Development of iminosugar-based glycosidase inhibitors as drug candidates for SARS-CoV-2 virus via molecular modelling and in vitro studies

We developed new iminosugar-based glycosidase inhibitors against SARS-CoV-2. Known drugs (miglustat, migalastat, miglitol, and swainsonine) were chosen as lead compounds to develop three classes of glycosidase inhibitors (α-glucosidase, α-galactosidase, and mannosidase). Molecular modelling of the lead compounds, synthesis of the compounds with the highest docking scores, enzyme inhibition tests, and in vitro antiviral assays afforded rationally designed inhibitors. Two highly active α-glucosidase inhibitors were discovered, where one of them is the most potent iminosugar-based anti-SARS-CoV-2 agent to date (EC90 = 1.94 µM in A549-ACE2 cells against Omicron BA.1 strain). However, galactosidase inhibitors did not exhibit antiviral activity, whereas mannosidase inhibitors were both active and cytotoxic. As our iminosugar-based drug candidates act by a host-directed mechanism, they should be more resilient to drug resistance. Moreover, this strategy could be extended to identify potential drug candidates for other viral infections.

Synthesis, characterization, molecular modeling, binding energies of β-cyclodextrin-inclusion complexes of quercetin: Modification of photo physical behavior upon β-CD complexation

We prepared a naturally occurring flavanoid namely quercetin from tea leaves and analyzed by Absorption, Emission, FT-IR, 1H, 13C nmr spectra and ESI-MS analysis. The inclusion behavior of quercetin in cyclodextrins like α-, β-, γ-, per-6-ABCD and mono-6-ABCD cavities were supported such as UV-vis., Emission, FT-IR and ICD spectra and energy minimization studies. From the absorption and emission results, the type of complexes formed were found to depend on stoichiometry of Host:Guest. FT-IR data of CD complexes of quercetin supported inclusion complex formation of the substrate with α-, β- and γ-CDs. The inclusion of host-guest complexation of quercetin with α-, β-, γ-CDs, per-6-ABCD and mono-6-ABCDs provides very valuable information about the CD:quercetin complexes, the study also shows that β-CD complexation improves water solubility, chemical stability and bioavailability of quercetin. Besides, phase solubility studies also supported the formation of 1:1 drug-CD soluble complexes. All these spectral results provide insight into the binding behavior of substrate into CD cavity in the order per-6-ABCD > Mono-6-ABCD > γ-CD > β-CD > α-CD. The proposed model also finds strong support from the fact with excess CD this exciton coupling disappears indicates the formation of only 1:1 complex.

Structural basis for the allosteric behaviour and substrate specificity of Lactococcus lactis Prolidase

Prolidase (EC 3.4.13.9) is an enzyme that specifically hydrolyzes Xaa-Pro dipeptides into free amino acids. We previously studied kinetic behaviours and solved the crystal structure of wild-type (WT) Lactococcus lactis prolidase (Llprol), showing that this homodimeric enzyme has unique characteristics: allosteric behaviour and substrate inhibition. In this study, we focused on solving the crystal structures of three Llprol mutants (D36S, H38S, and R293S) which behave differently in v-S plots. The D36S and R293S Llprol mutants do not show allosteric behaviour, and the Llprol mutant H38S has allosteric behaviour comparable to the WT enzyme (Hill constant 1.52 and 1.58, respectively). The crystal structures of Llprol variants suggest that the active site of Llprol formed with amino acid residues from both monomers, i.e., located in an interfacial area of dimer. The comparison between the structure models of Llprol indicated that the two monomers in the dimers of Llprol variants have different relative positions among Llprol variants. They showed different interatomic distances between the amino acid residues bridging the two monomers and varied sizes of the solvent-accessible interface areas in each Llprol variant. These observations indicated that Llprol could adapt to different conformational states with distinctive substrate affinities. It is strongly speculated that the domain movements required for productive substrate binding are restrained in allosteric Llprol (WT and H38S). At low substrate concentrations, only one out of the two active sites at the dimer interface could accept substrate; as a result, the asymmetrical activated dimer leads to allosteric behaviour.

In vitro and in silico investigation of FDA-approved drugs to be repurposed against Alzheimer's disease

Alzheimer's disease (AD), one of the main causes of dementia, is a neurodegenerative disorder. Cholinesterase inhibitors are used in the treatment of AD, but prolonged use of these drugs can lead to serious side effects. Drug repurposing is an approach that aims to reveal the effectiveness of drugs in different diseases beyond their clinical uses. In this work, we investigated in vitro and in silico inhibitory effects of 11 different drugs on cholinesterases. The results showed that trimebutine, theophylline, and levamisole had the highest acetylcholinesterase inhibitory actions among the tested drugs, and these drugs inhibited by 68.70 ± 0.46, 53.25 ± 3.40, and 44.03 ± 1.20%, respectively at 1000 µM. In addition, these drugs are bound to acetylcholinesterase via competitive manner. Molecular modeling predicted good fitness in acetylcholinesterase active site for these drugs and possible central nervous system action for trimebutine. All of these results demonstrated that trimebutine was determined to be the drug with the highest potential for use in AD.

Transient interdomain interactions in free USP14 shape its conformational ensemble

The deubiquitinase (DUB) ubiquitin-specific protease 14 (USP14) is a dual domain protein that plays a regulatory role in proteasomal degradation and has been identified as a promising therapeutic target. USP14 comprises a conserved USP domain and a ubiquitin-like (Ubl) domain separated by a 25-residue linker. The enzyme activity of USP14 is autoinhibited in solution, but is enhanced when bound to the proteasome, where the Ubl and USP domains of USP14 bind to the Rpn1 and Rpt1/Rpt2 units, respectively. No structure of full-length USP14 in the absence of proteasome has yet been presented, however, earlier work has described how transient interactions between Ubl and USP domains in USP4 and USP7 regulate DUB activity. To better understand the roles of the Ubl and USP domains in USP14, we studied the Ubl domain alone and in full-length USP14 by nuclear magnetic resonance spectroscopy and used small angle x-ray scattering and molecular modeling to visualize the entire USP14 protein ensemble. Jointly, our results show how transient interdomain interactions between the Ubl and USP domains of USP14 predispose its conformational ensemble for proteasome binding, which may have functional implications for proteasome regulation and may be exploited in the design of future USP14 inhibitors.

Sequence-dependent and -independent information in a combined random energy model for protein folding and coding

Random energy models (REMs) provide a simple description of the energy landscapes that guide protein folding and evolution. The requirement of a large energy gap between the native structure and unfolded conformations, considered necessary for cooperative, protein-like, folding behavior, indicates that proteins differ markedly from random heteropolymers. It has been suggested, therefore, that natural selection might have acted to choose nonrandom amino acid sequences satisfying this particular condition, implying that a large fraction of possible, unselected random sequences, would not fold to any structure. From an informational perspective, however, this scenario could indicate that protein structures, regarded as messages to be transmitted through a communication channel, would not be efficiently encoded in amino acid sequences, regarded as the communication channel for this transmission, since a large fraction of possible channel states would not be used. Here, we use a combined REM for conformations and sequences, with previously estimated parameters for natural proteins, to explore an alternative possibility in which the appropriate shape of the landscape results mainly from the deviation from randomness of possible native structures instead of sequences. We observe that this situation emerges naturally if the distribution of conformational energies happens to arise from two independent contributions corresponding to sequence-dependent and -independent terms. This construction is consistent with the hypothesis of a protein burial folding code, with native structures being determined by a modest amount of sequence-dependent atomic burial information with sequence-independent constraints imposed by unspecific hydrogen bond formation. More generally, an appropriate combination of sequence-dependent and -independent information accommodates the possibility of an efficient structural encoding with the main physical requirement for folding, providing possible insight not only on the folding process but also on several aspects sequence evolution such as neutral networks, conformational coverage, and de novo gene emergence.

Selectivity in the chiral self-assembly of nucleobase-arylazopyrazole photoswitches along DNA templates

The control of supramolecular DNA assembly through external stimuli such as light represents a promising approach to control bioreactions, and modulate hybridization or delivery processes. Here, we report on the design of nucleobase-containing arylazopyrazole photoswitches that undergo chiral organization upon self-assembly along short DNA templates. Chiroptical spectroscopy shows that the specific nucleobases allow selectivity in the resulting supramolecular DNA complexes, and UV light irradiation triggers partial desorption of the arylazopyrazole photoswitches. Molecular modeling studies reveal the differences of binding modes between the two configurations in the templated assembly. Remarkably, our results show that the photoswitching behaviour controls the self-assembly process along DNA, opening the way to potential applications as nano- and biomaterials.

Screening androgen receptor agonists of fish species using machine learning and molecular model in NORMAN water-relevant list

Androgen receptor (AR) agonists have strong endocrine disrupting effects in fish. Most studies mainly investigate AR binding capacity using human AR in vitro. However, there is still few methods to rapidly predict AR agonists in aquatic organisms. This study aimed to screen AR agonists of fish species using machine learning and molecular models in water-relevant list from NORMAN, a network of reference laboratories for monitoring contaminants of emerging concern in the environment. In this study, machine learning approaches (e.g., Deep Forest (DF)), Random Forests and artificial neural networks) were applied to predict AR agonists. Zebrafish, fathead minnow, mosquitofish, medaka fish and grass carp are all important aquatic model organisms widely used to evaluate the toxicity of new pollutants, and the molecular models of ARs from these five fish species were constructed to further screen AR agonists using AlphaFold2. The DF method showed the best performances with 0.99 accuracy, 0.97 sensitivity and 1 precision. The Asn705, Gln711, Arg752, and Thr877 residues in human AR and the corresponding sites in ARs from the five fish species were responsible for agonist binding. Overall, 245 substances were predicted as suspect AR agonists in the five fish species, including, certain glucocorticoids, cholesterol metabolites, and cardiovascular drugs in the NORMAN list. Using machine learning and molecular modeling hybrid methods rapidly and accurately screened AR agonists in fish species, and helping evaluate their ecological risk in fish populations.

DNA binding analysis of rare variants in homeodomains reveals homeodomain specificity-determining residues

Homeodomains (HDs) are the second largest class of DNA binding domains (DBDs) among eukaryotic sequence-specific transcription factors (TFs) and are the TF structural class with the largest number of disease-associated mutations in the Human Gene Mutation Database (HGMD). Despite numerous structural studies and large-scale analyses of HD DNA binding specificity, HD-DNA recognition is still not fully understood. Here, we analyze 92 human HD mutants, including disease-associated variants and variants of uncertain significance (VUS), for their effects on DNA binding activity. Many of the variants alter DNA binding affinity and/or specificity. Detailed biochemical analysis and structural modeling identifies 14 previously unknown specificity-determining positions, 5 of which do not contact DNA. The same missense substitution at analogous positions within different HDs often exhibits different effects on DNA binding activity. Variant effect prediction tools perform moderately well in distinguishing variants with altered DNA binding affinity, but poorly in identifying those with altered binding specificity. Our results highlight the need for biochemical assays of TF coding variants and prioritize dozens of variants for further investigations into their pathogenicity and the development of clinical diagnostics and precision therapies.

DFT/TDDFT calculations of geometry optimization, electronic structure and spectral properties of clevudine and telbivudine for treatment of chronic hepatitis B

Chronic hepatitis B remains a worldwide health concern. Presently, many drugs, such as Clevudine and Telbivudine, are recommended for the treatment of chronic hepatitis B disease. For this purpose, the quantum chemical analysis of ELUMO-HOMO (Egap), ionization potential (IP), electron affinity (EA), electronegativity (EN), chemical hardness (η), chemical potential (μ), chemical softness (S), electrophilicity index (ω), electron accepting capability (ω+), electron-donating capability (ω-), Nucleophilicity index (N), additional electronic charge (∆Nmax), Optical softness (σ0) and Dipole Moment, IR and UV-Vis spectra, molecular electrostatic potential (MEP) profile, Mulliken charge analysis, natural bond orbital (NBO) were examined in this study. The dipole moment of the compounds suggests their binding pose and predicted binding affinity. The electrophilic and nucleophilic regions were identified, and techniques such as NBO, UV-Vis, and IR were used to gain insights into the molecular structure, electronic transitions, and potential drug design for Hepatitis B treatment. Calculations for this study were carried out using the Gaussian 09 program package coupled with the DFT/TDDFT technique. The hybrid B3LYP functional method and the 6-311++G(d, p) basis set were used for the calculations.

Exploring Kinase Asp-Phe-Gly (DFG) Loop Conformational Stability with AlphaFold2-RAVE

Kinases compose one of the largest fractions of the human proteome, and their misfunction is implicated in many diseases, in particular, cancers. The ubiquitousness and structural similarities of kinases make specific and effective drug design difficult. In particular, conformational variability due to the evolutionarily conserved Asp-Phe-Gly (DFG) motif adopting in and out conformations and the relative stabilities thereof are key in structure-based drug design for ATP competitive drugs. These relative conformational stabilities are extremely sensitive to small changes in sequence and provide an important problem for sampling method development. Since the invention of AlphaFold2, the world of structure-based drug design has noticeably changed. In spite of it being limited to crystal-like structure prediction, several methods have also leveraged its underlying architecture to improve dynamics and enhanced sampling of conformational ensembles, including AlphaFold2-RAVE. Here, we extend AlphaFold2-RAVE and apply it to a set of kinases: the wild type DDR1 sequence and three mutants with single point mutations that are known to behave drastically differently. We show that AlphaFold2-RAVE is able to efficiently recover the changes in relative stability using transferable learned order parameters and potentials, thereby supplementing AlphaFold2 as a tool for exploration of Boltzmann-weighted protein conformations (Meller, A.; Bhakat, S.; Solieva, S.; Bowman, G. R. Accelerating Cryptic Pocket Discovery Using AlphaFold. J. Chem. Theory Comput. 2023, 19, 4355-4363).

The molecular basis of Abelson kinase regulation by its αI-helix

Abelson tyrosine kinase (Abl) is regulated by the arrangement of its regulatory core, consisting sequentially of the SH3, SH2, and kinase (KD) domains, where an assembled or disassembled core corresponds to low or high kinase activity, respectively. It was recently established that binding of type II ATP site inhibitors, such as imatinib, generates a force from the KD N-lobe onto the SH3 domain and in consequence disassembles the core. Here, we demonstrate that the C-terminal αI-helix exerts an additional force toward the SH2 domain, which correlates both with kinase activity and type II inhibitor-induced disassembly. The αI-helix mutation E528K, which is responsible for the ABL1 malformation syndrome, strongly activates Abl by breaking a salt bridge with the KD C-lobe and thereby increasing the force onto the SH2 domain. In contrast, the allosteric inhibitor asciminib strongly reduces Abl's activity by fixating the αI-helix and reducing the force onto the SH2 domain. These observations are explained by a simple mechanical model of Abl activation involving forces from the KD N-lobe and the αI-helix onto the KD/SH2SH3 interface.

Transformer-Based Molecular Generative Model for Antiviral Drug Design

Since the Simplified Molecular Input Line Entry System (SMILES) is oriented to the atomic-level representation of molecules and is not friendly in terms of human readability and editable, however, IUPAC is the closest to natural language and is very friendly in terms of human-oriented readability and performing molecular editing, we can manipulate IUPAC to generate corresponding new molecules and produce programming-friendly molecular forms of SMILES. In addition, antiviral drug design, especially analogue-based drug design, is also more appropriate to edit and design directly from the functional group level of IUPAC than from the atomic level of SMILES, since designing analogues involves altering the R group only, which is closer to the knowledge-based molecular design of a chemist. Herein, we present a novel data-driven self-supervised pretraining generative model called "TransAntivirus" to make select-and-replace edits and convert organic molecules into the desired properties for design of antiviral candidate analogues. The results indicated that TransAntivirus is significantly superior to the control models in terms of novelty, validity, uniqueness, and diversity. TransAntivirus showed excellent performance in the design and optimization of nucleoside and non-nucleoside analogues by chemical space analysis and property prediction analysis. Furthermore, to validate the applicability of TransAntivirus in the design of antiviral drugs, we conducted two case studies on the design of nucleoside analogues and non-nucleoside analogues and screened four candidate lead compounds against anticoronavirus disease (COVID-19). Finally, we recommend this framework for accelerating antiviral drug discovery.

Tuning Active Site Electron Density for Enhanced Molecular Recognition and Catalysis

Enzymes have an extraordinary ability to utilize aromatic interactions for molecular recognition and catalysis. We here report molecularly imprinted nanoparticle receptors. The aromatic "wall" material in the imprinted binding site is used to enhance the molecular recognition of aromatic guests that have similar charges, shapes, and sizes but differ in π-electron density. Additionally, aromatic interactions are employed to activate an electron-rich aryl leaving group on a glycoside, mimicking the nucleoside hydrolase of the parasite Trypanosoma vivax.

De novo design of cavity-containing proteins with a backbone-centered neural network energy function

The design of small-molecule-binding proteins requires protein backbones that contain cavities. Previous design efforts were based on naturally occurring cavity-containing backbone architectures. Here, we designed diverse cavity-containing backbones without predefined architectures by introducing tailored restraints into the backbone sampling driven by SCUBA (Side Chain-Unknown Backbone Arrangement), a neural network statistical energy function. For 521 out of 5816 designs, the root-mean-square deviations (RMSDs) of the Cα atoms for the AlphaFold2-predicted structures and our designed structures are within 2.0 Å. We experimentally tested 10 designed proteins and determined the crystal structures of two of them. One closely agrees with the designed model, while the other forms a domain-swapped dimer, where the partial structures are in agreement with the designed structures. Our results indicate that data-driven methods such as SCUBA hold great potential for designing de novo proteins with tailored small-molecule-binding function.

High-Throughput Interactome Determination via Sulfur Anomalous Scattering

We propose a novel approach for detecting the binding between proteins making use of the anomalous diffraction of natively present heavy elements, e.g., sulfurs, inside molecular three-dimensional structures. In particular, we analytically and numerically show that the diffraction patterns produced by the anomalous scattering of the sulfur atoms in a given direction depend additively on the relative distances between all couples of sulfur atoms. Thus, the differences in the patterns produced by bound proteins with respect to their nonbonded states can be exploited to rapidly assess protein complex formation. On the basis of our results, we suggest a possible experimental procedure for detecting protein-protein binding. Overall, the completely label-free and rapid method we propose may be readily extended to probe interactions on a large scale, thus paving the way for the development of a novel field of research based on a synchrotron light source.

Ligand-induced protein transition state stabilization switches the binding pathway from conformational selection to induced fit

Protein-ligand complex formation is fundamental to biological function. A central question is whether proteins spontaneously adopt binding-competent conformations to which ligands bind conformational selection (CS) or whether ligands induce the binding-competent conformation induced fit (IF). Here, we resolve the CS and IF binding pathways by characterizing protein conformational dynamics over a wide range of ligand concentrations using NMR relaxation dispersion. We determined the relative flux through the two pathways using a four-state binding model that includes both CS and IF. Experiments conducted without ligand show that galectin-3 exchanges between the ground-state conformation and a high-energy conformation similar to the ligand-bound conformation, demonstrating that CS is a plausible pathway. Near-identical crystal structures of the apo and ligand-bound states suggest that the high-energy conformation in solution corresponds to the apo crystal structure. Stepwise additions of the ligand lactose induce progressive changes in the relaxation dispersions that we fit collectively to the four-state model, yielding all microscopic rate constants and binding affinities. The ligand affinity is higher for the bound-like conformation than for the ground state, as expected for CS. Nonetheless, the IF pathway contributes greater than 70% of the total flux even at low ligand concentrations. The higher flux through the IF pathway is explained by considerably higher rates of exchange between the two protein conformations in the ligand-associated state. Thus, the ligand acts to decrease the activation barrier between protein conformations in a manner reciprocal to enzymatic transition-state stabilization of reactions involving ligand transformation.

Interactions between DNA and the acridine intercalator: A computational study

Cancer is a global public health problem characterized by deviations in the mechanisms that control cell proliferation, resulting in mutations and variations in the structure of DNA. The mechanisms of action of chemotherapeutic drugs are related to their interactions and binding with DNA; consequently, the development of antineoplastic agents that target DNA has extensively focused on use of acridine, a heterocyclic molecule that binds to deoxyribonucleic acid via intercalation, a process that modifies DNA and makes replication impossible. In this context, this study aimed to computationally investigate how acridine intercalators interact with DNA by evaluating the mechanism of interactions, binding, and interaction energies using quantum mechanics calculations. Molecular electrostatic potential (MEP) analysis revealed that acridine has well- distributed negative charges in the center of the molecule, indicative of a dominant electron-rich region. Acridine exhibits well-defined π orbitals (HOMO and LUMO) on the aromatic rings, suggesting that charge transfer occurs within the molecule and may be responsible for the pharmacological activity of the compound. Structural analysis revealed that acridine interacts with DNA mainly through hydrogen bonds between HAcridine… ODNA with bond lengths ranging from 2.370 Å to 3.472 Å. The Binding energy (ΔEBind) showed that acridine interacts with DNA effectively for all complexes and the electronic energy results (E+ZPE) for complexes revealed that the complexes are more stable when the DNA-centered acridine molecule. The Laplacian-analysis topological QTAIM parameter (∇2ρ(r)) and total energy (H(r)) categorized the interactions as being non-covalent in nature. The RGD peak distribution in the NCI analysis reveals the presence of van der Waals interactions, predominantly between the intercalator and DNA. Accordingly, we confirm that acridine/DNA interactions are relevant for understanding how the intercalator acts within nucleic acids.

Using atomistic solution scattering modelling to elucidate the role of the Fc glycans in human IgG4

Human immunoglobulin G (IgG) exists as four subclasses IgG1-4, each of which has two Fab subunits joined by two hinges to a Fc subunit. IgG4 has the shortest hinge with 12 residues. The Fc subunit has two glycan chains, but the importance of glycosylation is not fully understood in IgG4. Here, to evaluate the stability and structure of non-glycosylated IgG4, we performed a multidisciplinary structural study of glycosylated and deglycosylated human IgG4 A33 for comparison with our similar study of human IgG1 A33. After deglycosylation, IgG4 was found to be monomeric by analytical ultracentrifugation; its sedimentation coefficient of 6.52 S was reduced by 0.27 S in reflection of its lower mass. X-ray and neutron solution scattering showed that the overall Guinier radius of gyration RG and its cross-sectional values after deglycosylation were almost unchanged. In the P(r) distance distribution curves, the two M1 and M2 peaks that monitor the two most common distances within IgG4 were unchanged following deglycosylation. Further insight from Monte Carlo simulations for glycosylated and deglycosylated IgG4 came from 111,382 and 117,135 possible structures respectively. Their comparison to the X-ray and neutron scattering curves identified several hundred best-fit models for both forms of IgG4. Principal component analyses showed that glycosylated and deglycosylated IgG4 exhibited different conformations from each other. Within the constraint of unchanged RG and M1-M2 values, the glycosylated IgG4 models showed more restricted Fc conformations compared to deglycosylated IgG4, but no other changes. Kratky plots supported this interpretation of greater disorder upon deglycosylation, also observed in IgG1. Overall, these more variable Fc conformations may demonstrate a generalisable impact of deglycosylation on Fc structures, but with no large conformational changes in IgG4 unlike those seen in IgG1.

Classifying protein kinase conformations with machine learning

Protein kinases are key actors of signaling networks and important drug targets. They cycle between active and inactive conformations, distinguished by a few elements within the catalytic domain. One is the activation loop, whose conserved DFG motif can occupy DFG-in, DFG-out, and some rarer conformations. Annotation and classification of the structural kinome are important, as different conformations can be targeted by different inhibitors and activators. Valuable resources exist; however, large-scale applications will benefit from increased automation and interpretability of structural annotation. Interpretable machine learning models are described for this purpose, based on ensembles of decision trees. To train them, a set of catalytic domain sequences and structures was collected, somewhat larger and more diverse than existing resources. The structures were clustered based on the DFG conformation and manually annotated. They were then used as training input. Two main models were constructed, which distinguished active/inactive and in/out/other DFG conformations. They considered initially 1692 structural variables, spanning the whole catalytic domain, then identified ("learned") a small subset that sufficed for accurate classification. The first model correctly labeled all but 3 of 3289 structures as active or inactive, while the second assigned the correct DFG label to all but 17 of 8826 structures. The most potent classifying variables were all related to well-known structural elements in or near the activation loop and their ranking gives insights into the conformational preferences. The models were used to automatically annotate 3850 kinase structures predicted recently with the Alphafold2 tool, showing that Alphafold2 reproduced the active/inactive but not the DFG-in proportions seen in the Protein Data Bank. We expect the models will be useful for understanding and engineering kinases.

Synthesis, biological evaluation and molecular modeling studies of modulated benzyloxychalcones as potential acetylcholinesterase inhibitors

Acetylcholinesterase inhibitors (AChEIs) have become a significant target in the search for an efficient treatment of Alzheimer's disease. Chalcone-based compounds display a strong potency to hinder AChE. So, this study focused on the synthesis of a series of new chalcone derivatives with anti-cholinesterase potential and their structures were characterized based on spectroscopic methods including IR, 1H NMR, 13C NMR and HRMS. Chalcone derivatives were screened against AChE. Most of them exhibited potent inhibitory activity against AChE. Compound 11i showed the most potent activity toward acetylcholinesterase compared to the positive compound, Galantamine. Docking studies into the active site of the acetylcholinesterase enzyme ravealed the significant docking score of the synthesized compounds with docking score of -7.959 to -9.277 kcal/mol when compared to the co-crystallized ligand, Donepezil (-10.567 kcal/mol). The interaction's stability was further assessed using a conventional atomistic 100 ns dynamics simulation study, which revealed the conformational stability of representative compound 11i in the cavity of the acetylcholinesterase enzyme.Communicated by Ramaswamy H. Sarma.

Connectivity between surface and interior in catalytic subunits of acetylcholinesterases inferred from their X-ray structures

Catalytic activity and function of acetylcholinesterase (AChE; EC 3.1.1.7) have been recognized and studied for over a century and its quaternary and primary structures for about half a century, and its tertiary structure has been known for about 33 years. Clear understanding of relationships between the structure and the function is still pending for this enzyme. Hundreds of crystallographic, static snapshots of AChEs from different sources reveal largely one general backbone conformation with narrow entry into the active center gorge, tightly fit to accept one acetylcholine (ACh) molecule, in contrast to its high catalytic turnover. This short review of available X-ray structures of AChEs from electric ray Torpedo californica, mouse and human, finds some limited, yet consistent deviations in conformations of selected secondary structure elements of AChE relevant for its function. The observed conformational diversity of the acyl pocket loop of AChE, unlike the large Ω-loop, appears consistent with structurally dynamic INS data and solution-based SAXS experiments to explain its dominant role in controlling the size of the active center gorge opening, as well as connectivity between the immediate surroundings of the buried active Ser, and catalytically relevant sites on the AChE surface.

A trapped covalent intermediate as a key catalytic element in the hydrolysis of a GH3 β-glucosidase: An X-ray crystallographic and biochemical study

Glycoside hydrolases (GHs) are industrially important enzymes that hydrolyze glycosidic bonds in glycoconjugates. In this study, we found a GH3 β-glucosidase (CcBgl3B) from Cellulosimicrobium cellulans sp. 21 was able to selectively hydrolyze the β-1,6-glucosidic bond linked glucose of ginsenosides. X-ray crystallographic studies of the ligand complex ginsenoside-specific β-glucosidase provided a novel finding that support the catalytic mechanism of GH3. The substrate was clearly identified within the catalytic center of wild-type CcBgl3B, revealing that the C1 atom of the glucose was covalently bound to the Oδ1 group of the conserved catalytic nucleophile Asp264 as an enzyme-glycosyl intermediate. The glycosylated Asp264 could be identified by mass spectrometry. Through site-directed mutagenesis studies with Asp264, it was found that the covalent intermediate state formed by Asp264 and the substrate was critical for catalysis. In addition, Glu525 variants (E525A, E525Q and E525D) showed no or marginal activity against pNPβGlc; thus, this residue could supply a proton for the reaction. Overall, our study provides an insight into the catalytic mechanism of the GH3 enzyme CcBgl3B.

The structural landscape of the immunoglobulin fold by large-scale de novo design

De novo designing immunoglobulin-like frameworks that allow for functional loop diversification shows great potential for crafting antibody-like scaffolds with fully customizable structures and functions. In this work, we combined de novo parametric design with deep-learning methods for protein structure prediction and design to explore the structural landscape of 7-stranded immunoglobulin domains. After screening folding of nearly 4 million designs, we have assembled a structurally diverse library of ~50,000 immunoglobulin domains with high-confidence AlphaFold2 predictions and structures diverging from naturally occurring ones. The designed dataset enabled us to identify structural requirements for the correct folding of immunoglobulin domains, shed light on β-sheet-β-sheet rotational preferences and how these are linked to functional properties. Our approach eliminates the need for preset loop conformations and opens the route to large-scale de novo design of immunoglobulin-like frameworks.

Synthesis, crystal structure and in-silico evaluation of arylsulfonamide Schiff bases for potential activity against colon cancer

This report presents a comprehensive investigation into the synthesis and characterization of Schiff base compounds derived from benzenesulfonamide. The synthesis process, involved the reaction between N-cycloamino-2-sulfanilamide and various substituted o-salicylaldehydes, resulted in a set of compounds that were subjected to rigorous characterization using advanced spectral techniques, including 1H NMR, 13C NMR and FT-IR spectroscopy, and single-crystal X-ray diffraction. Furthermore, an in-depth assessment of the synthesized compounds was conducted through Absorption, Distribution, Metabolism, Excretion and Toxicity (ADMET) analysis, in conjunction with docking studies, to elucidate their pharmacokinetic profiles and potential. Impressively, the ADMET analysis showcased encouraging drug-likeness properties of the newly synthesized Schiff bases. These computational findings were substantiated by molecular properties derived from density functional theory (DFT) calculations using the B3LYP/6-31G* method within the Jaguar Module of Schrödinger 2023-2 from Maestro (Schrodinger LLC, New York, USA). The exploration of frontier molecular orbitals (HOMO and LUMO) enabled the computation of global reactivity descriptors (GRDs), encompassing charge separation (Egap) and global softness (S). Notably, within this analysis, one Schiff base, namely, 4-bromo-2-{N-[2-(pyrrolidine-1-sulfonyl)phenyl]carboximidoyl}phenol, 20, emerged with the smallest charge separation (ΔEgap = 3.5780 eV), signifying heightened potential for biological properties. Conversely, 4-bromo-2-{N-[2-(piperidine-1-sulfonyl)phenyl]carboximidoyl}phenol, 17, exhibited the largest charge separation (ΔEgap = 4.9242 eV), implying a relatively lower propensity for biological activity. Moreover, the synthesized Schiff bases displayed remarkeable inhibition of tankyrase poly(ADP-ribose) polymerase enzymes, integral in colon cancer, surpassing the efficacy of a standard drug used for the same purpose. Additionally, their bioavailability scores aligned closely with established medications such as trifluridine and 5-fluorouracil. The exploration of molecular electrostatic potential through colour mapping delved into the electronic behaviour and reactivity tendencies intrinsic to this diverse range of molecules.

Structural highlights of macromolecular complexes and assemblies

The structures of macromolecular assemblies have given us deep insights into cellular processes and have profoundly impacted biological research and drug discovery. We highlight the structures of macromolecular assemblies that have been modeled using integrative and computational methods and describe how open access to these structures from structural archives has empowered the research community. The arsenal of experimental and computational methods for structure determination ensures a future where whole organelles and cells can be modeled.

Automated model building and protein identification in cryo-EM maps

Interpreting electron cryo-microscopy (cryo-EM) maps with atomic models requires high levels of expertise and labour-intensive manual intervention in three-dimensional computer graphics programs1,2. Here we present ModelAngelo, a machine-learning approach for automated atomic model building in cryo-EM maps. By combining information from the cryo-EM map with information from protein sequence and structure in a single graph neural network, ModelAngelo builds atomic models for proteins that are of similar quality to those generated by human experts. For nucleotides, ModelAngelo builds backbones with similar accuracy to those built by humans. By using its predicted amino acid probabilities for each residue in hidden Markov model sequence searches, ModelAngelo outperforms human experts in the identification of proteins with unknown sequences. ModelAngelo will therefore remove bottlenecks and increase objectivity in cryo-EM structure determination.

Force-induced unzipping of DNA in the presence of solvent molecules

The melting of double-stranded DNA (dsDNA) in the presence of solvent molecules is a fundamental process with significant implications for understanding the thermal and mechanical behavior of DNA and its interactions with the surrounding environment. The solvents play an essential role in the structural transformation of DNA subjected to a pulling force. In this study, we simulate the thermal and force induced denaturation of dsDNA and elucidate the solvent dependent melting behavior, identifying key factors that influence the stability of DNA melting in presence of solvent molecules. Using a statistical model, we first find the melting profile of short heterogeneous DNA molecules in the presence of solvent molecules in Force ensemble. We also investigate the effect of solvent's strengths on the melting profile of DNA. In the force ensemble, we consider two homogeneous DNA chains and apply the force on different locations along the chain in the presence of solvent molecules. Different pathways manifest the melting of the molecule in both ensembles, and we found several interesting features of melting DNA in a constant force ensemble, such as lower critical force when the chain is pulled from the base pair close to a solvent molecule. The results provide new insights into the force-induced unzipping of DNA and could be used to develop new methods for controlling the unzipping process. By providing a better understanding of melting and unzipping of dsDNA in the presence of solvent molecules, this study provides valuable guidelines for predicting DNA thermodynamic quantities and for designing DNA nanostructures.

A comparative study of the predictive performance of different descriptor calculation tools: Molecular-based elution order modeling and interpretation of retention mechanism for isomeric compounds from METLIN database

In the pharmaceutical industry, the need for analytical standards is a bottleneck for comprehensive evaluation and quality control of intermediate and end products. These are complex mixtures containing structurally related molecules. In this regard, chromatographic peak annotation, especially for critical pairs of isomers and closest structural analogs, can be supported by using a Quantitative Structure Retention Relationship (QSRR) approach. In our study, we investigated the fundamental basis of the reversed-phase (RP) retention mechanism for 1141 isomeric compounds from the METLIN SMRT dataset. Nine different descriptor calculation tools combined with different feature selection methods (genetic algorithm (GA), stepwise, Boruta) and machine learning (ML) approaches (support vector machine (SVM), multiple linear regression (MLR), random forest (RF), XGBoost) were applied to provide a reliable molecular structure-based interpretation of RP retention behaviour of the isomeric compounds. Strict internal and external validation metrics were used to select models with the best predictive capabilities (rtest > 0.73, order of elution > 60 %). For the developed models, mean absolute errors were in the range of 60 to 110 s. Stepwise and GA showed the most suitable performance as descriptor selection methods, while SVM and XGBoost modeling gave satisfactory predictive characteristics in most cases. Validation performed on the published experimental data for structurally related pharmaceutical compounds confirmed the best accuracy of MLR modeling in combination with GA feature selection of general physico-chemical properties. The resulting models will be useful for the prediction of separation and identification of structurally related compounds in pharmaceutical analysis, providing a simultaneous understanding of the interaction mechanisms leading to their retention under RP conditions.

Characterization of chloroplast ribulose-5-phosphate-3-epimerase from the microalga Chlamydomonas reinhardtii

Carbon fixation relies on Rubisco and 10 additional enzymes in the Calvin-Benson-Bassham cycle. Epimerization of xylulose-5-phosphate (Xu5P) into ribulose-5-phosphate (Ru5P) contributes to the regeneration of ribulose-1,5-bisphosphate, the substrate of Rubisco. Ribulose-5-phosphate-3-epimerase (RPE, EC 5.1.3.1) catalyzes the formation of Ru5P, but it can also operate in the pentose-phosphate pathway by catalyzing the reverse reaction. Here, we describe the structural and biochemical properties of the recombinant RPE isoform 1 from Chlamydomonas (Chlamydomonas reinhardtii) (CrRPE1). The enzyme is a homo-hexamer that contains a zinc ion in the active site and exposes a catalytic pocket on the top of an α8β8 triose isomerase-type barrel as observed in structurally solved RPE isoforms from both plant and non-plant sources. By optimizing and developing enzyme assays to monitor the reversible epimerization of Ru5P to Xu5P and vice versa, we determined the catalytic parameters that differ from those of other plant paralogs. Despite being identified as a putative target of multiple thiol-based redox modifications, CrRPE1 activity is not affected by both reductive and oxidative treatments, indicating that enzyme catalysis is insensitive to possible redox alterations of cysteine residues. We mapped phosphorylation sites on the crystal structure, and the specific location at the entrance of the catalytic cleft supports a phosphorylation-based regulatory mechanism. This work provides an accurate description of the structural features of CrRPE1 and an in-depth examination of its catalytic and regulatory properties highlighting the physiological relevance of this enzyme in the context of photosynthetic carbon fixation.

MolFeSCue: enhancing molecular property prediction in data-limited and imbalanced contexts using few-shot and contrastive learning

MOTIVATION

Predicting molecular properties is a pivotal task in various scientific domains, including drug discovery, material science, and computational chemistry. This problem is often hindered by the lack of annotated data and imbalanced class distributions, which pose significant challenges in developing accurate and robust predictive models.

RESULTS

This study tackles these issues by employing pretrained molecular models within a few-shot learning framework. A novel dynamic contrastive loss function is utilized to further improve model performance in the situation of class imbalance. The proposed MolFeSCue framework not only facilitates rapid generalization from minimal samples, but also employs a contrastive loss function to extract meaningful molecular representations from imbalanced datasets. Extensive evaluations and comparisons of MolFeSCue and state-of-the-art algorithms have been conducted on multiple benchmark datasets, and the experimental data demonstrate our algorithm's effectiveness in molecular representations and its broad applicability across various pretrained models. Our findings underscore MolFeSCues potential to accelerate advancements in drug discovery.

AVAILABILITY AND IMPLEMENTATION

We have made all the source code utilized in this study publicly accessible via GitHub at http://www.healthinformaticslab.org/supp/ or https://github.com/zhangruochi/MolFeSCue. The code (MolFeSCue-v1-00) is also available as the supplementary file of this paper.

Rapid and automated design of two-component protein nanomaterials using ProteinMPNN

The design of protein-protein interfaces using physics-based design methods such as Rosetta requires substantial computational resources and manual refinement by expert structural biologists. Deep learning methods promise to simplify protein-protein interface design and enable its application to a wide variety of problems by researchers from various scientific disciplines. Here, we test the ability of a deep learning method for protein sequence design, ProteinMPNN, to design two-component tetrahedral protein nanomaterials and benchmark its performance against Rosetta. ProteinMPNN had a similar success rate to Rosetta, yielding 13 new experimentally confirmed assemblies, but required orders of magnitude less computation and no manual refinement. The interfaces designed by ProteinMPNN were substantially more polar than those designed by Rosetta, which facilitated in vitro assembly of the designed nanomaterials from independently purified components. Crystal structures of several of the assemblies confirmed the accuracy of the design method at high resolution. Our results showcase the potential of deep learning-based methods to unlock the widespread application of designed protein-protein interfaces and self-assembling protein nanomaterials in biotechnology.

Diverging co-translational protein complex assembly pathways are governed by interface energy distribution

Protein-protein interactions are at the heart of all cellular processes, with the ribosome emerging as a platform, orchestrating the nascent-chain interplay dynamics. Here, to study the characteristics governing co-translational protein folding and complex assembly, we combine selective ribosome profiling, imaging, and N-terminomics with all-atoms molecular dynamics. Focusing on conserved N-terminal acetyltransferases (NATs), we uncover diverging co-translational assembly pathways, where highly homologous subunits serve opposite functions. We find that only a few residues serve as "hotspots," initiating co-translational assembly interactions upon exposure at the ribosome exit tunnel. These hotspots are characterized by high binding energy, anchoring the entire interface assembly. Alpha-helices harboring hotspots are highly thermolabile, folding and unfolding during simulations, depending on their partner subunit to avoid misfolding. In vivo hotspot mutations disrupted co-translational complexation, leading to aggregation. Accordingly, conservation analysis reveals that missense NATs variants, causing neurodevelopmental and neurodegenerative diseases, disrupt putative hotspot clusters. Expanding our study to include phosphofructokinase, anthranilate synthase, and nucleoporin subcomplex, we employ AlphaFold-Multimer to model the complexes' complete structures. Computing MD-derived interface energy profiles, we find similar trends. Here, we propose a model based on the distribution of interface energy as a strong predictor of co-translational assembly.

Fluoride Ion Binding and Translocation in the CLCF Fluoride/Proton Antiporter: Molecular Insights from Combined Quantum-Mechanical/Molecular-Mechanical Modeling

CLCF fluoride/proton antiporters move fluoride ions out of bacterial cells, leading to fluoride resistance in these bacteria. However, many details about their operating mechanisms remain unclear. Here, we report a combined quantum-mechanical/molecular-mechanical (QM/MM) study of a CLCF homologue from Enterococci casseliflavus (Eca), in accord with the previously proposed windmill mechanism. Our multiscale modeling sheds light on two critical steps in the transport cycle: (i) the external gating residue E118 pushing a fluoride in the external binding site into the extracellular vestibule and (ii) an incoming fluoride reconquering the external binding site by forcing out E118. Both steps feature competitions for the external binding site between the negatively charged carboxylate of E118 and the fluoride. Remarkably, the displaced E118 by fluoride accepts a proton from the nearby R117, initiating the next transport cycle. We also demonstrate the importance of accurate quantum descriptions of fluoride solvation. Our results provide clues to the mysterious E318 residue near the central binding site, suggesting that the transport activities are unlikely to be disrupted by the glutamate interacting with a well-solvated fluoride at the central binding site. This differs significantly from the structurally similar CLC chloride/proton antiporters, where a fluoride trapped deep in the hydrophobic pore causes the transporter to be locked down. A free-energy barrier of 10-15 kcal/mol was estimated via umbrella sampling for a fluoride ion traveling through the pore to repopulate the external binding site.

Investigating the anticancer potential of 4-phenylthiazole derived Ru(II) and Os(II) metalacycles

In this contribution we report the synthesis, characterization and in vitro anticancer activity of novel cyclometalated 4-phenylthiazole-derived ruthenium(II) (2a-e) and osmium(II) (3a-e) complexes. Formation and sufficient purity of the complexes were unambigiously confirmed by 1H-, 13C- and 2D-NMR techniques, X-ray diffractometry, HRMS and elemental analysis. The binding preferences of these cyclometalates to selected amino acids and to DNA models including G-quadruplex structures were analyzed. Additionally, their stability and behaviour in aqueous solutions was determined by UV-Vis spectroscopy. Their cellular accumulation, their ability of inducing apoptosis, as well as their interference in the cell cycle were studied in SW480 colon cancer cells. The anticancer potencies were investigated in three human cancer cell lines and revealed IC50 values in the low micromolar range, in contrast to the biologically inactive ligands.

Discovery of N-(1-(2-hydroxyethyl)quinolin-2-one)-N'-(1-phenyl-1H-pyrazol-5-yl)methyl) urea as Mode-Selective TRPV1 antagonist

To discover mode-selective TRPV1 antagonists as thermoneutral drug candidates, the previous potent antagonist benzopyridone 2 was optimized based on the pharmacophore A- and C-regions. The structure activity relationship was investigated systematically by modifying the A-region by incorporating a polar side chain on the pyridone and then by changing the C-region with a variety of substituted pyridine and pyrazole moieties. The 3-t-butyl and 3-(1-methylcyclopropyl) pyrazole C-region analogs provided high potency as well as mode-selectivity. Among them, 51 and 54 displayed potent and capsaicin-selective antagonism with IC50 = 2.85 and 3.27 nM to capsaicin activation and 28.5 and 31.5 % inhibition at 3 µM concentration toward proton activation, respectively. The molecular modeling study of 51 with our homology model indicated that the hydroxyethyl side chain in the A-region interacted with Arg557 and Glu570, the urea B-region engaged in hydrogen bonding with Tyr511 and Thr550, respectively, and the pyrazole C-region made two hydrophobic interactions with the receptor. Optimization of antagonist 2, which has full antagonism for activators of all modes, lead to mode-selective antagonists 51 and 54. These observations will provide insight into the future development of clinical TRPV1 antagonists without target-based side effects.

Enhancing the Features of DNA Mimic Foldamers for Structural Investigations

DNA mimic foldamers based on aromatic oligoamide helices bearing anionic phosphonate side chains have been shown to bind to DNA-binding proteins sometimes orders of magnitude better than DNA itself. Here, we introduce new features in the DNA mimic foldamers to facilitate structural investigations of their interactions with proteins. Thirteen new foldamer sequences have been synthesized and characterized using NMR, circular dichroism, molecular modeling, and X-ray crystallography. The results show that foldamer helix handedness can be quantitatively biased by means of a single stereogenic center, that the foldamer structure can be made C2-symmetrical as in palindromic B-DNA sequences, and that associations between foldamer helices can be promoted utilizing dedicated C-terminal residues that act as sticky ends in B-DNA structures.

Assessment of Amino Acid Electrostatic Parametrizations of the Polarizable Gaussian Multipole Model

Accurate parametrization of amino acids is pivotal for the development of reliable force fields for molecular modeling of biomolecules such as proteins. This study aims to assess amino acid electrostatic parametrizations with the polarizable Gaussian Multipole (pGM) model by evaluating the performance of the pGM-perm (with atomic permanent dipoles) and pGM-ind (without atomic permanent dipoles) variants compared to the traditional RESP model. The 100-conf-combterm fitting strategy on tetrapeptides was adopted, in which (1) all peptide bond atoms (-CO-NH-) share identical set of parameters and (2) the total charges of the two terminal N-acetyl (ACE) and N-methylamide (NME) groups were set to neutral. The accuracy and transferability of electrostatic parameters across peptides with varying lengths and real-world examples were examined. The results demonstrate the enhanced performance of the pGM-perm model in accurately representing the electrostatic properties of amino acids. This insight underscores the potential of the pGM-perm model and the 100-conf-combterm strategy for the future development of the pGM force field.

Structure-guided engineering and molecular simulations to design a potent monoclonal antibody to target aP2 antigen for adaptive immune response instigation against type 2 diabetes

Introduction

Diabetes mellitus (DM) is recognized as one of the oldest chronic diseases and has become a significant public health issue, necessitating innovative therapeutic strategies to enhance patient outcomes. Traditional treatments have provided limited success, highlighting the need for novel approaches in managing this complex disease.

Methods

In our study, we employed graph signature-based methodologies in conjunction with molecular simulation and free energy calculations. The objective was to engineer the CA33 monoclonal antibody for effective targeting of the aP2 antigen, aiming to elicit a potent immune response. This approach involved screening a mutational landscape comprising 57 mutants to identify modifications that yield significant enhancements in binding efficacy and stability.

Results

Analysis of the mutational landscape revealed that only five substitutions resulted in noteworthy improvements. Among these, mutations T94M, A96E, A96Q, and T94W were identified through molecular docking experiments to exhibit higher docking scores compared to the wild-type. Further validation was provided by calculating the dissociation constant (KD), which showed a similar trend in favor of these mutations. Molecular simulation analyses highlighted T94M as the most stable complex, with reduced internal fluctuations upon binding. Principal components analysis (PCA) indicated that both the wild-type and T94M mutant displayed similar patterns of constrained and restricted motion across principal components. The free energy landscape analysis underscored a single metastable state for all complexes, indicating limited structural variability and potential for high therapeutic efficacy against aP2. Total binding free energy (TBE) calculations further supported the superior performance of the T94M mutation, with TBE values demonstrating the enhanced binding affinity of selected mutants over the wild-type.

Discussion

Our findings suggest that the T94M substitution, along with other identified mutations, significantly enhances the therapeutic potential of the CA33 antibody against DM by improving its binding affinity and stability. These results not only contribute to a deeper understanding of antibody-antigen interactions in the context of DM but also provide a valuable framework for the rational design of antibodies aimed at targeting this disease more effectively.

Data-Efficient Generation of Protein Conformational Ensembles with Backbone-to-Side-Chain Transformers

Excitement at the prospect of using data-driven generative models to sample configurational ensembles of biomolecular systems stems from the extraordinary success of these models on a diverse set of high-dimensional sampling tasks. Unlike image generation or even the closely related problem of protein structure prediction, there are currently no data sources with sufficient breadth to parametrize generative models for conformational ensembles. To enable discovery, a fundamentally different approach to building generative models is required: models should be able to propose rare, albeit physical, conformations that may not arise in even the largest data sets. Here we introduce a modular strategy to generate conformations based on "backmapping" from a fixed protein backbone that (1) maintains conformational diversity of the side chains and (2) couples the side-chain fluctuations using global information about the protein conformation. Our model combines simple statistical models of side-chain conformations based on rotamer libraries with the now ubiquitous transformer architecture to sample with atomistic accuracy. Together, these ingredients provide a strategy for rapid data acquisition and hence a crucial ingredient for scalable physical simulation with generative neural networks.

Impact of Glycosylation on Protein-Protein Self-Interactions of Monoclonal Antibodies

Protein self-interactions measured via second osmotic virial coefficients (B22) and dynamic light scattering interaction parameter values (kD) are often used as metrics for assessing the favorability of protein candidates and different formulations during monoclonal antibody (MAb) product development. Model predictions of B22 or kD typically do not account for glycans, though glycosylation can potentially impact experimental MAb self-interactions. To the best of our knowledge, the impact of MAb glycosylation on the experimentally measured B22 and kD values has not yet been reported. B22 and kD values of two fully deglycosylated MAbs and their native (i.e., fully glycosylated) counterparts were measured by light scattering over a range of pH and ionic strength conditions. Significant differences between B22 and kD of the native and deglycosylated forms were observed at a range of low to high ionic strengths used to modulate the effect of electrostatic contributions. Differences were most pronounced at low ionic strength, indicating that electrostatic interactions are a contributing factor. Though B22 and kD values were statistically equivalent at high ionic strengths where electrostatics were fully screened, we observed protein-dependent qualitative differences, which indicate that steric interactions may also play a role in the observed B22 and kD differences. A domain-level coarse-grained molecular model accounting for charge differences was considered to potentially provide additional insight but was not fully predictive of the behavior across all of the solution conditions investigated. This highlights that both the level of modeling and lack of inclusion of glycans may limit existing models in making quantitatively accurate predictions of self-interactions.

Monte Carlo Optimization Method Based QSAR Modeling of Placental Barrier Permeability

PURPOSE

In order to ensure that drug administration is safe during pregnancy, it is crucial to have the possibility to predict the placental permeability of drugs in humans. The experimental method which is most widely used for the said purpose is in vitro human placental perfusion, though the approach is highly expensive and time consuming. Quantitative structure-activity relationship (QSAR) modeling represents a powerful tool for the assessment of the drug placental transfer, and can be successfully employed to be an alternative in in vitro experiments.

METHODS

The conformation-independent QSAR models covered in the present study were developed through the use of the SMILES notation descriptors and local molecular graph invariants. What is more, the Monte Carlo optimization method, was used in the test sets and the training sets as the model developer with three independent molecular splits.

RESULTS

A range of different statistical parameters was used to validate the developed QSAR model, including the standard error of estimation, mean absolute error, root-mean-square error (RMSE), correlation coefficient, cross-validated correlation coefficient, Fisher ratio, MAE-based metrics and the correlation ideality index. Once the mentioned statistical methods were employed, an excellent predictive potential and robustness of the developed QSAR model was demonstrated. In addition, the molecular fragments, which are derived from the SMILES notation descriptors accounting for the decrease or increase in the investigated activity, were revealed.

CONCLUSION

The presented QSAR modeling can be an invaluable tool for the high-throughput screening of the placental permeability of drugs.

Theoretical analysis on D-π-A dye molecules with different acceptors and terminal branches for highly efficient dye-sensitized solar cells

Triphenylamine and 9-phenylcarbazole are the most common electron donor groups, now based on the two groups, eight D-π-A dyes are designed as sensitizers for dye-sensitized solar cells (DSSCs).The eight dyes use the same π-conjugated bridge (thiophene moiety and carbon-carbon double bond) and acceptor fragment (cyanoacrylic acid), and the donor group is added with additional electron-D groups to the original triphenylamine and 9-phenylcarbazole (C4H9 alkyl chain, C4F9 perfluoroalkyl chain, and methoxy), and comparing the properties of several donor groups and terminal branched chains while ensuring that the π-bridges and acceptors are identical. The photophysical properties, electronically excited states, and chemical reactivity affecting the performed dyes have been determined with DFT and TD-DFT calculations of bond lengths and dihedral angles between fragments, frontier molecular orbitals, density of states, isosurface molecular electrostatic potential, charge density differences, fragment transition density matrix, UV-Vis absorption spectra, quantum chemical, and photovoltaic parameters. Comparisons have been made between the dyes under study's photophysical characteristics, electrically excited states, and chemical reactivity. Among all the different donor dyes designed, SH-3 and ZD-3 are poorly molecularly planar compared to the same series of molecules with parameters such as large HOMO-LUMO energy gaps (2.78 eV, 3.28 eV), maximum excited energies (2.93 eV, 3.13 eV), and the shortest absorption peaks (422.76 nm, 396.48 nm), which are considered to be the worst material for photovoltaic applications. Whereas, SH-4 and ZD-4 have the smallest energy gap values (2.35 eV, 2.74 eV) and vertical excitation energies (2.66 eV, 3.04 eV) as well as having the longest absorption peaks (465.34 nm, 408.42 nm), the largest open circuit voltages (1.42 eV, 1.34 eV), which are the best designs among the two groups of molecules. The rest of the designed organic dyes have suitable photophysical properties and all of them are highly recommended for DSSCs.

The Functionality of the DC Pair in a Rhodopsin Guanylyl Cyclase from Catenaria anguillulae

Rhodopsin guanylyl cyclases (RGCs) belong to the class of enzymerhodopsins catalyzing the transition from GTP into the second messenger cGMP, whereas light-regulation of enzyme activity is mediated by a membrane-bound microbial rhodopsin domain, that holds the catalytic center inactive in the dark. Structural determinants for activation of the rhodopsin moiety eventually leading to catalytic activity are largely unknown. Here, we investigate the mechanistic role of the D283-C259 (DC) pair that is hydrogen bonded via a water molecule as a crucial functional motif in the homodimeric C. anguillulae RGC. Based on a structural model of the DC pair in the retinal binding pocket obtained by MD simulation, we analyzed formation and kinetics of early and late photocycle intermediates of the rhodopsin domain wild type and specific DC pair mutants by combined UV-Vis and FTIR spectroscopy at ambient and cryo-temperatures. By assigning specific infrared bands to S-H vibrations of C259 we are able to show that the DC pair residues are tightly coupled. We show that deprotonation of D283 occurs already in the inactive L state as a prerequisite for M state formation, whereas structural changes of C259 occur in the active M state and early cryo-trapped intermediates. We propose a comprehensive molecular model for formation of the M state that activates the catalytic moiety. It involves light induced changes in bond strength and hydrogen bonding of the DC pair residues from the early J state to the active M state and explains the retarding effect of C259 mutants.

Designed complexes combining brazilein and brazilin with betanidin for dye-sensitized solar cell application: DFT and TD-DFT study

Dye-sensitized solar cells (DSSCs) are promising third-generation photovoltaic cell technology owing to their easy fabrication, flexibility and better performance under diffuse light conditions. Natural pigment sensitizers are abundantly available and environmentally friendliness. However, narrow absorption spectra of natural pigments result in low efficiencies of the DSSCs. Therefore, combining two or more pigments with complementary absorption spectra is considered an appropriate method to broaden the absorption band and boost efficiency. This study reports three complex molecules: brazilin-betanidin-oxane (Braz-Bd-oxane), brazilin-betanidin-ether (Braz-Bd-ether) and brazilein-betanidin-ether (Braze-Bd-ether), obtained from the etherification and bi-etherification reactions of brazilin dye and brazilein dye with betanidin dye. The equilibrium geometrical structure properties, frontier molecular orbital, electrostatic surface potential, reorganization energy, chemical reactivities, and non-linear optical properties of the studied dyes were investigated using density functional theory (DFT)/B3LYP methods, with 6-31+G(d,p) basis sets and LANL2DZ for light atom and heavy atoms respectively. The optical-electronic properties were calculated using TD-DFT/B3LYP/6-31+G(d,p) for isolated dye and TD-DFT/CAM-B3LYP/6-31G(d,p)/LANL2DZ for dyes@(TiO2)9H4. The results reveal that spectra for Braz-Bd-oxane and Braze-Bd-ether complexes red-shifted compared to the individually selected dyes. The simulated absorption spectra of the adsorbed dyes on (TiO2)9H4 are red-shifted compared to the free dye. Moreover, Braz-Bd-oxane and Braz-Bd-ether exhibit better charge transfer and photovoltaic properties than the selected natural dyes forming these complexes. Based on the dyes' optoelectronic properties and photovoltaic properties, the designed molecules Braz-Bd-oxane and Braze-Bd-ether are considered better candidates to be used as photosensitizers in dye solar cells.

At the outer part of the active site in Trypanosoma cruzi glucokinase: The role of phenylalanine 337

The hole mutagenesis approach was used to interrogate the importance of F337 in Trypanosoma cruzi glucokinase (TcGlcK) in order to understand the complete set of binding interactions that are made by d-glucosamine analogue inhibitors containing aromatic tail groups that can extend to the outer part of the active site. An interesting inhibitor of this analogue class includes 2-N-carboxybenzyl-2-deoxy-d-glucosamine (CBZ-GlcN), which exhibits strong TcGlcK binding with a Ki of 710 nM. The residue F337 is found at the outer part of the active site that stems from the second protein subunit of the homodimeric assembly. In this study, F337 was changed to leucine and alanine so as to diminish phenylalanine's side chain size and attenuate intermolecular interactions in this region of the binding cavity. Results from enzyme - inhibitor assays revealed that the phenyl group of F337 made dominant hydrophobic interactions with the phenyl group of CBZ-GlcN as opposed to π - π stacking interactions. Moreover, enzymatic activity assays and X-ray crystallographic experiments indicated that each of these site-directed mutants primarily retained their activity and had high structural similarity of their protein fold. A computed structure model of T. cruzi hexokinase (TcHxK), which was produced by the artificial intelligence system AlphaFold, was compared to an X-ray crystal structure of TcGlcK. Our structural analysis revealed that TcHxK lacked an F337 counterpart residue and probably exists in the monomeric form. We proposed that the d-glucosamine analogue inhibitors that are structurally similar to CBZ-GlcN may not bind as strongly in TcHxK as they do in TcGlcK because of absent van der Waals contact from residue side chains.

Protein ensemble modeling and analysis with MMMx

Proteins, especially of eukaryotes, often have disordered domains and may contain multiple folded domains whose relative spatial arrangement is distributed. The MMMx ensemble modeling and analysis toolbox (https://github.com/gjeschke/MMMx) can support the design of experiments to characterize the distributed structure of such proteins, starting from AlphaFold2 predictions or folded domain structures. Weak order can be analyzed with reference to a random coil model or to peptide chains that match the residue-specific Ramachandran angle distribution of the loop regions and are otherwise unrestrained. The deviation of the mean square end-to-end distance of chain sections from their average over sections of the same sequence length reveals localized compaction or expansion of the chain. The shape sampled by disordered chains is visualized by superposition in the principal axes frame of their inertia tensor. Ensembles of different sizes and with weighted conformers can be compared based on a similarity parameter that abstracts from the ensemble width.

Spectroscopic, computational, cytotoxicity, and docking studies of 6-bromobenzimidazole as anti-breast cancer agent

6-Bromobenzimidazole (6BBZ) has been calculated in this study utilizing the 6-311++G(d,p) basis set and the Becke-3-Lee-Yang-Parr density functional approaches. The basic frequencies and geometric optimization are known. FTIR, FT-Raman, and UV-Vis spectra of the substance are compared between its computed and observed values. The energy gap between highest occupied molecular orbital-lowest unoccupied molecular orbital and molecule electrostatic potentials has been represented by charge density distributions that may be associated with the biological response. Time-dependent density functional theory calculations in the gas phase and dimethyl sulfoxide were carried out to ascertain the electronic properties and energy gap values using the same basis set. Molecular orbital contributions are investigated using the overlap population, partial, and total densities of states. Natural bond analysis was found to have strong electron delocalization by means of π(C4-C9) → π*(C5-C6), LP (N1) → π*(C7-C8), and LP(Br12) → π*(C5-C6) interactions. The Fukui function and Mulliken analysis have been explored on the atomic charges of the molecule. The nuclear magnetic resonance chemical shifts for 1 H and 13 C have been computed using the gauge-independent atomic orbital technique. With the highest binding affinity (-6.2 kcal mol-1 ) against estrogen sulfotransferase receptor (PDB ID: 1AQU) and low IC50 value of 17.23 μg/mL, 6BBZ demonstrated potent action against the MCF-7 breast cancer cell line. Studies on the antibacterial activity and ADMET prediction of the molecule have also been carried out.

Cryo-EM structure of the inactive ribosome complex accumulated in chick embryo cells in cold-stress conditions

Here, we present the high-resolution structure of the Gallus gallus 80S ribosome obtained from cold-treated chicken embryos. The translationally inactive ribosome complex contains elongation factor eEF2 with GDP, SERPINE1 mRNA binding protein 1 (SERBP1) and deacylated tRNA in the P/E position, showing common features with complexes already described in mammals. Modeling of most expansion segments of G. gallus 28S ribosomal RNA allowed us to identify specific features in their structural organization and to describe areas where a marked difference between mammalian and avian ribosomes could shed light on the evolution of the expansion segments. This study provides the first structure of an avian ribosome, establishing a model for future structural and functional studies on the translational machinery in Aves.