scholarly journals TorsiFlex: an automatic generator of torsional conformers. Application to the twenty proteinogenic amino acids

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
David Ferro-Costas ◽  
Irea Mosquera-Lois ◽  
Antonio Fernández-Ramos
Keyword(s):  
Amino Acids ◽  
Electronic Structure ◽  
Energy Surface ◽  
Random Search ◽  
Torsional Potential ◽  
Complete Work ◽  
Chemical Knowledge ◽  
Validation Tests ◽  
User Friendly

AbstractIn this work, we introduce , a user-friendly software written in Python 3 and designed to find all the torsional conformers of flexible acyclic molecules in an automatic fashion. For the mapping of the torsional potential energy surface, the algorithm implemented in combines two searching strategies: preconditioned and stochastic. The former is a type of systematic search based on chemical knowledge and should be carried out before the stochastic (random) search. The algorithm applies several validation tests to accelerate the exploration of the torsional space. For instance, the optimized structures are stored and this information is used to prevent revisiting these points and their surroundings in future iterations. operates with a dual-level strategy by which the initial search is carried out at an inexpensive electronic structure level of theory and the located conformers are reoptimized at a higher level. Additionally, the program takes advantage of conformational enantiomerism, when possible. As a case study, and in order to exemplify the effectiveness and capabilities of this program, we have employed to locate the conformers of the twenty proteinogenic amino acids in their neutral canonical form. has produced a number of conformers that roughly doubles the amount of the most complete work to date. Graphical Abstract

Torsiflex: an automatic generator of torsional conformers. Application to the twenty proteinogenic amino acids

2021 ◽  
Author(s):  
David Ferro-Costas ◽  
Irea Mosquera-Lois ◽  
Antonio Fernandez-Ramos
Keyword(s):  
Amino Acids ◽  
Electronic Structure ◽  
Energy Surface ◽  
Random Search ◽  
Torsional Potential ◽  
Complete Work ◽  
Chemical Knowledge ◽  
Validation Tests ◽  
User Friendly

Abstract In this work, we introduce TorsiFlex, a user-friendly software written in Python 3 and designed to find all the torsional conformers of flexible acyclic molecules in an automatic fashion. For the mapping of the torsional potential energy surface, the algorithm implemented in TorsiFlex combines two searching strategies: preconditioned and stochastic. The former is a type of systematic search based on chemical knowledge and should be carried out before the stochastic (random) search. The algorithm applies several validation tests to accelerate the exploration of the torsional space. For instance, the optimized structures are stored and this information is used to prevent revisiting these points and their surroundings in future iterations. TorsiFlex operates with a dual-level strategy by which the initial search is carried out at an inexpensive electronic structure level of theory and the located conformers are reoptimized at a higher level. Additionally, the program takes advantage of conformational enantiomerism, when possible. As a case study, and in order to exemplify the effectiveness and capabilities of this program, we have employed TorsiFlex to locate the conformers of the twenty proteinogenic amino acids in their neutral canonical form. TorsiFlex has produced a number of conformers that roughly doubles the amount of the most complete work to date.

Optimales Layout einer Walderschliessung

2016 ◽  
Vol 167 (5) ◽  
pp. 294-301
Author(s):  
Leo Bont
Keyword(s):  
Road Network ◽  
Spatial Arrangement ◽  
Problem Situation ◽  
Optimal Layout ◽  
Forest Road ◽  
The Road ◽  
Network Cost ◽  
User Friendly ◽  
Short Time

Optimal layout of a forest road network The road network is the backbone of forest management. When creating or redesigning a forest road network, one important question is how to shape the layout, this means to fix the spatial arrangement and the dimensioning standard of the roads. We consider two kinds of layout problems. First, new forest road network in an area without any such development yet, and second, redesign of existing road network for actual requirements. For each problem situation, we will present a method that allows to detect automatically the optimal road and harvesting layout. The method aims to identify a road network that concurrently minimizes the harvesting cost, the road network cost (construction and maintenance) and the hauling cost over the entire life cycle. Ecological issues can be considered as well. The method will be presented and discussed with the help of two case studies. The main benefit of the application of optimization tools consists in an objective-based planning, which allows to check and compare different scenarios and objectives within a short time. The responses coming from the case study regions were highly positive: practitioners suggest to make those methods a standard practice and to further develop the prototype to a user-friendly expert software.

pLoc_bal-mPlant: Predict Subcellular Localization of Plant Proteins by General PseAAC and Balancing Training Dataset

2019 ◽  
Vol 24 (34) ◽  
pp. 4013-4022 ◽  
Author(s):  
Xiang Cheng ◽  
Xuan Xiao ◽  
Kuo-Chen Chou
Keyword(s):  
Subcellular Localization ◽  
Basic Research ◽  
Training Dataset ◽  
Sequence Information ◽  
Plant Proteins ◽  
Protein Subcellular Localization ◽  
Computational Tools ◽  
Validation Tests ◽  
User Friendly ◽  
Better Than

Knowledge of protein subcellular localization is vitally important for both basic research and drug development. With the avalanche of protein sequences emerging in the post-genomic age, it is highly desired to develop computational tools for timely and effectively identifying their subcellular localization based on the sequence information alone. Recently, a predictor called “pLoc-mPlant” was developed for identifying the subcellular localization of plant proteins. Its performance is overwhelmingly better than that of the other predictors for the same purpose, particularly in dealing with multi-label systems in which some proteins, called “multiplex proteins”, may simultaneously occur in two or more subcellular locations. Although it is indeed a very powerful predictor, more efforts are definitely needed to further improve it. This is because pLoc-mPlant was trained by an extremely skewed dataset in which some subsets (i.e., the protein numbers for some subcellular locations) were more than 10 times larger than the others. Accordingly, it cannot avoid the biased consequence caused by such an uneven training dataset. To overcome such biased consequence, we have developed a new and bias-free predictor called pLoc_bal-mPlant by balancing the training dataset. Cross-validation tests on exactly the same experimentconfirmed dataset have indicated that the proposed new predictor is remarkably superior to pLoc-mPlant, the existing state-of-the-art predictor in identifying the subcellular localization of plant proteins. To maximize the convenience for the majority of experimental scientists, a user-friendly web-server for the new predictor has been established at http://www.jci-bioinfo.cn/pLoc_bal-mPlant/, by which users can easily get their desired results without the need to go through the detailed mathematics.

pLoc_bal-mEuk: Predict Subcellular Localization of Eukaryotic Proteins by General PseAAC and Quasi-balancing Training Dataset

2019 ◽  
Vol 15 (5) ◽  
pp. 472-485 ◽  
Author(s):  
Kuo-Chen Chou ◽  
Xiang Cheng ◽  
Xuan Xiao
Keyword(s):  
Drug Development ◽  
Subcellular Localization ◽  
Basic Research ◽  
The Other ◽  
Training Dataset ◽  
Sequence Information ◽  
Eukaryotic Proteins ◽  
Validation Tests ◽  
User Friendly ◽  
Better Than

<P>Background/Objective: Information of protein subcellular localization is crucially important for both basic research and drug development. With the explosive growth of protein sequences discovered in the post-genomic age, it is highly demanded to develop powerful bioinformatics tools for timely and effectively identifying their subcellular localization purely based on the sequence information alone. Recently, a predictor called “pLoc-mEuk” was developed for identifying the subcellular localization of eukaryotic proteins. Its performance is overwhelmingly better than that of the other predictors for the same purpose, particularly in dealing with multi-label systems where many proteins, called “multiplex proteins”, may simultaneously occur in two or more subcellular locations. Although it is indeed a very powerful predictor, more efforts are definitely needed to further improve it. This is because pLoc-mEuk was trained by an extremely skewed dataset where some subset was about 200 times the size of the other subsets. Accordingly, it cannot avoid the biased consequence caused by such an uneven training dataset. </P><P> Methods: To alleviate such bias, we have developed a new predictor called pLoc_bal-mEuk by quasi-balancing the training dataset. Cross-validation tests on exactly the same experimentconfirmed dataset have indicated that the proposed new predictor is remarkably superior to pLocmEuk, the existing state-of-the-art predictor in identifying the subcellular localization of eukaryotic proteins. It has not escaped our notice that the quasi-balancing treatment can also be used to deal with many other biological systems. </P><P> Results: To maximize the convenience for most experimental scientists, a user-friendly web-server for the new predictor has been established at http://www.jci-bioinfo.cn/pLoc_bal-mEuk/. </P><P> Conclusion: It is anticipated that the pLoc_bal-Euk predictor holds very high potential to become a useful high throughput tool in identifying the subcellular localization of eukaryotic proteins, particularly for finding multi-target drugs that is currently a very hot trend trend in drug development.</P>

The vibrationally adiabatic torsional potential energy surface of trans-stilbene

2007 ◽  
Vol 440 (1-3) ◽  
pp. 7-11 ◽  
Author(s):  
Praveen D. Chowdary ◽  
Todd J. Martinez ◽  
Martin Gruebele
Keyword(s):  
Potential Energy ◽  
Potential Energy Surface ◽  
Energy Surface ◽  
Torsional Potential ◽  
Trans Stilbene

scholarly journals How to Use Online Tools to Generate New Hypotheses for Mammary Gland Biology Research: A Case Study for Wnt7b

2021 ◽  
Author(s):  
Yorick Bernardus Cornelis van de Grift ◽  
Nika Heijmans ◽  
Renée van Amerongen
Keyword(s):  
Mammary Gland ◽  
In Silico ◽  
Online Tools ◽  
Mammary Gland Biology ◽  
User Friendly ◽  
In Silico Analyses ◽  
Biology Research ◽  
Selection Of

AbstractAn increasing number of ‘-omics’ datasets, generated by labs all across the world, are becoming available. They contain a wealth of data that are largely unexplored. Not every scientist, however, will have access to the required resources and expertise to analyze such data from scratch. Fortunately, a growing number of investigators is dedicating their time and effort to the development of user friendly, online applications that allow researchers to use and investigate these datasets. Here, we will illustrate the usefulness of such an approach. Using regulation of Wnt7b expression as an example, we will highlight a selection of accessible tools and resources that are available to researchers in the area of mammary gland biology. We show how they can be used for in silico analyses of gene regulatory mechanisms, resulting in new hypotheses and providing leads for experimental follow up. We also call out to the mammary gland community to join forces in a coordinated effort to generate and share additional tissue-specific ‘-omics’ datasets and thereby expand the in silico toolbox.

scholarly journals Stoichiometry and disorder influence over electronic structure in nanostructured VOx films

2021 ◽  
Vol 23 (1) ◽  
Author(s):  
A. D’Elia ◽  
S. J. Rezvani ◽  
N. Zema ◽  
F. Zuccaro ◽  
M. Fanetti ◽  
...  
Keyword(s):  
Electronic Structure ◽  
Distorted Octahedral Geometry ◽  
Edge Structure ◽  
Distorted Octahedral ◽  
X Ray Absorption ◽  
Complex Matter ◽  
Nanometric Size

AbstractWe present and discuss the role of nanoparticles size and stoichiometry over the local atomic environment of nanostructured VOx films. The samples have been characterized in situ using X-ray absorption near-edge structure (XANES) spectroscopy identifying the stoichiometry-dependent fingerprints of disordered atomic arrangement. In vanadium oxides, the ligand atoms arrange according to a distorted octahedral geometry depending on the oxidation state, e.g. trigonal distortion in V2O3 and tetragonal distortion in bulk VO2. We demonstrate, taking VO2 as a case study, that as a consequence of the nanometric size of the nanoparticles, the original ligands symmetry of the bulk is broken resulting in the coexistence of a continuum of distorted atomic conformations. The resulting modulation of the electronic structure of the nanostructured VOx as a function of the oxygen content reveals a stoichiometry-dependent increase of disorder in the ligands matrix. This work shows the possibility to produce VOx nanostructured films accessing new disordered phases and provides a unique tool to investigate the complex matter.

scholarly journals Evaluating electronic structure methods for accurate calculation of 19 F chemical shifts in fluorinated amino acids

2017 ◽  
Vol 38 (30) ◽  
pp. 2605-2617 ◽  
Author(s):  
Jayangika N. Dahanayake ◽  
Chandana Kasireddy ◽  
Jonathan M. Ellis ◽  
Derek Hildebrandt ◽  
Olivia A. Hull ◽  
...  
Keyword(s):  
Amino Acids ◽  
Electronic Structure ◽  
Chemical Shifts ◽  
Accurate Calculation ◽  
Fluorinated Amino Acids ◽  
Electronic Structure Methods

scholarly journals Computational Design of Sensitized Combustion Chemistry Experiments

2021 ◽  
Vol 7 ◽  
Author(s):  
Cody Ising ◽  
Pedro Rodriguez ◽  
Daniel Lopez ◽  
Jeffrey Santner
Keyword(s):  
Shock Tube ◽  
Initial Conditions ◽  
Computational Design ◽  
Reaction Rates ◽  
Oxidation Reactions ◽  
Combustion Chemistry ◽  
Experimental Conditions ◽  
As Species ◽  
User Friendly

In combustion chemistry experiments, reaction rates are often extracted from complex experiments using detailed models. To aid in this process, experiments are performed such that measurable quantities, such as species concentrations, flame speed, and ignition delay, are sensitive to reaction rates of interest. In this work, a systematic method for determining such sensitized experimental conditions is demonstrated. An open-source python script was created using the Cantera module to simulate thousands of 0D and hundreds of 1D combustion chemistry experiments in parallel across a broad, user-defined range of mixture conditions. The results of the simulation are post-processed to normalize and compare sensitivity values among reactions and across initial conditions for time-varying and steady-state simulations, in order to determine the “most useful” experimental conditions. This software can be utilized by researchers as a fast, user-friendly screening tool to determine the thermodynamic and mixture parameters for an experimental campaign. We demonstrate this software through two case studies comparing results of the 0D script against a shock tube experiment and results of the 1D script against a spherical flame experiment. In the shock tube case study we present mixture conditions compared to those used in the literature to study H + O2 (+M)→HO2(+M). In the flame case study, we present mixture conditions compared to those in the literature to study formyl radical (HCO) decomposition and oxidation reactions. The systematically determined experimental conditions identified in the present work are similar to the conditions chosen in the literature.

Sign in / Sign up

Export Citation Format

Share Document