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Sooram B, Mallikarjunachari U, Uddavesh S, Saudagar P. Pharmacophore-guided drug design using LdNMT as a model drug target for leishmaniasis. J Biomol Struct Dyn 2024; 42:863-875. [PMID: 37096664 DOI: 10.1080/07391102.2023.2196695] [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: 01/12/2023] [Accepted: 03/22/2023] [Indexed: 04/26/2023]
Abstract
Leishmaniasis is caused by Leishmania genus parasites and has a high mortality rate. The available drugs to treat leishmaniasis fail due to acquired resistance in parasites. Several enzymes of the Leishmania parasite have been used to design new therapeutic molecules against leishmaniasis. This study uses a pharmacophore-guided approach to design the drug candidate by targeting Leishmania N-Myristoyl transferase (LdNMT). From the initial sequence analysis of LdNMT, we have identified a unique 20 amino acid stretch exploited for screening and designing the small molecules. The pharmacophore for the myristate binding site on LdNMT was elucidated, and a heatmap was constructed. The leishmanial NMT pharmacophore has similarities with other pathogenic microorganisms. Moreover, substituting alanine in pharmacophoric residues elevates the affinity of myristate with NMT. Furthermore, a molecular dynamics (MD) simulation study was conducted to ascertain the stability of the mutants and or wild type. The wild-type NMT has a comparatively low affinity to myristate compared to alanine mutants, indicating that hydrophobic residues favor the myristate binding. The molecules were initially designed by using pharmacophore as a sieving mechanism. In subsequent steps, the selected molecules screened against leishmanial unique amino acid stretch and subsequently with human, leishmanial full-size NMTs. The compounds BP5, TYI, DMU, 3PE and 4UL were the top hits and chemical features similar to the myristate. The molecule 4UL was found to be highly specific towards leishmanial NMT over human NMT, suggesting the molecule is a strong leishmanial NMT inhibitor. The molecule can be taken further to assess it in in-vitro conditions.
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Affiliation(s)
- Banesh Sooram
- Department of Biotechnology, National Institute of Technology-Warangal, Warangal, Telangana, India
| | - Uppuladinne Mallikarjunachari
- Department of High Performance Computing-Medical and Bioinformatics Applications, Centre for Development for Advanced Computing (CDAC), Pune, Maharastra, India
| | - Sonavane Uddavesh
- Department of High Performance Computing-Medical and Bioinformatics Applications, Centre for Development for Advanced Computing (CDAC), Pune, Maharastra, India
| | - Prakash Saudagar
- Department of Biotechnology, National Institute of Technology-Warangal, Warangal, Telangana, India
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2
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Derry A, Altman RB. COLLAPSE: A representation learning framework for identification and characterization of protein structural sites. Protein Sci 2023; 32:e4541. [PMID: 36519247 PMCID: PMC9847082 DOI: 10.1002/pro.4541] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2022] [Revised: 12/02/2022] [Accepted: 12/08/2022] [Indexed: 12/23/2022]
Abstract
The identification and characterization of the structural sites which contribute to protein function are crucial for understanding biological mechanisms, evaluating disease risk, and developing targeted therapies. However, the quantity of known protein structures is rapidly outpacing our ability to functionally annotate them. Existing methods for function prediction either do not operate on local sites, suffer from high false positive or false negative rates, or require large site-specific training datasets, necessitating the development of new computational methods for annotating functional sites at scale. We present COLLAPSE (Compressed Latents Learned from Aligned Protein Structural Environments), a framework for learning deep representations of protein sites. COLLAPSE operates directly on the 3D positions of atoms surrounding a site and uses evolutionary relationships between homologous proteins as a self-supervision signal, enabling learned embeddings to implicitly capture structure-function relationships within each site. Our representations generalize across disparate tasks in a transfer learning context, achieving state-of-the-art performance on standardized benchmarks (protein-protein interactions and mutation stability) and on the prediction of functional sites from the Prosite database. We use COLLAPSE to search for similar sites across large protein datasets and to annotate proteins based on a database of known functional sites. These methods demonstrate that COLLAPSE is computationally efficient, tunable, and interpretable, providing a general-purpose platform for computational protein analysis.
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Affiliation(s)
- Alexander Derry
- Department of Biomedical Data ScienceStanford UniversityStanfordCaliforniaUSA
| | - Russ B. Altman
- Department of Biomedical Data ScienceStanford UniversityStanfordCaliforniaUSA
- Departments of Bioengineering, Genetics, and MedicineStanford UniversityStanfordCaliforniaUSA
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3
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Eguida M, Rognan D. Estimating the Similarity between Protein Pockets. Int J Mol Sci 2022; 23:12462. [PMID: 36293316 PMCID: PMC9604425 DOI: 10.3390/ijms232012462] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Revised: 10/15/2022] [Accepted: 10/16/2022] [Indexed: 10/28/2023] Open
Abstract
With the exponential increase in publicly available protein structures, the comparison of protein binding sites naturally emerged as a scientific topic to explain observations or generate hypotheses for ligand design, notably to predict ligand selectivity for on- and off-targets, explain polypharmacology, and design target-focused libraries. The current review summarizes the state-of-the-art computational methods applied to pocket detection and comparison as well as structural druggability estimates. The major strengths and weaknesses of current pocket descriptors, alignment methods, and similarity search algorithms are presented. Lastly, an exhaustive survey of both retrospective and prospective applications in diverse medicinal chemistry scenarios illustrates the capability of the existing methods and the hurdle that still needs to be overcome for more accurate predictions.
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Affiliation(s)
| | - Didier Rognan
- Laboratoire d’Innovation Thérapeutique, UMR7200 CNRS-Université de Strasbourg, 67400 Illkirch, France
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4
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Umezawa K, Kii I. Druggable Transient Pockets in Protein Kinases. Molecules 2021; 26:molecules26030651. [PMID: 33513739 PMCID: PMC7865889 DOI: 10.3390/molecules26030651] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Revised: 01/23/2021] [Accepted: 01/26/2021] [Indexed: 12/29/2022] Open
Abstract
Drug discovery using small molecule inhibitors is reaching a stalemate due to low selectivity, adverse off-target effects and inevitable failures in clinical trials. Conventional chemical screening methods may miss potent small molecules because of their use of simple but outdated kits composed of recombinant enzyme proteins. Non-canonical inhibitors targeting a hidden pocket in a protein have received considerable research attention. Kii and colleagues identified an inhibitor targeting a transient pocket in the kinase DYRK1A during its folding process and termed it FINDY. FINDY exhibits a unique inhibitory profile; that is, FINDY does not inhibit the fully folded form of DYRK1A, indicating that the FINDY-binding pocket is hidden in the folded form. This intriguing pocket opens during the folding process and then closes upon completion of folding. In this review, we discuss previously established kinase inhibitors and their inhibitory mechanisms in comparison with FINDY. We also compare the inhibitory mechanisms with the growing concept of “cryptic inhibitor-binding sites.” These sites are buried on the inhibitor-unbound surface but become apparent when the inhibitor is bound. In addition, an alternative method based on cell-free protein synthesis of protein kinases may allow the discovery of small molecules that occupy these mysterious binding sites. Transitional folding intermediates would become alternative targets in drug discovery, enabling the efficient development of potent kinase inhibitors.
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Affiliation(s)
- Koji Umezawa
- Department of Biomolecular Innovation, Institute for Biomedical Sciences, Shinshu University, 8304 Minami-Minowa, Kami-ina, Nagano 399-4598, Japan;
| | - Isao Kii
- Laboratory for Drug Target Research, Faculty & Graduate School of Agriculture, Shinshu University, 8304 Minami-Minowa, Kami-ina, Nagano 399-4598, Japan
- Correspondence: ; Tel.: +81-265-77-1521
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5
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A second shell residue modulates a conserved ATP-binding site with radically different affinities for ATP. Biochim Biophys Acta Gen Subj 2020; 1865:129766. [PMID: 33069831 DOI: 10.1016/j.bbagen.2020.129766] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2020] [Revised: 09/16/2020] [Accepted: 10/14/2020] [Indexed: 11/20/2022]
Abstract
BACKGROUND Prediction of ligand binding and design of new function in enzymes is a time-consuming and expensive process. Crystallography gives the impression that proteins adopt a fixed shape, yet enzymes are functionally dynamic. Molecular dynamics offers the possibility of probing protein movement while predicting ligand binding. Accordingly, we choose the bacterial F1Fo ATP synthase ε subunit to unravel why ATP affinity by ε subunits from Bacillus subtilis and Bacillus PS3 differs ~500-fold, despite sharing identical sequences at the ATP-binding site. METHODS We first used the Bacillus PS3 ε subunit structure to model the B. subtilis ε subunit structure and used this to explore the utility of molecular dynamics (MD) simulations to predict the influence of residues outside the ATP binding site. To verify the MD predictions, point mutants were made and ATP binding studies were employed. RESULTS MD simulations predicted that E102 in the B. subtilis ε subunit, outside of the ATP binding site, influences ATP binding affinity. Engineering E102 to alanine or arginine revealed a ~10 or ~54 fold increase in ATP binding, respectively, confirming the MD prediction that E102 drastically influences ATP binding affinity. CONCLUSIONS These findings reveal how MD can predict how changes in the "second shell" residues around substrate binding sites influence affinity in simple protein structures. Our results reveal why seemingly identical ε subunits in different ATP synthases have radically different ATP binding affinities. GENERAL SIGNIFICANCE This study may lead to greater utility of molecular dynamics as a tool for protein design and exploration of protein design and function.
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6
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Torng W, Altman RB. High precision protein functional site detection using 3D convolutional neural networks. Bioinformatics 2020; 35:1503-1512. [PMID: 31051039 PMCID: PMC6499237 DOI: 10.1093/bioinformatics/bty813] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2018] [Revised: 08/14/2018] [Accepted: 09/19/2018] [Indexed: 12/02/2022] Open
Abstract
Motivation Accurate annotation of protein functions is fundamental for understanding molecular and cellular physiology. Data-driven methods hold promise for systematically deriving rules underlying the relationship between protein structure and function. However, the choice of protein structural representation is critical. Pre-defined biochemical features emphasize certain aspects of protein properties while ignoring others, and therefore may fail to capture critical information in complex protein sites. Results In this paper, we present a general framework that applies 3D convolutional neural networks (3DCNNs) to structure-based protein functional site detection. The framework can extract task-dependent features automatically from the raw atom distributions. We benchmarked our method against other methods and demonstrate better or comparable performance for site detection. Our deep 3DCNNs achieved an average recall of 0.955 at a precision threshold of 0.99 on PROSITE families, detected 98.89 and 92.88% of nitric oxide synthase and TRYPSIN-like enzyme sites in Catalytic Site Atlas, and showed good performance on challenging cases where sequence motifs are absent but a function is known to exist. Finally, we inspected the individual contributions of each atom to the classification decisions and show that our models successfully recapitulate known 3D features within protein functional sites. Availability and implementation The 3DCNN models described in this paper are available at https://simtk.org/projects/fscnn. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Wen Torng
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Russ B Altman
- Department of Bioengineering, Stanford University, Stanford, CA, USA.,Department of Genetics, Stanford University, Stanford, CA, USA
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7
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Rey J, Rasolohery I, Tufféry P, Guyon F, Moroy G. PatchSearch: a web server for off-target protein identification. Nucleic Acids Res 2020; 47:W365-W372. [PMID: 31131411 PMCID: PMC6602448 DOI: 10.1093/nar/gkz478] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2019] [Revised: 04/26/2019] [Accepted: 05/21/2019] [Indexed: 01/17/2023] Open
Abstract
The large number of proteins found in the human body implies that a drug may interact with many proteins, called off-target proteins, besides its intended target. The PatchSearch web server provides an automated workflow that allows users to identify structurally conserved binding sites at the protein surfaces in a set of user-supplied protein structures. Thus, this web server may help to detect potential off-target protein. It takes as input a protein complexed with a ligand and identifies within user-defined or predefined collections of protein structures, those having a binding site compatible with this ligand in terms of geometry and physicochemical properties. It is based on a non-sequential local alignment of the patch over the entire protein surface. Then the PatchSearch web server proposes a ligand binding mode for the potential off-target, as well as an estimated affinity calculated by the Vinardo scoring function. This novel tool is able to efficiently detects potential interactions of ligands with distant off-target proteins. Furthermore, by facilitating the discovery of unexpected off-targets, PatchSearch could contribute to the repurposing of existing drugs. The server is freely available at http://bioserv.rpbs.univ-paris-diderot.fr/services/PatchSearch.
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Affiliation(s)
- Julien Rey
- Université Paris Diderot, Sorbonne Paris Cité, INSERM UMRS-973, Molécules Thérapeutiques in silico (MTi), F-75205 Paris, France.,Ressource Parisienne en Bioinformatique Structurale (RPBS), Paris, France
| | - Inès Rasolohery
- Université Paris Diderot, Sorbonne Paris Cité, INSERM UMRS-973, Molécules Thérapeutiques in silico (MTi), F-75205 Paris, France
| | - Pierre Tufféry
- Université Paris Diderot, Sorbonne Paris Cité, INSERM UMRS-973, Molécules Thérapeutiques in silico (MTi), F-75205 Paris, France.,Ressource Parisienne en Bioinformatique Structurale (RPBS), Paris, France
| | - Frédéric Guyon
- Université Paris Diderot, Sorbonne Paris Cité, INSERM UMRS-973, Molécules Thérapeutiques in silico (MTi), F-75205 Paris, France
| | - Gautier Moroy
- Université Paris Diderot, Sorbonne Paris Cité, INSERM UMRS-973, Molécules Thérapeutiques in silico (MTi), F-75205 Paris, France
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8
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Katigbak J, Li H, Rooklin D, Zhang Y. AlphaSpace 2.0: Representing Concave Biomolecular Surfaces Using β-Clusters. J Chem Inf Model 2020; 60:1494-1508. [PMID: 31995373 PMCID: PMC7093224 DOI: 10.1021/acs.jcim.9b00652] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Modern rational modulator design and structure-function characterization often concentrate on concave regions of biomolecular surfaces, ranging from well-defined small-molecule binding sites to large protein-protein interaction interfaces. Here, we introduce a β-cluster as a pseudomolecular representation of fragment-centric pockets detected by AlphaSpace [J. Chem. Inf. Model. 2015, 55, 1585], a recently developed computational analysis tool for topographical mapping of biomolecular concavities. By mimicking the shape as well as atomic details of potential molecular binders, this new β-cluster representation allows direct pocket-to-ligand shape comparison and can be used to guide ligand optimization. Furthermore, we defined the β-score, the optimal Vina score of the β-cluster, as an indicator of pocket ligandability and developed an ensemble β-cluster approach, which allows one-to-one pocket mapping and comparison among aligned protein structures. We demonstrated the utility of β-cluster representation by applying the approach to a wide variety of problems including binding site detection and comparison, characterization of protein-protein interactions, and fragment-based ligand optimization. These new β-cluster functionalities have been implemented in AlphaSpace 2.0, which is freely available on the web at http://www.nyu.edu/projects/yzhang/AlphaSpace2.
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Affiliation(s)
- Joseph Katigbak
- Department of Chemistry, New York University, New York, New York 10003, United States
| | - Haotian Li
- Department of Chemistry, New York University, New York, New York 10003, United States
| | - David Rooklin
- Department of Chemistry, New York University, New York, New York 10003, United States
| | - Yingkai Zhang
- Department of Chemistry, New York University, New York, New York 10003, United States
- NYU-ECNU Center for Computational Chemistry at NYU Shanghai, Shanghai 200062, China
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9
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Rensi S, Keys A, Lo YC, Derry A, McInnes G, Liu T, Altman R. Homology modeling of TMPRSS2 yields candidate drugs that may inhibit entry of SARS-CoV-2 into human cells. CHEMRXIV : THE PREPRINT SERVER FOR CHEMISTRY 2020:12009582. [PMID: 32511288 PMCID: PMC7263764 DOI: 10.26434/chemrxiv.12009582] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Revised: 03/20/2020] [Indexed: 04/16/2023]
Abstract
The most rapid path to discovering treatment options for the novel coronavirus SARS-CoV-2 is to find existing medications that are active against the virus. We have focused on identifying repurposing candidates for the transmembrane serine protease family member II (TMPRSS2), which is critical for entry of coronaviruses into cells. Using known 3D structures of close homologs, we created seven homology models. We also identified a set of serine protease inhibitor drugs, generated several conformations of each, and docked them into our models. We used three known chemical (non-drug) inhibitors and one validated inhibitor of TMPRSS2 in MERS as benchmark compounds and found six compounds with predicted high binding affinity in the range of the known inhibitors. We also showed that a previously published weak inhibitor, Camostat, had a significantly lower binding score than our six compounds. All six compounds are anticoagulants with significant and potentially dangerous clinical effects and side effects. Nonetheless, if these compounds significantly inhibit SARS-CoV-2 infection, they could represent a potentially useful clinical tool.
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Affiliation(s)
- Stefano Rensi
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Allison Keys
- Department of Computer Science, Stanford University, CA, USA
| | - Yu-Chen Lo
- Pediatrics, Bass Center for Childhood Cancer, Stanford School of Medicine, Stanford, CA, USA
| | - Alexander Derry
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - Greg McInnes
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - Tianyun Liu
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Russ Altman
- Department of Bioengineering, Stanford University, Stanford, CA, USA
- Department of Computer Science, Stanford University, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
- Departments of Genetics, Stanford University, Stanford, CA, USA
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10
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Naderi M, Lemoine JM, Govindaraj RG, Kana OZ, Feinstein WP, Brylinski M. Binding site matching in rational drug design: algorithms and applications. Brief Bioinform 2019; 20:2167-2184. [PMID: 30169563 PMCID: PMC6954434 DOI: 10.1093/bib/bby078] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2018] [Revised: 07/18/2018] [Accepted: 07/29/2018] [Indexed: 01/06/2023] Open
Abstract
Interactions between proteins and small molecules are critical for biological functions. These interactions often occur in small cavities within protein structures, known as ligand-binding pockets. Understanding the physicochemical qualities of binding pockets is essential to improve not only our basic knowledge of biological systems, but also drug development procedures. In order to quantify similarities among pockets in terms of their geometries and chemical properties, either bound ligands can be compared to one another or binding sites can be matched directly. Both perspectives routinely take advantage of computational methods including various techniques to represent and compare small molecules as well as local protein structures. In this review, we survey 12 tools widely used to match pockets. These methods are divided into five categories based on the algorithm implemented to construct binding-site alignments. In addition to the comprehensive analysis of their algorithms, test sets and the performance of each method are described. We also discuss general pharmacological applications of computational pocket matching in drug repurposing, polypharmacology and side effects. Reflecting on the importance of these techniques in drug discovery, in the end, we elaborate on the development of more accurate meta-predictors, the incorporation of protein flexibility and the integration of powerful artificial intelligence technologies such as deep learning.
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Affiliation(s)
- Misagh Naderi
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803, USA
| | - Jeffrey Mitchell Lemoine
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803, USA
- Division of Computer Science and Engineering, Louisiana State University, Baton Rouge, LA 70803, USA
| | | | - Omar Zade Kana
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803, USA
| | - Wei Pan Feinstein
- High-Performance Computing, Louisiana State University, Baton Rouge, LA 70803, USA
| | - Michal Brylinski
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803, USA
- Center for Computation & Technology, Louisiana State University, Baton Rouge, LA 70803, USA
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11
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A deep learning framework to predict binding preference of RNA constituents on protein surface. Nat Commun 2019; 10:4941. [PMID: 31666519 PMCID: PMC6821705 DOI: 10.1038/s41467-019-12920-0] [Citation(s) in RCA: 58] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2019] [Accepted: 10/08/2019] [Indexed: 12/21/2022] Open
Abstract
Protein-RNA interaction plays important roles in post-transcriptional regulation. However, the task of predicting these interactions given a protein structure is difficult. Here we show that, by leveraging a deep learning model NucleicNet, attributes such as binding preference of RNA backbone constituents and different bases can be predicted from local physicochemical characteristics of protein structure surface. On a diverse set of challenging RNA-binding proteins, including Fem-3-binding-factor 2, Argonaute 2 and Ribonuclease III, NucleicNet can accurately recover interaction modes discovered by structural biology experiments. Furthermore, we show that, without seeing any in vitro or in vivo assay data, NucleicNet can still achieve consistency with experiments, including RNAcompete, Immunoprecipitation Assay, and siRNA Knockdown Benchmark. NucleicNet can thus serve to provide quantitative fitness of RNA sequences for given binding pockets or to predict potential binding pockets and binding RNAs for previously unknown RNA binding proteins.
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12
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Lo YC, Liu T, Morrissey KM, Kakiuchi-Kiyota S, Johnson AR, Broccatelli F, Zhong Y, Joshi A, Altman RB. Computational analysis of kinase inhibitor selectivity using structural knowledge. Bioinformatics 2019; 35:235-242. [PMID: 29985971 DOI: 10.1093/bioinformatics/bty582] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2018] [Accepted: 07/05/2018] [Indexed: 12/11/2022] Open
Abstract
Motivation Kinases play a significant role in diverse disease signaling pathways and understanding kinase inhibitor selectivity, the tendency of drugs to bind to off-targets, remains a top priority for kinase inhibitor design and clinical safety assessment. Traditional approaches for kinase selectivity analysis using biochemical activity and binding assays are useful but can be costly and are often limited by the kinases that are available. On the other hand, current computational kinase selectivity prediction methods are computational intensive and can rarely achieve sufficient accuracy for large-scale kinome wide inhibitor selectivity profiling. Results Here, we present a KinomeFEATURE database for kinase binding site similarity search by comparing protein microenvironments characterized using diverse physiochemical descriptors. Initial selectivity prediction of 15 known kinase inhibitors achieved an >90% accuracy and demonstrated improved performance in comparison to commonly used kinase inhibitor selectivity prediction methods. Additional kinase ATP binding site similarity assessment (120 binding sites) identified 55 kinases with significant promiscuity and revealed unexpected inhibitor cross-activities between PKR and FGFR2 kinases. Kinome-wide selectivity profiling of 11 kinase drug candidates predicted novel as well as experimentally validated off-targets and suggested structural mechanisms of kinase cross-activities. Our study demonstrated potential utilities of our approach for large-scale kinase inhibitor selectivity profiling that could contribute to kinase drug development and safety assessment. Availability and implementation The KinomeFEATURE database and the associated scripts for performing kinase pocket similarity search can be downloaded from the Stanford SimTK website (https://simtk.org/projects/kdb). Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yu-Chen Lo
- Department of Bioengineering, Stanford, CA, USA
| | - Tianyun Liu
- Department of Bioengineering, Stanford, CA, USA.,Department of Genetics, Stanford University, Stanford, CA, USA
| | - Kari M Morrissey
- Department of Clinical Pharmacology, South San Francisco, CA, USA
| | | | - Adam R Johnson
- Biochemical and Cellular Pharmacology, South San Francisco, CA, USA
| | - Fabio Broccatelli
- Department of Drug Metabolism and Pharmacokinetic, Genentech Inc., South San Francisco, CA, USA
| | - Yu Zhong
- Department of Safety Assessment, South San Francisco, CA, USA
| | - Amita Joshi
- Department of Clinical Pharmacology, South San Francisco, CA, USA
| | - Russ B Altman
- Department of Bioengineering, Stanford, CA, USA.,Department of Genetics, Stanford University, Stanford, CA, USA
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13
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Torng W, Altman RB. Graph Convolutional Neural Networks for Predicting Drug-Target Interactions. J Chem Inf Model 2019; 59:4131-4149. [DOI: 10.1021/acs.jcim.9b00628] [Citation(s) in RCA: 126] [Impact Index Per Article: 25.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Affiliation(s)
- Wen Torng
- Deparment of Bioengineering, Stanford University, Stanford, California 94305, United States
| | - Russ B. Altman
- Deparment of Bioengineering, Stanford University, Stanford, California 94305, United States
- Department of Genetics, Stanford University, Stanford, California 94305, United States
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14
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Pocket similarity identifies selective estrogen receptor modulators as microtubule modulators at the taxane site. Nat Commun 2019; 10:1033. [PMID: 30833575 PMCID: PMC6399299 DOI: 10.1038/s41467-019-08965-w] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2018] [Accepted: 01/19/2019] [Indexed: 02/01/2023] Open
Abstract
Taxanes are a family of natural products with a broad spectrum of anticancer activity. This activity is mediated by interaction with the taxane site of beta-tubulin, leading to microtubule stabilization and cell death. Although widely used in the treatment of breast cancer and other malignancies, existing taxane-based therapies including paclitaxel and the second-generation docetaxel are currently limited by severe adverse effects and dose-limiting toxicity. To discover taxane site modulators, we employ a computational binding site similarity screen of > 14,000 drug-like pockets from PDB, revealing an unexpected similarity between the estrogen receptor and the beta-tubulin taxane binding pocket. Evaluation of nine selective estrogen receptor modulators (SERMs) via cellular and biochemical assays confirms taxane site interaction, microtubule stabilization, and cell proliferation inhibition. Our study demonstrates that SERMs can modulate microtubule assembly and raises the possibility of an estrogen receptor-independent mechanism for inhibiting cell proliferation. Taxanes are natural products which bind beta-tubulin, stabilize microtubules and have a broad spectrum of anticancer activity. Here authors employ a computational binding site similarity screen and cell-based assays to reveal a SERM cross-reactivity between the estrogen receptor and the beta-tubulin taxane binding pocket.
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15
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Sydow D, Burggraaff L, Szengel A, van Vlijmen HWT, IJzerman AP, van Westen GJP, Volkamer A. Advances and Challenges in Computational Target Prediction. J Chem Inf Model 2019; 59:1728-1742. [DOI: 10.1021/acs.jcim.8b00832] [Citation(s) in RCA: 58] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Affiliation(s)
- Dominique Sydow
- In silico Toxicology, Institute of Physiology, Charité − Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany
| | - Lindsey Burggraaff
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, P.O. Box 9502, 2300 RA, Leiden, The Netherlands
| | - Angelika Szengel
- In silico Toxicology, Institute of Physiology, Charité − Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany
| | - Herman W. T. van Vlijmen
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, P.O. Box 9502, 2300 RA, Leiden, The Netherlands
- Computational Chemistry, Janssen Research & Development, Turnhoutseweg 30, B-2340 Beerse, Belgium
| | - Adriaan P. IJzerman
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, P.O. Box 9502, 2300 RA, Leiden, The Netherlands
| | - Gerard J. P. van Westen
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, P.O. Box 9502, 2300 RA, Leiden, The Netherlands
| | - Andrea Volkamer
- In silico Toxicology, Institute of Physiology, Charité − Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany
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16
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Wilson JL, Racz R, Liu T, Adeniyi O, Sun J, Ramamoorthy A, Pacanowski M, Altman R. PathFX provides mechanistic insights into drug efficacy and safety for regulatory review and therapeutic development. PLoS Comput Biol 2018; 14:e1006614. [PMID: 30532240 PMCID: PMC6285459 DOI: 10.1371/journal.pcbi.1006614] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2018] [Accepted: 10/31/2018] [Indexed: 12/14/2022] Open
Abstract
Failure to demonstrate efficacy and safety issues are important reasons that drugs do not reach the market. An incomplete understanding of how drugs exert their effects hinders regulatory and pharmaceutical industry projections of a drug's benefits and risks. Signaling pathways mediate drug response and while many signaling molecules have been characterized for their contribution to disease or their role in drug side effects, our knowledge of these pathways is incomplete. To better understand all signaling molecules involved in drug response and the phenotype associations of these molecules, we created a novel method, PathFX, a non-commercial entity, to identify these pathways and drug-related phenotypes. We benchmarked PathFX by identifying drugs' marketed disease indications and reported a sensitivity of 41%, a 2.7-fold improvement over similar approaches. We then used PathFX to strengthen signals for drug-adverse event pairs occurring in the FDA Adverse Event Reporting System (FAERS) and also identified opportunities for drug repurposing for new diseases based on interaction paths that associated a marketed drug to that disease. By discovering molecular interaction pathways, PathFX improved our understanding of drug associations to safety and efficacy phenotypes. The algorithm may provide a new means to improve regulatory and therapeutic development decisions.
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Affiliation(s)
- Jennifer L. Wilson
- Department of Bioengineering, Stanford University, Palo Alto California, United States of America
| | - Rebecca Racz
- Division of Applied Regulatory Science, US Food and Drug Administration, Silver Spring Maryland, United States of America
| | - Tianyun Liu
- Department of Bioengineering, Stanford University, Palo Alto California, United States of America
| | - Oluseyi Adeniyi
- Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring Maryland, United States of America
| | - Jielin Sun
- Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring Maryland, United States of America
| | - Anuradha Ramamoorthy
- Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring Maryland, United States of America
| | - Michael Pacanowski
- Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring Maryland, United States of America
| | - Russ Altman
- Department of Bioengineering, Stanford University, Palo Alto California, United States of America
- Department of Genetics, Stanford University, Palo Alto California, United States of America
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17
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Ehrt C, Brinkjost T, Koch O. A benchmark driven guide to binding site comparison: An exhaustive evaluation using tailor-made data sets (ProSPECCTs). PLoS Comput Biol 2018; 14:e1006483. [PMID: 30408032 PMCID: PMC6224041 DOI: 10.1371/journal.pcbi.1006483] [Citation(s) in RCA: 41] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2018] [Accepted: 09/02/2018] [Indexed: 11/24/2022] Open
Abstract
The automated comparison of protein-ligand binding sites provides useful insights into yet unexplored site similarities. Various stages of computational and chemical biology research can benefit from this knowledge. The search for putative off-targets and the establishment of polypharmacological effects by comparing binding sites led to promising results for numerous projects. Although many cavity comparison methods are available, a comprehensive analysis to guide the choice of a tool for a specific application is wanting. Moreover, the broad variety of binding site modeling approaches, comparison algorithms, and scoring metrics impedes this choice. Herein, we aim to elucidate strengths and weaknesses of binding site comparison methodologies. A detailed benchmark study is the only possibility to rationalize the selection of appropriate tools for different scenarios. Specific evaluation data sets were developed to shed light on multiple aspects of binding site comparison. An assembly of all applied benchmark sets (ProSPECCTs–Protein Site Pairs for the Evaluation of Cavity Comparison Tools) is made available for the evaluation and optimization of further and still emerging methods. The results indicate the importance of such analyses to facilitate the choice of a methodology that complies with the requirements of a specific scientific challenge. Binding site similarities are useful in the context of promiscuity prediction, drug repurposing, the analysis of protein-ligand and protein-protein complexes, function prediction, and further fields of general interest in chemical biology and biochemistry. Many years of research have led to the development of a multitude of methods for binding site analysis and comparison. On the one hand, their availability supports research. On the other hand, the huge number of methods hampers the efficient selection of a specific tool. Our research is dedicated to the analysis of different cavity comparison tools. We use several binding site data sets to establish guidelines which can be applied to ensure a successful application of comparison methods by circumventing potential pitfalls.
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Affiliation(s)
- Christiane Ehrt
- Faculty of Chemistry and Chemical Biology, TU Dortmund University, Dortmund, Germany
| | - Tobias Brinkjost
- Faculty of Chemistry and Chemical Biology, TU Dortmund University, Dortmund, Germany
- Department of Computer Science, TU Dortmund University, Dortmund, Germany
| | - Oliver Koch
- Faculty of Chemistry and Chemical Biology, TU Dortmund University, Dortmund, Germany
- * E-mail: ,
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18
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Gangi Setty T, Mowers JC, Hobbs AG, Maiya SP, Syed S, Munson RS, Apicella MA, Subramanian R. Molecular characterization of the interaction of sialic acid with the periplasmic binding protein from Haemophilus ducreyi. J Biol Chem 2018; 293:20073-20084. [PMID: 30315109 DOI: 10.1074/jbc.ra118.005151] [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: 08/02/2018] [Revised: 10/11/2018] [Indexed: 12/18/2022] Open
Abstract
The primary role of bacterial periplasmic binding proteins is sequestration of essential metabolites present at a low concentration in the periplasm and making them available for active transporters that transfer these ligands into the bacterial cell. The periplasmic binding proteins (SiaPs) from the tripartite ATP-independent periplasmic (TRAP) transport system that transports mammalian host-derived sialic acids have been well studied from different pathogenic bacteria, including Haemophilus influenzae, Fusobacterium nucleatum, Pasteurella multocida, and Vibrio cholerae SiaPs bind the sialic acid N-acetylneuraminic acid (Neu5Ac) with nanomolar affinity by forming electrostatic and hydrogen-bonding interactions. Here, we report the crystal structure of a periplasmic binding protein (SatA) of the ATP-binding cassette (ABC) transport system from the pathogenic bacterium Haemophilus ducreyi The structure of Hd-SatA in the native form and sialic acid-bound forms (with Neu5Ac and N-glycolylneuraminic acid (Neu5Gc)), determined to 2.2, 1.5, and 2.5 Å resolutions, respectively, revealed a ligand-binding site that is very different from those of the SiaPs of the TRAP transport system. A structural comparison along with thermodynamic studies suggested that similar affinities are achieved in the two classes of proteins through distinct mechanisms, one enthalpically driven and the other entropically driven. In summary, our structural and thermodynamic characterization of Hd-SatA reveals that it binds sialic acids with nanomolar affinity and that this binding is an entropically driven process. This information is important for future structure-based drug design against this pathogen and related bacteria.
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Affiliation(s)
- Thanuja Gangi Setty
- From the Institute for Stem Cell Biology and Regenerative Medicine, GKVK Post, Bangalore 560065, India,; the University of Trans-Disciplinary Health Sciences and Technology (TDU), Bengaluru, Karnataka 560064, India
| | - Jonathan C Mowers
- the Departments of Biochemistry and Carver College of Medicine, University of Iowa, Iowa City, Iowa 52242
| | - Aaron G Hobbs
- the Departments of Biochemistry and Carver College of Medicine, University of Iowa, Iowa City, Iowa 52242
| | - Shubha P Maiya
- From the Institute for Stem Cell Biology and Regenerative Medicine, GKVK Post, Bangalore 560065, India
| | - Sanaa Syed
- From the Institute for Stem Cell Biology and Regenerative Medicine, GKVK Post, Bangalore 560065, India
| | - Robert S Munson
- the Center for Microbial Interface Biology, Ohio State University, Columbus, Ohio 43210, and
| | - Michael A Apicella
- Microbiology and Immunology, Carver College of Medicine, University of Iowa, Iowa City, Iowa 52242
| | - Ramaswamy Subramanian
- From the Institute for Stem Cell Biology and Regenerative Medicine, GKVK Post, Bangalore 560065, India,; the Departments of Biochemistry and Carver College of Medicine, University of Iowa, Iowa City, Iowa 52242.
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19
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Wilson JL. A scientist engineer's contribution to therapeutic discovery and development. Exp Biol Med (Maywood) 2018; 243:1125-1132. [PMID: 30458646 PMCID: PMC6327370 DOI: 10.1177/1535370218813974] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023] Open
Abstract
An engineering perspective views cells as complex circuits that process inputs – drugs, environmental cues – to create complex outcomes – disease, growth, death – and this perspective has immense potential for drug development. Logical rules can describe the features of cells and reductionist approaches have exploited these rules for drug development. In contrast, the reductionist approach serially characterizes cellular components and develops a deep understanding of each component’s specific role. This approach underutilizes the full system of biomolecules relevant to disease pathology and drug effects. An engineering perspective provides the tools to understand and leverage the full extent of biological systems; applying both reverse and forward engineering, a strength of the engineering approach has demonstrated progress in advancing understanding of disease and drug mechanisms. Drug development lacks sufficient engineering specifications, or empirical models, of drug pharmacodynamic effects and future efforts to derive empirical models of drug effects will streamline this development. At this stage of progress, the scientist engineer is uniquely poised to solve problems in therapeutics related to modulating multiple diseases with a single or multiple therapeutic agents and identifying pharmacodynamics biomarkers with knowledge of drug pathways. This article underscores the value of these principles in an age where drug development costs are soaring and finding efficacious therapies is challenging.
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Affiliation(s)
- Jennifer L Wilson
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
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20
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Govindaraj RG, Brylinski M. Comparative assessment of strategies to identify similar ligand-binding pockets in proteins. BMC Bioinformatics 2018. [PMID: 29523085 PMCID: PMC5845264 DOI: 10.1186/s12859-018-2109-2] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022] Open
Abstract
Background Detecting similar ligand-binding sites in globally unrelated proteins has a wide range of applications in modern drug discovery, including drug repurposing, the prediction of side effects, and drug-target interactions. Although a number of techniques to compare binding pockets have been developed, this problem still poses significant challenges. Results We evaluate the performance of three algorithms to calculate similarities between ligand-binding sites, APoc, SiteEngine, and G-LoSA. Our assessment considers not only the capabilities to identify similar pockets and to construct accurate local alignments, but also the dependence of these alignments on the sequence order. We point out certain drawbacks of previously compiled datasets, such as the inclusion of structurally similar proteins, leading to an overestimated performance. To address these issues, a rigorous procedure to prepare unbiased, high-quality benchmarking sets is proposed. Further, we conduct a comparative assessment of techniques directly aligning binding pockets to indirect strategies employing structure-based virtual screening with AutoDock Vina and rDock. Conclusions Thorough benchmarks reveal that G-LoSA offers a fairly robust overall performance, whereas the accuracy of APoc and SiteEngine is satisfactory only against easy datasets. Moreover, combining various algorithms into a meta-predictor improves the performance of existing methods to detect similar binding sites in unrelated proteins by 5–10%. All data reported in this paper are freely available at https://osf.io/6ngbs/.
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Affiliation(s)
| | - Michal Brylinski
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA, USA. .,Center for Computation & Technology, Louisiana State University, Baton Rouge, LA, USA.
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21
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Moult J, Fidelis K, Kryshtafovych A, Schwede T, Tramontano A. Critical assessment of methods of protein structure prediction (CASP)-Round XII. Proteins 2018; 86 Suppl 1:7-15. [PMID: 29082672 PMCID: PMC5897042 DOI: 10.1002/prot.25415] [Citation(s) in RCA: 245] [Impact Index Per Article: 40.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2017] [Revised: 10/25/2017] [Accepted: 10/27/2017] [Indexed: 12/24/2022]
Abstract
This article reports the outcome of the 12th round of Critical Assessment of Structure Prediction (CASP12), held in 2016. CASP is a community experiment to determine the state of the art in modeling protein structure from amino acid sequence. Participants are provided sequence information and in turn provide protein structure models and related information. Analysis of the submitted structures by independent assessors provides a comprehensive picture of the capabilities of current methods, and allows progress to be identified. This was again an exciting round of CASP, with significant advances in 4 areas: (i) The use of new methods for predicting three-dimensional contacts led to a two-fold improvement in contact accuracy. (ii) As a consequence, model accuracy for proteins where no template was available improved dramatically. (iii) Models based on a structural template showed overall improvement in accuracy. (iv) Methods for estimating the accuracy of a model continued to improve. CASP continued to develop new areas: (i) Assessing methods for building quaternary structure models, including an expansion of the collaboration between CASP and CAPRI. (ii) Modeling with the aid of experimental data was extended to include SAXS data, as well as again using chemical cross-linking information. (iii) A team of assessors evaluated the suitability of models for a range of applications, including mutation interpretation, analysis of ligand binding properties, and identification of interfaces. This article describes the experiment and summarizes the results. The rest of this special issue of PROTEINS contains papers describing CASP12 results and assessments in more detail.
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Affiliation(s)
- John Moult
- Institute for Bioscience and Biotechnology Research and Department of Cell Biology and Molecular Genetics, University of Maryland, 9600 Gudelsky Drive, Rockville, MD 20850, USA
| | - Krzysztof Fidelis
- Genome Center, University of California, Davis, 451 Health Sciences Drive, Davis, CA 95616, USA
| | - Andriy Kryshtafovych
- Genome Center, University of California, Davis, 451 Health Sciences Drive, Davis, CA 95616, USA
| | - Torsten Schwede
- University of Basel, Biozentrum & SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Anna Tramontano
- Department of Physics and Istituto Pasteur - Fondazione Cenci Bolognetti, Sapienza University of Rome, P.le Aldo Moro, 5, 00185 Rome, Italy
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22
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Liu T, Ish‐Shalom S, Torng W, Lafita A, Bock C, Mort M, Cooper DN, Bliven S, Capitani G, Mooney SD, Altman RB. Biological and functional relevance of CASP predictions. Proteins 2018; 86 Suppl 1:374-386. [PMID: 28975675 PMCID: PMC5820171 DOI: 10.1002/prot.25396] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2017] [Revised: 09/12/2017] [Accepted: 10/03/2017] [Indexed: 02/06/2023]
Abstract
Our goal is to answer the question: compared with experimental structures, how useful are predicted models for functional annotation? We assessed the functional utility of predicted models by comparing the performances of a suite of methods for functional characterization on the predictions and the experimental structures. We identified 28 sites in 25 protein targets to perform functional assessment. These 28 sites included nine sites with known ligand binding (holo-sites), nine sites that are expected or suggested by experimental authors for small molecule binding (apo-sites), and Ten sites containing important motifs, loops, or key residues with important disease-associated mutations. We evaluated the utility of the predictions by comparing their microenvironments to the experimental structures. Overall structural quality correlates with functional utility. However, the best-ranked predictions (global) may not have the best functional quality (local). Our assessment provides an ability to discriminate between predictions with high structural quality. When assessing ligand-binding sites, most prediction methods have higher performance on apo-sites than holo-sites. Some servers show consistently high performance for certain types of functional sites. Finally, many functional sites are associated with protein-protein interaction. We also analyzed biologically relevant features from the protein assemblies of two targets where the active site spanned the protein-protein interface. For the assembly targets, we find that the features in the models are mainly determined by the choice of template.
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Affiliation(s)
- Tianyun Liu
- Department of BioengineeringStanford UniversityStanfordCalifornia
| | - Shirbi Ish‐Shalom
- Biomedical Informatics Training Program, Stanford UniversityStanfordCalifornia
| | - Wen Torng
- Department of BioengineeringStanford UniversityStanfordCalifornia
| | - Aleix Lafita
- Laboratory of Biomolecular ResearchPaul Scherrer InstituteVilligenSwitzerland
- Department of Biosystems Science and EngineeringETH Zurich4058BaselSwitzerland
| | - Christian Bock
- Department of Biomedical Informatics and Medical EducationUniversity of WashingtonSeattleWashington
- Heidelberg UniversityHeidelbergGermany
| | - Matthew Mort
- Institute of Medical Genetics, Cardiff UniversityUnited Kingdom
| | - David N Cooper
- Institute of Medical Genetics, Cardiff UniversityUnited Kingdom
| | - Spencer Bliven
- Laboratory of Biomolecular ResearchPaul Scherrer InstituteVilligenSwitzerland
- National Center for Biotechnology Information, National Library of MedicineNational Institutes of HealthBethesdaMaryland
| | - Guido Capitani
- Laboratory of Biomolecular ResearchPaul Scherrer InstituteVilligenSwitzerland
- Department of BiologyETH ZurichZurichSwitzerland
| | - Sean D. Mooney
- Department of Biomedical Informatics and Medical EducationUniversity of WashingtonSeattleWashington
| | - Russ B. Altman
- Department of BioengineeringStanford UniversityStanfordCalifornia
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23
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Structure-based prediction of ligand-protein interactions on a genome-wide scale. Proc Natl Acad Sci U S A 2017; 114:13685-13690. [PMID: 29229851 DOI: 10.1073/pnas.1705381114] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
We report a template-based method, LT-scanner, which scans the human proteome using protein structural alignment to identify proteins that are likely to bind ligands that are present in experimentally determined complexes. A scoring function that rapidly accounts for binding site similarities between the template and the proteins being scanned is a crucial feature of the method. The overall approach is first tested based on its ability to predict the residues on the surface of a protein that are likely to bind small-molecule ligands. The algorithm that we present, LBias, is shown to compare very favorably to existing algorithms for binding site residue prediction. LT-scanner's performance is evaluated based on its ability to identify known targets of Food and Drug Administration (FDA)-approved drugs and it too proves to be highly effective. The specificity of the scoring function that we use is demonstrated by the ability of LT-scanner to identify the known targets of FDA-approved kinase inhibitors based on templates involving other kinases. Combining sequence with structural information further improves LT-scanner performance. The approach we describe is extendable to the more general problem of identifying binding partners of known ligands even if they do not appear in a structurally determined complex, although this will require the integration of methods that combine protein structure and chemical compound databases.
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24
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Torng W, Altman RB. 3D deep convolutional neural networks for amino acid environment similarity analysis. BMC Bioinformatics 2017; 18:302. [PMID: 28615003 PMCID: PMC5472009 DOI: 10.1186/s12859-017-1702-0] [Citation(s) in RCA: 75] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2017] [Accepted: 05/22/2017] [Indexed: 01/08/2023] Open
Abstract
Background Central to protein biology is the understanding of how structural elements give rise to observed function. The surfeit of protein structural data enables development of computational methods to systematically derive rules governing structural-functional relationships. However, performance of these methods depends critically on the choice of protein structural representation. Most current methods rely on features that are manually selected based on knowledge about protein structures. These are often general-purpose but not optimized for the specific application of interest. In this paper, we present a general framework that applies 3D convolutional neural network (3DCNN) technology to structure-based protein analysis. The framework automatically extracts task-specific features from the raw atom distribution, driven by supervised labels. As a pilot study, we use our network to analyze local protein microenvironments surrounding the 20 amino acids, and predict the amino acids most compatible with environments within a protein structure. To further validate the power of our method, we construct two amino acid substitution matrices from the prediction statistics and use them to predict effects of mutations in T4 lysozyme structures. Results Our deep 3DCNN achieves a two-fold increase in prediction accuracy compared to models that employ conventional hand-engineered features and successfully recapitulates known information about similar and different microenvironments. Models built from our predictions and substitution matrices achieve an 85% accuracy predicting outcomes of the T4 lysozyme mutation variants. Our substitution matrices contain rich information relevant to mutation analysis compared to well-established substitution matrices. Finally, we present a visualization method to inspect the individual contributions of each atom to the classification decisions. Conclusions End-to-end trained deep learning networks consistently outperform methods using hand-engineered features, suggesting that the 3DCNN framework is well suited for analysis of protein microenvironments and may be useful for other protein structural analyses. Electronic supplementary material The online version of this article (doi:10.1186/s12859-017-1702-0) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Wen Torng
- Deparment of Bioengineering, Stanford University, Stanford, CA, 94305, USA
| | - Russ B Altman
- Deparment of Bioengineering, Stanford University, Stanford, CA, 94305, USA. .,Department of Genetics, Stanford University, Stanford, CA, 94305, USA.
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25
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Evolutionary studies of ligand binding sites in proteins. Curr Opin Struct Biol 2016; 45:85-90. [PMID: 27992825 DOI: 10.1016/j.sbi.2016.11.024] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2016] [Revised: 11/30/2016] [Accepted: 11/30/2016] [Indexed: 01/27/2023]
Abstract
Biological processes at their most fundamental molecular aspects are defined by molecular interactions with ligand-protein interactions in particular at the core of cellular functions such as metabolism and signalling. Divergent and convergent processes shape the evolution of ligand binding sites. The competition between similar ligands and binding sites across protein families create evolutionary pressures that affect the specificity and selectivity of interactions. This short review showcases recent studies of the evolution of ligand binding-sites and methods used to detect binding-site similarities.
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26
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Liu T, Oprea T, Ursu O, Hasselgren C, Altman RB. Estimation of Maximum Recommended Therapeutic Dose Using Predicted Promiscuity and Potency. Clin Transl Sci 2016; 9:311-320. [PMID: 27736015 PMCID: PMC5161261 DOI: 10.1111/cts.12422] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2016] [Accepted: 09/01/2016] [Indexed: 01/08/2023] Open
Abstract
We report a simple model that predicts the maximum recommended therapeutic dose (MRTD) of small molecule drugs based on an assessment of likely protein-drug interactions. Previously, we reported methods for computational estimation of drug promiscuity and potency. We used these concepts to build a linear model derived from 238 small molecular drugs to predict MRTD. We applied this model successfully to predict MRTDs for 16 nonsteroidal antiinflammatory drugs (NSAIDs) and 14 antiretroviral drugs. Of note, based on the estimated promiscuity of low-dose drugs (and active chemicals), we identified 83 proteins as "high-risk off-targets" (HROTs) that are often associated with low doses; the evaluation of interactions with HROTs may be useful during early phases of drug discovery. Our model helps explain the MRTD for drugs with severe adverse reactions caused by interactions with HROTs.
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Affiliation(s)
- T Liu
- Department of Genetics, Stanford University, Stanford, California, USA
| | - T Oprea
- Department of Internal Medicine, Translational Informatics Division, University of New Mexico, Albuquerque, New Mexico, USA
| | - O Ursu
- Department of Internal Medicine, Translational Informatics Division, University of New Mexico, Albuquerque, New Mexico, USA
| | - C Hasselgren
- PureInfo Discovery Corp, Albuquerque, New Mexico, USA
| | - R B Altman
- Departments of Bioengineering and Genetics, Stanford University, Stanford, California, USA
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27
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Moll M, Finn PW, Kavraki LE. Structure-guided selection of specificity determining positions in the human Kinome. BMC Genomics 2016; 17 Suppl 4:431. [PMID: 27556159 PMCID: PMC5001202 DOI: 10.1186/s12864-016-2790-3] [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] [Indexed: 11/25/2022] Open
Abstract
Background The human kinome contains many important drug targets. It is well-known that inhibitors of protein kinases bind with very different selectivity profiles. This is also the case for inhibitors of many other protein families. The increased availability of protein 3D structures has provided much information on the structural variation within a given protein family. However, the relationship between structural variations and binding specificity is complex and incompletely understood. We have developed a structural bioinformatics approach which provides an analysis of key determinants of binding selectivity as a tool to enhance the rational design of drugs with a specific selectivity profile. Results We propose a greedy algorithm that computes a subset of residue positions in a multiple sequence alignment such that structural and chemical variation in those positions helps explain known binding affinities. By providing this information, the main purpose of the algorithm is to provide experimentalists with possible insights into how the selectivity profile of certain inhibitors is achieved, which is useful for lead optimization. In addition, the algorithm can also be used to predict binding affinities for structures whose affinity for a given inhibitor is unknown. The algorithm’s performance is demonstrated using an extensive dataset for the human kinome. Conclusion We show that the binding affinity of 38 different kinase inhibitors can be explained with consistently high precision and accuracy using the variation of at most six residue positions in the kinome binding site. We show for several inhibitors that we are able to identify residues that are known to be functionally important.
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Affiliation(s)
- Mark Moll
- Department of Computer Science, Rice University, PO Box 1892, Houston, 77251, TX, USA.
| | - Paul W Finn
- University of Buckingham, Hunter St, Buckingham, UK
| | - Lydia E Kavraki
- Department of Computer Science, Rice University, PO Box 1892, Houston, 77251, TX, USA
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28
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Siragusa L, Luciani R, Borsari C, Ferrari S, Costi MP, Cruciani G, Spyrakis F. Comparing Drug Images and Repurposing Drugs with BioGPS and FLAPdock: The Thymidylate Synthase Case. ChemMedChem 2016; 11:1653-66. [PMID: 27404817 DOI: 10.1002/cmdc.201600121] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2016] [Revised: 06/08/2016] [Indexed: 12/14/2022]
Abstract
Repurposing and repositioning drugs has become a frequently pursued and successful strategy in the current era, as new chemical entities are increasingly difficult to find and get approved. Herein we report an integrated BioGPS/FLAPdock pipeline for rapid and effective off-target identification and drug repurposing. Our method is based on the structural and chemical properties of protein binding sites, that is, the ligand image, encoded in the GRID molecular interaction fields (MIFs). Protein similarity is disclosed through the BioGPS algorithm by measuring the pockets' overlap according to which pockets are clustered. Co-crystallized and known ligands can be cross-docked among similar targets, selected for subsequent in vitro binding experiments, and possibly improved for inhibitory potency. We used human thymidylate synthase (TS) as a test case and searched the entire RCSB Protein Data Bank (PDB) for similar target pockets. We chose casein kinase IIα as a control and tested a series of its inhibitors against the TS template. Ellagic acid and apigenin were identified as TS inhibitors, and various flavonoids were selected and synthesized in a second-round selection. The compounds were demonstrated to be active in the low-micromolar range.
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Affiliation(s)
- Lydia Siragusa
- Molecular Discovery Limited, 215 Marsh Road, Pinner Middlesex, London, HA5 5NE, UK
| | - Rosaria Luciani
- Department of Life Sciences, University of Modena and Reggio Emilia, Via Campi 103, 41125, Modena, Italy
| | - Chiara Borsari
- Department of Life Sciences, University of Modena and Reggio Emilia, Via Campi 103, 41125, Modena, Italy
| | - Stefania Ferrari
- Department of Life Sciences, University of Modena and Reggio Emilia, Via Campi 103, 41125, Modena, Italy
| | - Maria Paola Costi
- Department of Life Sciences, University of Modena and Reggio Emilia, Via Campi 103, 41125, Modena, Italy
| | - Gabriele Cruciani
- Department of Chemistry, Biology and Biotechnology, University of Perugia, Via Elce di Sotto 8, 06123, Perugia, Italy
| | - Francesca Spyrakis
- Department of Life Sciences, University of Modena and Reggio Emilia, Via Campi 103, 41125, Modena, Italy. .,Department of Food Science, University of Parma, Viale delle Scienze 17A, 43124, Parma, Italy.
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Ehrt C, Brinkjost T, Koch O. Impact of Binding Site Comparisons on Medicinal Chemistry and Rational Molecular Design. J Med Chem 2016; 59:4121-51. [PMID: 27046190 DOI: 10.1021/acs.jmedchem.6b00078] [Citation(s) in RCA: 66] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Modern rational drug design not only deals with the search for ligands binding to interesting and promising validated targets but also aims to identify the function and ligands of yet uncharacterized proteins having impact on different diseases. Additionally, it contributes to the design of inhibitors with distinct selectivity patterns and the prediction of possible off-target effects. The identification of similarities between binding sites of various proteins is a useful approach to cope with those challenges. The main scope of this perspective is to describe applications of different protein binding site comparison approaches to outline their applicability and impact on molecular design. The article deals with various substantial application domains and provides some outstanding examples to show how various binding site comparison methods can be applied to promote in silico drug design workflows. In addition, we will also briefly introduce the fundamental principles of different protein binding site comparison methods.
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Affiliation(s)
- Christiane Ehrt
- Faculty of Chemistry and Chemical Biology, TU Dortmund University , Otto-Hahn-Straße 6, 44227 Dortmund, Germany
| | - Tobias Brinkjost
- Faculty of Chemistry and Chemical Biology, TU Dortmund University , Otto-Hahn-Straße 6, 44227 Dortmund, Germany.,Department of Computer Science, TU Dortmund University , Otto-Hahn-Straße 14, 44224 Dortmund, Germany
| | - Oliver Koch
- Faculty of Chemistry and Chemical Biology, TU Dortmund University , Otto-Hahn-Straße 6, 44227 Dortmund, Germany
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30
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Medvedeva IV, Demenkov PS, Ivanisenko VA. Computer analysis of protein functional sites projection on exon structure of genes in Metazoa. BMC Genomics 2015; 16 Suppl 13:S2. [PMID: 26693737 PMCID: PMC4686782 DOI: 10.1186/1471-2164-16-s13-s2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Study of the relationship between the structural and functional organization of proteins and their coding genes is necessary for an understanding of the evolution of molecular systems and can provide new knowledge for many applications for designing proteins with improved medical and biological properties. It is well known that the functional properties of proteins are determined by their functional sites. Functional sites are usually represented by a small number of amino acid residues that are distantly located from each other in the amino acid sequence. They are highly conserved within their functional group and vary significantly in structure between such groups. According to this facts analysis of the general properties of the structural organization of the functional sites at the protein level and, at the level of exon-intron structure of the coding gene is still an actual problem. RESULTS One approach to this analysis is the projection of amino acid residue positions of the functional sites along with the exon boundaries to the gene structure. In this paper, we examined the discontinuity of the functional sites in the exon-intron structure of genes and the distribution of lengths and phases of the functional site encoding exons in vertebrate genes. We have shown that the DNA fragments coding the functional sites were in the same exons, or in close exons. The observed tendency to cluster the exons that code functional sites which could be considered as the unit of protein evolution. We studied the characteristics of the structure of the exon boundaries that code, and do not code, functional sites in 11 Metazoa species. This is accompanied by a reduced frequency of intercodon gaps (phase 0) in exons encoding the amino acid residue functional site, which may be evidence of the existence of evolutionary limitations to the exon shuffling. CONCLUSIONS These results characterize the features of the coding exon-intron structure that affect the functionality of the encoded protein and allow a better understanding of the emergence of biological diversity.
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31
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Barelier S, Sterling T, O’Meara MJ, Shoichet BK. The Recognition of Identical Ligands by Unrelated Proteins. ACS Chem Biol 2015; 10:2772-84. [PMID: 26421501 DOI: 10.1021/acschembio.5b00683] [Citation(s) in RCA: 48] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
The binding of drugs and reagents to off-targets is well-known. Whereas many off-targets are related to the primary target by sequence and fold, many ligands bind to unrelated pairs of proteins, and these are harder to anticipate. If the binding site in the off-target can be related to that of the primary target, this challenge resolves into aligning the two pockets. However, other cases are possible: the ligand might interact with entirely different residues and environments in the off-target, or wholly different ligand atoms may be implicated in the two complexes. To investigate these scenarios at atomic resolution, the structures of 59 ligands in 116 complexes (62 pairs in total), where the protein pairs were unrelated by fold but bound an identical ligand, were examined. In almost half of the pairs, the ligand interacted with unrelated residues in the two proteins (29 pairs), and in 14 of the pairs wholly different ligand moieties were implicated in each complex. Even in those 19 pairs of complexes that presented similar environments to the ligand, ligand superposition rarely resulted in the overlap of related residues. There appears to be no single pattern-matching "code" for identifying binding sites in unrelated proteins that bind identical ligands, though modeling suggests that there might be a limited number of different patterns that suffice to recognize different ligand functional groups.
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Affiliation(s)
- Sarah Barelier
- Department of Pharmaceutical
Chemistry, University of California San Francisco, 1700 Fourth
Street, Byers Hall, San Francisco, California 94158, United States
| | - Teague Sterling
- Department of Pharmaceutical
Chemistry, University of California San Francisco, 1700 Fourth
Street, Byers Hall, San Francisco, California 94158, United States
| | - Matthew J. O’Meara
- Department of Pharmaceutical
Chemistry, University of California San Francisco, 1700 Fourth
Street, Byers Hall, San Francisco, California 94158, United States
| | - Brian K. Shoichet
- Department of Pharmaceutical
Chemistry, University of California San Francisco, 1700 Fourth
Street, Byers Hall, San Francisco, California 94158, United States
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32
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Bartolowits M, Davisson VJ. Considerations of Protein Subpockets in Fragment-Based Drug Design. Chem Biol Drug Des 2015; 87:5-20. [PMID: 26307335 DOI: 10.1111/cbdd.12631] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
While the fragment-based drug design approach continues to gain importance, gaps in the tools and methods available in the identification and accurate utilization of protein subpockets have limited the scope. The importance of these features of small molecule-protein recognition is highlighted with several examples. A generalized solution for the identification of subpockets and corresponding chemical fragments remains elusive, but there are numerous advancements in methods that can be used in combination to address subpockets. Finally, additional examples of approaches that consider the relative importance of small-molecule co-dependence of protein conformations are highlighted to emphasize an increased significance of subpockets, especially at protein interfaces.
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Affiliation(s)
- Matthew Bartolowits
- Department of Medicinal Chemistry and Molecular Pharmacology, College of Pharmacy, Purdue University, 575 Stadium Mall Dr., West Lafayette, IN, 47907, USA
| | - V Jo Davisson
- Department of Medicinal Chemistry and Molecular Pharmacology, College of Pharmacy, Purdue University, 575 Stadium Mall Dr., West Lafayette, IN, 47907, USA
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33
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Chartier M, Najmanovich R. Detection of Binding Site Molecular Interaction Field Similarities. J Chem Inf Model 2015; 55:1600-15. [PMID: 26158641 DOI: 10.1021/acs.jcim.5b00333] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
Protein binding-site similarity detection methods can be used to predict protein function and understand molecular recognition, as a tool in drug design for drug repurposing and polypharmacology, and for the prediction of the molecular determinants of drug toxicity. Here, we present IsoMIF, a method able to identify binding site molecular interaction field similarities across protein families. IsoMIF utilizes six chemical probes and the detection of subgraph isomorphisms to identify geometrically and chemically equivalent sections of protein cavity pairs. The method is validated using six distinct data sets, four of those previously used in the validation of other methods. The mean area under the receiver operator curve (AUC) obtained across data sets for IsoMIF is higher than those of other methods. Furthermore, while IsoMIF obtains consistently high AUC values across data sets, other methods perform more erratically across data sets. IsoMIF can be used to predict function from structure, to detect potential cross-reactivity or polypharmacology targets, and to help suggest bioisosteric replacements to known binding molecules. Given that IsoMIF detects spatial patterns of molecular interaction field similarities, its predictions are directly related to pharmacophores and may be readily translated into modeling decisions in structure-based drug design. IsoMIF may in principle detect similar binding sites with distinct amino acid arrangements that lead to equivalent interactions within the cavity. The source code to calculate and visualize MIFs and MIF similarities are freely available.
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Affiliation(s)
- Matthieu Chartier
- Department of Biochemistry, Faculty of Medicine and Health Sciences, University of Sherbrooke , 12e Avenue Nord, Sherbrooke, J1H 5N4 Québec, Canada
| | - Rafael Najmanovich
- Department of Biochemistry, Faculty of Medicine and Health Sciences, University of Sherbrooke , 12e Avenue Nord, Sherbrooke, J1H 5N4 Québec, Canada
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34
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Liu T, Altman RB. Relating Essential Proteins to Drug Side-Effects Using Canonical Component Analysis: A Structure-Based Approach. J Chem Inf Model 2015; 55:1483-94. [PMID: 26121262 DOI: 10.1021/acs.jcim.5b00030] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
The molecular mechanism of many drug side-effects is unknown and difficult to predict. Previous methods for explaining side-effects have focused on known drug targets and their pathways. However, low affinity binding to proteins that are not usually considered drug targets may also drive side-effects. In order to assess these alternative targets, we used the 3D structures of 563 essential human proteins systematically to predict binding to 216 drugs. We first benchmarked our affinity predictions with available experimental data. We then combined singular value decomposition and canonical component analysis (SVD-CCA) to predict side-effects based on these novel target profiles. Our method predicts side-effects with good accuracy (average AUC: 0.82 for side effects present in <50% of drug labels). We also noted that side-effect frequency is the most important feature for prediction and can confound efforts at elucidating mechanism; our method allows us to remove the contribution of frequency and isolate novel biological signals. In particular, our analysis produces 2768 triplet associations between 50 essential proteins, 99 drugs, and 77 side-effects. Although experimental validation is difficult because many of our essential proteins do not have validated assays, we nevertheless attempted to validate a subset of these associations using experimental assay data. Our focus on essential proteins allows us to find potential associations that would likely be missed if we used recognized drug targets. Our associations provide novel insights about the molecular mechanisms of drug side-effects and highlight the need for expanded experimental efforts to investigate drug binding to proteins more broadly.
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Affiliation(s)
- Tianyun Liu
- †Department of Genetics, Stanford University, Stanford, California 94305, United States
| | - Russ B Altman
- ‡Department of Genetics and Department of Bioengineering, Stanford University, Stanford, California 94305, United States
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35
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Assessing protein kinase target similarity: Comparing sequence, structure, and cheminformatics approaches. BIOCHIMICA ET BIOPHYSICA ACTA-PROTEINS AND PROTEOMICS 2015; 1854:1605-16. [PMID: 26001898 DOI: 10.1016/j.bbapap.2015.05.004] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2015] [Revised: 05/08/2015] [Accepted: 05/11/2015] [Indexed: 11/22/2022]
Abstract
In just over two decades, structure based protein kinase inhibitor discovery has grown from trial and error approaches, using individual target structures, to structure and data driven approaches that may aim to optimize inhibition properties across several targets. This is increasingly enabled by the growing availability of potent compounds and kinome-wide binding data. Assessing the prospects for adapting known compounds to new therapeutic uses is thus a key priority for current drug discovery efforts. Tools that can successfully link the diverse information regarding target sequence, structure, and ligand binding properties now accompany a transformation of protein kinase inhibitor research, away from single, block-buster drug models, and toward "personalized medicine" with niche applications and highly specialized research groups. Major hurdles for the transformation to data driven drug discovery include mismatches in data types, and disparities of methods and molecules used; at the core remains the problem that ligand binding energies cannot be predicted precisely from individual structures. However, there is a growing body of experimental data for increasingly successful focussing of efforts: focussed chemical libraries, drug repurposing, polypharmacological design, to name a few. Protein kinase target similarity is easily quantified by sequence, and its relevance to ligand design includes broad classification by key binding sites, evaluation of resistance mutations, and the use of surrogate proteins. Although structural evaluation offers more information, the flexibility of protein kinases, and differences between the crystal and physiological environments may make the use of crystal structures misleading when structures are considered individually. Cheminformatics may enable the "calibration" of sequence and crystal structure information, with statistical methods able to identify key correlates to activity but also here, "the devil is in the details." Examples from specific repurposing and polypharmacology applications illustrate these points. This article is part of a Special Issue entitled: Inhibitors of Protein Kinases.
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36
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Miguel A, Hsin J, Liu T, Tang G, Altman RB, Huang KC. Variations in the binding pocket of an inhibitor of the bacterial division protein FtsZ across genotypes and species. PLoS Comput Biol 2015; 11:e1004117. [PMID: 25811761 PMCID: PMC4374959 DOI: 10.1371/journal.pcbi.1004117] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2014] [Accepted: 01/08/2015] [Indexed: 01/28/2023] Open
Abstract
The recent increase in antibiotic resistance in pathogenic bacteria calls for new approaches to drug-target selection and drug development. Targeting the mechanisms of action of proteins involved in bacterial cell division bypasses problems associated with increasingly ineffective variants of older antibiotics; to this end, the essential bacterial cytoskeletal protein FtsZ is a promising target. Recent work on its allosteric inhibitor, PC190723, revealed in vitro activity on Staphylococcus aureus FtsZ and in vivo antimicrobial activities. However, the mechanism of drug action and its effect on FtsZ in other bacterial species are unclear. Here, we examine the structural environment of the PC190723 binding pocket using PocketFEATURE, a statistical method that scores the similarity between pairs of small-molecule binding sites based on 3D structure information about the local microenvironment, and molecular dynamics (MD) simulations. We observed that species and nucleotide-binding state have significant impacts on the structural properties of the binding site, with substantially disparate microenvironments for bacterial species not from the Staphylococcus genus. Based on PocketFEATURE analysis of MD simulations of S. aureus FtsZ bound to GTP or with mutations that are known to confer PC190723 resistance, we predict that PC190723 strongly prefers to bind Staphylococcus FtsZ in the nucleotide-bound state. Furthermore, MD simulations of an FtsZ dimer indicated that polymerization may enhance PC190723 binding. Taken together, our results demonstrate that a drug-binding pocket can vary significantly across species, genetic perturbations, and in different polymerization states, yielding important information for the further development of FtsZ inhibitors.
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Affiliation(s)
- Amanda Miguel
- Department of Bioengineering, Stanford University, Stanford, California, United States of America
| | - Jen Hsin
- Department of Bioengineering, Stanford University, Stanford, California, United States of America
| | - Tianyun Liu
- Department of Bioengineering, Stanford University, Stanford, California, United States of America
| | - Grace Tang
- Department of Bioengineering, Stanford University, Stanford, California, United States of America
| | - Russ B. Altman
- Department of Bioengineering, Stanford University, Stanford, California, United States of America
| | - Kerwyn Casey Huang
- Department of Bioengineering, Stanford University, Stanford, California, United States of America
- Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, California, United States of America
- * E-mail:
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37
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Carolan CG, Lamzin VS. Automated identification of crystallographic ligands using sparse-density representations. ACTA CRYSTALLOGRAPHICA. SECTION D, BIOLOGICAL CRYSTALLOGRAPHY 2014; 70:1844-53. [PMID: 25004962 PMCID: PMC4089483 DOI: 10.1107/s1399004714008578] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/15/2013] [Accepted: 04/15/2014] [Indexed: 12/05/2022]
Abstract
A novel procedure for the automatic identification of ligands in macromolecular crystallographic electron-density maps is introduced. It is based on the sparse parameterization of density clusters and the matching of the pseudo-atomic grids thus created to conformationally variant ligands using mathematical descriptors of molecular shape, size and topology. In large-scale tests on experimental data derived from the Protein Data Bank, the procedure could quickly identify the deposited ligand within the top-ranked compounds from a database of candidates. This indicates the suitability of the method for the identification of binding entities in fragment-based drug screening and in model completion in macromolecular structure determination.
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Affiliation(s)
- C. G. Carolan
- European Molecular Biology Laboratory (EMBL), c/o DESY, Notkestrasse 85, 22603 Hamburg, Germany
| | - V. S. Lamzin
- European Molecular Biology Laboratory (EMBL), c/o DESY, Notkestrasse 85, 22603 Hamburg, Germany
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38
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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: 65] [Impact Index Per Article: 6.5] [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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39
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Xie L, Ge X, Tan H, Xie L, Zhang Y, Hart T, Yang X, Bourne PE. Towards structural systems pharmacology to study complex diseases and personalized medicine. PLoS Comput Biol 2014; 10:e1003554. [PMID: 24830652 PMCID: PMC4022462 DOI: 10.1371/journal.pcbi.1003554] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Genome-Wide Association Studies (GWAS), whole genome sequencing, and high-throughput omics techniques have generated vast amounts of genotypic and molecular phenotypic data. However, these data have not yet been fully explored to improve the effectiveness and efficiency of drug discovery, which continues along a one-drug-one-target-one-disease paradigm. As a partial consequence, both the cost to launch a new drug and the attrition rate are increasing. Systems pharmacology and pharmacogenomics are emerging to exploit the available data and potentially reverse this trend, but, as we argue here, more is needed. To understand the impact of genetic, epigenetic, and environmental factors on drug action, we must study the structural energetics and dynamics of molecular interactions in the context of the whole human genome and interactome. Such an approach requires an integrative modeling framework for drug action that leverages advances in data-driven statistical modeling and mechanism-based multiscale modeling and transforms heterogeneous data from GWAS, high-throughput sequencing, structural genomics, functional genomics, and chemical genomics into unified knowledge. This is not a small task, but, as reviewed here, progress is being made towards the final goal of personalized medicines for the treatment of complex diseases.
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Affiliation(s)
- Lei Xie
- Department of Computer Science, Hunter College, The City University of New York, New York, New York, United States of America
- Ph.D. Program in Computer Science, Biology, and Biochemistry, The Graduate Center, The City University of New York, New York, New York, United States of America
- * E-mail:
| | - Xiaoxia Ge
- Department of Computer Science, Hunter College, The City University of New York, New York, New York, United States of America
| | - Hepan Tan
- Department of Computer Science, Hunter College, The City University of New York, New York, New York, United States of America
| | - Li Xie
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, California, United States of America
| | - Yinliang Zhang
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, California, United States of America
| | - Thomas Hart
- Department of Biological Sciences, Hunter College, The City University of New York, New York, New York, United States of America
| | - Xiaowei Yang
- School of Public Health, Hunter College, The City University of New York, New York, New York, United States of America
| | - Philip E. Bourne
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, California, United States of America
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40
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Tang GW, Altman RB. Knowledge-based fragment binding prediction. PLoS Comput Biol 2014; 10:e1003589. [PMID: 24762971 PMCID: PMC3998881 DOI: 10.1371/journal.pcbi.1003589] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2013] [Accepted: 03/11/2014] [Indexed: 11/18/2022] Open
Abstract
Target-based drug discovery must assess many drug-like compounds for potential activity. Focusing on low-molecular-weight compounds (fragments) can dramatically reduce the chemical search space. However, approaches for determining protein-fragment interactions have limitations. Experimental assays are time-consuming, expensive, and not always applicable. At the same time, computational approaches using physics-based methods have limited accuracy. With increasing high-resolution structural data for protein-ligand complexes, there is now an opportunity for data-driven approaches to fragment binding prediction. We present FragFEATURE, a machine learning approach to predict small molecule fragments preferred by a target protein structure. We first create a knowledge base of protein structural environments annotated with the small molecule substructures they bind. These substructures have low-molecular weight and serve as a proxy for fragments. FragFEATURE then compares the structural environments within a target protein to those in the knowledge base to retrieve statistically preferred fragments. It merges information across diverse ligands with shared substructures to generate predictions. Our results demonstrate FragFEATURE's ability to rediscover fragments corresponding to the ligand bound with 74% precision and 82% recall on average. For many protein targets, it identifies high scoring fragments that are substructures of known inhibitors. FragFEATURE thus predicts fragments that can serve as inputs to fragment-based drug design or serve as refinement criteria for creating target-specific compound libraries for experimental or computational screening.
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Affiliation(s)
- Grace W. Tang
- Department of Bioengineering, Stanford University, Stanford, California, United States of America
| | - Russ B. Altman
- Department of Bioengineering, Stanford University, Stanford, California, United States of America
- Department of Genetics, Stanford University, Stanford, California, United States of America
- * E-mail:
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41
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Identifying druggable targets by protein microenvironments matching: application to transcription factors. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2014; 3:e93. [PMID: 24452614 PMCID: PMC3910014 DOI: 10.1038/psp.2013.66] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/01/2013] [Accepted: 10/19/2013] [Indexed: 01/17/2023]
Abstract
Druggability of a protein is its potential to be modulated by drug-like molecules. It is important in the target selection phase. We hypothesize that: (i) known drug-binding sites contain advantageous physicochemical properties for drug binding, or “druggable microenvironments” and (ii) given a target, the presence of multiple druggable microenvironments similar to those seen previously is associated with a high likelihood of druggability. We developed DrugFEATURE to quantify druggability by assessing the microenvironments in potential small-molecule binding sites. We benchmarked DrugFEATURE using two data sets. One data set measures druggability using NMR-based screening. DrugFEATURE correlates well with this metric. The second data set is based on historical drug discovery outcomes. Using the DrugFEATURE cutoffs derived from the first, we accurately discriminated druggable and difficult targets in the second. We further identified novel druggable transcription factors with implications for cancer therapy. DrugFEATURE provides useful insight for drug discovery, by evaluating druggability and suggesting specific regions for interacting with drug-like molecules.
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42
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Konc J, Janežič D. Binding site comparison for function prediction and pharmaceutical discovery. Curr Opin Struct Biol 2013; 25:34-9. [PMID: 24878342 DOI: 10.1016/j.sbi.2013.11.012] [Citation(s) in RCA: 38] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2013] [Revised: 11/26/2013] [Accepted: 11/27/2013] [Indexed: 11/30/2022]
Abstract
While structural genomics resulted in thousands of new protein crystal structures, we still do not know the functions of most of these proteins. One reason for this shortcoming is their unique sequences or folds, which leaves them assigned as proteins of 'unknown function'. Recent advances in and applications of cutting edge binding site comparison algorithms for binding site detection and function prediction have begun to shed light on this problem. Here, we review these algorithms and their use in function prediction and pharmaceutical discovery. Finding common binding sites in weakly related proteins may lead to the discovery of new protein functions and to novel ways of drug discovery.
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Affiliation(s)
- Janez Konc
- National Institute of Chemistry, Ljubljana, Slovenia
| | - Dušanka Janežič
- University of Primorska, Faculty of Mathematics, Natural Sciences and Information Technologies, Koper, Slovenia.
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43
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Wang K, Sun J, Zhou S, Wan C, Qin S, Li C, He L, Yang L. Prediction of drug-target interactions for drug repositioning only based on genomic expression similarity. PLoS Comput Biol 2013; 9:e1003315. [PMID: 24244130 PMCID: PMC3820513 DOI: 10.1371/journal.pcbi.1003315] [Citation(s) in RCA: 46] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2013] [Accepted: 09/19/2013] [Indexed: 01/16/2023] Open
Abstract
Small drug molecules usually bind to multiple protein targets or even unintended off-targets. Such drug promiscuity has often led to unwanted or unexplained drug reactions, resulting in side effects or drug repositioning opportunities. So it is always an important issue in pharmacology to identify potential drug-target interactions (DTI). However, DTI discovery by experiment remains a challenging task, due to high expense of time and resources. Many computational methods are therefore developed to predict DTI with high throughput biological and clinical data. Here, we initiatively demonstrate that the on-target and off-target effects could be characterized by drug-induced in vitro genomic expression changes, e.g. the data in Connectivity Map (CMap). Thus, unknown ligands of a certain target can be found from the compounds showing high gene-expression similarity to the known ligands. Then to clarify the detailed practice of CMap based DTI prediction, we objectively evaluate how well each target is characterized by CMap. The results suggest that (1) some targets are better characterized than others, so the prediction models specific to these well characterized targets would be more accurate and reliable; (2) in some cases, a family of ligands for the same target tend to interact with common off-targets, which may help increase the efficiency of DTI discovery and explain the mechanisms of complicated drug actions. In the present study, CMap expression similarity is proposed as a novel indicator of drug-target interactions. The detailed strategies of improving data quality by decreasing the batch effect and building prediction models are also effectively established. We believe the success in CMap can be further translated into other public and commercial data of genomic expression, thus increasing research productivity towards valid drug repositioning and minimal side effects. Small drug molecules usually bind to unintended off-targets, leading to unexpected drug responses such as side effects or drug repositioning opportunities. Thus, identifying unintended drug-target interactions (DTI) is particularly required for understanding complicated drug actions. It remains expensive nowadays to experimentally determine DTI, so various computational methods are developed. In this study, we initiatively demonstrated that target binding is directly correlated with drug induced genomic expression profiles in Connectivity Map (CMap). By improving data quality of CMap, we illustrated three important facts: (1) Drugs binding to common targets show higher gene-expression similarity than random compounds, indicating that upstream ligand binding could be characterized by downstream gene-expression change. (2) It is found that some targets are better characterized by CMap than others. To guarantee efficiency of DTI discovery, prediction models should be specifically built for those well characterized targets. (3) It is broadly observed in the predicted DTI that ligands for the same target may collectively interact with common off-target. This observation is consistent with published experimental evidence and can help illustrate the mechanisms of unexplained drug reactions. Based on CMap, our work established an efficient pipeline of identifying potential DTI. By extending the success in CMap to other genomic data sources, we believe more DTI would be discovered.
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Affiliation(s)
- Kejian Wang
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Shanghai Jiao Tong University, Shanghai, China
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44
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Prediction and experimental validation of enzyme substrate specificity in protein structures. Proc Natl Acad Sci U S A 2013; 110:E4195-202. [PMID: 24145433 DOI: 10.1073/pnas.1305162110] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Structural Genomics aims to elucidate protein structures to identify their functions. Unfortunately, the variation of just a few residues can be enough to alter activity or binding specificity and limit the functional resolution of annotations based on sequence and structure; in enzymes, substrates are especially difficult to predict. Here, large-scale controls and direct experiments show that the local similarity of five or six residues selected because they are evolutionarily important and on the protein surface can suffice to identify an enzyme activity and substrate. A motif of five residues predicted that a previously uncharacterized Silicibacter sp. protein was a carboxylesterase for short fatty acyl chains, similar to hormone-sensitive-lipase-like proteins that share less than 20% sequence identity. Assays and directed mutations confirmed this activity and showed that the motif was essential for catalysis and substrate specificity. We conclude that evolutionary and structural information may be combined on a Structural Genomics scale to create motifs of mixed catalytic and noncatalytic residues that identify enzyme activity and substrate specificity.
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45
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Jalencas X, Mestres J. Identification of Similar Binding Sites to Detect Distant Polypharmacology. Mol Inform 2013; 32:976-90. [PMID: 27481143 DOI: 10.1002/minf.201300082] [Citation(s) in RCA: 41] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2013] [Accepted: 07/29/2013] [Indexed: 01/19/2023]
Abstract
The ability of small molecules to interact with multiple proteins is referred to as polypharmacology. This property is often linked to the therapeutic action of drugs but it is known also to be responsible for many of their side effects. Because of its importance, the development of computational methods that can predict drug polypharmacology has become an important line of research that led recently to the identification of many novel targets for known drugs. Nowadays, the majority of these methods are based on measuring the similarity of a query molecule against the hundreds of thousands of molecules for which pharmacological data on thousands of proteins are available in public sources. However, similarity-based methods are inherently biased by the chemical coverage offered by the active molecules present in those public repositories, which limits significantly their capacity to predict interactions with proteins structurally and functionally unrelated to any of the already known targets for drugs. It is in this respect that structure-based methods aiming at identifying similar binding sites may offer an alternative complementary means to ligand-based methods for detecting distant polypharmacology. The different existing approaches to binding site detection, representation, comparison, and fragmentation are reviewed and recent successful applications presented.
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Affiliation(s)
- Xavier Jalencas
- Systems Pharmacology, Research Program on Biomedical Informatics (GRIB), IMIM Hospital del Mar Research Institute & University Pompeu Fabra, Parc de Recerca Biomèdica, Doctor Aiguader 88, 08003 Barcelona, Catalonia, Spain fax: +34 93 3160550
| | - Jordi Mestres
- Systems Pharmacology, Research Program on Biomedical Informatics (GRIB), IMIM Hospital del Mar Research Institute & University Pompeu Fabra, Parc de Recerca Biomèdica, Doctor Aiguader 88, 08003 Barcelona, Catalonia, Spain fax: +34 93 3160550.
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46
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Giacomini KM, Yee SW, Ratain MJ, Weinshilboum RM, Kamatani N, Nakamura Y. Pharmacogenomics and patient care: one size does not fit all. Sci Transl Med 2013; 4:153ps18. [PMID: 23019654 DOI: 10.1126/scitranslmed.3003471] [Citation(s) in RCA: 43] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
The time is ripe to assess whether pharmacogenomics research--the study of the genetic basis for variation in drug response--has provided important insights into a personalized approach to prescribing and dosing medications. Here, we describe the status of the field and approaches for addressing some of the open questions in pharmacogenomics research and use of genetic testing in guiding drug therapy.
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Affiliation(s)
- Kathleen M Giacomini
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA 94143, USA.
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47
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Haupt VJ, Daminelli S, Schroeder M. Drug Promiscuity in PDB: Protein Binding Site Similarity Is Key. PLoS One 2013; 8:e65894. [PMID: 23805191 PMCID: PMC3689763 DOI: 10.1371/journal.pone.0065894] [Citation(s) in RCA: 107] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2012] [Accepted: 04/30/2013] [Indexed: 11/19/2022] Open
Abstract
Drug repositioning applies established drugs to new disease indications with increasing success. A pre-requisite for drug repurposing is drug promiscuity (polypharmacology) – a drug’s ability to bind to several targets. There is a long standing debate on the reasons for drug promiscuity. Based on large compound screens, hydrophobicity and molecular weight have been suggested as key reasons. However, the results are sometimes contradictory and leave space for further analysis. Protein structures offer a structural dimension to explain promiscuity: Can a drug bind multiple targets because the drug is flexible or because the targets are structurally similar or even share similar binding sites? We present a systematic study of drug promiscuity based on structural data of PDB target proteins with a set of 164 promiscuous drugs. We show that there is no correlation between the degree of promiscuity and ligand properties such as hydrophobicity or molecular weight but a weak correlation to conformational flexibility. However, we do find a correlation between promiscuity and structural similarity as well as binding site similarity of protein targets. In particular, 71% of the drugs have at least two targets with similar binding sites. In order to overcome issues in detection of remotely similar binding sites, we employed a score for binding site similarity: LigandRMSD measures the similarity of the aligned ligands and uncovers remote local similarities in proteins. It can be applied to arbitrary structural binding site alignments. Three representative examples, namely the anti-cancer drug methotrexate, the natural product quercetin and the anti-diabetic drug acarbose are discussed in detail. Our findings suggest that global structural and binding site similarity play a more important role to explain the observed drug promiscuity in the PDB than physicochemical drug properties like hydrophobicity or molecular weight. Additionally, we find ligand flexibility to have a minor influence.
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Affiliation(s)
| | | | - Michael Schroeder
- Biotechnology Center (BIOTEC), TU Dresden, Dresden, Germany
- * E-mail:
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48
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Combinatorial clustering of residue position subsets predicts inhibitor affinity across the human kinome. PLoS Comput Biol 2013; 9:e1003087. [PMID: 23754939 PMCID: PMC3675009 DOI: 10.1371/journal.pcbi.1003087] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2012] [Accepted: 04/22/2013] [Indexed: 11/22/2022] Open
Abstract
The protein kinases are a large family of enzymes that play fundamental roles in propagating signals within the cell. Because of the high degree of binding site similarity shared among protein kinases, designing drug compounds with high specificity among the kinases has proven difficult. However, computational approaches to comparing the 3-dimensional geometry and physicochemical properties of key binding site residue positions have been shown to be informative of inhibitor selectivity. The Combinatorial Clustering Of Residue Position Subsets (ccorps) method, introduced here, provides a semi-supervised learning approach for identifying structural features that are correlated with a given set of annotation labels. Here, ccorps is applied to the problem of identifying structural features of the kinase atp binding site that are informative of inhibitor binding. ccorps is demonstrated to make perfect or near-perfect predictions for the binding affinity profile of 8 of the 38 kinase inhibitors studied, while only having overall poor predictive ability for 1 of the 38 compounds. Additionally, ccorps is shown to identify shared structural features across phylogenetically diverse groups of kinases that are correlated with binding affinity for particular inhibitors; such instances of structural similarity among phylogenetically diverse kinases are also shown to not be rare among kinases. Finally, these function-specific structural features may serve as potential starting points for the development of highly specific kinase inhibitors. The kinases are a group of essential signaling proteins within the cell and are the largest family of enzymes encoded by the human genome. The high degree of binding site similarity shared across the protein kinases has made them difficult targets for which to design highly selective inhibitors, but kinome-wide binding site analysis can help predict unintended off-target inhibitions. Given the increasingly large number of available kinase structures, kinome-wide comparative analysis of binding sites is now possible. In this paper, the Combinatorial Clustering Of Residue Position Subsets (ccorps) method is introduced and used to synthesize kinome-wide structure datasets with a kinome-wide inhibitor affinity screening dataset consisting of 38 kinase inhibitors. ccorps identifies structural features of the kinase binding site that are correlated with an inhibitor binding and uses these features to predict if this inhibitor will be capable of binding to uncharacterized kinases. This paper demonstrates the ability of ccorps to accurately predict inhibitor binding and identify features of the kinase binding site that are unique to kinases capable of binding a given inhibitor.
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49
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Lahti JL, Tang GW, Capriotti E, Liu T, Altman RB. Bioinformatics and variability in drug response: a protein structural perspective. J R Soc Interface 2012; 9:1409-37. [PMID: 22552919 DOI: 10.1098/rsif.2011.0843] [Citation(s) in RCA: 57] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Marketed drugs frequently perform worse in clinical practice than in the clinical trials on which their approval is based. Many therapeutic compounds are ineffective for a large subpopulation of patients to whom they are prescribed; worse, a significant fraction of patients experience adverse effects more severe than anticipated. The unacceptable risk-benefit profile for many drugs mandates a paradigm shift towards personalized medicine. However, prior to adoption of patient-specific approaches, it is useful to understand the molecular details underlying variable drug response among diverse patient populations. Over the past decade, progress in structural genomics led to an explosion of available three-dimensional structures of drug target proteins while efforts in pharmacogenetics offered insights into polymorphisms correlated with differential therapeutic outcomes. Together these advances provide the opportunity to examine how altered protein structures arising from genetic differences affect protein-drug interactions and, ultimately, drug response. In this review, we first summarize structural characteristics of protein targets and common mechanisms of drug interactions. Next, we describe the impact of coding mutations on protein structures and drug response. Finally, we highlight tools for analysing protein structures and protein-drug interactions and discuss their application for understanding altered drug responses associated with protein structural variants.
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Affiliation(s)
- Jennifer L Lahti
- Department of Bioengineering, Stanford University, Stanford, CA, USA
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