Structural consensus in ligand-protein docking identifies recognition peptide motifs that bind streptavidin

Computational structure prediction of streptavidin-peptide complexes for known recognition sequences and a number of random di-, tri-, and tetrapeptides has been conducted, and mechanisms of peptide recognition with streptavidin have been investigated by a new computational protocol. The structural consensus criterion, which is computed from multiple docking simulations and measures the accessibility of the dominant binding mode, identifies recognition motifs from a set of random peptide sequences, whereas energetic analysis is less discriminatory. The predicted conformations of recognition tripeptide and tetrapeptide sequences are also in structural harmony and composed of peptide fragments that are individually unfrustrated in their bound conformation, resulting in a minimally frustrated energy landscape for recognition peptides. Proteins 28:421–433, 1997. © 1997 Wiley-Liss, Inc.     

Identification of putative antiviral bioactive compounds derived from family Asteraceae: An in silico approach

This computational study investigates 21 bioactive compounds from the Asteraceae family as potential inhibitors targeting the Spike protein (S protein) of SARS-CoV-2. Employing in silico methods and simulations, particularly CDOCKER and MM-GBSA, the study identifies two standout compounds, pterodontic acid and cichoric acid, demonstrating robust binding affinities (−46.1973 and −39.4265 kcal/mol) against the S protein. Comparative analysis with Favipiravir underscores their potential as promising inhibitors. Remarkably, these bioactives exhibit favorable ADMET properties, suggesting safety and efficacy. Molecular dynamics simulations validate their stability and interactions, signifying their potential as effective SARS-CoV-2 inhibitors.     

Computer vision-assisted photogrammetry and one-image 3D modeling in marine mammals

Image processing using traditional photogrammetric methods is a labor-intensive process. The collection of photogrammetry images during aerial surveys is expanding rapidly, creating new challenges to analyze images promptly and efficiently, while reducing human error during processing. Computer vision-assisted photogrammetry, a field of artificial intelligence (AI), can automate image processing, greatly enhancing the efficiency of photogrammetry. Here, we present a practical and efficient program capable of automatically extracting the fine-scale photogrammetry of East Asian finless porpoises (Neophocaena asiaeorientalis sunameri). Our results indicated that computer vision-assisted photogrammetry could achieve the same accuracy as traditional photogrammetry, and the results of the comparisons were validated against the direct measurements. Three-dimensional (3D) models using computer vision-assisted photogrammetric morphometrics generated trustworthy body volume estimates. We also explored the one image-based 3D modeling technique, which is less accurate, but still useful when only one image of the animal is available. Although several limitations exist in the current program, improvements could be made to narrow the virtual-reality gap when more images are available for machine learning and training. We recommend this program for analyzing images of marine mammals possessing a similar morphological contour.     

Synthesis and antimycobacterial activity of 7-O-substituted-4-methyl-2H-2-chromenone derivatives vs Mycobacterium tuberculosis

A series of 7-O-alkoxy-4-methylumbelliferone derivatives were prepared using a convenient one step synthesis. Additionally the bromo- and azido derivatives 7-O-(4-bromobutoxy)-, 7-O-(6-bromohexyloxy)- and 7-O-(6-azidohexyloxy)-4-methylumbelliferone derivatives were prepared. In vitro evaluation of antimycobacterial activity determined % inhibition and MIC vs M. tuberculosis H₃₇Rv with toxicity (IC₅₀) assessed in VERO cells. The coumarins with longer alkyl chains (nonyl and decyl) showed the optimum inhibitory activity in this series (MIC 3.13?μg/mL) and IC₅₀>10?μg/mL.     

Accuracy in the prediction of disease epidemics when ensembling simple but highly correlated models

Ensembling combines the predictions made by individual component base models with the goal of achieving a predictive accuracy that is better than that of any one of the constituent member models. Diversity among the base models in terms of predictions is a crucial criterion in ensembling. However, there are practical instances when the available base models produce highly correlated predictions, because they may have been developed within the same research group or may have been built from the same underlying algorithm. We investigated, via a case study on Fusarium head blight (FHB) on wheat in the U.S., whether ensembles of simple yet highly correlated models for predicting the risk of FHB epidemics, all generated from logistic regression, provided any benefit to predictive performance, despite relatively low levels of base model diversity. Three ensembling methods were explored: soft voting, weighted averaging of smaller subsets of the base models, and penalized regression as a stacking algorithm. Soft voting and weighted model averages were generally better at classification than the base models, though not universally so. The performances of stacked regressions were superior to those of the other two ensembling methods we analyzed in this study. Ensembling simple yet correlated models is computationally feasible and is therefore worth pursuing for models of epidemic risk.     

Efficient Immuno-Modulation of TH1/TH2 Biomarkers in 2,4-Dinitrofluorobenzene-Induced Atopic Dermatitis: Nanocarrier-Mediated Transcutaneous Co-Delivery of Anti-Inflammatory and Antioxidant Drugs

The present study was conducted with the aim to investigate the immuno-modulatory and histological stabilization effects of nanocarrier–based transcutaneous co-delivery of hydrocortisone (HC) and hydroxytyrosol (HT). In this investigation, the clinical and pharmacological efficacies of nanoparticle (NP)-based formulation to alleviate 2,4-dinitrofluorobenzene (DNFB)-induced atopic dermatitis (AD) was explored by using an NC/Nga mouse model. Ex vivo visual examination of AD induction in experimental mice indicated remarkable control of NP-based formulations in reducing pathological severity of AD-like skin lesions. Therapeutic effectiveness of NP-based formulations was also evaluated by comparing skin thickness of AD-induced NP-treated mice (456±27 µm) with that of atopic mice (916±37 µm). Analysis of the immuno-spectrum of AD also revealed the dominance of NP-based formulations in restraining immunoglobulin-E (IgE), histamine, prostaglandin-E₂ (PGE₂), vascular endothelial growth factor-α (VEGF-α), and T-helper cells (T_H1/T_H2) producing cytokines in serum and skin biopsies of tested mice. These anti-AD data were further supported by histological findings that revealed alleviated pathological features, including collagen fiber deposition, fibroblasts infiltration, and fragmentation of elastic fibers in experimental mice. Thus, NP-mediated transcutaneous co-delivery of HC and HT can be considered as a promising therapy for managing immunological and histological spectra associated with AD.     

Somatic embryogenesis of mangosteen

Young red leaf segments from 1 to 3?year-old seedlings of mangosteen (Garcinia mangostana L.) were incubated on different media, consisting of either B5 or Murashige and Skoog (MS) basal media and supplemented with several plant growth regulator at different levels, including 2,4-dichlorophenoxyacetic acid (2,4-D) and kinetin or 2,4-D and 6-benzyladenine (BA) or thidiazuron (TDZ) and BA. The highest percentage of globular structures (36.0?%) was obtained using MS medium with a combination of TDZ and BA, each at a concentration of 0.7?mg?l^?1. Histological observation of sections of globular structures showed accumulation of dense meristematic cells, a common characteristic of globular formation during somatic embryogenesis. Molecular analysis detected the gene somatic embryogenesis receptor-like kinase 1 (SERK1). This gene sequence showed a high similarity (73.0?C87.0?%) to previously reported plant SERK1.