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Cancer immunotherapy harnesses the immune system to combat tumors and has emerged as a major cancer treatment modality. The PD-1/PD-L1 immune checkpoint modulates interactions between tumor cells and T cells and has been extensively targeted in cancer immunotherapy. However, the monoclonal antibodies known to target this immune checkpoint have considerable side effects, and novel PD-1/PD-L1 inhibitors are therefore required. Herein, a peptide inhibitor to disrupt PD-1/PD-L1 interactions was designed through structure-driven phage display engineering coupled to computational modification and optimization. BetaPb, a novel peptide library constructed by using the known structure of PD-1/PD-L, was used to develop inhibitors against the immune checkpoint, and specific peptides with high affinity toward PD-1 were screened through enzyme-linked immunosorbent assays, homogeneous time-resolved fluorescence, and biolayer interferometry. A potential inhibitor, B8, was preliminarily screened through biopanning. The binding affinity of B8toward PD-1 was confirmed through computation-aided optimization. Assessment of B8variants (B8.1, B8.2, B8.3, B8.4, and B8.5) demonstrated their attenuation of PD-1/PD-L1 interactions. B8.4exhibited the strongest attenuation efficiency at a half-maximal effective concentration of 0.1 µM and the strongest binding affinity to PD-1 (equilibrium dissociation constant = 0.1 µM). B8.4outperformed the known PD-1/PD-L1 interaction inhibitor PL120131 in disrupting PD-1/PD-L1 interactions, revealing that B8.4has remarkable potential for modification to yield an antitumor agent. This study provides valuable information for the future development of peptide-based drugs, therapeutics, and immunotherapies for cancer.

B7 family members and their receptors have been confirmed to participate in T cell response signaling through the induction of immunosuppressive effects. The V-domain Ig suppressor of T-cell activation (VISTA) is expressed in myeloid cells as well as T lymphocytes and plays a crucial role in immune responses. In this study, the phage display technique was employed to isolate specific antibodies targeting VISTA in chickens. In in vitro experiments, VISTA could trigger the inhibition of T-cell activation through interaction during binding. However, isolated single-chain variable fragments (scFvs) effectively inhibited immune cells from producing exhaustion responses, freeing the T cells in peripheral blood mononuclear cells from inhibition and allowing them to remain activated. In addition, the isolated scFvs can block the interaction between VISTA and the VSIG3 ligand. Further analysis on one scFv VS10 revealed that a significant insertion mutation occurred at the CDR3 of the VH domain compared to the chicken germline sequence. Finally, the complex structures of scFv VS10 and VISTA were simulated using molecular docking, and the interaction produced was analyzed. The experimental results support the applicability of VISTA as a target for immune checkpoints and contribute to explaining geometric structures and properties in chicken antibodies.

Amidst growing concerns over COVID-19 aftereffects like fatigue and cognitive issues, NRICM101, a traditional Chinese medicine, has shown promise. Used by over 2 million people globally, it notably reduces hospitalizations and intubations in COVID-19 patients. To explore whether NRICM101 could combat COVID-19 brain fog, we tested NRICM101 on hACE2 transgenic mice administered the S1 protein of SARS-CoV-2, aiming to mitigate S1-induced cognitive issues by measuring animal behaviors, immunohistochemistry (IHC) staining, and next-generation sequencing (NGS) analysis. The study revealed that S1 protein-administered mice displayed marked signs of brain fog, characterized by reduced learning, memory, and nesting abilities. However, NRICM101 treatment in these animals ameliorated all these cognitive functions. S1 protein administration in mice induced notable inflammation, leading to the death of neurons (NeuN+) and neural stem cells (DCX+) in hACE2 transgenic mice. This was accompanied by heightened microglia activation (IBA1+/CD68+), increased cytokine production (IL1β, IL6), induction of neutrophil extracellular traps (NET), inflammation (NLRP3, CD11b), and platelet (CD31, vWF) and complement (C3) activation, ultimately damaging neurovasculature and disrupting the blood-brain barrier (B.B.B.). Administration of NRICM101 effectively alleviated all these pathological changes. In conclusion, NRICM101 has the potential to prevent COVID-19-associated brain fog by bolstering neurovascular integrity and protecting neurons and neural stem cells. This is achieved by the inhibition of S1 protein-induced complement activation, which in turn leads to the prevention of damage to the neurovasculature and the subsequent death of neurons.

Traditional herbal medicine offers opportunities to discover novel therapeutics against SARS-CoV-2 mutation. The dried aerial part of mint (Mentha canadensisL.) was chosen for bioactivity-guided extraction. Seven constituents were isolated and characterized by nuclear magnetic resonance (NMR) and mass spectrometry (MS). Syringic acid and methyl rosmarinate were evaluated in drug combination treatment. Ten amide derivatives of methyl rosmarinate were synthesized, and the dodecyl (13) and 3-ethylphenyl (19) derivatives demonstrated significant improvement in the anti-SARS-CoV-2 plaque reduction assay, achieving IC50of 0.77 and 2.70 μM, respectively, against Omicron BA.1 as compared to methyl rosmarinate’s IC50of 57.0 μM. Spike protein binding and 3CLproinhibition assays were performed to explore the viral inhibition mechanism. Molecular docking of compounds 13and 19to 3CLprowas performed to reveal potential interaction. In summary, natural products with anti-Omicron BA.1 activity were isolated from Mentha canadensisand derivatives of methyl rosmarinate were synthesized, showing 21- to 74-fold improvement in antiviral activity against Omicron BA.1.

Berberine is a pharmacologically active alkaloid present in widely used medicinal plants, such as Coptis chinensis(Huang-Lian). The hormone estradiol is oxidized by cytochrome P450 (CYP) 1B1 to primarily form the genotoxic metabolite 4-hydroxyestradiol, whereas CYP1A1 and CYP1A2 predominantly generate 2-hydroxyestradiol. To illustrate the effect of berberine on the regioselective oxidation of estradiol, effects of berberine and its metabolites on CYP1 activities were studied. Among CYP1s, CYP1B1.1, 1.3 (L432V), and 1.4 (N453S)-catalyzed 4-hydroxylation were preferentially inhibited by berberine. Differing from the competitive inhibition of CYP1B1.1 and 1.3, N453S substitution in CYP1B1 allowed a non-competitive or mixed-type pattern. An N228T in CYP1B1 highly decreased its activity and preference to 4-hydroxylation. A reverse mutation of T223N in CYP1A2 retained its 2-hydroxylation preference, but enhanced its inhibition susceptibility to berberine. Compared with berberine, metabolites demethyleneberberine and thalifendine caused weaker inhibition of CYP1A1 and CYP1B1 activities. Unexpectedly, thalifendine was more potent than berberine in the inhibition of CYP1A2, in which case an enhanced interaction through polar hydrogen-π bond was predicted from the docking analysis. These results demonstrate that berberine preferentially inhibits the estradiol 4-hydroxylation activity of CYP1B1 variants, suggesting that 4-hydroxyestradiol-mediated toxicity might be reduced by berberine, especially in tissues/tumors highly expressing CYP1B1.

The protoberberine alkaloid berberine carries methylenedioxy moiety and exerts a variety of pharmacological effects, such as anti-inflammation and lipid-lowering effects. Berberine causes potent CYP1B1 inhibition, whereas CYP1A2 shows resistance to the inhibition. To reveal the influence of oxidative metabolism on CYP1 inhibition by berberine, berberine oxidation and the metabolite-mediated inhibition were determined. After NADPH-fortified preincubation of berberine with P450, the inhibition of CYP1A1 and CYP1B1 variants (CYP1B1.1, CYP1B1.3, and CYP1B1.4) by berberine was not enhanced, and CYP1A2 remained resistant. Demethyleneberberine was identified as the most abundant metabolite of CYP1A1- and CYP1B1-catalyzed oxidations, and thalifendine was generated at a relatively low rate. CYP1A1-catalyzed berberine oxidation had the highest maximal velocity (Vmax) and exhibited positive cooperativity, suggesting the assistance of substrate binding when the first substrate was present. In contrast, the demethylenation by CYP1B1 showed the property of substrate inhibition. CYP1B1-catalyzed berberine oxidation had low Kmvalues, but it had Vmaxvalues less than 8% of those of CYP1A1. The dissociation constants generated from the binding spectrum and fluorescence quenching suggested that the low Kmvalues of CYP1B1-catalyzed oxidation might include more than the rate constants describing berberine binding. The natural protoberberine/berberine fmetabolites with methylenedioxy ring-opening (palmatine, jatrorrhizine, and demethyleneberberine) and the demethylation (thalifendine and berberrubine) caused weak CYP1 inhibition. These results demonstrated that berberine was not efficiently oxidized by CYP1B1, and metabolism-dependent irreversible inactivation was minimal. Metabolites of berberine caused a relatively weak inhibition of CYP1.

Tyrosinase, a key copper-containing enzyme involved in melanin biosynthesis, is closely associated with hyperpigmentation disorders, cancer, and neurodegenerative diseases, and as such, it is an essential target in medicine and cosmetics. Known tyrosinase inhibitors possess adverse side effects, and there are no safety regulations; therefore, it is necessary to develop new inhibitors with fewer side effects and less toxicity. Peptides are exquisitely specific to their in vivo targets, with high potencies and relatively few off-target side effects. Thus, we systematically and comprehensively investigated the tyrosinase-inhibitory abilities of N- and C-terminal cysteine/tyrosine-containing tetrapeptides by constructing a phage-display random tetrapeptide library and conducting computational molecular docking studies on novel tyrosinase tetrapeptide inhibitors. We found that N-terminal cysteine-containing tetrapeptides exhibited the most potent tyrosinase-inhibitory abilities. The positional preference of cysteine residues at the N terminus in the tetrapeptides significantly contributed to their tyrosinase-inhibitory function. The sulfur atom in cysteine moieties of N- and C-terminal cysteine-containing tetrapeptides coordinated with copper ions, which then tightly blocked substrate-binding sites. N- and C-terminal tyrosine-containing tetrapeptides functioned as competitive inhibitors against mushroom tyrosinase by using the phenol ring of tyrosine to stack with the imidazole ring of His263, thus competing for the substrate-binding site. The N-terminal cysteine-containing tetrapeptide CRVI exhibited the strongest tyrosinase-inhibitory potency (with an IC50of 2.7 ± 0.5 μM), which was superior to those of the known tyrosinase inhibitors (arbutin and kojic acid) and outperformed kojic acid–tripeptides, mimosine-FFY, and short-sequence oligopeptides at inhibiting mushroom tyrosinase.

Comparative Molecular Field Analysis (CoMFA) is a mainstream and down-to-earth 3D QSAR technique in the coverage of drug discovery and development. Even though CoMFA is remarkable for high predictive capacity, the intrinsic data-dependent characteristic still makes this methodology certainly be handicapped by noise. Its well known that the default settings in CoMFA can bring about predictive QSAR models, in the meanwhile optimized parameters was proven to provide more predictive results. Accordingly, so far numerous endeavors have been accomplished to ameliorate the CoMFA models robustness and predictive accuracy by considering various factors, including molecular conformation and alignment, field descriptors and grid spacing. Herein, we would like to make a comprehensive survey of the conceivable descriptors and their contribution to the CoMFA models predictive ability.

Human N-acetyl-β-hexosaminidase (Hex) isozymes are considered to be important targets for drug discovery. They are directly linked to osteoarthritis because Hex is the predominant glycosidase released by chondrocytes to degrade glycosaminoglycan. Hex is also associated with lysosomal storage disorders. We report the discovery of GlcNAc-type iminocyclitiols as potent and selective Hex inhibitors, likely contributed by the gain of extra electrostatic and hydrophobic interactions. The most potent inhibitor had a Kiof 0.69 nM against human Hex B and was 2.5 × 105times more selective for Hex B than for a similar human enzyme O-GlcNAcase. These glycosidase inhibitors were shown to modulate intracellular levels of glycolipids, including ganglioside-GM2 and asialoganglioside-GM2.

Severe acute respiratory syndrome coronavirus (SARS-CoV) main protease (Mpro), a protein required for the maturation of SARS-CoV, is vital for its life cycle, making it an attractive target for structure-based drug design of anti-SARS drugs. The structure-based virtual screening of a chemical database containing 58 855 compounds followed by the testing of potential compounds for SARS-CoV Mproinhibition leads to two hit compounds. The core structures of these two hits, defined by the docking study, are used for further analogue search. Twenty-one analogues derived from these two hits exhibited IC50values below 50 M, with the most potent one showing 0.3 M. Furthermore, the complex structures of two potent inhibitors with SARS-CoV Mprowere solved by X-ray crystallography. They bind to the protein in a distinct manner compared to all published SARS-CoV Mprocomplex structures. They inhibit SARS-CoV Mproactivity via intensive H-bond network and hydrophobic interactions, without the formation of a covalent bond. Interestingly, the most potent inhibitor induces protein conformational changes, and the inhibition mechanisms, particularly the disruption of catalytic dyad (His41 and Cys145), are elaborated.

A genetic algorithm optimized artificial neural network GNW has been designed to rank features for two diversified multivariate data sets. The dimensions of these data sets are 85 × 24 and 62 × 25 for 24 or 25 molecular descriptors being computed for 85 matrix metalloproteinase-1 inhibitors or 62 hepatitis C virus NS3 protease inhibitors, respectively. Each molecular descriptor computed is treated as a feature and input into an input layer node of the artificial neural network. To optimize the artificial neural network by the genetic algorithm, each interconnected weight between input and hidden or between hidden and output layer nodes is binary encoded as a 16 bits string in a chromosome, and the chromosome is evolved by crossover and mutation operations. Each input layer node and its associated weights of the trained GNW are systematically omitted once (the self-depleted weights), and the corresponding weight adjustments due to the omission are computed to keep the overall network behavior unchanged. The primary feature ranking index defined as the sum of self-depleted weights and the corresponding weight adjustments computed is found capable of separating good from bad features for some artificial data sets of known feature rankings tested. The final feature indexes used to rank the data sets are computed as a sum of the weighted frequency of each feature being ranked in a particular rank for each data set being partitioned into numerous clusters. The two data sets are also clustered by a standard K-means method and trained by a support vector machine (SVM) for feature ranking using the computed F-scores as feature ranking index. It is found that GNW outperforms the SVM method on three artificial as well as the matrix metalloproteinase-1 inhibitor data sets studied. A clear-cut separation of good from bad features is offered by the GNW but not by the SVM method for a feature pool of known feature ranking.

The severe acute respiratory syndrome-associated coronavirus (SARS-CoV) 3C-like protease (3CLpro or Mpro) is an attractive target for the development of anti-SARS drugs because of its crucial role in the viral life cycle. In this study, a compound database was screened by the structure-based virtual screening approach to identify initial hits as inhibitors of SARS-CoV 3CLpro. Out of the 59 363 compounds docked, 93 were selected for the inhibition assay, and 21 showed inhibition against SARS-CoV 3CLpro (IC50 ≤ 30 μM), with three of them having common substructures. Furthermore, a search for analogues with common substructure in the Maybridge, ChemBridge, and SPECS_SC databases led to the identification of another 25 compounds that exhibited inhibition against SARS-CoV 3CLpro (IC50 = 3−1000 μM). These compounds, 28 in total, were subjected to 3D-QSAR studies to elucidate the pharmacophore of SARS-CoV 3CLpro-i

Some three-dimensional quantitative structure−activity relationship (3D-QSAR) models have been constructed using the comparative molecular field analysis (CoMFA) and comparative molecular similarity indices (CoMSIA) for a series of 84 proline-based plus 12 structurally more diversified nonproline matrix metalloproteinase inhibitors. The structures of these inhibitors were built from a structure template extracted from the crystal structure of stromelysin. The structures built were divided into the training and test sets for both the CoMFA and CoMSIA analyses for each being composed of 60 and 24 inhibitors, respectively. The structures in the training set were aligned using some alignment rules derived from the analysis of the Ligplot program on a recent crystal structure of ligand-collagenase-1 complex. Some stepwise CoMSIA's were performed on the aligned training set on which the best CoMFA result was obtained. The best CoMSIA model was identified from the stepwise results, and the corresponding pharmacophore features were used for the construction of a pharmacophore hypothesis by the Catalyst 4.9 program. The training set was extended to include 11 structurally more diversified and nonproline inhibitors. To construct a pharmacophore hypothesis, the conformation of 60 structurally aligned proline-based inhibitors was fixed, while that of the 11 structurally more diversified nonproline inhibitors was allowed to vary during the hypothesis construction process. It was found that the predicted activities by the top hypothesis constructed for both the training and test sets were as good in statistics as those predicted by the best CoMSIA model from which the hypothesis was derived. The top hypothesis was mapped onto the structures of several highly active inhibitors selected from both the training and test sets. The goodness of mapping on each inhibitor was found to be correlated well with the activity of each inhibitor.

The Fisher's discriminant ratio has been used as a class separability criterion and implemented in a k-means clustering algorithm for performing simultaneous feature selection and data set trimming on a set of 221 HIV-1 protease inhibitors. The total number of molecular descriptors computed for each inhibitor is 43, and they are scaled to lie between 1 and 0 before being subjected to the feature selection process. Since the purpose is to select some of the most class sensitive descriptors, several feature evaluation indices such as the Shannon entropy, the linear regression of selected descriptors on the pKiof selected inhibitors, and a stepwise variable selection program are used to filter them. While the Shannon entropy provides the information content for each descriptor computed, more class sensitive descriptors are searched by both the linear regression and stepwise variable selection procedures. The inhibitors are divided into several different numbers of classes. They are subsequently divided into five classes due to the fact that the best feature selection result is obtained by the division. Most of the good features selected are the topological descriptors, and they are correlated well with the pKivalues. The outliers or the inhibitors with less class-sensitive descriptor values computed for each selected descriptor are identified and gathered by the k-means clustering algorithm. These are the trimmed inhibitors, while the remaining ones are retained or selected. We find that 44% or 98 inhibitors can be retained when the number of good descriptors selected for clustering is three. The descriptor values of these selected inhibitors are far more class sensitive than the original ones as evidenced by substantial increasing in statistical significance when they are subjected to both the SYBYL CoMFA PLS and Cerius2PLS regression analyses.

The tertiary structure of the central catalytic domain of insertion sequence ISLC3 isolated from Lactobacillus casei ATCC 393 was predicted using the homology modeling approach. The novel insertion sequence was isolated by us from the template bacteriophage φA3 of L.casei ATCC 393. The number of amino acid residues of the ISLC3 central catalytic domain was 116 and was treated as the query sequence. There were five Web‐available threading methods used to find some primary structure templates for the query sequence. These primary templates were further screened using the SWISS‐MODEL Protein Modeling Server and the default parameter settings therein to give six final structure templates. All of these final structure templates were the integrase (IN) protein of retroviruses. Multiple sequence alignment using these IN sequences against the query one revealed the signature DDE motif. Based on the structures of these final templates, the structure of the query sequence was constructed using the InsightII/Discover/Homology programs. A metal ion, Mg2+, was inserted into the center of the putative catalytic pocket formed by the DDE residues of the predicted structure in the final rounds of refinement by molecular dynamics (MD) simulations. The structure with a metal ion included was designated withMg and that without a metal ion was designated freeMg. The average exposed surface area of some hydrophobic residues of both the predicted freeMg and withMg structures were computed and compared with those computed for the six structure templates. Whereas the predicted withMg structure was slightly more exposed than the predicted freeMg structure, the former appeared to be more stable than the latter, as revealed by the lower conformation energy recorded for the former during the structure refinement by MD simulations. To verify further the predicted structures, the coordinates of both predicted structures were fed into the ERRAT Protein Verification Server. It was found that the quality of the predicted withMg structure was much better than that of the freeMg structure. The validation results also indicated that regions of the predicted withMg structure that can be rejected at the 95% confidence level were ∼20% whereas those which can be rejected at the same level for the six structure templates were ∼10%. The predicted withMg structure was also docked into a short oligonucleotide representing the substrate of the ISLC3 transposase using the DOCK_4.0.2 program. It was found that both Glu140 and Asp68 residues of the DDE motif of the predicted withMg structure were able to form hydrogen bonds with the DNA substrate, which was similar to what was observed in a docking study using the retrovirus IN 1asu and its DNA substrate.

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