Covalent docking in CDOCKER

CHARMM at 45: Enhancements in Accessibility, Functionality, and Speed

Since its inception nearly a half century ago, CHARMM has been playing a central role in computational biochemistry and biophysics. Commensurate with the developments in experimental research and advances in computer hardware, the range of methods and applicability of CHARMM have also grown. This review summarizes major developments that occurred after 2009 when the last review of CHARMM was published. They include the following: new faster simulation engines, accessible user interfaces for convenient workflows, and a vast array of simulation and analysis methods that encompass quantum mechanical, atomistic, and coarse-grained levels, as well as extensive coverage of force fields. In addition to providing the current snapshot of the CHARMM development, this review may serve as a starting point for exploring relevant theories and computational methods for tackling contemporary and emerging problems in biomolecular systems. CHARMM is freely available for academic and nonprofit research at https://academiccharmm.org/program.

Lead generation of UPPS inhibitors targeting MRSA: Using 3D-QSAR pharmacophore modeling, virtual screening, molecular docking, and molecular dynamic simulations

Undecaprenyl Pyrophosphate Synthase (UPPS) is a vital target enzyme in the early stages of bacterial cell wall biosynthesis. UPPS inhibitors have antibacterial activity against resistant strains such as MRSA and VRE. In this study, we used several consecutive computer-based protocols to identify novel UPPS inhibitors. The 3D QSAR pharmacophore model generation (HypoGen algorithm) protocol was used to generate a valid predictive pharmacophore model using a set of UPPS inhibitors with known reported activity. The developed model consists of four pharmacophoric features: one hydrogen bond acceptor, two hydrophobic, and one aromatic ring. It had a correlation coefficient of 0.86 and a null cost difference of 191.39, reflecting its high predictive power. Hypo1 was proven to be statistically significant using Fischer's randomization at a 95% confidence level. The validated pharmacophore model was used for the virtual screening of several databases. The resulting hits were filtered using SMART and Lipinski filters. The hits were docked into the binding site of the UPPS protein, affording 70 hits with higher docking affinities than the reference compound (6TC, - 21.17 kcal/mol). The top five hits were selected through extensive docking analysis and visual inspection based on docking affinities, fit values, and key residue interactions with the UPPS receptor. Moreover, molecular dynamic simulations of the top hits were performed to confirm the stability of the protein-ligand complexes, yielding five promising novel UPPS inhibitors.

Identification, characterization, and CADD analysis of Plasmodium DMAP1 reveals it as a potential molecular target for new anti-malarial discovery

Developing drug resistance in the malaria parasite is a reason for apprehension compelling the scientific community to focus on identifying new molecular targets that can be exploited for developing new anti-malarial compounds. Despite the availability of the Plasmodium genome, many protein-coding genes in Plasmodium are still not characterized or very less information is available about their functions. DMAP1 protein is known to be essential for growth and plays an important role in maintaining genomic integrity and transcriptional repression in vertebrate organisms. In this study, we have identified a homolog of DMAP1 in P. falciparum. Our sequence and structural analysis showed that although PfDMAP1 possesses a conserved SANT domain, parasite protein displays significant structural dissimilarities from human homolog at full-length protein level as well as within its SANT domain. PPIN analysis of PfDMAP1 revealed it to be vital for parasite and virtual High-throughput screening of various pharmacophore libraries using BIOVIA platform-identified compounds that pass ADMET profiling and showed specific binding with PfDMAP1. Based on MD simulations and protein-ligand interaction studies two best hits were identified that could be novel potent inhibitors of PfDMAP1 protein.Communicated by Ramaswamy H. Sarma.

Two-Step Covalent Docking with Attracting Cavities

Due to their various advantages, interest in the development of covalent drugs has been renewed in the past few years. It is therefore important to accurately describe and predict their interactions with biological targets by computer-aided drug design tools such as docking algorithms. Here, we report a covalent docking procedure for our in-house docking code Attracting Cavities (AC), which mimics the two-step mechanism of covalent ligand binding. Ligand binding to the protein cavity is driven by nonbonded interactions, followed by the formation of a covalent bond between the ligand and the protein through a chemical reaction. To test the performance of this method, we developed a diverse, high-quality, openly accessible re-docking benchmark set of 95 covalent complexes bound by 8 chemical reactions to 5 different reactive amino acids. Combination with structures from previous studies resulted in a set of 304 complexes, on which AC obtained a success rate (rmsd ≤ 2 Å) of 78%, outperforming two state-of-the-art covalent docking codes, genetic optimization for ligand docking (GOLD (66%)) and AutoDock (AD (35%)). Using a more stringent success criterion (rmsd ≤ 1.5 Å), AC reached a success rate of 71 vs 55% for GOLD and 26% for AD. We additionally assessed the cross-docking performance of AC on a set of 76 covalent complexes of the SARS-CoV-2 main protease. On this challenging test set of mainly small and highly solvent-exposed ligands, AC yielded success rates of 58 and 28% for re-docking and cross-docking, respectively, compared to 45 and 17% for GOLD.