Fragment-based drug discovery supports drugging 'undruggable' protein-protein interactions.

Protein-protein interactions (PPIs) have important roles in various cellular processes, but are commonly described as 'undruggable' therapeutic targets due to their large, flat, featureless interfaces. Fragment-based drug discovery (FBDD) has achieved great success in modulating PPIs, with more than ten compounds in clinical trials. Here, we highlight the progress of FBDD in modulating PPIs for therapeutic development. Targeting hot spots that have essential roles in both fragment binding and PPIs provides a shortcut for the development of PPI modulators via FBDD. We highlight successful cases of cracking the 'undruggable' problems of PPIs using fragment-based approaches. We also introduce new technologies and future trends. Thus, we hope that this review will provide useful guidance for drug discovery targeting PPIs.

Insights into 4-hydroxyphenylpyruvate dioxygenase-inhibitor interactions from comparative structural biology.

4-Hydroxyphenylpyruvate dioxygenase (HPPD) plays a key role in tyrosine metabolism and has been identified as a promising target for herbicide and drug discovery. The structures of HPPD complexed with different types of inhibitors have been determined previously. We summarize the structures of HPPD complexed with structurally diverse molecules, including inhibitors, natural products, substrates, and catalytic intermediates; from these structures, the detailed inhibitory mechanisms of different inhibitors were analyzed and compared, and the key structural factors determining the slow-binding behavior of inhibitors were identified. Further, we propose four subpockets that accommodate different inhibitor substructures. We believe that these analyses will facilitate in-depth understanding of the enzymatic reaction mechanism and enable the design of new inhibitors with higher potency and selectivity.

Human-virus protein-protein interactions maps assist in revealing the pathogenesis of viral infection.

Many significant viral infections have been recorded in human history, which have caused enormous negative impacts worldwide. Human-virus protein-protein interactions (PPIs) mediate viral infection and immune processes in the host. The identification, quantification, localization, and construction of human-virus PPIs maps are critical prerequisites for understanding the biophysical basis of the viral invasion process and characterising the framework for all protein functions. With the technological revolution and the introduction of artificial intelligence, the human-virus PPIs maps have been expanded rapidly in the past decade and shed light on solving complicated biomedical problems. However, there is still a lack of prospective insight into the field. In this work, we comprehensively review and compare the effectiveness, potential, and limitations of diverse approaches for constructing large-scale PPIs maps in human-virus, including experimental methods based on biophysics and biochemistry, databases of human-virus PPIs, computational methods based on artificial intelligence, and tools for visualising PPIs maps. The work aims to provide a toolbox for researchers, hoping to better assist in deciphering the relationship between humans and viruses.

ACFIS 2.0: an improved web-server for fragment-based drug discovery via a dynamic screening strategy.

Drug discovery, which plays a vital role in maintaining human health, is a persistent challenge. Fragment-based drug discovery (FBDD) is one of the strategies for the discovery of novel candidate compounds. Computational tools in FBDD could help to identify potential drug leads in a cost-efficient and time-saving manner. The Auto Core Fragment in silico Screening (ACFIS) server is a well-established and effective online tool for FBDD. However, the accurate prediction of protein-fragment binding mode and affinity is still a major challenge for FBDD due to weak binding affinity. Here, we present an updated version (ACFIS 2.0), that incorporates a dynamic fragment growing strategy to consider protein flexibility. The major improvements of ACFIS 2.0 include (i) increased accuracy of hit compound identification (from 75.4% to 88.5% using the same test set), (ii) improved rationality of the protein-fragment binding mode, (iii) increased structural diversity due to expanded fragment libraries and (iv) inclusion of more comprehensive functionality for predicting molecular properties. Three successful cases of drug lead discovery using ACFIS 2.0 are described, including drugs leads to treat Parkinson's disease, cancer, and major depressive disorder. These cases demonstrate the utility of this web-based server. ACFIS 2.0 is freely available at http://chemyang.ccnu.edu.cn/ccb/server/ACFIS2/.

Exploring the kinase-inhibitor fragment interaction space facilitates the discovery of kinase inhibitor overcoming resistance by mutations.

Protein kinases play crucial roles in many cellular signaling processes, making them become important targets for drug discovery. But drug resistance mediated by mutation puts a barrier to the therapeutic effect of kinase inhibitors. Fragment-based drug discovery has been successfully applied to overcome such resistance. However, the complicate kinase-inhibitor fragment interaction and fragment-to-lead process seriously limit the efficiency of kinase inhibitor discovery against resistance caused by mutation. Here, we constructed a comprehensive web platform KinaFrag for the fragment-based kinase inhibitor discovery to overcome resistance. The kinase-inhibitor fragment space was investigated from 7783 crystal kinase-inhibitor fragment complexes, and the structural requirements of kinase subpockets were analyzed. The core fragment-based virtual screening workflow towards specific subpockets was developed to generate new kinase inhibitors. A series of tropomyosin receptor kinase (TRK) inhibitors were designed, and the most potent compound YT9 exhibits up to 70-fold activity improvement than marketed drugs larotrectinib and selitrectinib against G595R, G667C and F589L mutations of TRKA. YT9 shows promising antiproliferative against tumor cells in vitro and effectively inhibits tumor growth in vivo for wild type TRK and TRK mutants. Our results illustrate the great potential of KinaFrag in the kinase inhibitor discovery to combat resistance mediated by mutation. KinaFrag is freely available at http://chemyang.ccnu.edu.cn/ccb/database/KinaFrag/.

PTMdyna: exploring the influence of post-translation modifications on protein conformational dynamics.

Protein post-translational modifications (PTM) play vital roles in cellular regulation, modulating functions by driving changes in protein structure and dynamics. Exploring comprehensively the influence of PTM on conformational dynamics can facilitate the understanding of the related biological function and molecular mechanism. Currently, a series of excellent computation tools have been designed to analyze the time-dependent structural properties of proteins. However, the protocol aimed to explore conformational dynamics of post-translational modified protein is still a blank. To fill this gap, we present PTMdyna to visually predict the conformational dynamics differences between unmodified and modified proteins, thus indicating the influence of specific PTM. PTMdyna exhibits an AUC of 0.884 tested on 220 protein-protein complex structures. The case of heterochromatin protein 1α complexed with lysine 9-methylated histone H3, which is critical for genomic stability and cell differentiation, was used to demonstrate its applicability. PTMdyna provides a reliable platform to predict the influence of PTM on protein dynamics, making it easier to interpret PTM functionality at the structure level. The web server is freely available at http://ccbportal.com/PTMdyna.

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.

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/.

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.

Cloud 3D-QSAR: a web tool for the development of quantitative structure-activity relationship models in drug discovery.

Effective drug discovery contributes to the treatment of numerous diseases but is limited by high costs and long cycles. The Quantitative Structure-Activity Relationship (QSAR) method was introduced to evaluate the activity of a large number of compounds virtually, reducing the time and labor costs required for chemical synthesis and experimental determination. Hence, this method increases the efficiency of drug discovery. To meet the needs of researchers to utilize this technology, numerous QSAR-related web servers, such as Web-4D-QSAR and DPubChem, have been developed in recent years. However, none of the servers mentioned above can perform a complete QSAR modeling and supply activity prediction functions. We introduce Cloud 3D-QSAR by integrating the functions of molecular structure generation, alignment, molecular interaction field (MIF) computing and results analysis to provide a one-stop solution. We rigidly validated this server, and the activity prediction correlation was R2 = 0.934 in 834 test molecules. The sensitivity, specificity and accuracy were 86.9%, 94.5% and 91.5%, respectively, with AUC = 0.981, AUCPR = 0.971. The Cloud 3D-QSAR server may facilitate the development of good QSAR models in drug discovery. Our server is free and now available at http://chemyang.ccnu.edu.cn/ccb/server/cloud3dQSAR/ and http://agroda.gzu.edu.cn:9999/ccb/server/cloud3dQSAR/.

J-Aggregates Formed by NaCl Treatment of Aza-Coating Heptamethine Cyanines and Their Application to Monitoring Salt Stress of Plants and Promoting Photothermal Therapy of Tumors.

The cationic nature of heptamethine cyanines gives them the capacity to form aggregates with salts by electrostatic interactions. In this work, NaCl promoted J-aggregate formation of aza-coating heptamethine cyanines is explored. NaCl can induce the N-benzyloxycarbonyl Cy-CO2 Bz to assemble into a J-aggregate having an absorption at 890 nm. Its excellent fluorescence response to NaCl implies that it has great potential for use as a probe for tracing salt stress in plants. Moreover, NaCl also promotes formation of J-aggregates from the N-ethyloxycarbonyl Cy-CO2 Et. The aggregate shows an intense absorption at 910 nm compared to the monomer which absorbs at 766 nm. Its J-aggregated form can serve as a photothermal agent. And the photothermal conversion efficiency is increased from 29.37 % to 57.59 %. This effort leads to the development of two applications of new cyanine J-aggregates including one for tracing salt stress of plants and the other for promoting photothermal therapy of tumors.

A Protocol for Activated Bioorthogonal Fluorescence Labeling and Imaging of 4-Hydroxyphenylpyruvate Dioxygenase in Plants.

4-Hydroxyphenylpyruvate dioxygenase (HPPD) plays a crucial role in the synthesis of nutrients needed to maintain optimal plant growth. Its level is closely linked to the extent of abiotic stress experienced by plants. Moreover, it is also the target of commercial herbicides. Therefore, labeling of HPPD in plants not only enables visualization of its tissue distribution and cellular uptake, it also facilitates assessment of abiotic stress of plants and provides information needed for the development of effective environmentally friendly herbicides. In this study, we created a method for fluorescence labeling of HPPD that avoids interference with the normal growth of plants. In this strategy, a perylene-linked dibenzyl-cyclooctyne undergoes strain-promoted azide-alkyne cycloaddition with an azide-containing HPPD ligand. The activation-based labeling process results in a significant emission enhancement caused by the change in the fluorescent forms from an excimer to a monomer. Notably, this activated bioorthogonal strategy is applicable to visualizing HPPD in Arabidopsis thaliana, and assessing its response to multiple abiotic stresses. Also, it can be employed to monitor in vivo levels and locations of HPPD in crops. Consequently, the labeling strategy will be a significant tool in investigations of HPPD-related abiotic stress mechanisms, discovering novel herbicides, and uncovering unknown biological functions.

Diagnosis of Bacterial Plant Diseases via a Nitroreductase-Activated Fluorescent Sensor.

Plant diseases caused by bacteria have become one of the serious problems that threaten human food security, which led to the remarkable reduction of agricultural yields and economic loss. Nitroreductase (NTR), as an important biomarker, is highly expressed in bacteria, and the level of NTR is closely related to the progression of pathogen infection. Therefore, the design of small-molecule fluorescent sensors targeting NTR is of great significance for the detection and diagnosis of plant pathogenic bacteria. In this study, a new fluorescent sensor targeting NTR was discovered and then successfully applied to the imaging of zebrafish and pathogenic bacteria. Most importantly, the developed sensor achieved the real-time diagnosis of Brassica napus L. infected with bacteria, which provides a promising tool for examining the temporal and spatial infection of plant pathogens in precision agriculture.

LARMD: integration of bioinformatic resources to profile ligand-driven protein dynamics with a case on the activation of estrogen receptor.

Protein dynamics is central to all biological processes, including signal transduction, cellular regulation and biological catalysis. Among them, in-depth exploration of ligand-driven protein dynamics contributes to an optimal understanding of protein function, which is particularly relevant to drug discovery. Hence, a wide range of computational tools have been designed to investigate the important dynamic information in proteins. However, performing and analyzing protein dynamics is still challenging due to the complicated operation steps, giving rise to great difficulty, especially for nonexperts. Moreover, there is a lack of web protocol to provide online facility to investigate and visualize ligand-driven protein dynamics. To this end, in this study, we integrated several bioinformatic tools to develop a protocol, named Ligand and Receptor Molecular Dynamics (LARMD, http://chemyang.ccnu.edu.cn/ccb/server/LARMD/ and http://agroda.gzu.edu.cn:9999/ccb/server/LARMD/), for profiling ligand-driven protein dynamics. To be specific, estrogen receptor (ER) was used as a case to reveal ERß-selective mechanism, which plays a vital role in the treatment of inflammatory diseases and many types of cancers in clinical practice. Two different residues (Ile373/Met421 and Met336/Leu384) in the pocket of ERß/ERα were the significant determinants for selectivity, especially Met336 of ERß. The helix H8, helix H11 and H7-H8 loop influenced the migration of selective agonist (WAY-244). These computational results were consistent with the experimental results. Therefore, LARMD provides a user-friendly online protocol to study the dynamic property of protein and to design new ligand or site-directed mutagenesis.

AIMMS suite: a web server dedicated for prediction of drug resistance on protein mutation.

Drug resistance is one of the most intractable issues for successful treatment in current clinical practice. Although many mutations contributing to drug resistance have been identified, the relationship between the mutations and the related pharmacological profile of drug candidates has yet to be fully elucidated, which is valuable both for the molecular dissection of drug resistance mechanisms and for suggestion of promising treatment strategies to counter resistant. Hence, effective prediction approach for estimating the sensitivity of mutations to agents is a new opportunity that counters drug resistance and creates a high interest in pharmaceutical research. However, this task is always hampered by limited known resistance training samples and accurately estimation of binding affinity. Upon this challenge, we successfully developed Auto In Silico Macromolecular Mutation Scanning (AIMMS), a web server for computer-aided de novo drug resistance prediction for any ligand-protein systems. AIMMS can qualitatively estimate the free energy consequences of any mutations through a fast mutagenesis scanning calculation based on a single molecular dynamics trajectory, which is differentiated with other web services by a statistical learning system. AIMMS suite is available at http://chemyang.ccnu.edu.cn/ccb/server/AIMMS/.

Unveiling toxicity profile for food risk components: A manually curated toxicological databank of food-relevant chemicals.

Rigorous risk assessment of chemicals in food and feed is essential to address the growing worldwide concerns about food safety. High-quality toxicological data on food-relevant chemicals are fundamental for risk modeling and assessment in the food safety area. The organization and analysis of substantial toxicity information can positively support decision-making by providing insight into toxicity trends. However, it remains challenging to systematically obtain fragmented toxicity data, and related toxicological resources are required to meet the current demands. In this study, we collected 221,439 experimental toxicity records for 5,657 food-relevant chemicals identified from extensive databases and literature, along with their information on chemical identification, physicochemical properties, environmental fates, and biological targets. Based on the aggregated data, a freely available web-based databank, Food-Relevant Available Chemicals Toxicology Databank (FRAC-TD) is presented, which supports multiple browsing ways and search criterions. Applying FRAC-TD for data-driven analysis, we revealed the underlying toxicity profiles of food-relevant chemicals in humans, mammals, and other species in the food chain. Expectantly, FRAC-TD could positively facilitate toxicological studies, toxicity prediction, and risk assessments in the food industry.

A mini-review on synthesis and antiviral activity of natural product oxetanocin A derivatives.

Oxetanocin A (Oxt-A), a novel oxetanosyl N-glycoside nucleoside, was isolated from Bacillus megaterium in 1986. It carries an oxetane ring on the sugar moiety of the nucleoside scaffold, which contributes to differences in its structure from those of common tetrahydrofuranyl-based nucleosides. In view of the unique 3D-spatial framework, the complete synthesis of Oxt-A has been achieved by multiple research groups. The pharmacological properties of this natural product have also been broadly investigated by pharmacists and chemists since its discovery. Notably, the potential antiviral effect of Oxt-A has captured attention of researchers in the field of antiviral agent development. Furthermore, epidemic outbreaks caused by viruses have been stimulating the preparation and modification of various Oxt-A analogs over the past few decades. However, none of the studies have overviewed the antiviral efficacies of this naturally occurring scaffold yet. Thus, the present review summarizes the synthesis, structural modification, and antiviral activities of Oxt-A and its derivatives. We believe that these comprehensive descriptions will provide a novel perspective for the discovery of antivirus drugs with well-improved performance and pave newer paths for combating sudden public health issues triggered by viruses in the future.

Phylogenetic comparison of 5' splice site determination in central spliceosomal proteins of the U1-70K gene family, in response to developmental cues and stress conditions.

Intron-containing genes have the ability to generate multiple transcript isoforms by splicing, thereby greatly expanding the eukaryotic transcriptome and proteome. In eukaryotic cells, precursor mRNA (pre-mRNA) splicing is performed by a mega-macromolecular complex defined as a spliceosome. Among its splicing components, U1 small nuclear ribonucleoprotein (U1 snRNP) is the smallest subcomplex involved in early spliceosome assembly and 5'-splice site recognition. Its central component, named U1-70K, has been extensively characterized in animals and yeast. Very few investigations on U1-70K genes have been conducted in plants, however. To this end, we performed a comprehensive study to systematically identify 115 U1-70K genes from 67 plant species, ranging from algae to angiosperms. Phylogenetic analysis suggested that the expansion of the plant U1-70K gene family was likely to have been driven by whole-genome duplications. Subsequent comparisons of gene structures, protein domains, promoter regions and conserved splicing patterns indicated that plant U1-70Ks are likely to preserve their conserved molecular function across plant lineages and play an important functional role in response to environmental stresses. Furthermore, genetic analysis using T-DNA insertion mutants suggested that Arabidopsis U1-70K may be involved in response to osmotic stress. Our results provide a general overview of this gene family in Viridiplantae and will act as a reference source for future mechanistic studies on this U1 snRNP-specific splicing factor.

Rational Redesign of Enzyme via the Combination of Quantum Mechanics/Molecular Mechanics, Molecular Dynamics, and Structural Biology Study.

Increasing demands for efficient and versatile chemical reactions have prompted innovations in enzyme engineering. A major challenge in engineering α-ketoglutarate-dependent oxygenases is to develop a rational strategy which can be widely used for directly evolving the desired mutant to generate new products. Herein, we report a strategy for rational redesign of a model enzyme, 4-hydroxyphenylpyruvate dioxygenase (HPPD), based on quantum mechanics/molecular mechanics (QM/MM) calculation and molecular dynamic simulations. This strategy enriched our understanding of the HPPD catalytic reaction pathway and led to the discovery of a series of HPPD mutants producing hydroxyphenylacetate (HPA) as the alternative product other than the native product homogentisate. The predicted HPPD-Fe(IV)═O-HPA intermediate was further confirmed by the crystal structure of Arabidopsis thaliana HPPD/S267W complexed with HPA. These findings not only provide a good understanding of the structure-function relationship of HPPD but also demonstrate a generally applicable platform for the development of biocatalysts.

Pyroglutamate Aminopeptidase I Promotes Hepatocellular Carcinoma via IL-6/STAT3 Activation as Revealed by a Specific Biosensor.

As a global health challenge, hepatocellular carcinoma (HCC) is strongly associated with chronic inflammation. Targeting inflammation, particularly inflammatory factors, is regarded as an important strategy for HCC diagnosis and treatment. Pyroglutamic aminopeptidase I (PGP-I), a common exopeptidase, was recently identified as a novel inflammatory cytokine in cells. However, whether PGP-I is involved in HCC development and can be regarded as a biomarker remains unclear. To address this issue, endogenous PGP-I was imaged in live cells and in vivo, and the related biochemical and pathological processes were analyzed accordingly with a newly developed fluorogenic PGP-I biosensor. Bioimaging with the specific biosensor demonstrated the aberrant expression of PGP-I in HCC cell lines and tumor-bearing nude mice. Moreover, overexpression of PGP-I in HCC cells promoted tumor progression, whereas knockdown of PGP-I significantly suppressed tumor cell growth and migration. The activity of PGP-I was further identified to be highly related to the phosphorylation of STAT3, which could be impeded by the natural product parthenolide. Collectively, these findings suggest that PGP-I, which can promote hepatocellular tumor progression through the classical inflammation-/tumor-related IL-6/STAT3 pathway, may serve as a potential HCC biomarker and therapeutic target.