Department of Applied Mathematics, Faculty of Mathematical Sciences, Tarbiat Modares University, Jalal Ale Ahmad Highway, P.O.Box: 14115-134, Tehran, Iran.
BMC Bioinformatics. 2019 Nov 21;20(1):604. doi: 10.1186/s12859-019-3186-6.
Evaluation of protein structure is based on trustworthy potential function. The total potential of a protein structure is approximated as the summation of all pair-wise interaction potentials. Knowledge-based potentials (KBP) are one type of potential functions derived by known experimentally determined protein structures. Although several KBP functions with different methods have been introduced, the key interactions that capture the total potential have not studied yet.
In this study, we seek the interaction types that preserve as much of the total potential as possible. We employ a procedure based on the principal component analysis (PCA) to extract the significant and key interactions in native protein structures. We call these interactions as principal interactions and show that the results of the model that considers only these interactions are very close to the full interaction model that considers all interactions in protein fold recognition. In fact, the principal interactions maintain the discriminative power of the full interaction model. This method was evaluated on 3 KBPs with different contact definitions and thresholds of distance and revealed that their corresponding principal interactions are very similar and have a lot in common. Additionally, the principal interactions consisted of 20 % of the full interactions on average, and they are between residues, which are considered important in protein folding.
This work shows that all interaction types are not equally important in discrimination of native structure. The results of the reduced model based on principal interactions that were very close to the full interaction model suggest that a new strategy is needed to capture the role of remaining interactions (non-principal interactions) to improve the power of knowledge-based potential functions.
蛋白质结构的评估基于可靠的势能。蛋白质结构的总势能近似为所有对相互作用势能的总和。基于知识的势能（KBP）是由已知的实验确定的蛋白质结构衍生的势能函数之一。尽管已经介绍了几种具有不同方法的 KBP 函数，但尚未研究捕获总势能的关键相互作用。
在这项研究中，我们寻求尽可能保留总势能的相互作用类型。我们采用基于主成分分析（PCA）的程序从天然蛋白质结构中提取重要的关键相互作用。我们将这些相互作用称为主要相互作用，并表明仅考虑这些相互作用的模型的结果与考虑蛋白质折叠识别中所有相互作用的全相互作用模型非常接近。事实上，主要相互作用保持了全相互作用模型的辨别能力。该方法在具有不同接触定义和距离阈值的 3 个 KBP 上进行了评估，结果表明它们对应的主要相互作用非常相似，并且有很多共同之处。此外，主要相互作用平均占全相互作用的 20％，它们存在于残基之间，这在蛋白质折叠中被认为是重要的。
这项工作表明，并非所有相互作用类型在区分天然结构方面都同等重要。基于主要相互作用的简化模型的结果与全相互作用模型非常接近，这表明需要一种新的策略来捕捉剩余相互作用（非主要相互作用）的作用，以提高基于知识的势能函数的能力。
