Biocomputing Group, Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy.
Department of Medical Sciences, University of Torino, Turin, Italy.
Methods Mol Biol. 2022;2449:169-185. doi: 10.1007/978-1-0716-2095-3_6.
After nearly two decades of research in the field of computational methods based on machine learning and knowledge-based potentials for ΔG and ΔΔG prediction upon variations, we now realize that all the approaches are poorly performing when tested on specific cases and that there is large space for improvement. Why this is so? Is it wrong the underlying assumption that experimental protein thermodynamics in solution reflects the thermodynamics of a single protein? Both machine learning and knowledge-based computational methods are rigorous and we know the solid theory behind. We are now in a critical situation, which suggests that predictions of protein instability upon variation should be considered with care. In the following, we will show how to cope with the problem of understanding which protein positions may be of interest for biotechnological and biomedical purposes. By applying a consensus procedure, we indicate possible strategies for the result interpretation.
经过近二十年的研究，我们在基于机器学习的计算方法和基于知识的势能领域取得了进展，这些方法可用于预测结构变化对 ΔG 和 ΔΔG 的影响。现在我们意识到，当在特定情况下进行测试时，所有方法的性能都很差，还有很大的改进空间。为什么会这样呢？是否错误地假设了溶液中实验蛋白质热力学反映了单个蛋白质的热力学？机器学习和基于知识的计算方法都是严谨的，我们也了解其背后的坚实理论。我们现在正处于一个关键的情况，这表明应该谨慎考虑对蛋白质变异不稳定性的预测。在下面，我们将展示如何应对理解哪些蛋白质位置可能对生物技术和生物医学目的有意义的问题。通过应用共识程序，我们为结果解释指明了可能的策略。
