We discuss the use of the Singular Value Decomposition as a model reduction technique in Protein Tertiary Structure prediction, alongside to the uncertainty analysis associated to the tertiary protein predictions via Particle Swarm Optimization (PSO). The algorithm presented in this paper corresponds to the category of the decoy-based modelling, since it first finds a good protein model located in the low energy region of the protein energy landscape, that is used to establish a three-dimensional space where the free-energy optimization and search is performed via an exploratory version of PSO. The ultimate goal of this algorithm is to get a representative sample of the protein backbone structure and the alternate states in an energy region equivalent or lower than the one corresponding to the protein model that is used to establish the expansion (model reduction), obtaining as result other protein structures that are closer to the native structure and a measure of the uncertainty in the protein tertiary protein reconstruction. The strength of this methodology is that it is simple and fast, and serves to alleviate the ill-posed character of the protein structure prediction problem, which is very highly dimensional, improving the results when it is performed in a good protein model of the low energy region. To prove this fact numerically we present the results of the application of the SVD-PSO algorithm to a set of proteins of the CASP competition whose native’s structures are known.
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Prediction of Protein Tertiary Structure via Regularized Template Classification Techniques
We discuss the use of the regularized linear discriminant analysis (LDA) as a model reduction technique combined with particle swarm optimization (PSO) in protein tertiary structure prediction, followed by structure refinement based on singular value decomposition (SVD) and PSO. The algorithm presented in this paper corresponds to the category of template-based modeling. The algorithm performs a preselection of protein templates before constructing a lower dimensional subspace via a regularized LDA. The protein coordinates in the reduced spaced are sampled using a highly explorative optimization algorithm, regressive–regressive PSO (RR-PSO). The obtained structure is then projected onto a reduced space via singular value decomposition and further optimized via RR-PSO to carry out a structure refinement. The final structures are similar to those predicted by best structure prediction tools, such as Rossetta and Zhang servers. The main advantage of our methodology is that alleviates the ill-posed character of protein structure prediction problems related to high dimensional optimization. It is also capable of sampling a wide range of conformational space due to the application of a regularized linear discriminant analysis, which allows us to expand the differences over a reduced basis set.
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- Award ID(s):
- 1661391
- NSF-PAR ID:
- 10172567
- Date Published:
- Journal Name:
- Molecules
- Volume:
- 25
- Issue:
- 11
- ISSN:
- 1420-3049
- Page Range / eLocation ID:
- 2467
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/molecules25112467
