Binding affinity prediction using a nonparametric regression model based on physicochemical and structural descriptors of the nano-environment for protein-ligand interactions.

Enter multiple e-mails separated by comma.

imagem

Author(s): BORRO, L.; YANO, I. H.; MAZONI, I.; NESHICH, G.

Summary: We propose a new empirical scoring function for binding affinity prediction modeled based on physicochemical and structural descriptors that characterize the nano-environment that encompass both ligand and binding pocket residues. Our hypothesis is that a more detailed characterization of protein-ligand complexes in terms of describing nano-environment as precisely as possible can lead to improvements in binding affinity prediction.

Publication year: 2016

Types of publication: Abstract in annals or event proceedings

Unit: Embrapa Digital Agriculture

Keywords: Binding affinity prediction model, Binding properties, Complexo proteína-ligante, Empiric nonparametric predictive model, Interações entre proteína e ligantes, Modelagem, Modelos, Models, Plataforma Sting, Protein-ligand complex

Further information Download publication (PDF)

Observation

Some of Embrapa's publications are published as ePub files. To read them, use or download one of the following free software options to your computer or mobile device. Android: Google Play Books; IOS: iBooks; Windows and Linux: Calibre.

 

Access other publications

Access the Agricultural Research Database (BDPA) to consult Embrapa's full library collection and records.
Visit Embrapa Bookstore to purchase books and other publications sold by Embrapa.