousness data on HMBS and GCK. These analyses further confirm FiTMuSiC’s qualities and accuracy, which compare favorably with those of other predictors. Additionally, FiTMuSiC returns two scores that separately describe the functional and structural effects of the variant, thus providing mechanistic insight into why the variant leads to fitness loss or gain. We also provide an easy-to-use webserver at https://babylone.ulb.ac.be/FiTMuSiC , which is freely available for academic use and does not require any bioinformatics expertise, which simplifies the accessibility of our tool for the entire scientific community.

中文翻译：

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FiTMuSiC：利用结构和（共同）进化数据进行蛋白质适应性预测

系统地预测突变对蛋白质适应性的影响对于理解遗传疾病至关重要。事实上，预测补充了分析变异如何导致功能失调的蛋白质进而导致疾病的实验工作。在这里，我们展示了新的适应度预测器 FiTMuSiC，它利用结构、进化和共同进化信息。我们表明，尽管 FiTMuSiC 的基础模型很简单，但其适应度预测准确度很高：它是第六轮基因组解释挑战关键评估 (CAGI6) 羟甲基双烷合酶 (HMBS) 目标的顶级预测因子之一，并且表现也很好以及更复杂的深度学习模型，例如 AlphaMissense。为了进一步证明 FiTMuSiC 的稳健性，我们将其预测与 HMBS 的体外活性数据、人葡萄糖激酶 (GCK) 的变异适应性数据以及 HMBS 和 GCK 的变异有害性数据进行了比较。这些分析进一步证实了 FiTMuSiC 的质量和准确性，与其他预测器相比具有优势。此外，FiTMuSiC 返回两个分数，分别描述变体的功能和结构效应，从而提供有关变体为何导致适应性损失或增益的机制见解。我们还在 https://babylone.ulb.ac.be/FiTMuSiC 上提供了一个易于使用的网络服务器，该服务器可免费供学术使用，并且不需要任何生物信息学专业知识，这简化了整个工具的可访问性科学界。