Perturbation Approaches for Exploring Protein Binding Site Flexibility to Predict Transient Binding Pockets.

Simulations of the long-time scale motions of a ligand binding pocket in a protein may open up new perspectives for the design of compounds with steric or chemical properties differing from those of known binders. However, slow motions of proteins are difficult to access using standard molecular dynamics (MD) simulations and are thus usually neglected in computational drug design. Here, we introduce two nonequilibrium MD approaches to identify conformational changes of a binding site and detect transient pockets associated with these motions. The methods proposed are based on the rotamerically induced perturbation (RIP) MD approach, which employs perturbation of side-chain torsional motion for initiating large-scale protein movement. The first approach, Langevin-RIP (L-RIP), entails a series of short Langevin MD simulations, each starting with perturbation of one of the side-chains lining the binding site of interest. L-RIP provides extensive sampling of conformational changes of the binding site. In less than 1 ns of MD simulation with L-RIP, we observed distortions of the α-helix in the ATP binding site of HSP90 and flipping of the DFG loop in Src kinase. In the second approach, RIPlig, a perturbation is applied to a pseudoligand placed in different parts of a binding pocket, which enables flexible regions of the binding site to be identified in a small number of 10 ps MD simulations. The methods were evaluated for four test proteins displaying different types and degrees of binding site flexibility. Both methods reveal all transient pocket regions in less than a total of 10 ns of simulations, even though many of these regions remained closed in 100 ns conventional MD. The proposed methods provide computationally efficient tools to explore binding site flexibility and can aid in the functional characterization of protein pockets, and the identification of transient pockets for ligand design.

[1]  V. Helms,et al.  Transient pockets on protein surfaces involved in protein-protein interaction. , 2007, Journal of medicinal chemistry.

[2]  Adrian H Elcock,et al.  Computational sampling of a cryptic drug binding site in a protein receptor: explicit solvent molecular dynamics and inhibitor docking to p38 MAP kinase. , 2006, Journal of molecular biology.

[3]  Tod D Romo,et al.  Unknown unknowns: the challenge of systematic and statistical error in molecular dynamics simulations. , 2014, Biophysical journal.

[4]  G. Ciccotti,et al.  Numerical Integration of the Cartesian Equations of Motion of a System with Constraints: Molecular Dynamics of n-Alkanes , 1977 .

[5]  Friedrich Rippmann,et al.  TRAPP: A Tool for Analysis of Transient Binding Pockets in Proteins , 2013, J. Chem. Inf. Model..

[6]  Albert C. Pan,et al.  Transitions to catalytically inactive conformations in EGFR kinase , 2013, Proceedings of the National Academy of Sciences.

[7]  David A. Agard,et al.  Probing the Flexibility of Large Conformational Changes in Protein Structures through Local Perturbations , 2009, PLoS Comput. Biol..

[8]  Harish Vashisth,et al.  "DFG-flip" in the insulin receptor kinase is facilitated by a helical intermediate state of the activation loop. , 2011, Biophysical journal.

[9]  Bert L de Groot,et al.  Molecular dynamics simulations using temperature-enhanced essential dynamics replica exchange. , 2007, Biophysical journal.

[10]  K Schulten,et al.  VMD: visual molecular dynamics. , 1996, Journal of molecular graphics.

[11]  A. Voter Parallel replica method for dynamics of infrequent events , 1998 .

[12]  James C. Phillips,et al.  Parallel Generalized Born Implicit Solvent Calculations with NAMD. , 2011, Journal of chemical theory and computation.

[13]  Conrad C. Huang,et al.  UCSF Chimera—A visualization system for exploratory research and analysis , 2004, J. Comput. Chem..

[14]  Holger Gohlke,et al.  The Amber biomolecular simulation programs , 2005, J. Comput. Chem..

[15]  Laxmikant V. Kalé,et al.  Scalable molecular dynamics with NAMD , 2005, J. Comput. Chem..

[16]  Federico Filomia,et al.  Insights into MAPK p38alpha DFG flip mechanism by accelerated molecular dynamics. , 2010, Bioorganic & medicinal chemistry.

[17]  A. Voter,et al.  Temperature-accelerated dynamics for simulation of infrequent events , 2000 .

[18]  David A Agard,et al.  Conformational dynamics of the molecular chaperone Hsp90 , 2011, Quarterly Reviews of Biophysics.

[19]  J. Reinstein,et al.  Conserved Conformational Changes in the ATPase Cycle of Human Hsp90* , 2008, Journal of Biological Chemistry.

[20]  Kalliopi K. Patapati,et al.  Three force fields' views of the 3(10) helix. , 2011, Biophysical journal.

[21]  Alexander D. MacKerell,et al.  All-atom empirical potential for molecular modeling and dynamics studies of proteins. , 1998, The journal of physical chemistry. B.

[22]  K. Fichthorn,et al.  Accelerated molecular dynamics of infrequent events , 1999 .

[23]  C. Lyttle,et al.  Molecular and Pharmacological Properties of a Potent and Selective Novel Nonsteroidal Progesterone Receptor Agonist Tanaproget* , 2005, Journal of Biological Chemistry.

[24]  Michelle R Arkin,et al.  Discovery and characterization of cooperative ligand binding in the adaptive region of interleukin-2. , 2003, Biochemistry.

[25]  Albert C. Pan,et al.  Pathway and mechanism of drug binding to G-protein-coupled receptors , 2011, Proceedings of the National Academy of Sciences.

[26]  A. Voter Hyperdynamics: Accelerated Molecular Dynamics of Infrequent Events , 1997 .

[27]  J. Mongan,et al.  Accelerated molecular dynamics: a promising and efficient simulation method for biomolecules. , 2004, The Journal of chemical physics.

[28]  Alexander D. MacKerell,et al.  Extending the treatment of backbone energetics in protein force fields: Limitations of gas‐phase quantum mechanics in reproducing protein conformational distributions in molecular dynamics simulations , 2004, J. Comput. Chem..