Genome pool strategy for structural coverage of protein families.

Even closely homologous proteins often have different crystallization properties and propensities. This observation can be used to introduce an additional dimension into crystallization trials by simultaneous targeting multiple homologs in what we call a "genome pool" strategy. We show that this strategy works because protein physicochemical properties correlated with crystallization success have a surprisingly broad distribution within most protein families. There are also "easy" and "difficult" families where this distribution is tilted in one direction. This leads to uneven structural coverage of protein families, with more "easy" ones solved. Increasing the size of the "genome pool" can improve chances of solving the "difficult" ones. In contrast, our analysis does not indicate that any specific genomes are "easy" or "difficult". Finally, we show that the group of proteins with known 3D structures is systematically different from the general pool of known proteins and we assess the structural consequences of these differences.

Prediction of signal peptides in protein sequences by neural networks.

We present here a neural network-based method for detection of signal peptides (abbreviation used: SP) in proteins. The method is trained on sequences of known signal peptides extracted from the Swiss-Prot protein database and is able to work separately on prokaryotic and eukaryotic proteins. A query protein is dissected into overlapping short sequence fragments, and then each fragment is analyzed with respect to the probability of it being a signal peptide and containing a cleavage site. While the accuracy of the method is comparable to that of other existing prediction tools, it provides a significantly higher speed and portability. The accuracy of cleavage site prediction reaches 73% on heterogeneous source data that contains both prokaryotic and eukaryotic sequences while the accuracy of discrimination between signal peptides and non-signal peptides is above 93% for any source dataset. As a consequence, the method can be easily applied to genome-wide datasets. The software can be downloaded freely from http://rpsp.bioinfo.pl/RPSP.tar.gz.

The challenge of protein structure determination--lessons from structural genomics.

The process of experimental determination of protein structure is marred with a high ratio of failures at many stages. With availability of large quantities of data from high-throughput structure determination in structural genomics centers, we can now learn to recognize protein features correlated with failures; thus, we can recognize proteins more likely to succeed and eventually learn how to modify those that are less likely to succeed. Here, we identify several protein features that correlate strongly with successful protein production and crystallization and combine them into a single score that assesses "crystallization feasibility." The formula derived here was tested with a jackknife procedure and validated on independent benchmark sets. The "crystallization feasibility" score described here is being applied to target selection in the Joint Center for Structural Genomics, and is now contributing to increasing the success rate, lowering the costs, and shortening the time for protein structure determination. Analyses of PDB depositions suggest that very similar features also play a role in non-high-throughput structure determination, suggesting that this crystallization feasibility score would also be of significant interest to structural biology, as well as to molecular and biochemistry laboratories.

XtalPred: a web server for prediction of protein crystallizability.

UNLABELLED: XtalPred is a web server for prediction of protein crystallizability. The prediction is made by comparing several features of the protein with distributions of these features in TargetDB and combining the results into an overall probability of crystallization. XtalPred provides: (1) a detailed comparison of the protein's features to the corresponding distribution from TargetDB; (2) a summary of protein features and predictions that indicate problems that are likely to be encountered during protein crystallization; (3) prediction of ligands; and (4) (optional) lists of close homologs from complete microbial genomes that are more likely to crystallize. AVAILABILITY: The XtalPred web server is freely available for academic users on http://ffas.burnham.org/XtalPred