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Structural Interaction Fingerprint (SIFt):  A Novel Method for Analyzing Three-Dimensional Protein−Ligand Binding Interactions

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Department of Structural Informatics, Biogen, Inc., 12 Cambridge Center, Cambridge, Massachusetts 02142
Cite this: J. Med. Chem. 2004, 47, 2, 337–344
Publication Date (Web):December 12, 2003
https://doi.org/10.1021/jm030331x
Copyright © 2004 American Chemical Society
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    Abstract

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    Representing and understanding the three-dimensional (3D) structural information of protein−ligand complexes is a critical step in the rational drug discovery process. Traditional analysis methods are proving inadequate and inefficient in dealing with the massive amount of structural information being generated from X-ray crystallography, NMR, and in silico approaches such as structure-based docking experiments. Here, we present SIFt (structural interaction fingerprint), a novel method for representing and analyzing 3D protein−ligand binding interactions. Key to this approach is the generation of an interaction fingerprint that translates 3D structural binding information from a protein−ligand complex into a one-dimensional binary string. Each fingerprint represents the “structural interaction profile” of the complex that can be used to organize, analyze, and visualize the rich amount of information encoded in ligand−receptor complexes and also to assist database mining. We have applied SIFt to tackle three common tasks in structure-based drug design. The first involved the analysis and organization of a typical set of results generated from a docking study. Using SIFt, docking poses with similar binding modes were identified, clustered, and subsequently compared with conventional scoring function information. A second application of SIFt was to analyze ∼90 known X-ray crystal structures of protein kinase−inhibitor complexes obtained from the Protein Databank. Using SIFt, we were able to organize the structures and reveal striking similarities and diversity between their small molecule binding interactions. Finally, we have shown how SIFt can be used as an effective molecular filter during the virtual chemical library screening process to select molecules with desirable binding mode(s) and/or desirable interaction patterns with the protein target. In summary, SIFt shows promise to fully leverage the wealth of information being generated in rational drug design.

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    Table of comparison of the SIFt bit strings of seven different poses of SB203580, docked into human p38 structures. Table of 89 crystal structures of protein kinases−ligand complexes and references. Table of 16 known database p38 inhibitors. This material is available free of charge via the Internet at http://pubs.acs.org.

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    72. Martin Weisel, Hans-Marcus Bitter, François Diederich, W. Venus So, and Rama Kondru . PROLIX: Rapid Mining of Protein–Ligand Interactions in Large Crystal Structure Databases. Journal of Chemical Information and Modeling 2012, 52 (6) , 1450-1461. https://doi.org/10.1021/ci300034x
    73. Satoshi Niijima, Akira Shiraishi, and Yasushi Okuno . Dissecting Kinase Profiling Data to Predict Activity and Understand Cross-Reactivity of Kinase Inhibitors. Journal of Chemical Information and Modeling 2012, 52 (4) , 901-912. https://doi.org/10.1021/ci200607f
    74. Peter C. Anderson, Vincent De Sapio, Kevin B. Turner, Sidney P. Elmer, Diana C. Roe, and Joseph S. Schoeniger . Identification of Binding Specificity-Determining Features in Protein Families. Journal of Medicinal Chemistry 2012, 55 (5) , 1926-1939. https://doi.org/10.1021/jm200979x
    75. Angelo D. Favia, Giovanni Bottegoni, Irene Nobeli, Paola Bisignano, and Andrea Cavalli . SERAPhiC: A Benchmark for in Silico Fragment-Based Drug Design. Journal of Chemical Information and Modeling 2011, 51 (11) , 2882-2896. https://doi.org/10.1021/ci2003363
    76. Sayan Ranu and Ambuj K. Singh . Novel Method for Pharmacophore Analysis by Examining the Joint Pharmacophore Space. Journal of Chemical Information and Modeling 2011, 51 (5) , 1106-1121. https://doi.org/10.1021/ci100503y
    77. Lirong Wang, Zhaojun Xie, Peter Wipf, and Xiang-Qun Xie . Residue Preference Mapping of Ligand Fragments in the Protein Data Bank. Journal of Chemical Information and Modeling 2011, 51 (4) , 807-815. https://doi.org/10.1021/ci100386y
    78. Ronak Y. Patel and Robert J. Doerksen . Protein Kinase−Inhibitor Database: Structural Variability of and Inhibitor Interactions with the Protein Kinase P-Loop. Journal of Proteome Research 2010, 9 (9) , 4433-4442. https://doi.org/10.1021/pr100662s
    79. Lu Tan, José Batista and Jürgen Bajorath . Rationalization of the Performance and Target Dependence of Similarity Searching Incorporating Protein−Ligand Interaction Information. Journal of Chemical Information and Modeling 2010, 50 (6) , 1042-1052. https://doi.org/10.1021/ci1001197
    80. Sara Núñez, Jennifer Venhorst and Chris G. Kruse. Assessment of a Novel Scoring Method Based on Solvent Accessible Surface Area Descriptors. Journal of Chemical Information and Modeling 2010, 50 (4) , 480-486. https://doi.org/10.1021/ci9004628
    81. Andrew R. Leach, Valerie J. Gillet, Richard A. Lewis and Robin Taylor . Three-Dimensional Pharmacophore Methods in Drug Discovery. Journal of Medicinal Chemistry 2010, 53 (2) , 539-558. https://doi.org/10.1021/jm900817u
    82. Tomohiro Sato, Teruki Honma and Shigeyuki Yokoyama. Combining Machine Learning and Pharmacophore-Based Interaction Fingerprint for in Silico Screening. Journal of Chemical Information and Modeling 2010, 50 (1) , 170-185. https://doi.org/10.1021/ci900382e
    83. José Batista, Lu Tan and Jürgen Bajorath. Atom-Centered Interacting Fragments and Similarity Search Applications. Journal of Chemical Information and Modeling 2010, 50 (1) , 79-86. https://doi.org/10.1021/ci9004223
    84. Xia Ning, Huzefa Rangwala and George Karypis . Multi-Assay-Based Structure−Activity Relationship Models: Improving Structure−Activity Relationship Models by Incorporating Activity Information from Related Targets. Journal of Chemical Information and Modeling 2009, 49 (11) , 2444-2456. https://doi.org/10.1021/ci900182q
    85. R. S. K. Vijayan, Indrani Bera, M. Prabu, Sangita Saha and Nanda Ghoshal . Combinatorial Library Enumeration and Lead Hopping using Comparative Interaction Fingerprint Analysis and Classical 2D QSAR Methods for Seeking Novel GABAA α3 Modulators. Journal of Chemical Information and Modeling 2009, 49 (11) , 2498-2511. https://doi.org/10.1021/ci900309s
    86. Izhar Wallach and Ryan Lilien. Predicting Multiple Ligand Binding Modes Using Self-Consistent Pharmacophore Hypotheses. Journal of Chemical Information and Modeling 2009, 49 (9) , 2116-2128. https://doi.org/10.1021/ci900199e
    87. Ravi K. Nandigam, Sangtae Kim, Juswinder Singh and Claudio Chuaqui . Position Specific Interaction Dependent Scoring Technique for Virtual Screening Based on Weighted Protein−Ligand Interaction Fingerprint Profiles. Journal of Chemical Information and Modeling 2009, 49 (5) , 1185-1192. https://doi.org/10.1021/ci800466n
    88. Violeta I. Pérez-Nueno, Obdulia Rabal, José I. Borrell and Jordi Teixidó. APIF: A New Interaction Fingerprint Based on Atom Pairs and Its Application to Virtual Screening. Journal of Chemical Information and Modeling 2009, 49 (5) , 1245-1260. https://doi.org/10.1021/ci900043r
    89. Fabrice Moriaud, Olivia Doppelt-Azeroual, Laetitia Martin, Ksenia Oguievetskaia, Kerstin Koch, Artem Vorotyntsev, Stewart A. Adcock and François Delfaud . Computational Fragment-Based Approach at PDB Scale by Protein Local Similarity. Journal of Chemical Information and Modeling 2009, 49 (2) , 280-294. https://doi.org/10.1021/ci8003094
    90. Ashutosh Kumar, Vinita Chaturvedi, Shalini Bhatnagar, Sudhir Sinha and Mohammad Imran Siddiqi . Knowledge Based Identification of Potent Antitubercular Compounds Using Structure Based Virtual Screening and Structure Interaction Fingerprints. Journal of Chemical Information and Modeling 2009, 49 (1) , 35-42. https://doi.org/10.1021/ci8003607
    91. Lu Tan, Eugen Lounkine and Jürgen Bajorath. Similarity Searching Using Fingerprints of Molecular Fragments Involved in Protein−Ligand Interactions. Journal of Chemical Information and Modeling 2008, 48 (12) , 2308-2312. https://doi.org/10.1021/ci800322y
    92. Thomas J. Crisman, Mihiret T. Sisay and Jürgen Bajorath . Ligand-Target Interaction-Based Weighting of Substructures for Virtual Screening. Journal of Chemical Information and Modeling 2008, 48 (10) , 1955-1964. https://doi.org/10.1021/ci800229q
    93. Esther Kellenberger, Nicolas Foata and Didier Rognan. Ranking Targets in Structure-Based Virtual Screening of Three-Dimensional Protein Libraries: Methods and Problems. Journal of Chemical Information and Modeling 2008, 48 (5) , 1014-1025. https://doi.org/10.1021/ci800023x
    94. Sebastian Radestock, Tanja Weil and Steffen Renner . Homology Model-Based Virtual Screening for GPCR Ligands Using Docking and Target-Biased Scoring. Journal of Chemical Information and Modeling 2008, 48 (5) , 1104-1117. https://doi.org/10.1021/ci8000265
    95. Eric J. Martin and David C. Sullivan. AutoShim: Empirically Corrected Scoring Functions for Quantitative Docking with a Crystal Structure and IC50 Training Data. Journal of Chemical Information and Modeling 2008, 48 (4) , 861-872. https://doi.org/10.1021/ci7004548
    96. Yanbei Li, Zhehuan Fan, Jingxin Rao, Zhiyi Chen, Qinyu Chu, Mingyue Zheng, Xutong Li. An overview of recent advances and challenges in predicting compound-protein interaction (CPI). Medical Review 2023, Article ASAP.
    97. George Nicolae Daniel Ion, George Mihai Nitulescu, Dragos Paul Mihai. A PIM-1 Kinase Inhibitor Docking Optimization Study Based on Logistic Regression Models and Interaction Analysis. Life 2023, 13 (8) , 1635. https://doi.org/10.3390/life13081635
    98. Izudin Redžepović, Boris Furtula. Chemical similarity of molecules with physiological response. Molecular Diversity 2023, 27 (4) , 1603-1612. https://doi.org/10.1007/s11030-022-10514-5
    99. Cillian Variot, Daniel Capule, Xhulio Arolli, Jackson Baumgartner, Cory Reidl, Charles Houseman, Miguel A. Ballicora, Daniel P. Becker, Misty L. Kuhn. Mapping roles of active site residues in the acceptor site of the PA3944 Gcn5‐related N ‐acetyltransferase enzyme. Protein Science 2023, 32 (8) https://doi.org/10.1002/pro.4725
    100. Natalia A Szulc, Zuzanna Mackiewicz, Janusz M Bujnicki, Filip Stefaniak. Structural interaction fingerprints and machine learning for predicting and explaining binding of small molecule ligands to RNA. Briefings in Bioinformatics 2023, 24 (4) https://doi.org/10.1093/bib/bbad187
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