Indian Institute of Technology Madras, India.
Indian Institute of Technology, Madras, India.
Brief Bioinform. 2021 Nov 5;22(6). doi: 10.1093/bib/bbab240.
Several prediction algorithms and tools have been developed in the last two decades to predict protein and peptide aggregation. These in silico tools aid to predict the aggregation propensity and amyloidogenicity as well as the identification of aggregation-prone regions. Despite the immense interest in the field, it is of prime importance to systematically compare these algorithms for their performance. In this review, we have provided a rigorous performance analysis of nine prediction tools using a variety of assessments. The assessments were carried out on several non-redundant datasets ranging from hexapeptides to protein sequences as well as amyloidogenic antibody light chains to soluble protein sequences. Our analysis reveals the robustness of the current prediction tools and the scope for improvement in their predictive performances. Insights gained from this work provide critical guidance to the scientific community on advantages and limitations of different aggregation prediction methods and make informed decisions about their research needs.
在过去的二十年中，已经开发出了几种预测算法和工具来预测蛋白质和肽的聚集。这些计算工具有助于预测聚集倾向和淀粉样变性，以及鉴定易于聚集的区域。尽管人们对该领域非常感兴趣，但系统地比较这些算法的性能至关重要。在这篇综述中，我们使用各种评估方法对九种预测工具进行了严格的性能分析。评估是在几个非冗余数据集上进行的，这些数据集范围从六肽到蛋白质序列，以及淀粉样变性抗体轻链到可溶性蛋白质序列。我们的分析揭示了当前预测工具的稳健性，以及提高其预测性能的空间。这项工作提供的见解为科学界提供了关于不同聚集预测方法的优缺点的关键指导，并为他们的研究需求做出明智的决策。
