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.

Nanotechnology has been an important tool for the production of nanoparticles with controlled release of drugs for therapeutic applications. Here, we produced solid lipid nanoparticles (SLN) loaded with curcumin and capsaicin (NCC) following the overarching goals of green chemistry. Currently, besides evaluating the composition, and size of these, it is necessary to understand the interactions between nanoparticles and the biomolecules present in the biological medium. For this, assays were conducted in order to evaluate the potential formation of the protein 'corona', and to better understand the results obtained in vitro, we also performed an interaction study, in silico, between the NCC components and the main serum protein, albumin. In the first hour of contact between the NCC and the culture medium showed fluctuation in the diameter of the NCC. However, after 24 and 48 h of the incubation period, all NCC concentrations showed an increase in size, which can be attributed to plasma protein adsorption. Since, hard corona takes a few seconds, while the soft corona can be formed in minutes up to a few hours. On the other hand, best docking binding-poses of interaction for the formed docking complexes evaluated suggest interactions following the docking affinity like free energy FEB (Tween 80bovine serum albumin) approximate to FEB (Span 80-bovine serum albumin) showing a pharmacodynamic pattern based in non-covalent hydrophobic interactions with the bovine serum albumin binding-site. Our in silico results clarify and reinforce our in vitro findings of corona formation, which represents the real interaction with cell membranes in vivo.