On the complexity of protein folding (extended abstract)
STOC '98 Proceedings of the thirtieth annual ACM symposium on Theory of computing
Using Constraint Programming and Local Search Methods to Solve Vehicle Routing Problems
CP '98 Proceedings of the 4th International Conference on Principles and Practice of Constraint Programming
Software—Practice & Experience
Protein Structure Prediction with Large Neighborhood Constraint Programming Search
CP '08 Proceedings of the 14th international conference on Principles and Practice of Constraint Programming
International Journal of Data Mining and Bioinformatics
Exploring protein fragment assembly using CLP
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
A filtering technique for fragment assembly- based proteins loop modeling with constraints
CP'12 Proceedings of the 18th international conference on Principles and Practice of Constraint Programming
Optimal valve placement in water distribution networks with CLP(FD)
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
A constraint solver for flexible protein models
Journal of Artificial Intelligence Research
Hi-index | 0.00 |
The paper investigates a novel approach, based on Constraint Logic Programming (CLP), to predict the 3D conformation of a protein via fragments assembly. The fragments are extracted by a preprocessor—also developed for this work—from a database of known protein structures that clusters and classifies the fragments according to similarity and frequency. The problem of assembling fragments into a complete conformation is mapped to a constraint solving problem and solved using CLP. The constraint-based model uses a medium discretization degree Cα-side chain centroid protein model that offers efficiency and a good approximation for space filling. The approach and adapts existing energy models to the protein representation used and applies a large neighboring search strategy. The results shows the feasibility and efficiency of the method. The declarative nature of the solution allows to include future extensions, e.g., different size fragments for better accuracy.