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.

No abstract

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.