Haliloglu Turkan, Hacisuleyman Aysima, Erman Burak
Polymer Research Center and Chemical Engineering Department, Bogazici University, İstanbul 34342, Turkey.
Institute of Bioengineering, Swiss Federal Institute of Technology (EPFL), Lausanne 1015, Switzerland.
Bioinformatics. 2022 Jul 11;38(14):3590-3599. doi: 10.1093/bioinformatics/btac380.
Allostery in proteins is an essential phenomenon in biological processes. In this article, we present a computational model to predict paths of maximum information transfer between active and allosteric sites. In this information theoretic study, we use mutual information as the measure of information transfer, where transition probability of information from one residue to its contacting neighbors is proportional to the magnitude of mutual information between the two residues. Starting from a given residue and using a Hidden Markov Model, we successively determine the neighboring residues that eventually lead to a path of optimum information transfer. The Gaussian approximation of mutual information between residue pairs is adopted. The limits of validity of this approximation are discussed in terms of a nonlinear theory of mutual information and its reduction to the Gaussian form.
Predictions of the model are tested on six widely studied cases, CheY Bacterial Chemotaxis, B-cell Lymphoma extra-large (Bcl-xL), Human proline isomerase cyclophilin A (CypA), Dihydrofolate reductase (DHFR), HRas GTPase and Caspase-1. The communication transmission rendering the propagation of local fluctuations from the active sites throughout the structure in multiple paths correlate well with the known experimental data. Distinct paths originating from the active site may likely represent a multi functionality such as involving more than one allosteric site and/or pre-existence of some other functional states. Our model is computationally fast and simple and can give allosteric communication pathways, which are crucial for the understanding and control of protein functionality.
Supplementary data are available at Bioinformatics online.
蛋白质中的别构现象是生物过程中的一个重要现象。在本文中，我们提出了一个计算模型来预测活性位点和别构位点之间最大信息传递路径。在这项信息理论研究中，我们使用互信息作为信息传递的度量，其中信息从一个残基传递到其相邻接触残基的转移概率与这两个残基之间互信息的大小成正比。从给定的残基开始，使用隐马尔可夫模型，我们依次确定最终导致最优信息传递路径的相邻残基。采用了残基对之间互信息的高斯近似。根据互信息的非线性理论及其简化为高斯形式，讨论了这种近似的有效性极限。
该模型的预测在六个广泛研究的案例中进行了测试，即细菌趋化蛋白CheY、B细胞淋巴瘤特大蛋白（Bcl-xL）、人脯氨酸异构酶亲环蛋白A（CypA）、二氢叶酸还原酶（DHFR）、HRas GTP酶和半胱天冬酶-1。使局部波动从活性位点在整个结构中通过多条路径传播的通信传递与已知实验数据密切相关。源自活性位点的不同路径可能代表多种功能，例如涉及不止一个别构位点和/或某些其他功能状态的预先存在。我们的模型计算快速且简单，能够给出别构通信路径，这对于理解和控制蛋白质功能至关重要。
补充数据可在《生物信息学》在线获取。
