Formation of p-hydroxybenzoic acid from phenylacetic acid by Poria weirii

1973 ◽  
Vol 51 (4) ◽  
pp. 827-828 ◽  
Author(s):  
C. Y. Li ◽  
K. C. Lu ◽  
J. M. Trappe ◽  
W. B. Bollen
Keyword(s):  
Phenylacetic Acid ◽  
Hydroxybenzoic Acid ◽  
Unknown Compound

Phenylacetic acid is known to inhibit growth of Poria weirii cultures at concentrations of 2.0 mM but not at 0.5 mM. In this study, the compound at the lower concentration was metabolized by the fungus by degradation to noninhibitory p-hydroxybenzoic acid and an unknown compound.

DEGRADATION OF AROMATIC AMINO ACIDS BY FUNGI: I. FATE OF L-PHENYLALANINE IN SCHIZOPHYLLUM COMMUNE

1967 ◽  
Vol 45 (11) ◽  
pp. 1659-1665 ◽  
Author(s):  
Keith Moore ◽  
G. H. N. Towers
Keyword(s):  
Amino Acids ◽  
Benzoic Acid ◽  
Protocatechuic Acid ◽  
Phenylacetic Acid ◽  
Schizophyllum Commune ◽  
Aromatic Amino Acids ◽  
Ring Cleavage ◽  
Hydroxybenzoic Acid ◽  
Phenyllactic Acid ◽  
Phenylalanine Metabolism

Growing cultures of Schizophyllum commune could produce 14CO2 from ring-labelled DL-phenylalanine-14C. Intermediates in the pathway of L-phenylalanine degradation prior to ring cleavage were shown to be cinnamic acid, benzoic acid, p-hydroxybenzoic acid, and protocatechuic acid. Phenylacetic acid and L(−)-β-phenyllactic acid were also identified as products of phenylalanine metabolism.

Induction of plantlets in axenically cultivated rhizoids of Fucus spiralis

1984 ◽  
Vol 62 (8) ◽  
pp. 1616-1620 ◽  
Author(s):  
Lisbeth Fries
Keyword(s):  
Phenylacetic Acid ◽  
Dry Weight ◽  
Hydroxybenzoic Acid ◽  
Dihydroxybenzoic Acid ◽  
Indolylacetic Acid ◽  
Plantlet Formation ◽  
Carbon Compounds ◽  
Hydroxyphenylacetic Acid ◽  
Fucus Spiralis ◽  
Active Substances

Rhizoids of Fucus spiralis were cultivated axenically in the artificial seawater ASP6 F2. Experiments were made to increase the filamental growth as well as to induce adventive primordia (plantlets). Additions of such carbon compounds as glucose, acetate, and formate had no favourable effects even in concentrations as low as 1∙10−4 M. Mannitol killed the rhizoids in higher concentrations and inhibited growth even in a concentration as low as 1∙10−5 M. Higher concentrations of glycerol also inhibited growth, but 1∙10−4 M was an exception as it initiated plantlets. Many simple phenolic compounds induced plantlets. Among the most active substances were phenylacetic acid, p-hydroxyphenylacetic acid, 3,4-dihydroxybenzoic acid, o-hydroxybenzoic acid, and o-acetoxybenzoic acid, with optimal effects in the concentration range of 1∙10−7 to 1∙10−6 M. β-Indolylacetic acid strongly influenced the dry weight as well as plantlet formation at concentrations of 1∙10−8 to 1∙10−7 M, with 1∙10−8 M favouring plantlet induction. It is obvious that β-indolylacetic acid plays an important role in the earlier stages of the development of Fucus.

THE METABOLISM OF AROMATIC COMPOUNDS WITH DIFFERENT SIDE CHAINS BY A PSEUDOMONAS

1967 ◽  
Vol 13 (7) ◽  
pp. 761-769 ◽  
Author(s):  
E. R. Blakley
Keyword(s):  
Aromatic Ring ◽  
Aromatic Compounds ◽  
Phenylacetic Acid ◽  
Oxidative Degradation ◽  
Homogentisic Acid ◽  
Side Chain ◽  
Hydroxybenzoic Acid ◽  
Side Chains ◽  
Acid Pathway ◽  
Phenylbutyric Acid

A strain of Pseudomonas previously used to study the oxidative degradation of phenylacetic acid and phenylpropionic acid has been used to study the degradation of p-hydroxybenzoic acid, L-tyrosine, L-phenylalanine, phenylbutyric acid, and phenylvaleric acid. p-Hydroxybenzoic acid was converted to 3, 4-dihydroxy-benzoic acid and the aromatic ring was cleaved between carbons 3 and 4. Previous results showed that cleavage of 3, 4-dihydroxyphenylacetic acid occurred between carbons 2 and 3. Phenylalanine and tyrosine were metabolized by the homogentisic acid pathway. These results, together with results of previous work, suggest that the pathway used for the degradation of aromatic compounds by this organism varies with the nature of the side chain. The metabolism of aromatic compounds with side chains longer than three carbons appears to involve oxidative shortening of the side chain prior to cleavage of the aromatic ring.

Biotransformation of 4-hydroxybenzoic acid in the rhizosphere of grapevine (Vitis vinifera L.)

2017 ◽  
Vol 40 (1) ◽  
pp. 95-102 ◽  
Author(s):  
B. Wang ◽  
T. Zhou1 ◽  
K. Li ◽  
X.W. Guo ◽  
Y.S. Guo ◽  
...  
Keyword(s):  
Vitis Vinifera ◽  
Vitis Vinifera L ◽  
Hydroxybenzoic Acid

Convolutional Neural Networks for ATC Classification

2019 ◽  
Vol 24 (34) ◽  
pp. 4007-4012 ◽  
Author(s):  
Alessandra Lumini ◽  
Loris Nanni
Keyword(s):  
Classification System ◽  
Feature Vector ◽  
Chemical Interaction ◽  
Anatomical Therapeutic Chemical ◽  
World Health ◽  
Therapeutic Effects ◽  
True Rate ◽  
Unknown Compound ◽  
The Absolute ◽  
Learned Features

Background: Anatomical Therapeutic Chemical (ATC) classification of unknown compound has raised high significance for both drug development and basic research. The ATC system is a multi-label classification system proposed by the World Health Organization (WHO), which categorizes drugs into classes according to their therapeutic effects and characteristics. This system comprises five levels and includes several classes in each level; the first level includes 14 main overlapping classes. The ATC classification system simultaneously considers anatomical distribution, therapeutic effects, and chemical characteristics, the prediction for an unknown compound of its ATC classes is an essential problem, since such a prediction could be used to deduce not only a compound’s possible active ingredients but also its therapeutic, pharmacological, and chemical properties. Nevertheless, the problem of automatic prediction is very challenging due to the high variability of the samples and the presence of overlapping among classes, resulting in multiple predictions and making machine learning extremely difficult. Methods: In this paper, we propose a multi-label classifier system based on deep learned features to infer the ATC classification. The system is based on a 2D representation of the samples: first a 1D feature vector is obtained extracting information about a compound’s chemical-chemical interaction and its structural and fingerprint similarities to other compounds belonging to the different ATC classes, then the original 1D feature vector is reshaped to obtain a 2D matrix representation of the compound. Finally, a convolutional neural network (CNN) is trained and used as a feature extractor. Two general purpose classifiers designed for multi-label classification are trained using the deep learned features and resulting scores are fused by the average rule. Results: Experimental evaluation based on rigorous cross-validation demonstrates the superior prediction quality of this method compared to other state-of-the-art approaches developed for this problem. Conclusion: Extensive experiments demonstrate that the new predictor, based on CNN, outperforms other existing predictors in the literature in almost all the five metrics used to examine the performance for multi-label systems, particularly in the “absolute true” rate and the “absolute false” rate, the two most significant indexes. Matlab code will be available at https://github.com/LorisNanni.

Synthesis and Evaluation of Aromatic Surfactants as Potential Antibacterial and Cytotoxic Agents

2019 ◽  
Vol 16 (6) ◽  
pp. 478-484
Author(s):  
Kenia Barrantes ◽  
Mary Fuentes ◽  
Luz Chacón ◽  
Rosario Achí ◽  
Jorge Granados-Zuñiga ◽  
...  
Keyword(s):  
Antibacterial Activity ◽  
Hela Cells ◽  
Cytotoxic Agents ◽  
Cytotoxic Activities ◽  
Hydroxybenzoic Acid ◽  
Derivatives Of

Two ether and one ester derivatives of the 4-nitro-3-hydroxybenzoic acid were synthesized and characterized. The in vitro antimicrobial and cytotoxic activities of the three novel compounds were also evaluated. The aromatic derivatives showed antibacterial activity against one of the four microorganisms tested and two compounds (C8 and NOBA) had a lower IC50 in HeLa cells.

DFT based computational methodology of IC50 prediction

2020 ◽  
Vol 16 ◽  
Author(s):  
Arijit Bag
Keyword(s):  
Present Method ◽  
Dipole Moments ◽  
Research Work ◽  
Organometallic Compounds ◽  
Computational Method ◽  
Unknown Compound ◽  
Drug Designing ◽  
Further Development ◽  
Similar Kind ◽  
Hiv 1

Background: IC50 is one of the most important parameters of a drug. But, it is very difficult to predict this value of a new compound without experiment. There are only a few QSAR based methods available for IC50 prediction which is also highly dependable on huge number of known data. Thus, there is an immense demand for a sophisticated computational method of IC50 prediction, in the field of in-silico drug designing. Objective: Recently developed quantum computation based method of IC50 prediction by Bag and Ghorai requires an affordable known data. In present research work further development of this method is carried out such that the requisite number of known data being minimal. Methods: To retrench the cardinal data span and shrink the effects of variant biological parameters on the computed value of IC50, a relative approach of IC50 computation is pursued in the present method. To predict an approximate value of IC50 of a small molecule, only the IC50 of a similar kind of molecule is required for this method. Results: The present method of IC50 computation is tested for both organic and organometallic compounds as HIV-1 capsid A inhibitor and cancer drugs. Computed results match very well with the experiment. Conclusion: This method is easily applicable to both organic and organometallic com- pounds with acceptable accuracy. Since this method requires only the dipole moments of an unknown compound and the reference compound, IC50 based drug search is possible with this method. An algorithm is proposed here for IC50 based drug search.

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