Quantitative Structure-affinity Relationship Research Articles

A fragment-based drug discovery developed on ciclopirox for inhibition of Hepatitis B virus core protein: An in silico study.

The Hepatitis B virus (HBV) core protein is an attractive target for preventing capsid assembly and viral replication. Drug repurposing strategies have introduced several drugs targeting HBV core protein. This study used a fragment-based drug discovery (FBDD) approach to reconstruct a repurposed core protein inhibitor to some novel antiviral derivatives. Auto Core Fragment in silico Screening (ACFIS) server was used for deconstruction-reconstruction of Ciclopirox in complex with HBV core protein. The Ciclopirox derivatives were ranked based on their free energy of binding (ΔGB). A quantitative structure affinity relationship (QSAR) was established on the Ciclopirox derivatives. The model was validated by a Ciclopirox-property-matched decoy set. A principal component analysis (PCA) was also assessed to define the relationship of the predictive variable of the QSAR model. 24-derivatives with a ΔGB (-16.56±1.46 Kcal.mol-1) more than Ciclopirox was highlighted. A QSAR model with a predictive power of 88.99% (F-statistics = 9025.78, corrected df(25), Pr > F = 0.0001) was developed by four predictive descriptors (ATS1p, nCs, Hy, F08[C-C]). The model validation showed no predictive power for the decoy set (Q2 = 0). No significant correlation was observed between predictors. By directly attaching to the core protein carboxyl-terminal domain, Ciclopirox derivatives may be able to suppress HBV virus assembly and subsequent viral replication inhibition. Hydrophobic residue Phe23 is a critical amino acid in the ligand binding domain. These ligands share the same physicochemical properties that lead to the development of a robust QSAR mode. The same strategy may also be used for future drug discovery of viral inhibitors.

Interaction mechanism of flavonoids and Tartary buckwheat bran protein: A fluorescence spectroscopic and 3D-QSAR study

Tartary buckwheat bran protein (TBBP) is a valuable by-product of Tartary buckwheat processing. Current studies have indicated that TBBP is a desirable carrier and protector of flavonoids. However, the quantitative structure-affinity relationship of flavonoids with TBBP still remains to be elucidated. Here, by using the fluorescence spectroscopy method to measure the binding constants of 16 flavonoids with TBBP, we employ a combination of the atom-based three dimensional-quantitative structure-affinity relationships (3D-QSAR) and quantum-chemical calculation to explore the underlying binding mechanisms. We show that flavonoids can cause the intrinsic fluorescence quenching and form non-covalent complexes with TBBP driven by hydrogen bonding and van der Waal forces. The atom-based 3D-QSAR model reveals that the rutoside group at the C-7 position of flavones is favorable to enhance the binding constants of flavonoids with TBBP. Quantum-chemical calculations consistently disclose that the rutoside group can intensify interaction forces of flavonoids, thereby strengthening the binding. This work provides theoretical evidence that TBBP has the potential application to be raw materials for develop functional food, and be the delivery carriers of flavonoids.

Protein-Ligand Complex Solvation Thermodynamics: Development, Parameterization, and Testing of GIST-Based Solvent Functionals.

In drug design, the importance of molecular solvation and desolvation is increasingly appreciated and water molecules are recognized as active contributors to protein-ligand binding. However, despite a number of theoretical approaches, computational tools are still far from routinely integrating solvation features into rational structure-affinity relationships (SARs). In this contribution, we present a set of solvent functional-based models, which calculate the relative binding free energy contributions resulting from solvation for a diverse set of 53 thrombin protein-ligand complexes. These protein-ligand complexes were further matched into chemically similar pairs of ligand molecules. Our solvent functionals are based on molecular dynamics simulations in conjunction with grid inhomogeneous solvation theory (GIST) processing, and they are calibrated using accurate experimental data from isothermal titration calorimetry (ITC) measurements. We found that excellent agreement with experimental measurements can be achieved by considering either the desolvation of the protein-binding pocket or the ligand in solution prior to binding. The incorporation of contributions from the protein-ligand complexes generally results in good agreement with experimental measurements but require additional adjustment of spatial cutoff parameters. In addition, we investigated the transfer of the trained solvent functionals to another protein target, which revealed deviating performance results, indicating a target-specific treatment of solvation features within the model. Together with our tool GIST-based processing of solvent functionals (Gips), we provide a way to automatically generate solvent functional parameters from GIST data and allow for the design of compounds with favorable solvation properties given the chemical similarity and affinity range of the matching pairs in the training set. Finally, we reflect on the resemblance with the popular three-dimensional quantitative SAR (3D-QSAR) method, as our study allows for (retrospective) insights on the high predictive power of this well-established method.

Interaction mechanism of flavonoids and bovine β-lactoglobulin: Experimental and molecular modelling studies

In this study, the quantitative structure-affinity relationship of 28 flavonoids with bovine β-lactoglobulin (β-lg) was investigated based on experimental and theoretical methods. The binding constants (Ka) of these flavonoids with β-lg were systematically compared by spectrofluorimetry, and a multivariate linear model (R2 = 0.8769, Q2 = 0.7963) was established that could explain the effect of the structure parameters of flavonoids on their affinity (lgKa) for β-lg. Then, the underlying interaction mechanism was further clarified with myricetin and baicalin as representative flavonoids. Thermodynamic analysis showed that hydrogen bonds and van der Waals force played the main roles in maintaining the complexes, which was consistent with the independent gradient model (IGM) and the MM/PBSA binding free energy results. The redshift of the maximum emission peak of tryptophan residues in the synchronous fluorescence was attributed to the interaction of myricetin or baicalin with Trp19 of β-lg, according to the root mean square fluctuation (RMSF) results.

Synthesis of novel 5-substituted-2-aminotetralin analogs: 5-HT1A and 5-HT7 G protein-coupled receptor affinity, 3D-QSAR and molecular modeling

The serotonin 5-HT7 G protein-coupled receptor (GPCR) is a proposed pharmacotherapeutic target for a variety of central and peripheral indications, albeit, there are no approved drugs selective for binding 5-HT7. We previously reported that a lead analog based on the 5-substituted-N,N-disubstituted-1,2,3,4-tetrahydronaphthalen-2-amine (5-substituted-2-aminotetralin, 5-SAT) scaffold binds with high affinity at the 5-HT7 GPCR, and can treat symptoms of autism in mouse models; subsequently, the lead was found to have high affinity at the 5-HT1A GPCR. Herein, we report the synthesis of novel 5-SAT analogs to develop a 3-dimensional quantitative structure—affinity relationship (3D-QSAR) at the human 5-HT7 receptor for comparison with similar studies at the highly homologous 5-HT1A receptor. We report 35 new 5-SAT ligands, some with very high affinity (Ki ≤ 1 nM) and stereoselectivity at 5-HT7 + or 5-HT1A receptors, several with modest selectivity (up to 12-fold) for binding at 5-HT7, and, several ligands with high selectivity (up to 40-fold) at the 5-HT1A receptor. 3D-QSAR results indicate that steric extensions at the C(5)-position improve selectivity for the 5-HT7 over 5-HT1A receptor, while steric and hydrophobic extensions at the chiral C(2)-amino position impart 5-HT1A selectivity. In silico receptor homology modeling studies, supplemented with molecular dynamics simulations and binding free energy calculations, were used to rationalize experimentally-determined receptor selectivity and stereoselective affinity results. The data from these studies indicate that the 5-SAT chemotype, previously shown to be safe and efficacious in rodent paradigms of neurodevelopmental and neuropsychiatric disorders, is amenable to structural modification to optimize affinity at serotonin 5-HT7 vs. 5-HT1A GPCRs, as may be required for successful clinical translation.

Molecular docking and quantitative structure-activity relationship study of anti-ulcer activity of quinazolinone derivatives

Proton pump inhibitors portray the first choice for treating various ulcer diseases because it inhibits H+/K+-ATPase enzyme, by covalently binding to a cysteine residue of either potassium or proton pump, therefore this enzyme is a validated target for anti-ulcer drugs. A Quantitative structure-affinity relationship (QSAR) and molecular docking analysis were carryout on 30 quinazolinone derivatives as H+/K+-ATPase inhibitors. QSAR study was performed using Material studio software version 8.0, while molecular docking analysis of all the novel quinazolinone derivatives was performed using Autodock vina version 4.0 of Pyrx software. The QSAR result reveal a strong correlation value of R2 = 0.9131, R2adj = 0.8914, Q2LOO = 0.8038 and R2 pred = 0.8946 which showed a highly predictive and statistically significant model. Molecular docking analysis revealed that the ligand 25 bind tightly deep to H+/K+-ATPase (protein target). Because of the high binding affinity of −9.3 kcal/mol. This research has revealed a significant correlation between binding score and biological activities of the molecules, and the results are even better than the one proposed by other authors, more also, both results of quantitative structure-affinity relationship (QSAR) and docking studies agree with each other which give chance for design and synthesis of novel anti-ulcer agents exhibiting good action against the receptor (H+/K+-ATPase) Furthermore, the present study prove more potent drugs than the ones already marketed.

Receptor binding profiles and quantitative structure-affinity relationships of some 5-substituted-N,N-diallyltryptamines.

N,N-Diallyltryptamine (DALT) and 5-methoxy-N,N-diallyltryptamine (5-MeO-DALT) are two tryptamines synthesized and tested by Alexander Shulgin. In self-experiments, 5-MeO-DALT was reported to be psychoactive in the 12-20mg range, while the unsubstituted compound DALT had few discernible effects in the 42-80 mg range. Recently, 5-MeO-DALT has been used in nonmedical settings for its psychoactive effects, but these effects have been poorly characterized and little is known of its pharmacological properties. We extended the work of Shulgin by synthesizing additional 5-substituted-DALTs. We then compared them to DALT and 5-MeO-DALT for their binding affinities at 45 cloned receptors and transporter proteins. Based on in vitro binding affinity, we identified 27 potential receptor targets for the 5-substituted-DALT compounds. Five of the DALT compounds had affinity in the 10-80 nM range for serotonin 5-HT1A and 5-HT2B receptors, while the affinity of DALT itself at 5-HT1A receptors was slightly lower at 100 nM. Among the 5-HT2 subtypes, the weakest affinity was at 5-HT2A receptors, spanning 250-730 nM. Five of the DALT compounds had affinity in the 50-400 nM range for serotonin 5-HT1D, 5-HT6, and 5-HT7 receptors; again, it was the unsubstituted DALT that had the weakest affinity at all three subtypes. The test drugs had even weaker affinity for 5-HT1B, 5-HT1E, and 5-HT5A subtypes and little or no affinity for the 5-HT3 subtype. These compounds also had generally nanomolar affinities for adrenergic α2A, α2B, and α2C receptors, sigma receptors σ1 and σ2, histamine H1 receptors, and norepinephrine and serotonin uptake transporters. They also bound to other targets in the nanomolar-to-low micromolar range. Based on these binding results, it is likely that multiple serotonin receptors, as well as several nonserotonergic sites are important for the psychoactive effects of DALT drugs. To learn whether any quantitative structure-affinity relationships existed, we evaluated correlations among physicochemical properties of the congeneric 5-substituted-DALT compounds. The descriptors included electronic (σp), hydrophobic (π), and steric (CMR) parameters. The binding affinity at 5-HT1A, 5-HT1D, 5-HT7, and κ opioid receptors was positively correlated with the steric volume parameter CMR. At α2A, α2B, and α2C receptors, and at the histamine H1 receptor, binding affinity was correlated with the Hammett substituent parameter σp; higher affinity was associated with larger σp values. At the σ2 receptor, higher affinity was correlated with increasing π. These correlations should aid in the development of more potent and selective drugs within this family of compounds.

High-Throughput Melanin-Binding Affinity and In Silico Methods to Aid in the Prediction of Drug Exposure in Ocular Tissue

Drugs possessing the ability to bind to melanin-rich tissue, such as the eye, are linked with higher ocular exposure, and therefore have the potential to affect the efficacy and safety profiles of therapeutics. A high-throughput melanin chromatographic affinity assay has been developed and validated, which has allowed the rapid melanin affinity assessment for a large number of compounds. Melanin affinity of compounds can be quickly assigned as low, medium, or high melanin binders. A high-throughput chromatographic method has been developed and fully validated to assess melanin affinity of pharmaceuticals and has been useful in predicting ocular tissue distribution in vivo studies. The high-throughput experimental approach has also allowed for a specific training set of 263 molecules for a quantitative structure-affinity relationships (QSAR) method to be developed, which has also been shown to be a predictor of ocular tissue exposure. Previous studies have reported the development of in silico QSAR models based on training sets of relatively small and mostly similar compounds; this model covers a broader range of melanin-binding affinities than what has been previously published and identified several physiochemical descriptors to be considered in the design of compounds where melanin-binding modulation is desired.

In vitro and in silico evaluation of twelve newly-synthesized 1-acetamide-5-methoxy-2-oxindoles as 5-Ht₇ receptor ligands.

Based on in vitro studies of twelve newly-synthesized 1-acetamide-5-methoxy-2-oxindoles as 5-Ht7 receptor ligands, Structure Affinity Relationship (SAR) and Quantitative Structure Affinity Relationship model (QSAR) are provided. Also, a ligand-based pharmacophore model is proposed through molecular docking techniques and Nucleus Independent Chemical Shift DFT calculations (NICS).

Reply to “Quantitative structure–affinity relationship study of azo dyes for cellulose fibers by multiple linear regression and artificial neural network”

A quantitative structure–property relationship study was recently published by Wang et al. (Chemom. Intell. Lab. Syst. 134 (2014) 1–9), to correlate 51 azo dye affinities for cellulose to dye molecular descriptors by multiple linear regression (MLR) and artificial neural network (ANN). They performed several tests to check the robustness and predictive power of their MLR and ANN models and compared their results with others previously published. Some erroneous calculations from their paper questioned the value of these comparisons. In this paper corrections on their published results are presented, and also additional metrics for model validation are added.

Computational approaches for drug discovery.

Cellular proteins are the mediators of multiple organism functions being involved in physiological mechanisms and disease. By discovering lead compounds that affect the function of target proteins, the target diseases or physiological mechanisms can be modulated. Based on knowledge of the ligand-receptor interaction, the chemical structures of leads can be modified to improve efficacy, selectivity and reduce side effects. One rational drug design technology, which enables drug discovery based on knowledge of target structures, functional properties and mechanisms, is computer-aided drug design (CADD). The application of CADD can be cost-effective using experiments to compare predicted and actual drug activity, the results from which can used iteratively to improve compound properties. The two major CADD-based approaches are structure-based drug design, where protein structures are required, and ligand-based drug design, where ligand and ligand activities can be used to design compounds interacting with the protein structure. Approaches in structure-based drug design include docking, de novo design, fragment-based drug discovery and structure-based pharmacophore modeling. Approaches in ligand-based drug design include quantitative structure-affinity relationship and pharmacophore modeling based on ligand properties. Based on whether the structure of the receptor and its interaction with the ligand are known, different design strategies can be seed. After lead compounds are generated, the rule of five can be used to assess whether these have drug-like properties. Several quality validation methods, such as cost function analysis, Fisher's cross-validation analysis and goodness of hit test, can be used to estimate the metrics of different drug design strategies. To further improve CADD performance, multi-computers and graphics processing units may be applied to reduce costs.

Quantitative structure–affinity relationship study of azo dyes for cellulose fibers by multiple linear regression and artificial neural network

A quantitative structure–property relationship study was performed to correlate descriptors representing the molecular structures to fiber affinities for azo dyes. The complete set of 51 compounds was divided into a training set of 41 compounds and a test set of 10 compounds by using DUPLEX algorithm. Multiple linear regression analysis was used to select the best subset of descriptors and to build linear models; nonlinear models were developed by means of artificial neural network. The robustness of the obtained models was assessed by different approaches, including leave-many-out cross-validation, Y-randomization test, and external validation through test set. The obtained models with four descriptors show good predictive power: for the test set, a squared correlation coefficient (r2) of 0.916 was achieved by the linear model; while the nonlinear model with r2 of 0.935 for the test set performs better than the linear model. Furthermore, the applicability domain of the models was analyzed based on the Williams plot. The donor atom number for H-bonds, group polarizability and electronegativity of the dye molecules are found to play important roles for the dye-fiber affinity.

Classifier Ensemble Based on Feature Selection and Diversity Measures for Predicting the Affinity of A2B Adenosine Receptor Antagonists

A(2B) adenosine receptor antagonists may be beneficial in treating diseases like asthma, diabetes, diabetic retinopathy, and certain cancers. This has stimulated research for the development of potent ligands for this subtype, based on quantitative structure-affinity relationships. In this work, a new ensemble machine learning algorithm is proposed for classification and prediction of the ligand-binding affinity of A(2B) adenosine receptor antagonists. This algorithm is based on the training of different classifier models with multiple training sets (composed of the same compounds but represented by diverse features). The k-nearest neighbor, decision trees, neural networks, and support vector machines were used as single classifiers. To select the base classifiers for combining into the ensemble, several diversity measures were employed. The final multiclassifier prediction results were computed from the output obtained by using a combination of selected base classifiers output, by utilizing different mathematical functions including the following: majority vote, maximum and average probability. In this work, 10-fold cross- and external validation were used. The strategy led to the following results: i) the single classifiers, together with previous features selections, resulted in good overall accuracy, ii) a comparison between single classifiers, and their combinations in the multiclassifier model, showed that using our ensemble gave a better performance than the single classifier model, and iii) our multiclassifier model performed better than the most widely used multiclassifier models in the literature. The results and statistical analysis demonstrated the supremacy of our multiclassifier approach for predicting the affinity of A(2B) adenosine receptor antagonists, and it can be used to develop other QSAR models.

Modeling Protein–Peptide Recognition Based on Classical Quantitative Structure–Affinity Relationship Approach: Implication for Proteome-Wide Inference of Peptide-Mediated Interactions

Peptide-mediated interactions are crucial to a variety of functions in the living cell and are estimated to be involved in up to 40 % of all cellular processes. Fast and reliable inference of such interactions is fundamentally important for our understanding and, then, reconstruction of complete virtual interactomics involved in a specific cell, tissue or organism. In the current study, we performed structure-level characterization, modeling and prediction of protein-peptide recognition specificity and stability in a high-throughput manner. To achieve this, the classical chemometrics methodology quantitative structure-activity relationship (QSAR), which is traditionally applied to small-molecule entities such as drug compounds and environmental chemicals, was employed to statistically correlate structure features with binding affinities for a panel of structure-solved, affinity-known protein-peptide complexes compiled from the PDB database and literatures. In the standard QSAR procedure, various structural descriptors including physicochemical, geometrical and constitutional parameters that characterize diverse aspects of protein-peptide interaction property were derived from the biomacromolecular complex structure architecture, and these descriptors were then correlated with experimentally measured affinities by using the partial least squares (PLS) regression and Gaussian process (GP) in conjunction with genetic algorithm (GA) variable selection. The nonlinear GA/GP method was found to perform much well as compared to linear GA/PLS modeling, suggesting that the protein-peptide interaction system is highly complicated that may involve strong noise and interactive effect. The optimal GA/GP model revealed that the interface size and solvent effect play a critical role in protein-peptide binding, and other properties such as peptide length and flexibility also contribute significantly to the binding. A further test on 2,018 human amphiphysin SH3 domain-binding peptides demonstrated that the purposed QSAR modeling procedure is very fast and effective, which can thus be readily used to perform proteome-wide inference of peptide-mediated interactions.

Applying Chemometrics Approaches to Model and Predict the Binding Affinities Between the Human Amphiphysin SH3 Domain and Its Peptide Ligands (Suplementary Material)

In this study, we used two categories of molecular descriptors as CODESSA and DPPS (divided physicochemical property scores of amino acids) to parameterize structural characteristics of 2015 human amphiphysin SH3 domain-binding decapeptides at atom and residue levels. Based upon that, several robust quantitative structure-affinity relationship (QSAR) models were then constructed using partial least squares regression (PLS) and least squares-support vector machine (LSSVM) coupled with genetic algorithm (GA)-variable selection. Results show that (1) GA is a powerful tool for variable selection by which the most informative variable combinations can be efficiently determined for PLS and LSSVM modeling, (2) regression models constructed using nonlinear LSSVM approach are more robust and predictable than those by linear PLS method, (3) the residue level descriptor (DPPS) performs better in capturing peptide structural characteristics, more amenable than those from the atom level descriptor (CODESSA). By investigating the optimal DPPS-based GA-LSSVM model, it is indicated that the core motif of SH3 domain-binding peptides contributes significantly to the binding affinity, whereas the two end residues, especially the N-terminal residue, have a little effect on the binding process.

CoMFA analyses of C-2 position Salvinorin A analogs at the kappa-opioid receptor provides insights into epimer selectivity

The highly potent and kappa-opioid (KOP) receptor-selective hallucinogen Salvinorin A and selected analogs have been analyzed using the 3D quantitative structure-affinity relationship technique Comparative Molecular Field Analysis (CoMFA) in an effort to derive a statistically significant and predictive model of salvinorin affinity at the KOP receptor and to provide additional statistical support for the validity of previously proposed structure-based interaction models. Two CoMFA models of Salvinorin A analogs substituted at the C-2 position are presented. Separate models were developed based on the radioligand used in the kappa-opioid binding assay, [(3)H]diprenorphine or [(125)I]6 beta-iodo-3,14-dihydroxy-17-cyclopropylmethyl-4,5 alpha-epoxymorphinan ([(125)I]IOXY). For each dataset, three methods of alignment were employed: a receptor-docked alignment derived from the structure-based docking algorithm GOLD, another from the ligand-based alignment algorithm FlexS, and a rigid realignment of the poses from the receptor-docked alignment. The receptor-docked alignment produced statistically superior results compared to either the FlexS alignment or the realignment in both datasets. The [(125)I]IOXY set (Model 1) and [(3)H]diprenorphine set (Model 2) gave q(2) values of 0.592 and 0.620, respectively, using the receptor-docked alignment, and both models produced similar CoMFA contour maps that reflected the stereoelectronic features of the receptor model from which they were derived. Each model gave significantly predictive CoMFA statistics (Model 1 PSET r(2)=0.833; Model 2 PSET r(2)=0.813). Based on the CoMFA contour maps, a binding mode was proposed for amine-containing Salvinorin A analogs that provides a rationale for the observation that the beta-epimers (R-configuration) of protonated amines at the C-2 position have a higher affinity than the corresponding alpha-epimers (S-configuration).

A novel method to estimate the affinity of HLA-A∗0201 restricted CTL epitope

A set of 70 peptides with affinity for the class I MHC HLA-A∗0201 molecule was subjected to quantitative structure–affinity relationship studies based on the SCORE function with good results ( r 2 = 0.6982, RMS = 0.280). Then the ‘leave-one-out’ cross-validation (LOO-CV) and an outer test set including 18 outer samples were used to validate the QSAR model. The results of the LOO-CV were q 2 = 0.6188, RMS = 0.315, and the results of outer test set were r 2 = 0.5633, RMS = 0.2292. All these show that the QSAR model has good predictability. Statistical analysis showed that the hydrophobic and hydrogen bond interaction played a significant role in peptide-MHC molecule binding. The study also provided useful information for structure modification of CTL epitope, and laid theoretical base for molecular design of therapeutic vaccine.

Toward Prediction of Binding Affinities Between the MHC Protein and Its Peptide Ligands Using Quantitative Structure-Affinity Relationship Approach

It is important but challenging to determine the binding specificity of MHC-peptide interactions accurately and to predict their binding affinity quantitatively. In this paper, we discuss the application of an effective amino acid descriptor to model and predict the binding affinities between the MHC protein and its peptide ligands. This amino acid descriptor was derived from 23 electronic properties, 37 steric properties, 54 hydrophobic properties and 5 hydrogen bond properties of coded amino acids using principal component analysis (PCA), called the divided physicochemical property scores (DPPS). The DPPS descriptor was used to characterize a set of mouse MHC (H-2K(K)) binding peptides, and genetic algorithm-partial least square (GA-PLS) models were then constructed. In analyses, these models were statistically consistent with previous reports and molecular graphics exhibition. Hydrophobic interactions and hydrogen bonds were important to antigen recognition and presentation, especially exerting effects on anchor residues of peptides.

A set of new amino acid descriptors applied in prediction of MHC class I binding peptides

A set of new amino acid descriptors, namely factor analysis scales of generalized amino acid information (FASGAI) involving hydrophobicity, alpha and turn propensities, bulky properties, compositional characteristics, local flexibility and electronic properties, was proposed to resolve the representation of peptide structures. FASGAI vectors were then used to represent the structures of 152 HLA-A ∗0201 restrictive T-cell epitopes with 9 amino acid residues. The features that are closely related to binding affinities were selected by genetic arithmetic, and the model based on partial least squares was developed to predict binding affinities. The model revealed promising predictive power, giving relatively high predictions for training and test samples. Further, the PreMHCbinding program at significantly lower computational complexity was exploited to predict MHC class I binding peptides. Quantitative structure–affinity relationship analyses demonstrated the bulky properties and hydrophobicity of the 3rd residue, bulky properties of the 2nd residue, hydrophobicity of the 9th residue that provided high positive contribution to the binding affinities, and that the hydrophobicity of the 4th residue and local flexibility of the 3rd residue were negative to binding affinities. The results showed that FASGAI vectors can be further utilized to represent the structures of other functional peptides; moreover, it has thus showed us further direction into the potential applications on relationship between structures and functions of proteins.

Quantitative structure-affinity relationship of 5-HT1A receptor ligands by the classification tree method

The influence of molecular structure of 346 ligands on their affinity for 5-HT1A receptors was investigated. It was shown that the effectiveness of the proposed novel approach for interpretation of decision tree models compared favourably with the PLS method. In the context of the proposed approach, molecular fragments and their values of the relative influence on the affinity for 5-HT1A receptors were defined.