Heuristic energy landscape paving for protein folding problem in the three-dimensional HP lattice model

A Novel Branch-and-Bound Algorithm for the Protein Folding Problem in the 3D HP Model

The protein folding problem (PFP) is an important issue in bioinformatics and biochemical physics. One of the most widely studied models of protein folding is the hydrophobic-polar (HP) model introduced by Dill. The PFP in the three-dimensional (3D) lattice HP model has been shown to be NP-complete; the proposed algorithms for solving the problem can therefore only find near-optimal energy structures for most long benchmark sequences within acceptable time periods. In this paper, we propose a novel algorithm based on the branch-and-bound approach to solve the PFP in the 3D lattice HP model. For 10 48-monomer benchmark sequences, our proposed algorithm finds the lowest energies so far within comparable computation times than previous methods.

Hybrid method to solve HP model on 3D lattice and to probe protein stability upon amino acid mutations

BACKGROUND

Predicting protein structure from amino acid sequence is a prominent problem in computational biology. The long range interactions (or non-local interactions) are known as the main source of complexity for protein folding and dynamics and play the dominant role in the compact architecture. Some simple but exact model, such as HP model, captures the pain point for this difficult problem and has important implications to understand the mapping between protein sequence and structure.

RESULTS

In this paper, we formulate the biological problem into optimization model to study the hydrophobic-hydrophilic model on 3D square lattice. This is a combinatorial optimization problem and known as NP-hard. Particle swarm optimization is utilized as the heuristic framework to solve the hard problem. To avoid premature in computation, we incorporated the Tabu search strategy. In addition, a pulling strategy was designed to accelerate the convergence of algorithm based on the characteristic of native protein structure. Together a novel hybrid method combining particle swarm optimization, Tabu strategy, and pulling strategy can fold the amino acid sequences on 3D square lattice efficiently. Promising results are reported in several examples by comparing with existing methods. This allows us to use this tool to study the protein stability upon amino acid mutation on 3D lattice. In particular, we evaluate the effect of single amino acid mutation and double amino acids mutation via 3D HP lattice model and some useful insights are derived.

CONCLUSION

We propose a novel hybrid method to combine several heuristic strategies to study HP model on 3D lattice. The results indicate that our hybrid method can predict protein structure more accurately and efficiently. Furthermore, it serves as a useful tools to probe the protein stability on 3D lattice and provides some biological insights.