Exhaustive exploration of the conformational landscape of small cyclic peptides using a robotics approach

Small cyclic peptides represent a promising class of therapeutic molecules with unique chemical properties. However, the poor knowledge of their structural characteristics makes their computational design and structure prediction a real challenge. In order to better describe their conformational space, we developed a method, named EGSCyP, for the exhaustive exploration of the energy landscape of small head-to-tail cyclic peptides. The method can be summarized by (i) a global exploration of the conformational space based on a mechanistic representation of the peptide and the use of robotics-based algorithms to deal with the closure constraint and (ii) an all-atom refinement of the obtained conformations. EGSCyP can handle D-form residues and N-methylations. Two strategies for the side-chains placement were implemented and compared. To validate our approach, we applied it to a set of three variants of cyclic RGDFV pentapeptides, including the drug candidate Cilengitide. A comparative analysis was made with respect to replica exchange molecular dynamics simulations in implicit solvent. Its results show that the EGSCyP method provides a very complete characterization of the conformational space of small cyclic pentapeptides.

[1]  Tamar Schlick,et al.  Molecular Modeling and Simulation: An Interdisciplinary Guide , 2010 .

[2]  David Baker,et al.  Accurate de novo design of hyperstable constrained peptides , 2016, Nature.

[3]  Nikolay V Dokholyan,et al.  Dynamic Docking of Conformationally Constrained Macrocycles: Methods and Applications. , 2016, ACS chemical biology.

[4]  Thierry Siméon,et al.  Geometric algorithms for the conformational analysis of long protein loops , 2004, J. Comput. Chem..

[5]  V J Hruby,et al.  Design of peptides, proteins, and peptidomimetics in chi space. , 1997, Biopolymers.

[6]  Adrian A Canutescu,et al.  Cyclic coordinate descent: A robotics algorithm for protein loop closure , 2003, Protein science : a publication of the Protein Society.

[7]  S. Goodman,et al.  Stereoisomerism and Biological Activity of the Selective and Superactive αvβ3 Integrin Inhibitor cyclo(-RGDfV-) and Its Retro-Inverso Peptide , 1997 .

[8]  Klaus R Liedl,et al.  Peptidic Macrocycles - Conformational Sampling and Thermodynamic Characterization , 2018, J. Chem. Inf. Model..

[9]  David Baker,et al.  Comprehensive computational design of ordered peptide macrocycles , 2017, Science.

[10]  Berk Hess,et al.  GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers , 2015 .

[11]  Chaok Seok,et al.  A kinematic view of loop closure , 2004, J. Comput. Chem..

[12]  David Curcó,et al.  Relaxation of amorphous multichain polymer systems using inverse kinematics , 2010 .

[13]  Christopher L. McClendon,et al.  Reaching for high-hanging fruit in drug discovery at protein–protein interfaces , 2007, Nature.

[14]  Ken A Dill,et al.  A test on peptide stability of AMBER force fields with implicit solvation. , 2008, The journal of physical chemistry. B.

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

[16]  Roland L. Dunbrack,et al.  proteins STRUCTURE O FUNCTION O BIOINFORMATICS Improved prediction of protein side-chain conformations with SCWRL4 , 2022 .

[17]  Hongtao Yu,et al.  Computational methods to design cyclic peptides. , 2016, Current opinion in chemical biology.

[18]  Thierry Siméon,et al.  Motion planning algorithms for molecular simulations: A survey , 2012, Comput. Sci. Rev..

[19]  Pierre Tufféry,et al.  PEP-FOLD: an updated de novo structure prediction server for both linear and disulfide bonded cyclic peptides , 2012, Nucleic Acids Res..

[20]  Didier Devaurs,et al.  Characterizing Energy Landscapes of Peptides Using a Combination of Stochastic Algorithms , 2015, IEEE Transactions on NanoBioscience.

[21]  P. Tufféry,et al.  Progress with peptide scanning to study structure-activity relationships: the implications for drug discovery , 2016, Expert opinion on drug discovery.

[22]  Meng Gao,et al.  Targeting protein−protein interfaces using macrocyclic peptides , 2015, Biopolymers.

[23]  Hongtao Yu,et al.  Insights into How Cyclic Peptides Switch Conformations. , 2016, Journal of chemical theory and computation.

[24]  Horst Kessler,et al.  N-methylated cyclic RGD peptides as highly active and selective αvβ3 integrin antagonists , 1999 .

[25]  D. Craik,et al.  Approaches to the stabilization of bioactive epitopes by grafting and peptide cyclization , 2016, Biopolymers.

[26]  J. A. V. BUTLER,et al.  Advances in Protein Chemistry , 1946, Nature.

[27]  L. Belvisi,et al.  Metadynamics Simulations Rationalise the Conformational Effects Induced by N-Methylation of RGD Cyclic Hexapeptides. , 2015, Chemistry.

[28]  J. Doye,et al.  Global Optimization by Basin-Hopping and the Lowest Energy Structures of Lennard-Jones Clusters Containing up to 110 Atoms , 1997, cond-mat/9803344.

[29]  K. Ramnarayan,et al.  Cyclic peptides — Small and big and their conformational aspects , 1985, Journal of Biosciences.

[30]  David A. Case,et al.  Effective Born radii in the generalized Born approximation: The importance of being perfect , 2002, J. Comput. Chem..

[31]  M. Khrestchatisky,et al.  Synthetic therapeutic peptides: science and market. , 2010, Drug discovery today.

[32]  A. Bondi.  van der Waals Volumes and Radii , 1964 .

[33]  Harold A. Scheraga,et al.  Conformational Analysis of Macromolecules. III. Helical Structures of Polyglycine and Poly‐L‐Alanine , 1966 .

[34]  Annick Thomas,et al.  In silico predictions of 3D structures of linear and cyclic peptides with natural and non‐proteinogenic residues , 2012, Journal of peptide science : an official publication of the European Peptide Society.

[35]  Yang Zhang,et al.  I-TASSER server for protein 3D structure prediction , 2008, BMC Bioinformatics.

[36]  M. Deem,et al.  Analytical rebridging Monte Carlo: Application to cis/trans isomerization in proline-containing, cyclic peptides , 1999, physics/9904057.

[37]  Stephen P. Hale,et al.  The exploration of macrocycles for drug discovery — an underexploited structural class , 2008, Nature Reviews Drug Discovery.

[38]  Miriam Góngora-Benítez,et al.  Multifaceted roles of disulfide bonds. Peptides as therapeutics. , 2014, Chemical reviews.

[39]  Erion Plaku,et al.  A Survey of Computational Treatments of Biomolecules by Robotics-Inspired Methods Modeling Equilibrium Structure and Dynamic , 2016, J. Artif. Intell. Res..

[40]  D. Craik,et al.  The Future of Peptide‐based Drugs , 2013, Chemical biology & drug design.

[41]  Horst Kessler,et al.  Cilengitide: The First Anti-Angiogenic Small Molecule Drug Candidate. Design, Synthesis and Clinical Evaluation , 2010, Anti-cancer agents in medicinal chemistry.

[42]  John D. Hunter,et al.  Matplotlib: A 2D Graphics Environment , 2007, Computing in Science & Engineering.

[43]  S. Hayward,et al.  Peptide‐plane flipping in proteins , 2001, Protein science : a publication of the Protein Society.

[44]  Annett Wechsler.  Current Advances In Mechanical Design And Production Iii , 2016 .

[45]  Paul W. Wojcik,et al.  Peptide-based inhibitors of protein-protein interactions. , 2016, Bioorganic & medicinal chemistry letters.

[46]  Roy L. Johnston,et al.  Exploring the Energy Landscapes of Cyclic Tetrapeptides with Discrete Path Sampling , 2012, Journal of chemical theory and computation.

[47]  Dinesh Manocha,et al.  Efficient inverse kinematics for general 6R manipulators , 1994, IEEE Trans. Robotics Autom..

[48]  Hong Y. Lee,et al.  Displacement analysis of the general spatial 7-link 7R mechanism , 1988 .

[49]  Joost Schymkowitz,et al.  Computational design of peptide ligands. , 2011, Trends in biotechnology.

[50]  Vincent A. Voelz,et al.  Molecular Simulation of Conformational Pre-Organization in Cyclic RGD Peptides , 2015, J. Chem. Inf. Model..

[51]  John F. Canny,et al.  Geometric Problems in Molecular Biology and Robotics , 1994, ISMB.

[52]  N. Go,et al.  Ring Closure and Local Conformational Deformations of Chain Molecules , 1970 .

[53]  Aaron R. Dinner,et al.  Local deformations of polymers with nonplanar rigid main-chain internal coordinates , 2000, J. Comput. Chem..

[54]  Phillip J. McKerrow,et al.  Introduction to robotics , 1991 .

[55]  Thierry Siméon,et al.  Segmenting Proteins into Tripeptides to Enhance Conformational Sampling with Monte Carlo Methods , 2018, Molecules.

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

[57]  Hongtao Yu,et al.  Toward structure prediction of cyclic peptides. , 2015, Physical chemistry chemical physics : PCCP.

[58]  Yu-Shan Lin,et al.  Understanding and designing head‐to‐tail cyclic peptides , 2018, Biopolymers.

[59]  D. Fairlie,et al.  Constraining Cyclic Peptides to Mimic Protein Structure Motifs , 2015 .

[60]  John A. Robinson,et al.  The design, structures and therapeutic potential of protein epitope mimetics. , 2008, Drug discovery today.

[61]  Hong Y. Lee,et al.  A new vector theory for the analysis of spatial mechanisms , 1988 .

[62]  P. Kollman.  Advances and Continuing Challenges in Achieving Realistic and Predictive Simulations of the Properties of Organic and Biological Molecules , 1996 .

[63]  Hongtao Yu,et al.  Toward accurately modeling N-methylated cyclic peptides. , 2017, Physical chemistry chemical physics : PCCP.

[64]  Horst Kessler,et al.  Receptor-bound conformation of cilengitide better represented by its solution-state structure than the solid-state structure. , 2014, Chemistry.

[65]  E. Novellino,et al.  Increasing αvβ3 selectivity of the anti-angiogenic drug cilengitide by N-methylation. , 2011, Angewandte Chemie.

[66]  F. Jiang,et al.  Accurate Structure Prediction and Conformational Analysis of Cyclic Peptides with Residue-Specific Force Fields. , 2016, The journal of physical chemistry letters.

[67]  Wim F Vranken,et al.  ACPYPE - AnteChamber PYthon Parser interfacE , 2012, BMC Research Notes.

[68]  J. Berg,et al.  Molecular dynamics simulations of biomolecules , 2002, Nature Structural Biology.

[69]  Piotr Cieplak,et al.  R.E.D. Server: a web service for deriving RESP and ESP charges and building force field libraries for new molecules and molecular fragments , 2011, Nucleic Acids Res..

[70]  X. Daura,et al.  Peptide Folding: When Simulation Meets Experiment , 1999 .

[71]  H. Scheraga,et al.  Monte Carlo-minimization approach to the multiple-minima problem in protein folding. , 1987, Proceedings of the National Academy of Sciences of the United States of America.

[72]  H. Kessler,et al.  N‐Methylation of Peptides: A New Perspective in Medicinal Chemistry , 2009 .