# Computer-Aided Drug Design Boosts RAS Inhibitor Discovery
## 3 Application of CADD Methods in the Development of RAS Inhibitors
### Determination of the Target Protein Structure  
To analyze the structural features of proteins, discover potential drug targets, screen potential drugs, and accelerate drug development, researchers need to determine the structures of target proteins. Many methods have been proposed for this purpose. The most traditional among them are nuclear magnetic resonance spectroscopy (NMR), X-ray crystallography, and cryo-electron microscopy (cryo-EM). Currently, there are approximately 400 open-access RAS structures in the Protein Data Bank (PDB) database [47], most of which were obtained using these methods. Although these approaches are widely used and have high measurement accuracy, they are time consuming and expensive. Therefore, the newly developed CADD method is crucial for predicting the structure of target proteins and can be used to discover potential drug binding sites. Homology modeling is a common method for estimating the structure of target proteins and evaluating structural properties based on the homologous sequence of proteins [48]. Many applications provide homology modeling, such as Modeller and Swiss-Model [49]. RAS-association domain family (RASSF) 2 is a tumor suppressor protein interacting with KRAS, whose epigenetic inactivation through promoter hypermethylation is frequently detected in multiple mutant RAS-containing primary tumors. Since RASSF2 acts as a proapoptotic KRAS-specific effector, some RAS inhibitors take effect through its overexpression to promote apoptosis and cell cycle arrest [50]. The typical amino acid sequences were retrieved from the Uniprot database by [PERSON] et al. [51]. Six templates were then observed by searching for templates based on the query sequence using the NCBI Basic Local Alignment Search Tool (PSI-BLAST). Finally, the 3D structure of the target protein was generated by comparative modeling using spatial constraint-based Modeller (9V15), I-Tasser, SwissModel, 3D-Jigsaw, and ModWeb, with quality checks on the ERRAT protein structure verification server [51]. Compared to traditional methods such as X-ray crystallography and NMR, homologous modeling has the advantage of being cost-effective and time-saving in predicting the 3D structure of proteins. However, the obtained 3D model is often improper and inapplicable when the homologous sequence is inadequate [48]. MD simulation is excellent for checking the quality of protein models by monitoring the atomic motion in real-time using [PERSON]'s equation of motion. A reliability structure with the optimal statistical eigenvalues of molecular dynamics and thermodynamics is then determined [52]. For example, [PERSON] et al. used two sets of classical MD simulation to determine the stability of the predictive model to be optimized [53]. However, the MD simulation requires high computational power to predict the protein structure in a given force field with multiple intramolecular interactions. The template-based protein-protein interaction complex structure prediction algorithm, PRISM, is useful for predicting the assembly of polymers or complex protein structures. PRISM compares the known protein-protein interface structure data with the protein interactions to be measured to derive the shape of the protein interaction region of interest [54], which drastically reduces the computational effort and research cost compared to ab initio methods. To study GTP-dependent KRAS dimerization, [PERSON] et al. [55] discovered two primary dimer interfaces dependent on the binding properties of KRAS to GTP by performing a series of conventional experiments including dynamic light scattering (DLS), isothermal calorimetry (ITC), microscale thermophoresis (MST), fluorescence spectroscopy, Forster resonance energy transfer (FRET), and NMR. From then on, PRISM was used to predict the dimer structure. In recent years, the burgeoning field of artificial intelligence (AI) has shown that it is possible to accurately predict protein structure with the increasing abundance of protein databases.