College of Applied Sciences, Umm Al Qura University, Mecca, Saudi Arabia.
School of Biological Sciences, University of Reading, Reading, UK.
Methods Mol Biol. 2023;2627:101-118. doi: 10.1007/978-1-0716-2974-1_6.
Protein structure modeling is one of the most advanced and complex processes in computational biology. One of the major problems for the protein structure prediction field has been how to estimate the accuracy of the predicted 3D models, on both a local and global level, in the absence of known structures. We must be able to accurately measure the confidence that we have in the quality predicted 3D models of proteins for them to become widely adopted by the general bioscience community. To address this major issue, it was necessary to develop new model quality assessment (MQA) methods and integrate them into our pipelines for building 3D protein models. Our MQA method, called ModFOLD, has been ranked as one of the most accurate MQA tools in independent blind evaluations. This chapter discusses model quality assessment in the protein modeling field, demonstrating both its strengths and limitations. We also present some of the best methods according to independent benchmarking data, which has been gathered in recent years.
蛋白质结构建模是计算生物学中最先进和最复杂的过程之一。在缺乏已知结构的情况下,蛋白质结构预测领域的主要问题之一是如何在局部和全局水平上估计预测的 3D 模型的准确性。我们必须能够准确地衡量我们对蛋白质预测 3D 模型质量的信心,以便它们被广大一般生物科学界所接受。为了解决这个主要问题,有必要开发新的模型质量评估(MQA)方法,并将其集成到我们构建 3D 蛋白质模型的管道中。我们的 MQA 方法称为 ModFOLD,在独立的盲评估中被评为最准确的 MQA 工具之一。本章讨论了蛋白质建模领域的模型质量评估,展示了其优缺点。我们还根据近年来收集的独立基准数据,介绍了一些最好的方法。