Advances in computational protein design.
Computational protein design continues to experience a variety of methodological advances. Several improvements have been suggested for the objective functions used to quantify sequence/structure compatibility. Disparate design strategies based upon dead-end elimination, simulated annealing and statistical design have each recently yielded striking successes involving de novo designed proteins with sizes on the order of 100 residues or greater. Such methods may be used to design new proteins, as well as to redesign natural proteins to facilitate structural and biophysical studies.
Makineni Theoretical Laboratories and Department of Chemistry, University of Pennsylvania, 231 South 34th Street, Philadelphia, Pennsylvania 19104, USA.
Sheldon
S
Park
Xi
X
Yang
Jeffery G
JG
Saven
D019295:Computational Biology / Q000379:methods*; D017076:Computer-Aided Design; D015233:Models, Statistical; D015202:Protein Engineering / Q000379:methods*; D017510:Protein Folding; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism; D013329:Structure-Activity Relationship
D011506:Proteins