The Quantitative Description of Amino Acid, Peptide, and Protein Properties and Bioactivities

Charton, Marvin

doi:10.1002/9780470171974.ch5

Cited by 29 publications

(4 citation statements)

References 96 publications

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“…We raised this question because there are similarities in several physical properties of compounds with acetylenic and chloro functions, such as the values of electronegativity calculated from bond vibrations [3.29 for R‐C≡CH and 3.04 for R‐C1 (Wells, 1968)] and the Hammett substituent constant, σ, in structure‐reactivity relationships of substituted benzoic acids [0.233 for R‐C≡CH (Landgrebe and Ryndbrandt, 1966) and 0.227 for R‐C1 (Ritchie and Sager, 1964)]. These values point to similar resonance and polar effects in aromatic compounds bearing these substituents, but they do not reflect any other important factors crucial to enzymatic reactions such as steric requirements, Van der Waals interaction, hydrogen bondings, or hydrophobicity (Charton, 1990). In these respects, there should be significant differences between the chlorine atom and the linear, ionizable C≡CH group, which would be able to act as a hydrogen acceptor and donor.…”

Section: Discussionmentioning

confidence: 99%

p‐Ethynylphenylalanine

Stokes¹,

Xu²,

Daunais³

et al. 2000

Journal of Neurochemistry

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Tryptophan hydroxylase (TPH) is the initial and rate-limiting enzyme in serotonin biosynthesis. The enzyme activity is dependent on molecular oxygen, a tetrahydropterin cosubstrate, and ferrous iron. The present study demonstrates that TPH is inhibited by a novel compound, p-ethynylphenylalanine (pEPA), produced by the Heck reaction of trimethylsilylacetylene with N-tertbutyloxycarbonyl-4-iodo-L-phenylalanine methyl ester. pEPA is a more potent and specific inhibitor of TPH than p-chlorophenylalanine (pCPA). In the present study, pEPA was demonstrated to inhibit competitively and reversibly TPH in vitro (K i ϭ 32.6 Ϯ 6.2 M vs. tryptophan). pEPA displayed little inhibitory activity toward tyrosine hydroxylase (EC 1.14.16.2), the initial and rate-limiting enzyme for catecholamine biosynthesis, and no inhibition of phenylalanine hydroxylase or tyrosinase. In addition, pEPA was a poor ligand for the serotonin transporter and several serotonin receptors. Administration of pEPA (30 mg/kg) to rats produced a 95 Ϯ 5% decrease in TPH activity in brain homogenates and a concomitant decrease in serotonin and 5-hydroxyindole-3-acetic acid levels (85%) at 24 h after injection. In contrast, pCPA produced a similar effect (87 Ϯ 5% decrease in TPH activity) only at 10 times the concentration (300 mg/kg). These results suggest that pEPA is a selective, reversible, and potent inhibitor of TPH both in vitro and in vivo. The potential for pEPA to inhibit selectively and reversibly the biosynthesis of serotonin may contribute to the characterization of the role of serotonin in behavioral and physiological activities.

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Section: Discussionmentioning

confidence: 99%

p‐Ethynylphenylalanine

Stokes¹,

Xu²,

Daunais³

et al. 2000

Journal of Neurochemistry

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“…Hammett parameter ( σ p ) is usually considered as a good quantitative assessment of the electron-donating/withdrawing effect of groups. 84,85 As can be seen, there is an apparently high correlation between Hammett constants and S 1 . Both electron-withdrawing groups (with positive σ p ) and electron-donating groups (with negative σ p ) can lead to the decrease of S 1 .…”

Section: Resultsmentioning

confidence: 80%

Design of BPEA-based derivatives with high singlet fission performance: a theoretical perspective

Song

Zhou

et al. 2023

Phys. Chem. Chem. Phys.

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“…In this context, quantitative amino acid descriptors have shown to be valuable. Since the pioneering work of Sneath [23] , who derived amino acid descriptors from semiqualitative physicochemical data for the 20 coded amino acids and used them in a quantitative sequence-activity model analysis of oxytocin-vasopressine analogues, many amino acid descriptors have been proposed for the 20 coded amino acids [24][25][26][27][28][29][30][31][32][33][34][35] . A notable development in QSAR is the use of amino acid "z scores" obtained by principal components analysis (PCA) based on 29 physicochemical variables of 20 coded amino acids [30][31][32] .…”

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confidence: 99%

“…The z-scores were extracted through PCA from a collected experimental data on various peptides, such as HPLC retention times, pK a 's, NMR-derived properties, and other measurable variables related to hydrophobicity, size, and electronic features. By using z-scores and multivariate statistics, some good regression models were generated for peptide QSARs on oxytocin, bradykinin and substance P receptors or on sweetener peptides by PLS [24][25][26][27][28][29][30][31][32][33] . In 1995, Collantes et al [34] established good 3D-QSAR models by using three-dimensional descriptors, both Isotropic Surface Area (ISA) and Electronic Charge Index (ECI).…”

mentioning

confidence: 99%

Structural parameterization and functional prediction of antigenic polypeptome sequences with biological activity through quantitative sequence-activity models (QSAM) by molecular electronegativity edge-distance vector (VMED)

Shi-rong

Chen

et al. 2007

SCI CHINA SER C

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Only from the primary structures of peptides, a new set of descriptors called the molecular electronegativity edge-distance vector (VMED) was proposed and applied to describing and characterizing the molecular structures of oligopeptides and polypeptides, based on the electronegativity of each atom or electronic charge index (ECI) of atomic clusters and the bonding distance between atom-pairs. Here, the molecular structures of antigenic polypeptides were well expressed in order to propose the automated technique for the computerized identification of helper T lymphocyte (Th) epitopes. Furthermore, a modified MED vector was proposed from the primary structures of polypeptides, based on the ECI and the relative bonding distance of the fundamental skeleton groups. The side-chains of each amino acid were here treated as a pseudo-atom. The developed VMED was easy to calculate and able to work. Some quantitative model was established for 28 immunogenic or antigenic polypeptides (AGPP) with 14 (1-14) A(d) and 14 other restricted activities assigned as "1"(+) and "0"(-), respectively. The latter comprised 6 A(b)(15-20), 3 A(k)(21-23), 2 E(k)(24-26), 2 H-2(k)(27 and 28) restricted sequences. Good results were obtained with 90% correct classification (only 2 wrong ones for 20 training samples) and 100% correct prediction (none wrong for 8 testing samples); while contrastively 100% correct classification (none wrong for 20 training samples) and 88% correct classification (1 wrong for 8 testing samples). Both stochastic samplings and cross validations were performed to demonstrate good performance. The described method may also be suitable for estimation and prediction of classes I and II for major histocompatibility antigen (MHC) epitope of human. It will be useful in immune identification and recognition of proteins and genes and in the design and development of subunit vaccines. Several quantitative structure activity relationship (QSAR) models were developed for various oligopeptides and polypeptides including 58 dipeptides and 31 pentapeptides with angiotensin converting enzyme (ACE) inhibition by multiple linear regression (MLR) method. In order to explain the ability to characterize molecular structure of polypeptides, a molecular modeling investigation on QSAR was performed for functional prediction of polypeptide sequences with antigenic activity and heptapeptide sequences with tachykinin activity through quantitative sequence-activity models (QSAMs) by the molecular electronegativity edge-distance vector (VMED). The results showed that VMED exhibited both excellent structural selectivity and good activity prediction. Moreover, the results showed that VMED behaved quite well for both QSAR and QSAM of poly-and oligopeptides, which exhibited both good estimation ability and prediction power, equal to or better than those reported in the previous references. Finally, a preliminary conclusion was drawn: both classical and modified MED vectors were very useful structural descriptors. Some suggestions were proposed for fu...

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The Quantitative Description of Amino Acid, Peptide, and Protein Properties and Bioactivities

Cited by 29 publications

References 96 publications

p‐Ethynylphenylalanine

p‐Ethynylphenylalanine

Design of BPEA-based derivatives with high singlet fission performance: a theoretical perspective

Structural parameterization and functional prediction of antigenic polypeptome sequences with biological activity through quantitative sequence-activity models (QSAM) by molecular electronegativity edge-distance vector (VMED)

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