Identification of potent schistosomicidal compounds predicted as type II-kinase inhibitors against c-Jun N-terminal kinase SMJNK.

Introduction: Schistosomiasis has for many years relied on a single drug, praziquantel (PZQ) for treatment of the disease. Immense efforts have been invested in the discovery of protein kinase (PK) inhibitors; however, given that the majority of PKs are still not targeted by an inhibitor with a useful level of selectivity, there is a compelling need to expand the chemical space available for synthesizing new, potent, and selective PK inhibitors. Small-molecule inhibitors targeting the ATP pocket of the catalytic domain of PKs have the potential to become drugs devoid of (major) side effects, particularly if they bind selectively.

Antimicrobial activity of compounds identified by artificial intelligence discovery engine targeting enzymes involved in Neisseria gonorrhoeae peptidoglycan metabolism.

Background: Neisseria gonorrhoeae (Ng) causes the sexually transmitted disease gonorrhoea. There are no vaccines and infections are treated principally with antibiotics. However, gonococci rapidly develop resistance to every antibiotic class used and there is a need for developing new antimicrobial treatments.

Discovery of a cryptic pocket in the AI-predicted structure of PPM1D phosphatase explains the binding site and potency of its allosteric inhibitors.

Virtual screening is a widely used tool for drug discovery, but its predictive power can vary dramatically depending on how much structural data is available. In the best case, crystal structures of a ligand-bound protein can help find more potent ligands. However, virtual screens tend to be less predictive when only ligand-free crystal structures are available, and even less predictive if a homology model or other predicted structure must be used.

Discovery of a cryptic pocket in the AI-predicted structure of PPM1D phosphatase explains the binding site and potency of its allosteric inhibitors.

Virtual screening is a widely used tool for drug discovery, but its predictive power can vary dramatically depending on how much structural data is available. In the best case, crystal structures of a ligand-bound protein can help find more potent ligands. However, virtual screens tend to be less predictive when only ligand-free crystal structures are available, and even less predictive if a homology model or other predicted structure must be used.

A chemical probe targeting the PWWP domain alters NSD2 nucleolar localization.

Nuclear receptor-binding SET domain-containing 2 (NSD2) is the primary enzyme responsible for the dimethylation of lysine 36 of histone 3 (H3K36), a mark associated with active gene transcription and intergenic DNA methylation. In addition to a methyltransferase domain, NSD2 harbors two proline-tryptophan-tryptophan-proline (PWWP) domains and five plant homeodomains (PHDs) believed to serve as chromatin reading modules. Here, we report a chemical probe targeting the N-terminal PWWP (PWWP1) domain of NSD2.

An atypical heterotrimeric Gα protein has substantially reduced nucleotide binding but retains nucleotide-independent interactions with its cognate RGS protein and Gβγ dimer.

Plants uniquely have a family of proteins called extra-large G proteins (XLG) that share homology in their C-terminal half with the canonical Gα subunits; we carefully detail here that Arabidopsis XLG2 lacks critical residues requisite for nucleotide binding and hydrolysis which is consistent with our quantitative analyses. Based on microscale thermophoresis, Arabidopsis XLG2 binds GTPγS with an affinity 100 times lower than that to canonical Gα subunits. This means that given the concentration range of guanine nucleotide in plant cells, XLG2 is not likely bound by GTP .

Off-Pocket Activity Cliffs: A Puzzling Facet of Molecular Recognition.

While accurate quantitative prediction of ligand-protein binding affinity remains an elusive goal, high-affinity ligands to therapeutic targets are being designed through heuristic optimization of ligand-protein contacts. However, herein, through large-scale data mining and analyses, we demonstrate that a ligand's binding can also be strongly affected through modifying its solvent-exposed portion that does not make contacts with the protein, thus resulting in "off-pocket activity cliffs" (). We then exposed the roots of the OAC phenomenon by means of molecular dynamics () simulations and MD data analyses.

Discovery of selective activators of PRC2 mutant EED-I363M.

Many common disease-causing mutations result in loss-of-function (LOF) of the proteins in which they occur. LOF mutations have proven recalcitrant to pharmacologic intervention, presenting a challenge for the development of targeted therapeutics. Polycomb repressive complex 2 (PRC2), which contains core subunits (EZH2, EED, and SUZ12), regulates gene activity by trimethylation of histone 3 lysine 27.

Site of Tagging Influences the Ochratoxin Recognition by Peptide NFO4: A Molecular Dynamics Study.

Molecular recognition by synthetic peptides is growing in importance in the design of biosensing elements used in the detection and monitoring of a wide variety of hapten bioanlaytes. Conferring specificity via bioimmobilization and subsequent recovery and purification of such sensing elements are aided by the use of affinity tags. However, the tag and its site of placement can potentially compromise the hapten recognition capabilities of the peptide, necessitating a detailed experimental characterization and optimization of the tagged molecular recognition entity.

Application of advanced sampling and analysis methods to predict the structure of adsorbed protein on a material surface.

The use of standard molecular dynamics simulation methods to predict the interactions of a protein with a material surface have the inherent limitations of lacking the ability to determine the most likely conformations and orientations of the adsorbed protein on the surface and to determine the level of convergence attained by the simulation. In addition, standard mixing rules are typically applied to combine the nonbonded force field parameters of the solution and solid phases the system to represent interfacial behavior without validation. As a means to circumvent these problems, the authors demonstrate the application of an efficient advanced sampling method (TIGER2A) for the simulation of the adsorption of hen egg-white lysozyme on a crystalline (110) high-density polyethylene surface plane.

Cluster analysis of molecular simulation trajectories for systems where both conformation and orientation of the sampled states are important.

Clustering methods have been widely used to group together similar conformational states from molecular simulations of biomolecules in solution. For applications such as the interaction of a protein with a surface, the orientation of the protein relative to the surface is also an important clustering parameter because of its potential effect on adsorbed-state bioactivity. This study presents cluster analysis methods that are specifically designed for systems where both molecular orientation and conformation are important, and the methods are demonstrated using test cases of adsorbed proteins for validation.

Parameterization of an interfacial force field for accurate representation of peptide adsorption free energy on high-density polyethylene.

Interfacial force field (IFF) parameters for use with the CHARMM force field have been developed for interactions between peptides and high-density polyethylene (HDPE). Parameterization of the IFF was performed to achieve agreement between experimental and calculated adsorption free energies of small TGTG-X-GTGT host-guest peptides (T = threonine, G = glycine, and X = variable amino-acid residue) on HDPE, with ±0.5 kcal/mol agreement.

Development of a tuned interfacial force field parameter set for the simulation of protein adsorption to silica glass.

Adsorption free energies for eight host-guest peptides (TGTG-X-GTGT, with X = N, D, G, K, F, T, W, and V) on two different silica surfaces [quartz (100) and silica glass] were calculated using umbrella sampling and replica exchange molecular dynamics and compared with experimental values determined by atomic force microscopy. Using the CHARMM force field, adsorption free energies were found to be overestimated (i.e.