A Computational Analysis of the Collision-Induced Dissociation Mechanisms of the Dipeptide Glycine-Serine Under Mass Spectrometry

Collision-induced dissociation (CID) of peptides using tandem mass spectrometry (MS) has been used to determine the identity of peptides and other large biological molecules. Mass spectrometry (MS) is a useful tool for determining the identity of molecules based on their interaction with electromagnetic fields. If coupled with another method like infrared (IR) vibrational spectroscopy, MS can provide structural information, but in its own right, MS can only provide the mass-to-charge (m/z) ratio of the fragments produced, which may not be enough information to determine the mechanism of the collision-induced dissociation (CID) of the molecule. In this case, theoretical calculations provide a useful companion for MS data and yield clues about the energetics of the dissociation. In this study, negative ion electrospray tandem MS was used to study the CID of the deprotonated dipeptide glycine-serine (Gly-Ser). Though negative ion MS is not as popular a choice as positive ion MS, studies by Bowie et al. show that it yields unique clues about molecular structure which complement positive ion spectroscopy, such as characteristic fragmentations like the loss of formaldehyde from the serine residue.2 The increase in the collision energy in the mass spectrometer alters the flexibility of the dipeptide backbone, enabling isomerizations (reactions not resulting in a fragment loss) and dissociations to take place. The mechanism of the CID of Gly-Ser was studied using two computational methods, B3LYP/6-311+G* and M06-2X/6-311++G**. The main pathway for molecular dissociation was analyzed in 5 conformers in an attempt to verify the initial mechanism proposed by Dr. James Swan after examination of the MS data. The results suggest that the loss of formaldehyde from serine, which Bowie et al. indicates is a characteristic of the presence of serine in a protein residue, is an endothermic reaction that is made possible by the conversion of the translational energy of the ion into internal energy as the ion collides with the inert collision gas. It has also been determined that the M06-2X functional¿s improved description of medium and long-range correlation makes it more effective than the B3LYP functional at finding elusive transition states. M06-2X also more accurately predicts the energy of those transition states than does B3LYP. A second CID mechanism, which passes through intermediates with the same m/z ratio as the main pathway for molecular dissociation, but different structures, including a diketopiperazine intermediate, was also studied. This pathway for molecular dissociation was analyzed with 3 conformers and the M06-2X functional, due to its previously determined effectiveness. The results suggest that the latter pathway, which meets the same intermediate masses as the first mechanism, is lower in overall energy and therefore a more likely pathway of dissociation than the first mechanism.

Computational selection of inhibitors of Abeta aggregation and neuronal toxicity.

Alzheimer's disease (AD) is characterized by the cerebral accumulation of misfolded and aggregated amyloid-beta protein (Abeta). Disease symptoms can be alleviated, in vitro and in vivo, by 'beta-sheet breaker' pentapeptides that reduce plaque load. However the peptide nature of these compounds, made them biologically unstable and unable to penetrate membranes with high efficiency. The main goal of this study was to use computational methods to identify small molecule mimetics with better drug-like properties. For this purpose, the docked conformations of the active peptides were used to identify compounds with similar activities. A series of related beta-sheet breaker peptides were docked to solid state NMR structures of a fibrillar form of Abeta. The lowest energy conformations of the active peptides were used to design three dimensional (3D)-pharmacophores, suitable for screening the NCI database with Unity. Small molecular weight compounds with physicochemical features and a conformation similar to the active peptides were selected, ranked by docking and biochemical parameters. Of 16 diverse compounds selected for experimental screening, 2 prevented and reversed Abeta aggregation at 2-3microM concentration, as measured by Thioflavin T (ThT) fluorescence and ELISA assays. They also prevented the toxic effects of aggregated Abeta on neuroblastoma cells. Their low molecular weight and aqueous solubility makes them promising lead compounds for treating AD.

Computational identification and functional validation of regulatory motifs in cartilage-expressed genes.

Chondrocyte gene regulation is important for the generation and maintenance of cartilage tissues. Several regulatory factors have been identified that play a role in chondrogenesis, including the positive transacting factors of the SOX family such as SOX9, SOX5, and SOX6, as well as negative transacting factors such as C/EBP and delta EF1. However, a complete understanding of the intricate regulatory network that governs the tissue-specific expression of cartilage genes is not yet available. We have taken a computational approach to identify cis-regulatory, transcription factor (TF) binding motifs in a set of cartilage characteristic genes to better define the transcriptional regulatory networks that regulate chondrogenesis. Our computational methods have identified several TFs, whose binding profiles are available in the TRANSFAC database, as important to chondrogenesis. In addition, a cartilage-specific SOX-binding profile was constructed and used to identify both known, and novel, functional paired SOX-binding motifs in chondrocyte genes. Using DNA pattern-recognition algorithms, we have also identified cis-regulatory elements for unknown TFs. We have validated our computational predictions through mutational analyses in cell transfection experiments. One novel regulatory motif, N1, found at high frequency in the COL2A1 promoter, was found to bind to chondrocyte nuclear proteins. Mutational analyses suggest that this motif binds a repressive factor that regulates basal levels of the COL2A1 promoter.

A packing computational method relating fractal particle size distribution and void fraction in granular media

The study of granular systems is of great interest to many fields of science and technology. The packing of particles affects to the physical properties of the granular system. In particular, the crucial influence of particle size distribution (PSD) on the random packing structure increase the interest in relating both, either theoretically or by computational methods. A packing computational method is developed in order to estimate the void fraction corresponding to a fractal-like particle size distribution.

A Packing Computational Method Relating Fractal Particle Size Distribution and Void Fraction in Granular Media

The study of granular systems is of great interest to many fields of science and technology. The packing of particles affects to the physical properties of the granular system. In particular, the crucial influence of particle size distribution (PSD) on the random packing structure increase the interest in relating both, either theoretically or by computational methods. A packing computational method is developed in order to estimate the void fraction corresponding to a fractal-like particle size distribution.

Assessing computational tools for the discovery of transcription factor binding sites

The prediction of regulatory elements is a problem where computational methods offer great hope. Over the past few years, numerous tools have become available for this task. The purpose of the current assessment is twofold: to provide some guidance to users regarding the accuracy of currently available tools in various settings, and to provide a benchmark of data sets for assessing future tools.

Substrate specifity of protein kinases and computational prediction of substrates

To ensure signalling fidelity, kinases must act only on a defined subset of cellular targets. Appreciating the basis for this substrate specificity is essential for understanding the role of an individual protein kinase in a particular cellular process. The specificity in the cell is determined by a combination of peptide specificity of the kinase (the molecular recognition of the sequence surrounding the phosphorylation site), substrate recruitment and phosphatase activity. Peptide specificity plays a crucial role and depends on the complementarity between the kinase and the substrate and therefore on their three-dimensional structures. Methods for experimental identification of kinase substrates and characterization of specificity are expensive and laborious, therefore, computational approaches are being developed to reduce the amount of experimental work required in substrate identification. We discuss the structural basis of substrate specificity of protein kinases and review the experimental and computational methods used to obtain specificity information. (c) 2005 Elsevier B.V. All rights reserved.

Computational modeling of optical trapping of vaterite microspheres

The ability to grow microscopic spherical birefringent crystals of vaterite, a calcium carbonate mineral, has allowed the development of an optical microrheometer based on optical tweezers. However, since these crystals are birefringent, and worse, are expected to have non-uniform birefringence, computational modeling of the microrheometer is a highly challenging task. Modeling the microrheometer - and optical tweezers in general - typically requires large numbers of repeated calculations for the same trapped particle. This places strong demands on the efficiency of computational methods used. While our usual method of choice for computational modelling of optical tweezers - the T-matrix method - meets this requirement of efficiency, it is restricted to homogeneous isotropic particles. General methods that can model complex structures such as the vaterite particles, such as finite-difference time-domain (FDTD) or finite-difference frequency-domain (FDFD) methods, are inefficient. Therefore, we have developed a hybrid FDFD/T-matrix method that combines the generality of volume-discretisation methods such as FDFD with the efficiency of the T-matrix method. We have used this hybrid method to calculate optical forces and torques on model vaterite spheres in optical traps. We present and compare the results of computational modelling and experimental measurements.

Impact of co-adsorbed oxygen on crotonaldehyde adsorption over gold nanoclusters:a computational study

Crotonaldehyde (2-butenal) adsorption over gold sub-nanometer particles, and the influence of co-adsorbed oxygen, has been systematically investigated by computational methods. Using density functional theory, the adsorption energetics of crotonaldehyde on bare and oxidised gold clusters (Au , d = 0.8 nm) were determined as a function of oxygen coverage and coordination geometry. At low oxygen coverage, sites are available for which crotonaldehyde adsorption is enhanced relative to bare Au clusters by 10 kJ mol. At higher oxygen coverage, crotonaldehyde is forced to adsorb in close proximity to oxygen weakening adsorption by up to 60 kJ mol relative to bare Au. Bonding geometries, density of states plots and Bader analysis, are used to elucidate crotonaldehyde bonding to gold nanoparticles in terms of partial electron transfer from Au to crotonaldehyde, and note that donation to gold from crotonaldehyde also becomes significant following metal oxidation. At high oxygen coverage we find that all molecular adsorption sites have a neighbouring, destabilising, oxygen adatom so that despite enhanced donation, crotonaldehyde adsorption is always weakened by steric interactions. For a larger cluster (Au, d = 1.1 nm) crotonaldehyde adsorption is destabilized in this way even at a low oxygen coverage. These findings provide a quantitative framework to underpin the experimentally observed influence of oxygen on the selective oxidation of crotyl alcohol to crotonaldehyde over gold and gold-palladium alloys. © 2014 the Partner Organisations.

Theory of hybrid computational and physical measurement-based integration of thermal and dimensional measurement

In dimensional metrology, often the largest source of uncertainty of measurement is thermal variation. Dimensional measurements are currently scaled linearly, using ambient temperature measurements and coefficients of thermal expansion, to ideal metrology conditions at 20˚C. This scaling is particularly difficult to implement with confidence in large volumes as the temperature is unlikely to be uniform, resulting in thermal gradients. A number of well-established computational methods are used in the design phase of product development for the prediction of thermal and gravitational effects, which could be used to a greater extent in metrology. This paper outlines the theory of how physical measurements of dimension and temperature can be combined more comprehensively throughout the product lifecycle, from design through to the manufacturing phase. The Hybrid Metrology concept is also introduced: an approach to metrology, which promises to improve product and equipment integrity in future manufacturing environments. The Hybrid Metrology System combines various state of the art physical dimensional and temperature measurement techniques with established computational methods to better predict thermal and gravitational effects.

Aerodynamic optimization using Adjoint methods and parametric CAD models

This paper describes an implementation of a method capable of integrating parametric, feature based, CAD models based on commercial software (CATIA) with the SU2 software framework. To exploit the adjoint based methods for aerodynamic optimisation within the SU2, a formulation to obtain geometric sensitivities directly from the commercial CAD parameterisation is introduced, enabling the calculation of gradients with respect to CAD based design variables. To assess the accuracy and efficiency of the alternative approach, two aerodynamic optimisation problems are investigated: an inviscid, 3D, problem with multiple constraints, and a 2D high-lift aerofoil, viscous problem without any constraints. Initial results show the new parameterisation obtaining reliable optimums, with similar levels of performance of the software native parameterisations. In the final paper, details of computing CAD sensitivities will be provided, including accuracy as well as linking geometric sensitivities to aerodynamic objective functions and constraints; the impact in the robustness of the overall method will be assessed and alternative parameterisations will be included.

Unconventional Catalysis in Organic Chemistry: a Computational Mechanistic Study

Catalysis plays a vital role in modern synthetic chemistry. However, even if conventional catalysis (organo-catalysis, metal-catalysis and enzyme-catalysis) has provided outstanding results, various unconventional ways to make chemical reactions more effective appear now very promising. Computational methods can be of great help to reach a deeper comprehension of these chemical processes. The methodologies employed in this thesis are Quantum-Mechanical (QM), Molecular Mechanics (MM) and hybrid Quantum-Mechanical/Molecular Mechanics (QM/MM) methods. In this abstract the results are briefly summarised. The first unconventional catalysis investigated consists in the application of Oriented External Electric Fields (OEEFs) to SN2 and 4e-electrocyclic reactions. SN2 reactions with back-side mechanism can be catalysed or inhibited by the presence of an OEEF. Moreover, OEEFs can inhibit back-side mechanism (Walden inversion of configuration) and promote the naturally unfavoured front-side mechanism (retention of configuration). Electrocyclic ring opening reaction of 3-substituted cyclobutene molecules can occur with inward or outward mechanisms depending on the nature of substituent groups on the cyclobutene structure (torquoselectivity principle). OEEFs can catalyse the naturally favoured pathway or circumvent the torquoselectivity principle leading to different stereoisomers. The second case study is based on Carbon Nanotubes (CNTs) working as nano-reactors: the reaction of ethyl chloride with chloride anion inside CNTs was investigated. In addition to the SN2 mechanism, syn and anti-E2 reactions are possible. These reactions inside CNTs of different radii were examined with hybrid QM/MM methods, finding that these processes can be both catalysed and inhibited by the CNT diameter. The results suggest that electrostatic effects govern the activation energy variations inside CNTs. Finally, a new biochemical approach, based on the use of DNA catalyst was investigated at QM level. Deoxyribozyme 9DB1 catalyses the RNA ligation allowing the regioselective formation of the 3'-5' bond, following an addition-elimination two-step mechanism.

Deep learning and embeddings for problems of computational biology

The development of Next Generation Sequencing promotes Biology in the Big Data era. The ever-increasing gap between proteins with known sequences and those with a complete functional annotation requires computational methods for automatic structure and functional annotation. My research has been focusing on proteins and led so far to the development of three novel tools, DeepREx, E-SNPs&GO and ISPRED-SEQ, based on Machine and Deep Learning approaches. DeepREx computes the solvent exposure of residues in a protein chain. This problem is relevant for the definition of structural constraints regarding the possible folding of the protein. DeepREx exploits Long Short-Term Memory layers to capture residue-level interactions between positions distant in the sequence, achieving state-of-the-art performances. With DeepRex, I conducted a large-scale analysis investigating the relationship between solvent exposure of a residue and its probability to be pathogenic upon mutation. E-SNPs&GO predicts the pathogenicity of a Single Residue Variation. Variations occurring on a protein sequence can have different effects, possibly leading to the onset of diseases. E-SNPs&GO exploits protein embeddings generated by two novel Protein Language Models (PLMs), as well as a new way of representing functional information coming from the Gene Ontology. The method achieves state-of-the-art performances and is extremely time-efficient when compared to traditional approaches. ISPRED-SEQ predicts the presence of Protein-Protein Interaction sites in a protein sequence. Knowing how a protein interacts with other molecules is crucial for accurate functional characterization. ISPRED-SEQ exploits a convolutional layer to parse local context after embedding the protein sequence with two novel PLMs, greatly surpassing the current state-of-the-art. All methods are published in international journals and are available as user-friendly web servers. They have been developed keeping in mind standard guidelines for FAIRness (FAIR: Findable, Accessible, Interoperable, Reusable) and are integrated into the public collection of tools provided by ELIXIR, the European infrastructure for Bioinformatics.

Propriedades dinâmicas de fluidos por simulação computacional: métodos híbridos atomístico-contínuo

Computational methods for the calculation of dynamical properties of fluids might consider the system as a continuum or as an assembly of molecules. Molecular dynamics (MD) simulation includes molecular resolution, whereas computational fluid dynamics (CFD) considers the fluid as a continuum. This work provides a review of hybrid methods MD/CFD recently proposed in the literature. Theoretical foundations, basic approaches of computational methods, and dynamical properties typically calculated by MD and CFD are first presented in order to appreciate the similarities and differences between these two methods. Then, methods for coupling MD and CFD, and applications of hybrid simulations MD/CFD, are presented.

GENERALIZED FINITE ELEMENT METHOD FOR NONLINEAR THREE-DIMENSIONAL ANALYSIS OF SOLIDS

The Generalized Finite Element Method (GFEM) is employed in this paper for the numerical analysis of three-dimensional solids tinder nonlinear behavior. A brief summary of the GFEM as well as a description of the formulation of the hexahedral element based oil the proposed enrichment strategy are initially presented. Next, in order to introduce the nonlinear analysis of solids, two constitutive models are briefly reviewed: Lemaitre`s model, in which damage and plasticity are coupled, and Mazars`s damage model suitable for concrete tinder increased loading. Both models are employed in the framework of a nonlocal approach to ensure solution objectivity. In the numerical analyses carried out, a selective enrichment of approximation at regions of concern in the domain (mainly those with high strain and damage gradients) is exploited. Such a possibility makes the three-dimensional analysis less expensive and practicable since re-meshing resources, characteristic of h-adaptivity, can be minimized. Moreover, a combination of three-dimensional analysis and the selective enrichment presents a valuable good tool for a better description of both damage and plastic strain scatterings.