Abstract：
Protein structure determination is a critical aspect of biological research, enabling us to understand protein function and potential applications. Recent advances in deep learning and artificial intelligence have led to the development of several protein structure prediction tools, such as AlphaFold2 and ColabFold. However, their performance has primarily been evaluated on well-characterised proteins and their ability to predict sturtctures of proteins lacking experimental structures, such as many snake venom toxins, has been less scrutinised. In this study, we evaluated three modelling tools on their prediction of over 1000 snake venom toxin structures for which no experimental structures exist. Our findings show that AlphaFold2 (AF2) performed the best across all assessed parameters. We also observed that ColabFold (CF) only scored slightly worse than AF2, while being computationally less intensive. All tools struggled with regions of intrinsic disorder, such as loops and propeptide regions, and performed well in predicting the structure of functional domains. Overall, our study highlights the importance of exercising caution when working with proteins with no experimental structures available, particularly those that are large and contain flexible regions. Nonetheless, leveraging computational structure prediction tools can provide valuable insights into the modelling of protein interactions with different targets and reveal potential binding sites, active sites, and conformational changes, as well as into the design of potential molecular binders for reagent, diagnostic, or therapeutic purposes.
摘要：
蛋白质结构测定是生物学研究的一个关键方面,使我们能够了解蛋白质的功能和潜在的应用。深度学习和人工智能的最新进展导致了几种蛋白质结构预测工具的开发。例如AlphaFold2和ColabFold。然而,它们的性能主要是在特征明确的蛋白质上进行评估的，它们在缺乏实验结构的蛋白质上的表现，比如很多蛇毒毒素,受到的审查较少。在这项研究中,我们评估了三种模型工具对1000多种蛇毒毒素结构的预测，这些结构不存在实验结构。我们的研究结果表明，AlphaFold2（AF2）在所有评估参数中表现最好。我们还观察到ColabFold（CF）的得分仅比AF2稍差，而计算强度较低。所有工具都在内在紊乱的区域挣扎，如环和前肽区域，并且在预测功能结构域的结构方面表现良好。总的来说,我们的研究强调了在处理没有实验结构的蛋白质时谨慎行事的重要性，特别是那些大的，包含灵活的区域。尽管如此，利用计算结构预测工具可以为蛋白质与不同靶标相互作用的建模提供有价值的见解，并揭示潜在的结合位点，活跃的网站，和构象变化，以及用于试剂的潜在分子粘合剂的设计，诊断,或治疗目的。
