ProFitFun: a protein tertiary structure fitness function for quantifying the accuracies of model structures.

MOTIVATION: An accurate estimation of the quality of protein model structures typifies as a cornerstone in protein structure prediction regimes. Despite the recent groundbreaking success in the field of protein structure prediction, there are certain prospects for the improvement in model quality estimation at multiple stages of protein structure prediction and thus, to further push the prediction accuracy. Here, a novel approach, named ProFitFun, for assessing the quality of protein models is proposed by harnessing the sequence and structural features of experimental protein structures in terms of the preferences of backbone dihedral angles and relative surface accessibility of their amino acid residues at the tripeptide level. The proposed approach leverages upon the backbone dihedral angle and surface accessibility preferences of the residues by accounting for its N-terminal and C-terminal neighbors in the protein structure. These preferences are used to evaluate protein structures through a machine learning approach and tested on an extensive dataset of diverse proteins. RESULTS: The approach was extensively validated on a large test dataset (n = 25 005) of protein structures, comprising 23 661 models of 82 non-homologous proteins and 1344 non-homologous experimental structures. In addition, an external dataset of 40 000 models of 200 non-homologous proteins was also used for the validation of the proposed method. Both datasets were further used for benchmarking the proposed method with four different state-of-the-art methods for protein structure quality assessment. In the benchmarking, the proposed method outperformed some state-of-the-art methods in terms of Spearman's and Pearson's correlation coefficients, average GDT-TS loss, sum of z-scores and average absolute difference of predictions over corresponding observed values. The high accuracy of the proposed approach promises a potential use of the sequence and structural features in computational protein design. AVAILABILITY AND IMPLEMENTATION: http://github.com/KYZ-LSB/ProTerS-FitFun. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Structure-based virtual screening of highly potent inhibitors of the nematode chitinase CeCht1.

Nematode chitinases play vital roles in various physiological processes, including egg hatching, larva moulting, and reproduction. Small-molecule inhibitors of nematode chitinases have potential applications for controlling nematode pests. On the basis of the crystal structure of CeCht1, a representative chitinase indispensable to the eggshell chitin degradation of the model nematode Caenorhabditis elegans, we have discovered a series of novel inhibitors bearing a (R)-3,4-diphenyl-4,5-dihydropyrrolo[3,4-c]pyrazol-6(2H)-one scaffold by hierarchical virtual screening. The crystal structures of CeCht1 complexed with two of these inhibitors clearly elucidated their interactions with the enzyme active site. Based on the inhibitory mechanism, several analogues with improved inhibitory activities were identified, among which the compound PP28 exhibited the most potent activity with a Ki value of 0.18 µM. This work provides the structural basis for the development of novel nematode chitinase inhibitors.

Identification of a Selective RelA Inhibitor Based on DSE-FRET Screening Methods.

Nuclear factor-κB (NF-κB) is an important transcription factor involved in various biological functions, including tumorigenesis. Hence, NF-κB has attracted attention as a target factor for cancer treatment, leading to the development of several inhibitors. However, existing NF-κB inhibitors do not discriminate between its subunits, namely, RelA, RelB, cRel, p50, and p52. Conventional methods used to evaluate interactions between transcription factors and DNA, such as electrophoretic mobility shift assay and luciferase assays, are unsuitable for high-throughput screening (HTS) and cannot distinguish NF-κB subunits. We developed a HTS method named DNA strand exchange fluorescence resonance energy transfer (DSE-FRET). This assay is suitable for HTS and can discriminate a NF-κB subunit. Using DSE-FRET, we searched for RelA-specific inhibitors and verified RelA inhibition for 32,955 compounds. The compound A55 (2-(3-carbamoyl-6-hydroxy-4-methyl-2-oxopyridin-1(2H)-yl) acetic acid) selectively inhibited RelA-DNA binding. We propose that A55 is a seed compound for RelA-specific inhibition and could be used in clinical applications.

Discovery of Fungal Denitrification Inhibitors by Targeting Copper Nitrite Reductase from Fusarium oxysporum.

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.

Application of Shape Similarity in Pose Selection and Virtual Screening in CSARdock2014 Exercise.

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 (r(2)) 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.

Hierarchical virtual screening approaches in small molecule drug discovery.

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.