Avdesh Mishra, Md Tamjidul Hoque email: {amishra2, thoque}@uno.edu Next Generation Evolutionary Sampling and Energy Function Guided Ab Initio Protein Structure Prediction Example of 3DIGARS-PSP modeling results on known Hard E. Coli and Protease Inhibitor proteins Avdesh Mishra, Md Tamjidul Hoque email: {amishra2, thoque}@uno.edu Department of Computer Science University of New Orleans, LA, USA The confirmation of a protein is vital to understand the function it performs within the cell. Towards this goal, we developed a computer program that applies a memory assisted evolutionary algorithm to sample the energy hyper-surface of the protein folding process, searching for the global minimum or the native fold of the protein. Sampling of the energy hyper-surface of the protein is achieved by novel mutation and crossover operations based on angular rotation and translation capabilities. Furthermore, the crossover operations in current generation are enhanced by the use of the best parents selected from previous generations. In addition, we employ a knowledge-based novel energy function, 3DIGARS3.0, which can differentiate the native structure that corresponds to the most thermodynamically stable state, compare to the possible decoy structures most effectively. The 3DIGARS3.0 energy function is an optimized combination of crucial properties such as hydrophobic versus hydrophilic, sequence-specific predicted accessibility and ubiquitous phi-psi characterization. Ongoing Research Effective use of Ramachandran Plot Effective initialization and use of associated memory Development of new operator to implement move sets Introduction Figure 1 | Cysteine Protease Inhibitor (PDB ID: 1nyc); towards left – superposition of 3DIGARS-PSP model on native (initial seeds from Rosetta); towards right – superposition of top Rosetta model (based on TMScore) on native. Figure 2 | E. Coli protein (PDB ID: 1pohA); towards left – superposition of 3DIGARS-PSP model on native (initial seeds from Rosetta); towards right – superposition of top Rosetta model (based on TMScore) on native. Figure 3 | E. Coli protein (PDB ID: 1pohA); towards left – superposition of 3DIGARS-PSP model on native (initial seeds from I-Tasser); towards right – superposition of top I-Tasser model (based on TMScore) on native. Figure 4 | E. Coli protein (PDB ID: 2z9hA); towards left – superposition of 3DIGARS-PSP model on native (initial seeds from Rosetta); towards right – superposition of top Rosetta model (based on TMScore) on native. Figure 5 | E. Coli protein (PDB ID: 2z9hA); towards left – superposition of 3DIGARS-PSP model on native (initial seeds from I-Tasser); towards right – superposition of top I-Tasser model (based on TMScore) on native. Figure 6 | E. Coli protein (PDB ID: 2p7vA); towards left – superposition of 3DIGARS-PSP model on native (initial seeds from Rosetta); towards right – superposition of top Rosetta model (based on TMScore) on native. Methods Backbone Models Dataset of 4332 Protein Structures Initialize Population for GA using Single Point Angular Mutation Obtain Secondary Structure (SS) and Φ, Ψ Angles using DSSP Save Best Model in Memory Figure 7 | E. Coli protein (PDB ID: 2p7vA); towards left – superposition of 3DIGARS-PSP model on native (initial seeds from I-Tasser); towards right – superposition of top I-Tasser model (based on TMScore) on native. Figure 8 | E. Coli protein (PDB ID: 1k4nA); towards left – superposition of 3DIGARS-PSP model on native (initial seeds from Rosetta); towards right – superposition of top Rosetta model (based on TMScore) on native. Figure 9 | E. Coli protein (PDB ID: 1k4nA); towards left – superposition of 3DIGARS-PSP model on native (initial seeds from I-Tasser); towards right – superposition of top I-Tasser model (based on TMScore) on native. Generate Frequency Distribution of Φ, Ψ Angles and SS Types Select 5% Elite Models Note: Natives are shown in cyan and pink and Models are shown in red and yellow Perform Memory Assisted Crossover @ 70 % In past we have shown that our energy function, 3DIGARS3.0 outperforms the state-of-arts method significantly. Also, in our prior work we have shown that our associate memory based sampling algorithm provides superior performance. In this work, we are working on to find the right combination of our energy function and the sampling algorithm to have better prediction of 3D structure of protein in comparison to the state-of-art approaches. To this end, we have been able to successfully apply dihedral angles mutation by rotation and crossover by protein segment translation rules to enhance the mutation and crossover operations of the sampling algorithms. We are working on case by case basis to obtain an accurate prediction of the useful secondary structures in a protein. Towards this, we have utilized the Ramachandran Plot information within our sampling algorithm. We have found that the use of Ramachandran Plot yields in significant improvement. We are exploring on the topics such as effective use of Ramachandran Plot, move sets and associated memory to find more efficient and effective rules to apply within the sampling algorithm. We plan to further improve the PSP problem by combining 3DIGARS and sDFIRE energy function in near future to make it further robust. Results Discussions and Conclusions Fill Rest Randomly Perform Angular Mutation @ 60% Calculate Fitness using 3DIGARS3.0 Save Models Generation < 2000 End Best Models Acknowledgements Authors gratefully acknowledge the Louisiana Board of Regents through the Board of Regents Support Fund, LEQSF (2013-16)-RD-A-19.