<p>The good performance of [10]CPP for catching fullerenes C₆₀ and C₇₀ is made clear. The largest complexation energy corresponds to the C₇₀@[10]CPP complex: −53.32 kcal mol⁻¹ at the B97-D2/def2-TZVP level.</p>

As more and more accurate density functional methods emerge, the transferability of Bronsted-Evans-Polanyi (BEP) relationships obtained with previous models is an open question. In this work, BEP relationships derived from different density functional theory based calculations are analyzed to answer this question. In particular, BEP relationships linking the activation energy of O-H bond breaking reactions taking place on metallic surfaces with the adsorption energy of the reaction products are chosen as a case study. These relationships are obtained with the widely used Perdew-Wang (PW91) generalized gradient approximation (GGA) exchange-correlation functional and with the more accurate meta-GGA Tao-Perdew Staroverov-Scuseria (TPSS) one. We provide compelling evidence that BEP relationships derived from PW91 and TPSS functionals are essentially coincidental. This finding validates previously published BEP relationships and indicates that the reaction activation energy barrier can be obtained by the determination of the energy reaction descriptor value at the less computationally demanding GGA level; an important aspect to consider in future studies aimed at the computational design of catalysts :with improved characteristics.

Background: The systemic information enclosed in microarray data encodes relevant clues to overcome the poorly understood combination of genetic and environmental factors in Parkinson's disease (PD), which represents the major obstacle to understand its pathogenesis and to develop disease-modifying therapeutics. While several gene prioritization approaches have been proposed, none dominate over the rest. Instead, hybrid approaches seem to outperform individual approaches. Methods: A consensus strategy is proposed for PD related gene prioritization from mRNA microarray data based on the combination of three independent prioritization approaches: Limma, machine learning, and weighted gene co-expression networks. Results: The consensus strategy outperformed the individual approaches in terms of statistical significance, overall enrichment and early recognition ability. In addition to a significant biological relevance, the set of 50 genes prioritized exhibited an excellent early recognition ability (6 of the top 10 genes are directly associated with PD). 40 % of the prioritized genes were previously associated with PD including well-known PD related genes such as SLC18A2, TH or DRD2. Eight genes (CCNH, DLK1, PCDH8, SLIT1, DLD, PBX1, INSM1, and BMI1) were found to be significantly associated to biological process affected in PD, representing potentially novel PD biomarkers or therapeutic targets. Additionally, several metrics of standard use in chemoinformatics are proposed to evaluate the early recognition ability of gene prioritization tools. Conclusions: The proposed consensus strategy represents an efficient and biologically relevant approach for gene prioritization tasks providing a valuable decision-making tool for the study of PD pathogenesis and the development of disease-modifying PD therapeutics.

Antimicrobial peptides (AMPs) represent promising alternatives to fight against bacterial pathogens. However, cellular toxicity remains one of the main concerns in the early development of peptide-based drugs. This work introduces the first multitasking (mtk) computational model focused on performing simultaneous predictions of antibacterial activities, and cytotoxicities of peptides. The model was created from a data set containing 3592 cases, and it displayed accuracy higher than 96% for classifying/predicting peptides in both training and prediction (test) sets. The technique known as alanine scanning was computationally applied to illustrate the calculation of the quantitative contributions of the amino acids (in their respective positions of the sequence) to the biological effects of a defined peptide. A small library formed by 10 peptides was generated, where peptides were designed by considering the interpretations of the different descriptors in the mtk-computational model. All the peptides were predicted to exhibit high antibacterial activities against multiple bacterial strains, and low cytotoxicity against various cell types. The present mtk-computational model can be considered a very useful tool to support high throughput research for the discovery of potent and safe AMPs.

Antimicrobial peptides (AMPs) have emerged as promising therapeutic alternatives to fight against the diverse infections caused by different pathogenic microorganisms. In this context, theoretical approaches in bioinformatics have paved the way toward the creation of several in silico models capable of predicting antimicrobial activities of peptides. All current models have several significant handicaps, which prevent the efficient search for highly active AMPs. Here, we introduce the first multitarget (mt) chemo-bioinformatic model devoted to performing alignment-free prediction of antibacterial activity of peptides against multiple Gram-positive bacterial strains. The model was constructed from a data set containing 2488 cases of AMPS sequences assayed against at least 1 out of 50 Gram-positive bacterial strains. This mt-chemo-bioinformatic model displayed percentages of correct classification higher than 90.00% in both training and prediction (test) sets. For the first time, two computational approaches derived from basic concepts in genetics and molecular biology were applied, allowing the calculations of the relative contributions of any amino acid (in a defined position) to the antibacterial activity of an AMP and depending on the bacterial strain used in the biological assay. The present mt-chemo-bioinformatic model constitutes a powerful tool to enable the discovery of potent and versatile AMPs.

Invasive bladder tumours express the cell-surface Sialyl-Tn (STn) antigen, which stems from a premature stop in protein O-glycosylation. The STn antigen favours invasion, immune escape, and possibly chemotherapy resistance, making it attractive for target therapeutics. However, the events leading to such deregulation in protein glycosylation are mostly unknown. Since hypoxia is a salient feature of advanced stage tumours, we searched into how it influences bladder cancer cells glycophenotype, with emphasis on STn expression. Therefore, three bladder cancer cell lines with distinct genetic and molecular backgrounds (T24, 5637 and HT1376) were submitted to hypoxia. To disclose HIF-1 alpha-mediated events, experiments were also conducted in the presence of Deferoxamine Mesilate (Dfx), an inhibitor of HIF-1 alpha proteasomal degradation. In both conditions all cell lines overexpressed HIF-1 alpha and its transcriptionally-regulated protein CA-IX. This was accompanied by increased lactate biosynthesis, denoting a shift toward anaerobic metabolism. Concomitantly, T24 and 5637 cells acquired a more motile phenotype, consistent with their more mesenchymal characteristics. Moreover, hypoxia promoted STn antigen overexpression in all cell lines and enhanced the migration and invasion of those presenting more mesenchymal characteristics, in an HIF-1 alpha-dependent manner. These effects were reversed by reoxygenation, demonstrating that oxygen affects O-glycan extension. Glycoproteomics studies highlighted that STn was mainly present

In this work, we combined various parameters found in the literature for the choline cation, chloride anion, and ethylene glycol to set up force field models (FFMs) for a eutectic mixture, namely, ethaline (1:2 choline chloride/ethylene glycol (ChCl:2EG)). The validation of these models was carried out on the basis of physical and chemical properties, such as the density, expansion coefficient, enthalpy of vaporization, self-diffusion coefficients, isothermal compressibility, surface tension, and shear viscosity. After the initial evaluation of the FFMs, a refinement was found necessary and accomplished by taking into account polarization effects in a mean-field manner. This was achieved by rescaling the electrostatic charges of the ions based on partial charges derived from ab initio molecular dynamics (MD) simulations of the bulk system. Classical all-atom MD simulations performed over a large range of temperatures (298.15-373.15 K) using the refined FFMs clearly showed improved results, allowing a better prediction of experimental properties. Specific structural properties (radial distribution functions and hydrogen bonding) were then analyzed in order to support the adequacy of the proposed refinement. The final selected FFM leads to excellent agreement between simulated and experimental data on dynamic and structural properties. Moreover, compared to the previously reported force field model (Perkins, S. L.; Painter, P.; Colina, C. M. Experimental and Computational Studies of Choline Chloride-Based Deep Eutectic Solvents. J. Chem. Eng. Data 2014, 59, 3652-3662), a 10% improvement in simulated transport properties, i.e., self-diffusion coefficients, was achieved. The isothermal compressibility, surface tension, and shear viscosity for ethaline are accessed in MD simulations for the first time.

The medium-chain acyl-CoA dehydrogenase (MCAD) is a mitochondrial enzyme that catalyzes the first step of mitochondrial fatty acid beta-oxidation (mFAO) pathway. Its deficiency is the most common genetic disorder of mFAO. Many of the MCAD disease-causing variants, including the most common p.K304E variant, show loss of function due to protein misfolding. Herein, we used molecular dynamics simulations to provide insights into the structural stability and dynamic behavior of MCAD wild-type (MCADwt) and validate a structure that would allow reliable new studies on its variants. Our results revealed that in both proteins the flavin adenine dinucleotide (FAD) has an important structural role on the tetramer stability and also in maintaining the volume of the enzyme catalytic pockets. We confirmed that the presence of substrate changes the dynamics of the catalytic pockets and increases FAD affinity. A comparison between the porcine MCADwt (pMCADwt) and human MCADwt (hMCADwt) structures revealed that both proteins are essentially similar and that the reversion of the double mutant E376G/T255E of hMCAD enzyme does not affect the structure of the protein neither its behavior in simulation. Our validated hMCADwt structure is crucial for complementing and accelerating the experimental studies aiming for the discovery and development of potential stabilizers of MCAD variants as candidates for the treatment of MCAD deficiency (MCADD).