Biomolecular Structure Information from High-Speed Quantum Mechanical Electronic Spectra Calculation

Machine-Learning-Assisted Rational Design of Si─Rhodamine as Cathepsin-pH-Activated Probe for Accurate Fluorescence Navigation

High-performance fluorescent probes stand as indispensable tools in fluorescence-guided imaging, and are crucial for precise delineation of focal tissue while minimizing unnecessary removal of healthy tissue. Herein, machine-learning-assisted strategy to investigate the current available xanthene dyes is first proposed, and a quantitative prediction model to guide the rational synthesis of novel fluorescent molecules with the desired pH responsivity is constructed. Two novel Si─rhodamine derivatives are successfully achieved and the cathepsin/pH sequentially activated probe Si─rhodamine─cathepsin-pH (SiR─CTS-pH) is constructed. The results reveal that SiR─CTS-pH exhibits higher signal-to-noise ratio of fluorescence imaging, compared to single pH or cathepsin-activated probe. Moreover, SiR─CTS-pH shows strong differentiation abilities for tumor cells and tissues and accurately discriminates the complex hepatocellular carcinoma tissues from normal ones, indicating its significant application potential in clinical practice. Therefore, the continuous development of xanthene dyes and the rational design of superior fluorescent molecules through machine-learning-assisted model broaden the path and provide more advanced methods to researchers.

Machine-learning screening of luminogens with aggregation-induced emission characteristics for fluorescence imaging

Due to the excellent biocompatible physicochemical performance, luminogens with aggregation-induced emission (AIEgens) characteristics have played a significant role in biomedical fluorescence imaging recently. However, screening AIEgens for special applications takes a lot of time and efforts by using conventional chemical synthesis route. Fortunately, artificial intelligence techniques that could predict the properties of AIEgen molecules would be helpful and valuable for novel AIEgens design and synthesis. In this work, we applied machine learning (ML) techniques to screen AIEgens with expected excitation and emission wavelength for biomedical deep fluorescence imaging. First, a database of various AIEgens collected from the literature was established. Then, by extracting key features using molecular descriptors and training various state-of-the-art ML models, a multi-modal molecular descriptors strategy has been proposed to extract the structure-property relationships of AIEgens and predict molecular absorption and emission wavelength peaks. Compared to the first principles calculations, the proposed strategy provided greater accuracy at a lower computational cost. Finally, three newly predicted AIEgens with desired absorption and emission wavelength peaks were synthesized successfully and applied for cellular fluorescence imaging and deep penetration imaging. All the results were consistent successfully with our expectations, which demonstrated the above ML has a great potential for screening AIEgens with suitable wavelengths, which could boost the design and development of novel organic fluorescent materials.

A deep transfer learning-based protocol accelerates full quantum mechanics calculation of protein

Effective full quantum mechanics (FQM) calculation of protein remains a grand challenge and of great interest in computational biology with substantial applications in drug discovery, protein dynamic simulation and protein folding. However, the huge computational complexity of the existing QM methods impends their applications in large systems. Here, we design a transfer-learning-based deep learning (TDL) protocol for effective FQM calculations (TDL-FQM) on proteins. By incorporating a transfer-learning algorithm into deep neural network (DNN), the TDL-FQM protocol is capable of performing calculations at any given accuracy using models trained from small datasets with high-precision and knowledge learned from large amount of low-level calculations. The high-level double-hybrid DFT functional and high-level quality of basis set is used in this work as a case study to evaluate the performance of TDL-FQM, where the selected 15 proteins are predicted to have a mean absolute error of 0.01 kcal/mol/atom for potential energy and an average root mean square error of 1.47 kcal/mol/$ {\rm A^{^{ \!\!\!o}}} $ for atomic forces. The proposed TDL-FQM approach accelerates the FQM calculation more than thirty thousand times faster in average and presents more significant benefits in efficiency as the size of protein increases. The ability to learn knowledge from one task to solve related problems demonstrates that the proposed TDL-FQM overcomes the limitation of standard DNN and has a strong power to predict proteins with high precision, which solves the challenge of high precision prediction in large chemical and biological systems.

Efficient Recognition and Removal of Persistent Organic Pollutants by a Bifunctional Molecular Material

Persistent organic pollutants (POPs) exist widely in the environment and place significant impact on human health by bioaccumulation. Efficient recognition of POPs and their removal are highly challenging tasks because their specific structures interact often very weakly with the capture materials. Herein, a molecular nanocage (1) is studied as an efficient sensing and sorbent material for POPs, which is demonstrated by a representative and stable perfluorooctane sulfonate (PFOS) substrate containing a hydrophilic sulfonic group and a hydrophobic fluoroalkyl chain. A highly sensitive and unusual turn-on fluorescence response within 10 s and a 97% total removal of PFOS from water in 20 min have been achieved owing to the strong host-guest interactions between 1 and PFOS. The binding constant of 1 to PFOS is 2 orders of magnitude higher than state-of-the-art adsorbents for PFOS and thus represents a new benchmark material for the recognition and removal of PFOS. The host-guest interaction has been elucidated by solid-state NMR spectroscopy and single-crystal X-ray diffraction, which provide key insights at a molecular level for the design of new advanced sensing/sorbent materials for POPs.

Nonconventional luminophores: characteristics, advancements and perspectives

Nonconventional luminophores devoid of remarkable conjugates have attracted considerable attention due to their unique luminescence behaviors, updated luminescence mechanism of organics and promising applications in optoelectronic, biological and medical fields. Unlike classic luminogens consisting of molecular segments with greatly extended electron delocalization, these unorthodox luminophores generally possess nonconjugated structures based on subgroups such as ether (-O-), hydroxyl (-OH), halogens, carbonyl (CO), carboxyl (-COOH), cyano (CN), thioether (-S-), sulfoxide (SO), sulfone (OSO), phosphate, and aliphatic amine, as well as their grouped functionalities like amide, imide, anhydride and ureido. They can exhibit intriguing intrinsic luminescence, generally featuring concentration-enhanced emission, aggregation-induced emission, excitation-dependent luminescence and prevailing phosphorescence. Herein, we review the recent progress in exploring these nonconventional luminophores and discuss the current challenges and future perspectives. Notably, different mechanisms are reviewed and the clustering-triggered emission (CTE) mechanism is highlighted, which emphasizes the clustering of the above mentioned electron rich moieties and consequent electron delocalization along with conformation rigidification. The CTE mechanism seems widely applicable for diversified natural, synthetic and supramolecular systems.

Closer Look at Inverse Electron Demand Diels–Alder and Nucleophilic Addition Reactions on s-Tetrazines Using Enhanced Sampling Methods

Inverse electron demand [4+2] Diels–Alder (iEDDA) reactions as well as unprecedented nucleophilic (azaphilic) additions of R-substituted silyl-enol ethers (where R is Phenyl, Methyl, and Hydrogen) to 1,2,4,5-tetrazine (s-tetrazine) catalyzed by \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hbox {BF}_{3}$$\end{document}BF3 have recently been discovered (Simon et al. in Org Lett 23(7):2426–2430, 2021), where static calculations were employed for calculation of activation energies. In order to have a more realistic dynamic description of these reactions in explicit solution at ambient conditions, in this work we use a semiempirical tight-binding method combined with enhanced sampling techniques to calculate free energy surfaces (FESs) of the iEDDA and azaphilic addition reactions. Relevant products of not only s-tetrazine but also its derivatives such as \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hbox {BF}_{3}$$\end{document}BF3-mediated s-tetrazine adducts are investigated. We reconstruct the FESs of the iEDDA and azaphilic addition reactions using metadynamics and blue moon ensemble, and compare the ability of different collective variables (CVs) including bond distances, Social PeRmutation INvarianT (SPRINT) coordinates, and path-CV to describe the reaction pathway. We find that when a bulky Phenyl is used as a substituent at the dienophile the azaphilic addition is preferred over the iEDDA reaction. In addition, we also investigate the effect of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hbox {BF}_{3}$$\end{document}BF3 in the diene and steric hindrance in the dienophile on the competition between the iEDDA and azaphilic addition reactions, providing chemical insight for reaction design.

A Machine-Learning Protocol for Ultraviolet Protein-Backbone Absorption Spectroscopy under Environmental Fluctuations

Ultraviolet (UV) absorption spectra are commonly used for characterizing the global structure of proteins. However, the theoretical interpretation of UV spectra is hindered by the large number of required expensive ab initio calculations of excited states spanning a huge conformation space. We present a machine-learning (ML) protocol for far-UV (FUV) spectra of proteins, which can predict FUV spectra of proteins with comparable accuracy to density functional theory (DFT) calculations but with 3-4 orders of magnitude reduced computational cost. It further shows excellent predictive power and transferability that can be used to probe structural mutations and protein folding pathways.

Perspective on Simplified Quantum Chemistry Methods for Excited States and Response Properties

We review recent developments in the framework of simplified quantum chemistry for excited state and optical response properties (sTD-DFT) and present future challenges for new method developments to improve accuracy and extend the range of application. In recent years, the scope of sTD-DFT was extended to molecular response calculations of the polarizability, optical rotation, first hyperpolarizability, two-photon absorption (2PA), and excited-state absorption for large systems with hundreds to thousands of atoms. The recently introduced spin-flip simplified time-dependent density functional theory (SF-sTD-DFT) variant enables an ultrafast treatment for diradicals and related strongly correlated systems. A few drawbacks were also identified, specifically for the computation of 2PA cross sections. We propose solutions to this problem and how to generally improve the accuracy of simplified schemes. New possible simplified schemes are also introduced for strongly correlated systems, e.g., with a second-order perturbative correlation correction. Interpretation tools that can extract chemical structure-property relationships from excited state or response calculations are also discussed. In particular, the recently introduced method-agnostic RespA approach based on natural response orbitals (NROs) as the key concept is employed.

Alternative Strategy to Obtain Artificial Imine Reductase by Exploiting Vancomycin/D-Ala-D-Ala Interactions with an Iridium Metal Complex

Based on the supramolecular interaction between vancomycin (Van), an antibiotic glycopeptide, and D-Ala-D-Ala (DADA) dipeptides, a novel class of artificial metalloenzymes was synthesized and characterized. The presence of an iridium(III) ligand at the N-terminus of DADA allowed the use of the metalloenzyme as a catalyst in the asymmetric transfer hydrogenation of cyclic imines. In particular, the type of link between DADA and the metal-chelating moiety was found to be fundamental for inducing asymmetry in the reaction outcome, as highlighted by both computational studies and catalytic results. Using the [IrCp*(m-I)Cl]Cl ⊂ Van complex in 0.1 M CH₃COONa buffer at pH 5, a significant 70% (S) e.e. was obtained in the reduction of quinaldine B.

Machine Learning Enables Highly Accurate Predictions of Photophysical Properties of Organic Fluorescent Materials: Emission Wavelengths and Quantum Yields

The development of functional organic fluorescent materials calls for fast and accurate predictions of photophysical parameters for processes such as high-throughput virtual screening, while the task is challenged by the limitations of quantum mechanical calculations. We establish a database covering >4300 solvated organic fluorescent dyes with 3000 distinct compounds and develop a new machine learning approach aimed at efficient and accurate predictions of emission wavelength and photoluminescence quantum yield (PLQY). Our feature engineering has given rise to a functionalized structure descriptor (FSD) and a comprehensive general solvent descriptor (CGSD), whereby a highly black-box computational framework is realized with consistently good accuracy across different dye families, ability of describing substitution effects and solvent effects, efficiency for large-scale predictions, and workability with on-the-fly learning. Evaluations with unseen molecules suggest a remarkable mean absolute error of 0.13 for PLQY and 0.080 eV for emission energy, the latter comparable to time-dependent density functional theory (TD-DFT) calculations. An online prediction platform was constructed based on the ensemble model to make predictions in various solvents. Our statistical learning methodology will complement quantum mechanical calculations as an efficient alternative approach for the prediction of these parameters.

Theoretical study on conformational energies of transition metal complexes

Conformational energies are an important chemical property for which a performance assessment of theoretical methods is mandatory. Existing benchmark sets are often limited to biochemical or main group element containing molecules, while organometallic systems are generally less studied. A key problem herein is to routinely generate conformers for these molecules due to their complexity and manifold of possible coordination patterns. In this study we used our recently published CREST protocol [Pracht et al., Phys. Chem. Chem. Phys., 2020, 22, 7169-7192] to generate conformer ensembles for a variety of 40 challenging transition metal containing molecules, which were then used to form a comprehensive conformational energy benchmark set termed TMCONF40. Several low-cost semiempirical, density functional theory (DFT) and force-field methods were compared to high level DLPNO-CCSD(T1) and double-hybrid DFT reference values. Close attention was paid to the energetic ordering of the conformers in the statistical evaluation. With respect to the double-hybrid references, both tested low-cost composite DFT methods produce high Pearson correlation coefficients of rp,mean,B97-3c//B97-3c = 0.922 and rp,mean,PBEh-3c//B97-3c = 0.890, with mean absolute deviations close to or below 1 kcal mol-1. This good performance also holds for a comparison to DLPNO-CCSD(T1) reference energies for a smaller subset termed TMCONF5. Based on DFT geometries, the GFNn-xTB methods yield reasonable Pearson correlation coefficients of rp,mean,GFN1-xTB//B97-3c = 0.617 (MADmean = 2.15 kcal mol-1) and rp,mean,GFN2-xTB//B97-3c = 0.567 (MADmean = 2.68 kcal mol-1), outperforming the widely used PMx methods on the TMCONF40 test set. Employing the low-cost composite DFT method B97-3c on GFN2-xTB geometries yields an slightly improved correlation of rp,mean,B97-3c//GFN2-xTB = 0.632. Furthermore, for 68% of the investigated complexes at least one low-energy conformer was found that is more stable than the respective crystal structure conformation, which signals the importance of conformational studies. General recommendations for the application of the CREST protocol and DFT methods for transition metal conformational energies are given.

Unfolding Dynamics of a Photoswitchable Helical Peptide

We present an atomistic force field for the azo-moiety of the photoswitchable FK-11-X peptide. We use the parameters to study the unfolding of the peptide through molecular dynamics simulations. The unfolded ensemble contains many different structures, ranging from a partially unfolded peptide to a fully unfolded structure. The averaged computed far-ultraviolet circular dichroism (CD) spectrum of the set of structures, which was simulated using the newly developed force field, agrees well with experiment. The rate of the simulated unfolding process was estimated to have a time constant of 5.80 ± 0.03 ns from the time evolution of the CD spectrum of the peptide, computed from the backbone conformations sampled over 40 simulated trajectories. Our estimated time constant is faster than, but not inconsistent with, previous experimental estimates from time-resolved infrared and optical rotatory dispersion spectroscopy.

Computation of Electromagnetic Properties of Molecular Ensembles

We outline a methodology for efficiently computing the electromagnetic response of molecular ensembles. The methodology is based on the link that we establish between quantum‐chemical simulations and the transfer matrix (T‐matrix) approach, a common tool in physics and engineering. We exemplify and analyze the accuracy of the methodology by using the time‐dependent Hartree‐Fock theory simulation data of a single chiral molecule to compute the T‐matrix of a cross‐like arrangement of four copies of the molecule, and then computing the circular dichroism of the cross. The results are in very good agreement with full quantum‐mechanical calculations on the cross. Importantly, the choice of computing circular dichroism is arbitrary: Any kind of electromagnetic response of an object can be computed from its T‐matrix. We also show, by means of another example, how the methodology can be used to predict experimental measurements on a molecular material of macroscopic dimensions. This is possible because, once the T‐matrices of the individual components of an ensemble are known, the electromagnetic response of the ensemble can be efficiently computed. This holds for arbitrary arrangements of a large number of molecules, as well as for periodic or aperiodic molecular arrays. We identify areas of research for further improving the accuracy of the method, as well as new fundamental and technological research avenues based on the use of the T‐matrices of molecules and molecular ensembles for quantifying their degrees of symmetry breaking. We provide T‐matrix‐based formulas for computing traditional chiro‐optical properties like (oriented) circular dichroism, and also for quantifying electromagnetic duality and electromagnetic chirality. The formulas are valid for light‐matter interactions of arbitrarily‐high multipolar orders.

Quantum Chemical Calculation of Molecular and Periodic Peptide and Protein Structures

Special-purpose classical force fields (FFs) provide good accuracy at very low computational cost, but their application is limited to systems for which potential energy functions are available. This excludes most metal-containing proteins or those containing cofactors. In contrast, the GFN2-xTB semiempirical quantum chemical method is parametrized for almost the entire periodic table. The accuracy of GFN2-xTB is assessed for protein structures with respect to experimental X-ray data. Furthermore, the results are compared with those of two special-purpose FFs, HF-3c, PM6-D3H4X, and PM7. The test sets include proteins without any prosthetic groups as well as metalloproteins. Crystal packing effects are examined for a set of smaller proteins to validate the molecular approach. For the proteins without prosthetic groups, the special purpose FF OPLS-2005 yields the smallest overall RMSD to the X-ray data but GFN2-xTB provides similarly good structures with even better bond-length distributions. For the metalloproteins with up to 5000 atoms, a good overall structural agreement is obtained with GFN2-xTB. The full geometry optimizations of protein structures with on average 1000 atoms in wall-times below 1 day establishes the GFN2-xTB method as a versatile tool for the computational treatment of various biomolecules with a good accuracy/computational cost ratio.

Automated exploration of the low-energy chemical space with fast quantum chemical methods

We propose and discuss an efficient scheme for the in silico sampling for parts of the molecular chemical space by semiempirical tight-binding methods combined with a meta-dynamics driven search algorithm. The focus of this work is set on the generation of proper thermodynamic ensembles at a quantum chemical level for conformers, but similar procedures for protonation states, tautomerism and non-covalent complex geometries are also discussed. The conformational ensembles consisting of all significantly populated minimum energy structures normally form the basis of further, mostly DFT computational work, such as the calculation of spectra or macroscopic properties. By using basic quantum chemical methods, electronic effects or possible bond breaking/formation are accounted for and a very reasonable initial energetic ranking of the candidate structures is obtained. Due to the huge computational speedup gained by the fast low-cost quantum chemical methods, overall short computation times even for systems with hundreds of atoms (typically drug-sized molecules) are achieved. Furthermore, specialized applications, such as sampling with implicit solvation models or constrained conformational sampling for transition-states, metal-, surface-, or noncovalently bound complexes are discussed, opening many possible applications in modern computational chemistry and drug discovery. The procedures have been implemented in a freely available computer code called CREST, that makes use of the fast and reliable GFNn-xTB methods.

Methodological Survey of Simplified TD-DFT Methods for Fast and Accurate Interpretation of UV-Vis-NIR Spectra of Phthalocyanines

A methodological survey of density functional theory (DFT) methods for the prediction of UV-visible (vis)-near-infrared (NIR) spectra of phthalocyanines is reported. Four methods, namely, full time-dependent (TD)-DFT and its Tamm-Dancoff approximation (TDA), together with their simplified modifications (sTD-DFT and sTDA, respectively), were tested by using the examples of unsubstituted and alkoxy-substituted metal-free ligands and zinc complexes. The theoretical results were compared with experimental data derived from UV-visible absorption and magnetic circular dichroism spectroscopy. Seven popular exchange-correlation functionals (BP86, B3LYP, TPSSh, M06, CAM-B3LYP, LC-BLYP, and ωB97X) were tested within these four approaches starting at a relatively modest level using 6-31G(d) basis sets and gas-phase BP86/def2-SVP optimized geometries. A gradual augmentation of the computational levels was used to identify the influence of starting geometry, solvation effects, and basis sets on the results of TD-DFT and sTD-DFT calculations. It was found that although these factors do influence the predicted energies of the vertical excitations, they do not affect the trends predicted in the spectral properties across series of structurally related substituted free bases and metallophthalocyanines. The best accuracy for the gas-phase vertical excitations was observed in the lower-energy Q-band region for calculations that made use of range-separated hybrids for both full and simplified TD-DFT approaches. The CAM-B3LYP functional provided particularly accurate results in the context of the sTD-DFT approach. The description of the higher-energy B-band region is considerably less accurate, and this demonstrates the need for further advances in the accuracy of theoretical calculations. Together with a general increase in accuracy, the application of simplified TD-DFT methods affords a 2-3 orders of magnitude speedup of the calculations in comparison to the full TD-DFT approach. It is anticipated that this approach will be widely used on desktop computers during the interpretation of UV-vis-NIR spectra of phthalocyanines and related macrocycles in the years ahead.

Exploration of Chemical Compound, Conformer, and Reaction Space with Meta-Dynamics Simulations Based on Tight-Binding Quantum Chemical Calculations

The semiempirical tight-binding based quantum chemistry method GFN2-xTB is used in the framework of meta-dynamics (MTD) to globally explore chemical compound, conformer, and reaction space. The biasing potential given as a sum of Gaussian functions is expressed with the root-mean-square-deviation (RMSD) in Cartesian space as a metric for the collective variables. This choice makes the approach robust and generally applicable to three common problems (i.e., conformer search, chemical reaction space exploration in a virtual nanoreactor, and for guessing reaction paths). Because of the inherent locality of the atomic RMSD, functional group or fragment selective treatments are possible facilitating the investigation of catalytic processes where, for example, only the substrate is thermally activated. Due to the approximate character of the GFN2-xTB method, the resulting structure ensembles require further refinement with more sophisticated, for example, density functional or wave function theory methods. However, the approach is extremely efficient running routinely on common laptop computers in minutes to hours of computation time even for realistically sized molecules with a few hundred atoms. Furthermore, the underlying potential energy surface for molecules containing almost all elements ( Z = 1-86) is globally consistent including the covalent dissociation process and electronically complicated situations in, for example, transition metal systems. As examples, thermal decomposition, ethyne oligomerization, the oxidation of hydrocarbons (by oxygen and a P450 enzyme model), a Miller-Urey model system, a thermally forbidden dimerization, and a multistep intramolecular cyclization reaction are shown. For typical conformational search problems of organic drug molecules, the new MTD(RMSD) algorithm yields lower energy structures and more complete conformer ensembles at reduced computational effort compared with its already well performing predecessor.

GFN2-xTB-An Accurate and Broadly Parametrized Self-Consistent Tight-Binding Quantum Chemical Method with Multipole Electrostatics and Density-Dependent Dispersion Contributions

An extended semiempirical tight-binding model is presented, which is primarily designed for the fast calculation of structures and noncovalent interaction energies for molecular systems with roughly 1000 atoms. The essential novelty in this so-called GFN2-xTB method is the inclusion of anisotropic second order density fluctuation effects via short-range damped interactions of cumulative atomic multipole moments. Without noticeable increase in the computational demands, this results in a less empirical and overall more physically sound method, which does not require any classical halogen or hydrogen bonding corrections and which relies solely on global and element-specific parameters (available up to radon, Z = 86). Moreover, the atomic partial charge dependent D4 London dispersion model is incorporated self-consistently, which can be naturally obtained in a tight-binding picture from second order density fluctuations. Fully analytical and numerically precise gradients (nuclear forces) are implemented. The accuracy of the method is benchmarked for a wide variety of systems and compared with other semiempirical methods. Along with excellent performance for the "target" properties, we also find lower errors for "off-target" properties such as barrier heights and molecular dipole moments. High computational efficiency along with the improved physics compared to its precursor GFN-xTB makes this method well-suited to explore the conformational space of molecular systems. Significant improvements are furthermore observed for various benchmark sets, which are prototypical for biomolecular systems in aqueous solution.

Polarization spectroscopy methods in the determination of interactions of small molecules with nucleic acids - tutorial

The structural characterization of non-covalent complexes between nucleic acids and small molecules (ligands) is of a paramount significance to bioorganic research. Highly informative methods about nucleic acid/ligand complexes such as single crystal X-ray diffraction or NMR spectroscopy cannot be performed under biologically compatible conditions and are extensively time consuming. Therefore, in search for faster methods which can be applied to conditions that are at least similar to the naturally occurring ones, a set of polarization spectroscopy methods has shown highly promising results. Electronic circular dichroism (ECD) is the most commonly used method for the characterization of the helical structure of DNA and RNA and their complexes with ligands. Less common but complementary to ECD, is flow-oriented linear dichroism (LD). Other methods such as vibrational CD (VCD) and emission-based methods (FDCD, CPL), can also be used for suitable samples. Despite the popularity of polarization spectroscopy in biophysics, aside several highly focused reviews on the application of these methods to DNA/RNA research, there is no systematic tutorial covering all mentioned methods as a tool for the characterization of adducts between nucleic acids and small ligands. This tutorial aims to help researchers entering the research field to organize experiments accurately and to interpret the obtained data reliably.