scholarly journals Lessons in Molecular Recognition:  The Effects of Ligand and Protein Flexibility on Molecular Docking Accuracy

2004 ◽  
Vol 47 (1) ◽  
pp. 45-55 ◽  
Author(s):  
Jon A. Erickson ◽  
Mehran Jalaie ◽  
Daniel H. Robertson ◽  
Richard A. Lewis ◽  
Michal Vieth
Keyword(s):  
Molecular Docking ◽  
Molecular Recognition ◽  
Protein Flexibility ◽  
Docking Accuracy

Importance of protein flexibility in molecular recognition: a case study on Type-I1/2 inhibitors of ALK

2018 ◽  
Vol 20 (7) ◽  
pp. 4851-4863 ◽  
Author(s):  
Xiaotian Kong ◽  
Huiyong Sun ◽  
Peichen Pan ◽  
Feng Zhu ◽  
Shan Chang ◽  
...  
Keyword(s):  
Molecular Recognition ◽  
Anaplastic Lymphoma Kinase ◽  
Protein Flexibility ◽  
Anaplastic Lymphoma ◽  
Promising Target

Anaplastic lymphoma kinase (ALK) has been regarded as a promising target for the therapy of various cancers.

Lessons in Molecular Recognition. 2. Assessing and Improving Cross-Docking Accuracy

2007 ◽  
Vol 47 (6) ◽  
pp. 2293-2302 ◽  
Author(s):  
Jeffrey J. Sutherland ◽  
Ravi K. Nandigam ◽  
Jon A. Erickson ◽  
Michal Vieth
Keyword(s):  
Molecular Recognition ◽  
Cross Docking ◽  
Docking Accuracy

PARTICLE SWARM OPTIMIZATION ON FLEXIBLE DOCKING

2012 ◽  
Vol 05 (05) ◽  
pp. 1250044 ◽  
Author(s):  
YU LIU ◽  
WENTAO LI ◽  
RUIXIN MA
Keyword(s):  
Particle Swarm Optimization ◽  
Molecular Docking ◽  
Optimization Problem ◽  
Particle Swarm ◽  
Scoring Function ◽  
Optimization Method ◽  
Flexible Docking ◽  
Swarm Optimization ◽  
Lamarckian Genetic Algorithm ◽  
Docking Accuracy

Molecular docking is an important tool in screening large libraries of compounds to determine the interactions between potential drugs and the target proteins. The molecular docking problem is how to locate a good conformation to dock a ligand to the large molecule. It can be formulated as a parameter optimization problem consisting of a scoring function and a global optimization method. Many docking methods have been developed with primarily these two parts varying. In this paper, a variety of particle swarm optimization (PSO) variants were introduced to cooperate with the semiempirical free energy force field in AutoDock 4.05. The search ability and the docking accuracy of these methods were evaluated by multiple redocking experiments. The results demonstrate that PSOs were more suitable than Lamarckian genetic algorithm (LGA). Among all of the PSO variants, FIPS takes precedence over others. Compared with the four state-of-art docking methods-GOLD, DOCK, FlexX and AutoDock with LGA, AutoDock cooperated with FIPS is more accurate. Thus, FIPS is an efficient PSO variant which has promising prospects that can be expected in the application to virtual screening.

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