# PNMAVis: Visual Analysis Tool of Protein Normal Mode for Understanding Cavity Dynamics
## 3 Related Work
### Molecular Cavity Calculation and Extraction  
Current methods for cavity calculation are divided into four categories: grid-based, probe-based, Voronoi-based, and surface-based. The first approach to compute and visualize cavities was POCKET [15], developed by [PERSON] and [PERSON] in 1992. This method extracts cavities by simplifying the protein to a hard-sphere model and simplifying the possible ligand positions to discrete points. Due to hardware limitations, the grid-based methodsare not suitable for larger structures without drastically reducing the grid resolution. One solution to solve this problem is to use the Voronoi-based method. The Voronoi-based method has been proven to be suitable for detecting pathways in molecules, and is suited for working with large molecules. The protein is often simplified by using the atom positions or the hard-sphere model. Moreover, the edges of Voronoi diagrams automatically provide geometrically optimal molecular paths based on the restriction. Because of the lining atom sequence is easier to achieve, a Voronoi-based method is adopted to extract the cavity in our work. The probe-based method uses the spatial expansion of ligands to calculate molecular paths and cavities. Most of the techniques approximate the ligand by using a single hard-sphere. In contrast to most of the previous approaches, the protein is not purely restricted to the hard-sphere model, but to a molecular surface model. The molecular surfaces-based method defines an interface of the molecule and its environment [16]. Therefore, they can be used to define cavities as well. Many computer-aided drug design tools supporting these computational models are available, such as POCKET [15], LIGSITE [17], and D3 Pockets [18], which use a grid computing model; CAVER and FPCRet [19] use a Vonoroi computing model. MSPocket [20] uses a surface computational model; PASS [21] and ROLL [22] use a probe ball computational model. In addition, there are also some hybrid computational models, such as KVFinder [23] and CavVis [24], which are based on a mixture of mesh and probe ball. Our work employs CAVER Analyst 2.0, which is a software program for the analysis of access pathways to buried active sites in proteins in long molecular dynamics (MD) simulations. This tool uses a method based on the Voronoi diagram to calculate the cavity. The calculation process of the tool is divided into two steps; firstly, the cavities of different time slices are calculated according to set parameters, and secondly, the correspondence between cavities and time is identified by via hierarchical clustering. The final output result is a cavity data set that changes with time, with other information such as cavity bottleneck, radius, length, and so on.  
For extracting the sequence of the molecular cavity, our first consideration is to calculate the set of amino acids around the molecular cavity. These amino acids are often referred to as cavity-lining amino acids. There are many ways to calculate cavity-lining amino acids. In most methods, the distance between two samples is set to 0.1 (A) on the centerline of the molecular cavity. In each sample, the four nearest atoms are detected. These four atoms are called cavity-lining atoms, and the amino acids to which they belong are labeled as molecular cavity-lining amino acids. In our work, the CAVER 3.0 [7] method is used to calculate the sequence of molecular cavities that need to be extracted. To take the sequence of the molecular cavity, users need to enter the list of spheres that form the molecular cavity and the list of surrounding amino acids, as well as additional information. The molecular cavity sphere with the largest radius relative to the surrounding atoms is located on the centerline of the cavity. The combination of molecular cavity spheres forms the approximate shape of the molecular cavity.