scholarly journals Exploring the conformational preferences of 20-residue peptides in isolation: Ac-Ala19-Lys + H+vs. Ac-Lys-Ala19 + H+ and the current reach of DFT

2015 ◽  
Vol 17 (11) ◽  
pp. 7373-7385 ◽  
Author(s):  
Franziska Schubert ◽  
Mariana Rossi ◽  
Carsten Baldauf ◽  
Kevin Pagel ◽  
Stephan Warnke ◽  
...  
Keyword(s):  
Amino Acid ◽  
Density Functional ◽  
Level Density ◽  
Exhaustive Search ◽  
Amino Acid Peptide ◽  
Conformational Preferences ◽  
High Level ◽  
Conformation Space

Using a high-level density functional and an exhaustive search of conformation space, the predicted conformation of a 20-amino acid peptide explains two seemingly contradictory experiments.

CAN SEMIEMPIRICAL QUANTUM MODELS CALCULATE THE BINDING ENERGY OF HYDROGEN BONDING FOR BIOLOGICAL SYSTEMS?

2009 ◽  
Vol 08 (04) ◽  
pp. 691-711 ◽  
Author(s):  
FENG FENG ◽  
HUAN WANG ◽  
WEI-HAI FANG ◽  
JIAN-GUO YU
Keyword(s):  
Hydrogen Bonding ◽  
Amino Acid ◽  
Amino Acid Residue ◽  
Density Functional ◽  
Biological Systems ◽  
Binding Energies ◽  
Semiempirical Model ◽  
Hydrogen Bonded Systems ◽  
High Level ◽  
Hydrogen Bonded

A modified semiempirical model named RM1BH, which is based on RM1 parameterizations, is proposed to simulate varied biological hydrogen-bonded systems. The RM1BH is formulated by adding Gaussian functions to the core–core repulsion items in original RM1 formula to reproduce the binding energies of hydrogen bonding of experimental and high-level computational results. In the parameterizations of our new model, 35 base-pair dimers, 18 amino acid residue dimers, 14 dimers between a base and an amino acid residue, and 20 other multimers were included. The results performed with RM1BH were compared with experimental values and the benchmark density-functional (B3LYP/6-31G**/BSSE) and Möller–Plesset perturbation (MP2/6-31G**/BSSE) calculations on various biological hydrogen-bonded systems. It was demonstrated that RM1BH model outperforms the PM3 and RM1 models in the calculations of the binding energies of biological hydrogen-bonded systems by very close agreement with the values of both high-level calculations and experiments. These results provide insight into the ideas, methods, and views of semiempirical modifications to investigate the weak interactions of biological systems.

scholarly journals A Data-Driven Approach to the Development and Understanding of Chiroptical Sensors for Alcohols with Remote γ-Stereocenters

2021 ◽  
Author(s):  
Jordan Dotson ◽  
Eric Anslyn ◽  
Matthew Sigman
Keyword(s):  
Density Functional ◽  
Level Density ◽  
Enantiomeric Excess ◽  
Data Driven ◽  
Functional Theory ◽  
Dynamic Covalent Chemistry ◽  
Sensor Optimization ◽  
Data Driven Approach ◽  
High Level ◽  
Sensor Molecules

Dynamic covalent chemistry-based sensors have recently emerged as powerful tools to rapidly determine the enantiomeric excess of organic small molecules. While a bevy of sensors have been developed, those for flexible molecules with stereocenters remote to the functional group that binds the chiroptical sensor remain scarce. In this study, we develop an iterative, data-driven workflow to design and analyze a chiroptical sensor capable of assessing challenging acyclic γ-stereogenic alcohols. Fol-lowing sensor optimization, the mechanism of sensing was probed with a combination of computational parameterization of the sensor molecules, statistical modeling, and high-level density functional theory (DFT) calculations. These were used to elucidate the mechanism of stereochemical recognition and revealed that competing attractive non-covalent interactions (NCIs) determine the overall performance of the sensor. It is anticipated that the data-driven workflows developed herein will be generally applicable to the development and understanding of dynamic covalent and supramolecular sensors.

A New Pathway for Activation of C - C and C - H Bonds by Transition Metals in the Gas Phase

2005 ◽  
Vol 58 (2) ◽  
pp. 82 ◽  
Author(s):  
Dongju Zhang ◽  
Ruoxi Wang ◽  
Rongxiu Zhu
Keyword(s):  
Transition Metals ◽  
Gas Phase ◽  
Density Functional Calculations ◽  
Density Functional ◽  
Bond Activation ◽  
Level Density ◽  
Elimination Mechanism ◽  
Activation Of Hydrocarbons ◽  
High Level ◽  
H2 Elimination

C–H and C–C bond activation of hydrocarbons at metal centres are of fundamental importance in biochemistry, organometallic chemistry, and catalysis. The present work aims to search for novel mechanisms for activation of C–C and C–H bonds by transition metals in the gas phase. Using high-level density functional calculations, we systemically studied the reactions of Ti+, V+, and Fe+ with ethane, and proposed new pathways of C–C and C–H bond activation—concerted activation of C–C and C–H bonds, and 1,2-H2 elimination. These two pathways clearly differ from the general addition–elimination mechanism.

Two crystallographic forms and the absolute structure of 5α,14α-androstane

2021 ◽  
Vol 77 (9) ◽  
pp. 537-543
Author(s):  
Christopher M. Crittenden ◽  
Antonio G. DiPasquale
Keyword(s):  
Density Functional ◽  
Level Density ◽  
Solvent System ◽  
Free Energy Calculations ◽  
Thin Plates ◽  
Diffusion Experiment ◽  
Space Group ◽  
Minor Form ◽  
High Level ◽  
Estimated Error

5α,14α-Androstane (C19H32) crystallizes in two different polymorphic forms in the same vapor diffusion experiment. The major form (Form I) crystallizes as thin plates in the space group P21, with Z = 4. These plates are twinned along a long c axis of length 43 Å and readily suffer from radiation damage when diffracted. The minor form (Form II) crystallizes as fine needles in the space group P212121, Z = 3. In the minor form, 5α,14α-androstane cocrystallizes with 5α,14α-androstan-17-one, an oxidation product of 5α,14α-androstane. The presence of 5α,14α-androstan-17-one in the minor form of the crystals was confirmed by HR-MS. Form II can be crystallized as a pure form without the ketone impurity using a different solvent system. High level density functional theory (DFT) lattice free energy calculations were performed and show that both pure forms are isoergic within the estimated error of the calculations.

Immunoelectron microscope detection of C-type natriuretic peptide in normal human atrial tissue

1993 ◽  
Vol 51 ◽  
pp. 388-389
Author(s):  
Chi-Ming Wei ◽  
Margaret Hukee ◽  
Christopher G.A. McGregor ◽  
John C. Burnett
Keyword(s):  
Amino Acid ◽  
Natriuretic Peptide ◽  
Vascular Tone ◽  
Cardiac Tissue ◽  
Ring Structure ◽  
Terminal Amino Acid ◽  
Amino Acid Peptide ◽  
Normal Human ◽  
Terminal Amino ◽  
The Brain

C-type natriuretic peptide (CNP) is a newly identified peptide that is structurally related to atrial (ANP) and brain natriuretic peptide (BNP). CNP exists as a 22-amino acid peptide and like ANP and BNP has a 17-amino acid ring formed by a disulfide bond. Unlike these two previously identified cardiac peptides, CNP lacks the COOH-terminal amino acid extension from the ring structure. ANP, BNP and CNP decrease cardiac preload, but unlike ANP and BNP, CNP is not natriuretic. While ANP and BNP have been localized to the heart, recent investigations have failed to detect CNP mRNA in the myocardium although small concentrations of CNP are detectable in the porcine myocardium. While originally localized to the brain, recent investigations have localized CNP to endothelial cells consistent with a paracrine role for CNP in the control of vascular tone. While CNP has been detected in cardiac tissue by radioimmunoassay, no studies have demonstrated CNP localization in normal human heart by immunoelectron microscopy.

Electronic Structure Benchmark Calculations of Inorganic and Biochemical Carboxylation Reactions

2018 ◽  
Author(s):  
Oscar A. Douglas-Gallardo ◽  
David A. Sáez ◽  
Stefan Vogt-Geisse ◽  
Esteban Vöhringer-Martinez
Keyword(s):  
Electronic Structure ◽  
Chemical Reactions ◽  
Density Functional ◽  
Correlation Energy ◽  
Electronic Energy ◽  
Basis Sets ◽  
Electronic Correlation ◽  
Correct Description ◽  
Carbon Dioxide Co2 ◽  
High Level

<div><div><div><p>Carboxylation reactions represent a very special class of chemical reactions that is characterized by the presence of a carbon dioxide (CO2) molecule as reactive species within its global chemical equation. These reactions work as fundamental gear to accomplish the CO2 fixation and thus to build up more complex molecules through different technological and biochemical processes. In this context, a correct description of the CO2 electronic structure turns out to be crucial to study the chemical and electronic properties associated with this kind of reactions. Here, a sys- tematic study of CO2 electronic structure and its contribution to different carboxylation reaction electronic energies has been carried out by means of several high-level ab-initio post-Hartree Fock (post-HF) and Density Functional Theory (DFT) calculations for a set of biochemistry and inorganic systems. We have found that for a correct description of the CO2 electronic correlation energy it is necessary to include post-CCSD(T) contributions (beyond the gold standard). These high-order excitations are required to properly describe the interactions of the four π-electrons as- sociated with the two degenerated π-molecular orbitals of the CO2 molecule. Likewise, our results show that in some reactions it is possible to obtain accurate reaction electronic energy values with computationally less demanding methods when the error in the electronic correlation energy com- pensates between reactants and products. Furthermore, the provided post-HF reference values allowed to validate different DFT exchange-correlation functionals combined with different basis sets for chemical reactions that are relevant in biochemical CO2 fixing enzymes.</p></div></div></div>

BAND NN: A Deep Learning Framework For Energy Prediction and Geometry Optimization of Organic Small Molecules

2019 ◽  
Author(s):  
Siddhartha Laghuvarapu ◽  
Yashaswi Pathak ◽  
U. Deva Priyakumar
Keyword(s):  
Machine Learning ◽  
Density Functional ◽  
Computational Cost ◽  
Geometry Optimization ◽  
Dft Methods ◽  
Energy Prediction ◽  
Machine Learning Model ◽  
Equilibrium Structures ◽  
High Level ◽  
Non Equilibrium

Recent advances in artificial intelligence along with development of large datasets of energies calculated using quantum mechanical (QM)/density functional theory (DFT) methods have enabled prediction of accurate molecular energies at reasonably low computational cost. However, machine learning models that have been reported so far requires the atomic positions obtained from geometry optimizations using high level QM/DFT methods as input in order to predict the energies, and do not allow for geometry optimization. In this paper, a transferable and molecule-size independent machine learning model (BAND NN) based on a chemically intuitive representation inspired by molecular mechanics force fields is presented. The model predicts the atomization energies of equilibrium and non-equilibrium structures as sum of energy contributions from bonds (B), angles (A), nonbonds (N) and dihedrals (D) at remarkable accuracy. The robustness of the proposed model is further validated by calculations that span over the conformational, configurational and reaction space. The transferability of this model on systems larger than the ones in the dataset is demonstrated by performing calculations on select large molecules. Importantly, employing the BAND NN model, it is possible to perform geometry optimizations starting from non-equilibrium structures along with predicting their energies.

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