New opportunities for protease ligand-binding site comparisons using SitesBase

Jatrophidin I, a cyclic peptide from Brazilian Jatropha curcas L.: isolation, characterization, conformational studies and biological activity

A cyclic peptide, jatrophidin I, was isolated from the latex of Jatropha curcas L. Its structure was elucidated by extensive 2D NMR spectroscopic analysis, with additional conformational studies performed using Molecular Dynamics/Simulated Annealing (MD/SA). Jatrophidin I had moderate protease inhibition activity when compared with pepstatin A; however, the peptide was inactive in antimalarial, cytotoxic and antioxidant assays.

Substrate-driven mapping of the degradome by comparison of sequence logos

Sequence logos are frequently used to illustrate substrate preferences and specificity of proteases. Here, we employed the compiled substrates of the MEROPS database to introduce a novel metric for comparison of protease substrate preferences. The constructed similarity matrix of 62 proteases can be used to intuitively visualize similarities in protease substrate readout via principal component analysis and construction of protease specificity trees. Since our new metric is solely based on substrate data, we can engraft the protease tree including proteolytic enzymes of different evolutionary origin. Thereby, our analyses confirm pronounced overlaps in substrate recognition not only between proteases closely related on sequence basis but also between proteolytic enzymes of different evolutionary origin and catalytic type. To illustrate the applicability of our approach we analyze the distribution of targets of small molecules from the ChEMBL database in our substrate-based protease specificity trees. We observe a striking clustering of annotated targets in tree branches even though these grouped targets do not necessarily share similarity on protein sequence level. This highlights the value and applicability of knowledge acquired from peptide substrates in drug design of small molecules, e.g., for the prediction of off-target effects or drug repurposing. Consequently, our similarity metric allows to map the degradome and its associated drug target network via comparison of known substrate peptides. The substrate-driven view of protein-protein interfaces is not limited to the field of proteases but can be applied to any target class where a sufficient amount of known substrate data is available.

FINDSITE: a threading-based approach to ligand homology modeling

Ligand virtual screening is a widely used tool to assist in new pharmaceutical discovery. In practice, virtual screening approaches have a number of limitations, and the development of new methodologies is required. Previously, we showed that remotely related proteins identified by threading often share a common binding site occupied by chemically similar ligands. Here, we demonstrate that across an evolutionarily related, but distant family of proteins, the ligands that bind to the common binding site contain a set of strongly conserved anchor functional groups as well as a variable region that accounts for their binding specificity. Furthermore, the sequence and structure conservation of residues contacting the anchor functional groups is significantly higher than those contacting ligand variable regions. Exploiting these insights, we developed FINDSITE^LHM that employs structural information extracted from weakly related proteins to perform rapid ligand docking by homology modeling. In large scale benchmarking, using the predicted anchor-binding mode and the crystal structure of the receptor, FINDSITE^LHM outperforms classical docking approaches with an average ligand RMSD from native of ∼2.5 Å. For weakly homologous receptor protein models, using FINDSITE^LHM, the fraction of recovered binding residues and specific contacts is 0.66 (0.55) and 0.49 (0.38) for highly confident (all) targets, respectively. Finally, in virtual screening for HIV-1 protease inhibitors, using similarity to the ligand anchor region yields significantly improved enrichment factors. Thus, the rather accurate, computationally inexpensive FINDSITE^LHM algorithm should be a useful approach to assist in the discovery of novel biopharmaceuticals.

As an integral part of drug development, high-throughput virtual screening is a widely used tool that could in principle significantly reduce the cost and time to discovery of new pharmaceuticals. In practice, virtual screening algorithms suffer from a number of limitations. The high sensitivity of all-atom ligand docking approaches to the quality of the target receptor structure restricts the selection of drug targets to those for which high-quality X-ray structures are available. Furthermore, the predicted binding affinity is typically strongly correlated with the molecular weight of the ligand, independent of whether or not it really binds. To address these significant problems, we developed FINDSITE^LHM, a novel threading-based approach that employs structural information extracted from weakly related proteins to perform rapid ligand docking and ranking that is very much in the spirit of homology modeling of protein structures. Particularly for low-quality modeled receptor structures, FINDSITE^LHM outperforms classical all-atom ligand docking approaches in terms of the accuracy of ligand binding pose prediction and requires considerably less CPU time. As an attractive alternative to classical molecular docking, FINDSITE^LHM offers the possibility of rapid structure-based virtual screening at the proteome level to improve and speed up the discovery of new biopharmaceuticals.