Polyakov Igor V, Meteleshko Yulia I, Mulashkina Tatiana I, Varentsov Mikhail I, Krinitskiy Mikhail A, Khrenova Maria G
Chemistry Department, Lomonosov Moscow State University, 119991 Moscow, Russia.
Faculty of Geography, Lomonosov Moscow State University, 119991 Moscow, Russia.
Int J Mol Sci. 2025 May 26;26(11):5097. doi: 10.3390/ijms26115097.
The active sites of enzymes are able to activate substrates and perform chemical reactions that cannot occur in solutions. We focus on the hydrolysis reactions catalyzed by enzymes and initiated by the nucleophilic attack of the substrate's carbonyl carbon atom. From an electronic structure standpoint, substrate activation can be characterized in terms of the Laplacian of the electron density. This is a simple and easily visible imaging technique that allows one to "visualize" the electrophilic site on the carbonyl carbon atom, which occurs only in the activated species. The efficiency of substrate activation by the enzymes can be quantified from the ratio of reactive and nonreactive states derived from the molecular dynamics trajectories executed with quantum mechanics/molecular mechanics potentials. We propose a neural network that assigns the species to reactive and nonreactive ones using the Laplacian of electron density maps. The neural network is trained on the cysteine protease enzyme-substrate complexes, and successfully validated on the zinc-containing hydrolase, thus showing a wide range of applications using the proposed approach.
酶的活性位点能够激活底物并进行在溶液中无法发生的化学反应。我们专注于由酶催化且由底物羰基碳原子的亲核攻击引发的水解反应。从电子结构的角度来看，底物激活可以用电荷密度的拉普拉斯算子来表征。这是一种简单且易于观察的成像技术，它能让人“看到”仅在活化物种中出现的羰基碳原子上的亲电位点。酶对底物激活的效率可以通过由量子力学/分子力学势执行的分子动力学轨迹得出的反应态和非反应态的比率来量化。我们提出一种神经网络，它使用电荷密度图的拉普拉斯算子将物种分类为反应性和非反应性物种。该神经网络在半胱氨酸蛋白酶 - 底物复合物上进行训练，并在含锌水解酶上成功验证，从而展示了所提出方法的广泛应用。
