CavityPlus 2022 Update: An Integrated Platform for Comprehensive Protein Cavity Detection and Property Analyses with User-friendly Tools and Cavity Databases.

Ligand binding sites provide essential information for uncovering protein functions and structure-based drug discovery. To facilitate cavity detection and property analysis process, we developed a comprehensive web server, CavityPlus in 2018. CavityPlus applies the CAVITY program to detect potential binding sites in a given protein structure. The CavPharmer, CorrSite, and CovCys tools can then be applied to generate receptor-based pharmacophore models, identify potential allosteric sites, or detect druggable cysteine residues for covalent drug design. While CavityPlus has been widely used, the constantly evolving knowledge and methods make it necessary to improve and extend its functions. This study presents a new version of CavityPlus, CavityPlus 2022 through a series of upgrades. We upgraded the CAVITY tool to greatly speed up cavity detection calculation. We optimized the CavPharmer tool for fast speed and more accurate results. We integrated the newly developed CorrSite2.0 into the CavityPlus 2022 web server for its improved performance of allosteric site prediction. We also added a new CavityMatch module for drug repurposing and protein function studies by searching similar cavities to a given cavity from pre-constructed cavity databases. The new version of CavityPlus is freely available at http://pkumdl.cn:8000/cavityplus/.

Anxiety prevalence and its association with physical activity in patients with non-communicable diseases during COVID-19 lockdown: a cross-sectional study in Shanghai, China.

BACKGROUND: Quarantine due to the COVID-19 pandemic may have created great psychological stress among vulnerable populations. We aimed to investigate the prevalence of anxiety and explore the association between physical activities (PA) and anxiety risk in people with non-communicable diseases during the period of COVID-19 lockdown. METHODS: We conducted a cross-sectional telephone survey from February 25 to April 20, 2020, the period of COVID-19 lockdown in Shanghai. Up to 8000 patients with type 2 diabetes and/or hypertension were selected using multi-stage cluster random sampling. PA level was measured based on the International Physical Activity Questionnaire using Metabolic Equivalent for Task scores, while symptoms of anxiety were assessed by the 7-item Generalized Anxiety Disorder scale. Multiple logistic regression analyses were performed to evaluate the associations of type and level of PA with the risk of anxiety. RESULTS: Of a total 4877 eligible patients, 2602 (53.4%) reported with anxiety, and 2463 (50.5%), 123 (2.5%) and 16 (0.3%) reported with mild, moderate, and severe anxiety. The prevalence of anxiety was higher in the females, the elders, non-smokers, non-drinkers, and patients with diabetes, and the associations of anxiety with sex, age, smoking, drinking and diagnosis of diabetes were significant. A significant negative association was observed for housework activities (OR 0.53, 95%CI: [0.45, 0.63], p < 0.001) and trip activities (OR 0.55, 95%CI: [0.48, 0.63], p < 0.001) with anxiety, but no significant was found for exercise activities (OR 1.06, 95%CI: [0.94, 1.20], p = 0.321). Compared with patients with a low PA level, those with a moderate (OR 0.53, 95%CI: [0.44, 0.64], p < 0.001) or a high PA level (OR 0.51, 95%CI: [0.43, 0.51], p < 0.001) had a lower prevalence of anxiety. CONCLUSION: This study demonstrates a higher prevalence of anxiety in patients with hypertension, diabetes, or both during the COVID-19 lockdown. The negative associations of housework and trip activities with anxiety highlight the potential benefit of PA among patients with non-communicable diseases.

A NR2E1-interacting peptide of LSD1 inhibits the proliferation of brain tumour initiating cells.

OBJECTIVES: Elimination of brain tumour initiating cells (BTICs) is important for the good prognosis of malignant brain tumour treatment. To develop a novel strategy targeting BTICs, we studied NR2E1(TLX) involved self-renewal mechanism of BTICs and explored the intervention means. MATERIALS AND METHODS: NR2E1 and its interacting protein-LSD1 in BTICs were studied by gene interference combined with cell growth, tumour sphere formation, co-immunoprecipitation and chromatin immunoprecipitation assays. NR2E1 interacting peptide of LSD1 was identified by Amide Hydrogen/Deuterium Exchange and Mass Spectrometry (HDX-MS) and analysed by in vitro functional assays. The in vivo function of the peptide was examined with intracranial mouse model by transplanting patient-derived BTICs. RESULTS: We found NR2E1 recruits LSD1, a lysine demethylase, to demethylate mono- and di-methylated histone 3 Lys4 (H3K4me/me2) at the Pten promoter and repress its expression, thereby promoting BTIC proliferation. Using Amide Hydrogen/Deuterium Exchange and Mass Spectrometry (HDX-MS) method, we identified four LSD1 peptides that may interact with NR2E1. One of the peptides, LSD1-197-211 that locates at the LSD1 SWIRM domain, strongly inhibited BTIC proliferation by promoting Pten expression through interfering NR2E1 and LSD1 function. Furthermore, overexpression of this peptide in human BTICs can inhibit intracranial tumour formation. CONCLUSION: Peptide LSD1-197-211 can repress BTICs by interfering the synergistic function of NR2E1 and LSD1 and may be a promising lead peptide for brain tumour therapy in future.

Cabazitaxel suppresses colorectal cancer cell growth via enhancing the p53 antitumor pathway.

There were approximately 1.93 million new cases and 940 000 deaths from colorectal cancer in 2020. The first-line chemotherapeutic drugs for colorectal cancer are mainly based on 5-fluorouracil, although the use of these drugs is limited by the development of drug resistance. Consequently, there is a need for novel chemotherapeutic drugs for the efficient treatment of colorectal cancer patients. In the present study, we screened 160 drugs approved by the Food and Drug Administration and identified that cabazitaxel (CBT), a microtube inhibitor, can suppress colony formation and cell migration of colorectal cancer cells in vitro. CBT also induces G2/M phase arrest and apoptosis of colorectal cancer cells. Most importantly, it inhibits the growth of colorectal cancer cell xenograft tumors in vivo. Transcriptome analysis by RNA-sequencing revealed that Tub family genes are abnormally expressed in CBT-treated colorectal cancer cells. The expression of several p53 downstream genes that are associated with cell cycle arrest, apoptosis, and inhibition of angiogenesis and metastasis is induced by CBT in colorectal cancer cells. Overall, our results suggests that CBT suppresses colorectal cancer by upregulating the p53 pathway, and thus CBT may have potential as an alternative chemotherapeutic drug for colorectal cancer.

A combined computational and experimental strategy identifies mutations conferring resistance to drugs targeting the BCR-ABL fusion protein.

Drug resistance is of increasing concern, especially during the treatments of infectious diseases and cancer. To accelerate the drug discovery process in combating issues of drug resistance, here we developed a computational and experimental strategy to predict drug resistance mutations. Using BCR-ABL as a case study, we successfully recaptured the clinically observed mutations that confer resistance imatinib, nilotinib, dasatinib, bosutinib, and ponatinib. We then experimentally tested the predicted mutants in vitro. We found that although all mutants showed weakened binding strength as expected, the binding constants alone were not a good indicator of drug resistance. Instead, the half-maximal inhibitory concentration (IC50) was shown to be a good indicator of the incidence of the predicted mutations, together with change in catalytic efficacy. Our suggested strategy for predicting drug-resistance mutations includes the computational prediction and in vitro selection of mutants with increased IC50 values beyond the drug safety window.

CavityPlus: a web server for protein cavity detection with pharmacophore modelling, allosteric site identification and covalent ligand binding ability prediction.

CavityPlus is a web server that offers protein cavity detection and various functional analyses. Using protein three-dimensional structural information as the input, CavityPlus applies CAVITY to detect potential binding sites on the surface of a given protein structure and rank them based on ligandability and druggability scores. These potential binding sites can be further analysed using three submodules, CavPharmer, CorrSite, and CovCys. CavPharmer uses a receptor-based pharmacophore modelling program, Pocket, to automatically extract pharmacophore features within cavities. CorrSite identifies potential allosteric ligand-binding sites based on motion correlation analyses between cavities. CovCys automatically detects druggable cysteine residues, which is especially useful to identify novel binding sites for designing covalent allosteric ligands. Overall, CavityPlus provides an integrated platform for analysing comprehensive properties of protein binding cavities. Such analyses are useful for many aspects of drug design and discovery, including target selection and identification, virtual screening, de novo drug design, and allosteric and covalent-binding drug design. The CavityPlus web server is freely available at http://repharma.pku.edu.cn/cavityplus or http://www.pkumdl.cn/cavityplus.

Identification of Serine 119 as an Effective Inhibitor Binding Site of M. tuberculosis Ubiquitin-like Protein Ligase PafA Using Purified Proteins and M. smegmatis.

Owing to the spread of multidrug resistance (MDR) and extensive drug resistance (XDR), there is a pressing need to identify potential targets for the development of more-effective anti-M. tuberculosis (Mtb) drugs. PafA, as the sole Prokaryotic Ubiquitin-like Protein ligase in the Pup-proteasome System (PPS) of Mtb, is an attractive drug target. Here, we show that the activity of purified Mtb PafA is significantly inhibited upon the association of AEBSF (4-(2-aminoethyl) benzenesulfonyl fluoride) to PafA residue Serine 119 (S119). Mutation of S119 to amino acids that resemble AEBSF has similar inhibitory effects on the activity of purified Mtb PafA. Structural analysis reveals that although S119 is distant from the PafA catalytic site, it is located at a critical position in the groove where PafA binds the C-terminal region of Pup. Phenotypic studies demonstrate that S119 plays critical roles in the function of Mtb PafA when tested in M. smegmatis. Our study suggests that targeting S119 is a promising direction for developing an inhibitor of M. tuberculosis PafA.

Statistical Analysis and Prediction of Covalent Ligand Targeted Cysteine Residues.

Targeted covalent compounds or drugs have good potency as they can bind to a specific target for a long time with low doses. Most currently known covalent ligands were discovered by chance or by modifying existing noncovalent compounds to make them covalently attached to a nearby reactive residue. Computational methods for novel covalent ligand binding prediction are highly demanded. We performed statistical analysis on protein complexes with covalent ligands attached to cysteine residues. We found that covalent modified cysteine residues have unique features compared to those not attached to covalent ligands, including lower pKa, higher exposure, and higher ligand binding affinity. SVM models were built to predict cysteine residues suitable for covalent ligand design with prediction accuracy of 0.73. Given a protein structure, our method can be used to automatically detect druggable cysteine residues for covalent ligand design, which is especially useful for identifying novel binding sites for covalent allosteric ligand design.

Computational Multitarget Drug Design.

Designing drugs that can simultaneously interact with multiple targets is a promising approach for treating complicated diseases. Compared to using combinations of single target drugs, multitarget drugs have advantages of higher efficacy, improved safety profile, and simpler administration. Many in silico methods have been developed to approach different aspects of this polypharmacology-guided drug design, particularly for drug repurposing and multitarget drug design. In this review, we summarize recent progress in computational multitarget drug design and discuss their advantages and limitations. Perspectives for future drug development will also be discussed.

Cadmium modulates hematopoietic stem and progenitor cells and skews toward myelopoiesis in mice.

The heavy metal cadmium (Cd) is known to modulate immunity and cause osteoporosis. However, how Cd influences on hematopoiesis remain largely unknown. Herein, we show that wild-type C57BL/6 (B6) mice exposed to Cd for 3months had expanded bone marrow (BM) populations of long-term hematopoietic stem cells (LT-HSCs), common myeloid progenitors (CMPs) and granulocyte-macrophage progenitors (GMPs), while having reduced populations of multipotent progenitors (MPPs) and common lymphoid progenitors (CLPs). A competitive mixed BM transplantation assay indicates that BM from Cd-treated mice had impaired LT-HSC ability to differentiate into mature cells. In accordance with increased myeloid progenitors and decreased lymphoid progenitors, the BM and spleens of Cd-treated mice had more monocytes and/or neutrophils and fewer B cells and T cells. Cd impaired the ability of the non-hematopoietic system to support LT-HSCs, in that lethally irradiated Cd-treated recipients transplanted with normal BM cells had reduced LT-HSCs after the hematopoietic system was fully reconstituted. This is consistent with reduced osteoblasts, a known critical component for HSC niche, observed in Cd-treated mice. Conversely, lethally irradiated control recipients transplanted with BM cells from Cd-treated mice had normal LT-HSC reconstitution. Furthermore, both control mice and Cd-treated mice that received Alendronate, a clinical drug used for treating osteoporosis, had BM increases of LT-HSCs. Thus, the results suggest Cd increase of LT-HSCs is due to effects on HSCs and not on osteoblasts, although, Cd causes osteoblast reduction and impaired niche function for maintaining HSCs. Furthermore, Cd skews HSCs toward myelopoiesis.

Mutation-induced protein interaction kinetics changes affect apoptotic network dynamic properties and facilitate oncogenesis.

It has been a consensus in cancer research that cancer is a disease caused primarily by genomic alterations, especially somatic mutations. However, the mechanism of mutation-induced oncogenesis is not fully understood. Here, we used the mitochondrial apoptotic pathway as a case study and performed a systematic analysis of integrating pathway dynamics with protein interaction kinetics to quantitatively investigate the causal molecular mechanism of mutation-induced oncogenesis. A mathematical model of the regulatory network was constructed to establish the functional role of dynamic bifurcation in the apoptotic process. The oncogenic mutation enrichment of each of the protein functional domains involved was found strongly correlated with the parameter sensitivity of the bifurcation point. We further dissected the causal mechanism underlying this correlation by evaluating the mutational influence on protein interaction kinetics using molecular dynamics simulation. We analyzed 29 matched mutant-wild-type and 16 matched SNP--wild-type protein systems. We found that the binding kinetics changes reflected by the changes of free energy changes induced by protein interaction mutations, which induce variations in the sensitive parameters of the bifurcation point, were a major cause of apoptosis pathway dysfunction, and mutations involved in sensitive interaction domains show high oncogenic potential. Our analysis provided a molecular basis for connecting protein mutations, protein interaction kinetics, network dynamics properties, and physiological function of a regulatory network. These insights provide a framework for coupling mutation genotype to tumorigenesis phenotype and help elucidate the logic of cancer initiation.

A comprehensive analysis of the influence of drug binding kinetics on drug action at molecular and systems levels.

Binding kinetics is closely related to the efficacy of drugs. Several aspects of binding kinetics, such as long residence or frequent dissociation, have been proposed to affect drug properties such as efficacy, selectivity, and multi-target potency. However, a comprehensive and balanced study of binding kinetics in various scenarios is still needed. We performed a comprehensive computational analysis of the role of drug binding kinetics in various situations such as enzyme inhibition, receptor binding, multi-target drug targeting, signal transduction pathways, and metabolic networks. Molecular studies of enzyme inhibition, receptor binding, and multi-target drugs have shown that at constant binding affinity, fast associating drugs show better enzyme inhibitory effects, earlier and higher receptor occupancy peaks, and better multi-target performances, while slow dissociating drugs show prolonged receptor occupancy, as suggested by others. Different situations exemplify slightly different kinetic-efficacy relationships, and each must be considered separately. At the systems level, binding kinetics can not only change the overall effect of drugs, but can also affect signaling dynamics. For example, in the tumor necrosis factor α-induced nuclear factor-κB pathway, inhibitor addition can delay the onset of oscillations and decrease their frequencies, with these changes varying with the binding kinetics of the inhibitor. The effects of drug binding kinetics also depend on network topology and where the target is located in the network. For successful drug discovery, both molecular binding kinetics and systems level requirements need to be considered.

14-3-3ζ interacts with stat3 and regulates its constitutive activation in multiple myeloma cells.

The 14-3-3 proteins are a family of regulatory signaling molecules that interact with other proteins in a phosphorylation-dependent manner and function as adapter or scaffold proteins in signal transduction pathways. One family member, 14-3-3ζ, is believed to function in cell signaling, cycle control, and apoptotic death. A systematic proteomic analysis done in our laboratory has identified signal transducers and activators of transcription 3 (Stat3) as a novel 14-3-3ζ interacting protein. Following our initial finding, in this study, we provide evidence that 14-3-3ζ interacts physically with Stat3. We further demonstrate that phosphorylation of Stat3 at Ser727 is vital for 14-3-3ζ interaction and mutation of Ser727 to Alanine abolished 14-3-3ζ/Stat3 association. Inhibition of 14-3-3ζ protein expression in U266 cells inhibited Stat3 Ser727 phosphorylation and nuclear translocation, and decreased both Stat3 DNA binding and transcriptional activity. Moreover, 14-3-3ζ is involved in the regulation of protein kinase C (PKC) activity and 14-3-3ζ binding to Stat3 protects Ser727 dephosphorylation from protein phosphatase 2A (PP2A). Taken together, our findings support the model that multiple signaling events impinge on Stat3 and that 14-3-3ζ serves as an essential coordinator for different pathways to regulate Stat3 activation and function in MM cells.

Quaternary structure, substrate selectivity and inhibitor design for SARS 3C-like proteinase.

The SARS coronavirus 3C-like proteinase is recognized as a potential drug design target for the treatment of severe acute respiratory syndrome. In the past few years, much work has been done to understand the catalytic mechanism of this target protein and to design its selective inhibitors. The protein exists as a dimer/monomer mixture in solution and the dimer was confirmed to be the active species for the enzyme reaction. Quantitative dissociation constants have been reported for the dimer by using analytic ultracentrifuge, gel filtration and enzyme assays. Though the enzyme is a cysteine protease with a chymotrypsin fold, SARS 3C-like proteinase follows the general base catalytic mechanism similar to chymotrypsin. As the enzyme can cut eleven different sites on the viral polyprotein, the substrate specificity has been studied by synthesized peptides corresponding or similar to the cleavage sites on the polyprotein. Predictive model was built for substrate structure and activity relationships and can be applied in inhibitor design. Due to the lack of potential drugs for the treatment of SARS, the discovery of inhibitors against SARS 3C-like proteinase, which can potentially be optimized as drugs appears to be highly desirable. Various groups have been working on inhibitor discovery by virtual screening, compound library screening, modification of existing compounds or natural products. High-throughput in vitro assays, auto-cleavage assays and viral replication assays have been developed for inhibition activity tests. Inhibitors with IC50 values as low as 60 nM have been reported.

Isatin compounds as noncovalent SARS coronavirus 3C-like protease inhibitors.

A series of isatin derivatives were synthesized and tested against SARS CoV 3C-like protease. Substitutions at the N-1 and C-5 positions were examined to elucidate the differences in substrate binding sites of the rhinovirus 3C protease and SARS CoV 3C-like protease. Compound 5f shows significant inhibition with an IC(50) of 0.37 microM. Further study showed that, unlike the irreversible covalent binding of isatin derivatives to human rhinovirus 3C protease, the compounds tested in this study are all noncovalent reversible inhibitors.

3D-QSAR studies on tripeptide aldehyde inhibitors of proteasome using CoMFA and CoMSIA methods.

The ubiquitin-proteasome pathway plays a crucial role in the regulation of many physiological processes and in the development of a number of major human diseases, such as cancer, Alzheimer's, Parkinson's, diabetes, etc. As a new target, the study on the proteasome inhibitors has received much attention recently. Three-dimensional quantitative structure-activity relationship (3D-QSAR) studies using comparative molecule field analysis (CoMFA) and comparative molecule similarity indices analysis (CoMSIA) techniques were applied to analyze the binding affinity of a set of tripeptide aldehyde inhibitors of 20S proteasome. The optimal CoMFA and CoMSIA models obtained for the training set were all statistically significant with cross-validated coefficients (q(2)) of 0.615, 0.591 and conventional coefficients (r(2)) of 0.901, 0.894, respectively. These models were validated by a test set of eight molecules that were not included in the training set. The predicted correlation coefficients (r(2)) of CoMFA and CoMSIA are 0.944 and 0.861, respectively. The CoMFA and CoMSIA field contour maps agree well with the structural characteristics of the binding pocket of beta5 subunit of 20S proteasome, which suggests that the 3D-QSAR models built in this paper can be used to guide the development of novel inhibitors of 20S proteasome.

Virtual screening of novel noncovalent inhibitors for SARS-CoV 3C-like proteinase.

The SARS coronavirus 3C-like proteinase is considered as a potential drug design target for the treatment of severe acute respiratory syndrome (SARS). Owing to the lack of available drugs for the treatment of SARS, the discovery of inhibitors for SARS coronavirus 3C-like proteinase that can potentially be optimized as drugs appears to be highly desirable. We have built a "flexible" three-dimensional model for SARS 3C-like proteinase by homology modeling and multicanonical molecular dynamics method and used the model for virtual screening of chemical databases. After Dock procedures, strategies including pharmocophore model, consensus scoring, and "drug-like" filters were applied in order to accelerate the process and improve the success rate of virtual docking screening hit lists. Forty compounds were purchased and tested by HPLC and colorimetric assay against SARS 3C-like proteinase. Three of them including calmidazolium, a well-known antagonist of calmodulin, were found to inhibit the enzyme with an apparent K(i) from 61 to 178 microM. These active compounds and their binding modes provide useful information for understanding the binding sites and for further selective drug design against SARS and other coronavirus.

Estimating protein-ligand binding free energy: atomic solvation parameters for partition coefficient and solvation free energy calculation.

Solvation energy calculation is one of the main difficulties for the estimation of protein-ligand binding free energy and the correct scoring in docking studies. We have developed a new solvation energy estimation method for protein-ligand binding based on atomic solvation parameter (ASP), which has been shown to improve the power of protein-ligand binding free energy predictions. The ASP set, designed to handle both proteins and organic compounds and derived from experimental n-octanol/water partition coefficient (log P) data, contains 100 atom types (united model that treats hydrogen atoms implicitly) or 119 atom types (all-atom model that treats hydrogen atoms explicitly). By using this unified ASP set, an algorithm was developed for solvation energy calculation and was further integrated into a score function for predicting protein-ligand binding affinity. The score function reproduced the absolute binding free energies of a test set of 50 protein-ligand complexes with a standard error of 8.31 kJ/mol. As a byproduct, a conformation-dependent log P calculation algorithm named ASPLOGP was also implemented. The predictive results of ASPLOGP for a test set of 138 compounds were r = 0.968, s = 0.344 for the all-atom model and r = 0.962, s = 0.367 for the united model, which were better than previous conformation-dependent approaches and comparable to fragmental and atom-based methods. ASPLOGP also gave good predictive results for small peptides. The score function based on the ASP model can be applied widely in protein-ligand interaction studies and structure-based drug design.

Biosynthesis, purification, and substrate specificity of severe acute respiratory syndrome coronavirus 3C-like proteinase.

The 3C-like proteinase of severe acute respiratory syndrome (SARS) coronavirus has been proposed to be a key target for structural-based drug design against SARS. In order to understand the active form and the substrate specificity of the enzyme, we have cloned, expressed, and purified SARS 3C-like proteinase. Analytic gel filtration shows a mixture of monomer and dimer at a protein concentration of 4 mg/ml and mostly monomer at 0.2 mg/ml, which correspond to the concentration used in the enzyme assays. The linear decrease of the enzymatic-specific activity with the decrease of enzyme concentration revealed that only the dimeric form is active and the dimeric interface could be targeted for structural-based drug design against SARS 3C-like proteinase. By using a high pressure liquid chromatography assay, SARS 3C-like proteinase was shown to cut the 11 peptides covering all of the 11 cleavage sites on the viral polyprotein with different efficiency. The two peptides corresponding to the two self-cleavage sites are the two with highest cleavage efficiency, whereas peptides with non-canonical residues at P2 or P1' positions react slower. The P2 position of the substrates seems to favor large hydrophobic residues. Secondary structure studies for the peptide substrates revealed that substrates with more beta-sheetlike structure tend to react fast. This study provides a basic understanding of the enzyme catalysis and a full substrate specificity spectrum for SARS 3C-like proteinase, which are helpful for structural-based inhibitor design against SARS and other coronavirus.

[Modeling of the SARS coronavirus main proteinase and conformational flexibility of the active site].

SARS coronavirus 3CL proteinase is the key enzyme for virus replication which may serve as the target for drug discovery against SARS. A 3D structure model has been built for SARS coronavirus 3CL proteinase by comparative protein modeling. A homodimer model of the proteinase was also built. Analysis of the dimeric interface suggests the 3CL proteinase may have dimer form in solution. The conformational flexibility of the active site has been simulated by molecular dynamics combined with multi-canonical sampling. The active site loops have two typical conformations which may be related to the conformational movement associated with the enzymatic reaction.