A comprehensive review of protein-centric predictors for biomolecular interactions: from proteins to nucleic acids and beyond.

Proteins interact with diverse ligands to perform a large number of biological functions, such as gene expression and signal transduction. Accurate identification of these protein-ligand interactions is crucial to the understanding of molecular mechanisms and the development of new drugs. However, traditional biological experiments are time-consuming and expensive. With the development of high-throughput technologies, an increasing amount of protein data is available. In the past decades, many computational methods have been developed to predict protein-ligand interactions. Here, we review a comprehensive set of over 160 protein-ligand interaction predictors, which cover protein-protein, protein-nucleic acid, protein-peptide and protein-other ligands (nucleotide, heme, ion) interactions. We have carried out a comprehensive analysis of the above four types of predictors from several significant perspectives, including their inputs, feature profiles, models, availability, etc. The current methods primarily rely on protein sequences, especially utilizing evolutionary information. The significant improvement in predictions is attributed to deep learning methods. Additionally, sequence-based pretrained models and structure-based approaches are emerging as new trends.

Efficient Homojunction/Heterojunction Photocatalyst via Integrating CsPbBr3 Quantum Dot Homojunction with TiO2 for Degradation of Organic Dyes.

A novel TiO2-CsPbBr3(Q) photocatalyst is proposed and rationally constructed, where CsPbBr3 perovskite quantum dots (QDs) of various sizes inside mesopore TiO2 (M-TiO2) are integrated. These perovskite QDs, generated in situ within M-TiO2, establish a type-II homojunction. Interestingly, a Z-scheme heterojunction is simultaneously formed at the interface between CsPbBr3 and TiO2. Due to the coexistence of the type-II homojunction and the Z-scheme heterojunction, photogenerated electrons are effectively transferred from TiO2 to CsPbBr3, thereby suppressing carrier recombination and thus enhancing the degradation of rhodamine B (RhB). Compared with pure CsPbBr3 and TiO2, TiO2-CsPbBr3(Q) shows significantly enhanced photocatalytic performance for RhB degradation. The degradation efficiency of RhB in the presence of the TiO2-CsPbBr3(Q) attains 97.7% in 5 min under light illumination, representing the highest efficiency observed among photocatalysts based on TiO2. This study will facilitate the development of superior semiconductor catalysts for photocatalytic applications.

Late-Stage Saturation of Drug Molecules.

The available methods of chemical synthesis have arguably contributed to the prevalence of aromatic rings, such as benzene, toluene, xylene, or pyridine, in modern pharmaceuticals. Many such sp2-carbon-rich fragments are now easy to synthesize using high-quality cross-coupling reactions that click together an ever-expanding menu of commercially available building blocks, but the products are flat and lipophilic, decreasing their odds of becoming marketed drugs. Converting flat aromatic molecules into saturated analogues with a higher fraction of sp3 carbons could improve their medicinal properties and facilitate the invention of safe, efficacious, metabolically stable, and soluble medicines. In this study, we show that aromatic and heteroaromatic drugs can be readily saturated under exceptionally mild rhodium-catalyzed hydrogenation, acid-mediated reduction, or photocatalyzed-hydrogenation conditions, converting sp2 carbon atoms into sp3 carbon atoms and leading to saturated molecules with improved medicinal properties. These methods are productive in diverse pockets of chemical space, producing complex saturated pharmaceuticals bearing a variety of functional groups and three-dimensional architectures. The rhodium-catalyzed method tolerates traces of dimethyl sulfoxide (DMSO) or water, meaning that pharmaceutical compound collections, which are typically stored in wet DMSO, can finally be reformatted for use as substrates for chemical synthesis. This latter application is demonstrated through the late-stage saturation (LSS) of 768 complex and densely functionalized small-molecule drugs.

Reaction sites of pyrimidine bases and nucleosides during chlorination: A computational study.

As important components of soluble microbial products in water, nucleobases have attracted much attention due to the high toxicity of their direct aromatic halogenated disinfection by-products (AH-DBPs) during chlorination. However, multiple halogenation sites of AH-DBPs pose challenges to identify them. In this study, reaction sites of pyrimidine bases and nucleosides during chlorination were investigated by quantum chemical computational method. The results indicate that the anion salt forms play key roles in chlorination of uracil, thymine, and their nucleosides, while neutral forms make predominant contributions to cytosine and cytidine. In view of both kinetics and thermodynamics, C5 is the most reactive site for uracil and thymine, N3/C5 and N3 for respective uridine and thymidine, N1/C5/N4 and N4 for respective cytosine and cytidine, whose estimated apparent rate constants kobs-est of ∼103, 103/102, 106/102/104, and 103 M-1 s-1, respectively, in consistent with the known experimental results. C6 in all pyrimidine compounds is hardly attacked by Cl+ in HOCl ascribed to its positive charge, but readily attacked by OH‾ in hydrolysis and the N1=C6 bond was found to possess the highest reactivity in hydrolysis among all double bonds. In addition, the structure-kinetic reactivity relationship study reveals a relatively strong correlation between lgkobs-est and APT charge in all pyrimidine compounds rather than FED2 (HOMO). The results are helpful to further understand the reactivity of various reaction sites in aromatic compounds during chlorination.

Identification of reaction sites and chlorinated products of purine bases and nucleosides during chlorination: a computational study.

Hypochlorous acid (HOCl) released from activated leukocytes plays a significant role in the human immune system, but is also implicated in numerous diseases due to its inappropriate production. Chlorinated nucleobases induce genetic changes that potentially enable and stimulate carcinogenesis, and thus have attracted considerable attention. However, their multiple halogenation sites pose challenges to identify them. As a good complement to experiments, quantum chemical computation was used to uncover chlorination sites and chlorinated products in this study. The results indicate that anion salt forms of all purine compounds play significant roles in chlorination except for adenosine. The kinetic reactivity order of all reaction sites in terms of the estimated apparent rate constant kobs-est (in M-1 s-1) is heterocyclic NH/N (102-107) > exocyclic NH2 (10-2-10) > heterocyclic C8 (10-5-10-1), but the order is reversed for thermodynamics. Combining kinetics and thermodynamics, the numerical simulation results show that N9 is the most reactive site for purine bases to form the main initial chlorinated product, while for purine nucleosides N1 and exocyclic N2/N6 are the most reactive sites to produce the main products controlled by kinetics and thermodynamics, respectively, and C8 is a possible site to generate the minor product. The formation mechanisms of biomarker 8-Cl- and 8-oxo-purine derivatives were also investigated. Additionally, the structure-kinetic reactivity relationship study reveals a good correlation between lg kobs-est and APT charge in all purine compounds compared to FED2 (HOMO), which proves again that the electrostatic interaction plays a key role. The results are helpful to further understand the reactivity of various reaction sites in aromatic compounds during chlorination.

High-Efficiency Blue-Emitting Mn-Ligand passivated CsPbBr3 nanoplatelets.

Perovskite nanoplatelets (NPLs), as a promising material to achieve pure blue emission, have attracted significant attention in high gamut displays. However, the high surface-to-volume ratio and the loosely connected ligands of NPLs make them susceptible to degradation from light, air and heat. As a result, NPLs often exhibit low photoluminescence (PL) intensity and instability. Here, an Mn-ligand passivation strategy is proposed, in which Mn-doped DMAPbBr3 is used as a precursor. During the perovskite transformation, Mn2+ ions migrate from the lattice of DMAPbBr3 to the surface of CsPbBr3 NPLs, which have strong binding forces with ligands. The final products Mn-CsPbBr3 (M-CPB) NPLs are then acquired by the ligand-induced ripening growth process, which not only exhibit pure blue emission with narrow full width at half maximum (FWHM), but also possess near-unity PL quantum yields (QYs). Besides, M-CPB NPLs show excellent stability due to the strong Mn-ligand passivation layer. Based on the new growth mechanism discovery, the reaction time can be shortened to several minutes by heating. The innovative growth model proposed in this work will provide a paradigm for designing and optimizing future synthesis schemes.

Achieving ultrafast and highly selective capture of radiotoxic tellurite ions on iron-based metal-organic frameworks through coordination bond-dominated conversion.

Chemically durable and effective adsorbents for radiotoxic TeOx2- (TeIV and TeVI) anions remain in great demand for contamination remediation. Herein, a low-cost iron-based metal-organic framework (MIL-101(Fe)) was used as an adsorbent to capture TeOx2- anions from contaminated solution with ultrafast kinetics and record-high adsorption capacity of 645 mg g-1 for TeO32- and 337 mg g-1 for TeO42-, outperforming previously reported adsorbents. Extended X-ray absorption fine structure (EXAFS) and density functional theory (DFT) calculations confirmed that the capture of TeOx2- by MIL-101(Fe) was mediated by the unique C-O-Te and Fe-O-Te coordination bonds at corresponding optimal adsorption sites, which enabled the selective adsorption of TeOx2- from solution and further irreversible immobilization under the geological environment. Meanwhile, MIL-101(Fe) works steadily over a wide pH range of 4-10 and at high concentrations of competing ions, and it is stable under ß-irradiation even at high dose of 200 kGy. Moreover, the MIL-101(Fe) membrane was fabricated to efficiently remove TeO32- ions from seawater for practical use, overcoming the secondary contamination and recovery problems in powder adsorption. Finally, the good sustainability of MIL-101(Fe) was evaluated from three perspectives of technology, environment, and society. Our strategy provides an alternative to traditional removal methods that should be attractive for Te contamination remediation.

A comprehensive computational benchmark for evaluating deep learning-based protein function prediction approaches.

Proteins play an important role in life activities and are the basic units for performing functions. Accurately annotating functions to proteins is crucial for understanding the intricate mechanisms of life and developing effective treatments for complex diseases. Traditional biological experiments struggle to keep pace with the growing number of known proteins. With the development of high-throughput sequencing technology, a wide variety of biological data provides the possibility to accurately predict protein functions by computational methods. Consequently, many computational methods have been proposed. Due to the diversity of application scenarios, it is necessary to conduct a comprehensive evaluation of these computational methods to determine the suitability of each algorithm for specific cases. In this study, we present a comprehensive benchmark, BeProf, to process data and evaluate representative computational methods. We first collect the latest datasets and analyze the data characteristics. Then, we investigate and summarize 17 state-of-the-art computational methods. Finally, we propose a novel comprehensive evaluation metric, design eight application scenarios and evaluate the performance of existing methods on these scenarios. Based on the evaluation, we provide practical recommendations for different scenarios, enabling users to select the most suitable method for their specific needs. All of these servers can be obtained from https://csuligroup.com/BEPROF and https://github.com/CSUBioGroup/BEPROF.

Design and Synthesis of Diphosphine Ligands Based on the Chiral Biindolyl Scaffold and Their Application in Transition-Metal Catalysis.

An extremely concise, scalable, and stereoselective synthesis of a privileged chiral skeleton based on 2,2'-biindolyl and commercially available chiral building blocks has been developed. This novel skeleton allows for easy access to a range of bisphosphine ligands (decagram scale, up to 58% total yield, only three steps). The synthetic method is characterized by an efficient central-to-axial chirality transfer strategy. In particular, the superior performance of the ligands has been demonstrated in diverse reactions, including several asymmetric hydrogenations, asymmetric conjugate reductions, and cycloisomerization reactions, indicating a great potential for the application of the newly developed chiral backbones in further modifications and exploration of novel chiral ligands and catalysts.

Comparative skin histological and transcriptomic analysis of Rana kukunoris with two different skin colors.

This study compares the skin structures of Rana kukunoris with two different skin colors living in the same area of Haibei in the Northeastern Qinghai-Tibet Plateau. The skin thickness of the khaki R. kukunoris was significantly greater than that of the brown R. kukunoris (P < 0.01), and significantly more mucous and granular glands were present on the dorsal skin of the khaki frog (P < 0.05). Meanwhile, the melanocytes on the dorsal skin of the brown frog were significantly larger than those on the khaki one (P < 0.05). Morphological changes in the expansion and aggregation of melanocytes seemed to deepen the skin color of R. kukunoris. Moreover, transcriptome sequencing identified tyrosine metabolism, melanogenesis, and riboflavin metabolism as the main pathways involved in melanin formation and metabolism in brown R. kukunoris. TYR, MC1R was upregulated as the skin color of R. kukunoris was deepened and contributed to melanin production and metabolism. In contrast, the khaki frog had significantly more upregulated genes and metabolic pathways related to autoimmunity. The khaki frog appeared to defend against ultraviolet (UV) radiation-induced damage by secreting mucus and small molecular peptides, whereas the brown frog protected itself by distributing a large amount of melanin. Hence, the different skin colors of R. kukunoris might represent different adaptation strategies for survival in the intense UV radiation environment of the Qinghai-Tibet Plateau.

Microplastics removal mechanisms in constructed wetlands and their impacts on nutrient (nitrogen, phosphorus and carbon) removal: A critical review.

Microplastics (MPs) are now prevalent in aquatic ecosystems, prompting the use of constructed wetlands (CWs) for remediation. However, the interaction between MPs and CWs, including removal efficiency, mechanisms, and impacts, remains a subject requiring significant investigation. This review investigates the removal of MPs in CWs and assesses their impact on the removal of carbon, nitrogen, and phosphorus. The analysis identifies crucial factors influencing the removal of MPs, with substrate particle size and CWs structure playing key roles. The review highlights substrate retention as the primary mechanism for MP removal. MPs hinder plant nitrogen uptake, microbial growth, community composition, and nitrogen-related enzymes, reducing nitrogen removal in CWs. For phosphorus and carbon removal, adverse effects of MPs on phosphorus elimination are observed, while their impact on carbon removal is minimal. Further research is needed to understand their influence fully. In summary, CWs are a promising option for treating MPs-contaminated wastewater, but the intricate relationship between MPs and CWs necessitates ongoing research to comprehend their dynamics and potential consequences.

Review on Soil Corrosion and Protection of Grounding Grids.

The corrosion of grounding grid materials in soil is a prominent factor in power and electrical equipment failure. This paper aims to delve into the corrosion characteristics of grounding grid materials and the corresponding methods of safeguarding against this phenomenon. Firstly, the influencing factors of the soil environment on the corrosion of the grounding grid are introduced, including soil physicochemical properties, microorganisms, and stray currents. Then, the corrosion behavior and durability of common grounding grid materials such as copper, carbon steel, and galvanized steel are discussed in detail and compared comprehensively. In addition, commonly used protective measures in China and outside China, including anti-corrosion coatings, electrochemical protection, and other technologies are introduced. Finally, it summarizes the current research progress and potential future directions of this field of study.

Highly efficient and stable Cs3Mn0.93Zn0.07Br5@SiO2 for wide color gamut backlight displays.

Mn-based perovskites have become a new candidate material for backlight display applications. However, low efficiency and poor stability are the key problems limiting the application of Mn-based perovskites. In this work, Zn-doped and SiO2-encapsulated Cs3MnBr5, denoted as Cs3Mn0.93Zn0.07Br5@SiO2 (CMZBS), was successfully synthesized to improve the photoluminescence quantum yield (PLQY) and stability. After Zn doping, the PLQY increased from 51% to 72% due to the reduction in the energy transfer between [MnBr4]2-. The PLQY can be further improved to 80% after coating SiO2. Compared with Cs3MnBr5 (CMB), CMZBS showed better stability against thermal, air, light, and polar solvents (ethanol and isopropanol). In addition, a white LED (WLED) device with a CIE of (0.323, 0.325) was fabricated by integrating CMZBS and the red phosphor K2SiF6:Mn4+ on a 465 nm blue GaN chip, which exhibited a high luminous efficiency of 92 lm W-1 and excellent stability, demonstrating its great potential application in wide color gamut displays.

Facilitating mitigation of agricultural non-point source pollution and improving soil nutrient conditions: The role of low temperature co-pyrolysis biochar in nitrogen and phosphorus distribution.

The current study generated co-pyrolysis biochar by pyrolyzing rice straw and pig manure at 300 °C and subsequently applying it in a field. Co-pyrolysis biochar demonstrated superior efficiency in mitigating agricultural non-point source pollution compared to biochar derived from individual sources. Furthermore, it displayed notable capabilities in retaining and releasing nutrients, resulting in increased soil levels of total nitrogen, total phosphorus, and organic matter during the maturation stage of rice. Moreover, co-pyrolysis biochar influences soil microbial communities, potentially impacting nutrient cycling. During the rice maturation stage, the soil treated with co-pyrolysis biochar exhibited significant increases in available nutrients and rice yield compared to the control (p < 0.05). These findings emphasize the potential of co-pyrolysis biochar for in-situ nutrient retention and enhanced soil nutrient utilization. To summarize, the co-pyrolysis of agricultural waste materials presents a promising approach to waste management, contributing to controlling non-point source pollution, improving soil fertility, and promoting crop production.

Ru-NHC-Catalyzed Asymmetric, Complete Hydrogenation of Indoles and Benzofurans: One Catalyst with Dual Function.

The highly enantioselective and complete hydrogenation of protected indoles and benzofurans has been developed, affording facile access to a range of chiral three-dimensional octahydroindoles and octahydrobenzofurans, which are prevalent in many bioactive molecules and organocatalysts. Remarkably, we are in control of the nature of the ruthenium N-heterocyclic carbene complex and employed the complex as both homogeneous and heterogeneous catalysts, providing new avenues for its potential applications in the asymmetric hydrogenation of more challenging aromatic compounds.

DeepPRObind: Modular Deep Learner that Accurately Predicts Structure and Disorder-Annotated Protein Binding Residues.

Current sequence-based predictors of protein-binding residues (PBRs) belong to two distinct categories: structure-trained vs. intrinsic disorder-trained. Since disordered PBRs differ from structured PBRs in several ways, including ability to bind multiple partners by folding into different conformations and enrichment in different amino acids, the structure-trained and disorder-trained predictors were shown to provide inaccurate results for the other annotation type. A simple consensus-based solution that combines structure- and disorder-trained methods provides limited levels of predictive performance and generates relatively many cross-predictions, where residues that interact with other ligand types are predicted as PBRs. We address this unsolved problem by designing a novel and fast deep-learner, DeepPRObind, that relies on carefully designed modular convolutional architecture and uses innovative aggregate input features. Comparative empirical tests on a low-similarity test dataset reveal that DeepPRObind generates accurate predictions of structured and disordered PBRs and low amounts of cross-predictions, outperforming a comprehensive collection of 12 predictors of PBRs. Given the relatively low runtime of DeepPRObind (40 seconds per protein), we further validate its results based on an analysis of putative PBRs in the yeast proteome, confirming that interactions in disordered regions are enriched among hub proteins. We release DeepPRObind as a convenient web server at https://www.csuligroup.com/DeepPRObind/.

HybridRNAbind: prediction of RNA interacting residues across structure-annotated and disorder-annotated proteins.

The sequence-based predictors of RNA-binding residues (RBRs) are trained on either structure-annotated or disorder-annotated binding regions. A recent study of predictors of protein-binding residues shows that they are plagued by high levels of cross-predictions (protein binding residues are predicted as nucleic acid binding) and that structure-trained predictors perform poorly for the disorder-annotated regions and vice versa. Consequently, we analyze a representative set of the structure and disorder trained predictors of RBRs to comprehensively assess quality of their predictions. Our empirical analysis that relies on a new and low-similarity benchmark dataset reveals that the structure-trained predictors of RBRs perform well for the structure-annotated proteins while the disorder-trained predictors provide accurate results for the disorder-annotated proteins. However, these methods work only modestly well on the opposite types of annotations, motivating the need for new solutions. Using an empirical approach, we design HybridRNAbind meta-model that generates accurate predictions and low amounts of cross-predictions when tested on data that combines structure and disorder-annotated RBRs. We release this meta-model as a convenient webserver which is available at https://www.csuligroup.com/hybridRNAbind/.

Plants boost pyrrhotite-driven nitrogen removal in constructed wetlands.

Pyrrhotite is a promising electron donor for autotrophic denitrification. Using pyrrhotite as the substrate in constructed wetlands (CWs) can enhance the nitrogen removal performance in carbon-limited wastewater treatment. However, the role of plants in pyrrhotite-integrated CW is under debate as the oxygen released from plant roots may destroy the anoxic condition for autotrophic denitrification. This study compared pyrrhotite-integrated CWs with and without plants and identified the effects of plants' presence in nitrogen removal, pyrrhotite oxidized dissolution, and microbial community. The results show that plants enhanced the TN removal significantly (from 41.6 ± 3.9 % to 97.1 ± 2.6 %). Plants can accelerate the PAD in CW through the strengthening of pyrrhotite dissolution. Enriched functional (Thiobacillus and Acidiferrobacter) and a more complex bacterial co-occurrence network has been found in CW with plants. This study identified the role of plants in PAD acceleration, providing an in-depth understanding of pyrrhotite in CW systems.

DeepCellEss: cell line-specific essential protein prediction with attention-based interpretable deep learning.

MOTIVATION: Protein essentiality is usually accepted to be a conditional trait and strongly affected by cellular environments. However, existing computational methods often do not take such characteristics into account, preferring to incorporate all available data and train a general model for all cell lines. In addition, the lack of model interpretability limits further exploration and analysis of essential protein predictions. RESULTS: In this study, we proposed DeepCellEss, a sequence-based interpretable deep learning framework for cell line-specific essential protein predictions. DeepCellEss utilizes a convolutional neural network and bidirectional long short-term memory to learn short- and long-range latent information from protein sequences. Further, a multi-head self-attention mechanism is used to provide residue-level model interpretability. For model construction, we collected extremely large-scale benchmark datasets across 323 cell lines. Extensive computational experiments demonstrate that DeepCellEss yields effective prediction performance for different cell lines and outperforms existing sequence-based methods as well as network-based centrality measures. Finally, we conducted some case studies to illustrate the necessity of considering specific cell lines and the superiority of DeepCellEss. We believe that DeepCellEss can serve as a useful tool for predicting essential proteins across different cell lines. AVAILABILITY AND IMPLEMENTATION: The DeepCellEss web server is available at http://csuligroup.com:8000/DeepCellEss. The source code and data underlying this study can be obtained from https://github.com/CSUBioGroup/DeepCellEss. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A Deep Learning Framework for Predicting Protein Functions With Co-Occurrence of GO Terms.

The understanding of protein functions is critical to many biological problems such as the development of new drugs and new crops. To reduce the huge gap between the increase of protein sequences and annotations of protein functions, many methods have been proposed to deal with this problem. These methods use Gene Ontology (GO) to classify the functions of proteins and consider one GO term as a class label. However, they ignore the co-occurrence of GO terms that is helpful for protein function prediction. We propose a new deep learning model, named DeepPFP-CO, which uses Graph Convolutional Network (GCN) to explore and capture the co-occurrence of GO terms to improve the protein function prediction performance. In this way, we can further deduce the protein functions by fusing the predicted propensity of the center function and its co-occurrence functions. We use Fmax and AUPR to evaluate the performance of DeepPFP-CO and compare DeepPFP-CO with state-of-the-art methods such as DeepGOPlus and DeepGOA. The computational results show that DeepPFP-CO outperforms DeepGOPlus and other methods. Moreover, we further analyze our model at the protein level. The results have demonstrated that DeepPFP-CO improves the performance of protein function prediction. DeepPFP-CO is available at https://csuligroup.com/DeepPFP/.