Optimization of minimum set of protein-DNA interactions: a quasi exact solution with minimum over-fitting.
BACKGROUND
A major limitation in modeling protein interactions is the difficulty of assessing the over-fitting of the training set. Recently, an experimentally based approach that integrates crystallographic information of C2H2 zinc finger-DNA complexes with binding data from 11 mutants, 7 from EGR finger I, was used to define an improved interaction code (no optimization). Here, we present a novel mixed integer programming (MIP)-based method that transforms this type of data into an optimized code, demonstrating both the advantages of the mathematical formulation to minimize over- and under-fitting and the robustness of the underlying physical parameters mapped by the code.


RESULTS
Based on the structural models of feasible interaction networks for 35 mutants of EGR-DNA complexes, the MIP method minimizes the cumulative binding energy over all complexes for a general set of fundamental protein-DNA interactions. To guard against over-fitting, we use the scalability of the method to probe against the elimination of related interactions. From an initial set of 12 parameters (six hydrogen bonds, five desolvation penalties and a water factor), we proceed to eliminate five of them with only a marginal reduction of the correlation coefficient to 0.9983. Further reduction of parameters negatively impacts the performance of the code (under-fitting). Besides accurately predicting the change in binding affinity of validation sets, the code identifies possible context-dependent effects in the definition of the interaction networks. Yet, the approach of constraining predictions to within a pre-selected set of interactions limits the impact of these potential errors to related low-affinity complexes.


BACKGROUND
Supplementary data are available at Bioinformatics online.
Temiz
N A
NA
Department of Computational Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15260, USA.
Trapp
A
A
Prokopyev
O A
OA
Camacho
C J
CJ
D000595:Amino Acid Sequence; D001483:Base Sequence; D001665:Binding Sites; D019295:Computational Biology / Q000379:methods*; D004247:DNA / Q000737:chemistry* / Q000378:metabolism; D008958:Models, Molecular; D008969:Molecular Sequence Data; D009154:Mutation; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism*; D016335:Zinc Fingers
D011506:Proteins; D004247:DNA