蛋白質-蛋白質對接(protein-protein docking, PPD)是一種可以從元件蛋白質結構去預測蛋白質複合體結構的計算方法，具有揭示原子結構細節以及窺探一些用其他方法不易發現的能力。在各種不同的PPD方法測試和監測其進展情況的結論之一是，數據驅動的PPD特別值得注意，因為它可以顯著增加PPD的低成功率，但需要納入實驗數據或資訊當做對蛋白質對接的約束因子。不過，目前尚不清楚當使用不正確或涉及含糊不清的數據時，數據驅動PPD結果的錯誤程度，解決這個問題將會對使用PPD計算預測蛋白質複合體結構的實際應用提供指南。在這項工作中，我們的目標是描述使用約束因子，即實驗數據或資訊的數量和質量，如何影響數據驅動PPD預測的精度。有兩種主要類型的數據可以從生化生物物理實驗或生物資訊學預測得到：1）兩個特定原子之間的距離，和2）在蛋白質複合體介面上的胺基酸資訊，以幫助 PPD預測。我們的研究結果顯示，即使只有很少的距離數據，甚至在這些數據中含有大量的噪訊，還是可以從傳統的PPD預測裡大大提高PPD的性能。相對而言，需要有相當多介面胺基酸資訊的數量和相當的數據品質才能實現數據驅動PPD預測的高成功率。這項研究的結果為使用數據驅動PPD預測蛋白質複合體結構所需要的數據提供一些必要的指引，並指出一些在這一領域的未來研究的方向。

Protein-protein docking (PPD), a computational method for predicting the structure of a protein complex from known component structures, has the ability to reveal atomic details of the complex structure and make otherwise unattainable discoveries. Among different kinds of PPD methods developed, one conclusion from monitoring their progress over the years is that data-driven PPD is particularly useful because it can significantly increase the low success rates of PPD, albeit requiring the incorporation of experimental data or information as constraints for the docking. It is unclear, however, how vulnerable of data-driven PPD is to incorrect or ambiguous data, answers to which will provide guides for practical applications of using PPD to computationally predict protein complex structures. In this work, we aim to characterize the effects of using constraints, i.e. the amount and quality of experimental data or information, on the accuracies of data-driven PPD predictions. There are two main types of data that can be obtained from biochemical and biophysical experiments, or from bioinformatics predictions: 1) distance between two specific atoms, and 2) information of interface residues, to aid PPD predictions. Our results showed that only few distances, even with a significant amount of noises in the data, can greatly improve the performance of PPD from those of conventional PPD predictions. In comparison, a larger amount and better quality of interface residue information is needed for achieving high success rates in data-driven PPD predictions. The results from this study provide some needed guidelines for using data-driven PPD to predict protein complex structures, and point out directions for future research in this field.

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