The efficient application of nitrogenous fertilizers is urgently required, as their excessive and inefficient use is causing substantial economic loss and environmental pollution. A significant amount of applied nitrogen in agricultural soils is lost as nitrous oxide (N2O) in the environment due to the microbial denitrification process. The widely distributed fungus Fusarium oxysporum is a major denitrifier in agricultural soils and its denitrification activity could be targeted to reduce nitrogen loss in the form of N2O from agricultural soils. Here, we report the discovery of first small molecule inhibitors of copper nitrite reductase (NirK) from F. oxysporum, which is a key enzyme in the fungal denitrification process. The inhibitors were discovered by a hierarchical in silico screening approach consisting of pharmacophore modeling and molecular docking. In vitro evaluation of F. oxysporum NirK activity revealed several pyrimidone and triazinone based compounds with potency in the low micromolar range. Some of these compounds suppressed the fungal denitrification in vivo as well. The compounds reported here could be used as starting points for the development of nitrogenous fertilizer supplements and coatings as a means to prevent nitrogen loss by targeting fungal denitrification.

Chitinases play important roles in pathogen invasion, arthropod molting, plant defense, and human inflammation. Inhibition of the activity of a typical chitinase by small molecules is of significance in drug development and biological research. On the basis of a recent reported crystal structure of OfChtI, the insect chitinase derived from the pest Ostrinia furnacalis, we computationally identified 17 compounds from a library of over 4 million chemicals by two rounds virtual screening. Among these, three compounds from one chemical class inhibited the activity of OfChtI with single-digit-micromolar IC50 values, and one compound from another chemical class exhibited a broad inhibitory activity not only toward OfChtI but also toward bacterial, fungal, and human chitinases. A new scaffold was discovered, and a structure–inhibitory activity relationship was proposed. This work may provide a novel starting point for the development of specific or broad-spectrum chitinase inhibitors.

To evaluate the applicability of shape similarity in docking-based pose selection and virtual screening, we participated in the CSARdock2014 benchmark exercise for identifying the correct docking pose of inhibitors targeting factor XA, spleen tyrosine kinase, and tRNA methyltransferase. This exercise provides a valuable opportunity for researchers to test their docking programs, methods, and protocols in a blind testing environment. In the CSARdock2014 benchmark exercise, we have implemented an approach that uses ligand 3D shape similarity to facilitate docking-based pose selection and virtual screening. We showed here that ligand 3D shape similarity between bound poses could be used to identify the native-like pose from an ensemble of docking-generated poses. Our method correctly identified the native pose as the top-ranking pose for 73% of test cases in a blind testing environment. Moreover, the pose selection results also revealed an excellent correlation between ligand 3D shape similarity scores and RMSD to X-ray crystal structure ligand. In the virtual screening exercise, the average RMSD for our pose prediction was found to be 1.02 Å, and it was one of the top performances achieved in CSARdock2014 benchmark exercise. Furthermore, the inclusion of shape similarity improved virtual screening performance of docking-based scoring and ranking. The coefficient of determination (r2) between experimental activities and docking scores for 276 spleen tyrosine kinase inhibitors was found to be 0.365 but reached 0.614 when the ligand 3D shape similarity was included.

Sumoylation involves the enzymatic conjugation of small ubiquitin-like modifier (SUMO) protein to their substrate proteins. Sumoylation is not only crucial for maintaining normal cellular physiology but also implicated in the development of several diseases including cancer. SUMO E1, the first protein in sumoylation pathway is of particular significance due to its confirmed role in tumorogenesis. However, notwithstanding its role as potential drug target, only a few small molecule inhibitors of SUMO E1 have been identified. Here, we report the identification of pyrazole and thiazole urea containing compounds as inhibitors of SUMO E1. We have utilized 3D-shape matching, electrostatic potential similarity evaluations and molecular docking to scaffold hop from previously known aryl urea scaffold with SUMO E1 activity to thiazole and pyrazole urea based scaffolds. These two classes of compounds were found to have moderate SUMO E1 inhibitory activity and can be used as starting points for the development of highly potent lead compounds against cancer.Download high-res image (102KB)Download full-size image

Molecular docking predicts the best pose of a ligand in the target protein binding site by sampling and scoring numerous conformations and orientations of the ligand. Failures in pose prediction are often due to either insufficient sampling or scoring function errors. To improve the accuracy of pose prediction by tackling the sampling problem, we have developed a method of pose prediction using shape similarity. It first places a ligand conformation of the highest 3D shape similarity with known crystal structure ligands into protein binding site and then refines the pose by repacking the side-chains and performing energy minimization with a Monte Carlo algorithm. We have assessed our method utilizing CSARdock 2012 and 2014 benchmark exercise datasets consisting of co-crystal structures from eight proteins. Our results revealed that ligand 3D shape similarity could substitute conformational and orientational sampling if at least one suitable co-crystal structure is available. Our method identified poses within 2 Å RMSD as the top-ranking pose for 85.7 % of the test cases. The median RMSD for our pose prediction method was found to be 0.81 Å and was better than methods performing extensive conformational and orientational sampling within target protein binding sites. Furthermore, our method was better than similar methods utilizing ligand 3D shape similarity for pose prediction.

Evaluation of ligand three-dimensional (3D) shape similarity is one of the commonly used approaches to identify ligands similar to one or more known active compounds from a library of small molecules. Apart from using ligand shape similarity as a virtual screening tool, its role in pose prediction and pose scoring has also been reported. We have recently developed a method that utilizes ligand 3D shape similarity with known crystallographic ligands to predict binding poses of query ligands. Here, we report the prospective evaluation of our pose prediction method through the participation in drug design data resource (D3R) Grand Challenge 2015. Our pose prediction method was used to predict binding poses of heat shock protein 90 (HSP90) and mitogen activated protein kinase kinase kinase kinase (MAP4K4) ligands and it was able to predict the pose within 2 Å root mean square deviation (RMSD) either as the top pose or among the best of five poses in a majority of cases. Specifically for HSP90 protein, a median RMSD of 0.73 and 0.68 Å was obtained for the top and the best of five predictions respectively. For MAP4K4 target, although the median RMSD for our top prediction was only 2.87 Å but the median RMSD of 1.67 Å for the best of five predictions was well within the limit for successful prediction. Furthermore, the performance of our pose prediction method for HSP90 and MAP4K4 ligands was always among the top five groups. Particularly, for MAP4K4 protein our pose prediction method was ranked number one both in terms of mean and median RMSD when the best of five predictions were considered. Overall, our D3R Grand Challenge 2015 results demonstrated that ligand 3D shape similarity with the crystal ligand is sufficient to predict binding poses of new ligands with acceptable accuracy.

Small ubiquitin like modifier (SUMO) specific proteases (SENPs) are cysteine proteases that carry out the proteolytic processing of SUMO from its pro form as well as the deconjugation of SUMO from substrate proteins. SENPs are attractive targets for drug discovery due to their crucial role in the development of various diseases. However, the SENPs inhibitor discovery efforts were limited, and only a few inhibitors or activity based probes have been identified until now. Here, we report a new class of SENP2 inhibitors identified by a combination of structure based virtual screening and quantitative FRET based assay. Our virtual screening protocol initially involves the identification of small molecules that have similar shape and electrostatic properties with the conjugate of SUMO1 C-terminal residues and substrate lysine. Molecular docking was then used to prioritize these small molecules for a FRET based assay that quantifies their SENP2 endopeptidase activity. The initial round of virtual screening followed by FRET based assay has enabled the identification of eight compounds with >40% SENP2 inhibition at 30 μM compound concentration. Five of these compounds belong to two scaffolds containing a 1,2,5-oxadiazole core that represent a novel class of SENP2 inhibitors. To improve the inhibitory potency and explore the structure–activity relationship of these two 1,2,5-oxadiazole scaffolds, structurally related compounds were identified in another round of virtual screening. The biological assay results confirmed SENP2 inhibitory activity of these two scaffolds. The most potent compound of each scaffold showed an IC50 of 5.9 and 3.7 μM. Most of the compounds also inhibited closely related isoform SENP1, while no detectable inhibition on other proteases, such as papain and trypsin, was observed. Our study suggests that 1,2,5-oxadiazoles could be used as a starting point for the development of novel therapeutic agents against various diseases targeting SENPs.

Sumoylation is a post-translational modification that plays an important role in a wide range of cellular processes. Among the proteins involved in the sumoylation pathway, Ubc9 is the sole E2-conjugating enzyme required for sumoylation and plays a central role by interacting with almost all of the partners required for sumoylation. Ubc9 has been implicated in a variety of human malignancies. In order to exploit the therapeutic potential of Ubc9, we have identified the potential site to target for rational drug design using molecular modeling approaches. The structural information derived was then used to prioritize hits from a small-molecule library for biological assay using a virtual screening protocol that involves shape matching with a known inhibitor inhibitors and docking of a small-molecule library utilizing computational approaches that incorporate both ligand and protein flexibility. Nineteen compounds were acquired from different chemical vendors and were tested for Ubc9 inhibitory activity. Five compounds showed inhibitory activity against Ubc9, out of which one compound was selected for further optimization. A similarity search was then carried out to retrieve commercially available derivatives, which were further acquired and assayed, resulting in two compounds with acceptable potency. These two compounds can be used as starting points for the development of more potent inhibitors of Ubc9 targeting the predicted site.

The SUMO–SIM is a challenging protein–protein interaction drug target. We present a virtual screening approach incorporating the consensus of protein interactions that led to the discovery of non-peptidic inhibitors. The most potent inhibitors have low micromolar potency and the binding affinity and interface was validated using multiple assays and HSQC-NMR.

The SAMPL challenges provide an ideal opportunity for unbiased evaluation and comparison of different approaches used in computational drug design. During the fourth round of this SAMPL challenge, we participated in the virtual screening and binding pose prediction on inhibitors targeting the HIV-1 integrase enzyme. For virtual screening, we used well known and widely used in silico methods combined with personal in cerebro insights and experience. Regular docking only performed slightly better than random selection, but the performance was significantly improved upon incorporation of additional filters based on pharmacophore queries and electrostatic similarities. The best performance was achieved when logical selection was added. For the pose prediction, we utilized a similar consensus approach that amalgamated the results of the Glide-XP docking with structural knowledge and rescoring. The pose prediction results revealed that docking displayed reasonable performance in predicting the binding poses. However, prediction performance can be improved utilizing scientific experience and rescoring approaches. In both the virtual screening and pose prediction challenges, the top performance was achieved by our approaches. Here we describe the methods and strategies used in our approaches and discuss the rationale of their performances.

The modular structure of many protein families, such as β-propeller proteins, strongly implies that duplication played an important role in their evolution, leading to highly symmetrical intermediate forms. Previous attempts to create perfectly symmetrical propeller proteins have failed, however. We have therefore developed a new and rapid computational approach to design such proteins. As a test case, we have created a sixfold symmetrical β-propeller protein and experimentally validated the structure using X-ray crystallography. Each blade consists of 42 residues. Proteins carrying 2–10 identical blades were also expressed and purified. Two or three tandem blades assemble to recreate the highly stable sixfold symmetrical architecture, consistent with the duplication and fusion theory. The other proteins produce different monodisperse complexes, up to 42 blades (180 kDa) in size, which self-assemble according to simple symmetry rules. Our procedure is suitable for creating nano-building blocks from different protein templates of desired symmetry.

Water molecules are routinely included in molecular docking methods and protocols because of their important role in mediating ligand protein interactions. However, it is still unclear that the inclusion of explicit water molecules improves docking accuracy. To explore the effect of including key water molecules on docking accuracy and performance, we participated in the CSARdock 2011 benchmark exercise. This exercise provides a valuable opportunity for researchers to test their docking programs, methods, and protocols in a blind testing environment. The benchmark exercise and its analysis presented in this paper showed that the performance of current docking programs can be improved by incorporating carefully selected water molecules. Our study showed that water mapping calculations can be used to select key water molecules from experimentally identified water positions for molecular dockings. We have observed that inclusion of all binding site water molecules led to reduced performance and erroneous results. Moreover, an overall improvement in binding pose prediction was achieved when computationally selected water molecules are included during docking simulations. The improvement in the docking performance by including water molecules also depends on protein system, chemical class of ligand, docking method, and scoring function.

SUMO activating enzyme 1 (SUMO E1) is responsible for the activation of SUMO in the first step of the sumoylation cascade. SUMO E1 is linked to many human diseases including cancer, thus making it a potential therapeutic target. There are few reported SUMO E1 inhibitors including several natural products. To identify small molecule inhibitors of SUMO E1 with better drug-like properties for potential therapeutic studies, we have used structure-based virtual screening to identify hits from the Maybridge small molecule library for biological assay. Our virtual screening protocol involves fast docking of the entire small molecule library with rigid protein and ligands followed by redocking of top hits using a method that incorporates both ligand and protein flexibility. Subsequently, the top-ranking compounds were prioritized using the molecular dynamics simulation-based binding free energy calculation. Out of 24 compounds that were acquired and tested using in vitro sumoylation assay, four of them showed more than 85% inhibition of sumoylation with the most active compound showing an IC50 of 14.4 μM. A similarity search with the most active compound in the ZINC database has identified three more compounds with improved potency. These compounds share a common phenyl urea scaffold and have been confirmed to inhibit SUMO E1 by in vitro SUMO-1 thioester bond formation assay. Our study suggests that these phenyl urea compounds could be used as a starting point for the development of novel therapeutic agents.

SUMO activating enzyme 1 (SUMO E1) is the first enzyme in sumoylation pathway and an important cancer drug target. However, only a few inhibitors were reported up to now that includes three natural products, semi-synthetic protein inhibitors and one AMP mimic. Here, we report the identification of quinazolinyloxy biaryl urea as a new class of SUMO E1 inhibitors. The most active compound of this class inhibited the in vitro sumoylation with an IC50 of 13.4 μM. This compound inhibits sumoylation by blocking the formation of SUMOE1-SUMO thioester intermediate. The biological activity of the most active compound is comparable to previously reported inhibitors with properties suitable for medicinal chemistry optimization for potency and druggability.

SAMPL3 fragment based virtual screening challenge provides a valuable opportunity for researchers to test their programs, methods and screening protocols in a blind testing environment. We participated in SAMPL3 challenge and evaluated our virtual fragment screening protocol, which involves RosettaLigand as the core component by screening a 500 fragments Maybridge library against bovine pancreatic trypsin. Our study reaffirmed that the real test for any virtual screening approach would be in a blind testing environment. The analyses presented in this paper also showed that virtual screening performance can be improved, if a set of known active compounds is available and parameters and methods that yield better enrichment are selected. Our study also highlighted that to achieve accurate orientation and conformation of ligands within a binding site, selecting an appropriate method to calculate partial charges is important. Another finding is that using multiple receptor ensembles in docking does not always yield better enrichment than individual receptors. On the basis of our results and retrospective analyses from SAMPL3 fragment screening challenge we anticipate that chances of success in a fragment screening process could be increased significantly with careful selection of receptor structures, protein flexibility, sufficient conformational sampling within binding pocket and accurate assignment of ligand and protein partial charges.

•Review of hierarchical virtual screening approaches is presented.•Hierarchical combination of ligand and structure-based methods is preferred over parallel combination.•Molecular docking is a key component of many hierarchical virtual screening schemes.•Hierarchical virtual screening may be useful in plucking high-hanging fruits.Virtual screening has played a significant role in the discovery of small molecule inhibitors of therapeutic targets in last two decades. Various ligand and structure-based virtual screening approaches are employed to identify small molecule ligands for proteins of interest. These approaches are often combined in either hierarchical or parallel manner to take advantage of the strength and avoid the limitations associated with individual methods. Hierarchical combination of ligand and structure-based virtual screening approaches has received noteworthy success in numerous drug discovery campaigns. In hierarchical virtual screening, several filters using ligand and structure-based approaches are sequentially applied to reduce a large screening library to a number small enough for experimental testing. In this review, we focus on different hierarchical virtual screening strategies and their application in the discovery of small molecule modulators of important drug targets. Several virtual screening studies are discussed to demonstrate the successful application of hierarchical virtual screening in small molecule drug discovery.