Computational toolbox for the analysis of protein–glycan interactions

• Ferran Nieto-Fabregat,
• Maria Pia Lenza,
• Angela Marseglia,
• Cristina Di Carluccio,
• Antonio Molinaro,
• Alba Silipo and
• Roberta Marchetti

Beilstein J. Org. Chem. 2024, 20, 2084–2107, doi:10.3762/bjoc.20.180

• modelling, and some of the most popular include: 1. AlphaFold2 [88]: It is an open-access protein structure prediction system based on artificial intelligence and machine learning. It is based on a neural network that can predict the 3D protein structure at a high accuracy level. The AlphaFold solution is

• composed of two steps. First, given a protein sequence, it generates multiple alignments with sequences from all the species, including evolutionary profiles from different sources. In the second step, a model refinement is generated based on structural refinement (where the network optimises the torsion

• ://github.com/BojarLab/LectinOracle). 4. CAPSIF [109]: CArbohydrate-Protein Site IdentiFier is a convolutional neural network able to predict protein–carbohydrate binding interface from a protein structure. In contrast to other DN algorithms, as GlyNet and LectinOracle, which predict lectin-carbohydrate binding

Cage-like microstructures via sequential Ugi reactions in aqueous emulsions

• Rita S. Alqubelat,
• Yaroslava A. Menzorova and
• Maxim A. Mironov

Beilstein J. Org. Chem. 2024, 20, 2078–2083, doi:10.3762/bjoc.20.179

• principle, two possible mechanisms were considered: (1) Soft CMC/chitosan gel particles interacted with the surface and flattened to form discs. (2) When particles were deposited on the surface, they did not completely cover it, forming a network. In this work, we tested these hypotheses and found arguments

• removing the solvent, the resulting structure could be easily identified using optical microscopy. They were cage-like microspheres with an average diameter corresponding to the diameter of droplets in the Pickering emulsion (Figure 2). The surface of the droplets was covered with a network of individual

• network. The repeated Ugi reaction resulted in the formation of a strong cage-like structure that was not destroyed upon removal of the solvent. We noticed a connection between the structure of the colloidal particles and their behavior at the interface. The developed surface of the particles, consisting

A new platform for the synthesis of diketopyrrolopyrrole derivatives via nucleophilic aromatic substitution reactions

• Vitor A. S. Almodovar and
• Augusto C. Tomé

Beilstein J. Org. Chem. 2024, 20, 1933–1939, doi:10.3762/bjoc.20.169

• /2020, UIDP/50006/2020 doi:10.54499/UIDP/50006/2020 and UIDB/50006/2020 doi:54499/UIDB/50006/2020, through PT national funds within the PT2020 Partnership Agreement, and to the Portuguese NMR Network. Vítor A. S. Almodovar thanks FCT/MCTES for his doctoral grant (SFRH/BD/135598/2018).

Discovery of antimicrobial peptides clostrisin and cellulosin from Clostridium: insights into their structures, co-localized biosynthetic gene clusters, and antibiotic activity

• Moisés Alejandro Alejo Hernandez,
• Katia Pamela Villavicencio Sánchez,
• Rosendo Sánchez Morales,
• Karla Georgina Hernández-Magro Gil,
• David Silverio Moreno-Gutiérrez,
• Eddie Guillermo Sanchez-Rueda,
• Yanet Teresa-Cruz,
• Brian Choi,
• Armando Hernández Garcia,
• Alba Romero-Rodríguez,
• Oscar Juárez,
• Siseth Martínez-Caballero,
• Mario Figueroa and
• Corina-Diana Ceapă

Beilstein J. Org. Chem. 2024, 20, 1800–1816, doi:10.3762/bjoc.20.159

• cluster of C. cellulovorans 743, clostrisin and cellulosin were compared with known lanthipeptides, generating a similarity network made using the percent identity between the precursor peptides from all experimentally characterized lanthipeptides to date (Figure 3A). We found it noteworthy to examine the

• Supporting Information File 1). For the similarity network, a database was created containing 145 precursor peptides of lanthipeptides reported in the MIBiG [38], RiPPMiner [5], and UniProt [57] databases. This input was utilized in the EFI-EST [51] web platform, employing the "FASTA" analysis option with

• the following parameters: E-value: 0.001; Fragments: disabled. Upon completion of the initial calculation, in the SNN Finishing section, an alignment score threshold of 40% was chosen, with default values for sequence length constraint, and neighborhood connectivity disabled. Network similarity was

pKalculator: A pK_a predictor for C–H bonds

• Rasmus M. Borup,
• Nicolai Ree and
• Jan H. Jensen

Beilstein J. Org. Chem. 2024, 20, 1614–1622, doi:10.3762/bjoc.20.144

• (DMSO) using a graph convolutional neural network (GCNN) [3]. Using a mix of experimental and computed pKa data, they achieved a mean absolute error (MAE) of 2.1 pKa units. Lee and co-workers also addressed this problem by creating a general machine learning (ML) model using either a neural network or

Mining raw plant transcriptomic data for new cyclopeptide alkaloids

• Draco Kriger,
• Michael A. Pasquale,
• Brigitte G. Ampolini and
• Jonathan R. Chekan

Beilstein J. Org. Chem. 2024, 20, 1548–1559, doi:10.3762/bjoc.20.138

• . ameranicus using UHPLC-HRMS/MS. Finally, we generated a global natural product social (GNPS) network to show the correlation between metabolites from these different plant species (Figure 5) [29]. Cyclopeptides in Ceanothus americanus The GNPS network showed the presence of multiple features from C

• , Figures S16–S26, Table S1). Indeed, our results support the proposed structure of ceanothine B while also assigning absolute stereochemistry consistent with all ʟ-amino acids (Figure 5). We next explored the GNPS network for new cyclopeptide alkaloids from C. americanus. We previously noted the presence

• analysis, which confirmed the presence of all ʟ-Phe in the structure (Supporting Information File 1, Figure S27). Beyond this, multiple other features in the GNPS network appeared to represent new molecules for C. americanus (Supporting Information File 1, Figures S4–S11). Xylopyrine-C was previously

Predicting bond dissociation energies of cyclic hypervalent halogen reagents using DFT calculations and graph attention network model

• Yingbo Shao,
• Zhiyuan Ren,
• Zhihui Han,
• Li Chen,
• Yao Li and
• Xiao-Song Xue

Beilstein J. Org. Chem. 2024, 20, 1444–1452, doi:10.3762/bjoc.20.127

• results of this study could aid in estimating the chemical stability and functional group transfer capabilities of hypervalent bromine(III) and chlorine(III) reagents, thereby facilitating their development. Keywords: BDE; cyclic hypervalent halogen reagents; DFT calculation; graph attention network

• DFT calculations for predicting key properties of organic molecules such as BDE, nucleophilicity, and electrophilicity [48][49][50][51][52][53][54][55][56][57][58][59][60]. Recently, applications of the Elastic Net model with Avalon fingerprints [55] and the deployment of artificial neural network

• reagents, we can obtain a rough estimation of the BDEs for others with different halogen centers. With these homolytic and heterolytic BDEs in hand, we next attempted to develop a predictive model for BDEs of hypervalent halogen compounds using machine learning algorithms. Graph attention network (GAT) [85

Synthesis and physical properties of tunable aryl alkyl ionic liquids based on 1-aryl-4,5-dimethylimidazolium cations

• Stefan Fritsch and
• Thomas Strassner

Beilstein J. Org. Chem. 2024, 20, 1278–1285, doi:10.3762/bjoc.20.110

• blocked at the C2 position [33]. The use of a substituent at the C2 position was found to have a strong influence on the properties of these ionic liquids due to changes in the hydrogen-bonding network. Here, we investigate the properties of TAAILs based on the 1-aryl-4,5-dimethylimidazolium cation. It is

Synthesis of 1,4-azaphosphinine nucleosides and evaluation as inhibitors of human cytidine deaminase and APOBEC3A

• Maksim V. Kvach,
• Stefan Harjes,
• Harikrishnan M. Kurup,
• Geoffrey B. Jameson,
• Elena Harjes and
• Vyacheslav V. Filichev

Beilstein J. Org. Chem. 2024, 20, 1088–1098, doi:10.3762/bjoc.20.96

• Molecular Biodiscovery, Massey University Research Fund (MURF 2015, 7003 and RM20734), Kiwi Innovation Network (KiwiNet), Massey Ventures Ltd (MU002391) and the School of Natural Sciences, Massey University.

Spin and charge interactions between nanographene host and ferrocene

• Akira Suzuki,
• Yuya Miyake,
• Ryoga Shibata and
• Kazuyuki Takai

Beilstein J. Org. Chem. 2024, 20, 1011–1019, doi:10.3762/bjoc.20.89

• three-dimensional disordered network of nanographite domains, each of which is a loose stack of 3–4 nanographene sheets with a mean in-plane size of 2–3 nm. ACFs have huge specific surface areas (about 2000 m2/g [24][25]) due to the presence of nanopores of ca. 1 nm in diameter between the nanographite

• as charge transfer and electromagnetic shielding. This is well consistent with the observed partial cationization of the guest molecules for FeCp2-ACFs-150 in XPS due to nanopore structure of the nanographene network in ACFs. The host–guest interaction between guest FeCp2 and host ACFs is most

Methodology for awakening the potential secondary metabolic capacity in actinomycetes

• Shun Saito and
• Midori A. Arai

Beilstein J. Org. Chem. 2024, 20, 753–766, doi:10.3762/bjoc.20.69

• undecylprodigiosin (21) is always triggered in the dying zone of the mycelial network of Streptomyces coelicolor M145 prior to morphological differentiation, right after an initial round of cell death [98]. They hypothesized that under certain circumstances, production of antiproliferative agents could play a role

A myo-inositol dehydrogenase involved in aminocyclitol biosynthesis of hygromycin A

• Michael O. Akintubosun and
• Melanie A. Higgins

Beilstein J. Org. Chem. 2024, 20, 589–596, doi:10.3762/bjoc.20.51

• efficiency can be attributed to the reduced kcat and increased KM for scyllo-inositol over myo-inositol. We also found that the catalytic efficiency for NAD+ was 452.4 ± 54.49 M−1 s−1 (Table 1 and Figure 2f). Sequence similarity network We generated a sequence similarity network (SSN) for the protein family

• determining the slope of the reaction from 0 to 5 min. Results were analyzed using Microsoft Excel and GraphPad Prism version 9.5.1 for Windows, GraphPad Software, Boston, Massachusetts USA, https://www.graphpad.com. SSN network generation and genome mining The sequence similarity network was generated using

• sequences and the UniRef50 function was used along with an E-value cut off of 1 × 10−55 and node network with 80% ID. Clusters with a single node were removed for simplicity. This produced an SSN with 28,698 nodes. A second SSN was generated for the IolG cluster from the PF01408 SSN. The Uniprot IDs for all

Possible bi-stable structures of pyrenebutanoic acid-linked protein molecules adsorbed on graphene: theoretical study

• Yasuhiro Oishi,
• Motoharu Kitatani and
• Koichi Kusakabe

Beilstein J. Org. Chem. 2024, 20, 570–577, doi:10.3762/bjoc.20.49

• , the magnitude of the activation barrier is expected to be close to that in PASE. This is expected to be the case since a protein weakly bound to the carbon graphitic network can be supplemented onto graphene only by linkers as PASE, which has often been observed experimentally [1]. This type of

Switchable molecular tweezers: design and applications

• Pablo Msellem,
• Maksym Dekthiarenko,
• Nihal Hadj Seyd and
• Guillaume Vives

Beilstein J. Org. Chem. 2024, 20, 504–539, doi:10.3762/bjoc.20.45

Ligand effects, solvent cooperation, and large kinetic solvent deuterium isotope effects in gold(I)-catalyzed intramolecular alkene hydroamination

• Ruichen Lan,
• Brock Yager,
• Yoonsun Jee,
• Cynthia S. Day and
• Amanda C. Jones

Beilstein J. Org. Chem. 2024, 20, 479–496, doi:10.3762/bjoc.20.43

• not correspond to alkylgold buildup. In protic solvents perhaps the strength of the H-bonding network controls the rate of reaction [82]. • Stronger donor ligands that would enhance protodeauration do not increase the rate of reaction. • Rates correlate most significantly with nucleophile effects

Green and sustainable approaches for the Friedel–Crafts reaction between aldehydes and indoles

• Periklis X. Kolagkis,
• Eirini M. Galathri and
• Christoforos G. Kokotos

Beilstein J. Org. Chem. 2024, 20, 379–426, doi:10.3762/bjoc.20.36

• hollow open interconnected network of CdS nanoparticles emerged as the optimal catalyst structure. This structure was found to promote the reaction between indole and p-chlorobenzaldehyde in solvent-free conditions after 5 hours at a catalyst loading of 3 mol %. Several aromatic aldehydes were also

Additive-controlled chemoselective inter-/intramolecular hydroamination via electrochemical PCET process

• Kazuhiro Okamoto,
• Naoki Shida and
• Mahito Atobe

Beilstein J. Org. Chem. 2024, 20, 264–271, doi:10.3762/bjoc.20.27

• phenoxide in PhOH, the PhOH molecule is included in the hydrogen bond network along with the tetrabutylammonium cation (Bu4N+) to form a large aggregate. The hydrogen bonding between the amide and phosphate base in the small aggregates was stronger than in the large aggregates, which significantly enhanced

• 1. Detailed CV analysis indicated that the size of the hydrogen bond complex determined the selectivity, and HFIP played a crucial role in expanding the hydrogen bond network. These results provide fundamental insights beneficial for the design of PCET-based redox reaction systems under

GlAIcomics: a deep neural network classifier for spectroscopy-augmented mass spectrometric glycans data

• Thomas Barillot,
• Baptiste Schindler,
• Baptiste Moge,
• Elisa Fadda,
• Franck Lépine and
• Isabelle Compagnon

Beilstein J. Org. Chem. 2023, 19, 1825–1831, doi:10.3762/bjoc.19.134

• been proposed as a very promising sequencing approach. However, its use as a generic analytical tool relies on the development of recognition techniques that can analyse complex vibrational fingerprints for a large number of monomers. In this study, we used a Bayesian deep neural network model to

• intelligence in combination with spectroscopy-augmented mass spectrometry for carbohydrates sequencing and glycomics applications. Keywords: Bayesian neural network; deep learning; glycomics; IR; spectroscopy; Introduction DNA and protein sequencing technologies that aim at determining the structure of a

• probabilistic deep neural network (Bayesian deep neural networks [12]) to support automated monosaccharide recognition for carbohydrate sequencing. We obtained a highly performing algorithm that we called "GlAIcomics", specifically trained on carbohydrates. Methodology Data production Our carbohydrate analysis

Effects of the aldehyde-derived ring substituent on the properties of two new bioinspired trimethoxybenzoylhydrazones: methyl vs nitro groups

• Dayanne Martins,
• Roberta Lamosa,
• Talis Uelisson da Silva,
• Carolina B. P. Ligiero,
• Sérgio de Paula Machado,
• Daphne S. Cukierman and
• Nicolás A. Rey

Beilstein J. Org. Chem. 2023, 19, 1713–1727, doi:10.3762/bjoc.19.125

• water molecules in the network (calcd.: 9.47% for C18H20O5N2·2H2O, MW = 380.39 g mol−1). On the other hand, hdz-NO2 did not show any mass loss below 250 °C, indicating the absence of solvation molecules in the sample (C17H17O7N3, MW = 375.34 g mol−1). Single crystals of both compounds, as monohydrates

A series of perylene diimide cathode interlayer materials for green solvent processing in conventional organic photovoltaics

• Kathryn M. Wolfe,
• Shahidul Alam,
• Eva German,
• Fahad N. Alduayji,
• Maryam Alqurashi,
• Frédéric Laquai and
• Gregory C. Welch

Beilstein J. Org. Chem. 2023, 19, 1620–1629, doi:10.3762/bjoc.19.119

• (EQE corrected). Here, the short circuit current is JSC; the open circuit voltage is VOC; the fill factor is FF, and the power conversion efficiency is η. Supporting Information Supporting Information File 14: Experimental part. Funding This work was supported by the NSERC Green Electronics Network

Photoredox catalysis harvesting multiple photon or electrochemical energies

• Mattia Lepori,
• Simon Schmid and
• Joshua P. Barham

Beilstein J. Org. Chem. 2023, 19, 1055–1145, doi:10.3762/bjoc.19.81

Recommendations for performing measurements of apparent equilibrium constants of enzyme-catalyzed reactions and for reporting the results of these measurements

• Robert N. Goldberg,
• Robert T. Giessmann,
• Peter J. Halling,
• Carsten Kettner and
• Hans V. Westerhoff

Beilstein J. Org. Chem. 2023, 19, 303–316, doi:10.3762/bjoc.19.26

• with T, pH, pMg, and I [21]. 3.11. Comparisons with values from the literature, network calculations, and the use of other methods A standard part of reporting any measurement is to make a comparison of the value(s) obtained in the current study with previously reported values of the measured quantity

• network or thermodynamic cycle calculations. It is recommended that the use of this approach be considered and, if the necessary data are present in the literature, that the thermodynamic pathway calculations be performed. Details regarding thermodynamic network calculations for biochemical substances are

Digyalipopeptide A, an antiparasitic cyclic peptide from the Ghanaian Bacillus sp. strain DE2B

• Adwoa P. Nartey,
• Aboagye K. Dofuor,
• Kofi B. A. Owusu,
• Anil S. Camas,
• Hai Deng,
• Marcel Jaspars and
• Kwaku Kyeremeh

Beilstein J. Org. Chem. 2022, 18, 1763–1771, doi:10.3762/bjoc.18.185

• to confirm the presence of the cyclic ester and the possible absolute stereochemistry around C-39 (δC 71.6) of the fatty acid chain we first of all consulted the global natural products social molecular networking (GNPS) [19][20][21]. The GNPS network data (Figure 6) provided the opportunity to

Synthetic study toward tridachiapyrone B

• Morgan Cormier,
• Florian Hernvann and
• Michaël De Paolis

Beilstein J. Org. Chem. 2022, 18, 1741–1748, doi:10.3762/bjoc.18.183

• helpful discussions. Funding MC thanks Ministère de la Recherche et de l’Education Nationale for a Ph.D. fellowship. This work has been partially supported by CNRS, Normandie University, INSA Rouen, Labex SynOrg (ANR-11-LABX-0029) and Région Normandie (CRUNCh network).

Navigating and expanding the roadmap of natural product genome mining tools

• Friederike Biermann,
• Sebastian L. Wenski and
• Eric J. N. Helfrich

Beilstein J. Org. Chem. 2022, 18, 1656–1671, doi:10.3762/bjoc.18.178

• ). An example of a deep learning architecture is the artificial neural network inspired by the human brain architecture. It consists of artificial neurons processing information organized in different layers and connected by synapses [73]. These advanced algorithms often provide higher accuracy in their

• prediction but are no longer interpretable as a result of their high level of abstraction [73]. NeuRiPP, for instance, utilizes a parallel convolutional neural network to predict novel RiPP precursor genes independent of their RiPP family. The neural network is trained on a RiPP precursor training set that

• annotations into numeric vectors using a shallow two-layer neural network, an approach adopted from natural language processing [41][72]. These high-dimensional vectors are then used as input for a second two-layer neural network trained on a set of BGC and non-BGC sequences to predict NP BGCs. In the last