tex Lv 1
This week we have released some new tools in Foldit for structure prediction. The update comprises several tools and some new levels that teach you how to use these tools. The rest of this blog post is to try and give an overview of why these tools are useful in structure prediction.
Often when we try to predict the structure of a protein, that protein has a similar amino acid sequence to one or more proteins of known structure. These known structures are called templates, as they can be used as starting points for predicting the structure of new proteins. We call this approach to structure prediction comparative modeling, as it is based on comparing a protein sequence to existing structures in order to build models. These tools are based on the same code that is being used within the Baker Lab for comparative modeling and structure refinement, so that comparisons between automated Rosetta protocols and manual structure prediction using Foldit are more fair now.
We compare protein sequences using a tool called a sequence alignment. A sequence alignment is simply a mapping of residues from one sequence to another. For comparative modeling the sequence alignment tells you which residues are analogous between the query sequence and the template sequence. In the context of comparative modeling, the sequence alignment tells you which sections you should copy from a template structure and which residues you should try to optimize more freely. By manipulating the sequence alignment, you can get different starting structures which you can then refine using the existing tools in Foldit.
In this post there are some videos that show you what the new tools and visualizations look like.
Many of the successful predictions from CASP8 were comparative modeling puzzles, but sequence alignment for these targets were done by scientists in the Baker Lab and left fixed for the Foldit players. With the latest Foldit tools for comparative modeling, you can manipulate the sequence alignment and use it to generate models interactively. Our hope in releasing these tools is that by being able to explicitly manipulate the sequence alignment and refine the resulting models, the players can both understand structure prediction better and build good comparative models of proteins using Foldit.