Dynamical Rearrangement of Human Epidermal Growth Factor Receptor 2 upon Antibody Binding: Effects on the Dimerization.

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 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 (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.

PTML Model of Enzyme Subclasses for Mining the Proteome of Biofuel Producing Microorganisms.

Predicting enzyme function and enzyme subclasses is always a key objective in fields such as biotechnology, biochemistry, medicinal chemistry, physiology, and so on. The Protein Data Bank (PDB) is the largest information archive of biological macromolecular structures, with more than 150 000 entries for proteins, nucleic acids, and complex assemblies. Among these entries, there are more than 4000 proteins whose functions remain unknown because no detectable homology to proteins whose functions are known has been found. The problem is that our ability to isolate proteins and identify their sequences far exceeds our ability to assign them a defined function. As a result, there is a growing interest in this topic, and several methods have been developed to identify protein function based on these innovative approaches. In this work, we have applied perturbation theory to an original data set consisting of 19 187 enzymes representing all 59 subclasses present in the protein data bank. In addition, we developed a series of artificial neural network models able to predict enzyme-enzyme pairs of query-template sequences with accuracy, specificity, and sensitivity greater than 90% in both training and validation series. As a likely application of this methodology and to further validate our approach, we used our novel model to predict a set of enzymes belonging to the yeast Pichia stipites. This yeast has been widely studied because it is commonly present in nature and produces a high ethanol yield by converting lignocellulosic biomass into bioethanol through the xylose reductase enzyme. Using this premise, we tested our model on 222 enzymes including xylose reductase, that is, the enzyme responsible for the conversion of biomass into bioethanol.

Computational MitoTarget Scanning Based on Topological Vacancies of Single-Walled Carbon Nanotubes with the Human Mitochondrial Voltage-Dependent Anion Channel (hVDAC1).

We present an in silico approach for modeling the noncovalent interactions between the human mitochondrial voltage-dependent anion channel (hVDAC1) and a family of single-walled carbon nanotubes (SWCNTs) with a defined pattern of topological vacancies ( v = 1-16), obtained by removing atoms from the SWCNT surface. The general results showed more stable docking interaction complexes (SWCNT-hVDAC1), with more negative Gibbs free energy of binding affinity values, and a strong dependence on the vacancy number ( R2 = 0.93) and vacancy formation energy ( R2 = 0.96). In addition, for most of the SWCNT vacancies that were analyzed, the interatomic distances for the interactions of the SWCNT-hVDAC1 complex with the functional catalytic residues (i.e., Pro7, Gln199, Gln182, Phe181, Val20, Asp19, Lys15, Gly14, Asp12, Ala11, and Arg18) that form the hVDAC1 active site (i.e., the voltage-sensing N-terminal α-helix segment) were very similar to or shorter than the interatomic distances of these residues for ATP-hVDAC1 interactions. In particular, the hVDAC1 residues that can be phosphorylated like Tyr10, Tyr198, and Se16 were significantly perturbed by the interactions with SWCNT with at least nine vacancies. In addition, the SWCNT vacancy family members can affect the flexibility properties of the hVDAC1 N-terminal α-helix segment inducing different patterns of local perturbations in inter-residue communication. Finally, vacancy quantitative structure-binding relationships (V-QSBRs) were unveiled for setting up a robust model that can predict the strength of docking interactions between SWCNTs with a specific topological vacancy and hVDAC1. The developed V-QSBR model classified properly all of the SWCNTs with a different number of SWCNT vacancies with exceptional sensitivity and specificity (both equal to 100%), indicating a strong potential to unequivocally predict the influence of SWCNT vacancies on the mitochondrial channel interactions.

Improved Force Field Model for the Deep Eutectic Solvent Ethaline: Reliable Physicochemical Properties.

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.

Effect of the Exchange-Correlation Potential on the Transferability of Brønsted-Evans-Polanyi Relationships in Heterogeneous Catalysis.

As more and more accurate density functional methods emerge, the transferability of Brønsted-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.

Ligand-Based Virtual Screening Using Tailored Ensembles: A Prioritization Tool for Dual A2AAdenosine Receptor Antagonists / Monoamine Oxidase B Inhibitors.

BACKGROUND: Virtual Screening methodologies have emerged as efficient alternatives for the discovery of new drug candidates. At the same time, ensemble methods are nowadays frequently used to overcome the limitations of employing a single model in ligand-based drug design. However, many applications of ensemble methods to this area do not consider important aspects related to both virtual screening and the modeling process. During the application of ensemble methods to virtual screening the proper validation of the models in virtual screening conditions is often neglected. No analysis of the diversity of the ensemble members is performed frequently or no considerations regarding the applicability domain of the base models are being made. METHODS: In this research, we review basic concepts and definitions related to virtual screening. We comment recent applications of ensemble methods to ligand-based virtual screening and highlight their advantages and limitations. RESULTS: Next, we propose a method based on genetic algorithms optimization for the generation of virtual screening tailored ensembles which address the previously identified problems in the current applications of ensemble methods to virtual screening. CONCLUSION: Finally, the proposed methodology is successfully applied to the generation of ensemble models for the ligand-based virtual screening of dual target A2A adenosine receptor antagonists and MAO-B inhibitors as potential Parkinson's disease therapeutics.

Density functional theory study of the water dissociation on platinum surfaces: general trends.

We report a comparative periodic density functional theory study of the reaction of water dissociation on five platinum surfaces, e.g., Pt(111) Pt(100), Pt(110), Pt(211), and Pt(321). These surfaces were chosen to study the surface structural effects in the reaction of water dissociation. It was found that water molecules adsorb stronger on surfaces presenting low coordinated atoms in the surface. In the cases of the stepped Pt(110) and kinked Pt(321) surfaces, the activation energy barriers are smaller than the adsorption energies for the water molecule on the corresponding surfaces. Therefore, the calculations suggest that the dissociation reaction will take place preferentially at corner or edge sites on platinum particles with the (110) orientation. The inclusion of the results obtained in this work in previous derived BEP relationships confirms that the adsorption energy of the reaction products arises as the most appropriate descriptor for water dissociation on transition metal surfaces.

Model for high-throughput screening of multitarget drugs in chemical neurosciences: synthesis, assay, and theoretic study of rasagiline carbamates.

The disappointing results obtained in recent clinical trials renew the interest in experimental/computational techniques for the discovery of neuroprotective drugs. In this context, multitarget or multiplexing QSAR models (mt-QSAR/mx-QSAR) may help to predict neurotoxicity/neuroprotective effects of drugs in multiple assays, on drug targets, and in model organisms. In this work, we study a data set downloaded from CHEMBL; each data point (>8000) contains the values of one out of 37 possible measures of activity, 493 assays, 169 molecular or cellular targets, and 11 different organisms (including human) for a given compound. In this work, we introduce the first mx-QSAR model for neurotoxicity/neuroprotective effects of drugs based on the MARCH-INSIDE (MI) method. First, we used MI to calculate the stochastic spectral moments (structural descriptors) of all compounds. Next, we found a model that classified correctly 2955 out of 3548 total cases in the training and validation series with Accuracy, Sensitivity, and Specificity values>80%. The model also showed excellent results in Computational-Chemistry simulations of High-Throughput Screening (CCHTS) experiments, with accuracy=90.6% for 4671 positive cases. Next, we reported the synthesis, characterization, and experimental assays of new rasagiline derivatives. We carried out three different experimental tests: assay (1) in the absence of neurotoxic agents, assay (2) in the presence of glutamate, and assay (3) in the presence of H2O2. Compounds 11 with 27.4%, 8 with 11.6%, and 9 with 15.4% showed the highest neuroprotective effects in assays (1), (2), and (3), respectively. After that, we used the mx-QSAR model to carry out a CCHTS of the new compounds in >400 unique pharmacological tests not carried out experimentally. Consequently, this model may become a promising auxiliary tool for the discovery of new drugs for the treatment of neurodegenerative diseases.

The impact of triamcinolone acetonide in early breast capsule formation in a rabbit model.

BACKGROUND: The etiology and clinical treatment of capsular contracture remain unresolved as the causes may be multifactorial. Triamcinolone acetonide applied in the pocket during surgery was reported to be ineffective in prevention of capsular contracture. However, if injected 4-6 weeks after surgery or as a treatment for capsular contracture, decreased applanation tonometry measurements and pain were observed. It was assumed that intraoperative application of triamcinolone was not effective because its effect does not last long enough. However, betadine, antibiotics, and fibrin were found to be effective in preventing capsular contracture with intraoperative applications and are more effective in the early phases of wound healing than in later stages. The role of triamcinolone acetonide in capsule formation is unknown. The purpose of this study was to determine if triamcinolone acetonide modulates breast capsule formation or capsular contracture in the early phases of wound healing in a rabbit model. METHODS: Rabbits (n=19) were implanted with one tissue expander and two breast implants and were killed at 4 weeks. Implant pocket groups were (1) Control (n=10) and (2) Triamcinolone (n=9). Pressure/volume curves and histological, immunological, and microbiological evaluations were performed. Operating room air samples and contact skin samples were collected for microbiological evaluation. RESULTS: In the triamcinolone group, a decreased capsular thickness, mild and mononuclear inflammation, and negative or mild angiogenesis were observed. There were no significant differences in intracapsular pressure, fusiform cell density, connective tissue, organization of collagen fibers, and microbiological results between the groups. There was no significant difference in the dialysate levels of IL-8 and TNF-α, but correlation between IL-8 and TNF-α was observed. CONCLUSION: Triamcinolone acetonide during breast implantation influences early capsule formation and may reduce capsular contracture. LEVEL OF EVIDENCE III: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors at www.springer.com/00266.

On the Need for Spin Polarization in Heterogeneously Catalyzed Reactions on Nonmagnetic Metallic Surfaces.

A series of reactions including water, oxygen, hydrogen and nitric oxide dissociation and carbon monoxide or nitric oxide oxidations catalyzed by metallic surfaces have been investigated by means of periodic density functional calculations with the main aim of establishing the importance of spin polarization when the substrate is nonmagnetic. Numerical differences in the calculated total energies and bond lengths of the breaking/forming bonds corresponding to spin restricted or spin unrestricted formalisms are usually smaller than the inherent error of density functional theory based methods. Nevertheless, it is important to insist on the fact that the spin polarized solution exists and is lower in energy than the one corresponding to the spin restricted formalism, as one would expect, and from a practical point of view, results obtained without taking spin polarization into account lead to the same description of the potential energy surface.