Deciphering the Selectivity of CBL-B Inhibitors Using All-Atom Molecular Dynamics and Machine Learning.

We employ a combination of accelerated molecular dynamics and machine learning to unravel how the dynamic characteristics of CBL-B and C-CBL confer their binding affinity and selectivity for ligands from subtle structural disparities within their binding pockets and dissociation pathways. Our predictive model of dissociation rate constants (k off) demonstrates a moderate correlation between predicted k off and experimental IC50 values, which is consistent with experimental k off and τ-random accelerated molecular dynamics (τRAMD) results. By employing a linear regression of dissociation trajectories, we identified key amino acids in binding pockets and along the dissociation paths responsible for activity and selectivity. These amino acids are statistically significant in achieving activity and selectivity and contribute to the primary structural discrepancies between CBL-B and C-CBL. Moreover, the binding free energies calculated from molecular mechanics with generalized Born and surface area solvation (MM/GBSA) highlight the ΔG difference between CBL-B and C-CBL. The k off prediction, together with the key amino acids, provides important guides for designing drugs with high selectivity.

Enhancing Molecular Energy Predictions with Physically Constrained Modifications to the Neural Network Potential.

Exclusively prioritizing the precision of energy prediction frequently proves inadequate in satisfying multifaceted requirements. A heightened focus is warranted on assessing the rationality of potential energy curves predicted by machine learning-based force fields (MLFFs), alongside evaluating the pragmatic utility of these MLFFs. This study introduces SWANI, an optimized neural network potential stemming from the ANI framework. Through the incorporation of supplementary physical constraints, SWANI aligns more cohesively with chemical expectations, yielding rational potential energy profiles. It also exhibits superior predictive precision compared with that of the ANI model. Additionally, a comprehensive comparison is conducted between SWANI and a prominent graph neural network-based model. The findings indicate that SWANI outperforms the latter, particularly for molecules exceeding the dimensions of the training set. This outcome underscores SWANI's exceptional capacity for generalization and its proficiency in handling larger molecular systems.

Farmland boundary extraction based on the AttMobile-DeeplabV3+ network and least squares fitting of straight lines.

The rapid extraction of farmland boundaries is key to implementing autonomous operation of agricultural machinery. This study addresses the issue of incomplete farmland boundary segmentation in existing methods, proposing a method for obtaining farmland boundaries based on unmanned aerial vehicle (UAV) remote sensing images. The method is divided into two steps: boundary image acquisition and boundary line fitting. To acquire the boundary image, an improved semantic segmentation network, AttMobile-DeeplabV3+, is designed. Subsequently, a boundary tracing function is used to track the boundaries of the binary image. Lastly, the least squares method is used to obtain the fitted boundary line. The paper validates the method through experiments on both crop-covered and non-crop-covered farmland. Experimental results show that on crop-covered and non-crop-covered farmland, the network's intersection over union (IoU) is 93.25% and 93.14%, respectively; the pixel accuracy (PA) for crop-covered farmland is 96.62%. The average vertical error and average angular error of the extracted boundary line are 0.039 and 1.473°, respectively. This research provides substantial and accurate data support, offering technical assistance for the positioning and path planning of autonomous agricultural machinery.

Structure-Kinetic Relationship for Drug Design Revealed by a PLS Model with Retrosynthesis-Based Pre-Trained Molecular Representation and Molecular Dynamics Simulation.

Drug design based on kinetic properties is growing in application. Here, we applied retrosynthesis-based pre-trained molecular representation (RPM) in machine learning (ML) to train 501 inhibitors of 55 proteins and successfully predicted the dissociation rate constant (koff) values of 38 inhibitors from an independent dataset for the N-terminal domain of heat shock protein 90α (N-HSP90). Our RPM molecular representation outperforms other pre-trained molecular representations such as GEM, MPG, and general molecular descriptors from RDKit. Furthermore, we optimized the accelerated molecular dynamics to calculate the relative retention time (RT) for the 128 inhibitors of N-HSP90 and obtained the protein-ligand interaction fingerprints (IFPs) on their dissociation pathways and their influencing weights on the koff value. We observed a high correlation among the simulated, predicted, and experimental -log(koff) values. Combining ML, molecular dynamics (MD) simulation, and IFPs derived from accelerated MD helps design a drug for specific kinetic properties and selectivity profiles to the target of interest. To further validate our koff predictive ML model, we tested our model on two new N-HSP90 inhibitors, which have experimental koff values and are not in our ML training dataset. The predicted koff values are consistent with experimental data, and the mechanism of their kinetic properties can be explained by IFPs, which shed light on the nature of their selectivity against N-HSP90 protein. We believe that the ML model described here is transferable to predict koff of other proteins and will enhance the kinetics-based drug design endeavor.

Electronic spectroscopy of differential mobility-selected prototropic isomers of protonated para-aminobenzoic acid.

para-Aminobenzoic acid (PABA) was electrosprayed from mixtures of protic and aprotic solvents, leading to formation of two prototropic isomers in the gas phase whose relative populations depended on the composition of the electrospray solvent. The two ion populations were separated in the gas phase using differential mobility spectrometry (DMS) within a nitrogen-only environment at atmospheric pressure. Under high-field conditions, the two prototropic isomers eluted with baseline signal separation with the N-protonated isomer having a more negative CV shift than the O-protonated isomer, in accord with previous DMS studies. The conditions most favorable for formation and separation of each tautomer were used to trap each prototropic isomer in a quadrupole ion trap for photodissociation action spectroscopy experiments. Spectral interrogation of each prototropic isomer in the UV region (3-6 eV) showed good agreement with previously recorded spectra, although a previously reported band (4.8-5.4 eV) was less intense for the O-protonated isomer in our measured spectrum. Without DMS selection, the measured spectra contained features corresponding to both protonated isomers even when solvent conditions were optimised for formation of a single isomer. Interconversion between protonated isomers within the ion trap was observed when protic ESI solvents were employed, leading to spectral cross contamination even with mobility selection. CCSD vertical excitation energies and vertical gradient (VG) Franck-Condon simulations are presented and reproduce the measured spectral features with near-quantitative agreement, providing supporting evidence for spectral assignments.

Multicomponent Spiropolymerization of Diisocyanides, Diethyl Acetylenedicarboxylate, and Halogenated Quinones.

Multicomponent spiropolymerization (MCSP) provides an efficient synthetic tool for the construction of spiropolymers based on nonspiro monomers. In this study, a method of MCSP using diisocyanides 1, diethyl acetylenedicarboxylate 2, and halogenated quinones 3 is developed for the in situ construction of bis-spiropolymers with high molecular weights (Mw up to 29 200) and good yields (up to 87.7%) under mild reaction conditions. The structure of the obtained bis-spiropolymers is confirmed by gel permeation chromatography, Fourier transform infrared spectroscopy, and nuclear magnetic resonance analysis. Halogenated bis-spiropolymers show good thermal stability, good solubility, and film-forming ability. The photosensitizer rhodamine B is used as a doping agent to induce the photodegradation of the polymer P1a3c into small-molecule segments, which results in the slow release of halogenated spiro-groups under irradiation with simulated sunlight. This finding reveals that P1a3c has the potential to be applied in pesticides. Therefore, this MCSP is a novel method for preparing halogen-containing bis-spiropolymers, which accelerates the development of multifunctional polymer materials.

Catalyst-Free Multicomponent Cyclopolymerizations of Diisocyanides, Activated Alkynes, and 1,4-Dibromo-2,3-Butanedione: a Facile Strategy toward Functional Polyiminofurans Containing Bromomethyl Groups.

Polymers containing iminofuran (PIFs) are rarely reported due to the lack of simple and effective synthesis methods. In this work, a novel multicomponent cyclopolymerization (MCCP) of diisocyanides, activated alkynes, and 1,4-dibromo-2,3-butanedione using catalyst-free one-pot reactions under mild conditions to prepare PIFs containing bromomethyl groups is reported. PIFs with good solubility and thermal stability are obtained with high Mw s (up to 19 600) and good yields (up to 89.5%) under optimized polymerization conditions. The structure of the PIFs is characterized by nuclear magnetic resonance, Fourier transform infrared spectroscopy, and gel permeation chromatography. The photophysical properties indicate that polymers P1a2b3 and P1c2b3 have cluster-triggered emission characteristics. Thin films made from PIFs quickly degrade under UV irradiation. Moreover, the obtained polymers are decorated with bromomethyl and carboxylate groups in the side chain, which can be postfunctionalized to prepare multifunctional materials, such as star branched polymers and biomedical carrier materials. Thus, this work not only enriches the field of polymerization based on isocyanates and activated alkynes but also provides a facile strategy toward functional iminofuran polymers.

MDM2-Associated Clusterization-Triggered Emission and Apoptosis Induction Effectuated by a Theranostic Spiropolymer.

Heteroatom-containing spiropolymers were constructed in a facile manner by a catalyst-free multicomponent spiropolymerization route. P1a2b as the most potent of these spiropolymers, demonstrates cluster-triggered emission resulting from strong interactions with the MDM2 protein. By preventing the anti-apoptotic p53/MDM2 interaction, P1a2b triggers apoptosis in cancerous cells, while demonstrating a good biocompatibility and non-toxicity in non-cancerous cells. The combined results from solution and cell-based cluster-triggered emission studies, docking, protein expression experiments and cytotoxicity data strongly support the MDM2-binding hypothesis and indicate a potential application as a fluorescent cancer marker as well as therapeutic for this spiropolymer.

Applying Machine Learning to Vibrational Spectroscopy.

The low-energy region of the potential energy surface (PES) of the protonated phenylalanine/serine dimer is mapped using the basin-hoping search algorithm, and 37 isomers are identified within 180 kJ·mol-1 of the global-minimum structure. Cluster structures are grouped using hierarchical clustering to partition the PES in terms of nuclear configuration. Calculated IR spectra for the various isomers are then compared with the isomer-specific IR spectra by means of the cosine distance metric to facilitate spectral assignment and identify which regions of the PES are populated in the electrospray ionization process.

Intramolecular cation-π interactions in protonated phenylalanine derivatives.

The structures and properties of a series of phenylalanine (Phe) derivatives have been investigated in a joint computational and experimental infrared multiple photon dissociation (IRMPD) study. IRMPD spectra in the 1000-2000 cm-1 region show that protonation is localized on the amine group in all cases. Intramolecular cation-π interactions between the ammonium group and the phenyl ring heavily influence molecular geometries and properties such as gas phase basicity and proton affinity. By varying substituents on the phenyl ring, one can sensitively tune the cation-π interaction and, therefore, the molecular structure and properties. Variations in molecular structures and properties as a function of phenyl ring substitution are shown to correlate with substituent Hammett parameters.