Automated Path Searching Reveals the Mechanism of Hydrolysis Enhancement by T4 Lysozyme Mutants

Abstract

Bacteriophage T4 lysozyme (T4L) is a glycosidase that is widely applied as a natural antimicrobial agent in the food industry. Due to its wide applications and small size, T4L has been regarded as a model system for understanding protein dynamics and for large-scale protein engineering. Through structural insights from the single conformation of T4L, a series of mutations (L99A,G113A,R119P) have been introduced, which have successfully raised the fractional population of its only hydrolysis-competent excited state to 96%. However, the actual impact of these substitutions on its dynamics remains unclear, largely due to the lack of highly efficient sampling algorithms. Here, using our recently developed travelling-salesman-based automated path searching (TAPS), we located the minimum-free-energy path (MFEP) for the transition of three T4L mutants from their ground states to their excited states. All three mutants share a three-step transition: the flipping of F114, the rearrangement of α0/α1 helices, and final refinement. Remarkably, the MFEP revealed that the effects of the mutations are drastically beyond the expectations of their original design: (a) the G113A substitution not only enhances helicity but also fills the hydrophobic Cavity I and reduces the free energy barrier for flipping F114; (b) R119P barely changes the stability of the ground state but stabilizes the excited state through rarely reported polar contacts S117OG:N132ND2, E11OE1:R145NH1, and E11OE2:Q105NE2; (c) the residue W138 flips into Cavity I and further stabilizes the excited state for the triple mutant L99A,G113A,R119P. These novel insights that were unexpected in the original mutant design indicated the necessity of incorporating path searching into the workflow of rational protein engineering.

Structural basis for lipid transfer by the ATG2A-ATG9A complex

“…To further dissect the starting stage of lipid-transfer by ATG2A NR , we located the minimum free energy path (MFEP) for this process via the recently matured travelling-salesman automated path searching (TAPS) algorithm [36]. Requiring minimal input information, TAPS is highly efficient and has successfully revealed high dimensional MFEPs for proteins with hundreds of residues [37][38][39]. Starting from the encounter DOPC-ATG2A NR complex (without micelle or ATG9A/WIP4), we used biased sampling dragging the single lipid to penetrate the ATG2A NR transfer-channel and generated an initial biased path for lipid-transfer.…”

Transition state searching for complex biomolecules: Algorithms and machine learning

“…标, 即集合变量(collective variable, CV), 强行定 向降维到该预选的低维CV空间, 而后在CV空间 内搜寻过渡态 [79][80][81][82][83][84][85][86][87][88][89][90][91][92][93][94][95] . 代表性方法: 温和爬升动力学 (gentlest ascent dynamics, GAD) [79][80][81] 、有限温度 弦 方 法 (finite temperature string, FTS) [82][83][84][85][86][87] 、 快 速断层扫描法(fast tomographic, FT) [88][89][90] 、基于 旅 行 商 的 路 径 搜 索 (travelling-salesman based automated path searching, TAPS) [91][92][93][94][95] .…”

“…x 如前所述, 为了准确阐明生物分子功能性动力 学的微观机制, 需要在传统采样算法的基础上, 发 展可获取上述转变过程过渡态信息的过渡态搜索 算法, 包括依赖CV [82][83][84][85][86][87][88][89][90][91][92][93][94][95] 和非CV依赖算法 [96][97][98][99][100][101] 两大类. 对于依赖CV的算法, 需在缺乏对体系的 先验数据和认知的条件下, 将高维相空间{ }"定 向降维"至少量的依据经验或直觉定义的CV上…”

“…的路径优化算法, 即基于TAPS算法 [91][92][93][94][95] , 此方法 中避免了高质量预选集合变量的困境, 可高效且快 速找到最优转变路径. 当构建完路径优化的低维空 间后, 需要从目标系统的两个稳定态结构出发, 产 生一条较为粗糙的转变路径 [114][115][116] , 而后对此路径 进行迭代优化(路径整体下山), 并最终收敛于最优 路径(MFEP) [82][83][84][85][86][87][88][89][90][91][92][93][94][95] ; 继而便可通过计算MFEP的 自由能图景, 准确给出微观转变机制和过渡态信 息 [57][58][59]117] .…”

“…在基于集合变量的搜索算法中, 还存在一种基 于路径集合变量PCV的新型算法 [120] , 即基于旅 行商问题的自动路径搜索算法(TAPS). TAPS巧 妙地避开了其他路径优化算法中集合变量的选取 问题, 同时基于并行化和GPU加速, 快速得到较 高维度空间中的最优路径(MFEP), 给出相应的微 观转变机制和过渡态信息(图2) [91][92][93][94][95] . 图 2 (a) PCV构建 [120] 和TAPS Method [91][92][93][94][95]121] 算法原理示意图; (b)基于伞形采样方法得到的TAPS算法确定的MEK1由 Loop-Out到达Loop-In转变过程最小自由能路径(MFEP)的自由能图景及相应的微观转变机制 [92] Fig.…”