First report on 3D-QSAR and molecular dynamics based docking studies of GCPII inhibitors for targeted drug delivery applications

Pandit, Amit; Sengupta, Sagnik; Krishnan, Mena Asha; Reddy, Ramesh B.; Sharma, Rajesh; Chelvam, Venkatesh

doi:10.1016/j.molstruc.2018.01.059

Cited by 8 publications

(6 citation statements)

References 19 publications

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“… 60 , 66 , 67 In this study, we aim to computationally analyze the interactions between PSMA and potential boron-containing PSMA-targeted inhibitors, which are proposed according to previous urea-based molecules and a structure–activity relationship (SAR) study. 56 In order to be clinically effective agents for BNCT, the PSMA inhibitors must possess high tumor-to-normal tissue ratios, high binding affinities, and high boron densities. A deeper understanding of interactions between the PSMA and inhibitors at the atomistic level will facilitate this goal.…”

Section: Introductionmentioning

confidence: 99%

“…The whole cavity around the active site can be separated into two substrate binding sites: S1′ site and S1 site, which accommodate the P1′ and P1 portions of inhibitors, respectively. It has been established that the S1′ site is more specific to glutamate residue or glutamate analogues, whereas the S1 site is more flexible and can accommodate different molecules. ,,− PSMA ligands can be classified into two categories (Figure ): the first class is the phosphorus-based ligands mimicking the transition state of hydrolytic reaction and the second class is urea-based inhibitors with the hydrolysis-resistant peptide bond surrogate. − The urea-based inhibitors can bind to both S1′ and S1 sites like the NAAG, which is the natural substrate of PSMA . They provide several advantages such as an ease of large-scale synthesis, penetration of the blood–brain barrier, and radiolabeling. , The unique chemical properties of these urea derivatives result in their better tumor uptake and higher binding affinity to a lipophilic pocket located near the active site of PSMA .…”

Section: Introductionmentioning

confidence: 99%

“…Previously reported PSMA-targeted BNCT compounds were mostly based upon the urea scaffold. Due to high boron content, a series of carborane derivatives have been synthesized and evaluated for inhibitory activity, tumor uptake, and biodistribution. ,, In this study, we aim to computationally analyze the interactions between PSMA and potential boron-containing PSMA-targeted inhibitors, which are proposed according to previous urea-based molecules and a structure–activity relationship (SAR) study . In order to be clinically effective agents for BNCT, the PSMA inhibitors must possess high tumor-to-normal tissue ratios, high binding affinities, and high boron densities.…”

Section: Introductionmentioning

confidence: 99%

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Interactions of Urea-Based Inhibitors with Prostate-Specific Membrane Antigen for Boron Neutron Capture Therapy

Padron

Hara

et al. 2021

ACS Omega

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In this study, molecular interactions of prostate-specific membrane antigen (PSMA) with five chemically distinct urea-based boron-containing inhibitors have been investigated at the atomic level using molecular docking and molecular dynamics simulations. The PSMA–inhibitor complexations have been analyzed by comparing their binding modes, secondary structures, root-mean-square deviations, noncovalent interactions, principal components, and binding free energies. PSMA is a cell surface glycoprotein upregulated in cancerous cells and can be targeted by boron-labeled inhibitors for boron neutron capture therapy (BNCT). The effective BNCT requires the selective boron delivery to the tumor area and highly specific PSMA-mediated cellular uptake by tumor. Thus, a potent inhibitor must exhibit both high binding affinity and high boron density. The computational results suggest that the chemical nature of inhibitors affects the binding mode and their association with PSMA is primarily dominated by hydrogen bonding, salt bridge, electrostatic, and π–π interactions. The binding free energies (−28.0, −15.2, −43.9, −23.2, and −38.2 kcal/mol) calculated using λ-dynamics for all inhibitors ( In1–5 ) predict preferential binding that is in accordance with experimental data. Among all inhibitors, In5 was found to be the best candidate for BNCT. The binding of this inhibitor to PSMA preserved its overall secondary structure. These results provide computational insights into the coordination flexibility of PSMA and its interaction with various inhibitors. They can be used for the design and synthesis of efficient BNCT agents with improved drug selectivity and high boron percentage.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Interactions of Urea-Based Inhibitors with Prostate-Specific Membrane Antigen for Boron Neutron Capture Therapy

Padron

Hara

et al. 2021

ACS Omega

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“…The screening step aims to reflect more structural information with the less molecular structure descriptors as possible. Many methods have been developed to screen molecular descriptors and can be mainly divided into two categories [7][8][9], traditional variable selection methods (such as the PLS method and its variants) and modern search algorithms based on the optimization strategy (genetic algorithm GA, simulated annealing algorithm SA, ant colony algorithm AC, particle swarm optimization PSO, and other swarm intelligence algorithms) [10][11][12]. Traditional variable selection methods are the most simple and efficient and can quickly screen descriptors, but their overall performances are low especially in the complex nonlinear data collection.…”

Section: Introductionmentioning

confidence: 99%

A Quantitative Structure-activity Relationships Model Based on Hybrid Artificial Intelligence Methods and its Application

M¹,

Zhang²,

Liu³

et al. 2018

Preprint

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10Abstract: Quantitative structure-activity relationship (QSAR) model is adopted to study the 11 relationship between the chemical and physical properties of various substances and the structure. 12Through QSAR studies, the internal relationship between the invisible structure and the activity 13 can be obtained. In this paper, a novel chaos-enhanced accelerate particle swarm algorithm 14 (CAPSO) is proposed, which is used to molecular descriptors screening and optimization of the 25Keywords: quantitative structure-activity relationship; hybrid intelligence; artificial neural 26 network; particle swarm optimization 27 28

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“…The step of molecular descriptor screening aims to reflect more structural information so that there is no noise in the descriptors. Many methods have been developed to screen molecular descriptors and can be mainly divided into two categories [16][17][18]. The first category includes the common methods, such as Akaike information criterion (AIC), Bayesian information criterion (BIC), and forward/backward/bi-directional stepwise multiple linear regression (MLR).…”

mentioning

confidence: 99%

A Quantitative Structure-Property Relationship Model Based on Chaos-Enhanced Accelerated Particle Swarm Optimization Algorithm and Back Propagation Artificial Neural Network

Zhang

Liu

et al. 2018

Applied Sciences

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Featured Application:The proposed hybrid intelligent model can be applied in engineering design, material performance prediction, numerical calculation, and the prediction of physical and chemical properties.Abstract: A quantitative structure-property relationship (QSPR) model is proposed to explore the relationship between the pKa of various compounds and their structures. Through QSPR studies, the relationship between the structure and properties can be obtained. In this study, a novel chaos-enhanced accelerated particle swarm algorithm (CAPSO) is adopted to screen molecular descriptors and optimize the weights of back propagation artificial neural network (BP ANN). Then, the QSPR model based on CAPSO and BP ANN is proposed and named the CAPSO BP ANN model. The prediction experiment showed that the CAPSO algorithm was a reliable method for screening molecular descriptors. The five molecular descriptors obtained by the CAPSO algorithm could well characterize the molecular structure of each compound in pKa prediction. The experimental results also showed that the CAPSO BP ANN model exhibited good performance in predicting the pKa values of various compounds. The absolute mean relative error, root mean square error, and square correlation coefficient are respectively 0.5364, 0.0632, and 0.9438, indicating the high prediction accuracy. The proposed hybrid intelligent model can be applied in engineering design and the prediction of physical and chemical properties.The establishment of the QSPR model mainly involves the following steps: acquisition of experimental data, construction and optimization of the molecular structure, calculation and screening of molecular descriptors, establishment and verification of the model, etc. First of all, the variable selection is important in many fields, such as spectroscopy [7,8], QSPR [9,10], and other fields [11,12]. The selection of molecular descriptors largely determines the quality of the QSPR model [13][14][15]. The step of molecular descriptor screening aims to reflect more structural information so that there is no noise in the descriptors. Many methods have been developed to screen molecular descriptors and can be mainly divided into two categories [16][17][18]. The first category includes the common methods, such as Akaike information criterion (AIC), Bayesian information criterion (BIC), and forward/backward/bi-directional stepwise multiple linear regression (MLR). The second includes the modern search algorithms, such as genetic algorithm (GA), simulated annealing algorithm (SA), ant colony algorithm (AC), particle swarm optimization (PSO), and other swarm intelligence algorithms [7,11,[19][20][21]. The common methods are the most simple and efficient, but their overall performances are low in complex nonlinear problems. The modern search algorithms based on the optimization strategy have obvious advantages and can search for optimal variables and deal with complex large data points. The model establishment is important in the QSPR study and commonly used QSPR model...

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First report on 3D-QSAR and molecular dynamics based docking studies of GCPII inhibitors for targeted drug delivery applications

Cited by 8 publications

References 19 publications

Interactions of Urea-Based Inhibitors with Prostate-Specific Membrane Antigen for Boron Neutron Capture Therapy

Interactions of Urea-Based Inhibitors with Prostate-Specific Membrane Antigen for Boron Neutron Capture Therapy

A Quantitative Structure-activity Relationships Model Based on Hybrid Artificial Intelligence Methods and its Application

A Quantitative Structure-Property Relationship Model Based on Chaos-Enhanced Accelerated Particle Swarm Optimization Algorithm and Back Propagation Artificial Neural Network

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