Optimal Template Research Articles

Modeling and affinity maturation of an anti-CD20 nanobody: a comprehensive in-silico investigation

B-cell Non-Hodgkin lymphomas are the malignancies of lymphocytes. CD20 is a membrane protein, which is highly expressed on the cell surface of the B-cells in NHL. Treatments using monoclonal antibodies (mAbs) have resulted in failure in some cases. Nanobodies (NBs), single-domain antibodies with low molecular weights and a high specificity in antigen recognition, could be practical alternatives for traditional mAbs with superior characteristics. To design an optimized NB as a candidate CD20 inhibitor with raised binding affinity to CD20, the structure of anti-CD20 NB was optimized to selectively target CD20. The 3D structure of the NB was constructed based on the optimal templates (6C5W and 5JQH), and the key residues were determined by applying a molecular docking study. After identifying the key residues, some mutations were introduced using a rational protocol to improve the binding affinity of the NB to CD20. The rational mutations were conducted using the experimental design (Taguchi method). Six residues (Ser27, Thr28, Phe29, Ile31, Asp99, and Asn100) were selected as the key residues, and five residues were targeted for rational mutation (Trp, Phe, His, Asp, and Tyr). Based on the mutations suggested by the experimental design, two optimized NB structures were constructed. NB2 showed a remarkable binding affinity to CD20 in docking studies with a binding energy of − 853 kcal/mol. The optimized NB was further evaluated using molecular dynamics simulation. The results revealed that CDR1 (complementarity determining regions1) and CDR3 are essential loops for recognizing the antigen. NB2 could be considered as a potential inhibitor of CD20, though experimental evaluations are needed to confirm it.

Feasibility-guided automated planning for stereotactic treatments of prostate cancer.

Significant improvements in plan quality using automated planning have been previously demonstrated. The aim of this study was to develop an optimal automated class solution for stereotactic radiotherapy (SBRT) planning of prostate cancer using the new Feasibility module implemented in the pinnacle evolution. Twelve patients were retrospectively enrolled in this planning study. Five plans were designed for each patient. Four plans were automatically generated using the 4 proposed templates for SBRT optimization implemented in the new pinnacle evolution treatment planning systems, differing for different settings of dose-fallout (low, medium, high and veryhigh). Based on the obtained results, the fifth plan (feas) was generated customizing the template with the optimal criteria obtained from the previous step and integrating in the template the "a-priori" knowledge of OARs sparing based on the Feasibility module, able to estimate the best possible dose-volume histograms of OARs before starting optimization. Prescribed dose was 35 Gy to the prostate in 5 fractions. All plans were generated with a full volumetric-modulated arc therapy arc and 6MV flattening filter-free beams, and optimized to ensure the same target coverage (95% of the prescription dose to 98% of the target). Plans were assessed according to dosimetric parameters and planning and delivery efficiency. Differences among the plans were evaluated using a Kruskal-Wallis 1-way analysis of variance. The requests for more aggressive objectives for dose falloff parameters (from low to veryhigh) translated in a statistically significant improvement of dose conformity, but at the expense of a dose homogeneity. The best automated plans in terms of best trade-off between target coverage and OARs sparing among the 4 plans automatically generated by the SBRT module were the high plans. The veryhigh plans reported a significant increase of high-doses to prostate, rectum, and bladder that was considered dosimetrically and clinically unacceptable. The feas plans were optimized on the basis on high plans, reporting significant reduction of rectum irradiation; Dmean, and V18 decreased by 19% to 23% (p = 0.031) and 4% to 7% (p = 0.059), respectively. No statistically significant differences were found in femoral heads and penile bulb irradiation for all dosimetric metrics. feas plans showed a significant increase of MU/Gy (mean: 368; p = 0.004), reflecting an increased level of fluence modulation. Thanks to the new efficient optimization engines implemented in pinnacle evolution (L-BFGS and layered graph), mean planning time was decreased to less than 10 minutes for all plans and all techniques. The integration of dose-volume histograms a-priori knowledge provided by the feasibility module in the automated planning process for SBRT planning has shown to significantly improve plan quality compared to generic protocol values as inputs.

How Analyst Attention Affects Inefficient Investment of Enterprises: The Mediating Role of Financial Constraints

This paper uses the data of Shanghai and Shenzhen A-share companies from the CSMAR database. The data range is from 2009 to 2019, studies the relationship between investment efficiency and financing constraints, and expounds on some of the problems faced by small and medium-sized enterprises today. The research uses a model to measure the optimal investment scale of the enterprise in the current period and uses the actual investment scale to gradually approach the optimal investment template to obtain the inefficient investment level of the enterprise. This paper studies the relationship between investment efficiency and financing constraints from an analyst's perspective. Research has found that analyst attention significantly impacts firms' inefficient investments. Analyst Attention and Report Attention can effectively alleviate corporate financing constraints and improve corporate investment efficiency, which is found that analyst attention and report attention have a more significant impact on small and medium-sized enterprises than on large enterprises.

Machine learning and hypothesis driven optimization of bull semen cryopreservation media

Cryopreservation provides a critical tool for dairy herd genetics management. Due to widely varying inter- and within-bull post thaw fertility, recent research on cryoprotectant extender medium has not dramatically improved suboptimal post-thaw recovery in industry. This progress is stymied by the interactions between samples and the many components of extender media and is often compounded by industry irrelevant sample sizes. To address these challenges, here we demonstrate blank-slate optimization of bull sperm cryopreservation media by supervised machine learning. We considered two supervised learning models: artificial neural networks and Gaussian process regression (GPR). Eleven media components and initial concentrations were identified from publications in bull semen cryopreservation, and an initial 200 extender-post-thaw motility pairs were used to train and 32 extender-post-thaw motility pairs to test the machine learning algorithms. The median post-thaw motility after coupling differential evolution with GPR the increased from 52.6 ± 6.9% to 68.3 ± 6.0% at generations 7 and 17 respectively, with several media performing dramatically better than control media counterparts. This is the first study in which machine learning was used to determine the best combination of constituents to optimize bull sperm cryopreservation media, and provides a template for optimization in other cell types.

Deep Cascade AdaBoost with Unsupervised Clustering in Autonomous Vehicles

In recent years, deep learning has achieved excellent performance in a growing number of application fields. With the help of high computation and large-scale datasets, deep learning models with huge parameters constantly enhance the performance of traditional algorithms. Additionally, the AdaBoost algorithm, as one of the traditional machine learning algorithms, has a minimal model and performs well on small datasets. However, it is still challenging to select the optimal classification feature template from a large pool of features in any scene quickly and efficiently. Especially in the field of autonomous vehicles, images taken by onboard cameras contain all kinds of targets on the road, which means the images are full of multiple features. In this paper, we propose a novel Deep Cascade AdaBoost model, which effectively combines the unsupervised clustering algorithm based on deep learning and the traditional AdaBoost algorithm. First, we use the unsupervised clustering algorithm to classify the sample data automatically. We can obtain classification subsets with small intra-class and large inter-class errors by specifying positive and negative samples. Next, we design a training framework for Cascade-AdaBoost based on clustering and mathematically demonstrate that our framework has better detection performance than the traditional Cascade-AdaBoost framework. Finally, experiments on the KITTI dataset demonstrate that our model performs better than the traditional Cascade-AdaBoost algorithm in terms of accuracy and time. The detection time was shortened by 30%, and the false detection rate was reduced by 20%. Meanwhile, the training time of our model is significantly shorter than the traditional Cascade-AdaBoost algorithm.

Defining the optimal template of salvage lymph node dissection for unilateral pelvic nodal recurrence of prostate cancer following radical prostatectomy.

Several retrospective studies have shown that salvage bilateral pelvic lymph node dissection (sLND) is a valid treatment option in the setting of oligorecurrent nodal prostate cancer following radical prostatectomy. Little is known about the optimal template of such sLND in patients with strictly unilateral pelvic recurrence on PET-CT imaging. In this study, we investigated whether a unilateral pelvic sLND could be sufficient in such a setting. We retrospectively collected data of patients treated with sLND between 2010 and 2019 at the University Hospitals, Leuven. Patients were included if they developed recurrence following radical prostatectomy, characterized by ≤3 unilateral pelvic lymph node metastases on Choline or PSMA PET-CT and received a super-extended bilateral pelvic sLND as first metastasis-directed therapy. As a primary endpoint, we investigated in how many cases a unilateral sLND would have been sufficient. In total, 44 patients with strictly unilateral pelvic recurrence were treated with super-extended bilateral pelvic sLND. In 5 out of 44 (11%) patients, histological examination showed presence of prostate cancer in the contralateral hemi-pelvis. In the group with a single positive node on imaging prior to sLND, only 1 out of 27 (3%) patients had contralateral disease at final pathology. No one (0%) in this group subsequently developed recurrence in the contralateral hemi-pelvis following sLND. In conclusion, this study suggests that unilateral pelvic sLND could be sufficient in patients with a single unilateral pelvic lymph node recurrence on PET/CT imaging.

Design, Synthesis and Biological Evaluation of Novel 1, 3, 4-Oxadiazole PD176252 Analogues as Potential GRPR Inhibitors

GRPR is over-expressed in cancer cells and is a potential drug target for the treatment of cancer. PD176252, as the most representative non-peptide inhibitor of GRPR, can inhibit the growth of cancer cells, but its low selectivity to cancer cells and normal cells limits its further application. The aim of this study was to design and synthesize novel GRPR inhibitor with stronger anti-cancer activity and higher affinity with GRPR than the lead compound PD176252. A series of 1, 3, 4-oxadiazole derivatives as PD176252 analogues (4a-4j, 6a-6q) were synthesized and investigated their cytotoxic activity on four cancer lines with high expression of GRPR (gastric (HGC-27), colon (HCT-116), prostate (PC-3), and lung (A549)) and one human cell line (gastric mucosal epithelial (GES-1)) by MTT assay. Flow cytometry analysis and Western Blot were used to determine whether the compound induced programmed apoptosis of cancer cells. Competitive binding experiment was used to verify the affinity between GRPR and the optimal compound. Compound 6m exhibited significant growth inhibition on all tested cancer cell lines, especially gastric cancer cells (HGC-27 cellular IC50 0.37±0.04μM). Also, the selectivity of 6m to HGC-27 was much higher than that of PD176252. Flow cytometric analysis and Western Blot proved that 6m significantly promoted the apoptosis of HGC-27 cells. Moreover, competitive binding experiment confirmed the close binding of 6m with GRPR, which indicated 6m with a higher affinity than lead compound PD176252. Our results suggested that 6m, as a novel GRPR inhibitor, had a higher affinity with GRPR and potential anti-cancer effect than PD176252, which can be used as a template for further optimization.

Virtual Screening Identifies Novel and Potent Inhibitors of Mycobacterium tuberculosis PknB with Antibacterial Activity.

Mycobacterium tuberculosis protein kinase B (PknB) is essential to mycobacterial growth and has received considerable attention as an attractive target for novel anti-tuberculosis drug development. Here, virtual screening, validated by biological assays, was applied to select candidate inhibitors of M. tuberculosis PknB from the Specs compound library (www.specs.net). Fifteen compounds were identified as hits and selected for in vitro biological assays, of which three indoles (2, AE-848/42799159; 4, AH-262/34335013; 10, AP-124/40904362) inhibited growth of M. tuberculosis H37Rv with minimal inhibitory concentrations of 6.2, 12.5, and 6.2 μg/mL, respectively. Two compounds, 2 and 10, inhibited M. tuberculosis PknB activity in vitro, with IC50 values of 14.4 and 12.1 μM, respectively, suggesting this to be the likely basis of their anti-tubercular activity. In contrast, compound 4 displayed anti-tuberculosis activity against M. tuberculosis H37Rv but showed no inhibition of PknB activity (IC50 > 128 μM). We hypothesize that hydrolysis of its ethyl ester to a carboxylate moiety generates an active species that inhibits other M. tuberculosis enzymes. Molecular dynamics simulations of modeled complexes of compounds 2, 4, and 10 bound to M. tuberculosis PknB indicated that compound 4 has a lower affinity for M. tuberculosis PknB than compounds 2 and 10, as evidenced by higher calculated binding free energies, consistent with experiment. Compounds 2 and 10 therefore represent candidate inhibitors of M. tuberculosis PknB that provide attractive starting templates for optimization as anti-tubercular agents.

A Search for New Mesoporous Carbon Solids with Cubic Ordered Porosity

A three-dimensional material comprised exclusively of carbon whose underlying structure lies on a triply periodic minimal surface (TPMS) is a member of the class of hypothetical carbon allotropes known as schwarzites. Such materials are highly sought-after due to their fundamental significance (the last remaining unknown crystalline allotrope of sp2-hybridized carbon) and likely interesting properties (e.g., ballistic conduction at room temperature). Other properties include a large pore volume and high surface area for gas and ion adsorption, making them a class of candidate materials for many applications such as supercapacitor electrodes and gas storage. A true schwarzite has never been synthesized, despite their predicted low energies of formation compared to fullerenes.This research focuses on the synthesis of an interpenetrating schwarzite-like material. A hard-templating strategy has been designed to achieve this through the synthesis of a free-standing graphene foam material whose structure lies on a cubic TPMS. The specific material of interest herein requires an ordered mesoporous silicate template that carries the Ia-3d space group with an optimal pore to wall ratio. Pore and wall structure as well as composition for these materials can be optimized by changing the hydrothermal synthesis temperature, the addition of different precursors, and by varying the cationic and neutral structure-directing agents. We present our recent progress in this effort and also propose future work to achieve optimal templates for schwarzite-like materials synthesis. Figure 1

Discovery of novel PRMT5 inhibitors bearing a methylpiperazinyl moiety.

Background: PRMT5 is an epigenetics-related enzyme, which plays a critical role in cancer development. Hence PRMT5 inhibition has been validated as a promising therapeutic strategy. Methods & Results: We synthesized a series of methylpiperazinyl derivatives as novel PRMT5 inhibitors that were achieved by scaffold-hopping from EPZ015666 by virtual screening followed by rational drug design. Among all compounds 43g, bearing a thiourea linker, showed antitumor activity across multiple cancer cell lines and reduced the level of symmetric arginine dimethylation of SmD3 dose-dependently. Moreover, 43g selectively inhibited PRMT5 among protein arginine methyltransferase isoforms. Further proteomics analysis revealed that 43g remarkably reduced the global arginine dimethylation level in a cellular context. Conclusion: This work provides new chemical templates for future structural optimization of PRMT5-related cancer treatments.

Nanogel-mediated drug delivery system for anticancer agent: pH stimuli responsive poly(ethylene glycol/acrylic acid) nanogel prepared by gamma irradiation

The popularity of nanogel as nano drug carrier lies in its adjustable physical properties, and the ability to encapsulate drug particles with improved properties is being developed to meet the diverse pH-sensitive nanogel for anticancer agent. Monitoring pH has been identified as an important diagnostic element during the treatment process. A pH-sensitive nanogel consisting of (PEG/PMAc) in the ratio of (50:50%) hasbeen cross-linkedby γ-irradiation techniques at an irradiation dose of 5 kGy. Compound 4 and its nanogel 5 were synthesized and assessed for their anticancer effects against HepG2, A549, MCF-7 and HCT-116 as dual VEGFR-2 and EGFR tyrosine kinases inhibitors. The molecular design was performed to investigate the binding mode of compound 4 with VEGFR-2 and EGFR receptors. Our compound 5 in nanogel showed enhanced anticancer activities against the four tested cancer cell lines and also showed higher inhibition activities against VEGFR-2 and EGFRT790M kinases than the derivative 4. Finally, our derivative 4 showed good in silico calculated ADMET profile. It was expected to show good GIT absorption in human, lower CNS side effects, no hepatotoxic actions and higher acute and oral chronic toxic doses in comparing to sorafenib and erlotinib. The obtained results showed that, our compound could be useful as a template for future design, optimization, adaptation and investigation to produce more potent and selective dual VEGFR-2/EGFRT790M inhibitors with higher anticancer activity.

Template Construction of Radar Target Recognition based on Maximum Information Profile

High resolution range profile (HRRP) has the advantages of low complexity in imaging and processing. These features make HRRP widely adopted in radar automatic target recognition technology. Template matching is a representative technique for radar target recognition using HRRP. To overcome the attitude sensitivity problem of HRRP data, it is necessary to subdivide the training database according to attitude domain, and construct corresponding templates for matching. However, templates from traditional methods usually cause the loss of details due to the smoothing process, with limited classification performance. This paper is motivated by proposing a template generation approach based on maximum information profile, which retains the detailed information in templates for accuracy optimization. The proposed method is verified by the recognition experiments of three types of aircraft targets. The experimental results indicate that the maximum information profile outperform the mean range profile contributed by the angle domain template.

Update of the Diagnostic and Therapeutic Role of the Pelvic Lymph Node Dissection Boundaries During Radical Cystectomy in Muscle Invasive Bladder Cancer

Radical cystectomy (RC) is the gold standard treatment option for muscle invasive bladder cancer (MIBC). However, up to 25% of patients who undergo RC show metastatic lymph node deposits during the procedure. In such cases, the 5-year survival rate is reported to be 25%–30%. Pelvic lymph node dissection (PLND) can also provide useful prognostic information, including data regarding the disease burden, lymph node density, and extracapsular extension of metastatic lymph nodes. Accordingly, the National Comprehensive Cancer Network guidelines recommend that PLND that includes the common iliac lymph node should be performed at the time of RC to allow reliable staging of MIBC. In addition to its diagnostic role, many studies have reported the potential therapeutic role of PLND. Data from clinical trials indicate a substantial oncological advantage in PLND cohorts compared to non-PLND cohorts, regardless of pathological nodal status, as a result of removal of metastatic and micrometastatic tumor cells nested in lymph nodes. As such, despite the diagnostic and therapeutic role of PLND in MIBC, the optimal PLND template remains controversial. Currently, extended PLND (E-PLND) is recommended for diagnostic purposes, however, E-PLND did not show therapeutic effectiveness in some recent preliminary randomized controlled trials. In this review, we will discuss the appropriate range of PLND for RC in terms of its diagnostic and therapeutic importance, and propose an appropriate range of PLNDs based on the evidence and randomized trials so far.

Heterogeneity of the GFP fitness landscape and data-driven protein design.

Studies of protein fitness landscapes reveal biophysical constraints guiding protein evolution and empower prediction of functional proteins. However, generalisation of these findings is limited due to scarceness of systematic data on fitness landscapes of proteins with a defined evolutionary relationship. We characterized the fitness peaks of four orthologous fluorescent proteins with a broad range of sequence divergence. While two of the four studied fitness peaks were sharp, the other two were considerably flatter, being almost entirely free of epistatic interactions. Mutationally robust proteins, characterized by a flat fitness peak, were not optimal templates for machine-learning-driven protein design - instead, predictions were more accurate for fragile proteins with epistatic landscapes. Our work paves insights for practical application of fitness landscape heterogeneity in protein engineering.

How many cores are enough? Optimizing the transperineal prostate biopsy template

Most urologists use a 10-12 core template during transrectal ultrasound guided prostate biopsy (TRUS-B). A similar consensus template does not exist for transperineal prostate biopsy (TP-B) including the optimal number and location of biopsy cores. We examined our institutional cohort to develop an optimal systematic template for TP-B. We prospectively monitored our first 200 consecutive free-hand TP-B. These included men who were biopsy naïve (n = 117), had elevated PSA with prior negative biopsy (n = 18), and men on active surveillance (n = 65). All men underwent a 20 core TP-B with each core placed in a separate specimen container. This allowed the 20-core TP-B to be easily broken down as though fewer cores had been taken in each patient. Ten, 12, and 16 core templates were designed a priori and compared within each patient to the 20 core template. The highest Grade Group (GG) at pathologic analysis was assigned to each biopsy. Primary outcome was detection of clinically significant prostate cancer, defined as ≥GG2. Secondary outcome was detection of GG1 prostate cancer. We performed sub-group analyses of biopsy naïve men and biopsy naïve men stratified by PSA density (<0.15 vs. ≥0.15 ng/mL/cc). An historic institutional cohort of 10-12 core TRUS-B (n = 170) was used to compare prostate cancer detection between techniques. P value of ≤0.05 was considered statistically significant. Clinically significant cancers were detected in 98 men (49%) using a 20 core TP-B technique. Had we sampled fewer cores we would have identified clinically significant cancers in 93 (47%, 16 core), 91 (46%, 12 core), and 82 (41%, 10 core) men. More clinically significant cancers were detected by the 20 core template compared to the 10 core template for both the whole cohort (49% vs. 41%, P = 0.02) and the biopsy naïve subset (48% vs. 40%, P = 0.05). Additional cores did not result in an increased detection of GG1 cancers (20-core: 35% vs. 10-core: 44%, P = 0.09). Less than one quarter of biopsy naïve men with a PSA density <0.15 were found to have clinically significant cancers. More clinically significant cancers were detected in the 12-core TP-B cohort compared to the 12-core TRUS-B series (46% vs. 38%, P < 0.001). A 20 core TP-B systematic biopsy template detected a greater number of clinically significant prostate cancers compared to a 10 core TP template. Cancer detection was similar for 12, 16, and 20 core templates. Higher core numbers did not result in greater detection of GG1 tumors reflecting increased detection of concomitant ≥GG2 with greater sampling. We propose a minimum 12 core systematic biopsy template for men undergoing TP-B.

BECT Spike Detection Algorithm Based on Optimal Template Matching and Morphological Feature Selection

Over 15% of children with epilepsy belong to benign childhood epilepsy with centro-temporal spikes (BECT), facing with educational difficulties. The accurate recognition of spikes in electroencephalogram (EEG) signals collected from BECT patients can help the doctor to effectively make diagnosis and give therapeutic schedule. Traditionally, template matching method can extract spike-like waves from EEG signals and is adopted by many researches. However, the patterns of the spikes appeared in different patients or different time in one patient varies greatly. The brief proposes a spike detection algorithm based on optimal template matching and morphological feature selection, which includes universal template matching, spike clustering, universal template optimization based on particle swarm optimization (PSO) algorithm and false positive spike (FPS) elimination based on spike morphological feature. Based on the testing EEG data set adopted in this brief, the sensitivity (Sen), specificity (Spe) and accuracy (AC) of the proposed algorithm reaches 98.2%, 95.1% and 96.5%, respectively.

Imprinted proteins for determination of ovalbumin

A strategy to design imprinted proteins (IPs) able to detect a glycoprotein (ovalbumin, OVA) was proposed. Glucose oxidase (GOx) was used as a matrix for obtaining the binding cavities with high specificity towards the template protein. The effective method to purify the obtained IPs from the template molecules was developed based on a combination of dialysis and gel filtration. HRP was chosen as a model template to discover the optimal production conditions, and the optimal template concentration (100µg⋅L-1) was chosen. The obtained imprinted proteins were characterized by the high adsorption selectivity towards the target protein (the imprinting factor towards OVA and HRP is 4.7). The developed method was transferred to the synthesis of the anti-OVA IPs. The binding properties of these IPs were estimated using the OVA conjugates with low- (FITC) and high- (HRP) molecular weight label molecules. The ability of the synthesized anti-OVA IPs as recognition elements in immunoassay was studied. Under the optimized experimental conditions, the proposed imprinted proteins exhibited a good linear response to OVA in the concentration range of 10-2000ng⋅mL-1 with a detection limit of 6ng⋅mL-1. The obtained recognition elements were tested for OVA determination in real samples of chicken egg white, and a sample of OVA-free cake spiked by OVA.

Triazoloquinazoline derived classical DNA intercalators: Design, synthesis, in silico ADME profile, docking, and antiproliferative evaluations.

Thirteen novel [1,2,4]triazolo[4,3-c]quinazoline derivatives as DNA intercalators were synthesized and their anticancer activities evaluated against HepG2 and HCT-116 cells. A docking study was carried out to explore how the new derivatives bind to active sites of DNA. The docking data were highly interrelated with that of biological testing. The HCT-116 cell line was the most sensitive one to the effect of the new derivatives. Compound 7c exhibited the highest anticancer activities against both the HepG2 and HCT116 cancer cell lines. Despite this compound displaying less activity than doxorubicin, it could be useful as a template for future manipulation, optimization, and investigation to produce other analogs with potential activity. The most active derivatives, 7c , 7b , and 7a were evaluated as DNA binders. Compound 7c displayed the highest binding affinity. Furthermore, the absorption, distribution, metabolism, excretion, and toxicity (ADMET) profile was calculated for the four most active compounds in comparison to doxorubicin as reference drug. Our derivatives 7a , 7b , and 7c displayed a very good calculated ADMET profile in comparison to doxorubicin.

Inhibitor-Dependent Usage of the S1' Specificity Pocket of ER Aminopeptidase 2.

Endoplasmic reticulum aminopeptidase 2 (ERAP2) is an intracellular enzyme involved in the processing of antigenic peptides intended for presentation by major histocompatibility complex class I (MHCI) molecules. Because of its role in regulating immune responses, ERAP2 is an emerging pharmacological target. Phosphinic pseudopeptides are potent transition-state analogue inhibitors of ERAP2. Previous structure-activity studies have revealed a complex but ambiguous relationship between the occupation of putative specificity pockets and the inhibitor efficacy. To address these problems, we solved crystal structures of ERAP2 in complex with two phosphinic pseudotripeptide inhibitors. Both compounds are found in the catalytic site in a canonical orientation for transition-state analogues and utilize the S1 and S2' pockets in a similar fashion. Strikingly, their P1' side chains exhibit different orientations and make interactions with distinct shallow pockets near the ERAP2 active site. These structures suggest that S1' pocket usage in ERAP2 may be inhibitor-dependent and constitute useful starting templates for the further optimization of this class of compounds.

Design, synthesis, in silico ADMET, docking, and antiproliferative evaluations of [1,2,4]triazolo[4,3-c]quinazolines as classical DNA intercalators.

Eleven novel [1,2,4]triazolo[4,3-c]quinazolines were designed, synthesized, and evaluated against HepG2 and HCT-116 cells. The molecular design was performed to investigate the binding mode of the proposed compounds with the DNA active site. The data obtained from biological testing highly correlated with that obtained from molecular modeling. HCT-116 was found to be the most sensitive cell line to the influence of the new derivatives. In particular, compounds 6f and 6e were found to be the most potent derivatives over all the tested compounds against the two HepG2 and HCT116 cancer cell lines, with IC50 = 23.44 ± 2.9, 12.63 ± 1.2, and 25.80 ± 2.1, and 14.32 ± 1.5 µM, respectively. Although compounds 6f and 6e displayed less activity than doxorubicin (IC50 = 7.94 ± 0.6 and 8.07 ± 0.8 µM, respectively), both could be useful as a template for future design, optimization, and investigation to produce more potent anticancer analogs. The most active derivatives 6a , 6c , 6e , and 6f were evaluated for their DNA-binding activities. Compound 6f displayed the highest binding affinity. This compound potently intercalates DNA at a decreased IC50 value (54.08 µM). Compounds 6a , 6c , and 6e exhibited good DNA-binding affinities, with IC50 values of 79.35, 84.08, and 59.35 µM, respectively. Furthermore, ADMET (absorption, distribution, metabolism, excretion, and toxicity) profiles were calculated for the four most active compounds in comparison to doxorubicin as a reference drug. Our derivatives 6a , 6c , 6e , and 6f displayed very good in-silico-predicted ADMET profiles. Doxorubicin violates three of Lipinski's rules, our derivatives 6a , 6c , 6e , and 6f do not violate any rule.