What Occurs by Replacing Mn2+with Co2+in Human Arginase I: First-Principles Computational Analysis

The diverse molecular architectures of terpene natural products are assembled by exquisite enzyme-catalyzed reactions. Successful recapitulation of these transformations using chemical synthesis is hard to predict from first principles and therefore challenging to execute. A means of evaluating the feasibility of such chemical reactions would greatly enable the development of concise syntheses of complex small molecules. Herein, we report the computational analysis of the energetic favorability of a key bio-inspired transformation, which we use to inform our synthetic strategy. This approach was applied to synthesize two constituents of the historically challenging indole diterpenoid class, resulting in a concise route to (–)-paspaline A in 9 steps from commercially available materials and the first pathway to and structural confirmation of emindole PB in 13 steps. This work highlights how traditional retrosynthetic design can be augmented with quantum chemical calculations to reveal energetically feasible synthetic disconnections, minimizing time-consuming and expensive empirical evaluation.

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Computational analysis of the contributions to the piezoelectric coefficient $$e_{33}$$ e 33 in ZnO nanowires: first-principles calculations

Journal of Computational Electronics ◽

10.1007/s10825-014-0620-x ◽

2014 ◽

Vol 13 (4) ◽

pp. 983-988 ◽

Cited By ~ 1

Author(s):

Seong Min Kim ◽

Tae Yun Kim ◽

Jung-Hoon Lee ◽

Sang-Woo Kim ◽

JeaWook Ha ◽

...

Keyword(s):

First Principles ◽

Computational Analysis ◽

Piezoelectric Coefficient ◽

Zno Nanowires ◽

First Principles Calculations

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Hydrogen generation by the reaction of H2O with Al2O3-based materials: a computational analysis

Physical Chemistry Chemical Physics ◽

10.1039/c4cp05789a ◽

2015 ◽

Vol 17 (10) ◽

pp. 6834-6843 ◽

Cited By ~ 20

Author(s):

Yu-Huan Lu ◽

Hsin-Tsung Chen

Keyword(s):

First Principles ◽

Reaction Mechanisms ◽

Md Simulation ◽

Computational Analysis ◽

Hydrogen Generation

By combined DFT and first-principles MD simulation, we propose detailed reaction mechanisms for H2O splitting and hydrogen generation on Al2O3-based materials.

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Graphene sheet with periodic vacancy: A first principles study

Physica Scripta ◽

10.1088/1402-4896/ac40d7 ◽

2021 ◽

Author(s):

Deepti Maikhuri ◽

Jaiparkash Jaiparkash ◽

Haider Abbas

Keyword(s):

Density Functional Theory ◽

Magnetic Properties ◽

Electronic Structure ◽

Band Gap ◽

Graphene Sheet ◽

First Principles ◽

Density Functional ◽

Computational Analysis ◽

Functional Theory ◽

First Principles Study

Abstract We present a comprehensive first-principles study of the electronic structure of graphene sheet with periodic vacancy. We report the structural, electronic, and magnetic properties of the graphene sheet with periodic vacancy that possess 48 C & 28 H atoms. Computational analysis based on density functional theory predicts that the periodic vacancy can modulate the properties of graphene sheet. Results show that periodic vacancies lead to the manipulation of band gap & could be utilized to tailor the electronic properties of the sheet. Also, it is found that, the graphene sheet with periodic vacancy is non-magnetic in nature.

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Computationally Augmented Retrosynthesis: Total Synthesis of Paspaline A and Emindole PB

10.26434/chemrxiv.7322330 ◽

2018 ◽

Author(s):

Timothy Newhouse ◽

Daria E. Kim ◽

Joshua E. Zweig

Keyword(s):

Chemical Synthesis ◽

Total Synthesis ◽

Small Molecules ◽

First Principles ◽

Quantum Chemical ◽

Computational Analysis ◽

Empirical Evaluation ◽

Synthetic Strategy ◽

Catalyzed Reactions ◽

Enzyme Catalyzed Reactions

The diverse molecular architectures of terpene natural products are assembled by exquisite enzyme-catalyzed reactions. Successful recapitulation of these transformations using chemical synthesis is hard to predict from first principles and therefore challenging to execute. A means of evaluating the feasibility of such chemical reactions would greatly enable the development of concise syntheses of complex small molecules. Herein, we report the computational analysis of the energetic favorability of a key bio-inspired transformation, which we use to inform our synthetic strategy. This approach was applied to synthesize two constituents of the historically challenging indole diterpenoid class, resulting in a concise route to (–)-paspaline A in 9 steps from commercially available materials and the first pathway to and structural confirmation of emindole PB in 13 steps. This work highlights how traditional retrosynthetic design can be augmented with quantum chemical calculations to reveal energetically feasible synthetic disconnections, minimizing time-consuming and expensive empirical evaluation.

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First-principles computational analysis of nanocomposite for detecting environmental polluting gas

New Polymer Nanocomposites for Environmental Remediation ◽

10.1016/b978-0-12-811033-1.00009-3 ◽

2018 ◽

pp. 207-222 ◽

Cited By ~ 1

Author(s):

Yoshitaka Fujimoto

Keyword(s):

First Principles ◽

Computational Analysis

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Automated Incorporation of Pairwise Dependency in Transcription Factor Binding Site Prediction Using Dinucleotide Weight Tensors

10.1101/078212 ◽

2016 ◽

Author(s):

Saeed Omidi ◽

Mihaela Zavolan ◽

Mikhail Pachkov ◽

Jeremie Breda ◽

Severin Berger ◽

...

Keyword(s):

Binding Site ◽

Gene Regulatory Networks ◽

First Principles ◽

Binding Sites ◽

Regulatory Networks ◽

Computational Analysis ◽

Nearest Neighbor ◽

Specific Weight ◽

Sequence Specificity ◽

Gene Regulatory

AbstractGene regulatory networks are ultimately encoded by the sequence-specific binding of (TFs) to short DNA segments. Although it is customary to represent the binding specificity of a TF by a position-specific weight matrix (PSWM), which assumes each position within a site contributes independently to the overall binding affinity, evidence has been accumulating that there can be significant dependencies between positions. Unfortunately, methodological challenges have so far hindered the development of a practical and generally-accepted extension of the PSWM model. On the one hand, simple models that only consider dependencies between nearest-neighbor positions are easy to use in practice, but fail to account for the distal dependencies that are observed in the data. On the other hand, models that allow for arbitrary dependencies are prone to overfitting, requiring regularization schemes that are difficult to use in practice for non-experts.Here we present a new regulatory motif model, called dinucleotide weight tensor (DWT), that incorporates arbitrary pairwise dependencies between positions in binding sites, rigorously from first principles, and free from tunable parameters. We demonstrate the power of the method on a large set of ChIP-seq data-sets, showing that DWTs outperform both PSWMs and motif models that only incorporate nearest-neighbor dependencies. We also demonstrate that DWTs outperform two previously proposed methods. Finally, we show that DWTs inferred from ChIP-seq data also outperform PSWMs on HT-SELEX data for the same TF, suggesting that DWTs capture inherent biophysical properties of the interactions between the DNA binding domains of TFs and their binding sites.We make a suite of DWT tools available at dwt.unibas.ch, that allow users to automatically perform ‘motif finding’, i.e. the inference of DWT motifs from a set of sequences, binding site prediction with DWTs, and visualization of DWT ‘dilogo’ motifs.Author SummaryGene regulatory networks are ultimately encoded in constellations of short binding sites in the DNA and RNA that are recognized by regulatory factors such as transcription factors (TFs). For several decades, computational analysis of regulatory networks has relied on a model of TF sequence-specificity, the position-specific weight-matrix (PSWM), that assumes different positions in a binding site contribute independently to the total binding energy of the TF. However, in recent years evidence has been accumulating that, at least for some TFs, this assumption does not hold. Here we present a new model for the sequence-specificity of TFs, the dinucleotide weight tensor (DWT), that takes arbitrary dependencies between positions in binding sites into account and show that it consistently outperforms PSWMs on high-throughput datasets on TF binding. Moreover, in contrast to previous approaches, DWTs are directly derived from first principles within a Bayesian framework, and contain no tunable parameters. This allows them to be easily applied in practice and we make a suite of tools available for computational analysis with DWTs.

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