Servers for protein structure prediction.
The 1990s cultivated a generation of protein structure human predictors. As a result of structural genomics and genome sequencing projects, and significant improvements in the performance of protein structure prediction methods, a generation of automated servers has evolved in the past few years. Servers for close and distant homology modeling are now routinely used by many biologists, and have already been applied to the experimental structure determination process itself, and to the interpretation and annotation of genome sequences. Because dozens of servers are currently available, it is hard for a biologist to know which server(s) to use; however, the state of the art of these methods is now assessed through the LiveBench and CAFASP experiments. Meta-servers--servers that use the results of other autonomous servers to produce a consensus prediction--have proven to be the best performers, and are already challenging all but a handful of expert human predictors. The difference in performance of the top ten autonomous (non-meta) servers is small and hard to assess using relatively small test sets. Recent experiments suggest that servers will soon free humans from most of the burden of protein structure prediction.
Buffalo Center of Excellence in Bioinformatics, and Department of Computer Science and Engineering, State University of New York at Buffalo, Buffalo, NY 14260, USA. df33@cse.buffalo.edu
Daniel
D
Fischer
D019295:Computational Biology; D030562:Databases, Protein; D020407:Internet; D011487:Protein Conformation; D012984:Software