PMC:4331677 / 29768-33033 JSONTXT

{"target":"https://pubannotation.org/docs/sourcedb/PMC/sourceid/4331677","sourcedb":"PMC","sourceid":"4331677","source_url":"https://www.ncbi.nlm.nih.gov/pmc/4331677","text":"Discussion and conclusion\nIn this work, we consider binding is a local event and emphasize the local information in target-ligand interaction prediction. We apply site-ligand interactions instead of target-ligand interactions and propose a chemical interpretable model to cover the site-ligand interactions. We first extract the ligand-binding sites from target-ligand complexes. Then we break the binding sites and ligands into fragments so that they can be encoded as fragment vectors based on target and ligand dictionary respectively. Finally, we assume that the fragments interactions determine the site-ligand interaction and propose a model, fragment interaction model (FIM), to generalize the assumption. The proposed model demonstrates higher AUC score (92%) with respect to two prevalence algorithms CS-PD (80%), BLM-NII (85%) and RF (85%). In addition, the fragment interaction network origined from FIM is chemical interpretable. Comparing to BLM-NII, RF and CS-PD model, it require crystal structure to extract local information (binding site) in FIM, which hinder the applying of FIM sometimes. However, with the increasing determination of protein crystal structures and the developing molecular modeling technique, we can model a 3D structure by computer, and extract the binding site.\nCompared with traditional target-based or ligand-based approaches, the proposed FIM method has the advantages of finding target candidates and ligand candidates simultaneously. Moreover, FIM can predict the interaction between previously unseen targets and ligand candidates. Different with other target-ligand based methods, our method emphasizes the basic chemical interactions between amine acids and ligand fragments, which is more general and could be applied beyond drugtarget interactions. Furthermore, we no longer represent the target as a whole but extract the ligand-binding sites from target-ligand complexes and apply the binding sites to describe the genomic space. For one hand, representing the genomic space by binding sites allows us provide site-ligand interaction prediction, which is important for multi-site targets. For another hand, the binding sites are local, which facilitate to achieve chemical interpretable model. Along this way, we break the binding sites and ligands into fragments, and regard the fragment interactions as genomic and chemical space interactions. We know clearly about how the genomic space interacts with chemical space under FIM.\nIn all, we highlight the local information during the binding process and attempt to figure out a clear relationship between the genomic and chemical spaces. The proposed model (FIM) applies the ligand binding sites as local information and views the binding site and ligand fragment interactions as genomic and chemical space interactions. The fragment interactions are straightforward and chemical interpretable, and the fragment interaction network reflect the chemical interactions. The comparison result shows that FIM outperforms other three approaches. The investigation on the role of global information shows that the local information dominate the predictive accuracy and integrating of the global information might promote the predictive ability to a very limited extent.","divisions":[{"label":"title","span":{"begin":0,"end":25}},{"label":"p","span":{"begin":26,"end":1301}},{"label":"p","span":{"begin":1302,"end":2482}}],"tracks":[]}