Multimode Ligand Binding in Receptor Site Modeling:  Implementation in CoMFA

Lukáčová, Viera; Baláž, Štefan

doi:10.1021/ci034100a

Cited by 33 publications

(62 citation statements)

References 31 publications

(56 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Similarly, RG is 1 and GG is 12. has resulted in a unique SAR table (Table 8.1), where each molecule occupies multiple rows, as opposed to a traditional SAR table, where one molecule has one corresponding row. Solving this problem is nontrivial, and a recently published mathematical scheme was adopted [19].…”

Section: Rd-qsar Methods Based On Pharmacophore and Pharmacophore Key Dmentioning

confidence: 99%

Receptor-dependent QSAR methods

Dong

Zheng

2013

In Silico Drug Discovery and Design

View full text Add to dashboard Cite

She has over 10 years of experience in computational science, ranging from material chemistry to biomedical physics to drug design. She has authored more than 30 peer-reviewed scientific publications. Following her PhD work in growth and nucleation mechanism of diamond film under chemical vapor deposition environment, she became the Assistant Professor of Chemistry at Jilin University. Her expertise is in computational drug discovery and medical informatics. She is currently a Senior Research Scientist at the Biomanufacturing Research Institute and Technology Enterprise (NC, USA).Quantitative structure-activity relationship (QSAR) techniques can be classified into receptor-independent and receptor-dependent (RD-QSAR) groups. In this chapter, we first review a few RD-QSAR methods, and then focus on two distinct RD-QSAR techniques based on the intuitive concept of structure-based pharmacophore and pharmacophore key descriptors. The alignment-dependent method employs docking tools to generate the ligandbinding poses, which form the basis for a multiconformation/ multipose QSAR method. On the other hand, the alignment-free RD-QSAR technique uses pharmacophore key descriptors generated by comparing ligand pharmacophore patterns with those of the binding pocket. Both techniques have been successfully applied to analyze several QSAR datasets. We conclude that RD-QSAR techniques will play more important roles in QSAR modeling when increasing numbers of 3D structures of important biological targets become available.

show abstract

Section: Rd-qsar Methods Based On Pharmacophore and Pharmacophore Key Dmentioning

confidence: 99%

Receptor-dependent QSAR methods

Dong

Zheng

2013

In Silico Drug Discovery and Design

View full text Add to dashboard Cite

show abstract

“…4 was also implemented in the most frequently used ligand-based method Comparative Molecular Field Analysis (CoMFA). 45 Notably, Eq. 4 contains the same adjustable parameters α, β, γ and κ as Eq.…”

Section: Methodsmentioning

confidence: 99%

Processing Multimode Binding Situations in Simulation-Based Prediction of Ligand−Macromolecule Affinities

et al. 2005

Self Cite

View full text Add to dashboard Cite

The Linear Response (LR) approximation and similar approaches belong to practical methods for estimation of ligand-receptor binding affinities. The approaches correlate experimental binding affinities with the changes upon binding of the ligand electrostatic and van der Waals energies, and of solvation characteristics. These attributes are expressed as ensemble averages that are obtained by conformational sampling of the protein-ligand complex and of the free ligand by molecular dynamics or Monte Carlo simulations. We observed that outliers in the LR correlations occasionally exhibit major conformational changes of the complex during sampling. We treated the situation as a multi-mode binding case, for which the observed association constant is the sum of the partial association constants of individual states/modes. The resulting nonlinear expression for the binding affinities contains all the LR variables for individual modes that are scaled by the same 2-4 adjustable parameters as in the one-mode LR equation. The multi-mode method was applied to inhibitors of a matrix metalloproteinase, where this treatment improved the explained variance in experimental activity from 75% for the uni-mode case to about 85%. The predictive ability scaled accordingly, as verified by extensive cross-validations.

show abstract

“…The CoMFA descriptor can also be extended to describe a series of conformations instead of a single molecular representation. This allows to illustrate the multimode binding mode of the ligands [19].…”

Section: Molecular Interaction Field Descriptors As the Input Argumenmentioning

confidence: 99%

Receptor Dependent Multidimensional QSAR for Modeling Drug - Receptor Interactions

Polański

2009

CMC

View full text Add to dashboard Cite

Quantitative Structure Activity Relationship (QSAR) is an approach of mapping chemical structure to properties. A significant development can be observed in the last two decades in this method which originated from the Hansch analysis based on the logP data and Hammett constant towards a growing importance of the molecular descriptors derived from 3D structure including conformational dynamics and solvation scenarios. However, molecular interactions in biological systems are complex phenomena generating extremely noisy data, if simulated in silico. This decides that activity modeling and predictions are a risky business. Molecular recognition uncertainty in traditional receptor independent (RI) m-QSAR cannot be eliminated but by the inclusion of the receptor data. Modeling ligand-receptor interactions is a complex computational problem. This has limited the development of the receptor dependent (RD) m-QSAR. However, a steady increase of computational power has also improved modeling ability in chemoinformatics and novel RD QSAR methods appeared. Following the RI m-QSAR terminology this is usually classified as RD 3/6D-QSAR. However, a clear systematic m-QSAR classification can be proposed, where dimension m refers to, the static ligand representation (3D), multiple ligand representation (4D), ligand-based virtual or pseudo receptor models (5D), multiple solvation scenarios (6D) and real receptor or target-based receptor model data (7D).

show abstract

Multimode Ligand Binding in Receptor Site Modeling: Implementation in CoMFA

Cited by 33 publications

References 31 publications

Receptor-dependent QSAR methods

Receptor-dependent QSAR methods

Processing Multimode Binding Situations in Simulation-Based Prediction of Ligand−Macromolecule Affinities

Receptor Dependent Multidimensional QSAR for Modeling Drug - Receptor Interactions

Contact Info

Product

Resources

About