Lanthanide chemical shift reagent들의 NMR induced chemical shift에 관한 연구 : phenylcyclopropane 유도체들의 입체적 구조 및 염기도가 Lanthanide induced shift에 미치는 영향

소정호 忠南大學校 大學院 1983 국내석사

Methyl-_cis and _trans-2-phenylcyclopropanecarboxylate, ethyl-_cis and _trans-2-phenylcyclopropanecarboxamide were synthesized and purified by gas chromatography. Lanthanide induced shifts (LIS) of above six compounds have been measured with Eu(fod). All protons in cyclopropane ring are assigned with chemicall shifts and lanthanide induced shifts. The trend of lanthanide induced shifts of the protons in cyclopropane ring is H_D>H_B>H_C>H_A in cis compounds. The geometric isomer can be determined by the trend of the lanthanide induced shifts of the protons in cyclopropane ring. The lanthanide induced shift of H_B in cis compounds are much smaller than those of trans compounds in consequence of the steric hinderance of phenyl grop for the formation fo lanthanide shift reagent-substrate complexes. The basicity of amino group in phenylcyclopropanecarboxamide cannot much contribute to the change of the lanthanide induced shifg, comparing to the steric factor. This explanation can be supported by the different formation constant of methyl-_trans-2-phenycyclopropanecarboxylate-Eu (fod)_3 complexes (K=33±17), methyl-_cis-2-phenycyclopropanecarboxylate-Eu (fod)_3 complexes(K=15?5) and N,N-dimethyl-_trans-2-phenylcyclopopanecarboxamide-Eu(fod)_3 complexes(K=18±2) * A thesis submitted to committee of the Graduate School of Chungnam National University in partial fulfillment of the requirements for the degree of Master of Science in january, 1983.

(A) geometric approach to separate the effects of magnetic susceptibility and chemical shift/exchange

은현성 서울대학교 대학원 2020 국내석사

In MRI, the frequency shift or phase image has been widely used to measure magnetic susceptibility of a material using quantitative susceptibility mapping (QSM). However, the frequency shift is also known to be affected by chemical shift/exchange, which may introduce errors in the susceptibility estimation. In this study, we proposed a geometric method that separates susceptibility-induced frequency shift from chemical shift/ exchange-induced frequency shift in a phantom using three axes scanned datasets. The method was successfully validated in numerical simulation and reported susceptibility and chemical shift/exchange in olive oil, bovine serum Albumin (BSA), ferritin, and iron oxide solutions. The proposed method is useful not only in measuring magnetic susceptibility and chemical shift/exchange but also in improving QSM reconstruction algorithms. 자기공명영상 (Magnetic Resonance Imaging, MRI) 분야에서, 주파수 변이 또는 위상 영상은 Quantitative Susceptibility Mapping (QSM) 기술을 활용해 자화율을 측정하는 데 이용된다. 하지만 주파수 변이는 자화율뿐만 아니라 화학전이 및 화학교환에 의해서 영향을 받는데, 이 때문에 자화율을 정확히 측정하는 것에 어려움이 있다. 본 연구에서는 위의 문제를 해결하기 위해 자화율에 의한 주파수 변이와 화학전이 및 화학교환에 의한 주파수 변이를 분리하는 방법론을 제안한다. 제안된 분리법은 서로 수직인 세 방향의 주 자기장에서 촬영한 영상들이 가지는 기하학적 특성을 활용하였다. 위 분리법의 유효성을 검증하기 위하여 가상의 인공물을 활용한 컴퓨터 시뮬레이션을 진행하였다. 또한, 올리브유, 알부민 수용액, 페리틴 수용액, 산화철 수용액을 이용하여 진행한 인공물 실험을 동반하여 효용성을 증명하였다. 본 연구에서 제안된 자화율과 화학전이 및 화학교환의 분리법은 각 물리량의 정확한 측정과 QSM 재구성 알고리즘의 기술적 향상에 공헌할 것으로 기대한다.

Ensemble-averaged 15N Chemical Shift Prediction in Peptides by Optimizing the Scaling Factor

김민지 서울대학교 대학원 2025 국내석사

핵자기공명(NMR) 분광학은 생체분자 역학을 이해하는 데 필수적인 원자 수준의 구조 정보를 제공한다. 그러나 본질적으로 무질서한 단백질(IDPs)을 분석하는 데 있어서는, 구조적 이질성과 빠른 구조 변동으로 인한 어려움이 존재한다. 본고에서는 빠르게 변하는 여러 구조를 포착하기 위한 계산 방법론을 제시한다. 이를 검증하기 위해 앙상블의 질소 NMR 화학적 이동을 밀도범함수 이론(DFT)을 통해 계산한다. 계산 방법론에는 알파-시누클레인의 C-말단에 포함된 잔기 중 DQLGK 펜타펩타이드를 활용하였다. 31개 유기 분자에 대한 구조 최적화와 NMR 계산에 882가지 범함수/기저 집합 조합을 적용 후, 실험값과의 상관관계를 비교하였다. 그 결과 구조 최적화에 B3LYP[GD3BJ]/6-311++G(d,p) 조합을, NMR 계산 시에 LC-wHPBE/6-31+G(d,p) 조합을 사용하는 최적의 계산 수준을 확립하였다. 펜타펩타이드 구조 앙상블은 40 ns 복제 교환 분자 동역학 시뮬레이션을 통해 생성되었으며, 이로부터 12개의 대표 구조가 추출되었다. 12개 대표 구조의 질소 화학적 이동을 볼츠만 가중 평균으로 계산할 시, 질소 화학적 이동의 실험 측정값과 2.821-3.719 ppm 정도의 오차를 보였다. 이때 라이신 잔기에 있어서는 0.503 ppm의 오차로 일치함을 보였다. 본 연구는 동적 펩타이드 시스템의 구조를 도출하기 위한 방법론을 확립하여, 내재적으로 무질서한 단백질에 대한 이론적 앙상블 예측 가능성을 제시한다. 추가적으로, 저자장 NMR 시스템에서 내부표준물질으로서 중수소화 에탄올(CD3CH2OH 및 CH3CD2OH)을 사용함으로써, 고유한 저자기장 한계에도 불구하고 메틸 및 메틸렌 신호를 성공적으로 분해했다. Nuclear magnetic resonance (NMR) spectroscopy provides essential atomic-level structural information for understanding biomolecular dynamics. However, analyzing intrinsically disordered proteins (IDPs) presents significant challenges due to their structural heterogeneity and rapid conformational fluctuations. This study presents a computational methodology for capturing rapidly interconverting conformers, validated through density functional theory (DFT) calculations of ensemble 15N NMR chemical shifts. Through application of 882 functional/basis set combinations for structure optimization and NMR calculations across 19 organic molecules (43 data points), NMR chemical shift correlation analysis with the reported experimental values was performed. This established an optimal computational protocol employing B3LYP[GD3BJ]/6-311++G(d,p) for structure optimization and LC-wHPBE/6-31+G(d,p) for NMR calculations. The pentapeptide conformational ensemble was generated through 40 ns replica-exchange molecular dynamics (REMD) simulations, from which 12 representative structures were extracted. Boltzmann-weighted averaging of 15N chemical shifts for these 12 conformers resulted deviations in 2.821-3.719 ppm upfield than experimental values, except lysine demonstrating closer agreement at 0.503 ppm deviation. Additionally, implementation of deuterated ethanol (CD3CH2OH and CH3CD2OH) as internal standards in low-field NMR systems successfully resolved methyl and methylene signals despite low-field limitation.

From Proteins, to Machines, to Protons, to Genes, and Back Again

Fraga, Keith Jeffrey University of California, Davis ProQuest Dissertat 2022 해외박사(DDOD)

The success of data standards and public databases in biology is the foundation for the current and continued success of machine learning in biology and medicine. This dissertation explores the interactions between biology, computers, and people in order to develop novel machine learning methods to model complex biological problems. Data is one of the main resources to do machine learning, and Chapters 1, 2, 3 are explicitly about data organization and quality assurance in the protein Nuclear Magnetic Resonance (NMR) spectroscopy discipline. Chapters 4 and 5 present new machine learning architectures to address learning tasks in genomic site recognition and NMR chemical shift prediction. Chapter 1 investigates the manner protein NMR chemical shift data is deposited at the Biological Magnetic Resonance Bank (BMRB) in order to build simple table look-up models to estimate protein chemical shifts. In Chapter 1, we find there is low sequence diversity and data redundancy in the BMRB that was a challenge to locate and filter out. Without filtering out BMRB entries with the same sequence, and possibly the same chemical shifts, look-up models will be more accurate due to data contamination in training and testing sets. Chapter 2 examines approaches to curate a large protein sample production and NMR database to create an NMR time-domain dataset. Quality assurance tests in this NMR sample/FID database uncovered data collisions and redundancies among the database records, which motivated the development of new NMR database management tools. Chapter 3 presents a relational database schema to archive protein NMR samples and associated time-domain data called SpecDB. SpecDB is open source and available at https://github.rpi.edu/RPIBioinformatics/SpecDB.git. Chapter 4 explores how deep neural networks can recognize genomic splice acceptor and donor sites from sequence alone, achieving 97% accuracy for highly used splice donor sites. Chapter 4 also investigates neural networks for intron/exon sequence classification, maximally reaching 77% accuracy. Chapter 5 presents the application of marginalized graph kernels to prediction of NMR chemical shifts for small organic molecules. Incorporating chemical descriptors to graph kernels reaches a 3.501 ppm mean absolute error for Carbon chemical shifts. In total, the following five dissertation chapters explore work in data integrity, organization, and learning techniques from data for applications to structural biology problems.

PROTON NMR STUDIES ON THE STRUCTURE OF SOME DIPEPTIDES

金斗姬 숙명여자대학교 1975 국내박사

10종의 다이�타이드(dipeptide), 글리실-엘-피닐알라닌(gl.-L-phe), 글리실-엘-밸린(gl.-L-val.), 글리실-디엘-알라닌(gl.-DL-al.), 글리실-디엘-시린(gl.-DL-ser.), 글리실-엘-아스파틱산(gl.-L-asp.), 엘-알라닐-엠-아스파릭산(L-al.-L-asp.), 디엘-알라닌-디엠-피닐알라닌(DL-al.-DL-phe.), 디엘-알라닐-디엘-시린(DL-al.-DL-ser.), 엘-피닐알라닐-엘-밸린(L-phe.-L-val.), 엘-밸린-엘-트립토판(L-val.-L-trp.)을 중수에 녹여 실온에서 얻은 양성자핵 자기공명 스펙트럼을 분석하여 그 구조에 대하여 연구하였다. 그 진동수와 천이확율을 양자역학과 "SDS" 방법을 이용하여 계산하였다. 글리실-디엘-알라닌 그리고 디엘-알라닐-디엘-피닐알라닌-, 엘-알라닐-엘-아스파틱산의 알라닌 부분, 글리실-디엘-시린을 제외한 다른 부분에서는 자기적으로 다른곳이 2개 또는 그이상이 있음이 발견되었다. 알파-프로톤(α-proton)에 대한 케미컬-쉬프트(chemical-shift)와 스핀-커플링-컨스탄트(spin coupling constant)의 값이 다른 프로톤(proton)등에 대한 값보다 큰 변화가 보이며, 따라서 이는 펩타이드 본드(peptide bond)위치를 확인하는 것이다. 이들 다이펩타이드의 천이 진동수, 시그날(signal)강도에 있어서 이 분석이론에 의한 값과 그들의 실험치의 사이에 좋은 일치를 보여주었다. 따라서 트리펩타이드(tripeptide) 보다 더 복잡한 포리펩타이드(polypeptide)와 단백질의 양성자 핵자기공명 스펙트럼 분석에 있어, 이 분석방법을 적용할 수 있는 가능성이 기대된다. Proton magnetic resonance spectra of tan dipeptides glycyl-L-phenylalanine, glycyl-L-valine, glycyl-DL-alanine, glycyl-DL-serine, glycyl-L-aspartic acid, L-alanyl-L-aspartic acid, DL-alanyl-DL-phenyl-alanine, DL-alanyl-DL-serine, L-phenylalanyl-L-valine, and L-valyl-L-tryptophan in deutrated water were investigated at room temperature. A complete analysis of the spectra has been made by SDS method and quantum mechanical calculation of frequencies and probabilities of NMR transitions between spin states. Expect for the signals in glycyl-DL-slanine, and for the alanyl-part signals of DL-alanyl-DL-phenylalanine, L-alanyl-L-aspartic acid, and the signals in glycyl-DL-serine, two or more magnetically non-equivalent sites were found. The values of the chemical shifts and the spin coupling constants are found to indicate ε bigger change for α protons with respect to β protons or γ protons, which in fact affirms the peptide linkage. The theoretically calculated and the observed values of the transition frequencies and relative intensities were found to be in good agreement with the experimental values for the dipeptides. The possibility of the extension of the analytical method to the analysis of NMR spectra for tripeptides, polypeptides and proteins has been suggested.

Determination of water content in alcohol mixture solvents using the 1H NMR chemical shift change

윤수연 중앙대학교 대학원 2020 국내석사

화학 공정 산업에서 유기용매에 함유된 수분 함량을 측정하는 것은 매우 중요한 과정이다. 칼 피셔 적정법과 가스 크로마토그래피 등을 활용하는 기존의 분석 방법은 측정을 수행하는 데 수분이 없는 조건, 상당한 시간을 필요로 한다. 이를 개선하기 위한 방법으로 수소 핵자기공명분광법의 화학적 이동 차이에 기반한 알코올 용매 내 수분 함량 분석법이 개발되었다. 본 연구에서는 수소 핵자기공명분광법의 화학적 이동 변화를 활용하여 각각 메탄올과 t-부탄올, 메탄올과 에탄올 혼합물의 수분 함량을 측정했다. 이를 통해 알코올 비율에 따라 수분 함량과 알코올 분자의 화학적 이동이 상관 관계를 보임을 확인하였다. 또한, 화학적 이동 변화를 이용하여 알코올 혼합 용매와 물 사이의 수소 결합의 경향성을 확인하였다. Determination of the water content in organic solvents is important in the chemical process industry. However, the widely used analytical methods such as Karl Fisher titration and gas chromatography require a significant amount of time or moisture-free conditions. Therefore, a simple method that relies on chemical shift difference between water and alcohol peaks in 1H NMR spectroscopy was developed for the determination of water content in alcohol solvents. This study observed the chemical shift changes in 1H NMR spectroscopy depending on water contents to test the feasibility of the method in alcohol-mixture solvents such as methanol-ethanol and methanol-t-butanol. The correlations between chemical shift change and water content with different alcohol ratios have been founded through the experimental results for alcohol mixtures. In addition, the chemical shift changes have shown the characteristic behaviors of hydrogen bonding between alcohol mixtures and water.

Combining Data-Driven Models With Physics-Based Approaches for Computational Molecule Characterization and Generation

Li, Jie University of California, Berkeley ProQuest Disser 2023 해외박사(DDOD)

Theoretical studies of molecules have historically relied on deterministic algorithms, stochastic simulations, and physical models. Recently modern data-driven methods are starting to infiltrate into various fields of molecular science, opening new possibilities for solving problems that are difficult to tackle through traditional approaches. The accumulation of data, advancement of machine learning algorithms and improvement in hardware enables a plethora of data-driven approaches to surpass traditional methods in terms of accuracy and efficiency, but questions remain about how well these data-driven methods can generalize to unseen data to do true prediction. In this dissertation, I will show that when data-driven models are combined with physics-based approaches, through either feature design, or exerting constrains on the machine learning models, new standards can be established in the fields of molecule characterization and generation.Nuclear magnetic resonance (NMR) chemical shifts (CS) are extremely sensitive to the local atomic environments for different nuclei in a molecule, and therefore is a common technique in molecule characterization. In chapter 2, I focus on the design of the UCBShift predictor for CSs for proteins in aqueous solution. The UCBShift method uniquely fuses a transfer prediction module, which employs sequence and structure alignments to select reference chemical shifts from a database, with a machine learning model that uses carefully curated and physics-inspired features, to predict CSs for proteins with higher accuracy and better reliability compared to all popular methods such as SHIFTX2 and SPARTA+. This chapter further delineates how UCBShift benefits from realistic data that has not been heavily curated, and surpasses existing CS calculators in terms of real-world performance without eliminating test predictions ad hoc.However, in order to achieve rigorous and consistent improvement for an arbitrary molecular system, carefully curated feature sets specifically for proteins can be limiting, and we seek features from theoretical calculations. In Chapter 3 I describe the development of a novel neural network model which employs quantum mechanical (QM) features from affordable Density Functional Theory (DFT) calculations, along with geometric features of the molecular systems, to predict NMR chemical shieldings. The resulting iShiftML model predicts chemical shieldings approaching the highest level of accuracy under the modern theoretical framework of CCSD(T) in the complete basis set limit, but without the computational burden that limits its applicability to large systems. Not only does the iShiftML model demonstrate excellent predictive performance when compared with small molecule gas phase experimental CSs, but it also offers a capability to predict chemical shifts for much more complex natural products, and can be used for differentiating diasteromers based on chemical shift assignments. This chapter unveils new possibilities for integrating machine learning and QM calculations for accurate and transferable molecular characterization.In Chapters 4 an 5, my research addresses fundamental issues for large and small molecule generation relevant to proteins and drug molecules. Chapter 4 describes the Int2Cart method that uses a recurrent neural network to predict the correlations between bond lengths, bond angles and backbone torsion angles and amino acid sequence of a protein. By incorporating these correlations, proteins reconstructed from just torsion angles display not only physically more accurate bond lengths and bond angles, but the reconstructed proteins are closer to their crystal structures than under the common assumption that bond lengths and bond angles are fixed, or that coming from a static library that only relies on local residue geometries. I have also shown potential applications of this method in estimating model quality for AlphaFold2 predicted structures, and reconstructing intrinsically disordered protein (IDP) ensembles with decreased steric overlap. Chapter 5 describes the combination of deep generative networks trained by reinforcement learning and physical docking study. I developed the iMiner method, which generates de novo drug-like molecules with an augmented binding potency towards specific protein targets, facilitating the discovery of potential new therapeutic targets. SARS-COV-2 Main Protease was used as an example to show that our generated molecules cover a broader chemical space than crowdsourcing efforts, and the newly generated molecules exert optimized interactions and correct shape for the compatibility with the binding pocket.To summarize, this dissertation contains multiple methods that harmonize data-driven models and physics-based approaches in the area of NMR spectroscopy, protein structure modelling and de novo drug discovery, which provides a new perspective for researchers striving to leverage computational methods in molecular science and chemical biology.

Biphenyldimethyldicarboxylate 및 그 가수분해물의 온도에 의한 chemical shihft의 거동

김진수 中央大學校 1994 국내석사

Measurement of the chemical shift of 129Xe dissolved in normal alkanes

Lim, Yoong-Ho Graduate Faculty of the University of Georgia 1989 국내박사

Determination of the pH of a solution based on the change of chemical shifts

양두경 서울대학교 대학원 1989 국내박사