Structure-Based Discovery of High-Affinity Small Molecule Ligands and Development of Tool Probes to Study the Role of Chitinase-3-Like Protein 1.

Chitinase-3-like-1 (CHI3L1), also known as YKL-40, is a glycoprotein linked to inflammation, fibrosis, and cancer. This study explored CHI3L1's interactions with various oligosaccharides using microscale thermophoresis (MST) and AlphaScreen (AS). These investigations guided the development of high-throughput screening assays to assess interference of small molecules in binding between CHI3L1 and biotinylated small molecules or heparan sulfate-based probes. Small molecule binders of YKL-40 were identified in our chitotriosidase inhibitors library with MST and confirmed through X-ray crystallography. Based on cocrystal structures of potent hit compounds with CHI3L1, small molecule probes 19 and 20 were designed for an AS assay. Structure-based optimization led to compounds 30 and 31 with nanomolar activities and drug-like properties. Additionally, an orthogonal AS assay using biotinylated heparan sulfate as a probe was developed. The compounds' affinity showed a significant correlation in both assays. These screening tools and compounds offer novel avenues for investigating the role of CHI3L1.

Novel orally bioavailable piperidine derivatives as extracellular arginase inhibitors developed by a ring expansion.

Arginase is a multifaced enzyme that plays an important role in health and disease being regarded as a therapeutic target for the treatment of various pathological states such as malignancies, asthma, and cardiovascular disease. The discovery of boronic acid-based arginase inhibitors in 1997 revolutionized attempts of medicinal chemistry focused on development of drugs targeting arginase. Unfortunately, these very polar compounds had limitations such as analysis and purification without chromophores, synthetically challenging space, and poor oral bioavailability. Herein, we present a novel class of boronic acid-based arginase inhibitors which are piperidine derivatives exhibiting a different pharmacological profile compared to our drug candidate in cancer immunotherapy -OATD-02 - dual ARG1/2 inhibitor with high intracellular activity. Compounds from this new series show low intracellular activity, hence they can inhibit mainly extracellular arginase, providing different therapeutic space compared to a dual intracellular ARG1/2 inhibitor. The disclosed series showed good inhibitory potential towards arginase enzyme in vitro (IC50 up to 160 nM), favorable pharmacokinetics in animal models, and encouraging preliminary in vitro and in vivo tolerability. Compounds from the new series have moderate-to-high oral bioavailability (up to 66 %) and moderate clearance in vivo. Herein we describe the development and optimization of the synthesis of the new class of boronic acid-based arginase inhibitors via a ring expansion approach starting from the inexpensive chirality source (d-hydroxyproline). This upgraded methodology facilitated a gram-scale delivery of the final compound and eliminated the need for costly and time-consuming chiral resolution.

Advanced Feature Extraction Methods from Images of Drillings in Melamine Faced Chipboard for Automatic Diagnosis of Drill Wear.

In this paper, a novel approach to evaluation of feature extraction methodologies is presented. In the case of machine learning algorithms, extracting and using the most efficient features is one of the key problems that can significantly influence overall performance. It is especially the case with parameter-heavy problems, such as tool condition monitoring. In the presented case, images of drilled holes are considered, where state of the edge and the overall size of imperfections have high influence on product quality. Finding and using a set of features that accurately describes the differences between the edge that is acceptable or too damaged is not always straightforward. The presented approach focuses on detailed evaluation of various feature extraction approaches. Each chosen method produced a set of features, which was then used to train a selected set of classifiers. Five initial feature sets were obtained, and additional ones were derived from them. Different voting methods were used for ensemble approaches. In total, 38 versions of the classifiers were created and evaluated. Best accuracy was obtained by the ensemble approach based on Weighted Voting methodology. A significant difference was shown between different feature extraction methods, with a total difference of 11.14% between the worst and best feature set, as well as a further 0.2% improvement achieved by using the best voting approach.

Vehicle Detection and Recognition Approach in Multi-Scale Traffic Monitoring System via Graph-Based Data Optimization.

Over the past few years, significant investments in smart traffic monitoring systems have been made. The most important step in machine learning is detecting and recognizing objects relative to vehicles. Due to variations in vision and different lighting conditions, the recognition and tracking of vehicles under varying extreme conditions has become one of the most challenging tasks. To deal with this, our proposed system presents an adaptive method for robustly recognizing several existing automobiles in dense traffic settings. Additionally, this research presents a broad framework for effective on-road vehicle recognition and detection. Furthermore, the proposed system focuses on challenges typically noticed in analyzing traffic scenes captured by in-vehicle cameras, such as consistent extraction of features. First, we performed frame conversion, background subtraction, and object shape optimization as preprocessing steps. Next, two important features (energy and deep optical flow) were extracted. The incorporation of energy and dense optical flow features in distance-adaptive window areas and subsequent processing over the fused features resulted in a greater capacity for discrimination. Next, a graph-mining-based approach was applied to select optimal features. Finally, the artificial neural network was adopted for detection and classification. The experimental results show significant performance in two benchmark datasets, including the LISA and KITTI 7 databases. The LISA dataset achieved a mean recognition rate of 93.75% on the LDB1 and LDB2 databases, whereas KITTI attained 82.85% accuracy on separate training of ANN.

Tracking Topological and Electronic Effects on the Folding and Stability of Guanine-Deficient RNA G-Quadruplexes, Engineered with a New Computational Tool for De Novo Quadruplex Folding.

The initial aim of this work was to elucidate the mutual influence of different single-stranded segments (loops and caps) on the thermodynamic stability of RNA G-quadruplexes. To this end, we used a new NAB-GQ-builder software program, to construct dozens of two-tetrad G-quadruplex topologies, based on a designed library of sequences. Then, to probe the sequence-morphology-stability relationships of the designed topologies, we performed molecular dynamics simulations. Their results provide guidance for the design of G-quadruplexes with balanced structures, and in turn programmable physicochemical properties for applications as biomaterials. Moreover, by comparative examinations of the single-stranded segments of three oncogene promoter G-quadruplexes, we assess their druggability potential for future therapeutic strategies. Finally, on the basis of a thorough analysis at the quantum mechanical level of theory on a series of guanine assemblies, we demonstrate how a valence tautomerism, triggered by a coordination of cations, initiates the process of G-quadruplex folding, and we propose a sequential folding mechanism, otherwise dictated by the cancellation of the dipole moments on guanines.

New Imaging Modality of COVID-19 Pneumonia Developed on the Basis of Alzheimer's Disease Research.

Viral pneumonia caused by highly infectious SARS-CoV-2 poses a higher risk to older people and those who have underlying health conditions, including Alzheimer's disease. In this work we present newly designed tacrine-based radioconjugates with physicochemical and biological properties that are crucial for the potential application as diagnostic radiopharmaceuticals. A set of ten tacrine derivatives was synthesized, labelled with gallium-68 and fully characterized in the context of their physicochemical properties. Based on these results, the final two most promising radioconjugates, [68Ga]Ga-NODAGA-Bn-NH(CH2)9Tac and [68Ga]Ga-THP-NH(CH2)9Tac, were selected for biodistribution studies. The latter compound was proven to be a good inhibitor of cholinesterases with significant affinity toward the lungs, according to the biodistribution studies. On the basis of molecular modelling combined with in vitro studies, we unraveled which structural properties of the developed tacrine derivatives are crucial for high affinity toward acetylcholinesterase, whose increased levels in lung tissues in the course of coronavirus disease indicate the onset of pneumonia. The radiopharmaceutical [68Ga]Ga-THP-NH(CH2)9Tac was ultimately selected due to its increased accuracy and improved sensitivity in PET imaging of lung tissue with high levels of acetylcholinesterase, and it may become a novel potential diagnostic modality for the determination of lung perfusion, including in inflammation after COVID-19.

Novel Cyclopentaquinoline and Acridine Analogs as Multifunctional, Potent Drug Candidates in Alzheimer's Disease.

A series of new cyclopentaquinoline derivatives with 9-acridinecarboxylic acid and a different alkyl chain length were synthesized, and their ability to inhibit cholinesterases was evaluated. All designed compounds, except derivative 3f, exhibited a selectivity for butyrylcholinesterase (BuChE) with IC50 values ranging from 103 to 539 nM. The 3b derivative revealed the highest inhibitory activity towards BuChE (IC50 = 103.73 nM) and a suitable activity against AChE (IC50 = 272.33 nM). The 3f derivative was the most active compound to AChE (IC50 = 113.34 nM) with satisfactory activity towards BuChE (IC50 = 203.52 nM). The potential hepatotoxic effect was evaluated for both 3b and 3f compounds. The 3b and 3f potential antioxidant activity was measured using the ORAC-FL method. The 3b and 3f derivatives revealed a significantly higher antioxidant potency, respectively 35 and 25 higher than tacrine. Theoretical, physicochemical, and pharmacokinetic properties were calculated using ACD Labs Percepta software. Molecular modeling and kinetic study were used to reveal the mechanism of cholinesterase inhibition in the most potent compounds: 3b and 3f.

Multiclass Image Classification Using GANs and CNN Based on Holes Drilled in Laminated Chipboard.

The multiclass prediction approach to the problem of recognizing the state of the drill by classifying images of drilled holes into three classes is presented. Expert judgement was made on the basis of the quality of the hole, by dividing the collected photographs into the classes: "very fine," "acceptable," and "unacceptable." The aim of the research was to create a model capable of identifying different levels of quality of the holes, where the reduced quality would serve as a warning that the drill is about to wear down. This could reduce the damage caused by a blunt tool. To perform this task, real-world data were gathered, normalized, and scaled down, and additional instances were created with the use of data-augmentation techniques, a self-developed transformation, and with general adversarial networks. This approach also allowed us to achieve a slight rebalance of the dataset, by creating higher numbers of images belonging to the less-represented classes. The datasets generated were then fed into a series of convolutional neural networks, with different numbers of convolution layers used, modelled to carry out the multiclass prediction. The performance of the so-designed model was compared to predictions generated by Microsoft's Custom Vision service, trained on the same data, which was treated as the benchmark. Several trained models obtained by adjusting the structure and hyperparameters of the model were able to provide better recognition of less-represented classes than the benchmark.

The universality of enzymatic rate-temperature dependency.

Organismal adaptation to extreme temperatures yields enzymes with distinct configurational stabilities, including thermophilic and psychrophilic enzymes, which are adapted to high and low temperatures, respectively. These enzymes are widely assumed to also have unique rate-temperature dependencies. Thermophilic enzymes, for example, are considered optimal at high temperatures and effectively inactive at low temperatures due to excess rigidity. Surveying published data, we find that thermophilic, mesophilic, and psychrophilic enzymes exhibit indistinguishable rate-temperature dependencies. Furthermore, given the nonenzymatic rate-temperature dependency, all enzymes, regardless of their operation temperatures, become >10-fold less powerful catalysts per 25 °C temperature increase. Among other factors, this loss of rate acceleration may be ascribed to thermally induced vibrations compromising the active-site catalytic configuration, suggesting that many enzymes are in fact insufficiently rigid.

Discovery of novel potent ΔF508-CFTR correctors that target the nucleotide binding domain.

The deletion of Phe508 (ΔF508) in the first nucleotide binding domain (NBD1) of CFTR is the most common mutation associated with cystic fibrosis. The ΔF508-CFTR mutant is recognized as improperly folded and targeted for proteasomal degradation. Based on molecular dynamics simulation results, we hypothesized that interaction between ΔF508-NBD1 and housekeeping proteins prevents ΔF508-CFTR delivery to the plasma membrane. Based on this assumption we applied structure-based virtual screening to identify new low-molecular-weight compounds that should bind to ΔF508-NBD1 and act as protein-protein interaction inhibitors. Using different functional assays for CFTR activity, we demonstrated that in silico-selected compounds induced functional expression of ΔF508-CFTR in transfected HeLa cells, human bronchial CF cells in primary culture, and in the nasal epithelium of homozygous ΔF508-CFTR mice. The proposed compounds disrupt keratin8-ΔF508-CFTR interaction in ΔF508-CFTR HeLa cells. Structural analysis of ΔF508-NBD1 in the presence of these compounds suggests their binding to NBD1. We conclude that our strategy leads to the discovery of new compounds that are among the most potent correctors of ΔF508-CFTR trafficking defect known to date.

Direct targeting of Arabidopsis cysteine synthase complexes with synthetic polypeptides to selectively deregulate cysteine synthesis.

Biosynthesis of cysteine is one of the fundamental processes in plants providing the reduced sulfur for cell metabolism. It is accomplished by the sequential action of two enzymes, serine acetyltransferase (SAT) and O-acetylserine (thiol) lyase (OAS-TL). Together they constitute the hetero-oligomeric cysteine synthase (CS) complex through specific protein-protein interactions influencing the rate of cysteine production. The aim of our studies was to deregulate the CS complex formation in order to investigate its function in the control of sulfur homeostasis and optimize cysteine synthesis. Computational modeling was used to build a model of the Arabidopsis thaliana mitochondrial CS complex. Several polypeptides based on OAS-TL C amino-acid sequence found at SAT-OASTL interaction sites were designed as probable competitors for SAT3 binding. After verification of the binding in a yeast two-hybrid assay, the most strongly interacting polypeptide was introduced to different cellular compartments of Arabidopsis cell via genetic transformation. Moderate increase in total SAT and OAS-TL activities, but not thiols content, was observed dependent on the transgenic line and sulfur availability in the hydroponic medium. Though our studies demonstrate the proof of principle, they also suggest more complex interaction of both enzymes underlying the mechanism of their reciprocal regulation.

Catalytic metal ion rearrangements underline promiscuity and evolvability of a metalloenzyme.

Although largely deemed as structurally conserved, catalytic metal ion sites can rearrange, thereby contributing to enzyme evolvability. Here, we show that in paraoxonase-1, a lipo-lactonase, catalytic promiscuity and divergence into an organophosphate hydrolase are correlated with an alternative mode of the catalytic Ca(2+). We describe the crystal structures of active-site mutants bearing mutations at position 115. The histidine at this position acts as a base to activate the lactone-hydrolyzing water molecule. Mutations to Trp or Gln indeed diminish paraoxonase-1's lactonase activity; however, the promiscuous organophosphate hydrolase activity is enhanced. The structures reveal a 1.8-Å upward displacement towards the enzyme's surface of the catalytic Ca(2+) in the His115 mutants and configurational changes in the ligating side chains and water molecules, relative to the wild-type enzyme. Biochemical analysis and molecular dynamics simulations suggest that this alternative, upward metal mode mediates the promiscuous hydrolysis of organophosphates. The upward Ca(2+) mode observed in the His115 mutants also appears to mediate the wild type's paraoxonase activity. However, whereas the upward mode dominates in the Trp115 mutant, it is scarcely populated in wild type. Thus, the plasticity of active-site metal ions may permit alternative, latent, promiscuous activities and also provide the basis for the divergence of new enzymatic functions.

Influence of macromolecular crowding on protein-protein association rates--a Brownian dynamics study.

The high total concentration of macromolecules, often referred to as macromolecular crowding, is one of the characteristic features of living cells. Macromolecular crowding influences interactions between many types of macromolecules, with consequent effects on, among others, the rates of reactions occurring in the cell. Simulations to study the influence of crowding on macromolecular association rate were performed using a modified Brownian dynamics protocol. The calculated values of the time-dependent self-diffusion coefficients in different crowding conditions are in a very good agreement with those obtained by other authors. Simulations of the complex formation between the monoclonal antibody HyHEL-5 and its antigen hen egg lysozyme, both represented at atomic level detail, show that the calculated association rates strongly depend on the volume excluded by crowding. The rate obtained for the highest concentration of crowding particles is greater than twofold higher than the rate for proteins without crowding.

MOFOID--not only the protein modeling server.

MOFOID is a new server developed mainly for automated modeling of protein structures by their homology to the structures deposited in the PDB database. Selection of a template and calculation of the alignment is performed with the Smith-Waterman or Needleman-Wunsch algorithms implemented in the EMBOSS package. The final model is built and optimised with programs from the JACKAL package. The wide spectrum of options in the web-based interface and the possibility of uploading user's own alignment make MOFOID a suitable platform for testing new approaches in the alignment building. The server is available at https:// valis.ibb.waw.pl/mofoid/.