Cyclopentadienyl-Based Amino Acids (Cp-aa) As Phenylalanine Analogues for Tumor Targeting: Syntheses and Biological Properties of [(Cp-aa)M(CO)3](M = Mn, Re,99mTc)

2012 ◽  
Vol 31 (19) ◽  
pp. 6880-6886 ◽  
Author(s):  
Samer Sulieman ◽  
Daniel Can ◽  
John Mertens ◽  
Harmel W. Peindy N’Dongo ◽  
Yu Liu ◽  
...  
Keyword(s):  
Amino Acids ◽  
Tumor Targeting ◽  
Biological Properties

Bio-organometallic Peptide Conjugates: Recent Advances in Their Synthesis and Prospects for Biomedical Application

2020 ◽  
Vol 24 (21) ◽  
pp. 2508-2523
Author(s):  
Johana Gómez ◽  
Diego Sierra ◽  
Constanza Cárdenas ◽  
Fanny Guzmán
Keyword(s):  
Amino Acids ◽  
Biomedical Application ◽  
Structural Diversity ◽  
Biological Properties ◽  
Therapeutic Agents ◽  
Organometallic Complexes ◽  
Chemical Behavior ◽  
Metal Compounds ◽  
Bioorganometallic Chemistry ◽  
Pharmacological Control

One area of organometallic chemistry that has attracted great interest in recent years is the syntheses, characterization and study of organometallic complexes conjugated to biomolecules with different steric and electronic properties as potential therapeutic agents against cancer and malaria, as antibiotics and as radiopharmaceuticals. This minireview focuses on the unique structural diversity that has recently been discovered in α- amino acids and the reactions of metallocene complexes with peptides having different chemical behavior and potential medical applications. Replacing α-amino acids with metallocene fragments is an effective way of selectively influencing the physicochemical, structural, electrochemical and biological properties of the peptides. Consequently, research in the field of bioorganometallic chemistry offers the opportunity to develop bioactive metal compounds as an innovative and promising approach in the search for pharmacological control of different diseases.

CHEMICAL AND BIOLOGICAL PROPERTIES OF CULTURE FILTRATES OF WESTIELLOPSIS PROLIFICA AND CHAETOPHORA ATTENUATA

1996 ◽  
Vol 44 (1) ◽  
pp. 43-48 ◽  
Author(s):  
S.C. Agrawal ◽  
U.K. Sharma
Keyword(s):  
Amino Acids ◽  
Aspartic Acid ◽  
Free Amino ◽  
Biological Properties ◽  
Total Chlorophyll ◽  
Total Chlorophyll Content ◽  
Westiellopsis Prolifica ◽  
Total Free Amino Acids ◽  
Zoospore Formation ◽  
Growth And Reproduction

Westiellopsis prolifica Janet and Chaetophora attenuata Hazen cultures released sugars (glucose, fructose, and sucrose), organic acids (oxaloacetic acid and oxalic acid), amino acids, and protein. W. prolifica cultures released the amino acids glycine, serine, cystine, glutamic acid, aspartic acid, and α-alanine, while C. attenuata cultures released glycine, serine, aspartic acid, and α-alanine. W. prolifica and C. attenuata cultures of all ages released more extracellular protein than total free amino acids. Cultures of C. attenuata released more protein than cultures of the same age of W. prolifica. The filtrates from old cultures of W. prolifica and C. attenuata decreased the total chlorophyll content of all algae tested, totally suppressed conjugation in Spirogyra decimino and zoospore formation in C. attenuata, and drastically decreased spore germination in W. prolifica, thus producing stressful conditions affecting the growth and reproduction of these and other algae.

ChemInform Abstract: SYNTHESIS AND BIOLOGICAL PROPERTIES OF CHOLINE ESTERS OF AMINO ACIDS

1983 ◽  
Vol 14 (28) ◽  
Author(s):  
V. O. TOPUZYAN ◽  
D. A. GERASIMYAN ◽  
A. L. BAGDASARYAN ◽  
O. L. MNDZHOYAN
Keyword(s):  
Amino Acids ◽  
Biological Properties ◽  
Choline Esters

scholarly journals Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences

2021 ◽  
Vol 118 (15) ◽  
pp. e2016239118
Author(s):  
Alexander Rives ◽  
Joshua Meier ◽  
Tom Sercu ◽  
Siddharth Goyal ◽  
Zeming Lin ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Amino Acids ◽  
Unsupervised Learning ◽  
Tertiary Structure ◽  
State Of The Art ◽  
Sequence Data ◽  
Protein Sequences ◽  
Representation Learning ◽  
Biological Properties ◽  
Biochemical Properties

In the field of artificial intelligence, a combination of scale in data and model capacity enabled by unsupervised learning has led to major advances in representation learning and statistical generation. In the life sciences, the anticipated growth of sequencing promises unprecedented data on natural sequence diversity. Protein language modeling at the scale of evolution is a logical step toward predictive and generative artificial intelligence for biology. To this end, we use unsupervised learning to train a deep contextual language model on 86 billion amino acids across 250 million protein sequences spanning evolutionary diversity. The resulting model contains information about biological properties in its representations. The representations are learned from sequence data alone. The learned representation space has a multiscale organization reflecting structure from the level of biochemical properties of amino acids to remote homology of proteins. Information about secondary and tertiary structure is encoded in the representations and can be identified by linear projections. Representation learning produces features that generalize across a range of applications, enabling state-of-the-art supervised prediction of mutational effect and secondary structure and improving state-of-the-art features for long-range contact prediction.

scholarly journals Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences

2019 ◽  
Author(s):  
Alexander Rives ◽  
Joshua Meier ◽  
Tom Sercu ◽  
Siddharth Goyal ◽  
Zeming Lin ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Amino Acids ◽  
Unsupervised Learning ◽  
Tertiary Structure ◽  
State Of The Art ◽  
Sequence Data ◽  
Protein Sequences ◽  
Representation Learning ◽  
Biological Properties ◽  
Biochemical Properties

In the field of artificial intelligence, a combination of scale in data and model capacity enabled by unsupervised learning has led to major advances in representation learning and statistical generation. In the life sciences, the anticipated growth of sequencing promises unprecedented data on natural sequence diversity. Protein language modeling at the scale of evolution is a logical step toward predictive and generative artificial intelligence for biology. To this end we use unsupervised learning to train a deep contextual language model on 86 billion amino acids across 250 million protein sequences spanning evolutionary diversity. The resulting model contains information about biological properties in its representations. The representations are learned from sequence data alone. The learned representation space has a multi-scale organization reflecting structure from the level of biochemical properties of amino acids to remote homology of proteins. Information about secondary and tertiary structure is encoded in the representations and can be identified by linear projections. Representation learning produces features that generalize across a range of applications, enabling state-of-the-art supervised prediction of mutational effect and secondary structure, and improving state-of-the-art features for long-range contact prediction.

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