Ciofalo Cosimo, Laveglia Vincenzo, Andreini Claudia, Rosato Antonio
Department of Chemistry, University of Florence, Via della Lastruccia 3, Sesto Fiorentino 50019, Italy.
Magnetic Resonance Center (CERM), University of Florence, Via Luigi Sacconi 6, Sesto Fiorentino 50019, Italy.
J Chem Inf Model. 2025 May 26;65(10):5205-5215. doi: 10.1021/acs.jcim.5c00549. Epub 2025 May 15.
Metalloproteins play crucial physiological roles across all domains of life, relying on metal ions for structural stability and catalytic activity. In recent years, computational approaches have emerged as powerful and increasingly reliable tools for predicting metal-binding sites in metalloproteins, enabling their application in the challenging field of metalloproteomics. Given the growing number of available tools, it is timely to design a reproducible approach to characterize their performance in specific usage scenarios. Thus, in this study, we selected some state-of-the-art structure-based predictors for zinc-binding sites and evaluated their performance on two data sets: experimental apoprotein structures and structural models generated by AlphaFold. Our results indicate that apoprotein structures pose significant challenges for predicting metal-binding sites. For these systems, the predictors achieved lower-than-expected performance due to the structural rearrangements occurring upon metalation. Conversely, predictions based on AlphaFold models yielded significantly better results, suggesting that they more closely resemble the holo forms of metalloproteins. Our findings highlight the great potential of metal-binding site predictions for advancing research in the field of metalloproteomics.
金属蛋白在生命的各个领域都发挥着关键的生理作用，依靠金属离子来维持结构稳定性和催化活性。近年来，计算方法已成为预测金属蛋白中金属结合位点的强大且日益可靠的工具，使其能够应用于具有挑战性的金属蛋白质组学领域。鉴于可用工具的数量不断增加，适时设计一种可重复的方法来表征它们在特定使用场景中的性能是很有必要的。因此，在本研究中，我们选择了一些用于锌结合位点预测的基于结构的先进预测器，并在两个数据集上评估了它们的性能：实验性脱辅基蛋白结构和由AlphaFold生成的结构模型。我们的结果表明，脱辅基蛋白结构对预测金属结合位点构成了重大挑战。对于这些系统，由于金属化时发生的结构重排，预测器的性能低于预期。相反，基于AlphaFold模型的预测产生了明显更好的结果，表明它们更接近金属蛋白的全酶形式。我们的研究结果凸显了金属结合位点预测在推进金属蛋白质组学领域研究方面的巨大潜力。
