bluuebunny/crossref_metadata_2025_split huggingface dataset:
Examining evolutionary scale modeling‐derived different‐dimensional embeddings in the antimicrobial peptide classification through a KNIME workflow
Authors: Martínez‐Mauricio Karla L. , García‐Jacas César R. , Cordoves‐Delgado Greneter 
Published: March 2024

AbstractMolecular features play an important role in different bio‐chem‐informatics tasks, such as the Quantitative Structure–Activity Relationships (QSAR) modeling. Several pre‐trained models have been recently created to be used in downstream tasks, either by fine‐tuning a specific model or by extracting features to feed traditional classifiers. In this regard, a new family of Evolutionary Scale Modeling models (termed as ESM‐2 models) was recently introduced, demonstrating outstanding results in protein structure prediction benchmarks. Herein, we studied the usefulness of the different‐dimensional embeddings derived from the ESM‐2 models to classify antimicrobial peptides (AMPs). To this end, we built a KNIME workflow to use the same modeling methodology across experiments in order to guarantee fair analyses. As a result, the 640‐ and 1280‐dimensional embeddings derived from the 30‐ and 33‐layer ESM‐2 models, respectively, are the most valuable since statistically better performances were achieved by the QSAR models built from them. We also fused features of the different ESM‐2 models, and it was concluded that the fusion contributes to getting better QSAR models than using features of a single ESM‐2 model. Frequency studies revealed that only a portion of the ESM‐2 embeddings is valuable for modeling tasks since between 43% and 66% of the features were never used. Comparisons regarding state‐of‐the‐art deep learning (DL) models confirm that when performing methodologically principled studies in the prediction of AMPs, non‐DL based QSAR models yield comparable‐to‐superior performances to DL‐based QSAR models. The developed KNIME workflow is available‐freely at https://github.com/cicese-biocom/classification-QSAR-bioKom. This workflow can be valuable to avoid unfair comparisons regarding new computational methods, as well as to propose new non‐DL based QSAR models.
Dual‐wield NTPases: A novel protein family mined from AlphaFold DB
Authors: Sakuma Koya , Koike Ryotaro , Ota Motonori 
Published: March 2024

AbstractAlphaFold protein structure database (AlphaFold DB) archives a vast number of predicted models. We conducted systematic data mining against AlphaFold DB and discovered an uncharacterized P‐loop NTPase family. The structure of the protein family was surprisingly novel, showing an atypical topology for P‐loop NTPases, noticeable twofold symmetry, and two pairs of independent putative active sites. Our findings show that structural data mining is a powerful approach to identifying undiscovered protein families.
The structural landscape of the immunoglobulin fold by large‐scale de novo design
Authors: Roel‐Touris Jorge , Carcelén Lourdes , Marcos Enrique 
Published: March 2024

AbstractDe 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.
A cellulosomal double‐dockerin module from Clostridium thermocellum shows distinct structural and cohesin‐binding features
Authors: Chen Chao , Yang Hongwu , Dong Sheng , You Cai , Moraïs Sarah , Bayer Edward A. , Liu Ya‐Jun , Xuan Jinsong , Cui Qiu , Mizrahi Itzhak , Feng Yingang 
Published: March 2024

AbstractCellulosomes are intricate cellulose‐degrading multi‐enzymatic complexes produced by anaerobic bacteria, which are valuable for bioenergy development and biotechnology. Cellulosome assembly relies on the selective interaction between cohesin modules in structural scaffolding proteins (scaffoldins) and dockerin modules in enzymes. Although the number of tandem cohesins in the scaffoldins is believed to determine the complexity of the cellulosomes, tandem dockerins also exist, albeit very rare, in some cellulosomal components whose assembly and functional roles are currently unclear. In this study, we characterized the structure and mode of assembly of a tandem bimodular double‐dockerin, which is connected to a putative S8 protease in the cellulosome‐producing bacterium, Clostridium thermocellum. Crystal and NMR structures of the double‐dockerin revealed two typical type I dockerin folds with significant interactions between them. Interaction analysis by isothermal titration calorimetry and NMR titration experiments revealed that the double‐dockerin displays a preference for binding to the cell‐wall anchoring scaffoldin ScaD through the first dockerin with a canonical dual‐binding mode, while the second dockerin module was unable to bind to any of the tested cohesins. Surprisingly, the double‐dockerin showed a much higher affinity to a cohesin from the CipC scaffoldin of Clostridium cellulolyticum than to the resident cohesins from C. thermocellum. These results contribute valuable insights into the structure and assembly of the double‐dockerin module, and provide the basis for further functional studies on multiple‐dockerin modules and cellulosomal proteases, thus highlighting the complexity and diversity of cellulosomal components.
Labile assembly of a tardigrade protein induces biostasis
Authors: Sanchez‐Martinez S. , Nguyen K. , Biswas S. , Nicholson V. , Romanyuk A. V. , Ramirez J. , Kc S. , Akter A. , Childs C. , Meese E. K. , Usher E. T. , Ginell G. M. , Yu F. , Gollub E. , Malferrari M. , Francia F. , Venturoli G. , Martin E. W. , Caporaletti F. , Giubertoni G. , Woutersen S. , Sukenik S. , Woolfson D. N. , Holehouse A. S. , Boothby T. C. 
Published: March 2024

AbstractTardigrades are microscopic animals that survive desiccation by inducing biostasis. To survive drying tardigrades rely on intrinsically disordered CAHS proteins, which also function to prevent perturbations induced by drying in vitro and in heterologous systems. CAHS proteins have been shown to form gels both in vitro and in vivo, which has been speculated to be linked to their protective capacity. However, the sequence features and mechanisms underlying gel formation and the necessity of gelation for protection have not been demonstrated. Here we report a mechanism of fibrillization and gelation for CAHS D similar to that of intermediate filament assembly. We show that in vitro, gelation restricts molecular motion, immobilizing and protecting labile material from the harmful effects of drying. In vivo, we observe that CAHS D forms fibrillar networks during osmotic stress. Fibrillar networking of CAHS D improves survival of osmotically shocked cells. We observe two emergent properties associated with fibrillization; (i) prevention of cell volume change and (ii) reduction of metabolic activity during osmotic shock. We find that there is no significant correlation between maintenance of cell volume and survival, while there is a significant correlation between reduced metabolism and survival. Importantly, CAHS D's fibrillar network formation is reversible and metabolic rates return to control levels after CAHS fibers are resolved. This work provides insights into how tardigrades induce reversible biostasis through the self‐assembly of labile CAHS gels.
Engineering of IF1‐susceptive bacterial F1‐ATPase
Authors: Hatasaki Yuichiro C. , Kobayashi Ryohei , Watanabe Ryo R. , Hara Mayu , Ueno Hiroshi , Noji Hiroyuki 
Published: March 2024

AbstractIF1, an inhibitor protein of mitochondrial ATP synthase, suppresses ATP hydrolytic activity of F1. One of the unique features of IF1 is the selective inhibition in mitochondrial F1 (MF1); it inhibits catalysis of MF1 but does not affect F1 with bacterial origin despite high sequence homology between MF1 and bacterial F1. Here, we aimed to engineer thermophilic Bacillus F1 (TF1) to confer the susceptibility to IF1 for elucidating the molecular mechanism of selective inhibition of IF1. We first examined the IF1‐susceptibility of hybrid F1s, composed of each subunit originating from bovine MF1 (bMF1) or TF1. It was clearly shown that only the hybrid with the β subunit of mitochondrial origin has the IF1‐susceptibility. Based on structural analysis and sequence alignment of bMF1 and TF1, the five non‐conserved residues on the C‐terminus of the β subunit were identified as the candidate responsible for the IF1‐susceptibility. These residues in TF1 were substituted with the bMF1 residues. The resultant mutant TF1 showed evident IF1‐susceptibility. Reversely, we examined the bMF1 mutant with TF1 residues at the corresponding sites, which showed significant suppression of IF1‐susceptibility, confirming the critical role of these residues. We also tested additional three substitutions with bMF1 residues in α and γ subunits that further enhanced the IF1‐susceptibility, suggesting the additive role of these residues. We discuss the molecular mechanism by which IF1 specifically recognizes F1 with mitochondrial origin, based on the present result and the structure of F1‐IF1 complex. These findings would help the development of the inhibitors targeting bacterial F1.
HadBD dehydratase from Mycobacterium tuberculosis fatty acid synthase type II: A singular structure for a unique function
Authors: Bories Pascaline , Rima Julie , Tranier Samuel , Marcoux Julien , Grimoire Yasmina , Tomaszczyk Mathilde , Launay Anne , Fata Karine , Marrakchi Hedia , Burlet‐Schiltz Odile , Mourey Lionel , Ducoux‐Petit Manuelle , Bardou Fabienne , Bon Cécile , Quémard Annaïk 
Published: March 2024

AbstractWorldwide, tuberculosis is the second leading infectious killer and multidrug resistance severely hampers disease control. Mycolic acids are a unique category of lipids that are essential for viability, virulence, and persistence of the causative agent, Mycobacterium tuberculosis (Mtb). Therefore, enzymes involved in mycolic acid biosynthesis represent an important class of drug targets. We previously showed that the (3R)‐hydroxyacyl‐ACP dehydratase (HAD) protein HadD is dedicated mainly to the production of ketomycolic acids and plays a determinant role in Mtb biofilm formation and virulence. Here, we discovered that HAD activity requires the formation of a tight heterotetramer between HadD and HadB, a HAD unit encoded by a distinct chromosomal region. Using biochemical, structural, and cell‐based analyses, we showed that HadB is the catalytic subunit, whereas HadD is involved in substrate binding. Based on HadBDMtb crystal structure and substrate‐bound models, we identified determinants of the ultra‐long‐chain lipid substrate specificity and revealed details of structure–function relationship. HadBDMtb unique function is partly due to a wider opening and a higher flexibility of the substrate‐binding crevice in HadD, as well as the drastically truncated central α‐helix of HadD hotdog fold, a feature described for the first time in a HAD enzyme. Taken together, our study shows that HadBDMtb, and not HadD alone, is the biologically relevant functional unit. These results have important implications for designing innovative antivirulence molecules to fight tuberculosis, as they suggest that the target to consider is not an isolated subunit, but the whole HadBD complex.
Flattening the curve—How to get better results with small deep‐mutational‐scanning datasets
Authors: Wirnsberger Gregor , Pritišanac Iva , Oberdorfer Gustav , Gruber Karl 
Published: March 2024

AbstractProteins are used in various biotechnological applications, often requiring the optimization of protein properties by introducing specific amino‐acid exchanges. Deep mutational scanning (DMS) is an effective high‐throughput method for evaluating the effects of these exchanges on protein function. DMS data can then inform the training of a neural network to predict the impact of mutations. Most approaches use some representation of the protein sequence for training and prediction. As proteins are characterized by complex structures and intricate residue interaction networks, directly providing structural information as input reduces the need to learn these features from the data. We introduce a method for encoding protein structures as stacked 2D contact maps, which capture residue interactions, their evolutionary conservation, and mutation‐induced interaction changes. Furthermore, we explored techniques to augment neural network training performance on smaller DMS datasets. To validate our approach, we trained three neural network architectures originally used for image analysis on three DMS datasets, and we compared their performances with networks trained solely on protein sequences. The results confirm the effectiveness of the protein structure encoding in machine learning efforts on DMS data. Using structural representations as direct input to the networks, along with data augmentation and pretraining, significantly reduced demands on training data size and improved prediction performance, especially on smaller datasets, while performance on large datasets was on par with state‐of‐the‐art sequence convolutional neural networks. The methods presented here have the potential to provide the same workflow as DMS without the experimental and financial burden of testing thousands of mutants. Additionally, we present an open‐source, user‐friendly software tool to make these data analysis techniques accessible, particularly to biotechnology and protein engineering researchers who wish to apply them to their mutagenesis data.
Reliability assessment models for competing failure processes with two types of correlative thresholds
Authors: Song Qingbiao , Tang Jiayin , Wei Honglei , Li Yong 
Published: March 2024

AbstractThe two dependent competing risks are soft failure due to aging degradation and fragmentation caused by shocks and hard failure due to spring breakage caused by the same shock process. Considering the complexity of the product itself and the instability of the working environment, this study proposed a generalized reliability model for systems experiencing dependent competing failure processes (DCFPs) of degradation and random shocks, which considered two kinds of DCFP: (1) shock process could affect soft failure thresholds; (2) degradation process could affect hard failure thresholds. In case (1), we considered the effect that a cumulative number of shocks above a certain magnitude could have on the change in the soft failure threshold. In case (2), we considered not only the shock process's impact on the soft failure threshold but also the total degradation (including continuous degradation and sudden degradation caused by shock) on the hard failure threshold. The model captures the features that the shocks experienced by the system affect the degradation process, accelerating the system degradation and causing soft failures; the degradation process of the system affects the shock process, making the system more susceptible to failure from external shocks. Finally, an example using micro‐electro‐mechanical systems devices illustrates the effectiveness of the proposed approach with sensitivity analysis.
Model‐free learning‐based distributed cooperative tracking control of human‐in‐the‐loop multi‐agent systems
Authors: Mei Di , Sun Jian , Xu Yong , Dou Lihua 
Published: March 2024

AbstractThis article studies the model‐free learning‐based distributed cooperative tracking control of human‐in‐the‐loop multi‐agent systems in the presence of an active leader. The core role of human‐in‐the‐loop is to use the ground station to send control commands to the non‐zero control input of the leader, and then directly or indirectly control a group of agents to complete complex tasks. Meanwhile, three essential demands including the completely unknown system model, the control objective obtained optimally, as well as no initial admissible control strategy requirement, are satisfied simultaneously. It is worth emphasizing that the relevant results only satisfy one or two demands at most, which are essentially not applicable to this problem. In this article, a model‐based human‐in‐the‐loop learning algorithm is first presented to achieve the optimal tracking control, as well as the convergence of the proposed learning algorithm is proved. Then, a bias‐based data‐driven learning algorithm is proposed, which provides the potential opportunities to overcome the difficulties caused by the above‐mentioned three demands. Finally, the validity of theoretical results is testified by a numerical example.
Discovery of Benzoxazepines as a New Class of PIM1 Kinase Inhibitors Through Structure Based Virtual Screening, Biochemical Evaluation and Cytotoxicity Studies
Authors: Sharma Omprakash , Srivastava Shubham , Sharma Manish , Malik Ruchi 
Published: March 2024

AbstractPIM kinase is a serine/threonine protein kinase implicated in human cancers because it promotes cell proliferation while inhibiting apoptosis and is a crucial target for malignancy. In the present study, we used 700000 commercially available Chembridge small molecules library for structure‐based drug discovery to identify novel potent PIM1 kinase inhibitors. Based on the virtual screening results, ten compounds were chosen and screened enzymatically at 10 μM. Out of ten compounds, only two compounds, 22876894 (61 %) and 58626268 (63 %), inhibited PIM1 kinase. Subsequently, the enzymatic IC50 value for compounds 22876894 and 58626268 was calculated and found to be 0.8 μM and 3.9 μM. Cell viability was evaluated on human malignant cell lines HCT‐116 (colorectal carcinoma), U‐2OS (osteosarcoma), and non‐cancerous hTERT‐RPE‐1. It was found that compound 22876894 possessed significant inhibitory activity against HCT‐116 (IC50=12.1 μM), U‐2OS (IC50=23.2 μM), and hTERT‐RPE‐1 (IC50=12.7 μM) cell lines. The compound 58626268 possessed significant inhibitory activities against HCT‐116 (IC50=28.1 μM), U‐2OS (IC50=23.5 μM), and hTERT‐RPE‐1 (IC50=21 μM) cell lines. Further, the molecular dynamics simulation of identified compounds (22876894 and 58626268) was performed. Based on kinase profiling, cytotoxicity studies, and molecular dynamics simulation results, we claim 5‐{[7‐(1‐benzothien‐3‐yl)‐9‐hydroxy‐2,3‐dihydro‐1,4‐benzoxazepin‐4(5H)‐yl]carbonyl}pyridin‐2(1H)‐one (22876894) and 4‐(2‐aminoisonicotinoyl)‐7‐(1‐benzothien‐3‐yl)‐2,3,4,5‐tetrahydro‐1,4‐benzoxazepin‐9‐ol (58626268) as a selective and potential PIM1 kinase inhibitors.
A New Model for Specific Heat Capacity of Real Gas
Authors: Cui Xiuyun , Zhu Bing , Ni Henmei 
Published: March 2024

AbstractBased on the new thermodynamic theory, a new model was suggested to calculate the specific heat capacities of monoatomic gas, He, and diatomic gas, O2. The theory suggests a portion of gas molecules with the highest kinetic energy affords the heat exchange to sustain the state equilibrium by inelastically colliding and liquefying on the container walls, whereas the rest elastically colliding with walls creates pressure. Accordingly, a general equation of specific heat capacity is established on the density of liquid and gas. Given
at isobar and
at isochore, Cp and Cv at any temperature can be calculated with the densities of gas and liquid (or solid). Employing the available data of He and O2 issued by NIST, Cps, and Cvs are calculated. The results fit the reference data well.
MgnBu2 Catalyzed the Hydroalkoxylation of Carbodiimides
Authors: Han Luming , Yan Ben , Ni Congjian , Pang Ziyuan , Ma Xiaoli , Yang Zhi 
Published: March 2024

AbstractThe commercially available alkaline earth‐metal compound MgnBu2 can act as a highly efficient pre‐catalyst under mild conditions for the catalytic addition of alcohols to carbodiimides. To the best of our knowledge, it has not been reported in alkaline earth‐metal catalyzing reactions. A possible mechanistic pathway is proposed based on the stoichiometric experiments and the previously published literature.
Synthesis and Properties of a Triphenylamine‐Based Fluorescent Probe for the Continuous Detection of Cu2+ and EDTA
Authors: Hu Hao , Yao Yelin , Deng Linlong , Wang Haibin , Xue Lei 
Published: March 2024

AbstractA fluorescent probe NS was successfully developed using triphenylamine aldehyde and 1‐hydroxy‐2‐naphthaldehyde. NS demonstrated selective recognition of Cu2+ in a DMSO/H2O solution, forming a stable 1 : 1 complex that led to fluorescence quenching. The addition of EDTA resulted in fluorescence recovery. The fluorescent intensity of NS showed a linear correlation with Cu2+ concentration within the range of 2~14 μmol ⋅ L−1, with a detection limit of 39 nmol ⋅ L−1 and a binding constant of 1.56×103 L mol−1. Additionally, the detection limit of the NS‐Cu2+ complex for EDTA was calculated to be 187 nmol ⋅ L−1. NS also displayed promising applications in the environmental and alcoholic drinks for detecting Cu2+ in water, wine, and Chinese Baijiu samples.
Access to 2,3‐Unsubstituted Imidazo[2,1‐b][1,3]benzothiazole Using Ethylene Glycol as a C2 Precursor and Subsequent Regioselective C3 Functionalization
Authors: Maity Sujit , Panda Niranjan 
Published: March 2024

AbstractSynthesis of imidazo[2,1‐b][1,3]benzothiazoles by oxidative coupling of 2‐aminobenzothiazoles with vicinal diol was reported. Expectedly, a combination of potassium persulfate and TEMPO oxidized ethylene glycol to α‐hydroxy acetaldehyde, which coupled with 2‐aminobenzothiazole to afford 2,3‐unsubstituted imidazo[2,1‐b][1,3]benzothiazoles. The later was exploited as a reliable handle to access 3‐substituted imidazo[2,1‐b][1,3]benzothiazoles regioselectively.
Regio‐ and Chemoselectivity of Tandem N‐Ketoalkylation/Cyclization of 2‐Thioxoimidazoline‐4‐One
Authors: Dorofeev Ivan A. , Zhilitskaya Larisa V. , Yarosh Nina O. , Shainyan Bagrat A. , Larina Lyudmila I. 
Published: March 2024

AbstractThe reaction of 2‐thioxoimidazolidin‐4‐one (thiohydantoin) with aliphatic, aromatic and heteroaromatic α‐iodomethylketones has been investigated in the absence of bases or catalysts. The reaction proceeds as N‐alkylation with subsequent intramolecular dehydrative cyclization. A series of new mono‐ and fused bicyclic iodide and triiodide salts was obtained based on the 5‐oxo‐2‐thioxoimidazolidin‐1‐ium and imidazo[2,1‐b]thiazolium cations. The observed regioselectivity has been rationalized and the mechanism was proposed.