Amino Acid Side Chain Descriptors for Quantitative Structure-Activity Relationship Studies of Peptide Analogs

1995 ◽  
Vol 38 (14) ◽  
pp. 2705-2713 ◽  
Author(s):  
Elizabeth R. Collantes ◽  
William J. Dunn
Keyword(s):  
Amino Acid ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Activity Relationship ◽  
Side Chain ◽  
Amino Acid Side Chain ◽  
Quantitative Structure ◽  
Peptide Analogs ◽  
Structure Activity

scholarly journals 4-Aminoquinolines Active against Chloroquine-Resistant Plasmodium falciparum: Basis of Antiparasite Activity and Quantitative Structure-Activity Relationship Analyses

2011 ◽  
Vol 55 (5) ◽  
pp. 2233-2244 ◽  
Author(s):  
Simon J. Hocart ◽  
Huayin Liu ◽  
Haiyan Deng ◽  
Dibyendu De ◽  
Frances M. Krogstad ◽  
...  
Keyword(s):  
Plasmodium Falciparum ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Structural Features ◽  
Activity Relationship ◽  
Side Chain ◽  
Quantitative Structure ◽  
Content Type ◽  
Structure Activity ◽  
Contour Plots

ABSTRACTChloroquine (CQ) is a safe and economical 4-aminoquinoline (AQ) antimalarial. However, its value has been severely compromised by the increasing prevalence of CQ resistance. This study examined 108 AQs, including 68 newly synthesized compounds. Of these 108 AQs, 32 (30%) were active only against CQ-susceptiblePlasmodium falciparumstrains and 59 (55%) were active against both CQ-susceptible and CQ-resistantP. falciparumstrains (50% inhibitory concentrations [IC50s], ≤25 nM). All AQs active against both CQ-susceptible and CQ-resistantP. falciparumstrains shared four structural features: (i) an AQ ring without alkyl substitution, (ii) a halogen at position 7 (Cl, Br, or I but not F), (iii) a protonatable nitrogen at position 1, and (iv) a second protonatable nitrogen at the end of the side chain distal from the point of attachment to the AQ ring via the nitrogen at position 4. For activity against CQ-resistant parasites, side chain lengths of ≤3 or ≥10 carbons were necessary but not sufficient; they were identified as essential factors by visual comparison of 2-dimensional (2-D) structures in relation to the antiparasite activities of the AQs and were confirmed by computer-based 3-D comparisons and differential contour plots of activity againstP. falciparum. The advantage of the method reported here (refinement of quantitative structure-activity relationship [QSAR] descriptors by random assignment of compounds to multiple training and test sets) is that it retains QSAR descriptors according to their abilities to predict the activities of unknown test compounds rather than according to how well they fit the activities of the compounds in the training sets.

scholarly journals Quantitative Structure–Activity Relationship Study of Bitter Di-, Tri- and Tetrapeptides Using Integrated Descriptors

Molecules ◽  
2019 ◽  
Vol 24 (15) ◽  
pp. 2846 ◽  
Author(s):  
Xu ◽  
Chung
Keyword(s):  
Amino Acids ◽  
Amino Acid ◽  
Electronic Properties ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Activity Relationship ◽  
Partial Specific Volume ◽  
Quantitative Structure ◽  
Structure Activity ◽  
Terminal Amino

New quantitative structure–activity relationship (QSAR) models for bitter peptides were built with integrated amino acid descriptors. Datasets contained 48 dipeptides, 52 tripeptides and 23 tetrapeptides with their reported bitter taste thresholds. Independent variables consisted of 14 amino acid descriptor sets. A bootstrapping soft shrinkage approach was utilized for variable selection. The importance of a variable was evaluated by both variable selecting frequency and standardized regression coefficient. Results indicated model qualities for di-, tri- and tetrapeptides with R2 and Q2 at 0.950 ± 0.002, 0.941 ± 0.001; 0.770 ± 0.006, 0.742 ± 0.004; and 0.972 ± 0.002, 0.956 ± 0.002, respectively. The hydrophobic C-terminal amino acid was the key determinant for bitterness in dipeptides, followed by the contribution of bulky hydrophobic N-terminal amino acids. For tripeptides, hydrophobicity of C-terminal amino acids and the electronic properties of the amino acids at the second position were important. For tetrapeptides, bulky hydrophobic amino acids at N-terminus, hydrophobicity and partial specific volume of amino acids at the second position, and the electronic properties of amino acids of the remaining two positions were critical. In summary, this study not only constructs reliable models for predicting the bitterness in different groups of peptides, but also facilitates better understanding of their structure-bitterness relationships and provides insights for their future studies.

Prediction of Lipophilicity of some Quinolone Derivatives by using Quantitative Structure- Activity Relationship

2019 ◽  
Vol 16 ◽  
Author(s):  
Meysam Shirmohammadi ◽  
Zakiyeh Bayat ◽  
Esmat Mohammadinasab
Keyword(s):  
Partition Coefficient ◽  
Principal Component ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Activity Relationship ◽  
Quantitative Structure ◽  
Selection Methods ◽  
Qsar Study ◽  
Structure Activity ◽  
Descriptor Selection

: Quantitative structure activity relationship (QSAR) was used to study the partition coefficient of some quinolones and their derivatives. These molecules are broad-spectrum antibiotic pharmaceutics. First, data were divided into two categories of train and test (validation) sets using random selection method. Second, three approaches including stepwise selection (STS) (forward), genetic algorithm (GA), and simulated annealing (SA) were used to select the descriptors, with the aim of examining the effect feature selection methods. To find the relation between descriptors and partition coefficient, multiple linear regression (MLR), principal component regression (PCR) and partial least squares (PLS) were used. QSAR study showed that the both regression and descriptor selection methods have vital role in the results. Different statistical metrics showed that the MLR-SA approach with (r2=0.96, q2=0.91, pred_r2=0.95) gives the best outcome. The proposed expression by MLR-SA approach can be used in the better design of novel quinolones and their derivatives.

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