QMrebind: incorporating quantum mechanical force field reparameterization at the ligand binding site for improved drug-target kinetics through milestoning simulations.

Understanding the interaction of ligands with biomolecules is an integral component of drug discovery and development. Challenges for computing thermodynamic and kinetic quantities for pharmaceutically relevant receptor-ligand complexes include the size and flexibility of the ligands, large-scale conformational rearrangements of the receptor, accurate force field parameters, simulation efficiency, and sufficient sampling associated with rare events. Our recently developed multiscale milestoning simulation approach, SEEKR2 (Simulation Enabled Estimation of Kinetic Rates v.2), has demonstrated success in predicting unbinding (koff) kinetics by employing molecular dynamics (MD) simulations in regions closer to the binding site. The MD region is further subdivided into smaller Voronoi tessellations to improve the simulation efficiency and parallelization. To date, all MD simulations are run using general molecular mechanics (MM) force fields. The accuracy of calculations can be further improved by incorporating quantum mechanical (QM) methods into generating system-specific force fields through reparameterizing ligand partial charges in the bound state. The force field reparameterization process modifies the potential energy landscape of the bimolecular complex, enabling a more accurate representation of the intermolecular interactions and polarization effects at the bound state. We present QMrebind (Quantum Mechanical force field reparameterization at the receptor-ligand binding site), an ORCA-based software that facilitates reparameterizing the potential energy function within the phase space representing the bound state in a receptor-ligand complex. With SEEKR2 koff estimates and experimentally determined kinetic rates, we compare and interpret the receptor-ligand unbinding kinetics obtained using the newly reparameterized force fields for model host-guest systems and HSP90-inhibitor complexes. This method provides an opportunity to achieve higher accuracy in predicting receptor-ligand koff rate constants.

Selectivity and Ranking of Tight-Binding JAK-STAT Inhibitors Using Markovian Milestoning with Voronoi Tessellations.

Janus kinases (JAK), a group of proteins in the nonreceptor tyrosine kinase (NRTKs) family, play a crucial role in growth, survival, and angiogenesis. They are activated by cytokines through the Janus kinase-signal transducer and activator of a transcription (JAK-STAT) signaling pathway. JAK-STAT signaling pathways have significant roles in the regulation of cell division, apoptosis, and immunity. Identification of the V617F mutation in the Janus homology 2 (JH2) domain of JAK2 leading to myeloproliferative disorders has stimulated great interest in the drug discovery community to develop JAK2-specific inhibitors. However, such inhibitors should be selective toward JAK2 over other JAKs and display an extended residence time. Recently, novel JAK2/STAT5 axis inhibitors (N-(1H-pyrazol-3-yl)pyrimidin-2-amino derivatives) have displayed extended residence times (hours or longer) on target and adequate selectivity excluding JAK3. To facilitate a deeper understanding of the kinase-inhibitor interactions and advance the development of such inhibitors, we utilize a multiscale Markovian milestoning with Voronoi tessellations (MMVT) approach within the Simulation-Enabled Estimation of Kinetic Rates v.2 (SEEKR2) program to rank order these inhibitors based on their kinetic properties and further explain the selectivity of JAK2 inhibitors over JAK3. Our approach investigates the kinetic and thermodynamic properties of JAK-inhibitor complexes in a user-friendly, fast, efficient, and accurate manner compared to other brute force and hybrid-enhanced sampling approaches.

DeepWEST: Deep Learning of Kinetic Models with the Weighted Ensemble Simulation Toolkit for Enhanced Sampling.

Recent advances in computational power and algorithms have enabled molecular dynamics (MD) simulations to reach greater time scales. However, for observing conformational transitions associated with biomolecular processes, MD simulations still have limitations. Several enhanced sampling techniques seek to address this challenge, including the weighted ensemble (WE) method, which samples transitions between metastable states using many weighted trajectories to estimate kinetic rate constants. However, initial sampling of the potential energy surface has a significant impact on the performance of WE, i.e., convergence and efficiency. We therefore introduce deep-learned kinetic modeling approaches that extract statistically relevant information from short MD trajectories to provide a well-sampled initial state distribution for WE simulations. This hybrid approach overcomes any statistical bias to the system, as it runs short unbiased MD trajectories and identifies meaningful metastable states of the system. It is shown to provide a more refined free energy landscape closer to the steady state that could efficiently sample kinetic properties such as rate constants.

Paper based field deployable sensor for naked eye monitoring of copper (II) ions; elucidation of binding mechanism by DFT studies.

The study demonstrates the fabrication of test strips made from newly synthesized ortho-Vanillin based colorimetric chemosensor (probe P) that could be employed as field deployable tool for rapid and naked eye detection of Cu2+. Upon addition of Cu2+ to the chemosensor, it exhibits rapid pink color from colorless and can be easily seen through the naked eye. This probe exhibits a remarkable colorimetric "ON" response and the absorbance intensity of the probe enhances significantly in presence of Cu2+. The sensing mechanism has been deduced using FTIR, XPS, LCMS and DFT studies. The binding mechanism of the probe to Cu2+ was substantiated by DFT studies. HOMO of the probe suggests that a high electronic density resides on O, N atoms and thus these are the favorable binding site for the metal ions. Study revealed that the P + Cu2+ complex is -35.64 eV more stable than individual reactants. The Cu2+ binds to the probe in 1:1 stoichiometry with a binding constant of 2.6 × 104 M-1 as calculated by Job's plot and Benesi-Hildebrand plot. The chemosensor shows 1.8 × 10-8 M detection limit, which is considerably lesser than that of the WHO admissible limit of [Cu2+] in drinking water. Possible interfering ions namely Ca2+, Mg2+, Fe2+, Co2+, Ni2+, Cd2+, Hg2+, Mn2+, Al3+ and Cr3+ do not show any appreciable interference in the colorimetric response of the probe towards Cu2+. Particularly, the colorimetric "ON-OFF-ON" responses are proved to be repeated over 5 times by the sequential inclusion of Cu2+ and S2-. Sensitivity of the probe in real-time water and blood samples is found at par with results with AAS and ICP-OES techniques. Further, the reversibility of the probe and the easy fabrication of deployable strips for real-field naked eye detection of Cu2+ suggest importance of synthesized probe.

Water-Stable Nanoscale Zirconium-Based Metal-Organic Frameworks for the Effective Removal of Glyphosate from Aqueous Media.

Two water-stable zirconium-based metal-organic frameworks (MOFs) (NU-1000 and UiO-67) have been synthesized in various size scales (100-2000 nm) for the adsorptive removal of glyphosate from the aqueous media. Both NU-1000 and UiO-67 possess a three-dimensional structure; NU-1000 consists of triangular micropores and wide mesoporous channels (31 Å), whereas UiO-67 has cage-like pores [octahedral (16 Å) and tetrahedral (14 Å) cages]. NU-1000 comprises Zr6(µ3-O)4(µ3-OH)4(H2O)4(OH)4, and UiO-67 contains Zr6O4(OH)4 as secondary building units. These units act as Lewis acid nodes and can interact with the Lewis base phosphate group of the glyphosate. The time taken for reaching equilibrium is found to be reduced considerably as the size of the MOF decreases. The smaller the particle size, the lesser is the diffusion barrier for the analyte, which enhances the interaction between Lewis acidic metal nodes and the Lewis basic center of the glyphosate molecule. NU-1000 was found to be better compared to UiO-67, both in terms of efficiency and reusability. This might be due to the larger pore diameters of the NU-1000. Theoretical calculations revealed that the interaction energy of glyphosate with the nodes of NU-1000 is higher (-37.63 KJ mol-1) compared to UiO-67 (-17.37 KJ mol-1), which might be the possible reason for the higher efficiency of NU-1000.

"Turn-on" Fluorescence Sensing and Discriminative Detection of Aliphatic Amines Using a 5-Fold-Interpenetrated Coordination Polymer.

A 5-fold-interpenetrated zinc-based coordination polymer can discriminately detect aliphatic amines through a fluorescence "turn-on" method. This compound can sense aliphatic amines in the solid state, solution state, and vapor phase. Theoretical calculations revealed that the ground-state dipole moment of the corresponding amines guides the order of enhancement.