The aim of this paper is to quantify the changes of the interionic and ion-solvent interactions in mixtures of imidazolium-based ionic liquids, having tetrafluoroborate (BmimBF(4)), hexafluorophosphate (BmimPF(6)), trifluoromethylsulfonate (BmimTFO), or bis(trifluoromethanesulfonyl)imide (BmimTFSI), anions, and polar aprotic molecular solvents, such as acetonitrile (AN), gamma-butyrolactone (GBL), and propylene carbonate (PC). For this purpose, we calculate, using the nearest-neighbor approach, the average distance between the imidazolium ring H atom in positions 2, 4, and 5 (H-2,H-4,H-5) and the nearest high-electronegativity atom of the solvent or anion (X) as distance descriptors, and the mean angle formed by the C-2,C-4,C-5-H-2,H-4,H-5 bond and the H-2,H-4,H-5 center dot center dot center dot X axis around the H-2,H-4,H-5 atom as angular descriptors of the cation-anion and cation-solvent interactions around the ring C-H groups. The behavior of these descriptors as a function of the ionic liquid mole fraction is analyzed in detail. The obtained results show that the extent of the change of these descriptors with respect to their values in the neat ionic liquid depends both on the nature of the anion and on the mixture composition. Thus, in the case of the mixtures of the molecular solvents with BmimBF(4) and BmimTFO, a small change of the distance and a drastic increase of the angular descriptor corresponding to the cation-anion interactions are observed with decreasing mole fraction of the ionic liquid, indicating that the anion moves from the above/below position (with respect to the imidazolium ring plane) to a position that is nearly linearly aligned with the C-2-H-2 bond and hinders the possible interaction between the C-2-H-2 group and the solvent molecules. On the other hand, in the case of mixtures of BmimTFSI and BmimPF6 with the molecular solvents, both the observed increase of the distance descriptor and the slight change of the angular descriptor with decreasing ionic liquid mole fraction are compatible with the direct interactions of the solvent with the C-2-H-2 group. The behavior of these descriptors is correlated with the experimentally observed H-1 chemical shift of the C-2-H-2 group and the red shift of the C-2-H-2 vibrational mode, particularly at low ionic liquid mole fractions. The present results are thus of great help in interpreting these experimental observations.

Human epidermal growth factor 2 (HER2) is a ligand-free tyrosine kinase receptor of the HER family that is overexpressed in some of the most aggressive tumours. Although it is known that HER2 dimerization involves a specific region of its extracellular domain, the so-called "dimerization arm", the mechanism of dimerization inhibition remains uncertain. However, uncovering how antibody interactions lead to inhibition of HER2 dimerization is of key importance in understanding its role in tumour progression and therapy. Herein, we employed several computational modelling techniques for a molecular-level understanding of the interactions between HER and specific anti-HER2 antibodies, namely an antigen-binding (Fab) fragment (F0178) and a single-chain variable fragment from Trastuzumab (scFv). Specifically, we investigated the effects of antibody-HER2 interactions on the key residues of "dimerization arm" from molecular dynamics (MD) simulations of unbound HER (in a total of 1 mu s), as well as ScFv:HER2 and F0178:HER2 complexes (for a total of 2.5 mu s). A deep surface analysis of HER receptor revealed that the binding of specific anti-HER2 antibodies induced conformational changes both in the interfacial residues, which was expected, and in the ECDII (extracellular domain), in particular at the "dimerization arm", which is critical in establishing protein-protein interface (PPI) interactions. Our results support and advance the knowledge on the already described trastuzumab effect on blocking HER2 dimerization through synergistic inhibition and/or steric hindrance. Furthermore, our approach offers a new strategy for fine-tuning target activity through allosteric ligands.

<jats:title>Abstract</jats:title><jats:p>Human epidermal growth factor 2 (HER2) is a ligand-free tyrosine kinase receptor of the HER family that is overexpressed in some of the most aggressive tumours. Treatment of HER2+ breast cancers with the humanized monoclonal anti-HER2 antibody (Trastuzumab) revealed highly effective, encouraging the development of various HER2-specific antibodies, kinase inhibitors and dimerization inhibitors for cancer therapy. Although it is known that HER2 dimerization involves a specific region of its extracellular domain, the so-called “dimerization arm”, the mechanism of dimerization inhibition remains uncertain. However, uncovering how antibody interactions lead to inhibition of HER2 dimerization is of key importance in understanding its role in tumour progression and therapy. Herein, we employed several computational modelling techniques for a molecular-level understanding of the interactions between HER and specific anti-HER2 antibodies, namely an antigen-binding (Fab) fragment (F0178) and a single chain variable fragment from Trastuzumab (scFv). Specifically, we investigated the effects of antibody-HER2 interactions on the key residues of “dimerization arm” from molecular dynamics (MD) simulations of unbound HER (in a total of 1 µs), as well as scFv:HER2 and F0178:HER2 complexes (for a total of 2.5 µs). A deep surface analysis of HER receptor revealed that the binding of specific anti-HER2 antibodies induced conformational changes both in the interfacial residues, which was expected, and in the ECDII, in particular at the “dimerization arm”, which is critical in establishing protein-protein interface (PPI) interactions. Our results support and advance the knowledge on the already described trastuzumab effect on blocking HER2 dimerization through synergistic inhibition and/or steric hindrance. Furthermore, our approach offers a new strategy for fine-tuning target activity through allosteric ligands.</jats:p><jats:sec><jats:title>Author summary</jats:title><jats:p>Increasing insight into the genetics and molecular biology of diseases has resulted in the identification of a high number of potential molecular targets for drug discovery and development. Human Epidermal Growth Factor Receptor 2 (HER2) is one of the most relevant Epidermal Growth Factor Receptor (EGFR) members, whose overexpression has been shown to play an important role in the development and progression of certain aggressive types of breast cancer. Thus, the development of novel approaches on anti-HER2 therapies is quiet relevant. Molecular modelling and simulation can be used to bring new perspectives, both in structure-based drug design and to provide atomistic information of the intermolecular coupling dynamics between inhibitors and receptors via interactive computographic software. Considering my interest in drug design and mechanisms of action using integrated in silico approaches, herein, I have used multiple methods to evaluate how HER2 coupling to its different partners could alter this functional mechanism. The results suggest that the antibodies fragments studied show different dynamic complexes with HER2 although both could contribute to downstream of the tumour cell receptor pathways. I do believe that future research breakthroughs with aid of chemo-bioinformatics will allow a more comprehensive perception on biomedicine.</jats:p></jats:sec>

<p>The endohedral alkali cations in M⁺@C₆₀⋯[10]CPP complexes boost the near infrared absorption bands associated with charge transfer from the nanoring to the fullerene.</p>

Background: Malaria or Paludism is a tropical disease caused by parasites of the Plasmodium genre and transmitted to humans through the bite of infected mosquitos of the Anopheles genre. This pathology is considered one of the first causes of death in tropical countries and, despite several existing therapies, they have a high toxicity. Computational methods based on Quantitative Structure-Activity Relationship studies have been widely used in drug design work flows. Objective: The main goal of the current research is to develop computational models for the identification of antimalarial hit compounds. Materials and Methods: For this, a data set suitable for the modeling of the antimalarial activity of chemical compounds was compiled from the literature and subjected to a thorough curation process. In addition, the performance of a diverse set of ensemble-based classification methodologies was evaluated and one of these ensembles was selected as the most suitable for the identification of antimalarial hits based on its virtual screening performance. Data curation was conducted to minimize noise. Among the explored ensemble-based methods, the one combining Genetic Algorithms for the selection of the base classifiers and Majority Vote for their aggregation showed the best performance. Results: Our results also show that ensemble modeling is an effective strategy for the QSAR modeling of highly heterogeneous datasets in the discovery of potential antimalarial compounds. Conclusion: It was determined that the best performing ensembles were those that use Genetic Algorithms as a method of selection of base models and Majority Vote as the aggregation method

Bioactive peptides participate in numerous metabolic functions of living organisms and have emerged as potential therapeutics on a diverse range of diseases. Albeit peptide design does not go without challenges, overwhelming advancements on in silico methodologies have increased the scope of peptidebased drug design and discovery to an unprecedented amount. Within an in silico model versus an experimental validation scenario, this review aims to summarize and discuss how different in silico techniques contribute at present to the design of peptide-based molecules. Published in silico results from 2014 to 2018 were selected and discriminated in major methodological groups, allowing a transversal analysis, promoting a landscape vision and asserting its increasing value in drug design.

Heterogeneously catalyzed reactions take place at the catalyst surface where, depending on the conditions and process, the reacting molecules are either in the gas or liquid phase. In the latter case, computational heterogeneous catalysis studies usually neglect solvent effects. In this work, we systematically analyze how the electrostatic contribution to solvent effects influences the atomic structure of the reactants and products as well as the adsorption, activation, and reaction energy for the dissociation of water on several planar and stepped transition metal surfaces. The solvent effects were accounted for through an implicit model that describes the effect of electrostatics, cavitation, and dispersion on the interaction between the solute and solvent. The present study shows that the activation energy barriers are only slightly influenced by the inclusion of the electrostatic solvent effects accounted for in a continuum solvent approach, whereas the adsorption energies of the reactants or products are significantly affected. Encouragingly, the linear equations corresponding to the Bronsted-Evans-Polanyi relationships (BEPs), relating the activation energies for the dissociation reaction with a suitable descriptor, e.g. the adsorption energies of the products of the reaction on the difference surfaces, are similar in the presence or in the absence of the solvent. Despite the associated uncertainties, this suggests that BEP relationships derived without the implicit consideration of the solvent are still valid for predicting the activation energy barriers of catalytic reactions from a reaction descriptor.

To streamline the interpretation of vibrational spectra, this work introduces the use of Bayesian linear regression with automatic relevance determination as a viable approach to decompose the atomic motions along any vibrational mode as a weighted combination of displacements along chemically meaningful internal coordinates. This novel approach denominated vibrational mode automatic relevance determination (VMARD) is presented and compared with the well-established potential energy decomposition (PED) scheme. Good agreement is generally attained between the two methods. VMARD returns a decomposition of the atomic displacement using only a small number of internal coordinates, thus aiding the interpretation of the vibrational spectra. Moreover, the results show that the VMARD descriptions are resilient toward the addition of additional internal coordinates, concise description of the vibrational modes despite the use of redundant internal coordinates. Potential applications of VMARD involving the gathering of physical insights on the atomic motions along the reaction coordinate at transition state structures, as well as the improvement of theoretically predicted vibrational frequencies, are also presented under a proof-of concept perspective.