Understanding Cryptic Pocket Formation in Protein Targets by Enhanced Sampling Simulations

Cryptic pockets, that is, sites on protein targets that only become apparent when drugs bind, provide a promising alternative to classical binding sites for drug development. Here, we investigate the nature and dynamical properties of cryptic sites in four pharmacologically relevant targets, while comparing the efficacy of various simulation-based approaches in discovering them. We find that the studied cryptic sites do not correspond to local minima in the computed conformational free energy landscape of the unliganded proteins. They thus promptly close in all of the molecular dynamics simulations performed, irrespective of the force-field used. Temperature-based enhanced sampling approaches, such as Parallel Tempering, do not improve the situation, as the entropic term does not help in the opening of the sites. The use of fragment probes helps, as in long simulations occasionally it leads to the opening and binding to the cryptic sites. Our observed mechanism of cryptic site formation is suggestive of an interplay between two classical mechanisms: induced-fit and conformational selection. Employing this insight, we developed a novel Hamiltonian Replica Exchange-based method “SWISH” (Sampling Water Interfaces through Scaled Hamiltonians), which combined with probes resulted in a promising general approach for cryptic site discovery. We also addressed the issue of “false-positives” and propose a simple approach to distinguish them from druggable cryptic pockets. Our simulations, whose cumulative sampling time was more than 200 μs, help in clarifying the molecular mechanism of pocket formation, providing a solid basis for the choice of an efficient computational method

Understanding Cryptic Pocket Formation in Protein Targets by Enhanced Sampling Simulations

Cryptic pockets, that is, sites on protein targets that only become apparent when drugs bind, provide a promising alternative to classical binding sites for drug development. Here, we investigate the nature and dynamical properties of cryptic sites in four pharmacologically relevant targets, while comparing the efficacy of various simulation-based approaches in discovering them. We find that the studied cryptic sites do not correspond to local minima in the computed conformational free energy landscape of the unliganded proteins. They thus promptly close in all of the molecular dynamics simulations performed, irrespective of the force-field used. Temperature-based enhanced sampling approaches, such as Parallel Tempering, do not improve the situation, as the entropic term does not help in the opening of the sites. The use of fragment probes helps, as in long simulations occasionally it leads to the opening and binding to the cryptic sites. Our observed mechanism of cryptic site formation is suggestive of an interplay between two classical mechanisms: induced-fit and conformational selection. Employing this insight, we developed a novel Hamiltonian Replica Exchange-based method “SWISH” (Sampling Water Interfaces through Scaled Hamiltonians), which combined with probes resulted in a promising general approach for cryptic site discovery. We also addressed the issue of “false-positives” and propose a simple approach to distinguish them from druggable cryptic pockets. Our simulations, whose cumulative sampling time was more than 200 μs, help in clarifying the molecular mechanism of pocket formation, providing a solid basis for the choice of an efficient computational method

The Different Flexibility of c-Src and c-Abl Kinases Regulates the Accessibility of a Druggable Inactive Conformation

c-Src and c-Abl are two closely related protein kinases that constitute important anticancer targets. Despite their high sequence identity, they show different sensitivities to the anticancer drug imatinib, which binds specifically to a particular inactive conformation in which the Asp of the conserved DFG motif points outward (DFG-out). We have analyzed the DFG conformational transition of the two kinases using massive molecular dynamics simulations, free energy calculations, and isothermal titration calorimetry. On the basis of the reconstruction of the free energy surfaces for the DFG-in to DFG-out conformational changes of c-Src and c-Abl, we propose that the different flexibility of the two kinases results in a different stability of the DFG-out conformation and might be the main determinant of imatinib selectivity

The Different Flexibility of c-Src and c-Abl Kinases Regulates the Accessibility of a Druggable Inactive Conformation

c-Src and c-Abl are two closely related protein kinases that constitute important anticancer targets. Despite their high sequence identity, they show different sensitivities to the anticancer drug imatinib, which binds specifically to a particular inactive conformation in which the Asp of the conserved DFG motif points outward (DFG-out). We have analyzed the DFG conformational transition of the two kinases using massive molecular dynamics simulations, free energy calculations, and isothermal titration calorimetry. On the basis of the reconstruction of the free energy surfaces for the DFG-in to DFG-out conformational changes of c-Src and c-Abl, we propose that the different flexibility of the two kinases results in a different stability of the DFG-out conformation and might be the main determinant of imatinib selectivity

The Different Flexibility of c-Src and c-Abl Kinases Regulates the Accessibility of a Druggable Inactive Conformation

c-Src and c-Abl are two closely related protein kinases that constitute important anticancer targets. Despite their high sequence identity, they show different sensitivities to the anticancer drug imatinib, which binds specifically to a particular inactive conformation in which the Asp of the conserved DFG motif points outward (DFG-out). We have analyzed the DFG conformational transition of the two kinases using massive molecular dynamics simulations, free energy calculations, and isothermal titration calorimetry. On the basis of the reconstruction of the free energy surfaces for the DFG-in to DFG-out conformational changes of c-Src and c-Abl, we propose that the different flexibility of the two kinases results in a different stability of the DFG-out conformation and might be the main determinant of imatinib selectivity

Free energy of the A-loop opening.

<p>Free energy surfaces of Abl, Src, and drug-resistant mutants projected on the optimal path describing the conformational change of the A-loop from open to closed in Src (CV1) and Abl (CV2). The free energy minima corresponding to an extended A-loop active-like conformation are labeled “A”, “B” is used for A-loop semi-closed (inactive) conformations and “C” for fully closed A-loop conformations. The contour lines are drawn every 1 kcal/mol.</p

Root mean square fluctuation analysis of TKs and Abl resistant mutants.

<p>(a) RMSF of Abl and Src. Fluctuations of the N-lobe (left) and of the A-loop (right) for the Abl mutants (b). and the TKs (c). Shades of red and blue identify strong and weak binders, respectively. Dotted lines are used for clarity.</p

K_d for the Src mutants, Abl and Src WT.

<p>The RMSF values have been averaged from MD simulations.</p

Free energy of imatinib (un-)binding to Abl and to the T315I ‘gatekeeper’ mutant.

<p>Free energy surfaces associated to the binding of imatinib to WT Abl (top panel) and the T315I Abl “gatekeeper” mutant (bottom panel). The deepest energy minima correspond to the crystallographic binding pose and are labeled A. On the way out, B and B’ correspond to an intermediate state (metastable in WT Abl) where imatinib is in between the DFG and the <i>α</i>C helix. States C and C’ correspond to the “external binding pose”. Interestingly in Abl T315I there are two exit channels and both have an higher barrier than in the WT. The contour lines are drawn every 2 kcal/mol.</p

Entropy and Enthalpy contributions to the DFG-in DFG-out flip as obtained from the linear regression of the Free Energy as a function of temperature.

<p>Entropy and Enthalpy contributions to the DFG-in DFG-out flip as obtained from the linear regression of the Free Energy as a function of temperature.</p