1
|
Rasti B. Quantitative Characterization of the Chemical Space Governed by Human Carbonic Anhydrases and selenium-containing derivatives of solfonamides. BRAZ J PHARM SCI 2022. [DOI: 10.1590/s2175-97902022e19704] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
|
2
|
Rasti B, Mazraedoost S, Panahi H, Falahati M, Attar F. New insights into the selective inhibition of the β-carbonic anhydrases of pathogenic bacteria Burkholderia pseudomallei and Francisella tularensis: a proteochemometrics study. Mol Divers 2018; 23:263-273. [PMID: 30120657 DOI: 10.1007/s11030-018-9869-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2018] [Accepted: 08/09/2018] [Indexed: 10/28/2022]
Abstract
Nowadays, antibiotic resistance has turned into one of the most important worldwide health problems. Biological end point of critical enzymes induced by potent inhibitors is recently being considered as a highly effective and popular strategy to defeat antibiotic-resistant pathogens. For instance, the simple but critical β-carbonic anhydrase has recently been in the center of attention for anti-pathogen drug discoveries. However, no β-carbonic anhydrase selective inhibitor has yet been developed. Available β-carbonic anhydrase inhibitors are also highly potent with regard to human carbonic anhydrases, leading to severe inevitable side effects in case of usage. Therefore, developing novel inhibitors with high selectivity against pathogenic β-carbonic anhydrases is of great essence. Herein, for the first time, we have conducted a proteochemometric study to explore the structural and the chemical aspects of the interactions governed by bacterial β-carbonic anhydrases and their inhibitors. We have found valuable information which can lead to designing novel inhibitors with better selectivity for bacterial β-carbonic anhydrases.
Collapse
Affiliation(s)
- Behnam Rasti
- Department of Microbiology, Faculty of Basic Sciences, Lahijan Branch, Islamic Azad University (IAU), Lahijan, Guilan, Iran.
| | - Sargol Mazraedoost
- Department of Microbiology, Faculty of Basic Sciences, Lahijan Branch, Islamic Azad University (IAU), Lahijan, Guilan, Iran
| | - Hanieh Panahi
- Department of Mathematics and Statistics, Lahijan Branch, Islamic Azad University, Lahijan, Iran
| | - Mojtaba Falahati
- Department of Nanotechnology, Faculty of Advance Science and Technology, Pharmaceutical Sciences Branch, Islamic Azad University (IAUPS), Tehran, Iran
| | - Farnoosh Attar
- Department of Biology, Faculty of Food Industry and Agriculture, Standard Research Institute (SRI), Karaj, Iran
| |
Collapse
|
3
|
Rasti B, Heravi YE. Probing the chemical interaction space governed by 4-aminosubstituted benzenesulfonamides and carbonic anhydrase isoforms. Res Pharm Sci 2018; 13:192-204. [PMID: 29853929 PMCID: PMC5921400 DOI: 10.4103/1735-5362.228940] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Isoform diversity, critical physiological roles and involvement in major diseases/disorders such as glaucoma, epilepsy, Alzheimer's disease, obesity, and cancers have made carbonic anhydrase (CA), one of the most interesting case studies in the field of computer aided drug design. Since applying non-selective inhibitors can result in major side effects, there have been considerable efforts so far to achieve selective inhibitors for different isoforms of CA. Using proteochemometrics approach, the chemical interaction space governed by a group of 4-amino-substituted benzenesulfonamides and human CAs has been explored in the present study. Several validation methods have been utilized to assess the validity, robustness and predictivity power of the proposed proteochemometric model. Our model has offered major structural information that can be applied to design new selective inhibitors for distinct isoforms of CA. To prove the applicability of the proposed model, new compounds have been designed based on the offered discriminative structural features.
Collapse
Affiliation(s)
- Behnam Rasti
- Department of Microbiology, Faculty of Basic Sciences, Lahijan Branch, Islamic Azad University (IAU), Lahijan, Guilan, I.R. Iran
| | | |
Collapse
|
4
|
Rasti B, Shahangian SS. Proteochemometric modeling of the origin of thymidylate synthase inhibition. Chem Biol Drug Des 2018; 91:1007-1016. [PMID: 29251822 DOI: 10.1111/cbdd.13163] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2017] [Revised: 11/09/2017] [Accepted: 12/01/2017] [Indexed: 12/11/2022]
Affiliation(s)
- Behnam Rasti
- Department of Microbiology; Faculty of Basic Sciences; Lahijan Branch; Islamic Azad University (IAU); Lahijan Guilan Iran
| | | |
Collapse
|
5
|
Cerisier N, Regad L, Triki D, Petitjean M, Flatters D, Camproux AC. Statistical Profiling of One Promiscuous Protein Binding Site: Illustrated by Urokinase Catalytic Domain. Mol Inform 2017; 36. [PMID: 28696518 DOI: 10.1002/minf.201700040] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2017] [Accepted: 06/26/2017] [Indexed: 12/21/2022]
Abstract
While recent literature focuses on drug promiscuity, the characterization of promiscuous binding sites (ability to bind several ligands) remains to be explored. Here, we present a proteochemometric modeling approach to analyze diverse ligands and corresponding multiple binding sub-pockets associated with one promiscuous binding site to characterize protein-ligand recognition. We analyze both geometrical and physicochemical profile correspondences. This approach was applied to examine the well-studied druggable urokinase catalytic domain inhibitor binding site, which results in a large number of complex structures bound to various ligands. This approach emphasizes the importance of jointly characterizing pocket and ligand spaces to explore the impact of ligand diversity on sub-pocket properties and to establish their main profile correspondences. This work supports an interest in mining available 3D holo structures associated with a promiscuous binding site to explore its main protein-ligand recognition tendency.
Collapse
Affiliation(s)
- Natacha Cerisier
- INSERM, UMRS-973, MTi,35, rue Hélène Brion, 75205, PARIS CEDEX 13.,University Paris Diderot, Sorbonne Paris Cité, UMRS-973, MTi
| | - Leslie Regad
- INSERM, UMRS-973, MTi,35, rue Hélène Brion, 75205, PARIS CEDEX 13.,University Paris Diderot, Sorbonne Paris Cité, UMRS-973, MTi
| | - Dhoha Triki
- INSERM, UMRS-973, MTi,35, rue Hélène Brion, 75205, PARIS CEDEX 13.,University Paris Diderot, Sorbonne Paris Cité, UMRS-973, MTi
| | - Michel Petitjean
- INSERM, UMRS-973, MTi,35, rue Hélène Brion, 75205, PARIS CEDEX 13.,University Paris Diderot, Sorbonne Paris Cité, UMRS-973, MTi
| | - Delphine Flatters
- INSERM, UMRS-973, MTi,35, rue Hélène Brion, 75205, PARIS CEDEX 13.,University Paris Diderot, Sorbonne Paris Cité, UMRS-973, MTi
| | - Anne-Claude Camproux
- INSERM, UMRS-973, MTi,35, rue Hélène Brion, 75205, PARIS CEDEX 13.,University Paris Diderot, Sorbonne Paris Cité, UMRS-973, MTi
| |
Collapse
|
6
|
Subramanian V, Ain QU, Henno H, Pietilä LO, Fuchs JE, Prusis P, Bender A, Wohlfahrt G. 3D proteochemometrics: using three-dimensional information of proteins and ligands to address aspects of the selectivity of serine proteases. MEDCHEMCOMM 2017; 8:1037-1045. [PMID: 30108817 PMCID: PMC6072133 DOI: 10.1039/c6md00701e] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/16/2016] [Accepted: 03/14/2017] [Indexed: 11/21/2022]
Abstract
The high similarity between certain sub-pockets of serine proteases may lead to low selectivity of protease inhibitors. Therefore the application of proteochemometrics (PCM), which quantifies the relationship between protein/ligand descriptors and affinity for multiple ligands and targets simultaneously, is useful to understand and improve the selectivity profiles of potential inhibitors. In this study, protein field-based PCM that uses knowledge-based and WaterMap derived fields to describe proteins in combination with 2D (RDKit and MOE fingerprints) and 3D (4 point pharmacophoric fingerprints and GRIND) ligand descriptors was used to model the bioactivities of 24 homologous serine proteases and 5863 inhibitors in an integrated fashion. Of the multiple field-based PCM models generated based on different ligand descriptors, RDKit fingerprints showed the best performance in terms of external prediction with Rtest2 of 0.72 and RMSEP of 0.81. Further, visual interpretation of the models highlights sub-pocket specific regions that influence affinity and selectivity of serine protease inhibitors.
Collapse
Affiliation(s)
- Vigneshwari Subramanian
- Division of Pharmaceutical Chemistry and Technology , Faculty of Pharmacy , University of Helsinki , 00014 Helsinki , Finland
- Computer-Aided Drug Design , Orion Pharma , Orionintie 1 , 02101 Espoo , Finland .
| | - Qurrat Ul Ain
- Centre for Molecular Informatics , Department of Chemistry , Lensfield Road , CB2 1EW Cambridge , UK
| | - Helena Henno
- Computer-Aided Drug Design , Orion Pharma , Orionintie 1 , 02101 Espoo , Finland .
| | - Lars-Olof Pietilä
- Computer-Aided Drug Design , Orion Pharma , Orionintie 1 , 02101 Espoo , Finland .
| | - Julian E Fuchs
- Centre for Molecular Informatics , Department of Chemistry , Lensfield Road , CB2 1EW Cambridge , UK
- Institute of General , Inorganic and Theoretical Chemistry , University of Innsbruck , Innrain 82 , 6020 Innsbruck , Austria
| | - Peteris Prusis
- Computer-Aided Drug Design , Orion Pharma , Orionintie 1 , 02101 Espoo , Finland .
| | - Andreas Bender
- Centre for Molecular Informatics , Department of Chemistry , Lensfield Road , CB2 1EW Cambridge , UK
| | - Gerd Wohlfahrt
- Computer-Aided Drug Design , Orion Pharma , Orionintie 1 , 02101 Espoo , Finland .
| |
Collapse
|
7
|
Rasti B, Schaduangrat N, Shahangian SS, Nantasenamat C. Exploring the origin of phosphodiesterase inhibition via proteochemometric modeling. RSC Adv 2017. [DOI: 10.1039/c7ra02332d] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
A proteochemometric study of a set of phosphodiesterase 4B and 4D inhibitors sheds light on the origin of their inhibition and selectivities.
Collapse
Affiliation(s)
- Behnam Rasti
- Department of Microbiology
- Faculty of Basic Sciences
- Lahijan Branch
- Islamic Azad University (IAU)
- Lahijan
| | - Nalini Schaduangrat
- Center of Data Mining and Biomedical Informatics
- Faculty of Medical Technology
- Mahidol University
- Bangkok 10700
- Thailand
| | - S. Shirin Shahangian
- Department of Biology
- Faculty of Sciences
- University of Guilan
- Rasht 41938-33697
- Iran
| | - Chanin Nantasenamat
- Center of Data Mining and Biomedical Informatics
- Faculty of Medical Technology
- Mahidol University
- Bangkok 10700
- Thailand
| |
Collapse
|
8
|
Shaikh N, Sharma M, Garg P. An improved approach for predicting drug-target interaction: proteochemometrics to molecular docking. MOLECULAR BIOSYSTEMS 2016; 12:1006-14. [PMID: 26822863 DOI: 10.1039/c5mb00650c] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
Proteochemometric (PCM) methods, which use descriptors of both the interacting species, i.e. drug and the target, are being successfully employed for the prediction of drug-target interactions (DTI). However, unavailability of non-interacting dataset and determining the applicability domain (AD) of model are a main concern in PCM modeling. In the present study, traditional PCM modeling was improved by devising novel methodologies for reliable negative dataset generation and fingerprint based AD analysis. In addition, various types of descriptors and classifiers were evaluated for their performance. The Random Forest and Support Vector Machine models outperformed the other classifiers (accuracies >98% and >89% for 10-fold cross validation and external validation, respectively). The type of protein descriptors had negligible effect on the developed models, encouraging the use of sequence-based descriptors over the structure-based descriptors. To establish the practical utility of built models, targets were predicted for approved anticancer drugs of natural origin. The molecular recognition interactions between the predicted drug-target pair were quantified with the help of a reverse molecular docking approach. The majority of predicted targets are known for anticancer therapy. These results thus correlate well with anticancer potential of the selected drugs. Interestingly, out of all predicted DTIs, thirty were found to be reported in the ChEMBL database, further validating the adopted methodology. The outcome of this study suggests that the proposed approach, involving use of the improved PCM methodology and molecular docking, can be successfully employed to elucidate the intricate mode of action for drug molecules as well as repositioning them for new therapeutic applications.
Collapse
Affiliation(s)
- Naeem Shaikh
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research (NIPER), S. A. S. Nagar, Punjab 160062, India.
| | - Mahesh Sharma
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research (NIPER), S. A. S. Nagar, Punjab 160062, India.
| | - Prabha Garg
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research (NIPER), S. A. S. Nagar, Punjab 160062, India.
| |
Collapse
|
9
|
Rasti B, Namazi M, Karimi-Jafari MH, Ghasemi JB. Proteochemometric Modeling of the Interaction Space of Carbonic Anhydrase and its Inhibitors: An Assessment of Structure-based and Sequence-based Descriptors. Mol Inform 2016; 36. [PMID: 27860295 DOI: 10.1002/minf.201600102] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2015] [Accepted: 10/26/2016] [Indexed: 11/08/2022]
Abstract
Due to its physiological and clinical roles, carbonic anhydrase (CA) is one of the most interesting case studies. There are different classes of CAinhibitors including sulfonamides, polyamines, coumarins and dithiocarbamates (DTCs). However, many of them hardly act as a selective inhibitor against a specific isoform. Therefore, finding highly selective inhibitors for different isoforms of CA is still an ongoing project. Proteochemometrics modeling (PCM) is able to model the bioactivity of multiple compounds against different isoforms of a protein. Therefore, it would be extremely applicable when investigating the selectivity of different ligands towards different receptors. Given the facts, we applied PCM to investigate the interaction space and structural properties that lead to the selective inhibition of CA isoforms by some dithiocarbamates. Our models have provided interesting structural information that can be considered to design compounds capable of inhibiting different isoforms of CA in an improved selective manner. Validity and predictivity of the models were confirmed by both internal and external validation methods; while Y-scrambling approach was applied to assess the robustness of the models. To prove the reliability and the applicability of our findings, we showed how ligands-receptors selectivity can be affected by removing any of these critical findings from the modeling process.
Collapse
Affiliation(s)
- Behnam Rasti
- Department of Bioinformatics, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Mohsen Namazi
- Department of Bioinformatics, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - M H Karimi-Jafari
- Department of Bioinformatics, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Jahan B Ghasemi
- Department of Analytical Chemistry, School of Chemistry, College of Science, University of Tehran, Tehran, Iran
| |
Collapse
|
10
|
Rasti B, Karimi-Jafari MH, Ghasemi JB. Quantitative Characterization of the Interaction Space of the Mammalian Carbonic Anhydrase Isoforms I, II, VII, IX, XII, and XIV and their Inhibitors, Using the Proteochemometric Approach. Chem Biol Drug Des 2016; 88:341-53. [PMID: 26990115 DOI: 10.1111/cbdd.12759] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2015] [Revised: 01/12/2016] [Accepted: 02/29/2016] [Indexed: 12/23/2022]
Affiliation(s)
- Behnam Rasti
- Department of Bioinformatics; Institute of Biochemistry and Biophysics; University of Tehran; PO Box 13145-1365 Tehran Iran
| | - Mohammad H. Karimi-Jafari
- Department of Bioinformatics; Institute of Biochemistry and Biophysics; University of Tehran; PO Box 13145-1365 Tehran Iran
| | - Jahan B. Ghasemi
- Department of Analytical Chemistry; School of Chemistry; College of Science; University of Tehran; PO Box 13145-1365 Tehran Iran
| |
Collapse
|
11
|
Qiu T, Qiu J, Feng J, Wu D, Yang Y, Tang K, Cao Z, Zhu R. The recent progress in proteochemometric modelling: focusing on target descriptors, cross-term descriptors and application scope. Brief Bioinform 2016; 18:125-136. [PMID: 26873661 DOI: 10.1093/bib/bbw004] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2015] [Revised: 12/09/2015] [Indexed: 12/17/2022] Open
Abstract
As an extension of the conventional quantitative structure activity relationship models, proteochemometric (PCM) modelling is a computational method that can predict the bioactivity relations between multiple ligands and multiple targets. Traditional PCM modelling includes three essential elements: descriptors (including target descriptors, ligand descriptors and cross-term descriptors), bioactivity data and appropriate learning functions that link the descriptors to the bioactivity data. Since its appearance, PCM modelling has developed rapidly over the past decade by taking advantage of the progress of different descriptors and machine learning techniques, along with the increasing amounts of available bioactivity data. Specifically, the new emerging target descriptors and cross-term descriptors not only significantly increased the performance of PCM modelling but also expanded its application scope from traditional protein-ligand interaction to more abundant interactions, including protein-peptide, protein-DNA and even protein-protein interactions. In this review, target descriptors and cross-term descriptors, as well as the corresponding application scope, are intensively summarized. Additionally, we look forward to seeing PCM modelling extend into new application scopes, such as Target-Catalyst-Ligand systems, with the further development of descriptors, machine learning techniques and increasing amounts of available bioactivity data.
Collapse
|
12
|
Subramanian V, Prusis P, Xhaard H, Wohlfahrt G. Predictive proteochemometric models for kinases derived from 3D protein field-based descriptors. MEDCHEMCOMM 2016. [DOI: 10.1039/c5md00556f] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Proteochemometric models of kinases derived from protein fields and ligand 4-point pharmacophoric fingerprints are predictive and visually interpretable.
Collapse
Affiliation(s)
- Vigneshwari Subramanian
- Computer-Aided Drug Design
- Orion Pharma
- FI-02101 Espoo
- Finland
- Division of Pharmaceutical Chemistry and Technology
| | - Peteris Prusis
- Computer-Aided Drug Design
- Orion Pharma
- FI-02101 Espoo
- Finland
| | - Henri Xhaard
- Division of Pharmaceutical Chemistry and Technology
- Faculty of Pharmacy
- University of Helsinki
- FI-00014 Helsinki
- Finland
| | - Gerd Wohlfahrt
- Computer-Aided Drug Design
- Orion Pharma
- FI-02101 Espoo
- Finland
| |
Collapse
|
13
|
Cortés-Ciriano I, Ain QU, Subramanian V, Lenselink EB, Méndez-Lucio O, IJzerman AP, Wohlfahrt G, Prusis P, Malliavin TE, van Westen GJP, Bender A. Polypharmacology modelling using proteochemometrics (PCM): recent methodological developments, applications to target families, and future prospects. MEDCHEMCOMM 2015. [DOI: 10.1039/c4md00216d] [Citation(s) in RCA: 80] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Proteochemometric (PCM) modelling is a computational method to model the bioactivity of multiple ligands against multiple related protein targets simultaneously.
Collapse
Affiliation(s)
- Isidro Cortés-Ciriano
- Unité de Bioinformatique Structurale
- Institut Pasteur and CNRS UMR 3825
- Structural Biology and Chemistry Department
- 75 724 Paris
- France
| | - Qurrat Ul Ain
- Unilever Centre for Molecular Informatics
- Department of Chemistry
- CB2 1EW Cambridge
- UK
| | | | - Eelke B. Lenselink
- Division of Medicinal Chemistry
- Leiden Academic Centre for Drug Research
- Leiden
- The Netherlands
| | - Oscar Méndez-Lucio
- Unilever Centre for Molecular Informatics
- Department of Chemistry
- CB2 1EW Cambridge
- UK
| | - Adriaan P. IJzerman
- Division of Medicinal Chemistry
- Leiden Academic Centre for Drug Research
- Leiden
- The Netherlands
| | - Gerd Wohlfahrt
- Computer-Aided Drug Design
- Orion Pharma
- FIN-02101 Espoo
- Finland
| | - Peteris Prusis
- Computer-Aided Drug Design
- Orion Pharma
- FIN-02101 Espoo
- Finland
| | - Thérèse E. Malliavin
- Unité de Bioinformatique Structurale
- Institut Pasteur and CNRS UMR 3825
- Structural Biology and Chemistry Department
- 75 724 Paris
- France
| | - Gerard J. P. van Westen
- European Molecular Biology Laboratory
- European Bioinformatics Institute
- Wellcome Trust Genome Campus
- Hinxton
- UK
| | - Andreas Bender
- Unilever Centre for Molecular Informatics
- Department of Chemistry
- CB2 1EW Cambridge
- UK
| |
Collapse
|
14
|
Nabu S, Nantasenamat C, Owasirikul W, Lawung R, Isarankura-Na-Ayudhya C, Lapins M, Wikberg JES, Prachayasittikul V. Proteochemometric model for predicting the inhibition of penicillin-binding proteins. J Comput Aided Mol Des 2014; 29:127-41. [DOI: 10.1007/s10822-014-9809-0] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2014] [Accepted: 10/21/2014] [Indexed: 12/17/2022]
|
15
|
Subramanian V, Prusis P, Pietilä LO, Xhaard H, Wohlfahrt G. Visually interpretable models of kinase selectivity related features derived from field-based proteochemometrics. J Chem Inf Model 2013; 53:3021-30. [PMID: 24116714 DOI: 10.1021/ci400369z] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
Achieving selectivity for small organic molecules toward biological targets is a main focus of drug discovery but has been proven difficult, for example, for kinases because of the high similarity of their ATP binding pockets. To support the design of more selective inhibitors with fewer side effects or with altered target profiles for improved efficacy, we developed a method combining ligand- and receptor-based information. Conventional QSAR models enable one to study the interactions of multiple ligands toward a single protein target, but in order to understand the interactions between multiple ligands and multiple proteins, we have used proteochemometrics, a multivariate statistics method that aims to combine and correlate both ligand and protein descriptions with affinity to receptors. The superimposed binding sites of 50 unique kinases were described by molecular interaction fields derived from knowledge-based potentials and Schrödinger's WaterMap software. Eighty ligands were described by Mold(2), Open Babel, and Volsurf descriptors. Partial least-squares regression including cross-terms, which describe the selectivity, was used for model building. This combination of methods allows interpretation and easy visualization of the models within the context of ligand binding pockets, which can be translated readily into the design of novel inhibitors.
Collapse
|
16
|
Gao J, Huang Q, Wu D, Zhang Q, Zhang Y, Chen T, Liu Q, Zhu R, Cao Z, He Y. Study on human GPCR–inhibitor interactions by proteochemometric modeling. Gene 2013; 518:124-31. [DOI: 10.1016/j.gene.2012.11.061] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2012] [Accepted: 11/27/2012] [Indexed: 11/15/2022]
|
17
|
Anderson PC, De Sapio V, Turner KB, Elmer SP, Roe DC, Schoeniger JS. Identification of binding specificity-determining features in protein families. J Med Chem 2012; 55:1926-39. [PMID: 22289061 DOI: 10.1021/jm200979x] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
We present a new approach for identifying features of ligand-protein binding interfaces that predict binding selectivity and demonstrate its effectiveness for predicting kinase inhibitor specificity. We analyzed a large set of human kinases and kinase inhibitors using clustering of experimentally determined inhibition constants (to define specificity classes of kinases and inhibitors) and virtual ligand docking (to extract structural and chemical features of the ligand-protein binding interfaces). We then used statistical methods to identify features characteristic of each class. Machine learning was employed to determine which combinations of characteristic features were predictive of class membership and to predict binding specificities and affinities of new compounds. Experiments showed predictions were 70% accurate. These results show that our method can automatically pinpoint on the three-dimensional binding interfaces pharmacophore-like features that act as "selectivity filters". The method is not restricted to kinases, requires no prior hypotheses about specific interactions, and can be applied to any protein families for which sets of structures and ligand binding data are available.
Collapse
Affiliation(s)
- Peter C Anderson
- Sandia National Laboratories, Box 969, MS 9291, Livermore, California 94551, USA
| | | | | | | | | | | |
Collapse
|
18
|
van Westen GJP, Wegner JK, IJzerman AP, van Vlijmen HWT, Bender A. Proteochemometric modeling as a tool to design selective compounds and for extrapolating to novel targets. MEDCHEMCOMM 2011. [DOI: 10.1039/c0md00165a] [Citation(s) in RCA: 123] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Proteochemometric modeling is founded on the principles of QSAR but is able to benefit from additional information in model training due to the inclusion of target information.
Collapse
Affiliation(s)
- Gerard J. P. van Westen
- Division of Medicinal Chemistry
- Leiden/Amsterdam Center for Drug Research
- Leiden
- The Netherlands
| | | | - Adriaan P. IJzerman
- Division of Medicinal Chemistry
- Leiden/Amsterdam Center for Drug Research
- Leiden
- The Netherlands
| | - Herman W. T. van Vlijmen
- Division of Medicinal Chemistry
- Leiden/Amsterdam Center for Drug Research
- Leiden
- The Netherlands
- Tibotec BVBA
| | - A. Bender
- Division of Medicinal Chemistry
- Leiden/Amsterdam Center for Drug Research
- Leiden
- The Netherlands
- Unilever Centre for Molecular Science Informatics
| |
Collapse
|
19
|
Proteochemometric modeling of the susceptibility of mutated variants of the HIV-1 virus to reverse transcriptase inhibitors. PLoS One 2010; 5:e14353. [PMID: 21179544 PMCID: PMC3002298 DOI: 10.1371/journal.pone.0014353] [Citation(s) in RCA: 25] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2010] [Accepted: 11/10/2010] [Indexed: 12/16/2022] Open
Abstract
Background Reverse transcriptase is a major drug target in highly active antiretroviral therapy (HAART) against HIV, which typically comprises two nucleoside/nucleotide analog reverse transcriptase (RT) inhibitors (NRTIs) in combination with a non-nucleoside RT inhibitor or a protease inhibitor. Unfortunately, HIV is capable of escaping the therapy by mutating into drug-resistant variants. Computational models that correlate HIV drug susceptibilities to the virus genotype and to drug molecular properties might facilitate selection of improved combination treatment regimens. Methodology/Principal Findings We applied our earlier developed proteochemometric modeling technology to analyze HIV mutant susceptibility to the eight clinically approved NRTIs. The data set used covered 728 virus variants genotyped for 240 sequence residues of the DNA polymerase domain of the RT; 165 of these residues contained mutations; totally the data-set covered susceptibility data for 4,495 inhibitor-RT combinations. Inhibitors and RT sequences were represented numerically by 3D-structural and physicochemical property descriptors, respectively. The two sets of descriptors and their derived cross-terms were correlated to the susceptibility data by partial least-squares projections to latent structures. The model identified more than ten frequently occurring mutations, each conferring more than two-fold loss of susceptibility for one or several NRTIs. The most deleterious mutations were K65R, Q151M, M184V/I, and T215Y/F, each of them decreasing susceptibility to most of the NRTIs. The predictive ability of the model was estimated by cross-validation and by external predictions for new HIV variants; both procedures showed very high correlation between the predicted and actual susceptibility values (Q2 = 0.89 and Q2ext = 0.86). The model is available at www.hivdrc.org as a free web service for the prediction of the susceptibility to any of the clinically used NRTIs for any HIV-1 mutant variant. Conclusions/Significance Our results give directions how to develop approaches for selection of genome-based optimum combination therapy for patients harboring mutated HIV variants.
Collapse
|
20
|
Strömbergsson H, Lapins M, Kleywegt GJ, Wikberg JES. Towards Proteome-Wide Interaction Models Using the Proteochemometrics Approach. Mol Inform 2010; 29:499-508. [PMID: 27463328 DOI: 10.1002/minf.201000052] [Citation(s) in RCA: 13] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2010] [Accepted: 05/25/2010] [Indexed: 02/02/2023]
Abstract
A proteochemometrics model was induced from all interaction data in the BindingDB database, comprizing in all 7078 protein-ligand complexes with representatives from all major drug target categories. Proteins were represented by alignment-independent sequence descriptors holding information on properties such as hydrophobicity, charge, and secondary structure. Ligands were represented by commonly used QSAR descriptors. The inhibition constant (pKi ) values of protein-ligand complexes were discretized into "high" and "low" interaction activity. Different machine-learning techniques were used to induce models relating protein and ligand properties to the interaction activity. The best was decision trees, which gave an accuracy of 80 % and an area under the ROC curve of 0.81. The tree pointed to the protein and ligand properties, which are relevant for the interaction. As the approach does neither require alignments nor knowledge of protein 3D structures virtually all available protein-ligand interaction data could be utilized, thus opening a way to completely general interaction models that may span entire proteomes.
Collapse
Affiliation(s)
- Helena Strömbergsson
- The Linnaeus Centre for Bioinformatics, Department of Cell and Molecular Biology, Biomedical Centre, Box 598, SE-751 24, Uppsala, Sweden.
| | - Maris Lapins
- Department of Pharmaceutical Pharmacology, Biomedical Centre, Box 591, SE-751 24 Uppsala, Sweden
| | - Gerard J Kleywegt
- Department of Cell and Molecular Biology, Biomedical Centre, Box 596, SE-751 24, Uppsala, Sweden
| | - Jarl E S Wikberg
- Department of Pharmaceutical Pharmacology, Biomedical Centre, Box 591, SE-751 24 Uppsala, Sweden
| |
Collapse
|
21
|
Strömbergsson H, Daniluk P, Kryshtafovych A, Fidelis K, Wikberg JES, Kleywegt GJ, Hvidsten TR. Interaction model based on local protein substructures generalizes to the entire structural enzyme-ligand space. J Chem Inf Model 2008; 48:2278-88. [PMID: 18937438 DOI: 10.1021/ci800200e] [Citation(s) in RCA: 29] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Chemogenomics is a new strategy in in silico drug discovery, where the ultimate goal is to understand molecular recognition for all molecules interacting with all proteins in the proteome. To study such cross interactions, methods that can generalize over proteins that vary greatly in sequence, structure, and function are needed. We present a general quantitative approach to protein-ligand binding affinity prediction that spans the entire structural enzyme-ligand space. The model was trained on a data set composed of all available enzymes cocrystallized with druglike ligands, taken from four publicly available interaction databases, for which a crystal structure is available. Each enzyme was characterized by a set of local descriptors of protein structure that describe the binding site of the cocrystallized ligand. The ligands in the training set were described by traditional QSAR descriptors. To evaluate the model, a comprehensive test set consisting of enzyme structures and ligands was manually curated. The test set contained enzyme-ligand complexes for which no crystal structures were available, and thus the binding modes were unknown. The test set enzymes were therefore characterized by matching their entire structures to the local descriptor library constructed from the training set. Both the training and the test set contained enzyme-ligand complexes from all major enzyme classes, and the enzymes spanned a large range of sequences and folds. The experimental binding affinities (p K i) ranged from 0.5 to 11.9 (0.7-11.0 in the test set). The induced model predicted the binding affinities of the external test set enzyme-ligand complexes with an r (2) of 0.53 and an RMSEP of 1.5. This demonstrates that the use of local descriptors makes it possible to create rough predictive models that can generalize over a wide range of protein targets.
Collapse
Affiliation(s)
- Helena Strömbergsson
- The Linnaeus Centre for Bioinformatics, Uppsala University, Uppsala, Sweden, Department of Biophysics, Faculty of Physics, University of Warsaw, Warsaw, Poland
| | | | | | | | | | | | | |
Collapse
|
22
|
Lapins M, Eklund M, Spjuth O, Prusis P, Wikberg JES. Proteochemometric modeling of HIV protease susceptibility. BMC Bioinformatics 2008; 9:181. [PMID: 18402661 PMCID: PMC2375133 DOI: 10.1186/1471-2105-9-181] [Citation(s) in RCA: 60] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2007] [Accepted: 04/10/2008] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND A major obstacle in treatment of HIV is the ability of the virus to mutate rapidly into drug-resistant variants. A method for predicting the susceptibility of mutated HIV strains to antiviral agents would provide substantial clinical benefit as well as facilitate the development of new candidate drugs. Therefore, we used proteochemometrics to model the susceptibility of HIV to protease inhibitors in current use, utilizing descriptions of the physico-chemical properties of mutated HIV proteases and 3D structural property descriptions for the protease inhibitors. The descriptions were correlated to the susceptibility data of 828 unique HIV protease variants for seven protease inhibitors in current use; the data set comprised 4792 protease-inhibitor combinations. RESULTS The model provided excellent predictability (R2 = 0.92, Q2 = 0.87) and identified general and specific features of drug resistance. The model's predictive ability was verified by external prediction in which the susceptibilities to each one of the seven inhibitors were omitted from the data set, one inhibitor at a time, and the data for the six remaining compounds were used to create new models. This analysis showed that the over all predictive ability for the omitted inhibitors was Q2 inhibitors = 0.72. CONCLUSION Our results show that a proteochemometric approach can provide generalized susceptibility predictions for new inhibitors. Our proteochemometric model can directly analyze inhibitor-protease interactions and facilitate treatment selection based on viral genotype. The model is available for public use, and is located at HIV Drug Research Centre.
Collapse
Affiliation(s)
- Maris Lapins
- Department of Pharmaceutical Pharmacology, Uppsala University, SE-751 24, Sweden.
| | | | | | | | | |
Collapse
|
23
|
Estrada E. Tight-binding "dihedral orbitals" approach to the degree of folding of macromolecular chains. J Phys Chem B 2007; 111:13611-8. [PMID: 17988111 DOI: 10.1021/jp074595x] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
We develop a tight-binding molecular approach to quantify the degree of folding of a macromolecular chain. This approach is based on the linear combination of "dihedral" orbitals to give molecular orbitals (LCDO-MO). The dihedral orbitals are a set of orbitals situated in each dihedral angle of the chain. The LCDO-MO approach remains basically topological, and we display its direct relation to known graph theoretical concepts. Using this approach, we define the dihedral electronic energy and the dihedral electronic partition function of a linear macromolecular chain. We show that the partition function per dihedral angle quantifies the degree of folding of the dihedral graph. We analyze the empirical relationship between these two functions by using a series of 100 proteins. We also study the relation between these two functions and the percentages of secondary structure for these proteins. Finally, we illustrate the use of the dihedral energy and the partition function in structure-property studies of proteins by analyzing the binding of steroids to DB3 antibody.
Collapse
Affiliation(s)
- Ernesto Estrada
- Complex Systems Research Group, X-rays Unit, RIAIDT, Edificio CACTUS, University of Santiago de Compostela, 15706 Santiago de Compostela, Spain.
| |
Collapse
|
24
|
Synthesis and biological activity of novel peptide mimetics as melanocortin receptor agonists. Bioorg Med Chem Lett 2007; 18:1223-8. [PMID: 18078748 DOI: 10.1016/j.bmcl.2007.11.109] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2007] [Revised: 11/25/2007] [Accepted: 11/28/2007] [Indexed: 11/24/2022]
Abstract
A series of novel peptidomimetic analogs was prepared containing cyclohexyl, phenyl, or heterocyclic groups to ostensibly orient the guanidine or mimic of an arginine in a putative melanocortin receptor ligand pharmacophore. Some binding affinity at the melanocortin receptors MC(3) and MC(4) was noted. In silico docking also indicated that the relative positions of the hydrogen-bonding sites and hydrophobic regions of the compounds are reasonably well matched to the receptor-binding site. This may present a lead entry into a selective series of MC(4)R agonists.
Collapse
|
25
|
Kontijevskis A, Petrovska R, Mutule I, Uhlen S, Komorowski J, Prusis P, Wikberg JES. Proteochemometric analysis of small cyclic peptides' interaction with wild-type and chimeric melanocortin receptors. Proteins 2007; 69:83-96. [PMID: 17557335 DOI: 10.1002/prot.21461] [Citation(s) in RCA: 14] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The melanocortin (MC) system confines unique G-protein coupled receptor pathways, which include the MC(1-5) receptors and their endogenous agonists and antagonists, the MCs and the agouti and agouti-related proteins. The MC4 receptor is an important target for development of drugs for treatment of obesity and cachexia. While natural MC peptides are selective for the MC1 receptor, some cyclic pentapeptides, such as the HS-129 peptide, show high selectivity for the MC4 receptor. Here we gained insight into the mechanisms for its recognition by MC receptors. To this end we correlated the interaction data of four HS peptide analogues with four wild-type and 14 multiple chimeric MC receptors to the binary and physicochemical descriptions of the studied entities by use of partial least squares regression, which resulted in highly valid proteochemometric models. Analysis of the models revealed that the recognition sites of the HS peptides are different from the earlier proteochemometrically mapped linear MSH peptides' recognitions sites, although they overlap partially. The analysis also revealed important amino acids that explain the selectivity of the HS-129 peptide for the MC4 receptor.
Collapse
|
26
|
Lapinsh M, Prusis P, Petrovska R, Uhlén S, Mutule I, Veiksina S, Wikberg JES. Proteochemometric modeling reveals the interaction site for Trp9 modified α-MSH peptides in melanocortin receptors. Proteins 2007; 67:653-60. [PMID: 17357163 DOI: 10.1002/prot.21323] [Citation(s) in RCA: 16] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
The interactions of alpha-MSH peptides with melanocortin receptors (MCRs) were located by proteochemometric modeling. Nine alpha-MSH peptide analogues were constructed by exchanging the Trp9 residue in the alpha-MSH core with the natural or artificial amino acids Arg, Asp, Cys, Gly, Leu, Nal, d-Nal, Pro, or d-Trp. The nine peptides created, and alpha-MSH itself, were evaluated for their interactions with the 4 wild-type MC(1,3-5)Rs and 15 multichimeric MCRs, each of the latter being constructed from three sequence segments, each taken from a different wild-type MC(1,3-5)R. The segments of the chimeric MCRs were selected according to the principles of statistical molecular design and were arranged so as to divide the receptors into five parts. By this approach, a set of 19 maximally diverse MC receptor proteins was obtained for which the interaction activity with the 10 peptides were measured by radioligand binding thus creating data for 190 ligand-protein pairs, which were subsequently analyzed by use of proteochemometric modeling. In proteochemometrics, the structural or physicochemical properties of both interaction partners, which represent the complementarity of the interacting entities, are used to create multivariate mathematical descriptions. (Here, physicochemical property descriptors of the receptors' and peptides' amino acids were used). A valid, highly predictive (Q2 = 0.74) and easily interpretable model was then obtained. The model was further validated by its ability to correctly predicting the affinity of alpha-MSH for new point and cassette-mutated MC4/MC1Rs, and it was then used to identify the receptor residues that are important for affording the high affinity and selectivity of alpha-MSH for the MC1R. It was revealed that these residues are located in several quite distant parts of the receptors' transmembrane cavity and must therefore cause their influence at various stages of the dynamic ligand-binding process, such as by affecting the conformation of the ligand at the vicinity of the receptor and taking part in the path of the ligand's entry into its binding pocket. Our study can be used as a template how to create high resolution proteochemometric models when there are a limited number of natural proteins and ligands available.
Collapse
Affiliation(s)
- Maris Lapinsh
- Department of Pharmaceutical Biosciences, Uppsala University, SE-751 24, Uppsala, Sweden
| | | | | | | | | | | | | |
Collapse
|
27
|
|
28
|
Strömbergsson H, Kryshtafovych A, Prusis P, Fidelis K, Wikberg JES, Komorowski J, Hvidsten TR. Generalized modeling of enzyme-ligand interactions using proteochemometrics and local protein substructures. Proteins 2006; 65:568-79. [PMID: 16948162 DOI: 10.1002/prot.21163] [Citation(s) in RCA: 34] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Modeling and understanding protein-ligand interactions is one of the most important goals in computational drug discovery. To this end, proteochemometrics uses structural and chemical descriptors from several proteins and several ligands to induce interaction-models. Here, we present a new and generalized approach in which proteins varying greatly in terms of sequence and structure are represented by a library of local substructures. Using linear regression and rule-based learning, we combine such local substructures with chemical descriptors from the ligands to model binding affinity for a training set of hydrolase and lyase enzymes. We evaluate the predictive performance of these models using cross validation and sets of unseen ligand with unknown three-dimensional structure. The models are shown to generalize by outperforming models using descriptors from only proteins or only ligands, or models using global structure similarities rather than local similarities. Thus, we demonstrate that this approach is capable of describing dependencies between local structural properties and ligands in otherwise dissimilar protein structures. These dependencies are often, but not always, associated with local substructures that are in contact with the ligands. Finally, we show that strongly bound enzyme-ligand complexes require the presence of particular local substructures, while weakly bound complexes may be described by the absence of certain properties. The results demonstrate that the alignment-independent approach using local substructures is capable of describing protein-ligand interaction for largely different proteins and hence opens up for proteochemometrics-analysis of the interaction-space of entire proteomes. Current approaches are limited to families of closely related proteins. families of closely related proteins.
Collapse
Affiliation(s)
- Helena Strömbergsson
- The Linnaeus Centre for Bioinformatics, Uppsala University, SE-751 24, Uppsala, Sweden
| | | | | | | | | | | | | |
Collapse
|
29
|
Strömbergsson H, Prusis P, Midelfart H, Lapinsh M, Wikberg JES, Komorowski J. Rough set-based proteochemometrics modeling of G-protein-coupled receptor-ligand interactions. Proteins 2006; 63:24-34. [PMID: 16435365 DOI: 10.1002/prot.20777] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
G-Protein-coupled receptors (GPCRs) are among the most important drug targets. Because of a shortage of 3D crystal structures, most of the drug design for GPCRs has been ligand-based. We propose a novel, rough set-based proteochemometric approach to the study of receptor and ligand recognition. The approach is validated on three datasets containing GPCRs. In proteochemometrics, properties of receptors and ligands are used in conjunction and modeled to predict binding affinity. The rough set (RS) rule-based models presented herein consist of minimal decision rules that associate properties of receptors and ligands with high or low binding affinity. The information provided by the rules is then used to develop a mechanistic interpretation of interactions between the ligands and receptors included in the datasets. The first two datasets contained descriptors of melanocortin receptors and peptide ligands. The third set contained descriptors of adrenergic receptors and ligands. All the rule models induced from these datasets have a high predictive quality. An example of a decision rule is "If R1_ligand(Ethyl) and TM helix 2 position 27(Methionine) then Binding(High)." The easily interpretable rule sets are able to identify determinative receptor and ligand parts. For instance, all three models suggest that transmembrane helix 2 is determinative for high and low binding affinity. RS models show that it is possible to use rule-based models to predict ligand-binding affinities. The models may be used to gain a deeper biological understanding of the combinatorial nature of receptor-ligand interactions.
Collapse
|
30
|
Prusis P, Uhlén S, Petrovska R, Lapinsh M, Wikberg JES. Prediction of indirect interactions in proteins. BMC Bioinformatics 2006; 7:167. [PMID: 16553946 PMCID: PMC1435945 DOI: 10.1186/1471-2105-7-167] [Citation(s) in RCA: 36] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2005] [Accepted: 03/22/2006] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Both direct and indirect interactions determine molecular recognition of ligands by proteins. Indirect interactions can be defined as effects on recognition controlled from distant sites in the proteins, e.g. by changes in protein conformation and mobility, whereas direct interactions occur in close proximity of the protein's amino acids and the ligand. Molecular recognition is traditionally studied using three-dimensional methods, but with such techniques it is difficult to predict the effects caused by mutational changes of amino acids located far away from the ligand-binding site. We recently developed an approach, proteochemometrics, to the study of molecular recognition that models the chemical effects involved in the recognition of ligands by proteins using statistical sampling and mathematical modelling. RESULTS A proteochemometric model was built, based on a statistically designed protein library's (melanocortin receptors') interaction with three peptides and used to predict which amino acids and sequence fragments that are involved in direct and indirect ligand interactions. The model predictions were confirmed by directed mutagenesis. The predicted presumed direct interactions were in good agreement with previous three-dimensional studies of ligand recognition. However, in addition the model could also correctly predict the location of indirect effects on ligand recognition arising from distant sites in the receptors, something that three-dimensional modelling could not afford. CONCLUSION We demonstrate experimentally that proteochemometric modelling can be used with high accuracy to predict the site of origin of direct and indirect effects on ligand recognitions by proteins.
Collapse
Affiliation(s)
- Peteris Prusis
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
- Linnaeus Center for Bioinformatics, Uppsala University, Uppsala, Sweden
| | - Staffan Uhlén
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
- Section for Pharmacology, The University of Bergen, Bergen, Norway
| | - Ramona Petrovska
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
| | - Maris Lapinsh
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
| | - Jarl ES Wikberg
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
| |
Collapse
|
31
|
Pogozheva ID, Chai BX, Lomize AL, Fong TM, Weinberg DH, Nargund RP, Mulholland MW, Gantz I, Mosberg HI. Interactions of human melanocortin 4 receptor with nonpeptide and peptide agonists. Biochemistry 2005; 44:11329-41. [PMID: 16114870 PMCID: PMC2532597 DOI: 10.1021/bi0501840] [Citation(s) in RCA: 77] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Specific interactions of human melanocortin-4 receptor (hMC4R) with its nonpeptide and peptide agonists were studied using alanine-scanning mutagenesis. The binding affinities and potencies of two synthetic, small-molecule agonists (THIQ, MB243) were strongly affected by substitutions in transmembrane alpha-helices (TM) 2, 3, 6, and 7 (residues Glu(100), Asp(122), Asp(126), Phe(261), His(264), Leu(265), and Leu(288)). In addition, a I129A mutation primarily affected the binding and potency of THIQ, while F262A, W258A, Y268A mutations impaired interactions with MB243. By contrast, binding affinity and potency of the linear peptide agonist NDP-MSH were substantially reduced only in D126A and H264A mutants. Three-dimensional models of receptor-ligand complexes with their agonists were generated by distance-geometry using the experimental, homology-based, and other structural constraints, including interhelical H-bonds and two disulfide bridges (Cys(40)-Cys(279), Cys(271)-Cys(277)) of hMC4R. In the models, all pharmacophore elements of small-molecule agonists are spatially overlapped with the corresponding key residues (His(6), d-Phe(7), Arg(8), and Trp(9)) of the linear peptide: their charged amine groups interact with acidic residues from TM2 and TM3, similar to His(6) and Arg(6) of NDP-MSH; their substituted piperidines mimic Trp(9) of the peptide and interact with TM5 and TM6, while the d-Phe aromatic rings of all three agonists contact with Leu(133), Trp(258), and Phe(261) residues.
Collapse
MESH Headings
- Amino Acid Sequence
- Binding Sites
- Cyclic AMP/pharmacology
- Humans
- Models, Molecular
- Molecular Sequence Data
- Mutagenesis, Site-Directed
- Peptides/pharmacology
- Piperazines/pharmacology
- Piperidines/pharmacology
- Protein Structure, Secondary
- Receptor, Melanocortin, Type 4/agonists
- Receptor, Melanocortin, Type 4/chemistry
- Receptor, Melanocortin, Type 4/drug effects
- Receptor, Melanocortin, Type 4/metabolism
- Recombinant Proteins/chemistry
- Recombinant Proteins/drug effects
- Recombinant Proteins/metabolism
- Rhodopsin/chemistry
- Sequence Alignment
- Sequence Homology, Amino Acid
- Transfection
Collapse
Affiliation(s)
- Irina D Pogozheva
- Department of Medicinal Chemistry, College of Pharmacy, University of Michigan, Ann Arbor, Michigan 48109, USA
| | | | | | | | | | | | | | | | | |
Collapse
|
32
|
Lapinsh M, Prusis P, Uhlén S, Wikberg JES. Improved approach for proteochemometrics modeling: application to organic compound--amine G protein-coupled receptor interactions. Bioinformatics 2005; 21:4289-96. [PMID: 16204343 DOI: 10.1093/bioinformatics/bti703] [Citation(s) in RCA: 68] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION Proteochemometrics is a novel technology for the analysis of interactions of series of proteins with series of ligands. We have here customized it for analysis of large datasets and evaluated it for the modeling of the interaction of psychoactive organic amines with all the five known families of amine G protein-coupled receptors (GPCRs). RESULTS The model exploited data for the binding of 22 compounds to 31 amine GPCRs, correlating chemical descriptions and cross-descriptions of compounds and receptors to binding affinity using a novel strategy. A highly valid model (q2 = 0.76) was obtained which was further validated by external predictions using data for 10 other entirely independent compounds, yielding the high q2ext = 0.67. Interpretation of the model reveals molecular interactions that govern psychoactive organic amines overall affinity for amine GPCRs, as well as their selectivity for particular amine GPCRs. The new modeling procedure allows us to obtain fully interpretable proteochemometrics models using essentially unlimited number of ligand and protein descriptors.
Collapse
Affiliation(s)
- Maris Lapinsh
- Department of Pharmaceutical Biosciences, Uppsala University Box 591 BMC, SE-751 24 Uppsala, Sweden
| | | | | | | |
Collapse
|
33
|
Lapinsh M, Veiksina S, Uhlén S, Petrovska R, Mutule I, Mutulis F, Yahorava S, Prusis P, Wikberg JES. Proteochemometric mapping of the interaction of organic compounds with melanocortin receptor subtypes. Mol Pharmacol 2004; 67:50-9. [PMID: 15470082 DOI: 10.1124/mol.104.002857] [Citation(s) in RCA: 35] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Proteochemometrics was applied in the analysis of the binding of organic compounds to wild-type and chimeric melanocortin receptors. Thirteen chimeric melanocortin receptors were designed based on statistical molecular design; each chimera contained parts from three of the MC(1,3-5) receptors. The binding affinities of 18 compounds were determined for these chimeric melanocortin receptors and the four wild-type melanocortin receptors. The data for 14 of these compounds were correlated to the physicochemical and structural descriptors of compounds, binary descriptors of receptor sequences, and cross-terms derived from ligand and receptor descriptors to obtain a proteochemometric model (correlation was performed using partial least-squares projections to latent structures; PLS). A well fitted mathematical model (R(2) = 0.92) with high predictive ability (Q(2) = 0.79) was obtained. In a further validation of the model, the predictive ability for ligands (Q(2)lig = 0.68) and receptors (Q(2)rec = 0.76) was estimated. The model was moreover validated by external prediction by using the data for the four additional compounds that had not at all been included in the proteochemometric model; the analysis yielded a Q(2)ext = 0.73. An interpretation of the results using PLS coefficients revealed the influence of particular properties of organic compounds on their affinity to melanocortin receptors. Three-dimensional models of melanocortin receptors were also created, and physicochemical properties of the amino acids inside the receptors' transmembrane cavity were correlated to the PLS modeling results. The importance of particular amino acids for selective binding of organic compounds was estimated and used to outline the ligand recognition site in the melanocortin receptors.
Collapse
MESH Headings
- Base Sequence
- Binding Sites
- Cloning, Molecular
- DNA Primers
- Humans
- Kinetics
- Ligands
- Models, Molecular
- Organic Chemicals/metabolism
- Protein Conformation
- Receptor, Melanocortin, Type 1/chemistry
- Receptor, Melanocortin, Type 1/metabolism
- Receptor, Melanocortin, Type 3/chemistry
- Receptor, Melanocortin, Type 3/metabolism
- Receptor, Melanocortin, Type 4/chemistry
- Receptor, Melanocortin, Type 4/metabolism
- Receptors, Corticotropin/chemistry
- Receptors, Corticotropin/metabolism
- Receptors, Melanocortin
- Recombinant Fusion Proteins/chemistry
- Recombinant Fusion Proteins/metabolism
Collapse
Affiliation(s)
- Maris Lapinsh
- Pharmaceutical Pharmacology, Box 591, BMC, SE751 24 Uppsala, Sweden
| | | | | | | | | | | | | | | | | |
Collapse
|
34
|
Lapinsh M, Prusis P, Mutule I, Mutulis F, Wikberg JES. QSAR and proteo-chemometric analysis of the interaction of a series of organic compounds with melanocortin receptor subtypes. J Med Chem 2003; 46:2572-9. [PMID: 12801221 DOI: 10.1021/jm020945m] [Citation(s) in RCA: 36] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
We have created quantitative structure-activity relationship (QSAR) models describing the interaction of a series of 54 organic compounds with four melanocortin (MC) receptor subtypes, MC(1), MC(3), MC(4), and MC(5). In addition to traditional QSAR analysis, we applied our recently developed proteo-chemometrics approach. Proteo-chemometrics is based on the combined analysis of series of receptors and ligands, wherein descriptions of ligands, proteins, and so-called ligand-protein cross-terms are correlated with interaction activities. The compounds were characterized by structural descriptors, including three-dimensional grid-independent descriptors (GRINDs), topological descriptors, and geometrical descriptors. Description of receptors was obtained by computing the receptors' amino acid sequence identities. Both the QSAR and proteo-chemometrics approaches resulted in models with essentially the same statistical significance: the cross-validated correlation coefficient q(2) for the proteo-chemometric model being 0.71, while for the QSAR models the q(2)s were 0.75, 0.68, 0.63, and 0.71 for the MC(1), MC(3)(-)(5) receptor, respectively. However, the proteo-chemometrics modeling provided more detailed information about receptor-ligand interactions and determinants for receptor subtype selectivity than did QSAR.
Collapse
Affiliation(s)
- Maris Lapinsh
- Department of Pharmaceutical Biosciences, Uppsala University, Box 591 BMC, SE751 24 Uppsala, Sweden
| | | | | | | | | |
Collapse
|
35
|
Wikberg JES, Mutulis F, Mutule I, Veiksina S, Lapinsh M, Petrovska R, Prusis P. Melanocortin receptors: ligands and proteochemometrics modeling. Ann N Y Acad Sci 2003; 994:21-6. [PMID: 12851294 DOI: 10.1111/j.1749-6632.2003.tb03158.x] [Citation(s) in RCA: 13] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
The melanocortin receptors exist in five subtypes, MC(1-5)R. These receptors participate in important regulations of the immune system, central behavior, and endocrine and exocrine glands. Here we provide a short review on MCR subtype selective peptides and organic compounds with activity on the MCRs, developed in our laboratory. Also provided is an overview of our new proteochemometric modeling technology, which has been applied to model the interaction of MSH peptides with the MCRs.
Collapse
Affiliation(s)
- J E S Wikberg
- Department of Pharmaceutical Biosciences, Uppsala University, Box 591, SE-751 24 Uppsala, Sweden.
| | | | | | | | | | | | | |
Collapse
|
36
|
Millhauser GL, McNulty JC, Jackson PJ, Thompson DA, Barsh GS, Gantz I. Loops and links: structural insights into the remarkable function of the agouti-related protein. Ann N Y Acad Sci 2003; 994:27-35. [PMID: 12851295 DOI: 10.1111/j.1749-6632.2003.tb03159.x] [Citation(s) in RCA: 14] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
The agouti-related protein (AGRP) is an endogenous antagonist of the melanocortin receptors MC3R and MC4R found in the hypothalamus and exhibits potent orexigenic activity. The cysteine-rich C-terminal domain of this protein, corresponding to AGRP(87-132), exhibits receptor binding affinity and antagonism equivalent to that of the full-length protein. We recently determined the NMR structure of AGRP(87-132) and demonstrated that a portion of the domain adopts the inhibitor cystine-knot fold. Remarkably, this is the first identification of a mammalian protein with this specific architecture. Further analysis of the structure suggests that melanocortin receptor contacts are made primarily by two loops presented within the cystine knot. (10) To test this hypothesis we designed a 34-residue AGRP analogue corresponding to only the cystine knot. We found that this designed miniprotein folds to a homogeneous product, retains the desired cystine-knot architecture, functions as a potent antagonist, and maintains the melanocortin receptor pharmacological profile of AGRP(87-132). (26) The AGRP-like activity of this molecule supports the hypothesis that indeed the cystine-knot region possesses the melanocortin receptor contacts. Based on these design and structure studies, we propose that the N-terminal loop of AGRP(87-132) makes contact with a receptor exoloop and helps confer AGRP's selectivity for the central MCRs.
Collapse
Affiliation(s)
- Glenn L Millhauser
- Department of Chemistry and Biochemistry, University of California, Santa Cruz, California 95064, USA.
| | | | | | | | | | | |
Collapse
|
37
|
Freyhult EK, Andersson K, Gustafsson MG. Structural modeling extends QSAR analysis of antibody-lysozyme interactions to 3D-QSAR. Biophys J 2003; 84:2264-72. [PMID: 12668435 PMCID: PMC1302793 DOI: 10.1016/s0006-3495(03)75032-2] [Citation(s) in RCA: 19] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
This work shows that quantitative multivariate modeling is an emerging possibility for unraveling protein-protein interactions using a combination of designed mutations with sequence and structure information. Using this approach, it is possible to stereochemically determine which residue properties contribute most to the interaction. This is illustrated by results from modeling of the interaction of the wild-type and 17 single and double mutants of a camel antibody specific for lysozyme. Linear multivariate models describing association and dissociation rates as well as affinity were developed. Sequence information in the form of amino acid property scales was combined with 3D structure information (obtained using molecular mechanics calculations) in the form of coordinates of the alpha-carbons and the center of the side chains. The results show that in addition to the amino acid properties of the mutated residues 101 and 105, the dissociation rate is controlled by the side-chain coordinate of residue 105, whereas the association is determined by the coordinates of residues 99, 100, 105 (side chain), 111, and 112. The great difference between the models for association and dissociation rates illustrates that the event of molecular recognition and the property of binding stability rely on different physical processes.
Collapse
Affiliation(s)
- Eva K Freyhult
- The Linnaeus Centre for Bioinformatics, Uppsala University, Sweden.
| | | | | |
Collapse
|
38
|
Lapinsh M, Prusis P, Lundstedt T, Wikberg JES. Proteochemometrics modeling of the interaction of amine G-protein coupled receptors with a diverse set of ligands. Mol Pharmacol 2002; 61:1465-75. [PMID: 12021408 DOI: 10.1124/mol.61.6.1465] [Citation(s) in RCA: 74] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
We have evaluated the proteochemometrics approach in the analysis of the interactions of a diverse set or organic ligands with subtypes of serotonin, dopamine, histamine, and adrenergic receptors. As used herein, proteochemometrics exploits affinity data for series of organic amines binding to wild-type amine G protein-coupled receptors, correlating it to descriptions and cross-description derived from the primary amino acid sequences of the receptors and the computed structures of the organic compounds. We show that after appropriate data preprocessing, statistically valid models that have good external predictive ability can be created. Evaluation of the models gave important quantitative insight into the mode of interactions of the amine G protein-coupled receptors with their ligands.
Collapse
Affiliation(s)
- Maris Lapinsh
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
| | | | | | | |
Collapse
|
39
|
Lapinsh M, Gutcaits A, Prusis P, Post C, Lundstedt T, Wikberg JES. Classification of G-protein coupled receptors by alignment-independent extraction of principal chemical properties of primary amino acid sequences. Protein Sci 2002; 11:795-805. [PMID: 11910023 PMCID: PMC2373523 DOI: 10.1110/ps.2500102] [Citation(s) in RCA: 85] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/16/2022]
Abstract
We have developed an alignment-independent method for classification of G-protein coupled receptors (GPCRs) according to the principal chemical properties of their amino acid sequences. The method relies on a multivariate approach where the primary amino acid sequences are translated into vectors based on the principal physicochemical properties of the amino acids and transformation of the data into a uniform matrix by applying a modified autocross-covariance transform. The application of principal component analysis to a data set of 929 class A GPCRs showed a clear separation of the major classes of GPCRs. The application of partial least squares projection to latent structures created a highly valid model (cross-validated correlation coefficient, Q(2) = 0.895) that gave unambiguous classification of the GPCRs in the training set according to their ligand binding class. The model was further validated by external prediction of 535 novel GPCRs not included in the training set. Of the latter, only 14 sequences, confined in rapidly expanding GPCR classes, were mispredicted. Moreover, 90 orphan GPCRs out of 165 were tentatively identified to GPCR ligand binding class. The alignment-independent method could be used to assess the importance of the principal chemical properties of every single amino acid in the protein sequences for their contributions in explaining GPCR family membership. It was then revealed that all amino acids in the unaligned sequences contributed to the classifications, albeit to varying extent; the most important amino acids being those that could also be determined to be conserved by using traditional alignment-based methods.
Collapse
Affiliation(s)
- Maris Lapinsh
- Department of Pharmaceutical Biosciences, Uppsala University, SE751 24 Uppsala, Sweden
| | | | | | | | | | | |
Collapse
|
40
|
Prusis P, Lundstedt T, Wikberg JES. Proteo-chemometrics analysis of MSH peptide binding to melanocortin receptors. Protein Eng Des Sel 2002; 15:305-11. [PMID: 11983931 DOI: 10.1093/protein/15.4.305] [Citation(s) in RCA: 28] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
The published data for six melanocortin peptides binding to wild-type and chimeric melanocortin MC(1)/MC(3) receptors were analysed using the novel proteo-chemometrics modelling approach. The chimeric receptors and the peptides were coded using binary descriptors and used to correlate with the experimental data for affinity or selectivity for peptides binding to receptors. Correlations were achieved using partial least squares projection to latent structures (PLS) and statistically valid models were obtained. The models were further improved by adding cross-terms and applying orthogonal signal correction. The models were validated using external prediction, with half of the data being excluded from the modelling. Interpretation of the results using PLS coefficient plots revealed that the binding pocket for the melanocortins is located between the first, second, third, sixth and seventh transmembrane regions of the melanocortin receptors, in good agreement with previous three-dimensional models for the interactions of melanocortins with melanocortin receptors. Further, analysis of cross-terms between peptide descriptors indicated that the proteo-chemometrics modelling is able to distinguish between differences in the conformational space of the peptides that affect binding affinity and selectivity.
Collapse
Affiliation(s)
- Peteris Prusis
- Department of Pharmaceutical Biosciences, Pharmacology, Uppsala University, Box 591, SE751 24 Uppsala, Sweden
| | | | | |
Collapse
|
41
|
Lapinsh M, Prusis P, Gutcaits A, Lundstedt T, Wikberg JE. Development of proteo-chemometrics: a novel technology for the analysis of drug-receptor interactions. BIOCHIMICA ET BIOPHYSICA ACTA 2001; 1525:180-90. [PMID: 11342268 DOI: 10.1016/s0304-4165(00)00187-2] [Citation(s) in RCA: 101] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
A novel method for the analysis of drug receptor interactions has been developed and used to explore mechanisms involved in the binding of 4-piperidyl oxazole antagonists to alpha1a-, alpha1b- and alpha1d-adrenoceptors. The method exploits affinity data for a series of organic chemical compounds binding to wild-type and artificially mutated receptors. The receptor sequences and compounds are assigned predictor variables that are correlated to the measured pharmacological activities using partial least-squares projections to latent structures. The predictor variables consist of one descriptor block derived from the chemical properties of the receptors' primary amino acid sequences and another descriptor block derived from the chemical properties of the organic compounds. The cross-terms generated from the two descriptor blocks are also derived. Using this approach, very sturdy models were generated describing the interactions of the chemical compounds with the receptors. Models are useful to predict binding affinity and receptor subtype selectivity of compounds prior to their synthesis, and may find use in rational drug design. Moreover, models also give quantitative information about the interactions of the amino acids of the receptors with the ligands, thereby giving an insight into the molecular mechanisms involved in ligand binding.
Collapse
Affiliation(s)
- M Lapinsh
- Departmenrt of Pharmaceutical Pharmacology, Upssala University, Sweden
| | | | | | | | | |
Collapse
|