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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. [ABSTRACT FROM AUTHOR]

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/. [ABSTRACT FROM AUTHOR]

Several secondary tropomyosin receptor kinase (TRK) mutations located in the solvent front, xDFG, and gatekeeper regions, are a common cause of clinical resistance. Mutations in the xDFG motif in particular limit sensitivity to second-generation TRK inhibitors, which represent an unmet clinical need. We designed a series of 3-pyrazolyl-substituted pyrazolo[1,5- a ]pyrimidine derivatives toward these secondary mutations using ring-opening and scaffold-hopping strategies. Compound 5n was the most potent, with IC 50 values of 2.3 nM, 0.4 nM, and 0.5 nM against TRKAG667C, TRKAF589L, and TRKAG595R, compared to selitrectinib with IC 50 values of 12.6 nM, 5.8 nM, and 7.6 nM, respectively (approximately 5.4, 14.5, and 15.2-fold increases). Furthermore, 5n displayed favorable pharmacokinetic properties and satisfactory antitumor efficacy (tumor growth inhibition of 97% at 30 mg/kg and 73% at 100 mg/kg) in TRKAWT and TRKAG667C xenograft mouse models. Collectively, 5n is a promising TRK inhibitor lead compound for overcoming clinically acquired resistance to second-generation inhibitors, particularly for resistant tumors harboring the TRKAG667C mutation in the xDFG motif. [Display omitted] • TRKA G667C mutation still limit sensitivity to the second-generation inhibitors. • A novel inhibitor 5n was rationally designed to overcome the TRKA G667C mutation. • Compound 5n displayed favorable pharmacokinetic profile and antitumor efficacy. [ABSTRACT FROM AUTHOR]

As a privileged scaffold, the quinazoline ring is widely used in the development of EGFR inhibitors, while few quinazoline-based MET inhibitors are reported. In our ongoing efforts to develop new MET-targeted anticancer drug candidates, a series of quinazoline-based 1,6-naphthyridinone derivatives were designed, synthesized, and evaluated for their biological activities. The preliminary SARs studies indicate that the quinazoline scaffold was also acceptable for the block A of class II MET inhibitors. The further pharmacokinetic studies led to the identification of the most promising compound 22a with favorable in vitro potency (MET, IC 50 = 9.0 nM), human microsomal metabolic stability (t 1/2 = 621.2 min) and oral bioavailability ( F = 42%). Moreover, 22a displayed good in vivo antitumor efficacy (IR of 81% in 75 mg/kg) in MET-positive human glioblastoma U-87 MG xenograft model. These positive results indicated that 22a is a potential new MET-targeted antitumor drug lead, which is worthy of further development. Image 1 • Quinazoline-based 1,6-naphthyridinones was identified as new MET inhibitor. • Lead compound 22a displayed favorable potency, PK profile and antitumor efficacy. • The quinazoline was a valuable scaffold for the discovery of novel MET inhibitors. [ABSTRACT FROM AUTHOR]

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/. [ABSTRACT FROM AUTHOR]

Drug repurposing offers a promising alternative to dramatically shorten the process of traditional de novo development of a drug. These efforts leverage the fact that a single molecule can act on multiple targets and could be beneficial to indications where the additional targets are relevant. Hence, extensive research efforts have been directed toward developing drug based computational approaches. However, many drug based approaches are known to incur low successful rates, due to incomplete modeling of drug-target interactions. There are also many technical limitations to transform theoretical computational models into practical use. Drug based approaches may, thus, still face challenges for drug repurposing task. Upon this challenge, we developed a consensus inverse docking (CID) workflow, which has a ~ 10% enhancement in success rate compared with current best method. Besides, an easily accessible web server named auto in silico consensus inverse docking (ACID) was designed based on this workflow (http://chemyang.ccnu.edu.cn/ccb/server/ACID). [ABSTRACT FROM AUTHOR]

Abstract Involved in the tyrosine degradation pathway, 4-hydroxyphenylpyruvate dioxygenase (HPPD) is an important target for treating type I tyrosinemia. To discover novel HPPD inhibitors, we proposed a hydrophobicity-oriented drug design (HODD) strategy based on the interactions between HPPD and the commercial drug NTBC. Most of the new compounds showed improved activity, compound d23 being the most active candidate (IC 50 = 0.047 μM) with about 2-fold more potent than NTBC (IC 50 = 0.085 μM). Therefore, compound d23 is a potential drug candidate to treat type I tyrosinemia. Graphical abstract Image 1 Highlights • A new strategy of hydrophobicity-oriented drug design (HODD) was developed for the design of HPPD inhibitors. • A series of 2-(3-(benzyloxy)benzoyl)-3-hydroxycyclohex-2-en-1-one analogs with new chemical scaffold were prepared and most of them showed good activity against recombinant human HPPD. • Compound d23 was identified as the most potent candidate (IC 50 = 47 nM), about 2-fold more potent than NTBC (IC 50 = 85 nM), demonstrating good potential for the treatment of type I tyrosinemia, alkaptunuria and hawkinsinuria. [ABSTRACT FROM AUTHOR]

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 R 2 = 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/. [ABSTRACT FROM AUTHOR]

Carboxypeptidase Y (CPY) is a vital enzyme with many well-known biological significances, such as determination of amino acid sequences and biosynthesis of polypeptides. Herein we developed a nonpeptide-based fluorescent probe to sense CPY activity accurately and successfully applied it to the inhibitory study. Based on the organic synthesis and comprehensive characterization, the refined probe CPY-P3 displayed excellent specificity towards CPY and exhibited over 400-fold enhancement of fluorescence intensity upon the hydrolysis of CPY. Compared with the commonly used peptide-based fluorescent substrate ( Suc-IIW-AMC ), CPY-P3 showed about 12-fold higher binding affinity and comparable catalytical efficiency for CPY. Moreover, CPY-P3 was successfully applied in CPY inhibitor characterization. To our knowledge, CPY-P3 is the first nonpeptide-based small-molecule fluorescent probe for the time-course CPY activity detection. Meanwhile, the generation of kelly color makes the detection directly visible without any complicated device. In all, this small-molecule probe should be a potentially useful tool in understanding the roles of CPY in protein sequencing and engineering, as well as the drug discovery of CPY-related diseases. [ABSTRACT FROM AUTHOR]