Literature DB >> 11341944

PLS modeling of chimeric MS04/MSH-peptide and MC1/MC3-receptor interactions reveals a novel method for the analysis of ligand-receptor interactions.

P Prusis1, R Muceniece, P Andersson, C Post, T Lundstedt, J E Wikberg.   

Abstract

A novel method has been developed for the analysis of ligand-receptor interactions. The method utilizes binding data generated from the analysis of chimeric proteins with chimeric peptides. To each chimeric part of the peptide and receptor are assigned descriptors, thus creating a matrix of X descriptors. These descriptors are then correlated with the experimentally determined interaction binding affinities for each chimeric receptor/peptide pair by use of partial least-squares projection to latent structures (PLS). The method was applied to analyze the interactions of chimeric MSH-peptides with wild-type MC1 and MC3 receptors, and MC1/MC3 receptor chimeras (in total 40 peptide-receptor combinations). Two types of PLS models could be created, one that revealed the relationships between receptor and peptide structure and peptide binding pK(i) values (i.e., affinity) (R2 and Q2 being 0.71 and 0.62, respectively), and another that revealed the relationships between peptide and receptor structure and peptide-receptor selectivity (R2 and Q2 being 0.64 and 0.57, respectively). After addition of cross-terms these models improved significantly; the R2 and Q2 being 0.93 and 0.75 for affinity, and 0.92 and 0.72 for selectivity, respectively. The analysis shows that the high affinity of the MSH-peptides is primarily achieved by interactions of the peptides' C-terminal amino acids with TM2 and TM3 of the receptor, and, to a lesser extent, by the interaction of the N-terminus with TM1, TM2 and TM3 of the receptor. However, in contrast, the MC1 receptor selectivity is primarily determined by an interaction of the peptides' N-termini with TM2/3 of the receptor. Moreover, the cross-terms of the PLS model revealed the existence of a strong interaction between TM6/7 and TM2/3 of the receptors.

Entities:  

Mesh:

Substances:

Year:  2001        PMID: 11341944     DOI: 10.1016/s0167-4838(00)00249-1

Source DB:  PubMed          Journal:  Biochim Biophys Acta        ISSN: 0006-3002


  9 in total

1.  Classification of G-protein coupled receptors by alignment-independent extraction of principal chemical properties of primary amino acid sequences.

Authors:  Maris Lapinsh; Alexandrs Gutcaits; Peteris Prusis; Claes Post; Torbjörn Lundstedt; Jarl E S Wikberg
Journal:  Protein Sci       Date:  2002-04       Impact factor: 6.725

2.  Structural modeling extends QSAR analysis of antibody-lysozyme interactions to 3D-QSAR.

Authors:  Eva K Freyhult; Karl Andersson; Mats G Gustafsson
Journal:  Biophys J       Date:  2003-04       Impact factor: 4.033

3.  Interactions of human melanocortin 4 receptor with nonpeptide and peptide agonists.

Authors:  Irina D Pogozheva; Biao-Xin Chai; Andrei L Lomize; Tung M Fong; David H Weinberg; Ravi P Nargund; Michael W Mulholland; Ira Gantz; Henry I Mosberg
Journal:  Biochemistry       Date:  2005-08-30       Impact factor: 3.162

4.  3D proteochemometrics: using three-dimensional information of proteins and ligands to address aspects of the selectivity of serine proteases.

Authors:  Vigneshwari Subramanian; Qurrat Ul Ain; Helena Henno; Lars-Olof Pietilä; Julian E Fuchs; Peteris Prusis; Andreas Bender; Gerd Wohlfahrt
Journal:  Medchemcomm       Date:  2017-03-15       Impact factor: 3.597

5.  Proteochemometric model for predicting the inhibition of penicillin-binding proteins.

Authors:  Sunanta Nabu; Chanin Nantasenamat; Wiwat Owasirikul; Ratana Lawung; Chartchalerm Isarankura-Na-Ayudhya; Maris Lapins; Jarl E S Wikberg; Virapong Prachayasittikul
Journal:  J Comput Aided Mol Des       Date:  2014-10-26       Impact factor: 3.686

6.  Proteochemometric modeling of the susceptibility of mutated variants of the HIV-1 virus to reverse transcriptase inhibitors.

Authors:  Muhammad Junaid; Maris Lapins; Martin Eklund; Ola Spjuth; Jarl E S Wikberg
Journal:  PLoS One       Date:  2010-12-15       Impact factor: 3.240

7.  Probing the chemical interaction space governed by 4-aminosubstituted benzenesulfonamides and carbonic anhydrase isoforms.

Authors:  Behnam Rasti; Yeganeh Entezari Heravi
Journal:  Res Pharm Sci       Date:  2018-06

8.  Prediction of indirect interactions in proteins.

Authors:  Peteris Prusis; Staffan Uhlén; Ramona Petrovska; Maris Lapinsh; Jarl E S Wikberg
Journal:  BMC Bioinformatics       Date:  2006-03-22       Impact factor: 3.169

9.  Proteochemometric modeling of HIV protease susceptibility.

Authors:  Maris Lapins; Martin Eklund; Ola Spjuth; Peteris Prusis; Jarl E S Wikberg
Journal:  BMC Bioinformatics       Date:  2008-04-10       Impact factor: 3.169

  9 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.