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AbstractColorectal cancer is a serious threat to people’s life due to its high incidence and high mortality. Quercetin can effectively treat colorectal carcinoma (CRC), but its exact mechanism of action is still unclear. Then quercetin-related target genes were obtained from Swiss Target Prediction database and Similarity Ensemble Approach (SEA) database, and CRC-related target genes were obtained from GeneCards database, respectively. Common target genes were obtained by FunRich software. String software was used to construct a protein–protein interaction (PPI) network. R package was used for gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Molecular docking, molecular dynamics (MD) simulation and post-dynamics simulation were used to explore the binding stability of quercetin to key targets. In total, 103 and 141 target information of quercetin were obtained from the Swiss Target Prediction database and SEA database, respectively. 1,649 CRC-related genes were obtained from GeneCards database. FunRich software was used to draw venny map and obtain 36 intersection targets of quercetin and CRC. String software was used to construct the PPI network. The core genes were AKT1, EGFR, MMP9, KDR, MET and PTK2. There were 532 items related to biological processes, 14 items related to cellular components, and 43 items related to molecular functions among the key target GO enrichment items. KEGG enrichment pathways of key targets involved cancer pathways, PI3K-Akt signal pathway, etc. The results of molecular docking, MD simulation and post-dynamics simulation showed they had a good affinity and formed a stable effect. So quercetin may play an important role in the treatment of CRC by acting on AKT1, EGFR, MMP9, KDR, MET and PTK2 to affect the development of CRC.Communicated by Ramaswamy H. Sarma

The commercialization process of perovskite solar cells (PSCs) is markedly restricted by the power conversion efficiency (PCE) and long-term stability. During fabrication and operation, the bottom interface of the organic–inorganic hybrid perovskite layer frequently exhibits voids and residual PbI2, while these defects inevitably act as recombination centers and degradation sites, affecting the efficiency and stability of the devices. Therefore, the degradation and nonradiative recombination originating from the buried interface should be thoroughly resolved. Here, we report a multifunctional passivator by introducing malonic dihydrazide as an interfacial chemical bridge between the electron transport layer and the perovskite (PVK) layer. MADH with hydrazine groups improves the surface affinity of SnO2and provides nucleation sites for the growth of PVK, leading to the reduced residual PbI2and the voids resulting from the inhomogeneous solvent volatilization at the bottom interface. Meanwhile, the hydrazine group and carbonyl group synergistically coordinate with Pb2+to improve the crystal growth environment, reducing the number of Pb-related defects. Eventually, the PCE of the PSCs is significantly enhanced benefiting from the reduced interfacial defects and the increased carrier transport. Moreover, the reductive nature of hydrazide further inhibits I2generation during long-term operation, and the device retains 90% of the initial PCE under a 1 sun continuous illumination exposure of 700 h.

Rhodium complexes have garnered extensive attention owing to their inherent potential for biological activity. The catalytic properties of Rh–N^N^N complexes have been investigated extensively, whereas there is a paucity of studies regarding their antitumor activity. In this study, we present four rhodium(I) complexes, each distinguished by a different anion. These complexes incorporate 2,6-bis(1,5-diphenyl-1H-pyrazol-3-yl)pyridine as the tridentate N^N^N-chelating ligand and one carbonyl ligand. These complexes underwent rigorous evaluation for cytotoxicity against a spectrum of human tumor cell lines, including MCF-7, HeLa, and HepG2, alongside a nontumor cell line, HUVEC, employing MTT assays and flow cytometry. Our results unveiled that complex 1Rh(N^N^N)(CO)Cl exhibited remarkable cytotoxicity and demonstrated a favorable selectivity index against tumor cells, with a particularly notable impact on breast adenocarcinoma (selectivity index = 5.2). To further ascertain its potential utility, we probed the interactions between complex 1and ctDNA/BSA. Both UV and fluorescence scan data indicated that complex 1was able to intercalate into ctDNA and bind with BSA. Meanwhile, comprehensive assessments, encompassing the mobility shift assay and the micronucleus test, consistently showed that complex 1could not induce DNA fragmentation in vitro and the formation of micronucleus in mouse erythrocytes. All of these findings suggest that complex 1could be a promising antitumor compound.

The CO2RR performance of M2@C2N DACs were computationally explored. It is revealed that Caffinity of dual-atom M2site matters most to CC bond coupling and C2H4formation while both C- and O-affinity control CH4formation.

Combining photodynamic therapy (PDT), chemodynamic therapy (CDT), and ferroptosis is a valuable means for an enhanced anticancer effect. However, traditional combination of PDT/CDT/ferroptosis faces several hurdles, including excess glutathione (GSH) neutralization and preparation complexity. In this work, a versatile multifunctional nanoparticle (HCNP) self-assembled from two porphyrin molecules, chlorin e6 and hemin, is developed. The as-constructed HCNPs exhibit a peroxidase-mimic catalytic activity, which can lead to the in situ generation of endogenous O2, thereby enhancing the efficacy of PDT. Furthermore, the generation of hydroxyl radicals (•OH) in the tumor environment in reaction to the high level of H2O2and the simultaneous disruption of intracellular GSH endow the HCNPs with the capacity of enhanced CDT, resulting in a more effective therapeutic outcome in combination with PDT. More importantly, GSH depletion further leads to the inactivation of GSH peroxide 4 and induced ferroptosis. Both in vitro and in vivo results showed that the combination of PDT/CDT/ferroptosis realizes highest antitumor efficacy significantly under laser irradiation. Therefore, by integrating the superiorities of O2and •OH generation capacity, GSH-depletion effect, and bioimaging into a single nanosystem, the HCNPs are a promising single therapeutic agent for tumor PDT/CDT/ferroptosis combination therapy.

Background: The outbreak of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has attracted worldwide attention due to its high infectivity and pathogenicity. Objective: The purpose of this study is to develop drugs with therapeutic potentials for COVID-19. Methods: we selected the crystal structure of 3CL pro to perform virtual screening against natural products in the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP). Then, molecular dynamics (MD) simulation was carried out to explore the binding mode between compounds and 3CL pro. Results and Discussion: A total of 6 candidates with good theoretical binding affinity to 3CL pro were identified. The binding mode after MD shows that hydrogen bonding and hydrophobic interaction play an important role in the binding process. Finally, based on the free binding energy analysis, the candidate natural product Gypenoside LXXV may bind to 3CL pro with high binding affinity. Conclusion: The natural product Gypenoside LXXV may have good potential anti-SARS-COV-2 activity.

AbstractCommunicated by Ramaswamy H. Sarma

The utilization of virtual compound libraries and virtual screening has become a routine method in drug discovery and has accelerated the research of pharmacy and translational medicine. With the development of computer-aided drug design (CADD), it is essential to incorporate virtual experiments into the education of medicinal chemistry. Herein, we describe a series of virtual experiments conducted by undergraduate students, including the construction of a virtual compound library, molecular docking, and the virtual screening of acetylcholinesterase inhibitors. Student feedback indicates that the virtual experiments are popular with students and effectively promote their interest in the drug discovery process.

Siloxane-based polymer films obtained by the sol–gel process are often porous and exhibit low refractive indices. To address this issue, we have prepared two intrinsic high refractive index siloxane–sulfide polymer networks via the Piers–Rubinsztajn reaction and thiol–ene click chemistry based on readily available tetraethoxysilane (TEOS). Our siloxane–sulfide polymers consisting of aromatic groups and sulfur display enhanced thermostability with high Tg(>120 °C) and T5d(>320 °C) as compared to related materials. Such good thermal properties significantly expand their application as high thermostable optical materials. Besides, both flexible and colorless polymer films P1and P2exhibit high refractive indices of 1.625 and 1.665 at 546 nm, respectively. P2also shows high refractive index of 1.648 at 1000 nm. Moreover, both siloxane–sulfide polymer films are uniform and homogeneous with high optical transmittance of 90% for wavelength ranging from 400 to 2000 nm. These data indicate that the new siloxane–sulfide polymers have potential application as optical materials such as antireflective coatings, encapsulation resin for the fabrication of LED, and the infrared transmitting materials.

Background: Dipeptides and their analogs have multiple biological functions, such as bitter taste, angiotensin-converting enzyme (ACE) inhibition, etc. That design and modification of dipeptides guided by theoretical models are of great significance for research and development on drug, especially in discovery and optimization of lead compound, which can reduce the experimental expenditure and improve the hit rate. Methods: We used conventional 3D-QSAR methods, Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA), and Topomer CoMFA to construct Three-Dimensional Quantitative Structure-Activity Relationship (3D-QSAR) models for four catalogues of dipeptides and their derivatives, which were bitter-tasting dipeptides, ACE-inhibitory dipeptides, anti-cryptococcal and anti-microtubule dipeptide derivatives. The CoMFA and CoMSIA models based on a training set were optimized through varying the force fields and their combination. Topomer CoMFA experientially completed identification and alignment of pose of the fragments. Results and Conclusion: We have achieved theoretical 3D-QSAR models with good reliability of prediction for correspondingly novel samples, which had good statistically significance with excellent cross-validated correlation coefficients (Q2 >0.5) and conventional correlation coefficients (R2 >0.9). The counter maps from the CoMFA and CoMSIA models could provide the direct information about contributions of the force fields for activity, aiding the design and modification of novel bioactive dipeptides and by which relationships between bitter dipeptides with ACEinhibitory dipeptides were explored briefly and a set of novel anti-cryptococcal dipeptide derivatives with predicted activities were designed. Topomer CoMFA models for bitter dipeptides, ACEinhibitory dipeptides and anti-cryptococcal dipeptidomimetics were ideal, proved the above results and could provide a tool for virtual screening.

Background: Dipeptides and their analogs have multiple biological functions, such as bitter taste, angiotensin-converting enzyme (ACE) inhibition, etc. That design and modification of dipeptides guided by theoretical models are of great significance for research and development on drug, especially in discovery and optimization of lead compound, which can reduce the experimental expenditure and improve the hit rate. Methods: We used conventional 3D-QSAR methods, Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA), and Topomer CoMFA to construct Three-Dimensional Quantitative Structure-Activity Relationship (3D-QSAR) models for four catalogues of dipeptides and their derivatives, which were bitter-tasting dipeptides, ACE-inhibitory dipeptides, anti-cryptococcal and anti-microtubule dipeptide derivatives. The CoMFA and CoMSIA models based on a training set were optimized through varying the force fields and their combination. Topomer CoMFA experientially completed identification and alignment of pose of the fragments. Results and Conclusion: We have achieved theoretical 3D-QSAR models with good reliability of prediction for correspondingly novel samples, which had good statistically significance with excellent cross-validated correlation coefficients (Q2 >0.5) and conventional correlation coefficients (R2 >0.9). The counter maps from the CoMFA and CoMSIA models could provide the direct information about contributions of the force fields for activity, aiding the design and modification of novel bioactive dipeptides and by which relationships between bitter dipeptides with ACEinhibitory dipeptides were explored briefly and a set of novel anti-cryptococcal dipeptide derivatives with predicted activities were designed. Topomer CoMFA models for bitter dipeptides, ACEinhibitory dipeptides and anti-cryptococcal dipeptidomimetics were ideal, proved the above results and could provide a tool for virtual screening.

A series of magnesia–silica binary composite catalysts with different molar ratios were prepared using various preparation methods and evaluated for the one-step production of 1,3-butadiene (BD) from bio-ethanol. The MgO–SiO2catalyst prepared by wet-kneading is distinguished from the samples prepared by other methods by its superior activity. An exceptionally high productivity of butadiene with the high ethanol conversion of 95% and BD selectivity of 77% was obtained over wet-kneaded hierarchical flower-like MgO nanostructures with the catalytic material made up of SiO2spheres (with a MgO : SiO2molar ratio of 65 : 35 at 450 °C and an WHSV of 4.1 h−1), which translates into an unprecedented BD yield of 0.025 mol gcat−1h−1. The as-prepared catalysts were characterized by XRD, FESEM, BET, FT-IR, UV/vis, CO2-TPD and NH3-TPD. The Mg precursor used and the preparation method are found to influence the extent of MgO and SiO2interactions during preparation, as estimated by various techniques, which, in turn, was found to correlate with the catalytic performance. A subtle balance of surface acidity/basicity was found to be necessary to achieve a high butadiene yield which can be obtained by variation of MgO : SiO2ratios in wet-kneaded samples. In view of the difference in catalytic activity and the physicochemical properties of the MgO–SiO2composite catalysts, the Si–O–Mg chemical bond formed by the strong MgO and SiO2interaction is suggested to be essential for the balanced surface acid/base sites in relation to the strong interaction between MgO and SiO2and the consequent high catalytic performance.

The activation of T cell immune responses, which relies on peptide antigens transported by TAP and bound to major histocompatibility complex (MHC) molecules, is recognized by T cell receptors (TCR). The quantitative prediction of MHC-epitope binding affinity can facilitate epitope screening and reduce cost and experimental efforts greatly. In this study, a comprehensive quantitative prediction method of binding affinity was established using quantitative structureactivity relationship (QSAR) modeling derived from amino acid physicochemical information. Firstly, the epitope was characterized by a set of amino acid physicochemical parameters. Secondly, the structural variables were optimized by the stepwise regression (STR). Finally, the robust quantitative models with were built by multiple linear regressions (MLR) for 31 MHC Class I subtypes. The normalized regression coefficients (NRCs) of QSAR model could demonstrate the mechanism of interaction of MHC, epitope, and TCR very well. The contribution of amino acid at each position of epitope, which was calculated by NRC, could determine which one was favorable for binding affinity or not. Therefore, the quantitative models established by STR-MLR could be used to guide virtual combinational design and high throughout screening of CTL epitope. Besides, they have many advantages, such as definite physiochemical indication, easier calculation and explanation, and good performances.

Ataxia telangiectasia-mutated and Rad3-related (ATR) protein kinase is an attractive anticancer target. In this study, comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) were performed on a series of aminopyrazine ATR inhibitors. The models generated by CoMFA had a cross-validated coefficient (q2) of 0.752 and a regression coefficient (r2) of 0.947. The CoMSIA models had a q2 of 0.728 and an r2 of 0.936. The reasonable quantitative structure-activity relationship model showed robust predictive ability. The contour map provided guidelines for building novel virtual compounds based on compound NO.40. In addition, the 3D structure of ATR was modeled by homology modeling. Molecular dynamic simulations were employed to optimize the structure. The docking results offered insights into the interactions between the inhibitors and the active site for potent analysis. This study provides useful guidance for the discovery of more potent compounds.

Drug resistance to existing antibiotics poses alarming threats to global public health, which inspires heightened interests in searching for new antibiotics, including antimicrobial peptides (AMPs). Accurate prediction of antibacterial activities of AMPs may expedite novel AMP design and reduce the costs and efforts involved in laboratory screening. In the present study, a novel quantitative prediction method of AMP was established by quantitative structure-activity relationship (QSAR) modeling based on the physicochemical properties of amino acids. The indices of these physicochemical properties were used to define AMP. The structural variables were optimized by stepwise regression (STR). Three series of AMPs from the QSAR model were constructed by multiple linear regressions (MLR). These QSAR models showed good performance in reliability and predictability. The normalized regression coefficients of the QSAR model and the contribution of amino acids at each position of AMP may determine the suitableness of a particular residue at any given position. QSAR models constructed by STR-MLR should prove to be useful tools in peptide design with respect to the calculation, explanation, good and reliable performance, and definition of physiochemical properties.

In this study, physicochemical scale (P-scale), was recruited as a novel set of physicochemical descriptors derived from component analysis on four short of physicochemical properties variables (hydrophobic, electronic, steric and hydrogen bond contribution) of 20 coded amino acids, By using partial least squares (PLS), we applied P-scale for the study of quantitative structure-activity relationship models (QASRs) on three angiotensin I converting enzyme (ACE) inhibitory peptides datasets (58 dipeptides, 55 tripeptides, and 50 tetrapeptides).The results of QSARs were superior to that of the earlier studies, with correlation coefficient (r2) and cross-validated(q2) equal to 0.902, 0.86; 0.985, 0.951 and 0.872, 0.77, respectively. By analysis, hydrophobic and steric properties of ACE-inhibitory peptide sequences play important roles in their bioactivities, and novel peptide sequence could be designed based on these properties of the amino acid residues. These results showed that P-scale descriptors can well represent the peptide sequence. Furthermore, the robust models show that P-scale descriptors can be further expanded for polypeptides and can serve as a useful quantitative tool for the rational drug design and discovery.

Organic–inorganic perovskites face the issues of being vulnerable to oxygen and moisture and the trap sites located at the surface and grain boundaries. Integration of two-dimensional (2D) perovskites as a capping layer is an effective route to enhance both photovoltaic efficiency and environmental stability of the three-dimensional (3D) underlayer. Here, we employ 1,4-butanediammonium diiodide (BDADI), which has a short chain length and diammonium cations, to construct a 3D/2D stacking perovskite solar cells (PSCs). The introduction of BDA2+could passivate surface defects in 3D perovskites by forming 2D Dion–Jacobson (DJ) phase perovskites and effectively suppressing nonradiative recombination, thus resulting in a longer carrier lifetime. The DJ 2D capping layer also regulate the energy level arrangement, enabling a better charge extraction and transport process. In addition, the water-resistance ability of 3D perovskite gets improved because of the hydrophobic characteristic of 1,4-butanediammonium cations. Consequently, the 3D/2D stacking PSCs yield an energy conversion efficiency of 20.32% in company with the enhanced long-term stability.