Protein structure prediction on 2D square HP lattice with revised fitness function

In order to understand the structure and folding of proteins, Hydrophobic-Polar (HP) model on 2D square lattice is one of the most explored models but parity problem of square lattice make it inefficient for biological applications. This work is dedicated to solve parity issues in 2D square lattice model. This work proposes a revised energy function and presents a case study for protein structure prediction (PSP). A novel approach to evaluate the structure modeling and protein folding problem over HP model is presented in this paper. This evaluation approach may enhance the quality and effectiveness of the computational approaches developed to address the PSP at coarse level.

[1]  R Unger,et al.  Genetic algorithms for protein folding simulations. , 1992, Journal of molecular biology.

[2]  Scott E. Decatur.  Protein Folding in the Generalized Hydrophobic-Polar Model on the Triangular Lattice , 1996 .

[3]  Jyh-Jong Tsay,et al.  An effective evolutionary algorithm for protein folding on 3D FCC HP model by lattice rotation and generalized move sets , 2013, Proteome Science.

[4]  Amarda Shehu,et al.  Probabilistic Search and Energy Guidance for Biased Decoy Sampling in Ab Initio Protein Structure Prediction , 2013, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[5]  N. Gopalakrishna Kini,et al.  A Comparative Study of Various Meta-Heuristic Algorithms for Ab Initio Protein Structure Prediction on 2D Hydrophobic-Polar Model , 2015, SocProS.

[6]  Glennie Helles,et al.  A comparative study of the reported performance of ab initio protein structure prediction algorithms , 2008, Journal of The Royal Society Interface.

[7]  Yang Zhang,et al.  Ab initio protein structure assembly using continuous structure fragments and optimized knowledge‐based force field , 2012, Proteins.

[8]  R A Sayle,et al.  RASMOL: biomolecular graphics for all. , 1995, Trends in biochemical sciences.

[9]  Cheng-Jian Lin,et al.  An effective hybrid of hill climbing and genetic algorithm for 2D triangular protein structure prediction , 2011, Proteome Science.

[10]  Frank Thomson Leighton,et al.  Protein folding in the hydrophobic-hydrophilic (HP) is NP-complete , 1998, RECOMB '98.

[11]  David Corne,et al.  An Introduction to Bioinformatics for Computer Scientists , 2003 .

[12]  Shuai Cheng Li,et al.  The difficulty of protein structure alignment under the RMSD , 2013, Algorithms for Molecular Biology.

[13]  Vincenzo Cutello,et al.  An Immune Algorithm for Protein Structure Prediction on Lattice Models , 2007, IEEE Transactions on Evolutionary Computation.

[14]  Helio J. C. Barbosa,et al.  A multiple minima genetic algorithm for protein structure prediction , 2014, Appl. Soft Comput..

[15]  Holger H. Hoos,et al.  An ant colony optimisation algorithm for the 2D and 3D hydrophobic polar protein folding problem , 2005, BMC Bioinformatics.

[16]  Mohammad Sohel Rahman,et al.  On the protein folding problem in 2D-triangular lattices , 2013, Algorithms for Molecular Biology.

[17]  Rolf Backofen,et al.  A Constraint-Based Approach to Fast and Exact Structure Prediction in Three-Dimensional Protein Models , 2006, Constraints.

[18]  Hans-Joachim Böckenhauer,et al.  Protein folding in the HP model on grid lattices with diagonals , 2007, Discret. Appl. Math..

[19]  Pascal Van Hentenryck,et al.  On Lattice Protein Structure Prediction Revisited , 2011, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[20]  Mohammad Sohel Rahman,et al.  Approximation Algorithms for Three Dimensional Protein Folding , 2016, WALCOM.

[21]  Daniel J. Rigden,et al.  From Protein Structure to Function with Bioinformatics , 2009 .

[22]  Sitao Wu,et al.  Ab Initio Protein Structure Prediction , 2009 .

[23]  Erich Bornberg-Bauer,et al.  Chain growth algorithms for HP-type lattice proteins , 1997, RECOMB '97.

[24]  Steven Skiena,et al.  Local Rules for Protein Folding on a Triangular Lattice and Generalized Hydrophobicity in the HP Model , 1997, J. Comput. Biol..