Expression, purification, and characterization of transmembrane protein homogentisate solanesyltransferase

Homogentisate solanesyltransferase (HST) is a crucial enzyme in the plastoquinone biosynthetic pathway and has recently emerged as a promising target for herbicides. In this study, we successfully expressed and purified a stable and highly pure form of seven times transmembrane protein Chlamydomonas reinhardtii HST (CrHST). The final yield of CrHST protein obtained was 12.2 mg per liter of M9 medium. We evaluated the inhibitory effect on CrHST using Des-Morpholinocarbony Cyclopyrimorate (DMC) and found its IC50 value to be 3.63 ± 0.53 μM, indicating significant inhibitory potential. Additionally, we investigated the substrate affinity of CrHST with two substrates, determining the Km values as 22.76 ± 1.70 μM for FPP and 48.54 ± 3.89 μM for HGA. Through sequence alignment analyses and three-dimensional structure predictions, we identified conserved amino acid residues forming the active cavity in the enzyme. The results from molecular docking and binding energy calculations indicate that DMC has a greater binding affinity with HST compared to HGA. These findings represent substantial progress in understanding CrHST's properties and potential for herbicide development. KEY POINTS: • First high-yield transmembrane CrHST protein via E. coli system • Preliminarily identified active cavity composition via activity testing • Determined substrate and inhibitor modes via molecular docking.

Web tools support predicting protein-nucleic acid complexes stability with affinity changes

Numerous biological processes, such as transcription, replication, and translation, rely on protein-nucleic acid interactions (PNIs). Demonstrating the binding stability of protein-nucleic acid complexes is vital to deciphering the code for PNIs. Numerous web-based tools have been developed to attach importance to protein-nucleic acid stability, facilitating the prediction of PNIs characteristics rapidly. However, the data and tools are dispersed and lack comprehensive integration to understand the stability of PNIs better. In this review, we first summarize existing databases for evaluating the stability of protein-nucleic acid binding. Then, we compare and evaluate the pros and cons of web tools for forecasting the interaction energies of protein-nucleic acid complexes. Finally, we discuss the application of combining models and capabilities of PNIs. We may hope these web-based tools will facilitate the discovery of recognition mechanisms for protein-nucleic acid binding stability. This article is categorized under: RNA Interactions with Proteins and Other Molecules > Protein-RNA Recognition RNA Interactions with Proteins and Other Molecules > RNA-Protein Complexes RNA Interactions with Proteins and Other Molecules > Protein-RNA Interactions: Functional Implications.

Protein-nucleic acid thermodynamic databases for specific uses

In response to Gromiha and Harini, we review the currently available thermodynamic databases for protein-nucleic acid interactions. These databases are designed for particular uses. We give general comments on them to facilitate browsing and exploration.

Rational Design of Esterase-Insensitive Fluorogenic Probes for In Vivo Imaging

Small-molecule fluorogenic probes are indispensable tools for performing research in biomedical fields and chemical biology. Although numerous cleavable fluorogenic probes have been developed to investigate various bioanalytes, few of them meet the baseline requirements for in vivo biosensing for disease diagnosis due to their insufficient specificity resulted from the remarkable esterase interferences. To address this critical issue, we developed a general approach called fragment-based fluorogenic probe discovery (FBFPD) to design esterase-insensitive probes for in vitro and in vivo applications. With the designed esterase-insensitive fluorogenic probe, we successfully achieved light-up in vivo imaging and quantitative analysis of cysteine. This strategy was further extended to design highly specific fluorogenic probes for other representative targets, sulfites, and chymotrypsin. The present study expands the bioanalytical toolboxes available and offers a promising platform to develop esterase-insensitive cleavable fluorogenic probes for in vivo biosensing and bioimaging for the early diagnosis of diseases.

Thermodynamic database supports deciphering protein-nucleic acid interactions

The thermodynamics of protein-nucleic acid interactions (PNIs) is crucial for elucidating the mechanisms of molecular recognition and pathological consequences. The Protein-Nucleic Acid Thermodynamics Database (PNATDB) is a database containing experimentally determined thermodynamic parameters along with sequence, structural, and function data, which is available free online.

In vivo diagnostics of abiotic plant stress responses via in situ real-time fluorescence imaging

A hydroxyphenylpyruvate dioxygenase-targeted fluorescent biosensor enables the early diagnostics of abiotic stresses in plants.

Pesticide Informatics Platform (PIP): An International Platform for Pesticide Discovery, Residue, and Risk Evaluation

Pesticides are widely used agrochemicals for crop protection. The need for novel pesticides becomes urgent as a result of the emergence of resistance and environmental toxicity. Pesticide informatics has been applied in different phase processes of pesticide target identification, active ingredient design, and impact evaluation. However, these valuable resources are scattered over the literature and web, limiting their availability. Here, we summarize and connect research on pesticide informatics resources. A pesticide informatics platform (PIP) was constructed to share these tools. We finally discuss the future direction of pesticide informatics, including pesticide contamination. We expect to share the pesticide informatics approaches and stimulate further research.

Conformational adjustment overcomes multiple drug-resistance mutants of tropomyosin receptor kinase

Mutation-induced resistance to targeted drug treatment poses a serious threat to successful chemotherapy. Multiple mutations underlying drug resistance remain a largely unsolved scientific issue. Tropomyosin receptor kinases (TRKs) are promising therapeutic targets for several malignant human cancers, but they have become less effective due to multiple resistance mutations. Thus, TRKs are representative cases to explore the problem of multiple resistance mutations. Here, we proposed a conformational adjustment strategy of drug design to overcome multiple resistance mutations in cancer treatments. A representative inhibitor, TIY-7, exhibited remarkable inhibitory activity against five TRK mutants, showing an IC₅₀ value of 1.1 nM against the most severe mutant TRKA-G595R. Moreover, it displayed superior tumor growth inhibitory activity compared with the clinically used drug selitrectinib. These results validated our strategy to design a new inhibitor structure to overcome multiple resistance mutations.

Web resources facilitate drug discovery in treatment of COVID-19

The infectious disease Coronavirus 2019 (COVID-19) continues to cause a global pandemic and, thus, the need for effective therapeutics remains urgent. Global research targeting COVID-19 treatments has produced numerous therapy-related data and established data repositories. However, these data are disseminated throughout the literature and web resources, which could lead to a reduction in the levels of their use. In this review, we introduce resource repositories for the development of COVID-19 therapeutics, from the genome and proteome to antiviral drugs, vaccines, and monoclonal antibodies. We briefly describe the data and usage, and how they advance research for therapies. Finally, we discuss the opportunities and challenges to preventing the pandemic from developing further.

HISNAPI: a bioinformatic tool for dynamic hot spot analysis in nucleic acid-protein interface with a case study

Protein-nucleic acid interactions play essential roles in many biological processes, such as transcription, replication and translation. In protein-nucleic acid interfaces, hotspot residues contribute the majority of binding affinity toward molecular recognition. Hotspot residues are commonly regarded as potential binding sites for compound molecules in drug design projects. The dynamic property is a considerable factor that affects the binding of ligands. Computational approaches have been developed to expedite the prediction of hotspot residues on protein-nucleic acid interfaces. However, existing approaches overlook hotspot dynamics, despite their essential role in protein function. Here, we report a web server named Hotspots In silico Scanning on Nucleic Acid and Protein Interface (HISNAPI) to analyze hotspot residue dynamics by integrating molecular dynamics simulation and one-step free energy perturbation. HISNAPI is capable of not only predicting the hotspot residues in protein-nucleic acid interfaces but also providing insights into their intensity and correlation of dynamic motion. Protein dynamics have been recognized as a vital factor that has an effect on the interaction specificity and affinity of the binding partners. We applied HISNAPI to the case of SARS-CoV-2 RNA-dependent RNA polymerase, a vital target of the antiviral drug for the treatment of coronavirus disease 2019. We identified the hotspot residues and characterized their dynamic behaviors, which might provide insight into the target site for antiviral drug design. The web server is freely available via a user-friendly web interface at http://chemyang.ccnu.edu.cn/ccb/server/HISNAPI/ and http://agroda.gzu.edu.cn:9999/ccb/server/HISNAPI/.

Computational methods for predicting hotspots at protein-RNA interfaces

Protein-RNA interactions play essential roles in many critical biological events. A comprehensive understanding of the mechanisms underlying these interactions is helpful when studying cellular activities and therapeutic applications. Hotspots are a small portion of residues contributing much toward protein-RNA binding affinity. In pharmaceutical research, the hotspot residues are seen as the best option for designing small molecules to target proteins of therapeutic interest. With the accumulation of experimental data about protein-RNA interactions, computational methods have been produced for hotspot prediction on a large scale. In this review, we first present an overview of the existing databases for protein-RNA binding data. Furthermore, we outline the most adopted computational methods for hotspots prediction in protein-RNA interactions. Finally, we discuss the applications of hotspot prediction. This article is categorized under: RNA Interactions with Proteins and Other Molecules > Protein-RNA Recognition RNA Interactions with Proteins and Other Molecules > Protein-RNA Interactions: Functional Implications RNA Methods > RNA Analyses In Vitro and In Silico.

Multienzyme-Targeted Fluorescent Probe as a Biosensing Platform for Broad Detection of Pesticide Residues

Pesticide residues, significantly hampering the overall environmental and human health, have become an increasingly severe issue. Thus, developing rapid, cost-effective, and sensitive tools for monitoring the pesticide residues in food and water is extremely important. Compared to the conventional and chromatographic techniques, enzyme inhibition-based biosensors conjugated with the fluorogenic probes provide effective alternative methods for detecting pesticide residues due to the inherent advantages including high selectivity and sensitivity, simple operation, and capability of providing in situ and real-time information. However, the detection efficiency of a single enzyme-targeted biosensor in practical samples is strongly impeded by the structural diversity of pesticides and their distinct targets. In this work, we developed a strategy of multienzyme-targeted fluorescent probe design and accordingly obtained a novel fluorescent probe (named as 3CP) for detecting the presence of wide variety of pesticides. The designed probe 3CP, targeting cholinesterases, carboxylesterases, and chymotrypsin simultaneously, yielded intense fluorescence in the solid state upon the enzyme-catalyzed hydrolysis. It showed excellent sensitivity against organophosphorus and carbamate pesticides, and the detection limit for dichlorvos achieved 1.14 pg/L. Moreover, it allowed for the diffusion-resistant in situ visualization of pesticides in live cells and zebrafish and the sensitive measurement of organophosphorus pesticides in fresh vegetables, demonstrating the promising potential for tracking the pesticide residues in environment and biological systems.

Expanding the Chemical Space of Succinate Dehydrogenase Inhibitors via the Carbon-Silicon Switch Strategy

The carbon-silicon switch strategy has become a key technique for structural optimization of drugs to widen the chemical space, increase drug activity against targeted proteins, and generate novel and patentable lead compounds. Flubeneteram, targeting succinate dehydrogenase (SDH), is a promising fungicide candidate recently developed in China. We describe the synthesis of novel SDH inhibitors with enhanced fungicidal activity to enlarge the chemical space of flubeneteram by employing the C-Si switch strategy. Several of the thus formed flubeneteram-silyl derivatives exhibited improved fungicidal activity against porcine SDH compared with the lead compound flubeneteram and the positive controls. Disease control experiments conducted in a greenhouse showed that trimethyl-silyl-substituted compound W2 showed comparable and even higher fungicidal activities compared to benzovindiflupyr and flubeneteram, respectively, even with a low concentration of 0.19 mg/L for soybean rust control. Furthermore, compound W2 encouragingly performed slightly better control than azoxystrobin and was less active than benzovindiflupyr at the concentration of 100 mg/L against soybean rust in field trials. The computational results showed that the silyl-substituted phenyl moiety in W2 could form strong van der Waals (VDW) interactions with SDH. Our results indicate that the C-Si switch strategy is an effective method for the development of novel SDH inhibitors.

PIIMS Server: A Web Server for Mutation Hotspot Scanning at the Protein-Protein Interface

Protein-protein interactions (PPIs) play vital roles in regulating biological processes, such as cellular and signaling pathways. Hotspots are certain residues located at protein-protein interfaces that contribute more in protein-protein binding than other residues. Research on the mutational effects of hotspots is important for understanding basic aspects of protein association. Hence, various computational tools have been developed to explore the impact of mutation hotspots, which will allow a better understanding of the forces that drive PPIs. However, tools that may provide comprehensive substitutions at hotspots are still rare. Hence, there is a strong need for a new free web server to explore mutational effects of hotspots. Herein we introduce a web server named PIIMS that integrates molecular dynamics simulation and one-step free energy perturbation. It contains two main computational functions: (1) computational alanine scanning analysis to identify hotspots and (2) full mutation scanning analysis to evaluate the effects of hotspot mutations. We rigidly validated its ability to predict binding free energy changes by using large and diverse datasets including 1,341 mutations from 50 PPIs with the correlation coefficient R = 0.75. The difference from the existing tools is that PIIMS can perform further evaluation of hotspot residues with regard to their different mutations. The PIIMS web server (accessible at http://chemyang.ccnu.edu.cn/ccb/server/PIIMS/index.php) is free and open to all users without login requirements.

Bioinformatics toolbox for exploring protein phosphorylation network

A clear systematic delineation of the interactions between phosphorylation sites on substrates and their effector kinases plays a fundamental role in revealing cellular activities, understanding signaling modulation mechanisms and proposing novel hypotheses. The emergence of bioinformatics tools contributes to studying phosphorylation network. Some of them feature the visualization of network, enabling more effective trace of the underlying biological problems in a clear and succinct way. In this review, we aimed to provide a toolbox for exploring phosphorylation network. We first systematically surveyed 19 tools that are available for exploring phosphorylation networks, and subsequently comparatively analyzed and summarized these tools to guide tool selection in terms of functionality, data sources, performance, network visualization and implementation, and finally briefly discussed the application cases of these tools. In different scenarios, the conclusion on the suitability of a tool for a specific user may vary. Nevertheless, easily accessible bioinformatics tools are proved to facilitate biological findings. Hopefully, this work might also assist non-specialists, students, as well as computational scientists who aim at developing novel tools in the field of phosphorylation modification.