Discovering and exploiting hidden pockets at protein interfaces

The number of three-dimensional structures of potential protein targets available in several platforms such as the Protein Data Bank is subjected to a constant increase over the last decades. This observation should be an additional motivation to use structure-based methodologies in drug discovery. In the recent years, different success stories of Structure Based Drug Design approach have been reported. However, it has also been shown that a lack of druggability is one of the major causes of failure in the development of a new compound.The concept of druggability can be used to describe proteins with the capability to bind drug-like compounds. A general consensus suggests that around 10% of the human genome codes for molecular targets that can be considered as druggable. Over the years, the protein druggability was studied with a particular interest to capture structural descriptors in order to develop computational methodologies for druggability assessment. Different computational methods have been published to detect and evaluate potential binding sites at protein surfaces. The majority of methods currently available are designed to assess druggability of a static structure. However it is well known that sometimes a few local rearrangements around the binding site can profoundly influence the affinity of a small molecule to its target. The use of techniques such as molecular dynamics (MD) or Metadynamics could be an interesting way to simulate those variations. The goal of this thesis was to design a new computational approach, called JEDI, for druggability assessment using a combination of empirical descriptors that can be collected ‘on-the-fly’ during MD simulations. JEDI is a grid-based approach able to perform the druggability assessment of a binding site in only a few seconds making it one of the fastest methodologies in the field. Agreement between computed and experimental druggability estimates is comparable to literature alternatives. In addition, the estimator is less sensitive than existing methodologies to small structural rearrangements and gives consistent druggability predictions for similar structures of the same protein. Since the JEDI function is continuous and differentiable, the druggability potential can be used as collective variable to rapidly detect cryptic druggable binding sites in proteins with a variety of MD free energy methods

The Impact of Small Molecule Binding on the Energy Landscape of the Intrinsically Disordered Protein C-Myc

Intrinsically disordered proteins are attractive therapeutic targets owing to their prevalence in several diseases. Yet their lack of well-defined structure renders ligand discovery a challenging task. An intriguing example is provided by the oncoprotein c-Myc, a transcription factor that is over expressed in a broad range of cancers. Transcriptional activity of c-Myc is dependent on heterodimerization with partner protein Max. This protein-protein interaction is disrupted by the small molecule 10058-F4 (1), that binds to monomeric and disordered c-Myc. To rationalize the mechanism of inhibition, structural ensembles for the segment of the c-Myc domain that binds to 1 were computed in the absence and presence of the ligand using classical force fields and explicit solvent metadynamics molecular simulations. The accuracy of the computed structural ensembles was assessed by comparison of predicted and measured NMR chemical shifts. The small molecule 1 was found to perturb the composition of the apo equilibrium ensemble and to bind weakly to multiple distinct c-Myc conformations. Comparison of the apo and holo equilibrium ensembles reveals that the c-Myc conformations binding 1 are already partially formed in the apo ensemble, suggesting that 1 binds to c-Myc through an extended conformational selection mechanism. The present results have important implications for rational ligand design efforts targeting intrinsically disordered proteins

A Collective Variable for the Rapid Exploration of Protein Druggability

An efficient molecular simulation methodology has been developed for the evaluation of the druggability (ligandability) of a protein. Previously proposed techniques were designed to assess the druggability of crystallographic structures and cannot be tightly coupled to molecular dynamics (MD) simulations. By contrast, the present approach, JEDI (<u>J</u>ust <u>E</u>xploring <u>D</u>ruggability at protein <u>I</u>nterfaces), features a druggability potential made of a combination of empirical descriptors that can be collected “on-the-fly” during MD simulations. Extensive validation studies indicate that JEDI analyses discriminate druggable and nondruggable protein binding site conformations with accuracy similar to alternative methodologies, and at a fraction of the computational cost. Since the JEDI function is continuous and differentiable, the druggability potential can be used as collective variable to rapidly detect cryptic druggable binding sites in proteins with a variety of MD free energy methods. Protocols for applications to flexible docking problems are outlined

Comparison of computed and observed secondary chemical shifts for apo c-Myc_402–412.

<p>A) ¹H_α chemical shifts. B) ¹³C_α chemical shifts. C) ¹H backbone amide chemical shifts. D) ¹³C_β chemical shifts. Black: experimental data. Solid red and blue: predicted by reweighting the biased BEMD simulations apoA and apoB respectively. Dotted red and blue: predicted from the neutral replicas of the BEMD simulations apoA and apoB respectively. Not all experimental ¹³C_β chemical shifts were reported. Camshift does not report chemical shifts for terminal residues.</p

Representative conformations from the computed equilibrium ensemble for the c-Myc_402–412/1 complex.

<p>The conformations depicted are those closest to the cluster center. The fractional cluster populations are: 0.021±0.008 (A), 0.019±0.002 (B), 0.018±0.005 (C), 0.015±0.010 (D), 0.014±0.003 (E), 0.011±0.008 (F), 0.011±0.005 (G), 0.011±0.001 (H), 0.010±0.003 (I). Figure prepared with the software VMD <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0041070#pone.0041070-Humphrey1" target="_blank">[78]</a>.</p

Free energy profiles for the c-Myc_402–412/1 holo simulations projected along several collective variables.

<p>Black: Simulation holoA, Red: Simulation holoB. A) CV1, B) CV2, C) CV3, D) CV4, E) CV5, F) CV6, G) CV7, H) CV8. See the <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0041070#s4" target="_blank">Methods</a> section in the main text for a definition of each CV.</p

Comparison of selected holo and apo conformations to the apo and holo ensembles.

<p>A) Probability distribution of backbone RMSD of conformations from the apo (black curve) and holo (red curve) ensembles to: A) holo cluster center 7A, B) holo cluster center 7B, C) holo cluster center 7C, D) apo cluster center 5A. The inset shows the low-RMSD regions. Each panel also shows an overlay of the lowest RMSD apo or holo structure to cluster centers from panels A–D. For clarity only the peptide backbone (tube representation, apo conformations in blue, holo conformations in orange) and ligand atoms (CPK) are shown. Figure prepared using VMD <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0041070#pone.0041070-Humphrey1" target="_blank">[78]</a>.</p

Free energy profiles for the c-Myc_402–412 apo simulations projected along several collective variables.

<p>Black: Simulation apoA, Red: Simulation apoB. A) CV1, B) CV2, C) CV3, D) CV4 E) CV5 F) CV6 G) CV7. See the <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0041070#s4" target="_blank">Methods</a> section in the main text for a definition of each CV.</p