Prediction of Protein Pairs Sharing Common Active Ligands Using Protein Sequence, Structure, and Ligand Similarity.

We benchmarked the ability of comparative computational approaches to correctly discriminate protein pairs sharing a common active ligand (positive protein pairs) from protein pairs with no common active ligands (negative protein pairs). Since the target and the off-targets of a drug share at least a common ligand, i.e., the drug itself, the prediction of positive protein pairs may help identify off-targets. We evaluated representative protein-centric and ligand-centric approaches, including (1) 2D and 3D ligand similarity, (2) several measures of protein sequence similarity in conjunction with different sequence sources (e.g., full protein sequence versus binding site residues), and (3) a newly described pocket shape similarity and alignment program called SiteHopper. While the sequence-based alignment of pocket residues achieved the best overall performance, SiteHopper outperformed sequence-based approaches for unrelated proteins with only 20-30% pocket residue identity. Analogously, among ligand-centric approaches, path-based fingerprints achieved the best overall performance, but ROCS-based ligand shape similarity outperformed path-based fingerprints for structurally dissimilar ligands (Tanimoto 25%-40%). A significant drop in recognition performance was observed for ligand-centric approaches when PDB ligands were used instead of ChEMBL ligands. Finally, we analyzed the relationship between pocket shape and ligand shape in our data set and found that similar ligands tend to bind to similar pockets while similar pockets may accept a range of different-shaped ligands.

Pre-existing soft modes of motion uniquely defined by native contact topology facilitate ligand binding to proteins.

Modeling protein flexibility constitutes a major challenge in accurate prediction of protein-ligand and protein-protein interactions in docking simulations. The lack of a reliable method for predicting the conformational changes relevant to substrate binding prevents the productive application of computational docking to proteins that undergo large structural rearrangements. Here, we examine how coarse-grained normal mode analysis has been advantageously applied to modeling protein flexibility associated with ligand binding. First, we highlight recent studies that have shown that there is a close agreement between the large-scale collective motions of proteins predicted by elastic network models and the structural changes experimentally observed upon ligand binding. Then, we discuss studies that have exploited the predicted soft modes in docking simulations. Two general strategies are noted: pregeneration of conformational ensembles that are then utilized as input for standard fixed-backbone docking and protein structure deformation along normal modes concurrent to docking. These studies show that the structural changes apparently "induced" upon ligand binding occur selectively along the soft modes accessible to the protein prior to ligand binding. They further suggest that proteins offer suitable means of accommodating/facilitating the recognition and binding of their ligand, presumably acquired by evolutionary selection of the suitable three-dimensional structure.

ProDy: protein dynamics inferred from theory and experiments.

SUMMARY: We developed a Python package, ProDy, for structure-based analysis of protein dynamics. ProDy allows for quantitative characterization of structural variations in heterogeneous datasets of structures experimentally resolved for a given biomolecular system, and for comparison of these variations with the theoretically predicted equilibrium dynamics. Datasets include structural ensembles for a given family or subfamily of proteins, their mutants and sequence homologues, in the presence/absence of their substrates, ligands or inhibitors. Numerous helper functions enable comparative analysis of experimental and theoretical data, and visualization of the principal changes in conformations that are accessible in different functional states. ProDy application programming interface (API) has been designed so that users can easily extend the software and implement new methods. AVAILABILITY: ProDy is open source and freely available under GNU General Public License from http://www.csb.pitt.edu/ProDy/.

Discovery of modulators of protein-protein interactions: current approaches and limitations.

Protein-protein interactions are involved in most of the essential processes that occur in living organisms from cell motility to DNA replication, which makes them interesting targets for drug discovery. However, due to the lack of deep pockets, and the large contact surfaces involved in these interactions, they are considered challenging targets and have been often times dismissed as "undruggable". Nonetheless, significant efforts in pharmaceutical and academic laboratories have been devoted to finding ways to exploit protein-protein interactions as drug targets. This article provides an overview of the principles underlying the main general strategies for discovering small-molecule modulators of protein-protein interactions, namely: high-throughput screening, fragment-based drug discovery, peptide-based drug discovery, protein secondary structure mimetics, and computer-aided drug discovery. In addition, examples of successful discovery of modulators of protein-protein interactions are discussed for each of those strategies.

ANCHOR: a web server and database for analysis of protein-protein interaction binding pockets for drug discovery.

ANCHOR is a web-based tool whose aim is to facilitate the analysis of protein-protein interfaces with regard to its suitability for small molecule drug design. To this end, ANCHOR exploits the so-called anchor residues, i.e. amino acid side-chains deeply buried at protein-protein interfaces, to indicate possible druggable pockets to be targeted by small molecules. For a given protein-protein complex submitted by the user, ANCHOR calculates the change in solvent accessible surface area (DeltaSASA) upon binding for each side-chain, along with an estimate of its contribution to the binding free energy. A Jmol-based tool allows the user to interactively visualize selected anchor residues in their pockets as well as the stereochemical properties of the surrounding region such as hydrogen bonding. ANCHOR includes a Protein Data Bank (PDB) wide database of pre-computed anchor residues from more than 30,000 PDB entries with at least two protein chains. The user can query according to amino acids, buried area (SASA), energy or keywords related to indication areas, e.g. oncogene or diabetes. This database provides a resource to rapidly assess protein-protein interactions for the suitability of small molecules or fragments with bioisostere anchor analogues as possible compounds for pharmaceutical intervention. ANCHOR web server and database are freely available at http://structure.pitt.edu/anchor.

1,4-Thienodiazepine-2,5-diones via MCR (I): synthesis, virtual space and p53-Mdm2 activity.

1,4-Thienodiazepine-2,5-diones have been synthesized via the Ugi-Deprotection-Cyclization (UDC) approach starting from Gewald 2-aminothiophenes in a convergent and versatile manner. The resulting scaffold is unprecedented, cyclic, and peptidomimetic with four points of diversity introduced from readily available starting materials. In addition to eighteen synthesized and characterized compounds, a virtual compound library was generated and evaluated for chemical space distribution and drug-like properties. A small focused compound library of 1,4-thienodiazepine-2,5-diones has been screened for the activity against p53-Mdm2 interaction. Biological evaluations demonstrated that some compounds exhibited promising antagonistic activity.

A Gibbs sampling approach to detection of tree motifs.

This paper introduces a method to detect tree patterns (tree motifs) in a database of rooted unordered labeled trees. The method can be viewed as an extension of the Gibbs sampling approach to detect sequence motifs. Basically, we enumerate tree topologies and for each topology we seek within the database for tree motifs with the given topology. A tree motif can be detected by matching the tree topology against the database of trees and then applying Gibbs sampling on the matching set. After completion of the process for a given tree topology, the process is restarted for the next enumerated tree topology. The method outputs for each topology the best tree motif found. We applied our method to an artificially created database of trees as well as to a database of carbohydrate (glycan) structures.