Autophagic cell death induced by pH modulation for enhanced iron-based chemodynamic therapy.

Iron-based chemodynamic therapy (CDT) exhibits commendable biocompatibility and selectivity, but its efficacy is constrained by the intracellular pH of tumors. To overcome this obstacle, we constructed a silica delivery platform loaded with autophagy-inducing reagents (rapamycin, RAPA) and iron-based Fenton reagents (Fe3O4). This platform was utilized to explore a novel strategy that leverages autophagy to decrease tumor acidity, consequently boosting the effectiveness of CDT. Both in vitro and in vivo experiments revealed that RAPA prompted the generation of acidic organelles (e.g., autophagic vacuoles and autophagosomes), effectively changing the intracellular pH in the tumor microenvironment. Furthermore, RAPA-induced tumor acidification significantly amplified the efficacy of Fe3O4-based Fenton reactions, consequently increasing the effectiveness of Fe3O4-based CDT. This innovative approach, which leverages the interplay between autophagy induction and iron-based CDT, shows promise in overcoming the limitations posed by tumor pH, thus offering a more efficient approach to tumor treatments.

Multi-modal deep learning enables efficient and accurate annotation of enzymatic active sites.

Annotating active sites in enzymes is crucial for advancing multiple fields including drug discovery, disease research, enzyme engineering, and synthetic biology. Despite the development of numerous automated annotation algorithms, a significant trade-off between speed and accuracy limits their large-scale practical applications. We introduce EasIFA, an enzyme active site annotation algorithm that fuses latent enzyme representations from the Protein Language Model and 3D structural encoder, and then aligns protein-level information with the knowledge of enzymatic reactions using a multi-modal cross-attention framework. EasIFA outperforms BLASTp with a 10-fold speed increase and improved recall, precision, f1 score, and MCC by 7.57%, 13.08%, 9.68%, and 0.1012, respectively. It also surpasses empirical-rule-based algorithm and other state-of-the-art deep learning annotation method based on PSSM features, achieving a speed increase ranging from 650 to 1400 times while enhancing annotation quality. This makes EasIFA a suitable replacement for conventional tools in both industrial and academic settings. EasIFA can also effectively transfer knowledge gained from coarsely annotated enzyme databases to smaller, high-precision datasets, highlighting its ability to model sparse and high-quality databases. Additionally, EasIFA shows potential as a catalytic site monitoring tool for designing enzymes with desired functions beyond their natural distribution.

Crystal Facets-Activity Correlation for Oxygen Evolution Reaction in Compositional Complex Alloys.

Compositional complex alloys, including high and medium-entropy alloys (HEAs/MEAs) have displayed significant potential as efficient electrocatalysts for the oxygen evolution reaction (OER), but their structure-activity relationship remains unclear. In particular, the basic question of which crystal facets are more active, especially considering the surface reconstructions, has yet to be answered. This study demonstrates that the lowest index {100} facets of FeCoNiCr MEAs exhibit the highest activity. The underlying mechanism associated with the {100} facet's low in-plane density, making it easier to surface reconstruction and form amorphous structures containing the true active species is uncovered. These results are validated by experiments on single crystals and polycrystal MEAs, as well as DFT calculations. The discoveries contribute to a fundamental comprehension of MEAs in electrocatalysis and offer physics-based strategies for developing electrocatalysts.

Amorphous conversion in pyrolytic symmetric trinuclear nickel clusters trigger trifunctional electrocatalysts.

The pursuit of multifunctional electrocatalysts holds significant importance due to their comprehension of material chemistry. Amorphous materials are particularly appealing, yet they pose challenges in terms of rational design due to their structural disorder and thermal instability. Herein, we propose a strategy that entails the tandem (low-temperature/250-350 °C) pyrolysis of molecular clusters, enabling preservation of the local short-range structures of the precursor Schiff base nickel (Ni3[2(C21H24N3Ni1.5O6)]). The temperature-dependent residuals demonstrate exceptional activity and stability for at least three distinct electrocatalytic processes, including the oxygen evolution reaction (η10 = 197 mV), urea oxidation reaction (η10 = 1.339 V), and methanol oxidation reaction (1358 mA cm-2 at 0.56 V). Three distinct nickel atom motifs are discovered for three efficient electrocatalytic reactions (Ni1 and Ni1' are preferred for UOR/MOR, while Ni2 is preferred for OER). Our discoveries pave the way for the potential development of multifunctional electrocatalysts through disordered engineering in molecular clusters under tandem pyrolysis.

Comprehensive Evaluation of 10 Docking Programs on a Diverse Set of Protein-Cyclic Peptide Complexes.

Cyclic peptides have emerged as a highly promising class of therapeutic molecules owing to their favorable pharmacokinetic properties, including stability and permeability. Currently, many clinically approved cyclic peptides are derived from natural products or their derivatives, and the development of molecular docking techniques for cyclic peptide discovery holds great promise for expanding the applications and potential of this class of molecules. Given the availability of numerous docking programs, there is a pressing need for a systematic evaluation of their performance, specifically on protein-cyclic peptide systems. In this study, we constructed an extensive benchmark data set called CPSet, consisting of 493 protein-cyclic peptide complexes. Based on this data set, we conducted a comprehensive evaluation of 10 docking programs, including Rosetta, AutoDock CrankPep, and eight protein-small molecule docking programs (i.e., AutoDock, AudoDock Vina, Glide, GOLD, LeDock, rDock, MOE, and Surflex). The evaluation encompassed the assessment of the sampling power, docking power, and scoring power of these programs. The results revealed that all of the tested protein-small molecule docking programs successfully sampled the binding conformations when using the crystal conformations as the initial structures. Among them, rDock exhibited outstanding performance, achieving a remarkable 94.3% top-100 sampling success rate. However, few programs achieved successful predictions of the binding conformations using tLEaP-generated conformations as the initial structures. Within this scheme, AutoDock CrankPep yielded the highest top-100 sampling success rate of 29.6%. Rosetta's scoring function outperformed the others in selecting optimal conformations, resulting in an impressive top-1 docking success rate of 87.6%. Nevertheless, all the tested scoring functions displayed limited performance in predicting binding affinity, with MOE@Affinity dG exhibiting the highest Pearson's correlation coefficient of 0.378. It is therefore suggested to use an appropriate combination of different docking programs for given tasks in real applications. We expect that this work will offer valuable insights into selecting the appropriate docking programs for protein-cyclic peptide complexes.

Assessing the performance of MM/PBSA and MM/GBSA methods. 10. Prediction reliability of binding affinities and binding poses for RNA-ligand complexes.

Ribonucleic acid (RNA)-ligand interactions play a pivotal role in a wide spectrum of biological processes, ranging from protein biosynthesis to cellular reproduction. This recognition has prompted the broader acceptance of RNA as a viable candidate for drug targets. Delving into the atomic-scale understanding of RNA-ligand interactions holds paramount importance in unraveling intricate molecular mechanisms and further contributing to RNA-based drug discovery. Computational approaches, particularly molecular docking, offer an efficient way of predicting the interactions between RNA and small molecules. However, the accuracy and reliability of these predictions heavily depend on the performance of scoring functions (SFs). In contrast to the majority of SFs used in RNA-ligand docking, the end-point binding free energy calculation methods, such as molecular mechanics/generalized Born surface area (MM/GBSA) and molecular mechanics/Poisson Boltzmann surface area (MM/PBSA), stand as theoretically more rigorous approaches. Yet, the evaluation of their effectiveness in predicting both binding affinities and binding poses within RNA-ligand systems remains unexplored. This study first reported the performance of MM/PBSA and MM/GBSA with diverse solvation models, interior dielectric constants (εin) and force fields in the context of binding affinity prediction for 29 RNA-ligand complexes. MM/GBSA is based on short (5 ns) molecular dynamics (MD) simulations in an explicit solvent with the YIL force field; the GBGBn2 model with higher interior dielectric constant (εin = 12, 16 or 20) yields the best correlation (Rp = -0.513), which outperforms the best correlation (Rp = -0.317, rDock) offered by various docking programs. Then, the efficacy of MM/GBSA in identifying the near-native binding poses from the decoys was assessed based on 56 RNA-ligand complexes. However, it is evident that MM/GBSA has limitations in accurately predicting binding poses for RNA-ligand systems, particularly compared with notably proficient docking programs like rDock and PLANTS. The best top-1 success rate achieved by MM/GBSA rescoring is 39.3%, which falls below the best results given by docking programs (50%, PLNATS). This study represents the first evaluation of MM/PBSA and MM/GBSA for RNA-ligand systems and is expected to provide valuable insights into their successful application to RNA targets.

RediscMol: Benchmarking Molecular Generation Models in Biological Properties.

Deep learning-based molecular generative models have garnered emerging attention for their capability to generate molecules with novel structures and desired physicochemical properties. However, the evaluation of these models, particularly in a biological context, remains insufficient. To address the limitations of existing metrics and emulate practical application scenarios, we construct the RediscMol benchmark that comprises active molecules extracted from 5 kinase and 3 GPCR data sets. A set of rediscovery- and similarity-related metrics are introduced to assess the performance of 8 representative generative models (CharRNN, VAE, Reinvent, AAE, ORGAN, RNNAttn, TransVAE, and GraphAF). Our findings based on the RediscMol benchmark differ from those of previous evaluations. CharRNN, VAE, and Reinvent exhibit a greater ability to reproduce known active molecules, while RNNAttn, TransVAE, and GraphAF struggle in this aspect despite their notable performance on commonly used distribution-learning metrics. Our evaluation framework may provide valuable guidance for advancing generative models in real-world drug design scenarios.

How Good Are Current Docking Programs at Nucleic Acid-Ligand Docking? A Comprehensive Evaluation.

Nucleic acid (NA)-ligand interactions are of paramount importance in a variety of biological processes, including cellular reproduction and protein biosynthesis, and therefore, NAs have been broadly recognized as potential drug targets. Understanding NA-ligand interactions at the atomic scale is essential for investigating the molecular mechanism and further assisting in NA-targeted drug discovery. Molecular docking is one of the predominant computational approaches for predicting the interactions between NAs and small molecules. Despite the availability of versatile docking programs, their performance profiles for NA-ligand complexes have not been thoroughly characterized. In this study, we first compiled the largest structure-based NA-ligand binding data set to date, containing 800 noncovalent NA-ligand complexes with clearly identified ligands. Based on this extensive data set, eight frequently used docking programs, including six protein-ligand docking programs (LeDock, Surflex-Dock, UCSF Dock6, AutoDock, AutoDock Vina, and PLANTS) and two specific NA-ligand docking programs (rDock and RLDOCK), were systematically evaluated in terms of binding pose and binding affinity predictions. The results demonstrated that some protein-ligand docking programs, specifically PLANTS and LeDock, produced more promising or comparable results compared with the specialized NA-ligand docking programs. Among the programs evaluated, PLANTS, rDock, and LeDock showed the highest performance in binding pose prediction, and their top-1 and best root-mean-square deviation (rmsd) success rates were as follows: PLANTS (35.93 and 76.05%), rDock (27.25 and 72.16%), and LeDock (27.40 and 64.37%). Compared with the moderate level of binding pose prediction, few programs were successful in binding affinity prediction, and the best correlation (Rp = -0.461) was observed with PLANTS. Finally, further comparison with the latest NA-ligand docking program (NLDock) on four well-established data sets revealed that PLANTS and LeDock outperformed NLDock in terms of binding pose prediction on all data sets, demonstrating their significant potential for NA-ligand docking. To the best of our knowledge, this study is the most comprehensive evaluation of popular molecular docking programs for NA-ligand systems.

Learning on topological surface and geometric structure for 3D molecular generation.

Highly effective de novo design is a grand challenge of computer-aided drug discovery. Practical structure-specific three-dimensional molecule generations have started to emerge in recent years, but most approaches treat the target structure as a conditional input to bias the molecule generation and do not fully learn the detailed atomic interactions that govern the molecular conformation and stability of the binding complexes. The omission of these fine details leads to many models having difficulty in outputting reasonable molecules for a variety of therapeutic targets. Here, to address this challenge, we formulate a model, called SurfGen, that designs molecules in a fashion closely resembling the figurative key-and-lock principle. SurfGen comprises two equivariant neural networks, Geodesic-GNN and Geoatom-GNN, which capture the topological interactions on the pocket surface and the spatial interaction between ligand atoms and surface nodes, respectively. SurfGen outperforms other methods in a number of benchmarks, and its high sensitivity on the pocket structures enables an effective generative-model-based solution to the thorny issue of mutation-induced drug resistance.

Network Structure and Properties of Lithium Aluminosilicate Glass.

Based on lithium aluminosilicate glass, the composition of glass was optimized by replacing SiO2 with B2O3, and the influence of glass composition on structure and performance was studied. With the increase in B2O3 concentrations from 0 to 6.5 mol%, Al2O3 always existed in the form of four-coordinated [AlO4] in the network structure, and B2O3 mainly entered the network in the form of four-coordinated [BO4]. The content of Si-O-Si linkages (Q4(0Al)) was always dominant. The incorporation of boron oxide improved the overall degree of polymerization and connectivity of the lithium aluminosilicate glass network structure. An increase in the degree of network polymerization led to a decrease in the thermal expansion coefficient of the glass and an increase in Vickers hardness and density. The durability of the glass in hydrofluoric acid and NaOH and KOH solutions was enhanced overall.

Two Complete Mitogenomes of Chalcididae (Hymenoptera: Chalcidoidea): Genome Description and Phylogenetic Implications.

The complete mitochondrial genomes of two species of Chalcididae were newly sequenced: Brachymeria lasus and Haltichella nipponensis. Both circular mitogenomes are 15,147 and 15,334 bp in total length, respectively, including 13 protein-coding genes (PCGs), two ribosomal RNA genes (rRNAs), and 22 transfer RNA genes (tRNAs) and an A+T-rich region. The nucleotide composition indicated a strong A/T bias. All PCGs of B. lasus and H. nipponensis began with the start codon ATD, except for B. lasus, which had an abnormal initiation codon TTG in ND1. Most PCGs of the two mitogenomes are terminated by a codon of TAR, and the remaining PCGs by the incomplete stop codon T or TA (ATP6, COX3, and ND4 in both species, with an extra CYTB in B. lasus). Except for trnS1 and trnF, all tRNAs can be folded into a typical clover structure. Both mitogenomes had similar control regions, and two repeat units of 135 bp were found in H. nipponensis. Phylogenetic analyses based on two datasets (PCG123 and PCG12) covering Chalcididae and nine families of Chalcidoidea were conducted using two methods (maximum likelihood and Bayesian inference); all the results support Mymaridae as the sister group of the remaining Chalcidoidea, with Chalcididae as the next successive group. Only analyses of PCG123 generated similar topologies of Mymaridae + (Chalcididae + (Agaonidae + remaining Chalcidoidea)) and provided one relative stable clade as Eulophidae + (Torymidae + (Aphelinidae + Trichogrammatidae)). Our mitogenomic phylogenetic results share one important similarity with earlier molecular phylogenetic efforts: strong support for the monophyly of many families, but a largely unresolved or unstable "backbone" of relationships among families.

A report of the complete mitochondrial genome of Bisetocreagris titanium (Arachnida: Pseudoscorpiones: Neobisiidae) from Yunnan Province, China.

A complete mitogenome of a cave dwelling pseudoscorpion Bisetocreagris titanium is reported here. The mitogenome is a circular DNA molecule with a length of 14,756 base pairs (bp), and it contains 13 protein coding genes (PCGs), 22 transfer RNAs (tRNAs), 2 ribosomal RNAs (rRNAs), and 1 putative control region. Phylogenetic analysis of 30 Arachnida species was performed based on the amino acid datasets of 13 PCGs, and the result indicated Pseudoscorpiones is the sister lineage of Acariformes. This result is congruent with the former phylogenetic results of mitogenomes, but incongruent with the results of morphological characters and/or ribosomal DNA data that indicated Pseudoscorpiones are positioned in a clade with the Solifugae.

A Novel Factor Graph and Cubature Kalman Filter Integrated Algorithm for Single-Transponder-Aided Cooperative Localization.

Cooperative localization (CL) of underwater multi-AUVs is vital for numerous underwater operations. Single-transponder-aided cooperative localization (STCL) is regarded as a promising scheme for multi-AUVs CL, benefiting from the fact that an accurate reference is adopted. To improve the positioning accuracy and robustness of STCL, a novel Factor Graph and Cubature Kalman Filter (FGCKF)-integrated algorithm is proposed in this paper. In the proposed FGCKF, historical information can be efficiently used in measurement updating to overcome uncertain observation environments, which greatly helps to improve the performance of filtering progress. Furthermore, Adaptive CKF, sum product, and Maximum Correntropy Criterion (MCC) methods are designed to deal with outliers of acoustic transmission delay, sound velocity, and motion velocity, respectively. Simulations and experiments are conducted, and it is verified that the proposed FGCKF algorithm can improve positioning accuracy and robustness greatly than traditional filtering methods.

Fe3O4 assembly for tumor accurate diagnosis by endogenous GSH responsive T2/T1 magnetic relaxation conversion.

Superparamagnetic iron oxide nanoparticles with high magnetization strength and good biological safety have been widely used as magnetic resonance imaging (MRI) contrast agents for tumors. However, the accuracy of tumor diagnosis is still low due to the lack of tumor targeting and the interference signals from normal tissues. Endogenous substances in tumor (such as high levels of GSH and pH) stimuli-responsive contrast agents could offer higher sensitivity for tumor diagnosis. Herein, based on the characteristic of overexpression of GSH in tumors, we propose an ultra-small Fe3O4 assembly as an endogenous GSH responsive MRI contrast agent. The ultra-small superparamagnetic Fe3O4 are bonded to the crosslinker cystamine to synthesize Fe3O4 nanoclusters, which exhibit a T2 imaging effect. When the contrast agent reaches the tumor tissue, the disulfide bond in cystamine is induced by GSH to break, the Fe3O4 nanoclusters are disassembled into ultra-small Fe3O4 nanoparticles, and the relaxation signal changes from T2 to T1, which is helpful for accurate diagnosis of tumors. In vivo experiments have shown that Fe3O4 nanoclusters can rapidly respond to overexpressed GSH in tumor sites for T2/T1 switchable imaging. This work not only designed an endogenous GSH responsive platform through simple synthesis methods, but also improved the accuracy of tumor diagnosis through the transformation of T2/T1 MRI signals.

The First Mitogenomes of the Subfamily Odontiinae (Lepidoptera, Crambidae) and Phylogenetic Analysis of Pyraloidea.

The complete mitochondrial genomes of three species of Odontiinae were newly sequenced: Dausara latiterminalis Yoshiyasu, Heortia vitessoides (Moore), and Pseudonoorda nigropunctalis (Hampson). These circular and double-stranded mitogenomes vary from 15,084 bp to 15,237 bp in size, including 13 protein-coding genes (PCGs), two ribosomal RNA genes (rRNAs), and 22 transfer RNA genes (tRNAs) and an A + T-rich region. The nucleotide composition indicated a strong A/T bias. Most PCGs are initiated with an ATN codon and terminated by a codon of TAR. All tRNAs could be folded into the clover-leaf structure with the exception of trnS1 (AGN), in which the dihydrouridine (DHU) arm formed a simple loop, and the motif 'ATAG' and 'ATTTA' in the A + T-rich region was also founded. The phylogenomic analyses covering Odontiinae + 11 subfamilies of Pyraloidea were conducted. Similar topologies were generated from both Bayesian inference (BI) and maximum likelihood (ML) analyses based on the nucleotide and amino acid sequence data. There was some discrepancy in the sister-group relationship of Odontiinae and Glaphyriinae, and the relationships among the subfamilies in the 'CAMMSS clade' of the Crambidae. The results of this study suggest that mitogenomic data are useful for resolving the deep-level relationships of Pyraloidea and the topologies generated from amino acid data might be more realistic and reliable. Moreover, more mitogenomic taxon sampling and larger scale analyses with more genes or a combination of mitogenomic and nuclear genes are needed to reconstruct a comprehensive framework of the pyraloid phylogeny.

Taxonomic revision of Telemidae (Arachnida, Araneae) from East and Southeast Asia.

Species of the spider family Telemidae Fage, 1913 from East and Southeast Asia are revised. Four new genera are erected: Mekonglema Zhao & Li, gen. nov. with the type species Mekonglema bailang Zhao & Li, sp. nov. (♂♀, Yunnan, China), Siamlema Zhao & Li, gen. nov. with the type species Siamlema changhai Zhao & Li, sp. nov. (♂♀, southern Thailand), Sundalema Zhao & Li, gen. nov. with the type species Sundalema bonjol Zhao & Li, sp. nov. (♂♀, Sumatra), and Zhuanlema Zhao & Li, gen. nov. with the type species Zhuanlema peteri Zhao & Li, sp. nov. (♂♀, northern Laos). Eight additional new species are described: Mekonglema kaorao Zhao & Li, sp. nov. (♂♀, northern Laos), M. walayaku Zhao & Li, sp. nov. (♂♀, Yunnan, China), M. yan Zhao & Li, sp. nov. (♂♀, Yunnan, China), Pinelema daguaiwan Zhao & Li, sp. nov. (♂♀, Guangxi, China), P. shiba Zhao & Li, sp. nov. (♂♀, Guangxi, China), P. tham Zhao & Li, sp. nov. (♂♀, northern Laos), Siamlema suea Zhao & Li, sp. nov. (♂♀, southern Thailand), and Sundalema khaorakkiat Zhao & Li, sp. nov. (♂♀, southern Thailand). Thirty species are transferred from the genus Telema Simon, 1882 to the genera Pinelema Wang & Li, 2012, Sundalema gen. nov., and Telemofila Wunderlich, 1995. Seychellia xinpingi Lin & Li, 2008 is transferred to Mekonglema gen. nov. as M. xinpingi comb. nov. Furthermore, the genus Pinelema is divided into seven species groups based on male morphological characters.

Reaction mechanism of synthetic thorium sulfides: theoretical calculation study.

Actinide sulfides are especially significant in actinide chemistry because of their potentials that are used as nuclear fuel and the wide variety of their stoichiometries and physical properties. It is essential for studying the synthesis mechanism of actinide sulfides. In this study, the reactions of thorium cation Th2+ with the facile sulfur-atom donor OCS to produce thorium sulfides have been systematically explored by using density functional. The detailed insights of the primary reaction and secondary reaction paths are reported. We investigated that the multiple bonding characters and complexes involved in reaction exhibit significant covalent character. The reaction rate indicated that the tunneling effect is small compared with the effect of temperature on the rate. This study addresses some of the current limitation in understanding the detailed reaction information of Th2++OCS.

Organic Compounds of Actinyls: Systematic Computational Assessment of Structural and Topological Properties in [AnO2(C2O4) n](2 n-2)- (An = U, Np, Pu, Am; n = 1-3) Complexes.

Exploring the bonding features between organics and actinide elements is a fundamental topic in nuclear waste separation. In this work, [AnO2(C2O4) n](2 n-2)- (An = U, Np, Pu, and Am; n = 1-3) complexes have been characterized by density functional theory. The actinyl oxalate complexes are found to exhibit the typical An-Oyl, An-Oeq bonds and Oyl-An-Oyl angles. Interatomic interaction analyzed by electron density difference, charge decomposition analysis, charges population, bond order, electron localization function, and quantum theory of atom in molecules indicates that An-Oeq bonds are ionic (closed-shell) bonding interaction with a small degree of covalent character. The similarities and differences between isomers have been discussed in the actinide coordination chemistry, and the orbital interactions also have been investigated through total, partial, and overlap population density of state diagrams. Besides, the electrostatic potential was used to predict the adsorption sites on the molecular vdW surface.

The formation mechanism of uranium and thorium hydride phosphorus: a systematically theoretical study.

Activation of prototypical bonds by actinide atoms is an important aspect of material activity, and the results can be used for the study of nuclear material storage. In this study, the activation of the P-H bonds of the PH3 molecule by U or Th to form uranium or thorium hydride phosphorus has been systematically explored using density functional theory. A detailed description of the reaction mechanism which includes the potential energy profiles and the properties of bond evolution is presented. There are two types of reaction channels, isomerization and dehydrogenation in U + PH3 and Th + PH3. The difference between the two reactions is the process of the first P-H bond dissociation. The evolution characteristics of the chemical bonds along reaction pathways is analyzed by using electron localization functions, quantum theory of atoms in molecules, Mayer bond orders and natural bond orbitals. The reaction rate constants are calculated at the variational transition state level, and rate-determining steps are predicted.