State-of-the-art computation of the rotational and IR spectra of the methyl-cyclopropyl cation: hints on its detection in space

2019 ◽  
Vol 21 (7) ◽  
pp. 3431-3439 ◽  
Author(s):  
Cristina Puzzarini ◽  
Nicola Tasinato ◽  
Julien Bloino ◽  
Lorenzo Spada ◽  
Vincenzo Barone
Keyword(s):  
Ir Spectra ◽  
State Of The Art ◽  
Spectroscopic Characterization ◽  
Computational Approaches

A route toward the detection of the methyl-cyclopropenyl cation in space: a spectroscopic characterization by state-of-the-art computational approaches.

scholarly journals Protein structure and sequence re-analysis of 2019-nCoV genome does not indicate snakes as its intermediate host or the unique similarity between its spike protein insertions and HIV-1

2020 ◽  
Author(s):  
Chengxin Zhang ◽  
Wei Zheng ◽  
Xiaoqiang Huang ◽  
Eric W. Bell ◽  
Xiaogen Zhou ◽  
...  
Keyword(s):  
State Of The Art ◽  
Careful Analysis ◽  
Spike Protein ◽  
The Novel ◽  
Intermediate Hosts ◽  
Cellular Mechanisms ◽  
Computational Approaches ◽  
Link Type ◽  
Hiv 1 ◽  
Existing Data

AbstractAs the infection of 2019-nCoV coronavirus is quickly developing into a global pneumonia epidemic, careful analysis of its transmission and cellular mechanisms is sorely needed. In this report, we re-analyzed the computational approaches and findings presented in two recent manuscripts by Ji et al. (https://doi.org/10.1002/jmv.25682) and by Pradhan et al. (https://doi.org/10.1101/2020.01.30.927871), which concluded that snakes are the intermediate hosts of 2019-nCoV and that the 2019-nCoV spike protein insertions shared a unique similarity to HIV-1. Results from our re-implementation of the analyses, built on larger-scale datasets using state-of-the-art bioinformatics methods and databases, do not support the conclusions proposed by these manuscripts. Based on our analyses and existing data of coronaviruses, we concluded that the intermediate hosts of 2019-nCoV are more likely to be mammals and birds than snakes, and that the “novel insertions” observed in the spike protein are naturally evolved from bat coronaviruses.

scholarly journals Conformational dynamics of carbohydrates: Raman optical activity of d-glucuronic acid and N-acetyl-d-glucosamine using a combined molecular dynamics and quantum chemical approach

2015 ◽  
Vol 17 (8) ◽  
pp. 6016-6027 ◽  
Author(s):  
Shaun T. Mutter ◽  
François Zielinski ◽  
James R. Cheeseman ◽  
Christian Johannessen ◽  
Paul L. A. Popelier ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Optical Activity ◽  
Quantum Chemical ◽  
Glucuronic Acid ◽  
State Of The Art ◽  
Conformational Dynamics ◽  
Conformational Space ◽  
Computational Approaches ◽  
Raman Optical Activity ◽  
Quantum Chemical Approach

Raman optical activity combined with state-of-the-art computational approaches successfully probes the conformational space of two important carbohydrates.

scholarly journals One-step electric-field driven methane and formaldehyde synthesis from liquid methanol

2017 ◽  
Vol 8 (3) ◽  
pp. 2329-2336 ◽  
Author(s):  
Giuseppe Cassone ◽  
Fabio Pietrucci ◽  
Franz Saija ◽  
François Guyot ◽  
A. Marco Saitta
Keyword(s):  
Electric Field ◽  
Chemical Reaction ◽  
State Of The Art ◽  
Computational Approaches ◽  
One Step ◽  
Formaldehyde Synthesis

By means of state-of-the-art computational approaches, a new fundamental chemical reaction, involving formaldehyde and methane, has been observed when an electric field is applied to liquid methanol.

Arsenic–nucleotides interactions: an experimental and computational investigation

2020 ◽  
Vol 49 (19) ◽  
pp. 6302-6311 ◽  
Author(s):  
Giuseppe Cassone ◽  
Donatella Chillè ◽  
Viviana Mollica Nardo ◽  
Ottavia Giuffrè ◽  
Rosina Celeste Ponterio ◽  
...  
Keyword(s):  
Aqueous Solutions ◽  
State Of The Art ◽  
Computational Investigation ◽  
Computational Approaches

By means of state-of-the-art computational approaches and experiments we characterize the chelation process established by As(iii) with AMP, ADP, and ATP in aqueous solutions.

Morphology

2017 ◽  
Author(s):  
Kemal Oflazer
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
State Of The Art ◽  
Morphological Processing ◽  
Machine Learning Techniques ◽  
Processing Application ◽  
Computational Approaches ◽  
Learning Techniques ◽  
Finite State ◽  
Basic Concepts

Morphology is the study of the structure of words and how words are forme3d by combining smaller units of linguistic information called morphemes. Any natural language processing application will need to computationally process the words in a language before any of the more complex processing is done. This is especially a must for morphologically complex languages. After a compact overview of the basic concepts in morphology, this chapter presents the state-of-the-art computational approaches to morphology, concentrating on two-level morphology and cascaded-rules and describing how morphographemics and morphotactics are handled in a finite-state setting. The chapter then summarizes recent approaches to how machine learning techniques are applied in morphological processing.

scholarly journals A Bittersweet Computational Journey among Glycosaminoglycans

Biomolecules ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 739
Author(s):  
Giulia Paiardi ◽  
Maria Milanesi ◽  
Rebecca C. Wade ◽  
Pasqualina D’Ursi ◽  
Marco Rusnati
Keyword(s):  
Core Protein ◽  
State Of The Art ◽  
Structural Diversity ◽  
Experimental Models ◽  
Substantial Improvement ◽  
Structure And Function ◽  
Computational Approaches ◽  
Free Molecules ◽  
Dynamics Simulations ◽  
And Function

Glycosaminoglycans (GAGs) are linear polysaccharides. In proteoglycans (PGs), they are attached to a core protein. GAGs and PGs can be found as free molecules, associated with the extracellular matrix or expressed on the cell membrane. They play a role in the regulation of a wide array of physiological and pathological processes by binding to different proteins, thus modulating their structure and function, and their concentration and availability in the microenvironment. Unfortunately, the enormous structural diversity of GAGs/PGs has hampered the development of dedicated analytical technologies and experimental models. Similarly, computational approaches (in particular, molecular modeling, docking and dynamics simulations) have not been fully exploited in glycobiology, despite their potential to demystify the complexity of GAGs/PGs at a structural and functional level. Here, we review the state-of-the art of computational approaches to studying GAGs/PGs with the aim of pointing out the “bitter” and “sweet” aspects of this field of research. Furthermore, we attempt to bridge the gap between bioinformatics and glycobiology, which have so far been kept apart by conceptual and technical differences. For this purpose, we provide computational scientists and glycobiologists with the fundamentals of these two fields of research, with the aim of creating opportunities for their combined exploitation, and thereby contributing to a substantial improvement in scientific knowledge.

scholarly journals An Introduction to Machine Learning Approaches for Biomedical Research

2021 ◽  
Vol 8 ◽  
Author(s):  
Juan Jovel ◽  
Russell Greiner
Keyword(s):  
Machine Learning ◽  
Reinforcement Learning ◽  
Biomedical Research ◽  
Applied Research ◽  
State Of The Art ◽  
Learning Approaches ◽  
Therapy Outcomes ◽  
Computational Approaches ◽  
Specific Goal ◽  
Basic And Applied Research

Machine learning (ML) approaches are a collection of algorithms that attempt to extract patterns from data and to associate such patterns with discrete classes of samples in the data—e.g., given a series of features describing persons, a ML model predicts whether a person is diseased or healthy, or given features of animals, it predicts weather an animal is treated or control, or whether molecules have the potential to interact or not, etc. ML approaches can also find such patterns in an agnostic manner, i.e., without having information about the classes. Respectively, those methods are referred to as supervised and unsupervised ML. A third type of ML is reinforcement learning, which attempts to find a sequence of actions that contribute to achieving a specific goal. All of these methods are becoming increasingly popular in biomedical research in quite diverse areas including drug design, stratification of patients, medical images analysis, molecular interactions, prediction of therapy outcomes and many more. We describe several supervised and unsupervised ML techniques, and illustrate a series of prototypical examples using state-of-the-art computational approaches. Given the complexity of reinforcement learning, it is not discussed in detail here, instead, interested readers are referred to excellent reviews on that topic. We focus on concepts rather than procedures, as our goal is to attract the attention of researchers in biomedicine toward the plethora of powerful ML methods and their potential to leverage basic and applied research programs.

Spin-state dependence of the structural and vibrational properties of solvated iron(ii) polypyridyl complexes from AIMD simulations: aqueous [Fe(bpy)3]Cl2, a case study

2018 ◽  
Vol 20 (9) ◽  
pp. 6236-6253 ◽  
Author(s):  
Latévi M. Lawson Daku
Keyword(s):  
Molecular Dynamics ◽  
Ir Spectra ◽  
Ir Spectrum ◽  
State Of The Art ◽  
State Dependence ◽  
Vibrational Properties ◽  
Polypyridyl Complexes ◽  
Dynamics Simulations

LS and HS IR spectra of aqueous [Fe(bpy)3]2+ and corresponding HS–LS difference IR spectrum as obtained from state-of-the-art ab initio molecular dynamics simulations applied to the determination of the structural and vibrational properties of the solvated complex.

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