[概念] Force Field Analysis

本文介绍了PMBOK指南中提及的质量规划工具之一——力场分析方法。该方法通过评估支持和反对变更的力量来帮助决策者全面考虑项目的各种影响因素。文章详细解释了如何实施力场分析,包括列出所有支持和反对力量、为每种力量打分及绘制力量对比图。

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PMBOK (2004 3rd 英)P186 质量规划工具中提到了一中方法 - Force Field Analysis,书中没有详细介绍。这里是一些补充 http://www.psywww.com/mtsite/forcefld.html

Force Field Analysis is a method used to get a whole view of all the forces for or against a plan so that a decision can be made which takes into account all interests. In effect this is a specialised method of weighing pros and cons.

Where a plan has been decided on, force field analysis allows you to look at all the forces for or against the plan. It helps you to plan or reduce the impact of the opposing forces, and strengthen and reinforce the supporting forces.

To carry out a force field analysis, follow the following steps: List all forces for change in one column, and all forces against change in another column. Assign a score to each force, from 1 (weak) to 5 (strong). Draw a diagram showing the forces for and against, and the size of the forces. 图例
翻译:3D Binding model analysis(FdGOGAT in pink and ACR11 in green) . The key residues are shown as sticks. H-bonds are shown as yellow dashed lines. Binding energy (Docking score: -5.7 kcal/mol) We studied the binding modes and interactions between the target proteins through molecular docking. As shown in the figure, the proteins were represented in cartoon, and the key residues were shown as sticks. Through docking, we found that target proteins has excellent binding energies. In addition, GLU-1264, PRO-1103, ASP-1608 of FdGOGAT can form 4 hydrogen bonds ARG-255, TYR-254, ASN-269, ARG-272 on ACR11. These interactions demonstrate the existence of ubiquitination binding between them. Molecular docking The sequences of the target proteins were obtained from Uniport and subsequently modeled using AlphaFold3, an advanced deep-learning-based protein structure prediction tool. AlphaFold3 employs an end-to-end transformer-based architecture, leveraging both evolutionary multiple sequence alignments (MSAs) and physical constraints to predict highly accurate three-dimensional protein structures. The model integrates an attention-based deep neural network with structural templates to refine its predictions, enabling the accurate determination of protein folding and domain organization. Following structural prediction, the modeled target protein was prepared for docking studies using AutoDockTools 1.5.6 (ADT). The protein structure refinement included: 1. Hydrogenation – Addition of polar hydrogens to optimize hydrogen bonding interactions. 2. Charge Distribution – Assignment of Gasteiger partial atomic charges, ensuring accurate electrostatic modeling. 3. Atomic Type Specification – Defining atomic parameters according to the AutoDock force field, which is essential for molecular docking simulations.For molecular docking, AutoDock Vina was employed, a Monte Carlo-based genetic algorithm that efficiently searches the conformational space of the ligand and evaluates bin
03-23
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