The huge conformational space stemming from the inherent flexibility of peptides is among the main obstacles to successful and efficient computational modeling of protein-peptide interactions. Current peptide docking methods typically overcome this challenge using prior knowledge from the structure of the complex. Here we introduce AnchorDock, a peptide docking approach, which automatically targets the docking search to the most relevant parts of the conformational space. This is done by precomputing the free peptide's structure and by computationally identifying anchoring spots on the protein surface. Next, a free peptide conformation undergoes anchor-driven simulated annealing molecular dynamics simulations around the predicted anchoring spots. In the challenging task of a completely blind docking test, AnchorDock produced exceptionally good results (backbone root-mean-square deviation ≤ 2.2Å, rank ≤15) for 10 of 13 unbound cases tested. The impressive performance of AnchorDock supports a molecular recognition pathway that is driven via pre-existing local structural elements.
The diverse selection of targets in the CAPRI experiments provides grounds for determining the limits of our rigid-body docking program MolFit, and for extending it. We find that the sensitivity of MolFit is high, enabling it to produce reasonably accurate docking solutions when the structures undergo moderate local conformation changes upon complex formation or when the docked molecules are modeled. Yet the ranks of these solutions are sometimes too low to meet the requirements of CAPRI assessment. This indicates that the selectivity of MolFit, which was optimized for docking of unbound X-ray structures, and which relies on the availability of external data from biochemical and bioinformatic sources, needs readjustment in order to meet the challenges presented by NMR or modeled structures. A different challenge is presented by large global conformation changes such as movements of domains. We show that such changes can be accommodated within the rigid-body approximation by employing rigid multibody multistage docking procedures. We also address the difficulty of ranking results from 2-body and multibody docking scans in cases in which there are no external data favoring one option over the other.
Elevated levels of activated protein kinase B (PKB/Akt) have been detected in many types of cancer. Substrate-based peptide inhibitors have the advantage of selectivity due to their extensive interactions with the kinase-specific substrate binding site but often lack necessary pharmacological properties. Chemical modifications of potent peptide inhibitors, such as cyclization, may overcome these drawbacks while maintaining potency. We present an extensive structure-activity relationship (SAR) study of a potent peptide-based PKB/Akt inhibitor. Two backbone cyclic (BC) peptide libraries with varying modes of cyclization, bridge chemistry, and ring size were synthesized and evaluated for in vitro PKB/Akt inhibition. Backbone-to-backbone urea BC peptides were more potent than N-terminus-to-backbone amide BC peptides. Several analogues were up to 10-fold more active than the parent linear peptide. Some activity trends could be rationalized using computational surface mapping of the PKB/Akt kinase catalytic domain. The novel molecules have enhanced pharmacological properties which make them promising lead candidates.
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