Akt1 prodigiosin docking and dynamic molecular

<b>Molecular docking </b><b>dynamic simulations</b>Protein-Ligand docking was performed using Molecular Operating Environment (MOE). Preparation of 3D Structure of Ligands and Protein Targets: The 3D structures built of Akt1. The three-dimensional (3D) structure of ligands such as PG (CID 135455579), ATP (CID 5957), IQO (CID 10196499) and Akt1 inhibitor VII (PubChem ID: 10196499), as well as the inhibitor evaluated, were retrieved from the PubChem compound database. The ligand molecules obtained from PubChem were optimized using molecular mechanics with the CHARMM27 force field until reaching a gradient of 0.01 kcal/(Å.mol). The optimized structures were saved in a mdb file format and compiled into a single database. Subsequently, both the receptor and ligands underwent 3D protonation in MOE using the default procedure. The preparation process involved adding restraint tethers to atoms, annotating ligand identification, adding hydrogen atoms, and refining coordinates. Identifying potential binding sites (also known as cavities or active sites) was accomplished using MOE’s Site Finder function. To perform molecular docking, the prepared ligand database was subjected to MOE’s DOCK module. The docking process utilized Placement Triangle Matcher and London dG rescoring, generating 100 poses. These poses then underwent Induced Fit Refinement and GBVI/WSA dG scoring to select the top five binding poses for further analysis, aiming to obtain the best possible score for each ligand. Finally, the most favorable pose with the lowest energy was chosen for subsequent useMoreover, the docking poses and ligand interactions were viewed using MOE (Frikha et al., 2024).<b>Molecular dynamic simulations</b>The molecular dynamics simulation was conducted to explore the dynamic interactions between the ligand and Akt1 protein complex. Following the methodology outlined by Faker et al. 2024,(Frikha et al., 2024) Here's a breakdown of the steps outlined: (1) Preparation of Inputs: The CHARMM-GUI was used to prepare inputs with the CHARMM36 force field for molecular simulations. (2) System Setup: The initial structure was placed in a rectangular box, solvated with water molecules using the TIP3P model, and neutralized by adding potassium (K+) and chloride (Cl-) ions. (3) Energy Minimization: The system underwent energy minimization using the steepest descent energy minimization algorithm for 50,000 steps to relieve steric clashes and correct bad contacts. (4) Pre-Equilibration Simulation: A pre-equilibration simulation was conducted for 125 picoseconds (ps) in the NVT ensemble (constant number of particles, volume, and temperature) at 300 Kelvin (K) using velocity rescaling with a stochastic term. (5) NPT Equilibrium Simulation: Each system underwent an NPT equilibrium simulation for 125 ps in the NPT ensemble (constant number of particles, pressure, and temperature). The pressure was controlled at 1 bar using the C-rescale barostat. (6) Constraint Handling: The linear constraint solver (LINCS) algorithm was applied to maintain bond constraints during the simulation. (6) Periodic Boundary Conditions (PBC): PBC were employed in the x, y, and z directions to minimize edge effects and simulate an infinitely repeating system. (7) Interactions Modeling: Short-range van der Waals interactions were modeled using the Lennard-Jones (LJ) potential with a cut-off radius of 1.2 nanometers (nm). Long-range electrostatic interactions were calculated using the particle-mesh Ewald (PME) algorithm, with a real space cutoff of 1.2 nm. (8) Initial Velocities Assignment: Initial velocities of particles were assigned based on Maxwell distributions to set the system in motion. (9) Molecular Dynamics Simulation: tow simulations have been performed between 10 and 100 nanosecond (ns) with sampling of each 5 ns, MD simulation was conducted to observe and analyze the system's behavior over an extended period. (10) Analysis: Various parameters including root mean square deviations (RMSD), residue root means square fluctuation (RMSF), number of hydrogen bonds (HB), radius of gyration (Rg) were calculated to analyze the system's dynamics and interactions. This protocol provides a comprehensive approach to studying the behavior of protein-ligand complexes using MD simulations

**分子对接**与**分子动力学模拟**
蛋白-配体对接实验依托分子操作环境（Molecular Operating Environment, MOE）完成。

配体与蛋白靶点的三维结构制备：Akt1的三维结构已预先构建，本研究从PubChem化合物数据库中获取了包括PG（CID 135455579）、ATP（CID 5957）、IQO（CID 10196499）、Akt1抑制剂VII（PubChem ID: 10196499）在内的已知配体，以及待评价的目标抑制剂的三维结构。从PubChem获取的配体分子采用CHARMM27力场通过分子力学方法进行结构优化，直至梯度收敛至0.01 kcal/(Å·mol)。优化后的配体结构以mdb文件格式存储，并整合为单一配体数据库。随后，采用MOE的默认流程对受体蛋白与配体进行三维质子化修饰，该制备流程涵盖：为原子添加约束 tether、标注配体识别信息、添加氢原子以及优化坐标。潜在结合位点（又称空腔或活性位点）的识别通过MOE的Site Finder工具完成。

为开展分子对接实验，将制备好的配体数据库导入MOE的DOCK模块。对接流程采用Placement Triangle Matcher算法与London dG重打分策略，初始生成100个配体构象（pose）。随后对这些构象进行诱导契合精修（Induced Fit Refinement）并通过GBVI/WSA dG打分进行筛选，最终为每个配体选取得分最优的前5个结合构象以开展后续分析，旨在获取每个配体的最佳结合评分。最后，选取能量最低的最优结合构象用于后续研究。此外，借助MOE可视化分析对接构象与配体-蛋白相互作用（Frikha等，2024）。

**分子动力学模拟**
本研究开展分子动力学模拟以探究配体与Akt1蛋白复合物的动态相互作用，模拟流程参照Faker等（2024）及Frikha等（2024）的方法设计，具体步骤如下：
1. 输入文件制备：采用CHARMM-GUI工具结合CHARMM36力场制备分子动力学模拟所需的输入文件。
2. 体系构建：将初始复合物结构置于矩形模拟盒中，采用TIP3P模型添加水分子进行溶剂化，并通过加入钾离子（K+）与氯离子（Cl-）中和体系电荷。
3. 能量最小化：采用最速下降法对体系进行50000步能量最小化，以消除空间位阻冲突并修正不良接触。
4. 预平衡模拟：在NVT系综（粒子数、体积、温度恒定）下，采用随机速度重标度方法于300开尔文（K）条件下开展125皮秒（ps）的预平衡模拟。
5. NPT平衡模拟：将每个体系置于NPT系综（粒子数、压力、温度恒定）下开展125 ps的平衡模拟，采用C-rescale恒温恒压器将体系压力控制为1巴（bar）。
6. 约束处理：模拟过程中采用线性约束求解器（LINCS）算法维持化学键的约束条件。
7. 周期性边界条件（PBC）：在x、y、z三个方向采用周期性边界条件以消除边界效应，模拟无限重复的周期性体系。
8. 相互作用建模：范德华短程相互作用采用Lennard-Jones（LJ）势函数建模，截断半径设为1.2纳米（nm）；长程静电相互作用采用粒子网格埃瓦尔德（PME）算法计算，实空间截断半径同样为1.2 nm。
9. 初始速度赋值：基于麦克斯韦分布为体系粒子分配初始速度，使体系进入运动状态。
10. 分子动力学模拟：本研究开展了时长为10~100纳秒（ns）的两次独立模拟，每5 ns进行一次数据采样，以长期观测并分析体系的动态行为。
11. 数据分析：计算包括均方根偏差（RMSD）、残基均方根波动（RMSF）、氢键数目（HB）以及回转半径（Rg）在内的多种参数，以分析体系的动态行为与相互作用。

本模拟流程为采用分子动力学（MD）模拟研究蛋白-配体复合物的动态行为提供了一套完整的研究方案。

搜集汇总

背景与挑战

背景概述

该数据集提供了Akt1蛋白与多种配体的分子对接和分子动力学模拟结果，包括对接评分、动态相互作用分析（如RMSD、RMSF、氢键数量等），适用于研究蛋白质-配体相互作用和药物设计。