Small molecules targeting the EGFR tyrosine kinase domain have been used with some success at treating patients with non-small cell lung cancer driven by activating mutations in the kinase domain. The initial class of inhibitors displaced ATP noncovalently but were rendered ineffective due to the development of resistance mutations in the kinase domain. These were overcome by the development of covalent inhibitors such as afatinib which also bind in the ATP pocket. However pooled analysis of two recent clinical trials LUX-3 and LUX-6 demonstrated an unprecedented overall survival benefit of afatinib over chemotherapy for the EGFR 19del, but not the EGFR L858R. In the current study we use modelling and simulations to show that structural constraints in EGFR 19del deletion result in significantly attenuated flexibilities in the binding pocket resulting in strong hydrogen and halogen bonds with afatinib in the EGFR 19del; these constraints are modulated by buried water and result in the differential affinities of afatinib for the different mutants. SNP analysis of residues surrounding the buried water points to the likelihood of further differential effects of afatinib and provides a compelling case for investigating the effects of the SNPs towards further stratification of patients for ensuring the most effective use of afatinib.

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Macrocycles and cyclic peptides are increasingly attractive therapeutic modalities as they often have improved affinity, are able to bind to extended protein interfaces and otherwise have favorable properties. Macrocyclization of a known binder molecule has the potential to stabilize its bioactive conformation, improve its metabolic stability, cell permeability and in certain cases oral bioavailability. Herein, we present an in silico approach that automatically generates, evaluates and proposes cyclizations utilizing a library of well-established chemical reactions and reagents. Using the three-dimensional (3D) conformation of the linear molecule in complex with a target protein as starting point, this approach identifies attachment points, generates linkers, evaluates the conformational landscape of suitable linkers and their geometric compatibility and ranks the resulting molecules with respect to their predicted conformational stability and interactions with the target protein. As we show here with several prospective and retrospective case studies, this procedure can be applied for the macrocyclization of small molecules and peptides and even PROTACs and proteins.The presented approach is an important step towards the enhanced utilization of macrocycles andcyclic peptides as attractive therapeutic modalities. File list (2) download file view on ChemRxiv DesignOfMacrocycles_chemrxiv.pdf (1.85 MiB) download file view on ChemRxiv SUPPORTING INFORMATION.pdf (458.87 KiB)

Inhibitors of PDZ-peptide interactions have important implications in a variety of biological processes including treatment of cancer and Parkinson’s disease. Even though experimental studies have reported characterization of peptidomimetic inhibitors of PDZ-peptide interactions, the binding modes for most of them have not been characterized by structural studies. In this study we have attempted to understand the structural basis of the small molecule-PDZ interactions by in silico analysis of the binding modes and binding affinities of a set of 38 small molecules with known Ki or Kd values for PDZ2 and PDZ3 domains of PSD-95 protein. These two PDZ domains show differential selectivity for these compounds despite having a high degree of sequence similarity and almost identical peptide binding pockets. Optimum binding modes for these ligands for PDZ2 and PDZ3 domains were identified by using a novel combination of semi-flexible docking and explicit solvent molecular dynamics (MD) simulations. Analysis of the binding modes revealed most of the peptidomimectic ligands which had high Ki or Kd moved away from the peptide binding pocket, while ligands with high binding affinities remained in the peptide binding pocket. The differential specificities of the PDZ2 and PDZ3 domains primarily arise from differences in the conformation of the loop connecting βB and βC strands, because this loop interacts with the N-terminal chemical moieties of the ligands. We have also computed the MM/PBSA binding free energy values for these 38 compounds with both the PDZ domains from multiple 5 ns MD trajectories on each complex i.e. a total of 228 MD trajectories of 5 ns length each. Interestingly, computational binding free energies show good agreement with experimental binding free energies with a correlation coefficient of approximately 0.6. Thus our study demonstrates that combined use of docking and MD simulations can help in identification of potent inhibitors of PDZ-peptide complexes.

No abstract

PDZ domains are peptide recognition modules which mediate specific protein-protein interactions and are known to have a complex specificity landscape. We have developed a novel structure-based multiscale approach which identifies crucial specificity determining residues (SDRs) of PDZ domains from explicit solvent molecular dynamics (MD) simulations on PDZ-peptide complexes and uses these SDRs in combination with knowledge-based scoring functions for proteomewide identification of their interaction partners. Multiple explicit solvent simulations ranging from 5 to 50 ns duration have been carried out on 28 PDZ-peptide complexes with known binding affinities. MM/PBSA binding energy values calculated from these simulations show a correlation coefficient of 0.755 with the experimental binding affinities. On the basis of the SDRs of PDZ domains identified by MD simulations, we have developed a simple scoring scheme for evaluating binding energies for PDZ-peptide complexes using residue based statistical pair potentials. This multiscale approach has been benchmarked on a mouse PDZ proteome array data set by calculating the binding energies for 217 different substrate peptides in binding pockets of 64 different mouse PDZ domains. Receiver operating characteristic (ROC) curve analysis indicates that, the area under curve (AUC) values for binder vs nonbinder classification by our structure based method is 0.780. Our structure based method does not require experimental PDZ-peptide binding data for training.

Reactivation of tumor-suppressing activity of p53 protein by targeting its negative regulator MDM2/MDMX has been pursued as a potential anticancer strategy. A promising dual inhibitor of MDM2/MDMX that has been developed and is currently in clinical trials is the stapled peptide ATSP-7041. The activity of this molecule is reported to be modulated in the presence of serum. Albumin is the most abundant protein in serum and is known to bind reversibly to several molecules. To study this interaction, we develop a protocol combining molecular modeling, docking, and simulations. Exhaustive docking of the peptide with representative simulated structures of human serum albumin led to the identification of probable binding sites on the surface of the protein, including both known canonical and novel binding sites. Sequence differences at putative peptide-binding sites in human and mouse albumin result in differing interaction energies with the peptide and enable us to rationalize the observed differences in vivo. In general, the findings should help in guiding the design of features in such peptides that may affect their distribution and cell permeability, opening a new window in structure-guided design strategies.

Protein-protein interactions mediated by phosphotyrosine binding (PTB) domains play a crucial role in various cellular processes. In order to understand the structural basis of substrate recognition by PTB domains, multiple explicit solvent atomistic simulations of 100ns duration have been carried out on 6 PTB-peptide complexes with known binding affinities. MM/PBSA binding energy values calculated from these MD trajectories and residue based statistical pair potential score show good correlation with the experimental dissociation constants. Our analysis also shows that the modeled structures of PTB domains can be used to develop less compute intensive residue level statistical pair potential based approaches for predicting interaction partners of PTB domains.