Molecular Structure Prediction Research Articles

A Comprehensive Genetic Analysis of Mycotoxin-Producing Penicillium expansum Isolated from River Water Using Molecular Profiling, DNA Barcoding, and Secondary Structure Prediction

This study marks the first report on the genetic characterization of Penicillium expansum strain capable of mycotoxin production isolated from river water. Situated in Ganagalawanipeta village, Srikakulam, Andhra Pradesh, India, where river water serves as a vital resource, our investigation probed the presence of pathogenic opportunistic fungi adept at mycotoxin synthesis. Over six months, 30 samples were collected to assess their occurrence. This article revolves around the use of morphological traits for Penicillium genus identification. Precise species determination involved PCR analysis using universal primers ITS1 and ITS4, followed by sequence analysis through NCBI-BLASTn and the ITS2 database. The analysis indicated a striking 99.49% genetic similarity to Penicillium expansum isolate MW559596 from CSIR-National Institute of Oceanography, Goa, an Indian isolate, with a resultant 600-base pair fragment. This sequence was officially cataloged as OR536221 in the NCBI GenBank database. Sequence and phylogenetic assessments were conducted to pinpoint the strain and geographical origin. Notably, the ribosomal nuclear ITS region displayed significant inter- and intra-specific divergence, manifested in DNA barcodes and secondary structures established via minimum free energy calculations. These findings provide crucial insights into the genetic diversity and potential mycotoxin production of P. expansum isolates, shedding light on the environmental repercussions and health risks associated with river water contamination from agricultural and aquaculture effluents. This pioneering research advances our understanding of mycotoxin-producing fungi in aquatic environments and underscores the imperative need for water quality monitoring in regions reliant on such water sources for their sustenance and livelihoods.

The Historical Evolution and Significance of Multiple Sequence Alignment in Molecular Structure and Function Prediction

Multiple sequence alignment (MSA) has evolved into a fundamental tool in the biological sciences, playing a pivotal role in predicting molecular structures and functions. With broad applications in protein and nucleic acid modeling, MSAs continue to underpin advancements across a range of disciplines. MSAs are not only foundational for traditional sequence comparison techniques but also increasingly important in the context of artificial intelligence (AI)-driven advancements. Recent breakthroughs in AI, particularly in protein and nucleic acid structure prediction, rely heavily on the accuracy and efficiency of MSAs to enhance remote homology detection and guide spatial restraints. This review traces the historical evolution of MSA, highlighting its significance in molecular structure and function prediction. We cover the methodologies used for protein monomers, protein complexes, and RNA, while also exploring emerging AI-based alternatives, such as protein language models, as complementary or replacement approaches to traditional MSAs in application tasks. By discussing the strengths, limitations, and applications of these methods, this review aims to provide researchers with valuable insights into MSA’s evolving role, equipping them to make informed decisions in structural prediction research.

The Long and Short of Coupling and Uncoupling via 2D IR Spectroscopy.

Determining dynamic structural changes along with the functional movements in biological systems has been a significant challenge for scientists for several decades. Utilizing vibrational coupling with the aid of 2D IR probe pairs has aided in uncovering structural dynamics and functional roles of chemical moieties involved in actions such as membrane peptide folding and transport, ion and water transport, and drug-protein interactions. Both native and non-native vibrational probe pairs have been developed for infrared studies, and their efficacy has been tested in various systems. With these probe pairs, 2D IR spectroscopy captures frozen snapshots of the structural events involved in biological function through vibrational coupling and correlated spectral diffusion. In this Perspective, different treatments of vibrational coupling and coupling models will be addressed, and a review of some of the specific vibrational probe pairs used to study these coupling mechanisms is presented. Overall, the intrinsic molecular dynamics detected on these ultrafast time scales will provide an atomic level view of how chosen structures traverse reaction paths. Thus, it is important to evaluate and assess the accuracy of the different vibrational coupling models and their consistency with the prediction of different molecular structures.

Computational insights into mercuric reductase from Pseudomonas fluorescens: a bioinformatic and molecular dynamics approach for mercury detoxification

Mercury (Hg) pollution poses significant threats to human health and ecosystems worldwide due to its persistence, bioaccumulation, and toxic effects. This study focuses on mercuric reductase from Pseudomonas fluorescens (UniProt ID Q51772), a key enzyme involved in detoxifying mercury through reduction to its less toxic elemental form, Hg(0). This study aims to explore the potential of this enzyme for bioremediation applications, focusing on structural insights, functional mechanisms, and biotechnological enhancements to facilitate mercury detoxification. The protein sequence of Q51772 was analyzed using bioinformatics tools to determine its structural and functional attributes. Physiochemical properties, including molecular weight, isoelectric point, and secondary structure predictions, were assessed. Virulence prediction tools confirmed the protein’s nontoxic and nonpathogenic nature. Homology modeling and docking studies provided insights into its three-dimensional structure and binding interactions with mercury substrates. Q51772 consists of 548 amino acids and belongs to the Pyridine nucleotide-disulphide oxidoreductase family, class I. It features specific domains crucial for mercury reduction, identified through Pfam and InterPro analyses. Physiochemical analysis indicated a stable protein with hydrophilic tendencies conducive to enzymatic function. Structural modeling validated by ProQ and PROCHECK confirmed the reliability of the predicted structure. Molecular docking of organic mercury compound with wild-type and mutant mercuric reductase revealed strong binding affinities, with key hydrogen bonds involving residues Gly95, Thr122, and Asp390. Mercuric reductase Q51772 from Pseudomonas fluorescens emerges as a promising candidate for bioremediation of mercury-contaminated environments. Future research should focus on optimizing its performance under diverse environmental conditions to advance sustainable solutions for mercury pollution control.

Molecular Prediction and Correlation of the Structure and Function of Universal Stress Protein A (UspA) from Salmonella Typhimurium.

Salmonella Typhimurium (ST) is a zoonotic pathogen that can cause gastroenteritis in humans when they consume contaminated food or water. When exposed to various stressors, both from living organisms (biotic) and the environment (abiotic), Salmonella Typhimurium produces Universal Stress Proteins (USPs). These proteins are gaining recognition for their crucial role in bacterial stress resistance and the ability to enter a prolonged state of growth arrest. Additionally, USPs exhibit diverse structures due to the fusion of the USP domain with different catalytic motifs, enabling them to participate in various reactions and cellular activities during stressful conditions. In this particular study, researchers cloned and analyzed the uspA gene obtained from poultry-derived strains of Salmonella Typhimurium. The gene comprises 435 base pairs, encoding a USP family protein consisting of 144 amino acids. Phylogenetic analysis demonstrated a close relationship between the uspA genes of Salmonella Typhimurium and those found in other bacterial species. Weused molecular dynamics simulations and 3D structure prediction to ensure thatthe USPA protein was stable.Furthermore, we alsocarried out motif search and network analysis of protein-protein interactions. The findings from this study offer valuable insights for the development of inhibitors targeted against Salmonella Typhimurium.

Molecular structure prediction, experimental and theoretical properties, and biological activities of (E)-5-nitro-3-(phenylimino) indolin-2-one- in-vitro against 60 lethal tumour cell lines

In this study an isatin derivative (E)-5-nitro-3-(phenylimino) indolin-2-one (5NPI) has been synthesized and examined utilizing quantum chemical calculations of DFT approach for structural optimization, electronic and vibrational characteristics. Functional groups have been detected using FT-IR spectra and compared with simulated spectra. To model UV–vis absorption and identify electronic characteristics in solvents and gas phase, TD-DFT computations have been performed. The GIAO technique was used to measure chemical shifting in NMR in several different solvents. Theoretical parameters coincide perfectly with experimental data. HOMO and LUMO band gaps reflect chemical activity and have satisfactory charge exchange inside the molecule. RDG analysis, topological and NBO investigations have been used to study weak intermolecular interactions. Reactive regions of the 5NPI compound have been determined using MEP and Fukui functions. NLO behavior has been computed using hyperpolarizability parameters. The anticancer activities of the compound 5NPI were evaluated from the National Cancer Institute (NCI), USA and subjected to in-silico analysis such as drug-likeness, ADMET and molecular docking. The compound 5NPI has been docked with 3RUX, 8ADA, 3TFU and 6TOJ proteins and showed moderate binding affinity as −7.14, −6.42, −6.68 and −8.41 kcal/mol respectively. Compound 5NPI is used as a lead molecule in the treatment of TB along with various tumors.

Analysis of AlphaFold and molecular dynamics structure predictions of mutations in serpins.

Serine protease inhibitors (serpins) include thousands of structurally conserved proteins playing key roles in many organisms. Mutations affecting serpins may disturb their conformation, leading to inactive forms. Unfortunately, conformational consequences of serpin mutations are difficult to predict. In this study, we integrate experimental data of patients with mutations affecting one serpin with the predictions obtained by AlphaFold and molecular dynamics. Five SERPINC1 mutations causing antithrombin deficiency, the strongest congenital thrombophilia were selected from a cohort of 350 unrelated patients based on functional, biochemical, and crystallographic evidence supporting a folding defect. AlphaFold gave an accurate prediction for the wild-type structure. However, it also produced native structures for all variants, regardless of complexity or conformational consequences in vivo. Similarly, molecular dynamics of up to 1000 ns at temperatures causing conformational transitions did not show significant changes in the native structure of wild-type and variants. In conclusion, AlphaFold and molecular dynamics force predictions into the native conformation at conditions with experimental evidence supporting a conformational change to other structures. It is necessary to improve predictive strategies for serpins that consider the conformational sensitivity of these molecules.

Machine learning for <i>in silico</i> protein research

<i>In silico</i> protein calculation has been an important research subject for a long time, while its recent combination with machine learning promotes the development greatly in related areas. This review focuses on four major fields of the <i>in silico</i> protein research that combines with machine learning, which are molecular dynamics, structure prediction, property prediction and molecule design. Molecular dynamics depend on the parameters of force field, which is necessary for obtaining accurate results. Machine learning can help researchers to obtain more accurate force field parameters. In molecular dynamics simulation, machine learning can also help to perform the free energy calculation in relatively low cost. Structure prediction is generally used to predict the structure given a protein sequence. Structure prediction is of high complexity and data volume, which is exactly what machine learning is good at. By the help of machine learning, scientists have gained great achievements in three-dimensional structure prediction of proteins. On the other hand, the predicting of protein properties based on its known information is also important to study protein. More challenging, however, is molecule design. Though marching learning has made breakthroughs in drug-like small molecule design and protein design in recent years, there is still plenty of room for exploration. This review focuses on summarizing the above four fields andlooks forward to the application of marching learning to the <i>in silico</i> protein research.

DeepSPInN – deep reinforcement learning for molecular structure prediction from infrared and 13C NMR spectra

DeepSPInI is a deep reinforcement learning method that predicts the molecular structure when given infrared and 13C nuclear magnetic resonance spectra with an accuracy of 91.5%.

Harnessing the power of curvilinear internal coordinates: from molecular structure prediction to vibrational spectroscopy

Different standard VPT2 codes employ Cartesian coordinates for the computation of rotational and vibrational spectroscopic parameters. However, curvilinear internal coordinates offer a number of advantages provided that a general non-redundant set of coordinates can be built and employed in an unsupervised workflow. In the present paper I summarize the main results and perspectives of a general engine employing curvilinear internal coordinates and perturbation theory for the computation of rotational and vibrational spectroscopic parameters of large molecules beyond the conventional rigid rotor/harmonic oscillator model. Some examples concerning biomolecule building blocks are discussed in some detail in order to better analyze the performance of the proposed strategy.

Antihypertensive potential of the stem bark of Canarium schweinfurthii Engl. (Burseraceae) in wistar rats: UPLC-ESI-QToF-MS/MS-based prediction of antihypertensive phytochemicals

Ethnopharmacological relevanceCanarium schweinfurthii, also called ‘’Elemierd’Afrique’’, is used in Cameroonian folk medicine (bark decoction) to treat patients suffering from hypertension.Aim of the study: This study aimed at evaluating the antihypertensive activities of the stem bark of Canarium schweinfurthii and identifying potential compounds present in its extract that may support or oppose its ethnomedicinial use. Materials and methodsStem bark extract of Canarium schweinfurthii was prepared by maceration using 70 % ethanol followed by redissolution in methanol and hyphenated. Ultra-Performance Liquid Chromatography-Tandem Mass Spectrometry (UPLC-MS/MS) analysis for the detection and characterisation of secondary metabolites. Antihypertensive effects were assessed in Wistar rats after induction of hypertension with sodium chloride (NaCl) 18 % at a dose of 0.01mL/gbody weight once a day for four weeks.Hemodynamic parameters were measured weekly by anon-invasive method using the CODA system. ResultsThe ethanolic bark extract of C. schweinfurthii significantly inhibited the increase of blood pressure with a maximum of 23.18 % (systolic pressure, p < 0.0001), 24.77 % (diastolic pressure, p < 0.001) and 22.95 % (mean pressure, p < 0.0001) at a dose of 200 mg/kgbody weight at the 4th week, compared to agroup of Wistar rats that received only NaCl (negative control). Similarly, the extract significantly inhibited the increase in heart rate by 18.84 % (p < 0.001) at 200 mg/kgbody weight at week four. Hematological parameters did not differ significantly between the extract-treated and control groups. The UPLC-MS/MS spectrometric analysis provided evidence for the presence of several C30 terpenoids containing three or five oxygen atoms and exhibiting pentacyclic triterpenoid structures, as well as C29 terpenoids and related compounds containing nitrogen in addition to oxygen, using spectral matching, and in silico molecular formula and structure prediction. Additionally, two features were annotated with high-confidence as lignans, structurally closely related to hinokinin and dehydrocubebin through MS/MS-based in silico structure prediction using CSI: Finger ID in SIRIUS5. The lignans have been previously reported from stem bark of plants belonging to the Burseraceae family. Conclusion: The ethanolic stem bark extract of C. schweinfurthii demonstrated antihypertensive properties on the tested Wistar rats. These results support the ethnopharmacological use of C. schweinfurthii concoctions for the treatment of hypertension and suggest a protective effect against salt damage, hypothetically by the up regulation of antioxidative enzymes and/or lipids, mitigatings membrane peroxidation.

Machine learning and DFT-based combined framework for predicting transmission spectra of quantum-confined bio-molecular nanotube.

The Adenine-based nanotube is theoretically designed, and its transmission spectra are investigated. The quantum-confined Adenine nanotube shows electronic transmission of the carrier at minimum stress. In this paper, the prediction of transmission spectra of the quantum-confined bio-molecular nanotube is investigated and deeply studied. Molecular level structure prediction and their electronic characterization can be possible with ab initio accuracy using a machine learning algorithmic approach. At the molecular level, it is difficult to predict quantum transmission spectra as these results are always hampered by the carrier backscattering effect. However, mostly these predictive models are available for intrinsic semi-conducting materials and other inorganic structures. Machine learning algorithms are designed to predict the electronic properties of the nano-scale structure. This task is even more difficult when quantum-confined molecular arrangements are considered, whose transmission spectra are sensitive to the confinements applied. This paper presents an effective machine learning algorithms framework for predicting transmission spectra of quantum-confined nanotubes from their geometries. In this paper, we consider regression machine learning algorithms to find maximum accuracy with varying configurations and geometries to excerpt their atoms' local environment information. The Hamiltonian components are then used to enable the utilization of the information to predict the electronic structure at any arbitrary sampling point or k-point. The theoretical basics introduced in this process help to capture and incorporate minor changes in quantum confinements into transmission spectra and provide the framework algorithm with more accuracy. This paper shows the ability to predict the accurate algorithmic models of the Adenine nanotube. In this framework, we have considered a tiny data set to achieve a rapid and reliable method for electronic structure determination and also propose the best algorithm for predictive model analysis.

Extracting Structural Information from Physicochemical Property Measurements Using Machine Learning─A New Approach for Structure Elucidation in Non-targeted Analysis.

Non-targeted analysis (NTA) has made critical contributions in the fields of environmental chemistry and environmental health. One critical bottleneck is the lack of available analytical standards for most chemicals in the environment. Our study aims to explore a novel approach that integrates measurements of equilibrium partition ratios between organic solvents and water (KSW) to predictions of molecular structures. These properties can be used as a fingerprint, which with the help of a machine learning algorithm can be converted into a series of functional groups (RDKit fragments), which can be used to search chemical databases. We conducted partitioning experiments using a chemical mixture containing 185 chemicals in 10 different organic solvents and water. Both a liquid chromatography quadrupole time-of-flight mass spectrometer (LC-QTOF MS) and a LC-Orbitrap MS were used to assess the feasibility of the experimental method and the accuracy of the algorithm at predicting the correct functional groups. The two methods showed differences in log KSW with the QTOF method showing a mean absolute error (MAE) of 0.22 and the Orbitrap method 0.33. The differences also culminated into errors in the predictions of RDKit fragments with the MAE for the QTOF method being 0.23 and for the Orbitrap method being 0.31. Our approach presents a new angle in structure elucidation for NTA and showed promise in assisting with compound identification.

Ensemble Reweighting Using Cryo-EM Particle Images.

Cryo-electron microscopy (cryo-EM) has recently become a leading method for obtaining high-resolution structures of biological macromolecules. However, cryo-EM is limited to biomolecular samples with low conformational heterogeneity, where most conformations can be well-sampled at various projection angles. While cryo-EM provides single-molecule data for heterogeneous molecules, most existing reconstruction tools cannot retrieve the ensemble distribution of possible molecular conformations from these data. To overcome these limitations, we build on a previous Bayesian approach and develop an ensemble refinement framework that estimates the ensemble density from a set of cryo-EM particle images by reweighting a prior conformational ensemble, e.g., from molecular dynamics simulations or structure prediction tools. Our work provides a general approach to recovering the equilibrium probability density of the biomolecule directly in conformational space from single-molecule data. To validate the framework, we study the extraction of state populations and free energies for a simple toy model and from synthetic cryo-EM particle images of a simulated protein that explores multiple folded and unfolded conformations.

DFT Method Used for Prediction of Molecular and Electronic Structures of Mn(VI) Macrocyclic Complexes with Porhyrazine/Phthalocyanine and Two Oxo Ligands.

By using the data of the DFT quantum chemical calculation in the OPBE/TZVP and B3PW91/TZVP levels, the possibility of the existence of a manganese(VI) heteroligand complex containing porphyrazine or its tetra[benzo] derivative (phthalocyanine) and two oxygen (O2-) ligands, which is still unknown for this element, is shown. The parameters of the molecular structure, multiplicity of the ground state, NBO analysis data and standard thermodynamic parameters (enthalpy ΔH0f, entropy S0f and Gibbs's energy ΔG0f of formation) of each of these metal macrocyclic compounds are presented. Additionally, it is noted that, based on the totality of structural data obtained by the above versions of the DFT method, the existence of a similar complex of manganese with di[benzo] derivative of porhyrazine and two oxygen (O2-) ligands seems doubtful.

Quantum-Chemical Prediction of Molecular and Electronic Structure of Carbon-Nitrogen Chemical Compound with Unusual Ratio Atoms: C(N20).

Using various versions of quantum-chemical calculation, namely four versions of density functional theory (DFT), (DFT B3PW91/TZVP, DFT M06/TZVP, DFT B3PW91/Def2TZVP, and DFT M06/Def2TZVP) and two versions of the MP method (MP2/TZVP and MP3/TZVP), the existence possibility of the carbon-nitrogen-containing compound having an unusual M: nitrogen ratio of 1:20, unknown for these elements at present, was shown. Structural parameters data are presented; it was noted that, as may be expected, CN4 grouping has practically a tetrahedral structure, and the chemical bond lengths formed by nitrogen atoms and a carbon atom in the frameworks of each of the calculation methods indicated above are equal to each other. Thermodynamical parameters, NBO analysis data, and HOMO/LUMO images for this compound are also presented. A good agreement between the calculated data obtained using the above three quantum-chemical methods was noticed, too.

An equivariant generative framework for molecular graph-structure Co-design.

Designing molecules with desirable physiochemical properties and functionalities is a long-standing challenge in chemistry, material science, and drug discovery. Recently, machine learning-based generative models have emerged as promising approaches for de novo molecule design. However, further refinement of methodology is highly desired as most existing methods lack unified modeling of 2D topology and 3D geometry information and fail to effectively learn the structure-property relationship for molecule design. Here we present MolCode, a roto-translation equivariant generative framework for molecular graph-structure Co-design. In MolCode, 3D geometric information empowers the molecular 2D graph generation, which in turn helps guide the prediction of molecular 3D structure. Extensive experimental results show that MolCode outperforms previous methods on a series of challenging tasks including de novo molecule design, targeted molecule discovery, and structure-based drug design. Particularly, MolCode not only consistently generates valid (99.95% validity) and diverse (98.75% uniqueness) molecular graphs/structures with desirable properties, but also generates drug-like molecules with high affinity to target proteins (61.8% high affinity ratio), which demonstrates MolCode's potential applications in material design and drug discovery. Our extensive investigation reveals that the 2D topology and 3D geometry contain intrinsically complementary information in molecule design, and provide new insights into machine learning-based molecule representation and generation.

Computational and Functional Insights of Protein Misfolding in Neurodegeneration.

Protein folding is the process by which a polypeptide chain self-assembles into the correct three-dimensional structure, so that it ends up in the biologically active, native state. Under conditions of proteotoxic stress, mutations, or cellular aging, proteins can begin to aggregate into non-native structures such as ordered amyloid fibrils and plaques. Many neurodegenerative diseases involve the misfolding and aggregation of specific proteins into abnormal, toxic species. Experimental approaches including crystallography and AFM (atomic force microscopy)-based force spectroscopy are used to exploit the folding and structural characterization of protein molecules. At the same time, computational techniques through molecular dynamics, fold recognition, and structure prediction are widely applied in this direction. Benchmarking analysis for combining and comparing computational methodologies with functional studies can decisively unravel robust interactions between the side groups of the amino acid sequence and monitor alterations in intrinsic protein dynamics with high precision as well as adequately determine potent conformations of the folded patterns formed in the polypeptide structure.

Molecular Docking, Multicomponent One-pot Synthesis of Pyrimidine Derivatives as Anti-mycobacterial Agents

Background: Molecular docking study is used significantly in the drug discovery process for predicting the interaction between drug and receptor. This technique has been used commonly to identify the binding affinity and orientation of drug molecules at the binding site of the target. The main objectives of docking studies include accurate modeling of molecular structure and precise prediction of the biological activity of the drug molecules. Methods: Based on this concept, a series of 2-amino-6-(substituted phenyl)-4-oxo-4,5-dihydropyrimidine-5-carbonitrile derivatives have been designed and synthesized via multi-component reaction. The synthetic protocol involves the one-pot, three-component reaction be-tween equimolar quantities of substituted benzaldehydes, ethyl cyanoacetate, and guanidine in an ethanolic sodium hydroxide solution. Results: The characterization of the titled compounds was carried out by assessing infrared spectroscopy (IR), proton nuclear magnetic resonance (1H-NMR), and mass spectrometry (MS) data. The synthesized compounds were screened for their in vitro anti-mycobacterial activity by using the luciferase reporter phage (LRP) assay method. Conclusion: The determination of anti-mycobacterial activity was carried out in terms of the percent reduction in the relative light unit (RLU). The test compounds displayed significant ac-tivity against Mycobacterium strain H37Rv in comparison to isoniazid as a standard drug.

PREDICTION OF DIPROPYL SULFONE PROPERTIES BY DENSITY FUNCTIONAL THEORY METHODS: CONFORMATIONAL ANALYSIS AND SIMULATED IR SPECTRUM

In this work, the conformational and vibrational analysis of dipropyl sulfone in its isolated gaseous state with identification of all stable conformers, their energy and degeneracy, relative population determined by Boltzmann distribution, as well as IR spectra have been performed by density functional theory (DFT) methods. Several DFT methods and basis sets were tested. It was demonstrated that the various local and hybrid DFT functionals such as well-known B3LYP, regardless of the size of basis sets, completely fail in the prediction of correct molecular structures, let alone the IR spectra. It was found that only long-range corrected hybrid density functionals, combined with decently sized basis sets are capable to predict correct values of dihedral angles between non-bonded atomic groups: the most important coordinates in conformational analysis. Thus, wB97XD/6-311++G(2df,2pd) method/basis set combination appears to be the best method for the titled system both in terms of geometry and IR spectra prediction. A detailed analysis of the potential energy surface revealed the existence of 28 distinct conformers with various populations at 298 K, which have significant impact in the simulated IR spectra. The linear scaling equation (LSE) fitting methodology was successfully adopted for the calibration of wavenumbers and achievement of the best match between theoretical and experimental absorption regions of functional groups in sulfones. Moreover, in the construction of the simulated IR spectra, the Lorentzian broadening of each calculated mode with different full widths at half maximum was considered to obtain extinction coefficients, thus more realistic ε(ν) dependency, that is directly comparable with experimental spectra. The authenticity of the results obtained have been verified by comparison with existing experimental literature data on sulfones.