Evaluating the peptide structure prediction capabilities of a purely ab-initio method

DEEPSAM is a relatively new global optimization algorithm aimed to predict the structure of bio-molecules from sequence, without any additional preliminary assumption. It is an evolutionary algorithm whose mutation operators are built by hybridizing the diffusion equation method, molecular dynamics simulated annealing, and a quasi-Newton local minimization method. The goal of this study was to evaluate the structure prediction capabilities of DEEPSAM by running it upon NMR structures of linear peptides (10-20 residues). The results indicate that DEEPSAM successfully predicted the conformations of these peptides, using modest computing resources.

[1]  Roland L. Dunbrack,et al.  The Rosetta all-atom energy function for macromolecular modeling and design , 2017, bioRxiv.

[2]  Pierre Tufféry,et al.  PEP-FOLD3: faster de novo structure prediction for linear peptides in solution and in complex , 2016, Nucleic Acids Res..

[3]  Shinya Honda,et al.  10 residue folded peptide designed by segment statistics. , 2004, Structure.

[4]  P. Derreumaux,et al.  Improved PEP-FOLD Approach for Peptide and Miniprotein Structure Prediction. , 2014, Journal of chemical theory and computation.

[5]  H. Scheraga,et al.  On the multiple-minima problem in the conformational analysis of molecules: deformation of the potential energy hypersurface by the diffusion equation method , 1989 .

[6]  R Matthew Fesinmeyer,et al.  Minimization and optimization of designed beta-hairpin folds. , 2006, Journal of the American Chemical Society.

[7]  S. Bhattacharjya,et al.  Cysteine deleted protegrin-1 (CDP-1): anti-bacterial activity, outer-membrane disruption and selectivity. , 2014, Biochimica et biophysica acta.

[8]  Masao Fukushima,et al.  Genetic algorithm with automatic termination and search space rotation , 2011, Memetic Comput..

[9]  J. W. Neidigh,et al.  Designing a 20-residue protein , 2002, Nature Structural Biology.

[10]  Shinya Honda,et al.  Crystal structure of a ten-amino acid protein. , 2008, Journal of the American Chemical Society.

[11]  Jorge Nocedal,et al.  On the limited memory BFGS method for large scale optimization , 1989, Math. Program..

[12]  Kenneth de Jong,et al.  Evolutionary computation: a unified approach , 2007, GECCO.

[13]  David J Brayden,et al.  Current status of selected oral peptide technologies in advanced preclinical development and in clinical trials. , 2016, Advanced drug delivery reviews.

[14]  M. Goldstein,et al.  On the crystallographic accuracy of structure prediction by implicit water models: Tests for cyclic peptides , 2013 .

[15]  A. Samson,et al.  NMR structure of the water soluble Aβ17–34 peptide , 2014, Bioscience reports.

[16]  Arne Elofsson,et al.  MaxSub: an automated measure for the assessment of protein structure prediction quality , 2000, Bioinform..

[17]  A. Telenti,et al.  Discovery and characterization of an endogenous CXCR4 antagonist. , 2015, Cell reports.

[18]  F. Lelj,et al.  Conformational energy minimization by simulated annealing using molecular dynamics: Some improvements to the monitoring procedure , 1991 .

[19]  Erick Fredj,et al.  A new hybrid algorithm for finding the lowest minima of potential surfaces: Approach and application to peptides , 2011, J. Comput. Chem..

[20]  C. Simmerling,et al.  ff14SB: Improving the Accuracy of Protein Side Chain and Backbone Parameters from ff99SB. , 2015, Journal of chemical theory and computation.

[21]  Gajendra P.S. Raghava,et al.  PEPstr: a de novo method for tertiary structure prediction of small bioactive peptides. , 2007, Protein and peptide letters.

[22]  Shugo Nakamura,et al.  CONFORMATIONAL ENERGY MINIMIZATION USING A TWO-STAGE METHOD , 1995 .

[23]  E. Segev,et al.  An atomistic structure of ubiquitin +13 relevant in mass spectrometry: Theoretical prediction and comparison with experimental cross sections , 2014 .

[24]  Jay W. Ponder,et al.  Analysis and Application of Potential Energy Smoothing and Search Methods for Global Optimization , 1998 .

[25]  T. Hoffmann,et al.  Peptide therapeutics: current status and future directions. , 2015, Drug discovery today.

[26]  Xin Yao,et al.  Evolutionary programming using mutations based on the Levy probability distribution , 2004, IEEE Transactions on Evolutionary Computation.

[27]  Kumardeep Chaudhary,et al.  PEPstrMOD: structure prediction of peptides containing natural, non-natural and modified residues , 2015, Biology Direct.