AnchorDock for Blind Flexible Docking of Peptides to Proteins.

Due to increasing interest in peptides as signaling modulators and drug candidates, several methods for peptide docking to their target proteins are under active development. The "blind" docking problem, where the peptide-binding site on the protein surface is unknown, presents one of the current challenges in the field. AnchorDock protocol was developed by Ben-Shimon and Niv to address this challenge.This protocol narrows the docking search to the most relevant parts of the conformational space. This is achieved by pre-folding the free peptide and by computationally detecting anchoring spots on the surface of the unbound protein. Multiple flexible simulated annealing molecular dynamics (SAMD) simulations are subsequently carried out, starting from pre-folded peptide conformations, constrained to the various precomputed anchoring spots.Here, AnchorDock is demonstrated using two known protein-peptide complexes. A PDZ-peptide complex provides a relatively easy case due to the relatively small size of the protein, and a typical peptide conformation and binding region; a more challenging example is a complex between USP7N-term and a p53-derived peptide, where the protein is larger, and the peptide conformation and a binding site are generally assumed to be unknown. AnchorDock returned native-like solutions ranked first and third for the PDZ and USP7 complexes, respectively. We describe the procedure step by step and discuss possible modifications where applicable.

AnchorDock: Blind and Flexible Anchor-Driven Peptide Docking.

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.

AMPK-derived peptides reduce blood glucose levels but lead to fat retention in the liver of obese mice.

AMP-activated protein kinase (AMPK) is a regulator of energy balance at both the cellular and the whole-body levels. Direct activation of AMPK has been highlighted as a potential novel, and possibly safer, alternative to treat type II diabetes and obesity. In this study, we aimed to design and characterize novel peptides that mimic the αG region of the α2 AMPK catalytic domain to modulate its activity by inhibiting interactions between AMPK domains or other interacting proteins. The derived peptides were tested in vivo and in tissue culture. The computationally predicted structure of the free peptide with the addition of the myristoyl (Myr) or acetyl (Ac) moiety closely resembled the protein structure that it was designed to mimic. Myr-peptide and Ac-peptide activated AMPK in muscle cells and led to reduced adipose tissue weight, body weight, blood glucose levels, insulin levels, and insulin resistance index, as expected from AMPK activation. In addition, triglyceride, cholesterol, leptin, and adiponectin levels were also lower, suggesting increased adipose tissue breakdown, a result of AMPK activation. On the other hand, liver weight and liver lipid content increased due to fat retention. We could not find an elevated pAMPK:AMPK ratio in the liver in vivo or in hepatocytes ex vivo, suggesting that the peptide does not lead to AMPK activation in hepatocytes. The finding that an AMPK-derived peptide leads to the activation of AMPK in muscle cells and in adipose tissue and leads to reduced glucose levels in obese mice, but to fat accumulation in the liver, demonstrates the differential effect of AMPK modulation in various tissues.

Protonation States in molecular dynamics simulations of peptide folding and binding.

Peptides are important signaling modules, acting both as individual hormones and as parts of larger molecules, mediating their protein-protein interactions. Many peptidic and peptidomimetic drugs have reached the marketplace and opportunities for peptide-based drug discovery are on the rise. pH-dependent behavior of peptides is well documented in the context of misfolding diseases and peptide translocation. Changes in the protonation states of peptide residues often have a crucial effect on a peptide's structure, dynamics and function, which may be exploited for biotechnological applications. The current review surveys the increasing levels of sophistication in the treatment of protonation states in computational studies involving peptides. Specifically we describe I) the common practice of assigning a single protonation state and using it throughout the dynamic simulation, II) approaches that consider multiple protonation states and compare computed observables to experimental ones, III) constant pH molecular dynamics methods that couple changes in protonation states with conformational dynamics "on the fly". Applications of conformational dynamics treatment of peptides in the context of binding, folding and interactions with the membrane are presented, illustrating the growing body of work in this field and highlighting the importance of careful handling of protonation states of peptidic residues.

Deciphering the Arginine-binding preferences at the substrate-binding groove of Ser/Thr kinases by computational surface mapping.

Protein kinases are key signaling enzymes that catalyze the transfer of γ-phosphate from an ATP molecule to a phospho-accepting residue in the substrate. Unraveling the molecular features that govern the preference of kinases for particular residues flanking the phosphoacceptor is important for understanding kinase specificities toward their substrates and for designing substrate-like peptidic inhibitors. We applied ANCHORSmap, a new fragment-based computational approach for mapping amino acid side chains on protein surfaces, to predict and characterize the preference of kinases toward Arginine binding. We focus on positions P-2 and P-5, commonly occupied by Arginine (Arg) in substrates of basophilic Ser/Thr kinases. The method accurately identified all the P-2/P-5 Arg binding sites previously determined by X-ray crystallography and produced Arg preferences that corresponded to those experimentally found by peptide arrays. The predicted Arg-binding positions and their associated pockets were analyzed in terms of shape, physicochemical properties, amino acid composition, and in-silico mutagenesis, providing structural rationalization for previously unexplained trends in kinase preferences toward Arg moieties. This methodology sheds light on several kinases that were described in the literature as having non-trivial preferences for Arg, and provides some surprising departures from the prevailing views regarding residues that determine kinase specificity toward Arg. In particular, we found that the preference for a P-5 Arg is not necessarily governed by the 170/230 acidic pair, as was previously assumed, but by several different pairs of acidic residues, selected from positions 133, 169, and 230 (PKA numbering). The acidic residue at position 230 serves as a pivotal element in recognizing Arg from both the P-2 and P-5 positions.

Backbone cyclic peptide inhibitors of protein kinase B (PKB/Akt).

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.

Computational mapping of anchoring spots on protein surfaces.

Protein-protein and protein-peptide interactions are often controlled by few strong contacts that involve hot spot residues. Computational detection of such contacts, termed here anchoring spots, is important for understanding recognition processes and for predicting interactions; it is an essential step in designing interaction interfaces and therapeutic agents. We describe ANCHORSMAP, an algorithm for computational mapping of amino acid side chains on protein surfaces. The algorithm consists of two stages: A geometry based stage (LSMdet), in which sub-pockets adequate for binding single side chains are detected and amino acid probes are scattered near them, and an energy based stage in which optimal positions of the probes are determined through repeated energy minimization and clustering of nearby poses and their DeltaG are calculated. ANCHORSMAP employs a new function for DeltaG calculations, which is specifically designed for the context of protein-protein recognition by introducing a correction in the electrostatic energy term that compensates for the dielectric shielding exerted by a hypothetical protein bound to the probe. The algorithm successfully detects known anchoring sites and accurately positions the probes. The calculated DeltaG rank high the correct anchoring spots in maps produced for unbound proteins. We find that Arg, Trp, Glu and Tyr, which are favorite hot spot residues, are also more selective of their binding environment. The usefulness of anchoring spots mapping is demonstrated by detecting the binding surfaces in the protein-protein complex barnase/barstar and the protein-peptide complex kinase/PKI, and by identifying phenylalanine anchoring sites on the surface of the nuclear transporter NTF2, C-terminus anchors on PDZ domains and phenol anchors on thermolysin. Finally, we discuss the role of anchoring spots in molecular recognition processes.

CAPRI targets T29-T42: proving ground for new docking procedures.

The critical assessment of protein interactions (CAPRI) experiment provides a unique opportunity for unbiased assessment of docking procedures. The recent CAPRI targets T29-T42 entailed docking of bound, unbound, and modeled structures, presenting a wide range of prediction difficulty. We submitted accurate predictions for targets T40, T41, and T42, a good prediction for T32 and acceptable predictions for T29 and T34. The accuracy of our docking results generally matched the prediction difficulty; hence, docking of modeled proteins produced less accurate results. However, there were interesting exceptions: an accurate prediction was submitted for the dimer of modeled tetratricopeptide repeat (T42) and only an acceptable prediction for the bound/unbound case T29. The ensembles of docking models produced in the scans included an acceptable or better prediction for every target. We show here that our recently developed postscan reevaluation procedure, which tests propensity and solvation measures of the whole interface and the interface core, successfully distinguished these predictions from false docking models. For enzyme-inhibitor targets, we show that the distance of the interface from the enzyme's centroid ranked high native like docking models. Also, for one case we demonstrate that docking of an ensemble of conformers produced by normal modes analysis can improve the accuracy of the prediction.

Looking at enzymes from the inside out: the proximity of catalytic residues to the molecular centroid can be used for detection of active sites and enzyme-ligand interfaces.

Analysis of the distances of the exposed residues in 175 enzymes from the centroids of the molecules indicates that catalytic residues are very often found among the 5% of residues closest to the enzyme centroid. This property of catalytic residues is implemented in a new prediction algorithm (named EnSite) for locating the active sites of enzymes and in a new scheme for re-ranking enzyme-ligand docking solutions. EnSite examines only 5% of the molecular surface (represented by surface dots) that is closest to the centroid, identifying continuous surface segments and ranking them by their area size. EnSite ranks the correct prediction 1-4 in 97% of the cases in a dataset of 65 monomeric enzymes (rank 1 for 89% of the cases) and in 86% of the cases in a dataset of 176 monomeric and multimeric enzymes from all six top-level enzyme classifications (rank 1 in 74% of the cases). Importantly, identification of buried or flat active sites is straightforward because EnSite "looks" at the molecular surface from the inside out. Detailed examination of the results indicates that the proximity of the catalytic residues to the centroid is a property of the functional unit, defined as the assembly of domains or chains that form the active site (in most cases the functional unit corresponds to a single whole polypeptide chain). Using the functional unit in the prediction further improves the results. The new property of active sites is also used for re-evaluating enzyme-inhibitor unbound docking results. Sorting the docking solutions by the distance of the interface to the centroid of the enzyme improves remarkably the ranks of nearly correct solutions compared to ranks based on geometric-electrostatic-hydrophobic complementarity scores.