Articles & Third-Party References

Recent journal articles and third-party references about ACD/Labs from the last year

Targeted Dereplication of Microbial Natural Products by High-Resolution MS and Predicted LC Retention Time
Apr 26, 2017
J. Chervin, M. Stierhof, M.H. Tong, D. Peace, K. Østnes Hansen, D.S. Urgast, J.H. Andersen, Y. Yu, R. Ebel, K. Kyeremeh, V. Paget, G. Cimpan, A. Van Wyk, H. Deng, M. Jaspars, and J.N. Tabudravu
Third-Party Reference
Journal of Natural Products
A new strategy for the identification of known compounds in Streptomyces extracts that can be applied in the discovery of natural products is presented. The strategy incorporates screening a database of 5555 natural products including 5098 structures from Streptomyces sp., using a high-throughput LCMS data processing algorithm that utilizes HRMS data and predicted LC retention times (tR) as filters for rapid identification of known compounds in the natural product extract. The database, named StrepDB, contains for each compound the structure, molecular formula, molecular mass, and predicted LC retention time. All identified compounds are annotated and color coded for easier visualization. It is an indirect approach to quickly assess masses (which are not annotated) that may potentially lead to the discovery of new or novel structures. In addition, a spectral database named MbcDB was generated using the ACD/Spectrus DB Platform. MbcDB contains 665 natural products, each with structure, experimental HRESIMS, MS/MS, UV, and NMR spectra. StrepDB was used to screen a mutant Streptomyces albus extract, which led to the identification and isolation of two new compounds, legonmaleimides A and B, the structures of which were elucidated with the aid of MbcDB and spectroscopic techniques. The structures were confirmed by computer-assisted structure elucidation (CASE) methods using ACD/Structure Elucidator Suite. The developed methodology suggests a pipeline approach to the dereplication of extracts and discovery of novel natural products.
J. Nat. Prod., 2017, 80 (5): 1370-1377

Determination of acid dissociation constants (pKa) of cephalosporin antibiotics: Computational and experimental approaches
Nov 18, 2016
Alyson R. Ribeiro, Torsten C. Schmidt
Third-Party Reference
Cefapirin (CEPA) and ceftiofur (CEF) are two examples of widely used veterinarian cephalosporins presenting multiple ionization centers. However, the acid dissociation constants (pKa) of CEF are missing and experimental data about CEPA are rare. The same is true for many cephalosporins, where available data are either incomplete or even wrong. Environmentally relevant biotic and abiotic processes depend primordially on the antibiotic pH-dependent speciation. Consequently, this physicochemical parameter should be reliable, including the correct ionization center identification. In this direction, two experimental techniques, potentiometry and spectrophotometry, along with two well-known pKa predictors, Marvin and ACD/Percepta, were used to study the macro dissociation constants of CEPA and CEF. Additionally, the experimental dissociation constants of 14 cephalosporins available in the literature were revised, compiled and compared with data obtained in silico. Only one value was determined experimentally for CEF (2.68 ± 0.05), which was associated to the carboxylic acid group deprotonation. For CEPA two values were obtained experimentally: 2.74 ± 0.01 for the carboxylic acid deprotonation and 5.13 ± 0.01 for the pyridinium ring deprotonation. In general, experimentally obtained values agree with the in silico predicted data (ACD/Percepta RMSE: 0.552 and Marvin RMSE: 0.706, n = 88). However, for cephalosporins having imine and aminothiazole groups structurally close, Marvin presented problems in pKa predictions. For the biological and environmental fate and effect discussion, it is important to recognize that CEPA and CEF, as well as many other cephalosporins, are present as anionic species in the biologic and environmentally relevant pH values of 6–7.5.
Chemosphere. 169:524–533.

Facing a formidable challenge
Oct 31, 2016
S. Ktori (Scientific Computing World)
Third-Party Reference
Scientific Computing World
Andrew Anderson and Graham A. McGibbon discuss ACD/Labs' partnership with the Allotrope Foundation.
Read Article

A simple approach to multifunctionalized N1-alkylated 7-amino-6-azaoxindole derivatives using their in situ stabilized tautomer form
Oct 13, 2016
N.T. Tzvetkov, B. Neumann, H. Stammler, L. Antonov
Third-Party Reference
A simple approach for the synthesis of multifunctionalized N1-alkyl 7-amino-6-azaoxindole derivatives was developed and investigated. Formation of 5-amino- and 7-amino-6-aza-2-oxindoles 12a and 13a, respectively, was achieved using an intramolecular reductive cyclization as a key step. Subsequent alkylation of the pyrrole N1 atom in 12a led to the desired N1-alkylated compounds 22a–24 comprising different functionalities. Alkylation of 5-amino-substituted regioisomer 13a under the same conditions as used for 12a did not resulted in N1-alkylated products. To find a plausible explanation for the observed differences in reactivity, we investigated the possible tautomers of 12a and 13a and the distribution of their neutral and ionized forms in a gas phase. The relevant physicochemical properties of compounds 12a and 23 were determined.
Tetrahedron, 72(41): 6455-66, 2016.

Structure Elucidation Software Review in Magnetic Resonance in Chemistry
Aug 18, 2016
Press Release
Magn. Reson. Chem.
The latest edition of Magnetic Resonance in Chemistry features a special introductory tutorial to Computer-Assisted Structure Elucidation (CASE) aimed at undergraduate and graduate students, and utilizes a free CASE tutorial software package available from ACD/Labs.
Structure Elucidation Software Review in Magnetic Resonance in Chemistry

Prediction Models of Retention Indices for Increased Confidence in Structural Elucidation during Complex Matrix Analysis: Application to Gas Chromatography Coupled with High-Resolution Mass Spectrometry
Aug 02, 2016
Dossin E, Martin E, Diana P, Castellon A, Monge A, Pospisil P, Bentley M, Guy PA
Third-Party Reference
Analytical Chemistry
Monitoring of volatile and semivolatile compounds was performed using gas chromatography (GC) coupled to high-resolution electron ionization mass spectrometry, using both headspace and liquid injection modes. A total of 560 reference compounds, including 8 odd n-alkanes, were analyzed and experimental linear retention indices (LRI) were determined. These reference compounds were randomly split into training (n = 401) and test (n = 151) sets. LRI for all 552 reference compounds were also calculated based upon computational Quantitative Structure-Property Relationship (QSPR) models, using two independent approaches RapidMiner (coupled to Dragon) and ACD/ChromGenius software. Correlation coefficients for experimental versus predicted LRI values calculated for both training and test set compounds were calculated at 0.966 and 0.949 for RapidMiner and at 0.977 and 0.976 for ACD/ChromGenius, respectively. In addition, the cross-validation correlation was calculated at 0.96 from RapidMiner and the residual standard error value obtained from ACD/ChromGenius was 53.635. These models were then used to predict LRI values for several thousand compounds reported present in tobacco and tobacco-related fractions, plus a range of specific flavor compounds. It was demonstrated that using the mean of the LRI values predicted by RapidMiner and ACD/ChromGenius, in combination with accurate mass data, could enhance the confidence level for compound identification from the analysis of complex matrixes, particularly when the two predicted LRI values for a compound were in close agreement. Application of this LRI modeling approach to matrixes with unknown composition has already enabled the confirmation of 23 postulated compounds, demonstrating its ability to facilitate compound identification in an analytical workflow. The goal is to reduce the list of putative candidates to a reasonable relevant number that can be obtained and measured for confirmation.
Dossin E, et al.. (2016). Prediction Models of Retention Indices for Increased Confidence in Structural Elucidation during Complex Matrix Analysis: Application to Gas Chromatography Coupled with High-Resolution Mass Spectrometry. Anal. Chem., 88(15): 7539-47.

To view all of our past articles and third-party references, please use our resource search.