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In proteins, shape matters

A meta-genomic database is now helping fill in 10 percent of previously unknown protein shapes that control growth and influence mobility. They act as catalysts, or transport and store molecules.


Proteins are the important molecules forming a cell's structure and carrying out its functions. Made up of long amino acid chains, one-dimensional amino acid sequences look meaningless on paper. Yet when viewed in three dimensions, researchers can see what a protein's structure is and how the way it folds determines its function.


Close to 15,000 protein families - groups that share an evolutionary origin - are in the Pfam database.


Almost a third (4,752) of these protein families, have at least one protein in each already with an experimentally determined structure. For another third (4,886) of those protein families, comparative models can be built with some degree of confidence. But, the last third (5,211) of the protein families in the database has no structural information.

In the January 20, 2017 issue of Science, a team led by David Baker of the University of Washington (USA) in collaboration with researchers at the U.S. Department of Energy Joint Genome Institute (DOE JGI), a DOE Office of Science User Facility, reports that structural models are now being generated for 614 or 12 percent of those protein families without structural information.

"That this could be accomplished using computational modeling methods was not at all apparent 5 years ago," the team noted in their paper.


This accomplishment was possible with a collaboration between Baker lab and its protein structure prediction server, Rosetta, and the Integrated Microbial Genomes (IMG) system run by the DOE JGI, which analyzed the metagenomic sequences publicly available.


"A large number of protein families (Pfam) have low numbers of sequences," said study first author Sergey Ovchinnikov, a graduate student in the Baker lab. "This resulted in two consequences: 1) nobody cared about these families (since they were small); and, 2) co-evolution methods could not be applied to study them.

"With metagenomics, we found that some of these neglected families — with only a handful of sequences (so far) — can now become as large as some of the most studied ones, when metagenomics data are taken into account! Moreover, we can offer a 3D model of a representative sequence from the family. We hope this will spark interest in some of these families."


Armed with genome sequences, researchers have been able to identify sets of amino acids that evolve simultaneously, even when nowhere near each other on an unfolded DNA chain.

This suggests these same amino acids are neighbors on a folded protein, hinting to a protein's structure.

Proximity suggests functional coordination with natural selection acting on function, which can favor not just one amino acid but all in a set.


Nikos Kyrpides, DOE JGI Prokaryote Super Program head, feels the collaboration between the Baker lab and the DOE JGI allowed their teams to uncover a powerful way to predict structures and structural alignments: "Such efforts were previously restricted to protein families generated from sequences found on the isolate genome only. These genomes make up about 200 million sequences. As expected, when we added our metagenomics data — harnessing the 5 billion assembled metagenome sequences available on our IMG/M database — we were able to dramatically increase the coverage of many known protein families."

Kyrpides would like to encourage another kind of collaboration: "People came to us because we maintain the largest integration of assembled metagenomes. Application of such tools to our data is a great example of how the larger community can use JGI resources for discovery. We would like to see more success stories like this one through new Data Science between JGI and the National Energy Research Scientific Computing Center (NERSC)."


JGI-NERSC Microbiome Data Science will enable state-of-the-art computational genomics and metagenomics research to help translate genomic sequence information into biological discovery.


Abstract
Despite decades of work by structural biologists, there are still ~5200 protein families with unknown structure outside the range of comparative modeling. We show that Rosetta structure prediction guided by residue-residue contacts inferred from evolutionary information can accurately model proteins that belong to large families and that metagenome sequence data more than triple the number of protein families with sufficient sequences for accurate modeling. We then integrate metagenome data, contact-based structure matching, and Rosetta structure calculations to generate models for 614 protein families with currently unknown structures; 206 are membrane proteins and 137 have folds not represented in the Protein Data Bank. This approach provides the representative models for large protein families originally envisioned as the goal of the Protein Structure Initiative at a fraction of the cost.

The U.S. Department of Energy Joint Genome Institute, a DOE Office of Science User Facility at Lawrence Berkeley National Laboratory, is committed to advancing genomics in support of DOE missions related to clean energy generation and environmental characterization and cleanup. DOE JGI, headquartered in Walnut Creek, Calif., provides integrated high-throughput sequencing and computational analysis that enable systems-based scientific approaches to these challenges. Follow @doe_jgi on Twitter.

DOE's Office of Science is the largest supporter of basic research in the physical sciences in the United States, and is working to address some of the most pressing challenges of our time. For more information, please visit science.energy.gov.
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Jan 25, 2017   Fetal Timeline   Maternal Timeline   News   News Archive   



Left: Metagenomic sequences: Each row is a different sequence (different colors are amino acid groups). Each position (or column) is compared to all other positions to detect patterns of co-evolution.

Bottom: Strength of co-evolving gene residues is shown as blue dots and colored lines.
The goal is to make a structure that reflects as many of these contacts as possible.

Right: a cartoon of the protein structure predicted. The protein domain shown is from
the Pfam DUF3794, which is part of a Spore coat assembly protein SafA.

Image Credit: Pic of Great Boiling Spring by Brian Hedlund, UNLV.
Protein structure and composite image by Sergey Ovchinnikov, UW

 


 


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