The researchers for the Human Proteome Projects have published a paper in Genome Research entitled The proteome folding project: proteome-scale prediction of structure and function.

The paper describes how they were able to use the computation results from World Community Grid to predict protein structure and protein function. Protein structure determines the function of proteins in life processes. Knowing the structure of these proteins helps scientists studying biological and medical processes and can, for example, hasten the process of discovering treatments for diseases. The human genome as well as 93 other genomes of importance to humans were processed.

The paper describes the methods used to validate the accuracy of their predictions, which are now publicly available in a data base for all scientists to use.


More. . .

Paper published in the journal Genome Biology and Evolution using Human Proteome Folding project results


Paper Title:

The Plant Proteome Folding Project: Structure and Positive Selection in Plant Protein Families


Lay Person Abstract:

Melissa Pentony et al. have presented work considering components of proteins exhibiting faster-than-average evolution in the proteomes of five major plant species, including rice (Oryza sativa) and Arabidopsis thaliana (an important model organism for plant study). They describe new information on the relationship between evolution and protein structure in plants.

The World Community Grid has contributed to this study by providing a much more structurally complete view of unknown and understudied proteins from five plant families than was previously available. The results from the Human Proteome Folding project produced 29,202 protein structures contributing to this project, of which 4,764 were very high-confidence. This should eventually assist agricultural scientists to better understand important plant and food crops, how to breed them for disease resistance, better nutrition and to better handle environmental stress.


More. . .

World Community Grid Post - HPF2 Update, Fall/Winter 2012


Holiday greetings to all you crunchers (and other interested parties!) out there - here's an update on what we're working on, to keep everyone motivated through the long winter ahead (or, for those of you in the southern hemisphere… the hot summer?)

As mentioned in the last status update, we have been working feverishly on improving, testing, and generally developing a method to predict protein and gene function using custom machine learning techniques. As we mentioned last time, machine learning is a concept that has been around for quite some time, but has in the past few years become the hot topic in computer science and math, and generally any field that strives to predict features and outcomes in some system (biology and finance come to mind…).

To avoid digressing and poorly covering what some truly brilliant others have already done, I encourage you to check out Andrew Ng's Machine Learning course (available free from Stanford Uni. online) if interested - even watching the seven-minute Welcome and What is Machine Learning? lectures will give you a great idea of what this topic is about and why it's so valuable. They're available here: https://www.coursera.org/course/ml (just click Preview).


More. . .