A fragment based step-by-step strategy for determining the most stable conformers of biomolecules

Li, Hong-Bao; Lin, Zijing; Luo, Yi

doi:10.1016/j.cplett.2014.07.054

Cited by 14 publications

(14 citation statements)

References 30 publications

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“…17,37,38 As shown in Table 1, all the low energy conformers were taken from ref. The conformational distributions at 298 K are calculated based on the Boltzmann distribution form in the same way we did previously 39,40 by considering the Gibbs free energy correction. The conformational distributions at 298 K are calculated based on the Boltzmann distribution form in the same way we did previously 39,40 by considering the Gibbs free energy correction.…”

Section: Methodsmentioning

confidence: 99%

“…21. The conformational distributions at 298 K are calculated based on the Boltzmann distribution form in the same way we did previously 39,40 by considering the Gibbs free energy correction. The transition states for the transformation from the most populated zwitterion (Z 1 ) to the two canonical groups (RG 1a and RG 2a ), as well as between the two canonical groups were identified at the BHandHLYP/6-311++G(d,p) level.…”

Section: Methodsmentioning

confidence: 99%

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Theoretical spectroscopic studies on chemical and electronic structures of arginylglycine

Jiang

et al. 2015

Phys. Chem. Chem. Phys.

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The energy differences between canonical and zwitterionic isomers of arginylglycine (ArgGly) at the CCSD/aug-cc-pVDZ level are too small (less than 1 kcal mol(-1)) to determine the dominant form in the gas phase from the energetic point of view. First-principles simulations have been performed for near-edge X-ray absorption fine-structure (NEXAFS) spectra and X-ray photoelectron spectra (XPS) at C, N and O K-edges, as well as for infrared (IR) spectra of neutral ArgGly. Noticeable spectral differences were found which enable the unambiguous identification of different neutral groups. We thus demonstrate X-ray spectroscopy as a powerful technique to study the conformation dependent chemical and electronic properties of neutral ArgGly.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Theoretical spectroscopic studies on chemical and electronic structures of arginylglycine

Jiang

et al. 2015

Phys. Chem. Chem. Phys.

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“…In this text, the PES of these two amino acids and their derivatives were searched based on the efficient fragment based step-by-step strategy 35 and the low-energy conformers were finally determined by the advanced CBS-QB3 and DSD-PBEP86-D3(BJ)/aug-cc-pVTZ methods for Sec and Pyl, respectively. The spectral characteristics of the vibrational frequencies (infrared spectra, IR) at room temperature were simulated and analyzed compared with the measured IR spectra of cysteine and lysine.…”

Section: Introductionmentioning

confidence: 99%

Theoretical Spectroscopic Studies on Chemical and Electronic Structures of Selenocysteine and Pyrrolysine

Hua

Wang

et al. 2020

J. Phys. Chem. A

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The chemical and electronic structures of the 21st and 22nd proteinogenic amino acid selenocysteine (Sec), pyrrolysine (Pyl), and their derivatives (deprotonated and protonated ions) were extensively characterized for the first time. Through the fragment based step-by-step research on their potential energy surface (PES), electronic energies of the most stable conformers of Sec, Pyl and the related ions were finally determined at the advanced CBS-QB3 and DSD-PBEP86-D3(BJ)/ aug-cc-pVTZ levels, respectively, with the identification of many new low-energy conformers. The infrared spectra (IR) at 298 K of the most abundant conformers in different forms were scaled by comparison with the anharmonic frequency calculations and analyzed comparing with the experimental spectra of similar molecules. The characteristic soft X-ray spectra (including X-ray photoelectron spectra (XPS) and near-edge X-ray absorption finestructure spectra (NEXAFS)) of the most stable conformers at 498 K were also simulated. In particular, the two possible protonated configurations of Pyl can be clearly distinguished by their different spectral features. Furthermore, a small binding energy intersection appeared around 293 eV at the C 1s edge between the canonical and protonated Pyl conformers, which is different from all the previous studies. This work thus filled the gap in our knowledge by providing detailed information on the chemical and electronic structures of Sec and Pyl and will be a useful guidance for future experimental research.

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“…The conformations of the constituting peptide fragments are then properly combined to yield the low energy conformations of the target peptide 2 , 15 , 16 . The “divide and conquer” method possess a highly desirable feature that the required computational cost increases moderately with the number of amino acid (AA) residues in a peptide 2 , 17 . Consequently, the “divide and conquer” approach is expected to be useful for the structure prediction of large peptide.…”

Section: Introductionmentioning

confidence: 99%

A random forest learning assisted “divide and conquer” approach for peptide conformation search

Chen

Yang

Lin

2018

Sci Rep

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Computational determination of peptide conformations is challenging as it is a problem of finding minima in a high-dimensional space. The “divide and conquer” approach is promising for reliably reducing the search space size. A random forest learning model is proposed here to expand the scope of applicability of the “divide and conquer” approach. A random forest classification algorithm is used to characterize the distributions of the backbone φ-ψ units (“words”). A random forest supervised learning model is developed to analyze the combinations of the φ-ψ units (“grammar”). It is found that amino acid residues may be grouped as equivalent “words”, while the φ-ψ combinations in low-energy peptide conformations follow a distinct “grammar”. The finding of equivalent words empowers the “divide and conquer” method with the flexibility of fragment substitution. The learnt grammar is used to improve the efficiency of the “divide and conquer” method by removing unfavorable φ-ψ combinations without the need of dedicated human effort. The machine learning assisted search method is illustrated by efficiently searching the conformations of GGG/AAA/GGGG/AAAA/GGGGG through assembling the structures of GFG/GFGG. Moreover, the computational cost of the new method is shown to increase rather slowly with the peptide length.

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A fragment based step-by-step strategy for determining the most stable conformers of biomolecules

Cited by 14 publications

References 30 publications

Theoretical spectroscopic studies on chemical and electronic structures of arginylglycine

Theoretical spectroscopic studies on chemical and electronic structures of arginylglycine

Theoretical Spectroscopic Studies on Chemical and Electronic Structures of Selenocysteine and Pyrrolysine

A random forest learning assisted “divide and conquer” approach for peptide conformation search

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