2024-02-23T04:46:04Z
https://ir.soken.ac.jp/oai
oai:ir.soken.ac.jp:00005328
2023-06-20T14:16:45Z
1:440
Estimation of statistical binding properties of ligand population during in vitro selection based on population dynamics theory.
Estimation of statistical binding properties of ligand population during in vitro selection based on population dynamics theory.
AITA, Takuyo
NISHIGAKI, Koichi
HUSIMI, Yuzuru
metadata only access
© 2013 Elsevier Inc. All rights reserved.
Computer Simulation
In Vitro Techniques
Ligands
Models, Chemical
Peptides
Thermodynamics
During in vitro selection process, it is very valuable to monitor the binding properties of the ligand population in real time, particularly the population average of the association constant in the population. If this monitoring can be realized, the selection process can be controlled in a rational way. In this paper, we present a simple method to estimate the binding properties of the ligand population during in vitro selection. The framework of the method is as follows. First, the number of all the collected ligand molecules, which are eluted after incubation and washing, is measured. Ideally, this number corresponds to the number of all the ligand molecules bound with the target-receptor or other materials in a test tube. This measurement is performed through several successive rounds of selection. Second, the measured numbers of molecules are subjected to a theoretical analysis, based on the mathematical theory of population dynamics in the selection process. Then, we can estimate the probability density of the binding free energy in the ligand population. The validity of our method was confirmed by several computer simulations based on a physicochemical model.
American Elsevier / New York
2014-01
eng
journal article
https://ir.soken.ac.jp/records/5328
24239675
http://dx.doi.org/10.1016/j.mbs.2013.10.012
10.1016/j.mbs.2013.10.012
http://www.sciencedirect.com/science/journal/00255564
Mathematical Biosciences ＜ELSEVIER ScienceDirect＞
AA11534002
0025-5564
Mathematical biosciences
Mathematical biosciences
247
59
68