Researchers in George Church's laboratory at Harvard Medical School have developed a computational method for predicting post-translational modifications that they said cannot be identified with current with mass spectrometry-based techniques.

Using their strategy, described in a research paper in the current issue of Molecular & Cellular Proteomics, the authors were able to identify novel phosphorylation and acetylation sites across several organisms.

They added that the strategy also proved that some PTMs occur across different species, something that has mostly been only assumed.

The strategy devised by lead author Daniel Schwartz and co-researchers uses an algorithm called motif-x to extract motifs from a sequence database. They used motif-x to determine phosphorylation motifs in yeast, fly, mouse, and man; and lysine acetylation motifs in man. They then scanned the motifs against proteomic sequence data with a new algorithm called scan-x to predict other potential modifications sites.

Scwhartz developed the motif-x method in 2005 while he was still doing his graduate work. Now a post-doc in Church's lab, Schwartz said the algorithm was originally devised as a way to take sequence data from all the studies that were coming out about phosphorylation sites and "getting potential motifs out … and to maybe understand the kinases that are active [and] to do this in automated fashion."

"It was going to extract out over-represented patterns from mass-spec experiments that had thousands upon thousands of phosphorylation sites and to do it in an automated fashion," Schwartz told ProteoMonitor this week.

Once that was achieved, the next step was to take the motifs and scan them against a proteome "to make additional sites that might be phosphorylated based on your initial dataset," Schwartz said. And that's where scan-x comes in: By locating and scoring motifs in a protein sequence, scan-x can predict PTMs.

For their MCP study, Schwartz and his colleagues concentrated on phosphorylation data, "but it certainly doesn't have to be limited to that," Schwartz said. "It can scan anything that goes into motif-x and extract out motifs."

Comparing their approach on phosphorylation prediction against two recently published tools, the author reported "substantial improvements" in both sensitivity and specificity.

NetPhosYeast is an artificial neural network-based serine and threonine phosphorylation predictor specifically for yeast. In their work using NetPhosYeast, the researchers achieved a sensitivity of 93.7 percent and a specificity of 39.2 percent.

Phosida is a tool aimed at human and mouse serine and threonine phosphorylation prediction using a support vector machine strategy. Using it against a dataset of human serine and threonine, Schwartz and his colleagues achieved a sensitivity of 12.2 percent and a specificity value of 97.3 percent. In the mouse dataset, sensitivity rose to 22.2 percent at a specificity of 97.1 percent.

By comparison, they reported a sensitivity of 23.3 percent and a specificity of 97.3 percent with their computational approach using motif-x and scan-x.

They also compared their method against Scansite, a program that uses position-specific scoring matrices "derived experimentally for individual kinases to make phosphorylation predictions," and is one of the most commonly used tools for phosphorylation predictions, the authors wrote.

Applied against the human serine and threonin phosphorylation test sets in their experiment, Scansite recorded a sensitivity of 13.2 percent at a specificity of 97.6 percent. Meanwhile, using their own method, Schwartz and his co-researchers achieved a sensitivity of 21.4 percent at "the equivalent specificity."

They then compared their human tyrosine phosphorylation predictions against the Scansite tyrosine kinase prediction tool at medium stringency: Scansite achieved sensitivity of 5.1 percent at a specificity of 97.5 percent. Scan-x yielded a 9.1-percent sensitivity at a 97.5-percent specificity.