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Published on: July 25, 2013
Never born proteins as a test case for ab initio protein structures prediction
Giovanni Minervini1, Giuseppe Evangelista, Fabio Polticelli
1Department of Biology, University Roma Tre, Viale G. Marconi 446, 00146 Rome, Italy.
Bioinformation
|February 25, 2009
Summary
Researchers explored "never born proteins" (NBPs) to test protein structure prediction software. Comparing 10,000 random protein sequences revealed software limitations but also significant prediction accuracy for these theoretical proteins.
Area of Science:
- Biochemistry
- Computational Biology
- Structural Biology
Background:
- The theoretical number of possible proteins exceeds natural proteins, creating a vast unexplored space of "never born proteins" (NBPs).
- Studying NBPs offers insights into the origins and unique properties of natural proteins.
- NBPs serve as an excellent benchmark for assessing the accuracy of *ab initio* protein structure prediction software.
Purpose of the Study:
- To evaluate the efficiency and limitations of *ab initio* protein structure prediction software.
- To compare predicted structures of NBPs generated using established computational models.
Main Methods:
- Generated 10,000 three-dimensional protein structures from completely random amino acid sequences.
- Utilized the ROSETTA and FOD models for random protein sequence generation.
- Compared the generated structures against predictions from *ab initio* software packages.
Main Results:
- The study identified limitations in current *ab initio* protein structure prediction software.
- A significant agreement was observed between predicted and generated structures for many NBPs.
- The findings highlight areas for improvement in computational protein modeling.
Conclusions:
- Current *ab initio* protein structure prediction tools have limitations when modeling NBPs.
- Despite limitations, these tools show promising accuracy in predicting structures for certain random protein sequences.
- NBPs provide a valuable testbed for refining and validating protein structure prediction algorithms.
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