Conformational Energy Research Articles

Unexpected discovery: “A new 3,3′-bipyrazolo[3,4-b]pyridine scaffold and its comprehensive analysis”

In this study, a novel 3,3′-bipyrazolo [3,4-b]pyridine-type structure was synthesized from 5-acetylamino-3-methyl-1-phenylpyrazole using the Vilsmeier-Haack reaction as a key step. The spectroscopic properties and structural elucidation of the compound were determined with the use of FT-IR, HRMS, 1H NMR, and 13C NMR. Likewise, the theoretical analysis of the IR and NMR spectra allowed peaks to be assigned and a solid correlation was demonstrated between the experimental and theoretical results. Finally, ab initio calculations based on the density functional theory method at the B3LYP/6-311G (d,p) level of theory were used to determine the conformational energy barrier, facilitating the identification of the most probable conformers of the synthesized compound. Overall, our findings contribute to the understanding of bipyrazolo [3,4-b]pyridine derivatives.

A journey in unraveling the enantiorecognition mechanism of 3,5-dinitrobenzoyl-amino acids with two Cinchona alkaloid-based chiral stationary phases: The power of molecular dynamic simulations

BackgroundInnovations in computer hardware and software capabilities have paved the way for advances in molecular modelling techniques and methods, leading to an unprecedented expansion of their potential applications. In contrast to the docking technique, which usually identifies the most stable selector-selectand (SO-SA) complex for each enantiomer, the molecular dynamics (MD) technique enables the consideration of a distribution of the SO-SA complexes based on their energy profile. This approach provides a more truthful representation of the processes occurring within the column. However, benchmark procedures and focused guidelines for computational treatment of enantioselectivity at the molecular level are still missing. ResultsTwenty-eight molecular dynamics simulations were performed to study the enantiorecognition mechanisms of seven N-3,5-dinitrobenzoylated α- and β-amino acids (DNB-AAs), occurring with the two quinine- and quinidine-based (QN-AX and QD-AX) chiral stationary phases (CSPs), under polar-ionic conditions. The MD protocol was optimized in terms of box size, simulation run time, and frame recording frequency. Subsequently, all the trajectories were analyzed by calculating both the type and amount of the interactions engaged by the selectands (SAs) with the two chiral selectors (SOs), as well as the conformational and interaction energy profiles of the formed SA-SO associates. All the MDs were in strict agreement with the experimental enantiomeric elution order and allowed to establish (i) that salt-bridge and H-bond interactions play a pivotal role in the enantiorecognition mechanisms, and (ii) that the π-cation and π-π interactions are the discriminant chemical features between the two SOs in ruling the chiral recognition mechanism. SignificanceThe results of this work clearly demonstrate the high contribution given by MD simulations in the comprehension of the enantiorecognition mechanism with Cinchona alkaloid-based CSPs. However, from this research endeavor it clearly emerged that the MD protocol optimization is crucial for the quality of the produced results.

Comparing ANI-2x, ANI-1ccx neural networks, force field, and DFT methods for predicting conformational potential energy of organic molecules

In this study, the conformational potential energy surfaces of Amylmetacresol, Benzocaine, Dopamine, Betazole, and Betahistine molecules were scanned and analyzed using the neural network architecture ANI-2 × and ANI-1ccx, the force field method OPLS, and density functional theory with the exchange-correlation functional B3LYP and the basis set 6-31G(d). The ANI-1ccx and ANI-2 × methods demonstrated the highest accuracy in predicting torsional energy profiles, effectively capturing the minimum and maximum values of these profiles. Conformational potential energy values calculated by B3LYP and the OPLS force field method differ from those calculated by ANI-1ccx and ANI-2x, which account for non-bonded intramolecular interactions, since the B3LYP functional and OPLS force field weakly consider van der Waals and other intramolecular forces in torsional energy profiles. For a more comprehensive analysis, electronic parameters such as dipole moment, HOMO, and LUMO energies for different torsional angles were calculated at two levels of theory, B3LYP/6-31G(d) and ωB97X/6-31G(d). These calculations confirmed that ANI predictions are more accurate than density functional theory calculations with B3LYP functional and OPLS force field for determining potential energy surfaces. This research successfully addressed the challenges in determining conformational potential energy levels and shows how machine learning and deep neural networks offer a more accurate, cost-effective, and rapid alternative for predicting torsional energy profiles.

In Silico Study of the Early Stages of Aggregation of β-Sheet Forming Antimicrobial Peptide GL13K.

Antimicrobial peptides (AMPs) are of growing interest as potential candidates that may offer more resilience against antimicrobial resistance than traditional antibiotic agents. In this article, we perform the first in silico study of the synthetic ß sheet-forming AMP GL13K. Through atomistic simulations of single and multi-peptide systems under different conditions, we are able to shine a light on the short timescales of early aggregation. We find that isolated peptide conformations are primarily dictated by sequence rather than charge, whereas changing charge has a significant impact on the conformational free energy landscape of multi-peptide systems. We demonstrate that the loss of charge-charge repulsion is a sufficient minimal model for experimentally observed aggregation. Overall, our work explores the molecular biophysical underpinnings of the first stages of aggregation of a unique AMP, laying necessary groundwork for its further development as an antibiotic candidate.

In Silico Screening of Some Active Phytochemicals to Identify Promising Inhibitors Against SARS-CoV-2 Targets.

There are very few small-molecule drug candidates developed against SARS-CoV-2 that have been revealed since the epidemic began in November 2019. The typical medicinal chemistry discovery approach requires more than a decade of the year of painstaking research and development and a significant financial guarantee, which is not feasible in the challenge of the current epidemic. This current study proposes to find and identify the most effective and promising phytomolecules against SARS-CoV-2 in six essential proteins (3CL protease, Main protease, Papain- Like protease, N-protein RNA binding domain, RNA-dependent RNA polymerase, and Spike receptor binding domain target through in silico screening of 63 phytomolecules from six different Ayurveda medicinal plants. The phytomolecules and SARS-CoV-2 proteins were taken from public domain databases such as PubChem and RCSB Protein Data Bank. For in silico screening, the molecular interactions, binding energy, and ADMET properties were investigated. The structure-based molecular docking reveals some molecules' greater affinity towards the target than the co-crystal ligand. Our results show that tannic acid, cyanidin-3-rutinoside, zeaxanthin, and carbolactone are phytomolecules capable of inhibiting SARS-CoV-2 target proteins in the least energy conformations. Tannic acid had the least binding energy of -8.8 kcal/mol, which is better than the binding energy of its corresponding co-crystal ligand (-7.5 kcal/mol) against 3 CL protease. Also, it has shown the least binding energy of -9.9 kcal/mol with a more significant number of conventional hydrogen bond interactions against the RdRp target. Cyanidin-3-rutinoside showed binding energy values of -8.8 and -7.6 kcal/mol against Main protease and Papain-like protease, respectively. Zeaxanthin was the top candidate in the N protein RBD with a binding score of - 8.4 kcal/mol, which is slightly better when compared to a co-crystal ligand (-8.2 kcal/mol). In the spike, carbolactone was the suitable candidate with the binding energy of -7.2 kcal/mol and formed a conventional hydrogen bond and two hydrophobic interactions. The best binding affinity-scored phytomolecules were selected for the MD simulations studies. The present in silico screening study suggested that active phytomolecules from medicinal plants could inhibit SARS-CoV-2 targets. The elite docked compounds with drug-like properties have a harmless ADMET profile, which may help to develop promising COVID-19 inhibitors.

COSMIC: Molecular Conformation Space Modeling in Internal Coordinates with an Adversarial Framework.

The fast and accurate conformation space modeling is an essential part of computational approaches for solving ligand and structure-based drug discovery problems. Recent state-of-the-art diffusion models for molecular conformation generation show promising distribution coverage and physical plausibility metrics but suffer from a slow sampling procedure. We propose a novel adversarial generative framework, COSMIC, that shows comparable generative performance but provides a time-efficient sampling and training procedure. Given a molecular graph and random noise, the generator produces a conformation in two stages. First, it constructs a conformation in a rotation and translation invariant representation─internal coordinates. In the second step, the model predicts the distances between neighboring atoms and performs a few fast optimization steps to refine the initial conformation. The proposed model considers conformation energy, achieving comparable space coverage, and diversity metrics results.

How to Personalize General Anesthesia-A Prospective Theoretical Approach to Conformational Changes of Halogenated Anesthetics in Fire Smoke Poisoning.

Smoke intoxication is a central event in mass burn incidents, and toxic smoke acts at different levels of the body, blocking breathing and oxygenation. The majority of these patients require early induction of anesthesia to preserve vital functions. We studied the influence of hemoglobin (HMG) and myoglobin (MGB) blockade by hydrochloric acid (HCl) in an interaction model with gaseous anesthetics using molecular docking techniques. In the next part of the study, molecular dynamics (MD) simulations were performed on the top-scoring ligand-receptor complexes to investigate the stability of the ligand-receptor complexes and the interactions between ligands and receptors in more detail. Through docking analysis, we observed that hemoglobin creates more stable complexes with anesthetic gases than myoglobin. Intoxication with gaseous hydrochloric acid produces conformational and binding energy changes of anesthetic gases to the substrate (both the pathway and the binding site), the most significant being recorded in the case of desflurane and sevoflurane, while for halothane and isoflurane, they remain unchanged. According to our theoretical model, the selection of anesthetic agents for patients affected by fire smoke containing hydrochloric acid is critical to ensure optimal anesthetic effects. In this regard, our model suggests that halothane and isoflurane are the most suitable choices for predicting the anesthetic effects in such patients when compared to sevoflurane and desflurane.

Tell Machine Learning Potentials What They Are Needed For: Simulation-Oriented Training Exemplified for Glycine.

Machine learning potentials (MLPs) are widely applied as an efficient alternative way to represent potential energy surfaces (PESs) in many chemical simulations. The MLPs are often evaluated with the root-mean-square errors on the test set drawn from the same distribution as the training data. Here, we systematically investigate the relationship between such test errors and the simulation accuracy with MLPs on an example of a full-dimensional, global PES for the glycine amino acid. Our results show that the errors in the test set do not unambiguously reflect the MLP performance in different simulation tasks, such as relative conformer energies, barriers, vibrational levels, and zero-point vibrational energies. We also offer an easily accessible solution for improving the MLP quality in a simulation-oriented manner, yielding the most precise relative conformer energies and barriers. This solution also passed the stringent test by diffusion Monte Carlo simulations.

Modeling Zinc Complexes Using Neural Networks.

Understanding the energetic landscapes of large molecules is necessary for the study of chemical and biological systems. Recently, deep learning has greatly accelerated the development of models based on quantum chemistry, making it possible to build potential energy surfaces and explore chemical space. However, most of this work has focused on organic molecules due to the simplicity of their electronic structures as well as the availability of data sets. In this work, we build a deep learning architecture to model the energetics of zinc organometallic complexes. To achieve this, we have compiled a configurationally and conformationally diverse data set of zinc complexes using metadynamics to overcome the limitations of traditional sampling methods. In terms of the neural network potentials, our results indicate that for zinc complexes, partial charges play an important role in modeling the long-range interactions with a neural network. Our developed model outperforms semiempirical methods in predicting the relative energy of zinc conformers, yielding a mean absolute error (MAE) of 1.32 kcal/mol with reference to the double-hybrid PWPB95 method.

Energy landscapes of homopolymeric RNAs revealed by deep unsupervised learning

Conformational dynamics of RNA plays important roles in a variety of cellular functions such as transcriptional regulation, catalysis, scaffolding, and sensing. Recently, RNAs with low-complexity sequences have been shown to phase separate and form condensate phases similar to lowcomplexity protein domains. The affinity for phase separation and the material characteristics of RNA condensates are strongly dependent on sequence composition and patterning. We hypothesize that differences in the affinities for RNA phase separation can be uncovered by studying sequence-dependent conformational dynamics of single RNA chains. To this end, we have employed atomistic simulations and deep dimensionality reduction techniques to map temperature-dependent conformational free energy landscapes for 20 base-long homopolymeric RNA sequences: poly(U), poly(G), poly(C), and poly(A). The energy landscapes of homopolymeric RNAs reveal a plethora of metastable states with qualitatively different populations stemming from differences in base chemistry. Through detailed analysis of base, phosphate, and sugar interactions, we show that experimentally observed temperature-driven shifts in metastable state populations align with experiments on RNA phase transitions. Specifically, we find that the thermodynamics of unfolding of homopolymeric RNA follows the poly(G) > poly(A) > poly(C) > poly(U) order of stability, mirroring the propensity of RNA to form condensates. To conclude, this work shows that at least for homopolymeric RNA sequences the single-chain conformational dynamics contains sufficient information for predicting and quantifying condensate forming affinities of RNAs. Thus, we anticipate that atomically detailed studies of temeprature -dependent energy landscapes of RNAs will be a useful guide for understanding the propensity of various RNA molecules to form condensates.

Computational insights into the conformational transition of STING: Mechanistic, energetic considerations, and the influence of crucial mutations

STING (stimulator of interferon genes) is a crucial protein in the innate immune system's response to viral and bacterial infections. In this study, we investigated the mechanistic and energetic mechanism of the conformational transition process of STING activated by cGAMP binding. We found that the STING connector region undergoes an energetically unfavorable rotation during this process, which is compensated by the favorable interaction between cGAMP and the STING ligand binding domain. We further studied several disease-causing mutations and found that the V155 M mutation facilitates a smoother transition in the STING connector region. However, the V147L mutation exhibits unfavorable conformational transition energy, suggesting it may hinder STING activation pathway that relies on connector region rotation. Despite being labeled as hyperactive, the widespread prevalence of V147L/V147I mutations across species implies a neutral character, indicating complexity in its role. Overall, our analysis deepens the understanding of STING activation within the connector region, and targeting this region with compounds may provide an alternative approach to interfering with STING's function.

Influence of Lipid Conformations on the Interaction Energy between a Membrane and a Peripheral Protein

Peripheral membrane proteins are temporarily coupled to the surface of a membrane, penetrating into the lipid layer. In this work, it has been shown that the fraction of trans configurations of dihedral angles in hydrophobic chains of lipids decreases in the region of contact of peripheral membrane proteins with the membrane. This effect differs for different lipid chains and for dihedral angles at different distances from the beginning of a chain. A gosh configuration has a higher energy than a trans configuration. Consequently, the decrease in the fraction of trans configurations leads to an increase in the energy of the chain. The energy of chain conformations for the peripheral membrane protein considered in this work increases by ≈2 kJ/mol. A chain in chain conformations is involved in molecular mechanisms determining the elastic modulus of membranes. The energy stored in a conformation chain can be spent to the desorption of protein from the surface of the membrane and can be considered as a reason why the interaction of peripheral membrane proteins with the membrane is temporal.

Autobench V1.0: Benchmarking Automation for Electronic Structure Calculations.

This work reports on new software for automatic conformer energy benchmarking calculations for flexible molecules. The software workflow consists of four parts: conformational search, preoptimization, optimization, and frequency calculations at a higher level and last calculations using several theoretical levels. The software was written to be user-friendly and versatile to be used by nonexperts in computational chemistry. Any theoretical levels available in either Gaussian 16 or ORCA 5 may be applied in the benchmarking study. The workflow will automatically run conformational search calculations and deal with conformers that converge to the same minimum and those that show a negative frequency. At the end of the workflow, the user will have the mean absolute deviations and the most accurate method/DFT functional and basis set in comparison to the benchmark to be applied for the molecular system of interest. Case examples are given at the end of the paper that may help users to get insight into the software's main features.

Accurate Structures and Rotational Constants of Steroid Hormones at DFT Cost: Androsterone, Testosterone, Estrone, β-Estradiol, and Estriol.

A comprehensive analysis of the structural, conformational, and spectroscopic properties in the gas phase has been performed for five prototypical steroid hormones, namely, androsterone, testosterone, estrone, β-estradiol, and estriol. The revDSD-PBEP86 double-hybrid functional in conjunction with the D3BJ empirical dispersion and a suitable triple-ζ basis set provides accurate conformational energies and equilibrium molecular structures, with the latter being further improved by proper account of core-valence correlation. Average deviations within 0.1% between computed and experimental ground state rotational constants are reached when adding to those equilibrium values vibrational corrections obtained at the cost of standard harmonic frequencies thanks to the use of a new computational tool. Together with the intrinsic interest of the studied hormones, the accuracy of the results obtained at DFT cost for molecules containing about 50 atoms paves the way toward the accurate investigations of other flexible bricks of life.

In Vitro and In Silico Explorations of the Protein Conformational Changes of Corynebacterium glutamicum MshA, a Model Retaining GT-B Glycosyltransferase.

MshA is a GT-B glycosyltransferase catalyzing the first step in the biosynthesis of mycothiol. While many GT-B enzymes undergo an open-to-closed transition, MshA is unique because its 97° rotation is beyond the usual range of 10-25°. Molecular dynamics (MD) simulations were carried out for MshA in both ligand bound and unbound states to investigate the effect of ligand binding on localized protein dynamics and its conformational free energy landscape. Simulations showed that both the unliganded "opened" and liganded "closed" forms of the enzyme sample a wide degree of dihedral angles and interdomain distances with relatively low overlapping populations. Calculation of the free energy surface using replica exchange MD for the apo "opened" and an artificial generated apo "closed" structure revealed overlaps in the geometries sampled, allowing calculation of a barrier of 2 kcal/mol for the open-to-closed transition in the absence of ligands. MD simulations of fully liganded MshA revealed a smaller sampling of the dihedral angles. The localized protein fluctuation changes suggest that UDP-GlcNAc binding activates the motions of loops in the 1-l-myo-inositol-1-phosphate (I1P)-binding site despite little change in the interactions with UDP-GlcNAc. Circular dichroism, intrinsic fluorescence spectroscopy, and mutagenesis studies were used to confirm the ligand-induced structural changes in MshA. The results support a proposed mechanism where UDP-GlcNAc binds with rigid interactions to the C-terminal domain of MshA and activates flexible loops in the N-terminal domain for binding and positioning of I1P. This model can be used for future structure-based drug development of inhibitors of the mycothiol biosynthetic pathway.

Molecular Insights into the Impact of Mutations on the Binding Affinity of Targeted Covalent Inhibitors of BTK.

Targeted covalent inhibitors (TCIs) have witnessed a significant resurgence in recent years, particularly in the kinase drug discovery field for treating diverse clinical indications. The inhibition of Bruton's tyrosine kinase (BTK) for treating B-cell cancers is a classic example where TCIs such as ibrutinib have had breakthroughs in targeted therapy. However, selectivity remains challenging, and the emergence of resistance mutations is a critical concern for clinical efficacy. Computational methods that can accurately predict the impact of mutations on inhibitor binding affinity could prove helpful in informing targeted approaches─providing insights into drug resistance mechanisms. In addition, such systems could help guide the systematic evaluation and impact of mutations in disease models for optimal experimental design. Here, we have employed in silico physics-based methods to understand the effects of mutations on the binding affinity and conformational dynamics of select TCIs of BTK. The TCIs studied include ibrutinib, acalabrutinib, and zanubrutinib─all of which are FDA-approved drugs for treating multiple forms of leukemia and lymphoma. Our results offer useful molecular insights into the structural determinants, thermodynamics, and conformational energies that impact ligand binding for this biological target of clinical relevance.

A spectrophotometric determination and the quantum chemical investigation of Pd(II)-3-hydroxy-2-(4-methoxyphenyl)-4-oxo-4H-1-benzopyran complex

An analytical approach has been instigated for spectrophotometric inquisition of trace amounts of palladium in its bivalent oxidation state employing a novel 1-benzopyran derivative viz. A 1:2 [Pd(II):HMPB] light yellow complex is formed by a spontaneous interaction between Pd(II) and HMPB. The complex is quantitatively extracted from a weakly basic medium into dichloromethane absorbing prevalently and consistently at 405-425 nm. The method coheres to linearity up to 1.5 µg mL-1 of palladium(II). Analytical parameters such as the molar absorption coefficient (ε=3.011 × 104 L mol-1 cm-1), Sandell's sensitivity [0.0035 µg Pd(II) cm-2], % RSD (0.62%) and the limit of detection [0.0147 µg Pd(II) mL-1] indicate that the study satisfies all the criteria for good sensitivity, precision and accuracy. In addition, intervention with regard to numerous anions/complexing agents and cations of primary analytical importance has been performed to assess the method's flexibility and usefulness. The results show that the majority of them do not cause any interference during determination. To better understand chemistry of the prepared complex, spectroscopic quantum chemical studies including DFT and MEP mapping based on examination of the electronic characteristics of the complex in its most stable least energy conformation are used.
 KEY WORDS: Palladium, 3-Hydroxy-2-(4-methoxyphenyl)-4-oxo-4H-1-benzopyran, Extractive spectrophotometric determination, DFT, MEP, Docking
 Bull. Chem. Soc. Ethiop. 2024, 38(3), 591-603. 
 DOI: https://dx.doi.org/10.4314/bcse.v38i3.4

Incorporating Neural Networks into the AMOEBA Polarizable Force Field.

Neural network potentials (NNPs) offer significant promise to bridge the gap between the accuracy of quantum mechanics and the efficiency of molecular mechanics in molecular simulation. Most NNPs rely on the locality assumption that ensures the model's transferability and scalability and thus lack the treatment of long-range interactions, which are essential for molecular systems in the condensed phase. Here we present an integrated hybrid model, AMOEBA+NN, which combines the AMOEBA potential for the short- and long-range noncovalent atomic interactions and an NNP to capture the remaining local covalent contributions. The AMOEBA+NN model was trained on the conformational energy of the ANI-1x data set and tested on several external data sets ranging from small molecules to tetrapeptides. The hybrid model demonstrated substantial improvements over the baseline models in term of accuracy as the molecule size increased, suggesting its potential as a next-generation approach for chemically accurate molecular simulations.

Uncovering the Association Mechanism between Two Intrinsically Flexible Proteins.

The understanding of protein-protein interaction mechanisms is key to the atomistic description of cell signaling pathways and for the development of new drugs. In this context, the mechanism of intrinsically disordered proteins folding upon binding has attracted attention. The VirB9 C-terminal domain (VirB9Ct) and the VirB7 N-terminal motif (VirB7Nt) associate with VirB10 to form the outer membrane core complex of the Type IV Secretion System injectisome. Despite forming a stable and rigid complex, VirB7Nt behaves as a random coil, while VirB9Ct is intrinsically dynamic in the free state. Here we combined NMR, stopped-flow fluorescence, and computer simulations using structure-based models to characterize the VirB9Ct-VirB7Nt coupled folding and binding mechanism. Qualitative data analysis suggested that VirB9Ct preferentially binds to VirB7Nt by way of a conformational selection mechanism at lower temperatures. However, at higher temperatures, energy barriers between different VirB9Ct conformations are more easily surpassed. Under these conditions the formation of non-native initial encounter complexes may provide alternative pathways toward the native complex conformation. These observations highlight the intimate relationship between folding and binding, calling attention to the fact that the two molecular partners must search for the most favored intramolecular and intermolecular interactions on a rugged and funnelled conformational energy landscape, along which multiple intermediates may lead to the final native state.

Solvent efficiency and role of dispersion and electrostatic forces for chiral discrimination of sulfur-containing amino acids by tetra-protonated CBPQT macrocycle

Two enantiomeric forms of amino acids in a chiral medium behave quite differently because of the different orientation of their functional groups in space. Thus, the phenomenon of chiral recognition is crucial with the focus on applications in molecular sensing and enantioselective separations. The present research work is focused on the illustration of the potential chiral recognition of a porous CBPQT4+ macrocycle for sulfur-containing amino acids, which is elucidated by the conformational energies landscape with quantitative non-covalent analysis and their electronic behaviour. Herein, we report the chiral recognition of sulfur-containing amino acids e.g., cysteine (CY), homocysteine (HCY), and methionine (MT), by tetra-protonated (4+) CBPQT macrocycle via density functional theory (DFT) calculations. Geometry optimization, thermodynamic stability, noncovalent interaction analysis, symmetry-adapted perturbation (SAPT), and electronic properties analyses are employed to characterize the chiral response of the complexes formed by CBPQT4+ with D- and L-isomers of selected amino acids. The interactive conformations of complexes indicate physisorption of amino acids through the central cavity of the macrocycle. The maximum chiral discrimination is observed in the case of D- and L-cysteine isomers, which is 3.56 kcal/mol. It is revealed that the complex; D-CY@CBPQT4+ is energetically more stable than the L-CY analogue, whereas, L-HCY and L-MT show higher stability as compared to D-type counterparts, probably due to the interaction of the thiol groups with π-electrons of macrocycle in respective stable complexes. Non-covalent interaction (NCI) analyses including, reduced density gradient-based NCI index and SAPT reveal that the methionine-based complexes show the highest attractive components e.g., electrostatic, dispersion, and induction with the lowest repulsive exchange contribution, which is followed by homocysteine and cysteine. Overall, results show that the CBPQT4+ macrocycle has an excellent ability to differentiate between L- and D-amino acids, the difference is more pronounced when the structure of amino acid is small and rigid.