Drug discovery utilizes chemical biology and computational drug design approaches for the efficient identification and optimization of lead compounds. Chemical biology is mostly involved in the elucidation of the biological function of a target and the mechanism of action of a chemical modulator. On the other hand, computer-aided drug design makes use of the structural knowledge of either the target (structure-based) or known ligands with bioactivity (ligand-based) to facilitate the determination of promising candidate drugs. Various virtual screening techniques are now being used by both pharmaceutical companies and academic research groups to reduce the cost and time required for the discovery of a potent drug. Despite the rapid advances in these methods, continuous improvements are critical for future drug discovery tools. Advantages presented by structure-based and ligand-based drug design suggest that their complementary use, as well as their integration with experimental routines, has a powerful impact on rational drug design. In this article, we give an overview of the current computational drug design and their application in integrated rational drug development to aid in the progress of drug discovery research.

Cancer is the second leading cause of death globally, and use of therapeutic peptides to target and kill cancer cells has received considerable attention in recent years. Identification of anticancer peptides (ACPs) through wet-lab experimentation is expensive and often time consuming; therefore, development of an efficient computational method is essential to identify potential ACP candidates prior to in vitro experimentation. In this study, we developed support vector machine- and random forest-based machine-learning methods for the prediction of ACPs using the features calculated from the amino acid sequence, including amino acid composition, dipeptide composition, atomic composition, and physicochemical properties. We trained our methods using the Tyagi-B dataset and determined the machine parameters by 10-fold cross-validation. Furthermore, we evaluated the performance of our methods on two benchmarking datasets, with our results showing that the random forest-based method outperformed the existing methods with an average accuracy and Matthews correlation coefficient value of 88.7% and 0.78, respectively. To assist the scientific community, we also developed a publicly accessible web server at www.thegleelab.org/MLACP.html.

Knowledge of the cellular targets of ROS (reactive oxygen species) and their regulation is an essential prerequisite for understanding ROS-mediated signalling. GAPDH (glyceraldehyde-3-phosphate dehydrogenase) is known as a major target protein in oxidative stresses and becomes thiolated in its active site. However, the molecular and functional changes of oxidized GAPDH, the inactive form, have not yet been characterized. To examine the modifications of GAPDH under oxidative stress, we separated the oxidation products by two-dimensional gel electrophoresis and identified them using nanoLC-ESI-q-TOF MS/MS (nano column liquid chromatography coupled to electrospray ionization quadrupole time-of-flight tandem MS). Intracellular GAPDH subjected to oxidative stress separated into multiple acidic spots on two-dimensional gel electrophoresis and were identified as cysteine disulfide and cysteic acids on Cys152 in the active site. We identified the interacting proteins of oxidized inactive GAPDH as p54nrb (54 kDa nuclear RNA-binding protein) and PSF (polypyrimidine tract-binding protein-associated splicing factor), both of which are known to exist as heterodimers and bind to RNA and DNA. Interaction between oxidized GAPDH and p54nrb was abolished upon expression of the GAPDH active site mutant C152S. The C-terminal of p54nrb binds to GAPDH in the cytosol in a manner dependent on the dose of hydrogen peroxide. The GAPDH-p54nrb complex enhances the intrinsic topoisomerase I activation by p54nrb-PSF binding. These results suggest that GAPDH exerts other functions beyond glycolysis, and that oxidatively modified GAPDH regulates its cellular functions by changing its interacting proteins, i.e. the RNA splicing by interacting with the p54nrb-PSF complex.

Redox-active cysteine, a highly reactive sulfhydryl, is one of the major targets of ROS. Formation of disulfide bonds and other oxidative derivatives of cysteine including sulfenic, sulfinic, and sulfonic acids, regulates the biological function of various proteins. We identified novel lowabundant cysteine modifications in cellular GAPDH purified on 2-dimensional gel electrophoresis (2D-PAGE) by employing selectively excluded mass screening analysis for nano ultraperformance liquid chromatography-electrospray-quadrupole-time of flight tandem mass spectrometry, in conjunction with MOD i and MODmap algorithm. We observed unexpected mass shifts (⌬m ‫؍‬ ؊16, ؊34, ؉64, ؉87, and ؉103 Da) at redox-active cysteine residue in cellular GAPDH purified on 2D-PAGE, in oxidized NDP kinase A, peroxiredoxin 6, and in various mitochondrial proteins. Mass differences of ؊16, ؊34, and ؉64 Da are presumed to reflect the conversion of cysteine to serine, dehydroalanine (DHA), and Cys-SO 2 -SH respectively. To determine the plausible pathways to the formation of these products, we prepared model compounds and examined the hydrolysis and hydration of thiosulfonate (Cys-S-SO 2 -Cys) either to DHA (⌬m ‫؍‬ ؊34 Da) or serine along with Cys-SO 2 -SH (⌬m ‫؍‬ ؉64 Da). We also detected acrylamide adducts of sulfenic and sulfinic acids (؉87 and ؉103 Da). These findings suggest that oxidations take place at redox-active cysteine residues in cellular proteins, with the formation of thiosulfonate, Cys-SO 2 -SH, and DHA, and conversion of cysteine to serine, in addition to sulfenic, sulfinic and sulfonic acids of reactive cysteine.

The primary goal of rational drug discovery is the identification of selective ligands which act on single or multiple drug targets to achieve the desired clinical outcome through the exploration of total chemical space. To identify such desired compounds, computational approaches are necessary in predicting their drug-like properties. G Protein-Coupled Receptors (GPCRs) represent one of the largest and most important integral membrane protein families. These receptors serve as increasingly attractive drug targets due to their relevance in the treatment of various diseases, such as inflammatory disorders, metabolic imbalances, cardiac disorders, cancer, monogenic disorders, etc. In the last decade, multitudes of three-dimensional (3D) structures were solved for diverse GPCRs, thus referring to this period as the “golden age for GPCR structural biology.” Moreover, accumulation of data about the chemical properties of GPCR ligands has garnered much interest toward the exploration of GPCR chemical space. Due to the steady increase in the structural, ligand, and functional data of GPCRs, several cheminformatics approaches have been implemented in its drug discovery pipeline. In this review, we mainly focus on the cheminformatics-based paradigms in GPCR drug discovery. We provide a comprehensive view on the ligand– and structure-based cheminformatics approaches which are best illustrated via GPCR case studies. Furthermore, an appropriate combination of ligand-based knowledge with structure-based ones, i.e., integrated approach, which is emerging as a promising strategy for cheminformatics-based GPCR drug design is also discussed.