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Li Z, Huang R, Xia M, Patterson TA, Hong H. Fingerprinting Interactions between Proteins and Ligands for Facilitating Machine Learning in Drug Discovery. Biomolecules 2024; 14:72. [PMID: 38254672 PMCID: PMC10813698 DOI: 10.3390/biom14010072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Revised: 12/26/2023] [Accepted: 12/28/2023] [Indexed: 01/24/2024] Open
Abstract
Molecular recognition is fundamental in biology, underpinning intricate processes through specific protein-ligand interactions. This understanding is pivotal in drug discovery, yet traditional experimental methods face limitations in exploring the vast chemical space. Computational approaches, notably quantitative structure-activity/property relationship analysis, have gained prominence. Molecular fingerprints encode molecular structures and serve as property profiles, which are essential in drug discovery. While two-dimensional (2D) fingerprints are commonly used, three-dimensional (3D) structural interaction fingerprints offer enhanced structural features specific to target proteins. Machine learning models trained on interaction fingerprints enable precise binding prediction. Recent focus has shifted to structure-based predictive modeling, with machine-learning scoring functions excelling due to feature engineering guided by key interactions. Notably, 3D interaction fingerprints are gaining ground due to their robustness. Various structural interaction fingerprints have been developed and used in drug discovery, each with unique capabilities. This review recapitulates the developed structural interaction fingerprints and provides two case studies to illustrate the power of interaction fingerprint-driven machine learning. The first elucidates structure-activity relationships in β2 adrenoceptor ligands, demonstrating the ability to differentiate agonists and antagonists. The second employs a retrosynthesis-based pre-trained molecular representation to predict protein-ligand dissociation rates, offering insights into binding kinetics. Despite remarkable progress, challenges persist in interpreting complex machine learning models built on 3D fingerprints, emphasizing the need for strategies to make predictions interpretable. Binding site plasticity and induced fit effects pose additional complexities. Interaction fingerprints are promising but require continued research to harness their full potential.
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Affiliation(s)
- Zoe Li
- National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR 72079, USA; (Z.L.); (T.A.P.)
| | - Ruili Huang
- National Center for Advancing Translational Sciences, National Institutes of Health, Bethesda, MD 20892, USA; (R.H.); (M.X.)
| | - Menghang Xia
- National Center for Advancing Translational Sciences, National Institutes of Health, Bethesda, MD 20892, USA; (R.H.); (M.X.)
| | - Tucker A. Patterson
- National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR 72079, USA; (Z.L.); (T.A.P.)
| | - Huixiao Hong
- National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR 72079, USA; (Z.L.); (T.A.P.)
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2
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Tan S, Lu R, Yao D, Wang J, Gao P, Xie G, Liu H, Yao X. Identification of LRRK2 Inhibitors through Computational Drug Repurposing. ACS Chem Neurosci 2023; 14:481-493. [PMID: 36649061 DOI: 10.1021/acschemneuro.2c00672] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
Parkinson's disease (PD) is the second most common neurodegenerative disorder that affects more than ten million people worldwide. However, the current PD treatments are still limited and alternative treatment strategies are urgently required. Leucine-rich repeat kinase 2 (LRRK2) has been recognized as a promising target for PD treatment. However, there are no approved LRRK2 inhibitors on the market. To rapidly identify potential drug repurposing candidates that inhibit LRRK2 kinase, we report a structure-based drug repurposing workflow that combines molecular docking, recursive partitioning model, molecular dynamics (MD) simulation, and molecular mechanics-generalized Born surface area (MM-GBSA) calculation. Thirteen compounds screened from our drug repurposing workflow were further evaluated through the experiment. The experimental results showed six drugs (Abivertinib, Aumolertinib, Encorafenib, Bosutinib, Rilzabrutinib, and Mobocertinib) with IC50 less than 5 μM that were identified as potential LRRK2 kinase inhibitors. The most potent compound Abivertinib showed potent inhibitions with IC50 toward G2019S mutation and wild-type LRRK2 of 410.3 nM and 177.0 nM, respectively. Our combination screening strategy had a 53% hit rate in this repurposing task. MD simulations and MM-GBSA free energy analysis further revealed the atomic binding mechanism between the identified drugs and G2019S LRRK2. In summary, the results showed that our drug repurposing workflow could be used to identify potent compounds for LRRK2. The potent inhibitors discovered in our work can be a starting point to develop more effective LRRK2 inhibitors.
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Affiliation(s)
- Shuoyan Tan
- College of Chemistry and Chemical Engineering, Lanzhou University, Lanzhou730000, China
| | - Ruiqiang Lu
- College of Chemistry and Chemical Engineering, Lanzhou University, Lanzhou730000, China
| | - Dahong Yao
- School of Pharmaceutical Sciences, Shenzhen Technology University, Shenzhen518060, China
| | - Jun Wang
- Ping An Healthcare Technology, Beijing100000, China
| | - Peng Gao
- Ping An Healthcare Technology, Beijing100000, China
| | - Guotong Xie
- Ping An Healthcare Technology, Beijing100000, China
| | - Huanxiang Liu
- Faculty of Applied Science, Macao Polytechnic University, Macao, China
| | - Xiaojun Yao
- College of Chemistry and Chemical Engineering, Lanzhou University, Lanzhou730000, China.,State Key Laboratory of Quality Research in Chinese Medicines, Macau University of Science and Technology, Macau, China
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Zhao Z, Bourne PE. Harnessing systematic protein-ligand interaction fingerprints for drug discovery. Drug Discov Today 2022; 27:103319. [PMID: 35850431 DOI: 10.1016/j.drudis.2022.07.004] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2022] [Revised: 07/04/2022] [Accepted: 07/12/2022] [Indexed: 12/15/2022]
Abstract
Determining protein-ligand interaction characteristics and mechanisms is crucial to the drug discovery process. Here, we review recent progress and successful applications of a systematic protein-ligand interaction fingerprint (IFP) approach for investigating proteome-wide protein-ligand interactions for drug development. Specifically, we review the use of this IFP approach for revealing polypharmacology across the kinome, predicting promising targets from which to design allosteric inhibitors and covalent kinase inhibitors, uncovering the binding mechanisms of drugs of interest, and demonstrating resistant mechanisms of specific drugs. Together, we demonstrate that the IFP strategy is efficient and practical for drug design research for protein kinases as targets and is extensible to other protein families.
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Affiliation(s)
- Zheng Zhao
- School of Data Science and Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22904, USA.
| | - Philip E Bourne
- School of Data Science and Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22904, USA.
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PLAS-5k: Dataset of Protein-Ligand Affinities from Molecular Dynamics for Machine Learning Applications. Sci Data 2022; 9:548. [PMID: 36071074 PMCID: PMC9451116 DOI: 10.1038/s41597-022-01631-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Accepted: 08/15/2022] [Indexed: 11/08/2022] Open
Abstract
Computational methods and recently modern machine learning methods have played a key role in structure-based drug design. Though several benchmarking datasets are available for machine learning applications in virtual screening, accurate prediction of binding affinity for a protein-ligand complex remains a major challenge. New datasets that allow for the development of models for predicting binding affinities better than the state-of-the-art scoring functions are important. For the first time, we have developed a dataset, PLAS-5k comprised of 5000 protein-ligand complexes chosen from PDB database. The dataset consists of binding affinities along with energy components like electrostatic, van der Waals, polar and non-polar solvation energy calculated from molecular dynamics simulations using MMPBSA (Molecular Mechanics Poisson-Boltzmann Surface Area) method. The calculated binding affinities outperformed docking scores and showed a good correlation with the available experimental values. The availability of energy components may enable optimization of desired components during machine learning-based drug design. Further, OnionNet model has been retrained on PLAS-5k dataset and is provided as a baseline for the prediction of binding affinities.
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Wang DD, Chan MT, Yan H. Structure-based protein-ligand interaction fingerprints for binding affinity prediction. Comput Struct Biotechnol J 2021; 19:6291-6300. [PMID: 34900139 PMCID: PMC8637032 DOI: 10.1016/j.csbj.2021.11.018] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 11/09/2021] [Accepted: 11/13/2021] [Indexed: 11/17/2022] Open
Abstract
Binding affinity prediction (BAP) using protein–ligand complex structures is crucial to computer-aided drug design, but remains a challenging problem. To achieve efficient and accurate BAP, machine-learning scoring functions (SFs) based on a wide range of descriptors have been developed. Among those descriptors, protein–ligand interaction fingerprints (IFPs) are competitive due to their simple representations, elaborate profiles of key interactions and easy collaborations with machine-learning algorithms. In this paper, we have adopted a building-block-based taxonomy to review a broad range of IFP models, and compared representative IFP-based SFs in target-specific and generic scoring tasks. Atom-pair-counts-based and substructure-based IFPs show great potential in these tasks.
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Affiliation(s)
- Debby D Wang
- School of Health Science and Engineering, University of Shanghai for Science and Technology, 516 Jungong Rd, Shanghai 200093, China
| | - Moon-Tong Chan
- School of Science and Technology, Hong Kong Metropolitan University, 30 Good Shepherd St, Ho Man Tin, Hong Kong
| | - Hong Yan
- Department of Electrical Engineering, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong
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Mordalski S, Wojtuch A, Podolak I, Kurczab R, Bojarski AJ. 2D SIFt: a matrix of ligand-receptor interactions. J Cheminform 2021; 13:66. [PMID: 34496955 PMCID: PMC8424890 DOI: 10.1186/s13321-021-00545-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Accepted: 08/21/2021] [Indexed: 11/10/2022] Open
Abstract
Depicting a ligand-receptor complex via Interaction Fingerprints has been shown to be both a viable data visualization and an analysis tool. The spectrum of its applications ranges from simple visualization of the binding site through analysis of molecular dynamics runs, to the evaluation of the homology models and virtual screening. Here we present a novel tool derived from the Structural Interaction Fingerprints providing a detailed and unique insight into the interactions between receptor and specific regions of the ligand (grouped into pharmacophore features) in the form of a matrix, a 2D-SIFt descriptor. The provided implementation is easy to use and extends the python library, allowing the generation of interaction matrices and their manipulation (reading and writing as well as producing the average 2D-SIFt). The library for handling the interaction matrices is available via repository http://bitbucket.org/zchl/sift2d.
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Affiliation(s)
- Stefan Mordalski
- Department of Medicinal Chemistry, Maj Institute of Pharmacology Polish Academy of Sciences, Krakow, Poland.
| | - Agnieszka Wojtuch
- Faculty of Mathematics and Computer Science, Jagiellonian University, Krakow, Poland
| | - Igor Podolak
- Faculty of Mathematics and Computer Science, Jagiellonian University, Krakow, Poland
| | - Rafał Kurczab
- Department of Medicinal Chemistry, Maj Institute of Pharmacology Polish Academy of Sciences, Krakow, Poland
| | - Andrzej J Bojarski
- Department of Medicinal Chemistry, Maj Institute of Pharmacology Polish Academy of Sciences, Krakow, Poland
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Kimber TB, Chen Y, Volkamer A. Deep Learning in Virtual Screening: Recent Applications and Developments. Int J Mol Sci 2021; 22:4435. [PMID: 33922714 PMCID: PMC8123040 DOI: 10.3390/ijms22094435] [Citation(s) in RCA: 65] [Impact Index Per Article: 21.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Revised: 04/13/2021] [Accepted: 04/14/2021] [Indexed: 01/03/2023] Open
Abstract
Drug discovery is a cost and time-intensive process that is often assisted by computational methods, such as virtual screening, to speed up and guide the design of new compounds. For many years, machine learning methods have been successfully applied in the context of computer-aided drug discovery. Recently, thanks to the rise of novel technologies as well as the increasing amount of available chemical and bioactivity data, deep learning has gained a tremendous impact in rational active compound discovery. Herein, recent applications and developments of machine learning, with a focus on deep learning, in virtual screening for active compound design are reviewed. This includes introducing different compound and protein encodings, deep learning techniques as well as frequently used bioactivity and benchmark data sets for model training and testing. Finally, the present state-of-the-art, including the current challenges and emerging problems, are examined and discussed.
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Affiliation(s)
| | | | - Andrea Volkamer
- In Silico Toxicology and Structural Bioinformatics, Institute of Physiology, Charité-Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany; (T.B.K.); (Y.C.)
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Podlewska S, Bugno R, Lacivita E, Leopoldo M, Bojarski AJ, Handzlik J. Low Basicity as a Characteristic for Atypical Ligands of Serotonin Receptor 5-HT2. Int J Mol Sci 2021; 22:ijms22031035. [PMID: 33494248 PMCID: PMC7864501 DOI: 10.3390/ijms22031035] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Revised: 01/15/2021] [Accepted: 01/16/2021] [Indexed: 02/07/2023] Open
Abstract
Serotonin receptors are extensively examined by academic and industrial researchers, due to their vital roles, which they play in the organism and constituting therefore important drug targets. Up to very recently, it was assumed that the basic nitrogen in compound structure is a necessary component to make it active within this receptor system. Such nitrogen interacts in its protonated form with the aspartic acid from the third transmembrane helix (D3x32) forming a hydrogen bond tightly fitting the ligand in the protein binding site. However, there are several recent studies that report strong serotonin receptor affinity also for compounds without a basic moiety in their structures. In the study, we carried out a comprehensive in silico analysis of the low-basicity phenomenon of the selected serotonin receptor ligands. We focused on the crystallized representatives of the proteins of 5-HT1B, 5-HT2A, 5-HT2B, and 5-HT2C receptors, and examined the problem both from the ligand- and structure-based perspectives. The study was performed for the native proteins, and for D3x32A mutants. The investigation resulted in the determination of nonstandard structural requirements for activity towards serotonin receptors, which can be used in the design of new nonbasic ligands.
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Affiliation(s)
- Sabina Podlewska
- Department of Technology and Biotechnology of Drugs, Faculty of Pharmacy, Jagiellonian University Medical College, 9 Medyczna Street, 30-688 Kraków, Poland
- Maj Institute of Pharmacology, Polish Academy of Sciences, 12 Smętna Street, 31-343 Kraków, Poland; (R.B.); (A.J.B.)
- Correspondence: (S.P.); (J.H.); Tel.: +48-12-66-23-301 (S.P.); +48-12-620-55-84 (J.H.)
| | - Ryszard Bugno
- Maj Institute of Pharmacology, Polish Academy of Sciences, 12 Smętna Street, 31-343 Kraków, Poland; (R.B.); (A.J.B.)
| | - Enza Lacivita
- Department of Pharmacy-Drug Sciences, University of Bari “Aldo Moro”, via E. Orabona 4, 70125 Bari, Italy; (E.L.); (M.L.)
| | - Marcello Leopoldo
- Department of Pharmacy-Drug Sciences, University of Bari “Aldo Moro”, via E. Orabona 4, 70125 Bari, Italy; (E.L.); (M.L.)
| | - Andrzej J. Bojarski
- Maj Institute of Pharmacology, Polish Academy of Sciences, 12 Smętna Street, 31-343 Kraków, Poland; (R.B.); (A.J.B.)
| | - Jadwiga Handzlik
- Department of Technology and Biotechnology of Drugs, Faculty of Pharmacy, Jagiellonian University Medical College, 9 Medyczna Street, 30-688 Kraków, Poland
- Correspondence: (S.P.); (J.H.); Tel.: +48-12-66-23-301 (S.P.); +48-12-620-55-84 (J.H.)
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Bremer PL, De Boer D, Alvarado W, Martinez X, Sorin EJ. Overcoming the Heuristic Nature of k-Means Clustering: Identification and Characterization of Binding Modes from Simulations of Molecular Recognition Complexes. J Chem Inf Model 2020; 60:3081-3092. [PMID: 32383869 DOI: 10.1021/acs.jcim.9b01137] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
The accurate and reproducible detection and description of thermodynamic states in computational data is a nontrivial problem, particularly when the number of states is unknown a priori and for large, flexible chemical systems and complexes. To this end, we report a novel clustering protocol that combines high-resolution structural representation, brute-force repeat clustering, and optimization of clustering statistics to reproducibly identify the number of clusters present in a data set (k) for simulated ensembles of butyrylcholinesterase in complex with two previously studied organophosphate inhibitors. Each structure within our simulated ensembles was depicted as a high-dimensionality vector with components defined by specific protein-inhibitor contacts at the chemical group level and the magnitudes of these components defined by their respective extents of pair-wise atomic contact, thus allowing for algorithmic differentiation between varying degrees of interaction. These surface-weighted interaction fingerprints were tabulated for each of over 1 million structures from more than 100 μs of all-atom molecular dynamics simulation per complex and used as the input for repetitive k-means clustering. Minimization of cluster population variance and range afforded accurate and reproducible identification of k, thereby allowing for the characterization of discrete binding modes from molecular simulation data in the form of contact tables that concisely encapsulate the observed intermolecular contact motifs. While the protocol presented herein to determine k and achieve non-heuristic clustering is demonstrated on data from massive atomistic simulation, our approach is generalizable to other data types and clustering algorithms, and is tractable with limited computational resources.
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Karadag M, Arslan M, Kaleli NE, Kalyoncu S. Physicochemical determinants of antibody-protein interactions. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2019; 121:85-114. [PMID: 32312427 DOI: 10.1016/bs.apcsb.2019.08.011] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
Antibodies are specialized proteins generated by immune system for high specificity and affinity binding to target antigens. Because of their essential roles in immune system, antibodies have been successfully developed and engineered as biopharmaceuticals for treatment of various diseases. Analysis of antibody-protein interactions is always required to get detailed information on effectivity of such antibody-based therapeutics. Although physicochemical rules cannot be generalized for every antibody-protein interaction, there are some features which should be taken into account during antibody development and engineering efforts. In this chapter, physicochemical analysis of antibody paratope-protein epitope interactions will be discussed to highlight important characteristics. First, paratope and non-paratope regions of antibodies will be described and important roles of these regions on binding and biophysical features of antibodies will be discussed. Then, general features of epitope regions of protein antigens will be introduced along with several computational/experimental tools to identify them. Lastly, a rising star of antibody biopharmaceuticals, nanobodies, will be described to show importance of next-generation antibody fragment based biopharmaceuticals in drug development.
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Affiliation(s)
- Murat Karadag
- Izmir Biomedicine and Genome Center, İzmir, Turkey; Izmir Biomedicine and Genome Institute, Dokuz Eylul University, İzmir, Turkey
| | - Merve Arslan
- Izmir Biomedicine and Genome Center, İzmir, Turkey; Izmir Biomedicine and Genome Institute, Dokuz Eylul University, İzmir, Turkey
| | - Nazli Eda Kaleli
- Izmir Biomedicine and Genome Center, İzmir, Turkey; Izmir Biomedicine and Genome Institute, Dokuz Eylul University, İzmir, Turkey
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Rácz A, Bajusz D, Héberger K. Life beyond the Tanimoto coefficient: similarity measures for interaction fingerprints. J Cheminform 2018; 10:48. [PMID: 30288626 PMCID: PMC6755604 DOI: 10.1186/s13321-018-0302-y] [Citation(s) in RCA: 61] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2018] [Accepted: 09/27/2018] [Indexed: 12/22/2022] Open
Abstract
Background Interaction fingerprints (IFP) have been repeatedly shown to be valuable tools in virtual screening to identify novel hit compounds that can subsequently be optimized to drug candidates. As a complementary method to ligand docking, IFPs can be applied to quantify the similarity of predicted binding poses to a reference binding pose. For this purpose, a large number of similarity metrics can be applied, and various parameters of the IFPs themselves can be customized. In a large-scale comparison, we have assessed the effect of similarity metrics and IFP configurations to a number of virtual screening scenarios with ten different protein targets and thousands of molecules. Particularly, the effect of considering general interaction definitions (such as Any Contact, Backbone Interaction and Sidechain Interaction), the effect of filtering methods and the different groups of similarity metrics were studied. Results The performances were primarily compared based on AUC values, but we have also used the original similarity data for the comparison of similarity metrics with several statistical tests and the novel, robust sum of ranking differences (SRD) algorithm. With SRD, we can evaluate the consistency (or concordance) of the various similarity metrics to an ideal reference metric, which is provided by data fusion from the existing metrics. Different aspects of IFP configurations and similarity metrics were examined based on SRD values with analysis of variance (ANOVA) tests. Conclusion A general approach is provided that can be applied for the reliable interpretation and usage of similarity measures with interaction fingerprints. Metrics that are viable alternatives to the commonly used Tanimoto coefficient were identified based on a comparison with an ideal reference metric (consensus). A careful selection of the applied bits (interaction definitions) and IFP filtering rules can improve the results of virtual screening (in terms of their agreement with the consensus metric). The open-source Python package FPKit was introduced for the similarity calculations and IFP filtering; it is available at: https://github.com/davidbajusz/fpkit.![]() Electronic supplementary material The online version of this article (10.1186/s13321-018-0302-y) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Anita Rácz
- Plasma Chemistry Research Group, Research Centre for Natural Sciences, Hungarian Academy of Sciences, Magyar tudósok krt. 2, Budapest, 1117, Hungary
| | - Dávid Bajusz
- Medicinal Chemistry Research Group, Research Centre for Natural Sciences, Hungarian Academy of Sciences, Magyar tudósok krt. 2, Budapest, 1117, Hungary.
| | - Károly Héberger
- Plasma Chemistry Research Group, Research Centre for Natural Sciences, Hungarian Academy of Sciences, Magyar tudósok krt. 2, Budapest, 1117, Hungary
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Miszta P, Jakowiecki J, Rutkowska E, Turant M, Latek D, Filipek S. Approaches for Differentiation and Interconverting GPCR Agonists and Antagonists. Methods Mol Biol 2018; 1705:265-296. [PMID: 29188567 DOI: 10.1007/978-1-4939-7465-8_12] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
Abstract
Predicting the functional preferences of the ligands was always a highly demanding task, much harder that predicting whether a ligand can bind to the receptor. This is because of significant similarities of agonists, antagonists and inverse agonists which are binding usually in the same binding site of the receptor and only small structural changes can push receptor toward a particular activation state. For G protein-coupled receptors, due to a large progress in crystallization techniques and also in receptor thermal stabilization, it was possible to obtain a large number of high-quality structures of complexes of these receptors with agonists and non-agonists. Additionally, the long-time-scale molecular dynamics simulations revealed how the activation processes of GPCRs can take place. Using both theoretical and experimental knowledge it was possible to employ many clever and sophisticated methods which can help to differentiate agonists and non-agonists, so one can interconvert them in search of the optimal drug.
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Affiliation(s)
- Przemysław Miszta
- Biological and Chemical Research Centre, Faculty of Chemistry, University of Warsaw, ul. Pasteura 1, 02-093, Warsaw, Poland
| | - Jakub Jakowiecki
- Biological and Chemical Research Centre, Faculty of Chemistry, University of Warsaw, ul. Pasteura 1, 02-093, Warsaw, Poland
| | - Ewelina Rutkowska
- Biological and Chemical Research Centre, Faculty of Chemistry, University of Warsaw, ul. Pasteura 1, 02-093, Warsaw, Poland
| | - Maria Turant
- Biological and Chemical Research Centre, Faculty of Chemistry, University of Warsaw, ul. Pasteura 1, 02-093, Warsaw, Poland
| | - Dorota Latek
- Biological and Chemical Research Centre, Faculty of Chemistry, University of Warsaw, ul. Pasteura 1, 02-093, Warsaw, Poland
| | - Sławomir Filipek
- Biological and Chemical Research Centre, Faculty of Chemistry, University of Warsaw, ul. Pasteura 1, 02-093, Warsaw, Poland.
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Zou B, Lee VHF, Yan H. Prediction of sensitivity to gefitinib/erlotinib for EGFR mutations in NSCLC based on structural interaction fingerprints and multilinear principal component analysis. BMC Bioinformatics 2018. [PMID: 29514601 PMCID: PMC5842518 DOI: 10.1186/s12859-018-2093-6] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Background Non-small cell lung cancer (NSCLC) with activating EGFR mutations, especially exon 19 deletions and the L858R point mutation, is particularly responsive to gefitinib and erlotinib. However, the sensitivity varies for less common and rare EGFR mutations. There are various explanations for the low sensitivity of EGFR exon 20 insertions and the exon 20 T790 M point mutation to gefitinib/erlotinib. However, few studies discuss, from a structural perspective, why less common mutations, like G719X and L861Q, have moderate sensitivity to gefitinib/erlotinib. Results To decode the drug sensitivity/selectivity of EGFR mutants, it is important to analyze the interaction between EGFR mutants and EGFR inhibitors. In this paper, the 30 most common EGFR mutants were selected and the technique of protein-ligand interaction fingerprint (IFP) was applied to analyze and compare the binding modes of EGFR mutant-gefitinib/erlotinib complexes. Molecular dynamics simulations were employed to obtain the dynamic trajectory and a matrix of IFPs for each EGFR mutant-inhibitor complex. Multilinear Principal Component Analysis (MPCA) was applied for dimensionality reduction and feature selection. The selected features were further analyzed for use as a drug sensitivity predictor. The results showed that the accuracy of prediction of drug sensitivity was very high for both gefitinib and erlotinib. Targeted Projection Pursuit (TPP) was used to show that the data points can be easily separated based on their sensitivities to gefetinib/erlotinib. Conclusions We can conclude that the IFP features of EGFR mutant-TKI complexes and the MPCA-based tensor object feature extraction are useful to predict the drug sensitivity of EGFR mutants. The findings provide new insights for studying and predicting drug resistance/sensitivity of EGFR mutations in NSCLC and can be beneficial to the design of future targeted therapies and innovative drug discovery. Electronic supplementary material The online version of this article (10.1186/s12859-018-2093-6) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Bin Zou
- Department of Electronic Engineering, City University of Hong Kong, Kowloon, Hong Kong, China.
| | - Victor H F Lee
- Department of Clinical Oncology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong, China
| | - Hong Yan
- Department of Electronic Engineering, City University of Hong Kong, Kowloon, Hong Kong, China
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Design, synthesis, and biological evaluation of novel combretastatin A-4 thio derivatives as microtubule targeting agents. Eur J Med Chem 2017; 144:797-816. [PMID: 29291446 DOI: 10.1016/j.ejmech.2017.11.050] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2017] [Revised: 11/17/2017] [Accepted: 11/18/2017] [Indexed: 11/20/2022]
Abstract
A series of novel combretastatin A-4 (CA-4) thio derivatives containing different molecular cores, namely α-phenylcinnamic acids (core 1), (Z)-stilbenes (core 2), 4,5-disubstituted oxazoles (core 3), and 4,5-disubstituted N-methylimidazoles (core 4), as cis-restricted analogues were designed and synthesized. They were selected with the use of a parallel virtual screening protocol including the generation of a virtual combinatorial library based on an elaborated synthesis protocol of CA-4 analogues. The selected compounds were evaluated for antiproliferative activity against a panel of six human cancer cell lines (A431, HeLa, MCF7, MDA-MB-231, A549 and SKOV) and two human non-cancer cell lines (HaCaT and CCD39Lu). Moreover, the effect of the test compounds on the inhibition of tubulin polymerization in vitro was estimated. In the series studied here, oxazole-bridged analogues exhibited the most potent antiproliferative activity. Compounds 23a, 23e, and 23i efficiently inhibited tubulin polymerization with IC50 values of 0.86, 1.05, and 0.85 μM, respectively. Thio derivative 23i, when compared to its oxygen analogue 23j, showed a 5-fold higher inhibitory impact on tubulin polymerization. Compounds 23e and 23i, which showed both best cytotoxic and antitubulin activity, were further studied in terms of their effect on cell cycle distribution and proapoptotic activity. Compound 23e induced a statistically significant block of the cell cycle at the G2/M phase in A431, HaCaT, HeLa, MCF-7, MDA-MB-231, and SKOV-3 cells to an extent comparable to that observed in CA-4. In HeLa and SKOV-3 cells incubated with 23i, a concentration-dependent block of the G2/M phase was observed. The proapoptotic effect of 23e and 23i in A431, HaCaT, MCF-7, MDA-MB-231, and SKOV-3 was demonstrated with ELISA assay and double staining with Annexin V-FITC/PI. The results indicated that compound 23e and 23i may serve as novel lead compounds in research on more effective anticancer agents.
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Freyd T, Warszycki D, Mordalski S, Bojarski AJ, Sylte I, Gabrielsen M. Ligand-guided homology modelling of the GABAB2 subunit of the GABAB receptor. PLoS One 2017; 12:e0173889. [PMID: 28323850 PMCID: PMC5360267 DOI: 10.1371/journal.pone.0173889] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2016] [Accepted: 02/28/2017] [Indexed: 11/18/2022] Open
Abstract
γ-aminobutyric acid (GABA) is the main inhibitory neurotransmitter in the central nervous system, and disturbances in the GABAergic system have been implicated in numerous neurological and neuropsychiatric diseases. The GABAB receptor is a heterodimeric class C G protein-coupled receptor (GPCR) consisting of GABAB1a/b and GABAB2 subunits. Two GABAB receptor ligand binding sites have been described, namely the orthosteric GABA binding site located in the extracellular GABAB1 Venus fly trap domain and the allosteric binding site found in the GABAB2 transmembrane domain. To date, the only experimentally solved three-dimensional structures of the GABAB receptor are of the Venus fly trap domain. GABAB receptor allosteric modulators, however, show great therapeutic potential, and elucidating the structure of the GABAB2 transmembrane domain may lead to development of novel drugs and increased understanding of the allosteric mechanism of action. Despite the lack of x-ray crystal structures of the GABAB2 transmembrane domain, multiple crystal structures belonging to other classes of GPCRs than class A have been released within the last years. More closely related template structures are now available for homology modelling of the GABAB receptor. Here, multiple homology models of the GABAB2 subunit of the GABAB receptor have been constructed using templates from class A, B and C GPCRs, and docking of five clusters of positive allosteric modulators and decoys has been undertaken to select models that enrich the active compounds. Using this ligand-guided approach, eight GABAB2 homology models have been chosen as possible structural representatives of the transmembrane domain of the GABAB2 subunit. To the best of our knowledge, the present study is the first to describe homology modelling of the transmembrane domain of the GABAB2 subunit and the docking of positive allosteric modulators in the receptor.
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Affiliation(s)
- Thibaud Freyd
- Department of Medical Biology, Faculty of Health Sciences, UiT - the Arctic University of Norway, Tromsø, Norway
| | - Dawid Warszycki
- Department of Medicinal Chemistry, Institute of Pharmacology, Polish Academy of Sciences, Kraków, Poland
| | - Stefan Mordalski
- Department of Medicinal Chemistry, Institute of Pharmacology, Polish Academy of Sciences, Kraków, Poland
| | - Andrzej J. Bojarski
- Department of Medicinal Chemistry, Institute of Pharmacology, Polish Academy of Sciences, Kraków, Poland
| | - Ingebrigt Sylte
- Department of Medical Biology, Faculty of Health Sciences, UiT - the Arctic University of Norway, Tromsø, Norway
- * E-mail:
| | - Mari Gabrielsen
- Department of Medical Biology, Faculty of Health Sciences, UiT - the Arctic University of Norway, Tromsø, Norway
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16
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Warszycki D, Rueda M, Mordalski S, Kristiansen K, Satała G, Rataj K, Chilmonczyk Z, Sylte I, Abagyan R, Bojarski AJ. From Homology Models to a Set of Predictive Binding Pockets-a 5-HT 1A Receptor Case Study. J Chem Inf Model 2017; 57:311-321. [PMID: 28055203 PMCID: PMC5361891 DOI: 10.1021/acs.jcim.6b00263] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
Despite its remarkable importance in the arena of drug design, serotonin 1A receptor (5-HT1A) has been elusive to the X-ray crystallography community. This lack of direct structural information not only hampers our knowledge regarding the binding modes of many popular ligands (including the endogenous neurotransmitter-serotonin), but also limits the search for more potent compounds. In this paper we shed new light on the 3D pharmacological properties of the 5-HT1A receptor by using a ligand-guided approach (ALiBERO) grounded in the Internal Coordinate Mechanics (ICM) docking platform. Starting from a homology template and set of known actives, the method introduces receptor flexibility via Normal Mode Analysis and Monte Carlo sampling, to generate a subset of pockets that display enriched discrimination of actives from inactives in retrospective docking. Here, we thoroughly investigated the repercussions of using different protein templates and the effect of compound selection on screening performance. Finally, the best resulting protein models were applied prospectively in a large virtual screening campaign, in which two new active compounds were identified that were chemically distinct from those described in the literature.
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Affiliation(s)
- Dawid Warszycki
- Institute of Pharmacology, Polish Academy of Sciences, 12 Smetna Street, 31-343 Kraków, Poland
| | - Manuel Rueda
- University of California, San Diego, Skaggs School of Pharmacy & Pharmaceutical Sciences, 9500 Gilman Drive, MC 0747 La Jolla, CA 92093-0747, U.S
| | - Stefan Mordalski
- Institute of Pharmacology, Polish Academy of Sciences, 12 Smetna Street, 31-343 Kraków, Poland
| | - Kurt Kristiansen
- Department of Medical Biology, Faculty of Health Sciences, University of Tromsø, N-9037 Tromsø, Norway
| | - Grzegorz Satała
- Institute of Pharmacology, Polish Academy of Sciences, 12 Smetna Street, 31-343 Kraków, Poland
| | - Krzysztof Rataj
- Institute of Pharmacology, Polish Academy of Sciences, 12 Smetna Street, 31-343 Kraków, Poland
| | - Zdzisław Chilmonczyk
- Department of Cell Biology, National Medicines Institute, 30/34 Chełmska Street, 00-725 Warszawa, Poland
| | - Ingebrigt Sylte
- Department of Medical Biology, Faculty of Health Sciences, University of Tromsø, N-9037 Tromsø, Norway
| | - Ruben Abagyan
- University of California, San Diego, Skaggs School of Pharmacy & Pharmaceutical Sciences, 9500 Gilman Drive, MC 0747 La Jolla, CA 92093-0747, U.S
| | - Andrzej J. Bojarski
- Institute of Pharmacology, Polish Academy of Sciences, 12 Smetna Street, 31-343 Kraków, Poland
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Practical application of the Average Information Content Maximization (AIC-MAX) algorithm: selection of the most important structural features for serotonin receptor ligands. Mol Divers 2017; 21:407-412. [PMID: 28185036 PMCID: PMC5438429 DOI: 10.1007/s11030-017-9729-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2016] [Accepted: 01/16/2017] [Indexed: 12/12/2022]
Abstract
The Average Information Content Maximization algorithm (AIC-MAX) based on mutual information maximization was recently introduced to select the most discriminatory features. Here, this methodology was applied to select the most significant bits from the Klekota-Roth fingerprint for serotonin receptors ligands as well as to select the most important features for distinguishing ligands with activity for one receptor versus another. The interpretation of selected bits and machine-learning experiments performed using the reduced interpretations outperformed the raw fingerprints and indicated the most important structural features of the analyzed ligands in terms of activity and selectivity. Moreover, the AIC-MAX methodology applied here for serotonin receptor ligands can also be applied to other target classes.
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18
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HajiEbrahimi A, Ghafouri H, Ranjbar M, Sakhteman A. Protein Ligand Interaction Fingerprints. PHARMACEUTICAL SCIENCES 2017. [DOI: 10.4018/978-1-5225-1762-7.ch041] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
Abstract
A most challenging part in docking-based virtual screening is the scoring functions implemented in various docking programs in order to evaluate different poses of the ligands inside the binding cavity of the receptor. Precise and trustable measurement of ligand-protein affinity for Structure-Based Virtual Screening (SB-VS) is therefore, an outstanding problem in docking studies. Empirical post-docking filters can be helpful as a way to provide various types of structure-activity information. Different types of interaction have been presented between the ligands and the receptor so far. Based on the diversity and importance of PLIF methods, this chapter will focus on the comparison of different protocols. The advantages and disadvantages of all methods will be discussed explicitly in this chapter as well as future sights for further progress in this field. Different classifications approaches for the protein-ligand interaction fingerprints were also discussed in this chapter.
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19
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Strasser A, Wittmann HJ. Molecular Modelling Approaches for the Analysis of Histamine Receptors and Their Interaction with Ligands. Handb Exp Pharmacol 2017; 241:31-61. [PMID: 28110354 DOI: 10.1007/164_2016_113] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Several experimental techniques to analyse histamine receptors are available, e.g. pharmacological characterisation of known or new compounds by different types of assays or mutagenesis studies. To obtain insights into the histamine receptors on a molecular and structural level, crystal structures have to be determined and molecular modelling studies have to be performed. It is widely accepted to generate homology models of the receptor of interest based on an appropriate crystal structure as a template and to refine the resulting models by molecular dynamic simulations. A lot of modelling techniques, e.g. docking, QSAR or interaction fingerprint methods, are used to predict binding modes of ligands and pharmacological data, e.g. affinity or even efficacy. However, within the last years, molecular dynamic simulations got more and more important: First of all, molecular dynamic simulations are very helpful to refine the binding mode of a ligand to a histamine receptor, obtained by docking studies. Furthermore, with increasing computational performance it got possible to simulate complete binding pathways of ions or ligands from the aqueous extracellular phase into the allosteric or orthosteric binding pocket of histamine receptors.
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Affiliation(s)
- Andrea Strasser
- Department of Pharmaceutical/Medicinal Chemistry II, Institute of Pharmacy, University of Regensburg, Universitäts-Str. 31, Regensburg, 93040, Germany.
| | - Hans-Joachim Wittmann
- Department of Pharmaceutical/Medicinal Chemistry II, Institute of Pharmacy, University of Regensburg, Universitäts-Str. 31, Regensburg, 93040, Germany
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Zhu J, Li X, Zhang S, Ye H, Zhao H, Jin H, Han W. Exploring stereochemical specificity of phosphotriesterase by MM-PBSA and MM-GBSA calculation and steered molecular dynamics simulation. J Biomol Struct Dyn 2016; 35:3140-3151. [DOI: 10.1080/07391102.2016.1244494] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Affiliation(s)
- Jingxuan Zhu
- Key Laboratory for Molecular Enzymology and Engineering of the Ministry of Education, School of Life Science, Jilin University, Changchun 130023, China
| | - Xin Li
- Key Laboratory for Molecular Enzymology and Engineering of the Ministry of Education, School of Life Science, Jilin University, Changchun 130023, China
| | - Siqi Zhang
- Key Laboratory for Molecular Enzymology and Engineering of the Ministry of Education, School of Life Science, Jilin University, Changchun 130023, China
| | - Hen Ye
- Key Laboratory for Molecular Enzymology and Engineering of the Ministry of Education, School of Life Science, Jilin University, Changchun 130023, China
| | - Hui Zhao
- Institute of Tropical Bioscience and Biotechnology, Chinese Academy of Tropical Agricultural Sciences, Changchun 130023, China
| | - Hanyong Jin
- Key Laboratory for Molecular Enzymology and Engineering of the Ministry of Education, School of Life Science, Jilin University, Changchun 130023, China
| | - Weiwei Han
- Key Laboratory for Molecular Enzymology and Engineering of the Ministry of Education, School of Life Science, Jilin University, Changchun 130023, China
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21
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Molecular interaction fingerprint approaches for GPCR drug discovery. Curr Opin Pharmacol 2016; 30:59-68. [PMID: 27479316 DOI: 10.1016/j.coph.2016.07.007] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2016] [Revised: 07/11/2016] [Accepted: 07/12/2016] [Indexed: 01/23/2023]
Abstract
Protein-ligand interaction fingerprints (IFPs) are binary 1D representations of the 3D structure of protein-ligand complexes encoding the presence or absence of specific interactions between the binding pocket amino acids and the ligand. Various implementations of IFPs have been developed and successfully applied for post-processing molecular docking results for G Protein-Coupled Receptor (GPCR) ligand binding mode prediction and virtual ligand screening. Novel interaction fingerprint methods enable structural chemogenomics and polypharmacology predictions by complementing the increasing amount of GPCR structural data. Machine learning methods are increasingly used to derive relationships between bioactivity data and fingerprint descriptors of chemical and structural information of binding sites, ligands, and protein-ligand interactions. Factors that influence the application of IFPs include structure preparation, binding site definition, fingerprint similarity assessment, and data processing and these factors pose challenges as well possibilities to optimize interaction fingerprint methods for GPCR drug discovery.
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22
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An Algorithm to Identify Target-Selective Ligands - A Case Study of 5-HT7/5-HT1A Receptor Selectivity. PLoS One 2016; 11:e0156986. [PMID: 27271158 PMCID: PMC4896471 DOI: 10.1371/journal.pone.0156986] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2015] [Accepted: 05/23/2016] [Indexed: 11/19/2022] Open
Abstract
A computational procedure to search for selective ligands for structurally related protein targets was developed and verified for serotonergic 5-HT7/5-HT1A receptor ligands. Starting from a set of compounds with annotated activity at both targets (grouped into four classes according to their activity: selective toward each target, not-selective and not-selective but active) and with an additional set of decoys (prepared using DUD methodology), the SVM (Support Vector Machines) models were constructed using a selective subset as positive examples and four remaining classes as negative training examples. Based on these four component models, the consensus classifier was then constructed using a data fusion approach. The combination of two approaches of data representation (molecular fingerprints vs. structural interaction fingerprints), different training set sizes and selection of the best SVM component models for consensus model generation, were evaluated to determine the optimal settings for the developed algorithm. The results showed that consensus models with molecular fingerprints, a larger training set and the selection of component models based on MCC maximization provided the best predictive performance.
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23
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Zhao Z, Xie L, Xie L, Bourne PE. Delineation of Polypharmacology across the Human Structural Kinome Using a Functional Site Interaction Fingerprint Approach. J Med Chem 2016; 59:4326-41. [PMID: 26929980 DOI: 10.1021/acs.jmedchem.5b02041] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
Targeted polypharmacology of kinases has emerged as a promising strategy to design efficient and safe therapeutics. Here, we perform a systematic study of kinase-ligand binding modes for the human structural kinome at scale (208 kinases, 1777 unique ligands, and their complexes) by integrating chemical genomics and structural genomics data and by introducing a functional site interaction fingerprint (Fs-IFP) method. New insights into kinase-ligand binding modes were obtained. We establish relationships between the features of binding modes, the ligands, and the binding pockets, respectively. We also drive the intrinsic binding specificity and which correlation with amino acid conservation. Third, we explore the landscape of the binding modes and highlight the regions of "selectivity pocket" and "selectivity entrance". Finally, we demonstrate that Fs-IFP similarity is directly correlated to the experimentally determined profile. These improve our understanding of kinase-ligand interactions and contribute to the design of novel polypharmacological therapies targeting kinases.
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Affiliation(s)
- Zheng Zhao
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health , Bethesda, Maryland 20894, United States
| | - Li Xie
- Scripps Ranch , San Diego, California 92131, United States
| | - Lei Xie
- Department of Computer Science, Hunter College, The City University of New York , New York, New York 10065, United States.,The Graduate Center, The City University of New York , New York, New York 10016, United States
| | - Philip E Bourne
- Office of the Director, National Institutes of Health, Bethesda, Maryland 20892, United States
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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.
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25
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Kucwaj-Brysz K, Warszycki D, Podlewska S, Witek J, Witek K, González Izquierdo A, Satała G, Loza MI, Lubelska A, Latacz G, Bojarski AJ, Castro M, Kieć-Kononowicz K, Handzlik J. Rational design in search for 5-phenylhydantoin selective 5-HT7R antagonists. Molecular modeling, synthesis and biological evaluation. Eur J Med Chem 2016; 112:258-269. [PMID: 26900658 DOI: 10.1016/j.ejmech.2016.02.024] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2015] [Revised: 02/08/2016] [Accepted: 02/08/2016] [Indexed: 01/13/2023]
Abstract
A series of novel arylpiperazine 5-(4-fluorophenyl)-5-methylhydantoins with 2-hydroxypropyl linker (2-15) was synthesized and evaluated on their affinity towards serotonin 5-HT7 receptor (5-HT7R) in comparison to other closely related GPCRs: serotonin 5-HT1A, and dopamine D2 receptors. The functional activity studied through the measurement of 5-HT7R-mediated cyclic AMP production in Human Embryonic Kidney 293 cells (HEK293) stably expressing human 5-HT7 proved their antagonistic properties. The lead structure was also examined in the preliminary metabolic stability study using human liver microsomes (HMLs). The process of selection of candidates for synthesis was supported by a special molecular modeling workflow including combinatorial library generation, docking, and machine learning-based assessment. Additionally, in silico predictions of selectivity over 5-HT1AR and D2R, as well as functional activity were carried out. The newly synthesized compounds were proved to possess a potent affinity for 5-HT7R, similar to that of the lead structure of 5-(4-fluorophenyl)-3-(3-(4-(2-methoxyphenyl)piperazin-1-yl)-2-hydroxypropyl)-5-methylimidazolidine-2,4-dione (1). For several derivatives, significant selectivity both over 5-HT1AR and D2R was found.
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Affiliation(s)
- Katarzyna Kucwaj-Brysz
- Department of Technology and Biotechnology of Drugs, Jagiellonian University Medical College, Medyczna 9, PL 30-688 Kraków, Poland
| | - Dawid Warszycki
- Department of Medicinal Chemistry Institute of Pharmacology, Polish Academy of Sciences, Smętna 12, PL 31-343 Kraków, Poland
| | - Sabina Podlewska
- Department of Medicinal Chemistry Institute of Pharmacology, Polish Academy of Sciences, Smętna 12, PL 31-343 Kraków, Poland; Faculty of Chemistry, Jagiellonian University, Ingardena 3, PL 30-060, Kraków, Poland
| | - Jagna Witek
- Department of Medicinal Chemistry Institute of Pharmacology, Polish Academy of Sciences, Smętna 12, PL 31-343 Kraków, Poland
| | - Karolina Witek
- Department of Technology and Biotechnology of Drugs, Jagiellonian University Medical College, Medyczna 9, PL 30-688 Kraków, Poland
| | - Andrea González Izquierdo
- Department of Pharmacology, Center for Research in Molecular Medicine and Chronic Diseases (CIMUS), University of Santiago de Compostela, Spain
| | - Grzegorz Satała
- Department of Medicinal Chemistry Institute of Pharmacology, Polish Academy of Sciences, Smętna 12, PL 31-343 Kraków, Poland
| | - María I Loza
- Department of Pharmacology, Center for Research in Molecular Medicine and Chronic Diseases (CIMUS), University of Santiago de Compostela, Spain
| | - Annamaria Lubelska
- Department of Technology and Biotechnology of Drugs, Jagiellonian University Medical College, Medyczna 9, PL 30-688 Kraków, Poland
| | - Gniewomir Latacz
- Department of Technology and Biotechnology of Drugs, Jagiellonian University Medical College, Medyczna 9, PL 30-688 Kraków, Poland
| | - Andrzej J Bojarski
- Department of Medicinal Chemistry Institute of Pharmacology, Polish Academy of Sciences, Smętna 12, PL 31-343 Kraków, Poland
| | - Marián Castro
- Department of Pharmacology, Center for Research in Molecular Medicine and Chronic Diseases (CIMUS), University of Santiago de Compostela, Spain
| | - Katarzyna Kieć-Kononowicz
- Department of Technology and Biotechnology of Drugs, Jagiellonian University Medical College, Medyczna 9, PL 30-688 Kraków, Poland
| | - Jadwiga Handzlik
- Department of Technology and Biotechnology of Drugs, Jagiellonian University Medical College, Medyczna 9, PL 30-688 Kraków, Poland.
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Qiu T, Xiao H, Zhang Q, Qiu J, Yang Y, Wu D, Cao Z, Zhu R. Proteochemometric modeling of the antigen-antibody interaction: new fingerprints for antigen, antibody and epitope-paratope interaction. PLoS One 2015; 10:e0122416. [PMID: 25901362 PMCID: PMC4406442 DOI: 10.1371/journal.pone.0122416] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2014] [Accepted: 02/20/2015] [Indexed: 01/12/2023] Open
Abstract
Despite the high specificity between antigen and antibody binding, similar epitopes can be recognized or cross-neutralized by paratopes of antibody with different binding affinities. How to accurately characterize this slight variation which may or may not change the antigen-antibody binding affinity is a key issue in this area. In this report, by combining cylinder model with shell structure model, a new fingerprint was introduced to describe both the structural and physical-chemical features of the antigen and antibody protein. Furthermore, beside the description of individual protein, the specific epitope-paratope interaction fingerprint (EPIF) was developed to reflect the bond and the environment of the antigen-antibody interface. Finally, Proteochemometric Modeling of the antigen-antibody interaction was established and evaluated on 429 antigen-antibody complexes. By using only protein descriptors, our model achieved the best performance ( R2=0.91,Qtest2=0.68) among peers. Further, together with EPIF as a new cross-term, our model ( R2=0.92,Qtest2=0.74) can significantly outperform peers with multiplication of ligand and protein descriptors as a cross-term ( R2≤0.81,Qtest2≤0.44). Results illustrated that: 1) our newly designed protein fingerprints and EPIF can better describe the antigen-antibody interaction; 2) EPIF is a better and specific cross-term in Proteochemometric Modeling for antigen-antibody interaction. The fingerprints designed in this study will provide assistance to the description of antigen-antibody binding, and in future, it may be valuable help for the high-throughput antibody screening. The algorithm is freely available on request.
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Affiliation(s)
- Tianyi Qiu
- Department of Bioinformatics, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China
| | - Han Xiao
- Department of Computer Science, University of Helsinki, Helsinki, FI-00014, Finland
| | - Qingchen Zhang
- Department of Bioinformatics, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China
| | - Jingxuan Qiu
- Department of Bioinformatics, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China
| | - Yiyan Yang
- Department of Bioinformatics, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China
| | - Dingfeng Wu
- Department of Bioinformatics, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China
| | - Zhiwei Cao
- Department of Bioinformatics, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China
- Shanghai Center for Bioinformation Technology, Shanghai 201203, China
- * E-mail: (RZ); (ZC)
| | - Ruixin Zhu
- Department of Bioinformatics, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China
- School of Pharmacy, Liaoning University of Traditional Chinese Medicine, Dalian 116600, Liaoning, China
- * E-mail: (RZ); (ZC)
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Zhan D, Bai A, Yu L, Han W, Feng Y. Characterization of the PH1704 protease from Pyrococcus horikoshii OT3 and the critical functions of Tyr120. PLoS One 2014; 9:e103902. [PMID: 25192005 PMCID: PMC4156298 DOI: 10.1371/journal.pone.0103902] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2014] [Accepted: 07/03/2014] [Indexed: 12/02/2022] Open
Abstract
The PH1704 protease from hyperthermophilic archaean Pyrococcus horikoshii OT3 is a member of DJ-1/ThiJ/PfpI superfamily with diverse functional subclasses. The recombinant PH1704 was efficiently purified and was systematically characterized by a combination of substrate specificity analysis, steady-state kinetics study and molecular docking research. The homogeneous protease was obtained as a presumed dodecamer with molecular weight of ∼240 kDa. Iodoacetamide strongly inhibited the peptidase activity, confirming that Cys100 is a nucleophilic residue. The recombinant protein was identified as both an aminopeptidase and an endopeptidase. Experimental data showed that L-R-amc was the best substrate of PH1704. Structural interaction fingerprint analysis (SIFt) indicated the binding pose of PH1704 and showed that Tyr120 is important in substrate binding. Kinetic parameters Kcat and Kcat/Km of the Y120P mutant with L-R-amc was about 7 and 7.8 times higher than that of the wild type (WT). For the endopeptidase Y120P with AAFR-amc, Kcat and Kcat/Km is 10- and 21- fold higher than that of WT. Experimental data indicate the important functions of Tyr120: involvement in enzyme activity to form a hydrogen bond with Cys100 and as an entrance gate of the substrate with Lys43. The results of this study can be used to investigate the DJ-1/ThiJ/PfpI superfamily.
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Affiliation(s)
- Dongling Zhan
- Key Laboratory for Molecular Enzymology and Engineering of Ministry of Education, School of Life Science, Jilin University, Changchun, China
- College of Food Science and Engineering, Jilin Agricultural University, Changchun, China
| | - Aixi Bai
- Key Laboratory for Molecular Enzymology and Engineering of Ministry of Education, School of Life Science, Jilin University, Changchun, China
| | - Lei Yu
- Key Laboratory for Molecular Enzymology and Engineering of Ministry of Education, School of Life Science, Jilin University, Changchun, China
| | - Weiwei Han
- Key Laboratory for Molecular Enzymology and Engineering of Ministry of Education, School of Life Science, Jilin University, Changchun, China
- * E-mail: (YF); (WH)
| | - Yan Feng
- Key Laboratory for Molecular Enzymology and Engineering of Ministry of Education, School of Life Science, Jilin University, Changchun, China
- State Key Laboratory of Microbial Metabolism, College of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
- * E-mail: (YF); (WH)
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Salentin S, Haupt VJ, Daminelli S, Schroeder M. Polypharmacology rescored: protein-ligand interaction profiles for remote binding site similarity assessment. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2014; 116:174-86. [PMID: 24923864 DOI: 10.1016/j.pbiomolbio.2014.05.006] [Citation(s) in RCA: 72] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/06/2014] [Revised: 05/20/2014] [Accepted: 05/26/2014] [Indexed: 11/27/2022]
Abstract
Detection of remote binding site similarity in proteins plays an important role for drug repositioning and off-target effect prediction. Various non-covalent interactions such as hydrogen bonds and van-der-Waals forces drive ligands' molecular recognition by binding sites in proteins. The increasing amount of available structures of protein-small molecule complexes enabled the development of comparative approaches. Several methods have been developed to characterize and compare protein-ligand interaction patterns. Usually implemented as fingerprints, these are mainly used for post processing docking scores and (off-)target prediction. In the latter application, interaction profiles detect similarities in the bound interactions of different ligands and thus identify essential interactions between a protein and its small molecule ligands. Interaction pattern similarity correlates with binding site similarity and is thus contributing to a higher precision in binding site similarity assessment of proteins with distinct global structure. This renders it valuable for existing drug repositioning approaches in structural bioinformatics. Current methods to characterize and compare structure-based interaction patterns - both for protein-small-molecule and protein-protein interactions - as well as their potential in target prediction will be reviewed in this article. The question of how the set of interaction types, flexibility or water-mediated interactions, influence the comparison of interaction patterns will be discussed. Due to the wealth of protein-ligand structures available today, predicted targets can be ranked by comparing their ligand interaction pattern to patterns of the known target. Such knowledge-based methods offer high precision in comparison to methods comparing whole binding sites based on shape and amino acid physicochemical similarity.
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Chin SP, Buckle MJC, Chalmers DK, Yuriev E, Doughty SW. Toward activated homology models of the human M1 muscarinic acetylcholine receptor. J Mol Graph Model 2014; 49:91-8. [PMID: 24631873 DOI: 10.1016/j.jmgm.2014.02.002] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2013] [Revised: 02/14/2014] [Accepted: 02/17/2014] [Indexed: 10/25/2022]
Abstract
Structure-based virtual screening offers a good opportunity for the discovery of selective M1 muscarinic acetylcholine receptor (mAChR) agonists for the treatment of Alzheimer's disease. However, no 3-D structure of an M1 mAChR is yet available and the homology models that have been previously reported are only able to identify antagonists in virtual screening experiments. In this study, we generated a homology model of the human M1 mAChR, based on the crystal structure of an M3 mAChR as the template. This initial model was modified, using the agonist-bound crystal structure of a β2-adrenergic receptor as a guide, to give two possible activated structures. The T192 side chain was adjusted in both structures and one of the structures also had the whole of transmembrane (TM) 5 rotated and tilted toward the inner channel of the transmembrane region. The binding sites of all three structures were then refined by induced-fit docking (IFD) with acetylcholine. Virtual screening experiments showed that all three refined models could efficiently differentiate agonists from decoy molecules, with the TM5-modified models also giving good agonist/antagonist selectivity. The whole range of agonists and antagonists was observed to bind within the orthosteric site of the structure obtained by IFD refinement alone, implying that it has inactive state character. In contrast, the two TM5-modified structures were unable to accommodate the antagonists, supporting the proposition that they possess activated state character.
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Affiliation(s)
- Sek Peng Chin
- Department of Pharmacy, Faculty of Medicine, University of Malaya, 50603 Kuala Lumpur, Malaysia
| | - Michael J C Buckle
- Department of Pharmacy, Faculty of Medicine, University of Malaya, 50603 Kuala Lumpur, Malaysia.
| | - David K Chalmers
- Medicinal Chemistry, Monash Institute of Pharmaceutical Sciences, Monash University (Parkville Campus), 381 Royal Parade, Parkville, Victoria 3052, Australia
| | - Elizabeth Yuriev
- Medicinal Chemistry, Monash Institute of Pharmaceutical Sciences, Monash University (Parkville Campus), 381 Royal Parade, Parkville, Victoria 3052, Australia
| | - Stephen W Doughty
- The School of Pharmacy, University of Nottingham Malaysia Campus, Jalan Broga, 43500 Semenyih, Selangor, Malaysia.
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Witek J, Smusz S, Rataj K, Mordalski S, Bojarski AJ. An application of machine learning methods to structural interaction fingerprints—a case study of kinase inhibitors. Bioorg Med Chem Lett 2014; 24:580-5. [DOI: 10.1016/j.bmcl.2013.12.017] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2013] [Revised: 11/30/2013] [Accepted: 12/03/2013] [Indexed: 11/26/2022]
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Kurczab R, Bojarski AJ. New strategy for receptor-based pharmacophore query construction: a case study for 5-HT₇ receptor ligands. J Chem Inf Model 2013; 53:3233-43. [PMID: 24245803 DOI: 10.1021/ci4005207] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
In this paper, a new approach for generating receptor-based 3D pharmacophore models for rapid in silico virtual screening is presented. The method combines information from docking poses of known ligands of different structures and further ligand-receptor complexes analyses using structural interaction fingerprints (SIFts). Next, the best linear combination of three-, four-, and five-feature pharmacophores in terms of selected performance parameter (i.e., recall, F-score, and MCC) is constructed. The resultant queries showed significantly better VS performance and new scaffold recognition when compared with the known ligand- and receptor-based pharmacophore models. The approach was developed and validated on 5-HT₇ receptor homology models created on available crystal structure templates. The efficiency of the obtained linear combinations exhibited only a minor dependence on the template selection.
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Affiliation(s)
- Rafał Kurczab
- Department of Medicinal Chemistry, Institute of Pharmacology Polish Academy of Sciences , 12 Smętna Street, Kraków, 31-343, Poland
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Zajdel P, Kurczab R, Grychowska K, Satała G, Pawłowski M, Bojarski AJ. The multiobjective based design, synthesis and evaluation of the arylsulfonamide/amide derivatives of aryloxyethyl- and arylthioethyl- piperidines and pyrrolidines as a novel class of potent 5-HT7 receptor antagonists. Eur J Med Chem 2012; 56:348-60. [DOI: 10.1016/j.ejmech.2012.07.043] [Citation(s) in RCA: 35] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2012] [Revised: 07/20/2012] [Accepted: 07/26/2012] [Indexed: 12/12/2022]
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