1
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Milon TI, Wang Y, Fontenot RL, Khajouie P, Villinger F, Raghavan V, Xu W. Development of a novel representation of drug 3D structures and enhancement of the TSR-based method for probing drug and target interactions. Comput Biol Chem 2024; 112:108117. [PMID: 38852360 DOI: 10.1016/j.compbiolchem.2024.108117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Revised: 05/13/2024] [Accepted: 05/31/2024] [Indexed: 06/11/2024]
Abstract
Understanding the mechanisms underlying interactions between drugs and target proteins is critical for drug discovery. In our earlier studies, we introduced the Triangular Spatial Relationship (TSR)-based algorithm, which enables the representation of a protein's 3D structure as a vector of integers (TSR keys). These TSR keys correspond to substructures of the 3D structure of a protein and are computed based on the triangles constructed by all possible triples of Cα atoms within the protein. In this study, we report on a new TSR-based algorithm for probing drug and target interactions. Specifically, we have extended the previous algorithm in three novel directions: TSR keys for representing the 3D structure of a drug or a ligand, cross TSR keys between drugs and their targets and intra-residual TSR keys for phosphorylated amino acids. The outcomes illustrate the key contributions as follows: (i) The TSR-based method, which uses the TSR keys as features, is unique in its capability to interpret hierarchical relationships of drugs as well as drug - target complexes using common and specific TSR keys. (ii) The method can distinguish not only the binding sites from the rest of the protein structures, but also the binding sites of primary targets from those of off-targets. (iii) The method has the potential to correlate the 3D structures of drugs with their functions. (iv) Representation of 3D structures by TSR keys has its unique advantage in terms of ease of making searching for similar substructures across structure datasets easier. In summary, this study presents a novel computational methodology, with significant advantages, for providing insights into the mechanism underlying drug and target interactions.
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Affiliation(s)
- Tarikul I Milon
- Department of Chemistry, University of Louisiana at Lafayette, P.O. Box 44370, Lafayette, LA 70504, USA
| | - Yuhong Wang
- National Center for Advancing Translational Sciences, 9800 Medical Center Drive, Rockville, MD 20850, USA
| | - Ryan L Fontenot
- Department of Chemistry, University of Louisiana at Lafayette, P.O. Box 44370, Lafayette, LA 70504, USA
| | - Poorya Khajouie
- Department of Chemistry, University of Louisiana at Lafayette, P.O. Box 44370, Lafayette, LA 70504, USA; The Center for Advanced Computer Studies, University of Louisiana at Lafayette, LA 70504, USA
| | - Francois Villinger
- Department of Biology, University of Louisiana at Lafayette, New Iberia, LA 70560, USA
| | - Vijay Raghavan
- The Center for Advanced Computer Studies, University of Louisiana at Lafayette, LA 70504, USA
| | - Wu Xu
- Department of Chemistry, University of Louisiana at Lafayette, P.O. Box 44370, Lafayette, LA 70504, USA.
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2
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Miyata A, Ito S, Fujinami D. Structure Prediction and Genome Mining-Aided Discovery of the Bacterial C-Terminal Tryptophan Prenyltransferase PalQ. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2307372. [PMID: 38059776 PMCID: PMC10853753 DOI: 10.1002/advs.202307372] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Revised: 11/13/2023] [Indexed: 12/08/2023]
Abstract
Post-translational prenylations, found in eukaryotic primary metabolites and bacterial secondary metabolites, play crucial roles in biomolecular interactions. Employing genome mining methods combined with AlphaFold2-based predictions of protein interactions, PalQ , a prenyltransferase responsible for the tryptophan prenylation of RiPPs produced by Paenibacillus alvei, is identified. PalQ differs from cyanobactin prenyltransferases because of its evolutionary relationship to isoprene synthases, which enables PalQ to transfer extended prenyl chains to the indole C3 position. This prenylation introduces structural diversity to the tryptophan side chain and also leads to conformational dynamics in the peptide backbone, attributed to the cis/trans isomerization that arises from the formation of a pyrrolidine ring. Additionally, PalQ exhibited pronounced positional selectivity for the C-terminal tryptophan. Such enzymatic characteristics offer a toolkit for peptide therapeutic lipidation.
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Affiliation(s)
- Azusa Miyata
- Graduate Division of Nutritional and Environmental SciencesUniversity of Shizuoka52‐1 Yada, Suruga‐kuShizuoka422‐8526Japan
| | - Sohei Ito
- Graduate Division of Nutritional and Environmental SciencesUniversity of Shizuoka52‐1 Yada, Suruga‐kuShizuoka422‐8526Japan
| | - Daisuke Fujinami
- Graduate Division of Nutritional and Environmental SciencesUniversity of Shizuoka52‐1 Yada, Suruga‐kuShizuoka422‐8526Japan
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3
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Mukherjee P, Agarwal S, Mallick SB, Dasgupta J. PAS domain of flagellar histidine kinase FlrB has a unique architecture and binds heme as a sensory ligand in an unconventional fashion. Structure 2024; 32:200-216.e5. [PMID: 38157857 DOI: 10.1016/j.str.2023.11.014] [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: 06/29/2023] [Revised: 09/28/2023] [Accepted: 11/29/2023] [Indexed: 01/03/2024]
Abstract
Phosphorylation of the σ54-dependent transcription activator FlrC by the sensor histidine kinase FlrB is essential for flagellar synthesis of Vibrio cholerae. Despite that, the structure, sensory signal, and mechanistic basis of function of FlrB were elusive. Here, we report the crystal structure of the sensory PAS domain of FlrB in its functional dimeric state that exhibits a unique architecture. Series of biochemical/biophysical experiments on different constructs and mutants established that heme binds hydrophobically as sensory ligand in the shallow ligand-binding cleft of FlrB-PAS without axial coordination. Intriguingly, ATP binding to the C-terminal ATP-binding (CA) domain assists PAS domain to bind heme, vis-à-vis, heme binding to the PAS facilitates ATP binding to the CA domain. We hypothesize that synergistic binding of heme and ATP triggers conformational signaling in FlrB, leading to downstream flagellar gene transcription. Enhanced swimming motility of V. cholerae with increased heme uptake supports this proposition.
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Affiliation(s)
- Peeali Mukherjee
- Department of Biotechnology, St. Xavier's College (Autonomous), 30 Mother Teresa Sarani, Kolkata 700016, India
| | - Shubhangi Agarwal
- Department of Biotechnology, St. Xavier's College (Autonomous), 30 Mother Teresa Sarani, Kolkata 700016, India
| | - Sritapa Basu Mallick
- Department of Biotechnology, St. Xavier's College (Autonomous), 30 Mother Teresa Sarani, Kolkata 700016, India
| | - Jhimli Dasgupta
- Department of Biotechnology, St. Xavier's College (Autonomous), 30 Mother Teresa Sarani, Kolkata 700016, India.
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4
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Ravnik V, Jukič M, Bren U. Identifying Metal Binding Sites in Proteins Using Homologous Structures, the MADE Approach. J Chem Inf Model 2023; 63:5204-5219. [PMID: 37557084 PMCID: PMC10466382 DOI: 10.1021/acs.jcim.3c00558] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Indexed: 08/11/2023]
Abstract
In order to identify the locations of metal ions in the binding sites of proteins, we have developed a method named the MADE (MAcromolecular DEnsity and Structure Analysis) approach. The MADE approach represents an evolution of our previous toolset, the ProBiS H2O (MD) methodology, for the identification of conserved water molecules. Our method uses experimental structures of proteins homologous to a query, which are subsequently superimposed upon it. Areas with a particular species present in a similar location among many homologous protein structures are identified using a clustering algorithm. Dense clusters likely represent positions containing species important to the query protein structure or function. We analyze well-characterized apo protein structures and show that the MADE approach can identify clusters corresponding to the expected positions of metal ions in their binding sites. The greatest advantage of our method lies in its generality. It can in principle be applied to any species found in protein records; it is not only limited to metal ions. We additionally demonstrate that the MADE approach can be successfully applied to predict the location of cofactors in computer-modeled structures, e.g., via AlphaFold. We also conduct a careful protein superposition method comparison and find our methodology robust and the results largely independent of the selected protein superposition algorithm. We postulate that with increasing structural data availability, additional applications of the MADE approach will be possible such as non-protein systems, water network identification, protein binding site elaboration, and analysis of binding events, all in a dynamic manner. We have implemented the MADE approach as a plugin for the PyMOL molecular visualization tool. The MADE plugin is available free of charge at https://gitlab.com/Jukic/made_software.
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Affiliation(s)
- Vid Ravnik
- Faculty
of Chemistry and Chemical Engineering, University
of Maribor, Smetanova
ulica 17, Maribor SI-2000, Slovenia
| | - Marko Jukič
- Faculty
of Chemistry and Chemical Engineering, University
of Maribor, Smetanova
ulica 17, Maribor SI-2000, Slovenia
- The
Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska, Glagoljaška 8, Koper SI-6000, Slovenia
- Institute
for Environmental Protection and Sensors, Beloruska ulica 7, Maribor SI-2000, Slovenia
| | - Urban Bren
- Faculty
of Chemistry and Chemical Engineering, University
of Maribor, Smetanova
ulica 17, Maribor SI-2000, Slovenia
- The
Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska, Glagoljaška 8, Koper SI-6000, Slovenia
- Institute
for Environmental Protection and Sensors, Beloruska ulica 7, Maribor SI-2000, Slovenia
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Zhang T, Jiang S, Li T, Liu Y, Zhang Y. Identified Isosteric Replacements of Ligands' Glycosyl Domain by Data Mining. ACS OMEGA 2023; 8:25165-25184. [PMID: 37483233 PMCID: PMC10357434 DOI: 10.1021/acsomega.3c02243] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Accepted: 06/09/2023] [Indexed: 07/25/2023]
Abstract
Biologically equivalent replacements of key moieties in molecules rationalize scaffold hopping, patent busting, or R-group enumeration. Yet, this information may depend upon the expert-defined space, and might be subjective and biased toward the chemistries they get used to. Most importantly, these practices are often informatively incomplete since they are often compromised by a try-and-error cycle, and although they depict what kind of substructures are suitable for the replacement occurrence, they fail to explain the driving forces to support such interchanges. The protein data bank (PDB) encodes a receptor-ligand interaction pattern and could be an optional source to mine structural surrogates. However, manual decoding of PDB has become almost impossible and redundant to excavate the bioisosteric know-how. Therefore, a text parsing workflow has been developed to automatically extract the local structural replacement of a specific structure from PDB by finding spatial and steric interaction overlaps between the fragments in endogenous ligands and particular ligand fragments. Taking the glycosyl domain for instance, a total of 49 520 replacements that overlap on nucleotide ribose were identified and categorized based on their SMILE codes. A predominately ring system, such as aliphatic and aromatic rings, was observed; yet, amide and sulfonamide replacements also occur. We believe these findings may enlighten medicinal chemists on the structure design and optimization of ligands using the bioisosteric replacement strategy.
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Affiliation(s)
- Tinghao Zhang
- Xi’an
Institute of Flexible Electronics (IFE) and Xi’an Institute
of Biomedical Materials & Engineering (IBME), Northwestern Polytechnical
University, 127 West Youyi Road, Xi’an 710072, China
| | - Shenghao Jiang
- School of
Computer Science, Northwestern Polytechnical
University, 127 West
Youyi Road, Xi’an 710072, China
| | - Ting Li
- Xi’an
Institute of Flexible Electronics (IFE) and Xi’an Institute
of Biomedical Materials & Engineering (IBME), Northwestern Polytechnical
University, 127 West Youyi Road, Xi’an 710072, China
| | - Yan Liu
- Xi’an
Institute of Flexible Electronics (IFE) and Xi’an Institute
of Biomedical Materials & Engineering (IBME), Northwestern Polytechnical
University, 127 West Youyi Road, Xi’an 710072, China
| | - Yuezhou Zhang
- Xi’an
Institute of Flexible Electronics (IFE) and Xi’an Institute
of Biomedical Materials & Engineering (IBME), Northwestern Polytechnical
University, 127 West Youyi Road, Xi’an 710072, China
- Ningbo
Institute of Northwestern Polytechnical University, Frontiers Science
Center for Flexible Electronics (FSCFE), Key laboratory of Flexible
Electronics of Zhejiang Province, Ningbo Institute of Northwestern
Polytechnical University, 218 Qingyi Road, Ningbo 315103, China
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6
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Structure-based discovery and in vitro validation of inhibitors of chloride intracellular channel 4 protein. Comput Struct Biotechnol J 2022; 21:688-701. [PMID: 36659928 PMCID: PMC9826898 DOI: 10.1016/j.csbj.2022.12.040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Revised: 12/22/2022] [Accepted: 12/23/2022] [Indexed: 12/25/2022] Open
Abstract
The use of computer-aided methods have continued to propel accelerated drug discovery across various disease models, interestingly allowing the specific inhibition of pathogenic targets. Chloride Intracellular Channel Protein 4 (CLIC4) is a novel class of intracellular ion channel highly implicated in tumor and vascular biology. It regulates cell proliferation, apoptosis and angiogenesis; and is involved in multiple pathologic signaling pathways. Absence of specific inhibitors however impedes its advancement to translational research. Here, we integrate structural bioinformatics and experimental research approaches for the discovery and validation of small-molecule inhibitors of CLIC4. High-affinity allosteric binders were identified from a library of 1615 Food and Drug Administration (FDA)-approved drugs via a high-performance computing-powered blind-docking approach, resulting in the selection of amphotericin B and rapamycin. NMR assays confirmed the binding and conformational disruptive effects of both drugs while they also reversed stress-induced membrane translocation of CLIC4 and inhibited endothelial cell migration. Structural and dynamics simulation studies further revealed that the inhibitory mechanisms of these compounds were hinged on the allosteric modulation of the catalytic glutathione (GSH)-like site loop and the extended catalytic β loop which may elicit interference with the catalytic activities of CLIC4. Structure-based insights from this study provide the basis for the selective targeting of CLIC4 to treat the associated pathologies.
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Key Words
- A9C, 9-Anthracenecarboxylic acid
- AMPhB, Amphotericin B
- Ad, Adenovirus
- Allosteric inhibition
- Bad, BCL2 associated agonist of cell death
- Bcl-2, B-cell lymphoma 2
- Bcl-xL, B-cell lymphoma-extra large
- CDK, Cyclin-dependent kinases
- CLIC, Chloride intracellular channel protein
- Chloride intracellular channel protein 4
- Computational high-throughput screening
- DAPI, 4′,6-diamidino-2-phenylindole
- DIDS, 4,4′-Diisothiocyano-2,2′-stilbenedisulfonic acid
- DMSO, Dimethyl sulfoxide
- DOPE, Discrete optimized protein energy
- GPU, Graphics Processing Unit
- GSH-like catalytic site
- GST, glutathione S-transferases
- GUI, Graphical User Interface
- HEPES, (4-(2-hydroxyethyl)− 1-piperazineethanesulfonic acid;
- HIF, Hypoxia-inducible factor
- HSQC, Heteronuclear single quantum coherence spectroscopy
- HUVEC, Human umbilical vein endothelial cells
- IKKβ, Inhibitor of nuclear kappa-B-kinase subunit beta
- JNK, c-Jun N-terminal kinase
- MKK6, Mitogen-activated protein kinase kinase-6
- MOI, Multiplicity of infection
- NF-κB, Nuclear factor kappa-light-chain-enhancer of activated B cells
- NMR, Nuclear magnetic resonance
- NPT, The constant-temperature, constant-pressure ensemble
- NaCL, Sodium chloride
- Nuclear magnetic resonance
- PAH, Pulmonary arterial hypertension
- RAPA, Rapamycin
- SASA, Solvent accessible surface area
- SEK1, Dual specificity mitogen-activated protein kinase kinase 4
- Smad, Suppressor of Mothers against Decapentaplegic
- Structure-based drug discovery
- TEV, Tobacco etch virus
- TIP3P, Transferable intermolecular potential 3 P
- TROSY, Transverse relaxation optimized spectroscopy
- UCSF, University of California, San Francisco
- VEGF, Vascular endothelial growth factor
- p38, Mitogen activated protein kinases
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Konc J, Janežič D. Protein binding sites for drug design. Biophys Rev 2022; 14:1413-1421. [PMID: 36532870 PMCID: PMC9734416 DOI: 10.1007/s12551-022-01028-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Accepted: 12/01/2022] [Indexed: 12/13/2022] Open
Abstract
Drug development is a lengthy and challenging process that can be accelerated at early stages by new mathematical approaches and modern computers. To address this important issue, we are developing new mathematical solutions for the detection and characterization of protein binding sites that are important for new drug development. In this review, we present algorithms based on graph theory combined with molecular dynamics simulations that we have developed for studying biological target proteins to provide important data for optimizing the early stages of new drug development. A particular focus is the development of new protein binding site prediction algorithms (ProBiS) and new web tools for modeling pharmaceutically interesting molecules-ProBiS Tools (algorithm, database, web server), which have evolved into a full-fledged graphical tool for studying proteins in the proteome. ProBiS differs from other structural algorithms in that it can align proteins with different folds without prior knowledge of the binding sites. It allows detection of similar binding sites and can predict molecular ligands of various types of pharmaceutical interest that could be advanced to drugs to treat a disease, based on the entire Protein Data Bank (PDB) and AlphaFold database, including proteins not yet in the PDB. All ProBiS Tools are freely available to the academic community at http://insilab.org and https://probis.nih.gov.
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Affiliation(s)
- Janez Konc
- Theory Department, National Institute of Chemistry, Hajdrihova 19, SI-1001 Ljubljana, Slovenia
| | - Dušanka Janežič
- Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska, Glagoljaška 8, SI-6000 Koper, Slovenia
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8
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Konc J, Janežič D. ProBiS-Fold Approach for Annotation of Human Structures from the AlphaFold Database with No Corresponding Structure in the PDB to Discover New Druggable Binding Sites. J Chem Inf Model 2022; 62:5821-5829. [PMID: 36269348 DOI: 10.1021/acs.jcim.2c00947] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
ProBiS (Protein Binding Sites), a local structure-based comparison algorithm, is used in the new ProBiS-Fold web server to annotate human structures from the AlphaFold database without a corresponding structure in the Protein Data Bank (PDB) to discover new druggable binding sites. The ProBiS algorithm is used to compare each query protein structure predicted by the AlphaFold approach with the protein structures from the PDB to identify similarities between known binding sites found in the PDB and yet unknown binding sites in the AlphaFold database. Ligands bound in these identified similar PDB sites are then transferred to each query protein from the AlphaFold database, and binding sites are identified as ligand clusters on an AlphaFold protein. Small molecule binding sites are assigned druggability scores based on the similarity of their ligands to known drugs, allowing them to be ranked according to their perceived and actual potential for drug development. ProBiS-Fold provides interactive and downloadable binding sites for the entire human structural proteome, including more than 3000 new druggable binding sites that have no corresponding structure in the PDB, taking into account AlphaFold's model quality, to enable protein structure-function relationship studies and pharmaceutical drug discovery research. The web server is freely accessible to academic users at http://probis-fold.insilab.org.
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Affiliation(s)
- Janez Konc
- Theory Department, National Institute of Chemistry, Hajdrihova 19, SI-1001 Ljubljana, Slovenia
| | - Dušanka Janežič
- Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska, Glagoljaška 8, SI-6000 Koper, Slovenia
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9
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Shi W, Singha M, Pu L, Srivastava G, Ramanujam J, Brylinski M. GraphSite: Ligand Binding Site Classification with Deep Graph Learning. Biomolecules 2022; 12:1053. [PMID: 36008947 PMCID: PMC9405584 DOI: 10.3390/biom12081053] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Revised: 07/18/2022] [Accepted: 07/20/2022] [Indexed: 12/10/2022] Open
Abstract
The binding of small organic molecules to protein targets is fundamental to a wide array of cellular functions. It is also routinely exploited to develop new therapeutic strategies against a variety of diseases. On that account, the ability to effectively detect and classify ligand binding sites in proteins is of paramount importance to modern structure-based drug discovery. These complex and non-trivial tasks require sophisticated algorithms from the field of artificial intelligence to achieve a high prediction accuracy. In this communication, we describe GraphSite, a deep learning-based method utilizing a graph representation of local protein structures and a state-of-the-art graph neural network to classify ligand binding sites. Using neural weighted message passing layers to effectively capture the structural, physicochemical, and evolutionary characteristics of binding pockets mitigates model overfitting and improves the classification accuracy. Indeed, comprehensive cross-validation benchmarks against a large dataset of binding pockets belonging to 14 diverse functional classes demonstrate that GraphSite yields the class-weighted F1-score of 81.7%, outperforming other approaches such as molecular docking and binding site matching. Further, it also generalizes well to unseen data with the F1-score of 70.7%, which is the expected performance in real-world applications. We also discuss new directions to improve and extend GraphSite in the future.
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Affiliation(s)
- Wentao Shi
- Division of Electrical and Computer Engineering, Louisiana State University, Baton Rouge, LA 70803, USA; (W.S.); (J.R.)
| | - Manali Singha
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803, USA; (M.S.); (G.S.)
| | - Limeng Pu
- Center for Computation and Technology, Louisiana State University, Baton Rouge, LA 70803, USA;
| | - Gopal Srivastava
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803, USA; (M.S.); (G.S.)
| | - Jagannathan Ramanujam
- Division of Electrical and Computer Engineering, Louisiana State University, Baton Rouge, LA 70803, USA; (W.S.); (J.R.)
- Center for Computation and Technology, Louisiana State University, Baton Rouge, LA 70803, USA;
| | - Michal Brylinski
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803, USA; (M.S.); (G.S.)
- Center for Computation and Technology, Louisiana State University, Baton Rouge, LA 70803, USA;
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10
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Majumder A, Kanti Mondal S, Mukhoty S, Bag S, Mondal A, Begum Y, Sharma K, Banik A. Virtual screening and docking analysis of novel ligands for selective enhancement of tea ( Camellia sinensis) flavonoids. Food Chem X 2022; 13:100212. [PMID: 35498963 PMCID: PMC9039891 DOI: 10.1016/j.fochx.2022.100212] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2021] [Revised: 12/15/2021] [Accepted: 01/13/2022] [Indexed: 12/12/2022] Open
Abstract
Tea-specific flavonoid biosynthetic pathway (FBP) was retrieved from KEGG. Putative ligands were predicted to enhance enzymes-substrate binding affinity. FBP genes showed moderately higher expression & relatively strong codon adaptation. Most of the genes were AT-rich and biased to A/U-ending synonymous codons. Mutational selection was determining the selective constraints on codon bias.
Flavour of tea is mainly contributed by a group of polyphenols – flavonoids. However, the content of flavonoid fluctuates seasonally and is found to be higher in the first flush of tea, when compared to the second flush. This disparity in the flavonoid content, and hence taste, incurs heavy economic losses to the tea plantation industry each harvest season. For our present study, four key product-specific enzymes (PAL, FNS, FLS and ANS) of the tea-specific flavonoid pathway were selected to perform molecular docking studies with specific virtually screened allosteric modulators. Results of docking analyses showed Naringenin, 2-Morpholin-4-ium-4-ylethanesulfonate, 6-C-Glucosylquercetin, 2-Oxoglutaric acid, 3,5,7,3′,4′-pentahydroxyflavone to be capable of improving the spontaneity of the enzyme-substrate reactions in terms of docking score, RMSD values, and non-covalent interactions (H-bond,hydrophobic interaction, Π-stacking, salt bridge, etc.). Further, the evolutionary relationship of tea flavonoid pathway enzymes was constructed and compared with related taxa. The codon usage-based of tea flavonoid biosynthetic genes indicated the non-biasness of their nucleotide composition. Overall this study will provide a direction towards putative ligand-dependent enhancement of flavonoid content, irrespective of seasonal variation.
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Key Words
- 4CL, Tyrosine ammonia lyase
- AMF, Arbuscular Mycorrhizal Fungi
- ANR, anthocyanidin reductase
- ANS, anthocyanidinsynthase
- C4H, trans-cinnamate-4-
- CAI, Codon Adaptation Index
- CHI, chalcone isomerase
- CHS, 4-coumarat
- CoA, ligase chalcone synthase
- Codon usage indices
- DFR, dihydroflavonol 4-reductase
- ENc, Effective number of codons
- F3H, flavanone 3-hydroxylase
- F3′5′H, flavonoid 3′5′-hydroxylase
- F3′H, flavonoid 3′-hydroxylase
- FLS, Flavonol synthase
- FNS, flavone synthase
- Flavonoids
- GC1, GC2, and GC3-GC, content at the first, second, and third codon positions
- GC3s, frequency of either G or C at the third codon position of synonymous codons
- H 0, null hypothesisno selection
- IAA, Indole acetic acid
- LAR, leucoanthocyanidin reductase
- Ligands
- Molecular docking
- PAL, phenylalanine ammonia-lyase
- RMSD, root-mean-square deviation
- RSCU, Relative Synonymous Codon Usage
- TAL, monooxygenase
- Tea flush
- UGT72, UDP-3 glycosyltransferases
- Virtual screening
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Affiliation(s)
- Anusha Majumder
- Laboratory of Microbial Interaction, School of Biotechnology, Presidency University, Kolkata, West Bengal, India
| | - Sunil Kanti Mondal
- Department of Biotechnology, The University of Burdwan, Burdwan, West Bengal, India
| | - Samyabrata Mukhoty
- Department of Biotechnology, The University of Burdwan, Burdwan, West Bengal, India
| | - Sagar Bag
- Laboratory of Microbial Interaction, School of Biotechnology, Presidency University, Kolkata, West Bengal, India
| | - Anupam Mondal
- Laboratory of Microbial Interaction, School of Biotechnology, Presidency University, Kolkata, West Bengal, India
| | - Yasmin Begum
- Department of Biophysics, Molecular Biology, and Bioinformatics, University of Calcutta, 92, APC Road, Kolkata 700009, West Bengal, India.,Center of Excellence in Systems Biology and Biomedical Engineering (TEQIP Phase-III), University of Calcutta, JD-2, Sector III, Salt Lake, Kolkata 700106, West Bengal, India
| | - Kalpna Sharma
- R&D Centre, Danguajhar Tea Garden, Goodricke Group Ltd., Jalpaiguri, West Bengal, India
| | - Avishek Banik
- Laboratory of Microbial Interaction, School of Biotechnology, Presidency University, Kolkata, West Bengal, India
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11
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Konc J, Lešnik S, Škrlj B, Sova M, Proj M, Knez D, Gobec S, Janežič D. ProBiS-Dock: A Hybrid Multitemplate Homology Flexible Docking Algorithm Enabled by Protein Binding Site Comparison. J Chem Inf Model 2022; 62:1573-1584. [PMID: 35289616 DOI: 10.1021/acs.jcim.1c01176] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
The protein data bank (PDB) is a rich source of protein ligand structures, but ligands are not explicitly used in current docking algorithms. We have developed ProBiS-Dock, a docking algorithm complementary to the ProBiS-Dock Database (J. Chem. Inf. Model. 2021, 61, 4097-4107) that treats small molecules and proteins as fully flexible entities and allows conformational changes in both after ligand binding. A new scoring function is described that consists of a binding site-specific scoring function (ProBiS-Score) and a general statistical scoring function. ProBiS-Dock enables rapid docking of small molecules to proteins and has been successfully validated in silico against standard benchmarks. It enables rapid search for new active ligands by leveraging existing knowledge in the PDB. The potential of the software for drug development has been confirmed in vitro by the discovery of new inhibitors of human indoleamine 2,3-dioxygenase 1, an enzyme that is an attractive target for cancer therapy and catalyzes the first rate-determining step of l-tryptophan metabolism via the kynurenine pathway. The software is freely available to academic users at http://insilab.org/probisdock.
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Affiliation(s)
- Janez Konc
- National Institute of Chemistry, Theory Department, Hajdrihova 19, SI-1001 Ljubljana, Slovenia
| | - Samo Lešnik
- National Institute of Chemistry, Theory Department, Hajdrihova 19, SI-1001 Ljubljana, Slovenia
| | - Blaž Škrlj
- National Institute of Chemistry, Theory Department, Hajdrihova 19, SI-1001 Ljubljana, Slovenia.,Jozef Stefan International Postgraduate School, Jamova cesta 39, SI-1000 Ljubljana, Slovenia.,Jozef Stefan Institute, Jamova cesta 39, SI-1000 Ljubljana, Slovenia
| | - Matej Sova
- Faculty of Pharmacy, The Chair of Pharmaceutical Chemistry, Aškerčeva cesta 7, SI-1000 Ljubljana, Slovenia
| | - Matic Proj
- Faculty of Pharmacy, The Chair of Pharmaceutical Chemistry, Aškerčeva cesta 7, SI-1000 Ljubljana, Slovenia
| | - Damijan Knez
- Faculty of Pharmacy, The Chair of Pharmaceutical Chemistry, Aškerčeva cesta 7, SI-1000 Ljubljana, Slovenia
| | - Stanislav Gobec
- Faculty of Pharmacy, The Chair of Pharmaceutical Chemistry, Aškerčeva cesta 7, SI-1000 Ljubljana, Slovenia
| | - Dušanka Janežič
- Faculty of Mathematics, Natural Sciences and Information Technologies, Glagoljaška ulica 8, SI-6000 Koper, Slovenia
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12
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Kenneth K W To, Cho WCS. Drug repurposing for cancer therapy in the era of precision medicine. Curr Mol Pharmacol 2022; 15:895-903. [PMID: 35156588 DOI: 10.2174/1874467215666220214104530] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Revised: 10/15/2021] [Accepted: 11/07/2021] [Indexed: 11/22/2022]
Abstract
Drug repurposing refers to the identification of clinically approved drugs, with the known safety profiles and defined pharmacokinetic properties, to new indications. Despite the advances in oncology research, cancers are still associated with the most unmet medical needs. Drug repurposing has emerged as a useful approach for the search for effective and durable cancer treatment. It may also represent a promising strategy to facilitate precision cancer treatment and to overcome drug resistance. The repurposing of non-cancer drugs for precision oncology effectively extends the inventory of actionable molecular targets and thus increases the number of patients who may benefit from precision cancer treatment. In cancer types where genetic heterogeneity is so high that it is not feasible to identify strong repurposed drug candidates for standard treatment, the precision oncology approach offers individual patients access to novel treatment options. For repurposed candidates with low potency, a combination of multiple repurposed drugs may produce a synergistic therapeutic effect. Precautions should be taken when combining repurposed drugs with anticancer agents to avoid detrimental drug-drug interactions and unwanted side effects. New multifactorial data analysis and artificial intelligence methods are needed to untangle the complex association of molecular signatures influencing specific cancer subtypes to facilitate drug repurposing in precision oncology.
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Affiliation(s)
- Kenneth K W To
- School of Pharmacy, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - William C S Cho
- Department of Clinical Oncology, Queen Elizabeth Hospital, Hong Kong SAR, China
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13
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Vankayala SL, Warrensford LC, Pittman AR, Pollard BC, Kearns FL, Larkin JD, Woodcock HL. CIFDock: A novel CHARMM-based flexible receptor-flexible ligand docking protocol. J Comput Chem 2022; 43:84-95. [PMID: 34741467 DOI: 10.1002/jcc.26759] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Revised: 01/28/2021] [Accepted: 03/25/2021] [Indexed: 12/13/2022]
Abstract
Docking studies play a critical role in the current workflow of drug discovery. However, limitations may often arise through factors including inadequate ligand sampling, a lack of protein flexibility, scoring function inadequacies (e.g., due to metals, co-factors, etc.), and difficulty in retaining explicit water molecules. Herein, we present a novel CHARMM-based induced fit docking (CIFDock) workflow that can circumvent these limitations by employing all-atom force fields coupled to enhanced sampling molecular dynamics procedures. Self-guided Langevin dynamics simulations are used to effectively sample relevant ligand conformations, side chain orientations, crystal water positions, and active site residue motion. Protein flexibility is further enhanced by dynamic sampling of side chain orientations using an expandable rotamer library. Steps in the procedure consisting of fixing individual components (e.g., the ligand) while sampling the other components (e.g., the residues in the active site of the protein) allow for the complex to adapt to conformational changes. Ultimately, all components of the complex-the protein, ligand, and waters-are sampled simultaneously and unrestrained with SGLD to capture any induced fit effects. This modular flexible docking procedure is automated using CHARMM scripting, interfaced with SLURM array processing, and parallelized to use the desired number of processors. We validated the CIFDock procedure by performing cross-docking studies using a data set comprised of 21 pharmaceutically relevant proteins. Five variants of the CHARMM-based SWISSDOCK scoring functions were created to quantify the results of the final generated poses. Results obtained were comparable to, or in some cases improved upon, commercial docking program data.
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Affiliation(s)
- Sai L Vankayala
- Department of Chemistry, Eckerd College, St. Petersburg, Florida, USA
| | | | - Amanda R Pittman
- Department of Chemistry, Eckerd College, St. Petersburg, Florida, USA
| | - Benjamin C Pollard
- Department of Chemistry, University of South Florida, Tampa, Florida, USA
| | - Fiona L Kearns
- Department of Chemistry, Eckerd College, St. Petersburg, Florida, USA
| | - Joseph D Larkin
- Department of Chemistry, University of South Florida, Tampa, Florida, USA
| | - H Lee Woodcock
- Department of Chemistry, Eckerd College, St. Petersburg, Florida, USA
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14
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Škrlj B, Kralj J, Konc J, Robnik‐Šikonja M, Lavrač N. Deep node ranking for neuro‐symbolic structural node embedding and classification. INT J INTELL SYST 2022. [DOI: 10.1002/int.22651] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- Blaž Škrlj
- Jožef Stefan Institute Ljubljana Slovenia
- Jožef Stefan International Postgraduate School Ljubljana Slovenia
| | - Jan Kralj
- Jožef Stefan Institute Ljubljana Slovenia
- Cosylab d.o.o. Ljubljana Slovenia
| | - Janez Konc
- National Institute of Chemistry Ljubljana Slovenia
| | | | - Nada Lavrač
- Jožef Stefan Institute Ljubljana Slovenia
- University of Nova Gorica Ajdovščina Slovenia
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15
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Bai B, Zou R, Chan HCS, Li H, Yuan S. MolADI: A Web Server for Automatic Analysis of Protein-Small Molecule Dynamic Interactions. Molecules 2021; 26:molecules26154625. [PMID: 34361778 PMCID: PMC8347168 DOI: 10.3390/molecules26154625] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 07/28/2021] [Accepted: 07/28/2021] [Indexed: 11/16/2022] Open
Abstract
Protein-ligand interaction analysis is important for drug discovery and rational protein design. The existing online tools adopt only a single conformation of the complex structure for calculating and displaying the interactions, whereas both protein residues and ligand molecules are flexible to some extent. The interactions evolved with time in the trajectories are of greater interest. MolADI is a user-friendly online tool which analyzes the protein-ligand interactions in detail for either a single structure or a trajectory. Interactions can be viewed easily with both 2D graphs and 3D representations. MolADI is available as a web application.
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Affiliation(s)
- Bing Bai
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Avenue, Shenzhen 518055, China; (B.B.); (R.Z.); (H.C.S.C.)
- Shenzhen Institute of Advanced Technology, University of Chinese Academy of Sciences, 1068 Xueyuan Avenue, Shenzhen 518055, China
| | - Rongfeng Zou
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Avenue, Shenzhen 518055, China; (B.B.); (R.Z.); (H.C.S.C.)
- Shenzhen Institute of Advanced Technology, University of Chinese Academy of Sciences, 1068 Xueyuan Avenue, Shenzhen 518055, China
- AlphaMol Science Ltd., 1068 Xueyuan Avenue, Shenzhen 518055, China
| | - H. C. Stephen Chan
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Avenue, Shenzhen 518055, China; (B.B.); (R.Z.); (H.C.S.C.)
- Shenzhen Institute of Advanced Technology, University of Chinese Academy of Sciences, 1068 Xueyuan Avenue, Shenzhen 518055, China
| | - Hongchun Li
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Avenue, Shenzhen 518055, China; (B.B.); (R.Z.); (H.C.S.C.)
- Shenzhen Institute of Advanced Technology, University of Chinese Academy of Sciences, 1068 Xueyuan Avenue, Shenzhen 518055, China
- Correspondence: (H.L.); (S.Y.)
| | - Shuguang Yuan
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Avenue, Shenzhen 518055, China; (B.B.); (R.Z.); (H.C.S.C.)
- Shenzhen Institute of Advanced Technology, University of Chinese Academy of Sciences, 1068 Xueyuan Avenue, Shenzhen 518055, China
- AlphaMol Science Ltd., 1068 Xueyuan Avenue, Shenzhen 518055, China
- Correspondence: (H.L.); (S.Y.)
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16
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Konc J, Lešnik S, Škrlj B, Janežič D. ProBiS-Dock Database: A Web Server and Interactive Web Repository of Small Ligand-Protein Binding Sites for Drug Design. J Chem Inf Model 2021; 61:4097-4107. [PMID: 34319727 DOI: 10.1021/acs.jcim.1c00454] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
We have developed a new system, ProBiS-Dock, which can be used to determine the different types of protein binding sites for small ligands. The binding sites identified this way are then used to construct a new binding site database, the ProBiS-Dock Database, that allows for the ranking of binding sites according to their utility for drug development. The newly constructed database currently has more than 1.4 million binding sites and offers the possibility to investigate potential drug targets originating from different biological species. The interactive ProBiS-Dock Database, a web server and repository that consists of all small-molecule ligand binding sites in all of the protein structures in the Protein Data Bank, is freely available at http://probis-dock-database.insilab.org. The ProBiS-Dock Database will be regularly updated to keep pace with the growth of the Protein Data Bank, and our anticipation is that it will be useful in drug discovery.
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Affiliation(s)
- Janez Konc
- Theory Department, National Institute of Chemistry, Hajdrihova 19, SI-1000 Ljubljana, Slovenia
| | - Samo Lešnik
- Theory Department, National Institute of Chemistry, Hajdrihova 19, SI-1000 Ljubljana, Slovenia
| | - Blaž Škrlj
- Theory Department, National Institute of Chemistry, Hajdrihova 19, SI-1000 Ljubljana, Slovenia.,Jozef Stefan International Postgraduate School, Jamova cesta 39, SI-1000 Ljubljana, Slovenia
| | - Dušanka Janežič
- Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska, Glagoljaška ulica 8, SI-6000 Koper, Slovenia
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17
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Hassan S, Töpel M, Aronsson H. Ligand Binding Site Comparison - LiBiSCo - a web-based tool for analyzing interactions between proteins and ligands to explore amino acid specificity within active sites. Proteins 2021; 89:1530-1540. [PMID: 34240464 DOI: 10.1002/prot.26175] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Revised: 06/18/2021] [Accepted: 06/23/2021] [Indexed: 11/12/2022]
Abstract
Interaction between protein and ligands are ubiquitous in a biological cell, and understanding these interactions at the atom level in protein-ligand complexes is crucial for structural bioinformatics and drug discovery. Here, we present a web-based protein-ligand interaction application named Ligand Binding Site Comparison (LiBiSCo) for comparing the amino acid residues interacting with atoms of a ligand molecule between different protein-ligand complexes available in the Protein Data Bank (PDB) database. The comparison is performed at the ligand atom level irrespectively of having binding site similarity or not between the protein structures of interest. The input used in LiBiSCo is one or several PDB IDs of protein-ligand complex(es) and the tool returns a list of identified interactions at ligand atom level including both bonded and non-bonded interactions. A sequence profile for the interaction for each ligand atoms is provided as a WebLogo. The LiBiSco is useful in understanding ligand binding specificity and structural promiscuity among families that are structurally unrelated. The LiBiSCo tool can be accessed through https://albiorix.bioenv.gu.se/LiBiSCo/HomePage.py.
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Affiliation(s)
- Sameer Hassan
- Department of Biological and Environmental Sciences, University of Gothenburg, Gothenburg, Sweden.,Karolinska Institutet, Division of Neurogeriatrics, Stockholm, Sweden
| | - Mats Töpel
- Department of Marine Science, University of Gothenburg, Gothenburg, Sweden
| | - Henrik Aronsson
- Department of Biological and Environmental Sciences, University of Gothenburg, Gothenburg, Sweden
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18
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NOD: a web server to predict New use of Old Drugs to facilitate drug repurposing. Sci Rep 2021; 11:13540. [PMID: 34188160 PMCID: PMC8241987 DOI: 10.1038/s41598-021-92903-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Accepted: 06/15/2021] [Indexed: 11/08/2022] Open
Abstract
Computational methods accelerate the drug repurposing pipelines that are a quicker and cost-effective alternative to discovering new molecules. However, there is a paucity of web servers to conduct fast, focussed, and customized investigations for identifying new uses of old drugs. We present the NOD web server, which has the mentioned characteristics. NOD uses a sensitive sequence-guided approach to identify close and distant homologs of a protein of interest. NOD then exploits this evolutionary information to suggest potential compounds from the DrugBank database that can be repurposed against the input protein. NOD also allows expansion of the chemical space of the potential candidates through similarity searches. We have validated the performance of NOD against available experimental and/or clinical reports. In 65.6% of the investigated cases in a control study, NOD is able to identify drugs more effectively than the searches made in DrugBank. NOD is freely-available at http://pauling.mbu.iisc.ac.in/NOD/NOD/ .
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19
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Reem A, Sami R, Y.F. Koko M, Essam Noma A, Algabri YA, Kumar RA, Khojah E, Zhong ZH. Functional and Structural Annotation of a Hypothetical Protein (PA2373) from Pseudomonas aeruginosa PA01. INT J PHARMACOL 2021. [DOI: 10.3923/ijp.2021.262.270] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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20
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Sarkar T, Raghavan VV, Chen F, Riley A, Zhou S, Xu W. Exploring the effectiveness of the TSR-based protein 3-D structural comparison method for protein clustering, and structural motif identification and discovery of protein kinases, hydrolases, and SARS-CoV-2's protein via the application of amino acid grouping. Comput Biol Chem 2021; 92:107479. [PMID: 33951604 DOI: 10.1016/j.compbiolchem.2021.107479] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Revised: 03/14/2021] [Accepted: 03/23/2021] [Indexed: 12/26/2022]
Abstract
Development of protein 3-D structural comparison methods is essential for understanding protein functions. Some amino acids share structural similarities while others vary considerably. These structures determine the chemical and physical properties of amino acids. Grouping amino acids with similar structures potentially improves the ability to identify structurally conserved regions and increases the global structural similarity between proteins. We systematically studied the effects of amino acid grouping on the numbers of Specific/specific, Common/common, and statistically different keys to achieve a better understanding of protein structure relations. Common keys represent substructures found in all types of proteins and Specific keys represent substructures exclusively belonging to a certain type of proteins in a data set. Our results show that applying amino acid grouping to the Triangular Spatial Relationship (TSR)-based method, while computing structural similarity among proteins, improves the accuracy of protein clustering in certain cases. In addition, applying amino acid grouping facilitates the process of identification or discovery of conserved structural motifs. The results from the principal component analysis (PCA) demonstrate that applying amino acid grouping captures slightly more structural variation than when amino acid grouping is not used, indicating that amino acid grouping reduces structure diversity as predicted. The TSR-based method uniquely identifies and discovers binding sites for drugs or interacting proteins. The binding sites of nsp16 of SARS-CoV-2, SARS-CoV and MERS-CoV that we have defined will aid future antiviral drug design for improving therapeutic outcome. This approach for incorporating the amino acid grouping feature into our structural comparison method is promising and provides a deeper insight into understanding of structural relations of proteins.
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Affiliation(s)
- Titli Sarkar
- The Center for Advanced Computer Studies, University of Louisiana at Lafayette, Lafayette, LA 70504, USA
| | - Vijay V Raghavan
- The Center for Advanced Computer Studies, University of Louisiana at Lafayette, Lafayette, LA 70504, USA
| | - Feng Chen
- High Performance Computing, 329 Frey Computing Services Center, Louisiana State University, Baton Rouge, LA 70803, USA
| | - Andrew Riley
- The Center for Advanced Computer Studies, University of Louisiana at Lafayette, Lafayette, LA 70504, USA
| | - Sophia Zhou
- Department of Chemistry, University of Louisiana at Lafayette, P.O. Box 44370, Lafayette, LA 70504, USA
| | - Wu Xu
- Department of Chemistry, University of Louisiana at Lafayette, P.O. Box 44370, Lafayette, LA 70504, USA.
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21
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Kores K, Konc J, Bren U. Mechanistic Insights into Side Effects of Troglitazone and Rosiglitazone Using a Novel Inverse Molecular Docking Protocol. Pharmaceutics 2021; 13:315. [PMID: 33670968 PMCID: PMC7997210 DOI: 10.3390/pharmaceutics13030315] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Revised: 02/23/2021] [Accepted: 02/25/2021] [Indexed: 12/15/2022] Open
Abstract
Thiazolidinediones form drugs that treat insulin resistance in type 2 diabetes mellitus. Troglitazone represents the first drug from this family, which was removed from use by the FDA due to its hepatotoxicity. As an alternative, rosiglitazone was developed, but it was under the careful watch of FDA for a long time due to suspicion, that it causes cardiovascular diseases, such as heart failure and stroke. We applied a novel inverse molecular docking protocol to discern the potential protein targets of both drugs. Troglitazone and rosiglitazone were docked into predicted binding sites of >67,000 protein structures from the Protein Data Bank and examined. Several new potential protein targets with successfully docked troglitazone and rosiglitazone were identified. The focus was devoted to human proteins so that existing or new potential side effects could be explained or proposed. Certain targets of troglitazone such as 3-oxo-5-beta-steroid 4-dehydrogenase, neutrophil collagenase, stromelysin-1, and VLCAD were pinpointed, which could explain its hepatoxicity, with additional ones indicating that its application could lead to the treatment/development of cancer. Results for rosiglitazone discerned its interaction with members of the matrix metalloproteinase family, which could lead to cancer and neurodegenerative disorders. The concerning cardiovascular side effects of rosiglitazone could also be explained. We firmly believe that our results deepen the mechanistic understanding of the side effects of both drugs, and potentially with further development and research maybe even help to minimize them. On the other hand, the novel inverse molecular docking protocol on the other hand carries the potential to develop into a standard tool to predict possible cross-interactions of drug candidates potentially leading to adverse side effects.
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Affiliation(s)
- Katarina Kores
- Laboratory of Physical Chemistry and Chemical Thermodynamics, Faculty for Chemistry and Chemical Technology, University of Maribor, Smetanova 17, SI-2000 Maribor, Slovenia; (K.K.); (J.K.)
| | - Janez Konc
- Laboratory of Physical Chemistry and Chemical Thermodynamics, Faculty for Chemistry and Chemical Technology, University of Maribor, Smetanova 17, SI-2000 Maribor, Slovenia; (K.K.); (J.K.)
- Laboratory for Molecular Modeling, Theory Department, National Institute of Chemistry, Hajdrihova 19, SI-1000 Ljubljana, Slovenia
| | - Urban Bren
- Laboratory of Physical Chemistry and Chemical Thermodynamics, Faculty for Chemistry and Chemical Technology, University of Maribor, Smetanova 17, SI-2000 Maribor, Slovenia; (K.K.); (J.K.)
- Department of Applied Natural Sciences, Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska, Glagoljaška 8, SI-6000 Koper, Slovenia
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Brackenridge DA, McGuffin LJ. Proteins and Their Interacting Partners: An Introduction to Protein-Ligand Binding Site Prediction Methods with a Focus on FunFOLD3. Methods Mol Biol 2021; 2365:43-58. [PMID: 34432238 DOI: 10.1007/978-1-0716-1665-9_3] [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] [Indexed: 06/13/2023]
Abstract
Proteins are essential molecules with a diverse range of functions; elucidating their biological and biochemical characteristics can be difficult and time consuming using in vitro and/or in vivo methods. Additionally, in vivo protein-ligand binding site elucidation is unable to keep place with current growth in sequencing, leaving the majority of new protein sequences without known functions. Therefore, the development of new methods, which aim to predict the protein-ligand interactions and ligand-binding site residues directly from amino acid sequences, is becoming increasingly important. In silico prediction can utilise either sequence information, structural information or a combination of both. In this chapter, we will discuss the broad range of methods for ligand-binding site prediction from protein structure and we will describe our method, FunFOLD3, for the prediction of protein-ligand interactions and ligand-binding sites based on template-based modelling. Additionally, we will describe the step-by-step instructions using the FunFOLD3 downloadable application along with examples from the Critical Assessment of Techniques for Protein Structure Prediction (CASP) where FunFOLD3 has been used to aid ligand and ligand-binding site prediction. Finally, we will introduce our newer method, FunFOLD3-D, a version of FunFOLD3 which aims to improve template-based protein-ligand binding site prediction through the integration of docking, using AutoDock Vina.
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Abstract
INTRODUCTION Molecular docking has been consolidated as one of the most important methods in the molecular modeling field. It has been recognized as a prominent tool in the study of protein-ligand complexes, to describe intermolecular interactions, to accurately predict poses of multiple ligands, to discover novel promising bioactive compounds. Molecular docking methods have evolved in terms of their accuracy and reliability; but there are pending issues to solve for improving the connection between the docking results and the experimental evidence. AREAS COVERED In this article, the author reviews very recent innovative molecular docking applications with special emphasis on reverse docking, treatment of protein flexibility, the use of experimental data to guide the selection of docking poses, the application of Quantum mechanics(QM) in docking, and covalent docking. EXPERT OPINION There are several issues being worked on in recent years that will lead to important breakthroughs in molecular docking methods in the near future These developments are related to more efficient exploration of large datasets and receptor conformations, advances in electronic description, and the use of structural information for guiding the selection of results.
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Affiliation(s)
- Julio Caballero
- Departamento De Bioinformática, Centro De Bioinformática, Simulación Y Modelado (CBSM), Facultad De Ingeniería, Universidad De Talca, Talca, Chile
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Santonastaso A, Maggi M, De Jonge H, Scotti C. High resolution structure of human apolipoprotein (a) kringle IV type 2: beyond the lysine binding site. J Lipid Res 2020; 61:1687-1696. [PMID: 32907988 PMCID: PMC7707183 DOI: 10.1194/jlr.ra120001023] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
Lipoprotein (a) [Lp(a)] is characterized by an LDL-like composition in terms of lipids and apoB100, and by one copy of a unique glycoprotein, apo(a). The apo(a) structure is mainly based on the repetition of tandem kringle domains with high homology to plasminogen kringles 4 and 5. Among them, kringle IV type 2 (KIV-2) is present in a highly variable number of genetically encoded repeats, whose length is inversely related to Lp(a) plasma concentration and cardiovascular risk. Despite it being the major component of apo(a), the actual function of KIV-2 is still unclear. Here, we describe the first high-resolution crystallographic structure of this domain. It shows a general fold very similar to other KIV domains with high and intermediate affinity for the lysine analog, ε-aminocaproic acid. Interestingly, KIV-2 presents a lysine binding site (LBS) with a unique shape and charge distribution. KIV-2 affinity for predicted small molecule binders was found to be negligible in surface plasmon resonance experiments; and with the LBS being nonfunctional, we propose to rename it "pseudo-LBS". Further investigation of the protein by computational small-molecule docking allowed us to identify a possible heparin-binding site away from the LBS, which was confirmed by specific reverse charge mutations abolishing heparin binding. This study opens new possibilities to define the pathogenesis of Lp(a)-related diseases and to facilitate the design of specific therapeutic drugs.
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Affiliation(s)
- Alice Santonastaso
- Department of Molecular Medicine, Unit of Immunology and General Pathology, University of Pavia, Pavia, Italy
| | - Maristella Maggi
- Department of Molecular Medicine, Unit of Immunology and General Pathology, University of Pavia, Pavia, Italy
| | - Hugo De Jonge
- Department of Molecular Medicine, Unit of Immunology and General Pathology, University of Pavia, Pavia, Italy
| | - Claudia Scotti
- Department of Molecular Medicine, Unit of Immunology and General Pathology, University of Pavia, Pavia, Italy.
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25
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Computational Drug Repositioning: Current Progress and Challenges. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10155076] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Novel drug discovery is time-consuming, costly, and a high-investment process due to the high attrition rate. Therefore, many trials are conducted to reuse existing drugs to treat pressing conditions and diseases, since their safety profiles and pharmacokinetics are already available. Drug repositioning is a strategy to identify a new indication of existing or already approved drugs, beyond the scope of their original use. Various computational and experimental approaches to incorporate available resources have been suggested for gaining a better understanding of disease mechanisms and the identification of repurposed drug candidates for personalized pharmacotherapy. In this review, we introduce publicly available databases for drug repositioning and summarize the approaches taken for drug repositioning. We also highlight and compare their characteristics and challenges, which should be addressed for the future realization of drug repositioning.
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Lešnik S, Hodošček M, Podobnik B, Konc J. Loop Grafting between Similar Local Environments for Fc-Silent Antibodies. J Chem Inf Model 2020; 60:5475-5486. [PMID: 32379970 PMCID: PMC7686954 DOI: 10.1021/acs.jcim.9b01198] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
![]()
Reduction
of the affinity of the fragment crystallizable (Fc) region with immune
receptors by substitution of one or a few amino acids, known as Fc-silencing,
is an established approach to reduce the immune effector functions
of monoclonal antibody therapeutics. This approach to Fc-silencing,
however, is problematic as it can lead to instability and immunogenicity
of the developed antibodies. We evaluated loop grafting as a novel
approach to Fc-silencing in which the Fc loops responsible for immune
receptor binding were replaced by loops of up to 20 amino acids from
similar local environments in other human and mouse antibodies. Molecular
dynamics simulations of the designed variants of an Fc region in a
complex with the immune receptor FcγIIIa confirmed that loop
grafting potentially leads to a significant reduction in the binding
of the antibody variants to the receptor, while retaining their stability.
In comparison, standard variants with less than eight substituted
amino acids showed possible instability and a lower degree of Fc-silencing
due to the occurrence of compensatory interactions. The presented
approach to Fc-silencing is general and could be used to modulate
undesirable side effects of other antibody therapeutics without affecting
their stability or increasing their immunogenicity.
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Affiliation(s)
- Samo Lešnik
- National Institute of Chemistry, Hajdrihova 19, SI-1000 Ljubljana, Slovenia
| | - Milan Hodošček
- National Institute of Chemistry, Hajdrihova 19, SI-1000 Ljubljana, Slovenia
| | - Barbara Podobnik
- Biologics Technical Development Mengeš, Technical Research & Development Novartis, Lek Pharmaceuticals d.d., Kolodvorska 27, SI-1234 Mengeš, Slovenia
| | - Janez Konc
- National Institute of Chemistry, Hajdrihova 19, SI-1000 Ljubljana, Slovenia
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Ab Ghani NS, Ramlan EI, Firdaus-Raih M. Drug ReposER: a web server for predicting similar amino acid arrangements to known drug binding interfaces for potential drug repositioning. Nucleic Acids Res 2020; 47:W350-W356. [PMID: 31106379 PMCID: PMC6602481 DOI: 10.1093/nar/gkz391] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2019] [Revised: 04/24/2019] [Accepted: 05/02/2019] [Indexed: 11/29/2022] Open
Abstract
A common drug repositioning strategy is the re-application of an existing drug to address alternative targets. A crucial aspect to enable such repurposing is that the drug's binding site on the original target is similar to that on the alternative target. Based on the assumption that proteins with similar binding sites may bind to similar drugs, the 3D substructure similarity data can be used to identify similar sites in other proteins that are not known targets. The Drug ReposER (DRug REPOSitioning Exploration Resource) web server is designed to identify potential targets for drug repurposing based on sub-structural similarity to the binding interfaces of known drug binding sites. The application has pre-computed amino acid arrangements from protein structures in the Protein Data Bank that are similar to the 3D arrangements of known drug binding sites thus allowing users to explore them as alternative targets. Users can annotate new structures for sites that are similarly arranged to the residues found in known drug binding interfaces. The search results are presented as mappings of matched sidechain superpositions. The results of the searches can be visualized using an integrated NGL viewer. The Drug ReposER server has no access restrictions and is available at http://mfrlab.org/drugreposer/.
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Affiliation(s)
- Nur Syatila Ab Ghani
- Faculty of Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Selangor 43600, Malaysia
| | - Effirul Ikhwan Ramlan
- School of Computing, Engineering and Intelligent Systems, Ulster University, Northlands Road, Magee Campus, Londonderry BT48 7JL, UK.,Malaysia Genome Institute, National Institutes of Biotechnology Malaysia, Jalan Bangi, 43000 Kajang, Selangor, Malaysia
| | - Mohd Firdaus-Raih
- Faculty of Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Selangor 43600, Malaysia.,Institute of Systems Biology, Universiti Kebangsaan Malaysia, Bangi, Selangor 43600, Malaysia
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28
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In Silico Laboratory: Tools for Similarity-Based Drug Discovery. Methods Mol Biol 2019. [PMID: 31773644 DOI: 10.1007/978-1-0716-0163-1_1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register]
Abstract
Computational methods that predict and evaluate binding of ligands to receptors implicated in different pathologies have become crucial in modern drug design and discovery. Here, we describe protocols for using the recently developed package of computational tools for similarity-based drug discovery. The ProBiS stand-alone program and web server allow superimposition of protein structures against large protein databases and predict ligands based on detected binding site similarities. GenProBiS allows mapping of human somatic missense mutations related to cancer and non-synonymous single nucleotide polymorphisms and subsequent visual exploration of specific interactions in connection to these mutations. We describe protocols for using LiSiCA, a fast ligand-based virtual screening software that enables easy screening of large databases containing billions of small molecules. Finally, we show the use of BoBER, a web interface that enables user-friendly access to a large database of bioisosteric and scaffold hopping replacements.
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Afroz M, Zihad SMNK, Uddin SJ, Rouf R, Rahman MS, Islam MT, Khan IN, Ali ES, Aziz S, Shilpi JA, Nahar L, Sarker SD. A systematic review on antioxidant and antiinflammatory activity of Sesame (
Sesamum indicum
L.) oil and further confirmation of antiinflammatory activity by chemical profiling and molecular docking. Phytother Res 2019; 33:2585-2608. [DOI: 10.1002/ptr.6428] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2019] [Revised: 05/10/2019] [Accepted: 06/10/2019] [Indexed: 12/20/2022]
Affiliation(s)
- Mohasana Afroz
- Pharmacy Discipline, Life Science SchoolKhulna University Khulna Bangladesh
| | | | - Shaikh Jamal Uddin
- Pharmacy Discipline, Life Science SchoolKhulna University Khulna Bangladesh
| | - Razina Rouf
- Department of Pharmacy, Faculty of Life ScienceBangabandhu Sheikh Mujibur Rahman Science & Technology University Gopalganj Bangladesh
| | - Md. Shamim Rahman
- Biotechnology and Genetic Engineering Discipline, Life Science SchoolKhulna University Khulna Bangladesh
| | - Muhammad Torequl Islam
- Department for Management of Science and Technology DevelopmentTon Duc Thang University Ho Chi Minh City Vietnam
- Faculty of PharmacyTon Duc Thang University Ho Chi Minh City Vietnam
| | - Ishaq N. Khan
- PK‐NeuroOncology Research Group, Institute of Basic Medical SciencesKhyber Medical University Peshawar Pakistan
| | - Eunüs S. Ali
- Department of Biochemistry and Molecular GeneticsNorthwestern University Feinberg School of Medicine Chicago Illinois
| | - Shahin Aziz
- Chemical Research DivisionBangladesh Council of Scientific and Industrial Research Dhaka Bangladesh
| | - Jamil A. Shilpi
- Pharmacy Discipline, Life Science SchoolKhulna University Khulna Bangladesh
| | - Lutfun Nahar
- Centre for Natural Products Discovery, School of Pharmacy and Biomolecular Sciences, Faculty of ScienceLiverpool John Moores University Liverpool UK
| | - Satyajit D. Sarker
- Centre for Natural Products Discovery, School of Pharmacy and Biomolecular Sciences, Faculty of ScienceLiverpool John Moores University Liverpool UK
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30
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Gazerani P. Identification of novel analgesics through a drug repurposing strategy. Pain Manag 2019; 9:399-415. [DOI: 10.2217/pmt-2018-0091] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Abstract
The identification of new indications for approved or failed drugs is a process called drug repositioning or drug repurposing. The motivation includes overcoming the productivity gap that exists in drug development, which is a high-cost–high-risk process. Repositioning also includes rescuing drugs that have safely entered the market but have failed to demonstrate sufficient efficiency for the initial clinical indication. Considering the high prevalence of chronic pain, the lack of sufficient efficacy and the safety issues of current analgesics, repositioning seems to be an attractive approach. This review presents example of drugs that already have been repositioned and highlights new technologies that are available for the identification of additional compounds to stimulate the curiosity of readers for further exploration.
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Affiliation(s)
- Parisa Gazerani
- Biomedicine, Department of Health Science & Technology, Aalborg University, Frederik Bajers Vej 3 B, 9220 Aalborg East, Denmark
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31
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Konc J, Skrlj B, Erzen N, Kunej T, Janezic D. GenProBiS: web server for mapping of sequence variants to protein binding sites. Nucleic Acids Res 2019; 45:W253-W259. [PMID: 28498966 PMCID: PMC5570222 DOI: 10.1093/nar/gkx420] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2017] [Accepted: 05/02/2017] [Indexed: 02/02/2023] Open
Abstract
Discovery of potentially deleterious sequence variants is important and has wide implications for research and generation of new hypotheses in human and veterinary medicine, and drug discovery. The GenProBiS web server maps sequence variants to protein structures from the Protein Data Bank (PDB), and further to protein–protein, protein–nucleic acid, protein–compound, and protein–metal ion binding sites. The concept of a protein–compound binding site is understood in the broadest sense, which includes glycosylation and other post-translational modification sites. Binding sites were defined by local structural comparisons of whole protein structures using the Protein Binding Sites (ProBiS) algorithm and transposition of ligands from the similar binding sites found to the query protein using the ProBiS-ligands approach with new improvements introduced in GenProBiS. Binding site surfaces were generated as three-dimensional grids encompassing the space occupied by predicted ligands. The server allows intuitive visual exploration of comprehensively mapped variants, such as human somatic mis-sense mutations related to cancer and non-synonymous single nucleotide polymorphisms from 21 species, within the predicted binding sites regions for about 80 000 PDB protein structures using fast WebGL graphics. The GenProBiS web server is open and free to all users at http://genprobis.insilab.org.
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Affiliation(s)
- Janez Konc
- National Institute of Chemistry, Hajdrihova 19, 1000 Ljubljana, Slovenia.,University of Primorska, Faculty of Mathematics, Natural Sciences and Information Technologies, 6000 Koper, Slovenia
| | - Blaz Skrlj
- National Institute of Chemistry, Hajdrihova 19, 1000 Ljubljana, Slovenia
| | - Nika Erzen
- National Institute of Chemistry, Hajdrihova 19, 1000 Ljubljana, Slovenia
| | - Tanja Kunej
- Biotechnical Faculty, University of Ljubljana, 1000 Ljubljana, Slovenia
| | - Dusanka Janezic
- University of Primorska, Faculty of Mathematics, Natural Sciences and Information Technologies, 6000 Koper, Slovenia
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32
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Kores K, Lešnik S, Bren U, Janežič D, Konc J. Discovery of Novel Potential Human Targets of Resveratrol by Inverse Molecular Docking. J Chem Inf Model 2019; 59:2467-2478. [PMID: 30883115 DOI: 10.1021/acs.jcim.8b00981] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Resveratrol is a polyphenol known for its antioxidant and anti-inflammatory properties, which support its use as a treatment for variety of diseases. There are already known connections of resveratrol to chemoprevention of cancer because of its ability to prevent tumor initiation and inhibit tumor promotion and progression. Resveratrol is also believed to be important in cardiovascular diseases and neurological disorders, such as Alzheimer's disease. Using an inverse molecular docking approach, we sought to find new potential targets of resveratrol. Docking of resveratrol into each ProBiS predicted binding site of >38 000 protein structures from the Protein Data Bank was examined, and a number of novel potential targets into which resveratrol was docked successfully were found. These explain known actions or predict new effects of resveratrol. The results included three human proteins that are already known to bind resveratrol. A majority of proteins discovered however have no already described connections with resveratrol. We report new potential target human proteins and proteins connected with different organisms into which resveratrol can dock. Our results reveal previously unknown potential target human proteins, whose connection with cardiovascular and neurological disorders could lead to new potential treatments for variety of diseases. We believe that our research could help in future experimental studies on revestratol bioactivity in humans.
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Affiliation(s)
- Katarina Kores
- University of Maribor , Faculty for Chemistry and Chemical Technology Maribor , Smetanova ulica 17 , SI-2000 Maribor , Slovenia
| | - Samo Lešnik
- National Institute of Chemistry , Hajdrihova 19 , SI-1000 Ljubljana , Slovenia
| | - Urban Bren
- University of Maribor , Faculty for Chemistry and Chemical Technology Maribor , Smetanova ulica 17 , SI-2000 Maribor , Slovenia.,National Institute of Chemistry , Hajdrihova 19 , SI-1000 Ljubljana , Slovenia.,University of Primorska , Faculty of Mathematics, Natural Sciences and Information Technology , Glagoljaška 8 , SI-6000 Koper , Slovenia
| | - Dušanka Janežič
- University of Primorska , Faculty of Mathematics, Natural Sciences and Information Technology , Glagoljaška 8 , SI-6000 Koper , Slovenia
| | - Janez Konc
- National Institute of Chemistry , Hajdrihova 19 , SI-1000 Ljubljana , Slovenia.,University of Primorska , Faculty of Mathematics, Natural Sciences and Information Technology , Glagoljaška 8 , SI-6000 Koper , Slovenia
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33
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Abstract
INTRODUCTION The success of binding site comparisons in drug discovery is based on the recognized fact that many different proteins have similar binding sites. Indeed, binding site comparisons have found many uses in drug development and have the potential to dramatically cut the cost and shorten the time necessary for the development of new drugs. Areas covered: The authors review recent methods for comparing protein binding sites and their use in drug repurposing and polypharmacology. They examine emerging fields including the use of binding site comparisons in precision medicine, the prediction of structured water molecules, the search for targets of natural compounds, and their application in the development of protein-based drugs by loop modeling and for comparison of RNA binding sites. Expert opinion: Binding site comparisons have produced many interesting results in drug development, but relatively little work has been done on protein-protein interaction sites, which are particularly relevant in view of the success of biological drugs. Growth of protein loop modeling for modulating biological drugs is anticipated. The fusion of currently distinct methods for the comparison of RNA and protein binding sites into a single comprehensive approach could allow the search for new selective ribosomal antibiotics and initiate pharmaceutical research into other nucleoproteins.
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Affiliation(s)
- Janez Konc
- a Theory Department , National Institute of Chemistry , Ljubljana , Slovenia.,b Faculty of Pharmacy , University of Ljubljana , Ljubljana , Slovenia.,c Faculty of Mathematics , Natural Sciences and Information Technologies, University of Primorska , Koper , Slovenia.,d Faculty of Chemistry and Chemical Technology , University of Maribor , Maribor , Slovenia
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34
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Susec M, Sencanski M, Glisic S, Veljkovic N, Pedersen C, Drinovec L, Stojan J, Nøhr J, Vrecl M. Functional characterization of β 2-adrenergic and insulin receptor heteromers. Neuropharmacology 2019; 152:78-89. [PMID: 30707913 DOI: 10.1016/j.neuropharm.2019.01.025] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2018] [Revised: 01/01/2019] [Accepted: 01/23/2019] [Indexed: 01/06/2023]
Abstract
This study aimed to functionally characterize β2-adrenergic (β2AR) and insulin receptor (IR) heteromers in regard to β-arrestin 2 (βarr2) recruitment and cAMP signaling and to examine the involvement of the cytoplasmic portion of the IR β chain in heteromerization with β2AR. Evidence for β2AR:IR:βarr2 complex formation and the specificity of the IR:βarr2 interaction was first provided by bioinfomatics analysis. Receptor-heteromer investigation technology (HIT) then provided functional evidence of β2AR:IR heterodimerization by showing isoproterenol-induced but not insulin-induced GFP2-βarr2 recruitment to the β2AR:IR complex; the IR:βarr2 interaction was found to only be constitutive. The constitutive IR:βarr2 BRET signal (BRETconst) was significantly smaller in cells coexpressing IR-RLuc8 and a GFP2-βarr2 1-185 mutant lacking the proposed IR binding domain. β2AR:IR heteromerization also influenced the pharmacological phenotype of β2AR, i.e., its efficacy in recruiting βarr2 and activating cAMP signaling. Evidence suggesting involvement of the cytoplasmic portion of the IR β chain in the interaction with β2AR was provided by BRET2 saturation and HIT assays using an IR 1-1271 stop mutant lacking the IR C-terminal tail region. For the complex consisting of IR 1-1271-RLuc8:β2AR-GFP2, saturation was not reached, most likely reflecting random collisions between IR 1-1271 and β2AR. Furthermore, in the HIT assay, no substantial agonist-induced increase in the BRET2 signal was detected that would be indicative of βarr2 recruitment to the IR 1-1271:β2AR heteromer. Complementary 3D visualization of β2AR:IR provided supporting evidence for stability of the heterotetramer complex and identified amino acid residues involved in β2AR:IR heteromerization. This article is part of the Special Issue entitled 'Receptor heteromers and their allosteric receptor-receptor interactions'.
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Affiliation(s)
- Maja Susec
- Institute of Preclinical Sciences, Veterinary Faculty, University of Ljubljana, Slovenia
| | - Milan Sencanski
- Center for Multidisciplinary Research, Institute of Nuclear Sciences VINCA, University of Belgrade, Belgrade, Serbia
| | - Sanja Glisic
- Center for Multidisciplinary Research, Institute of Nuclear Sciences VINCA, University of Belgrade, Belgrade, Serbia
| | - Nevena Veljkovic
- Center for Multidisciplinary Research, Institute of Nuclear Sciences VINCA, University of Belgrade, Belgrade, Serbia
| | - Christina Pedersen
- Department of Incretin & Islet Biology, Novo Nordisk A/S, Måløv, Denmark
| | - Luka Drinovec
- Department of Condensed Matter Physics, Jožef Stefan Institute, Slovenia
| | - Jurij Stojan
- Institute of Biochemistry, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Jane Nøhr
- Department of Incretin & Islet Biology, Novo Nordisk A/S, Måløv, Denmark
| | - Milka Vrecl
- Institute of Preclinical Sciences, Veterinary Faculty, University of Ljubljana, Slovenia.
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35
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Nishimura Y, Hara H. Editorial: Drug Repositioning: Current Advances and Future Perspectives. Front Pharmacol 2018; 9:1068. [PMID: 30294274 PMCID: PMC6158627 DOI: 10.3389/fphar.2018.01068] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2018] [Accepted: 09/03/2018] [Indexed: 01/08/2023] Open
Affiliation(s)
- Yuhei Nishimura
- Department of Integrative Pharmacology, Mie University Graduate School of Medicine, Tsu, Japan
| | - Hideaki Hara
- Molecular Pharmacology, Department of Biofunctional Evaluation, Gifu Pharmaceutical University, Gifu, Japan
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36
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Samec N, Jovcevska I, Stojan J, Zottel A, Liovic M, Myers MP, Muyldermans S, Šribar J, Križaj I, Komel R. Glioblastoma-specific anti-TUFM nanobody for in-vitro immunoimaging and cancer stem cell targeting. Oncotarget 2018; 9:17282-17299. [PMID: 29707108 PMCID: PMC5915116 DOI: 10.18632/oncotarget.24629] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2017] [Accepted: 02/24/2018] [Indexed: 11/25/2022] Open
Abstract
Glioblastoma multiforme (GBM) is the most common and lethal form of brain tumor. The prognosis for patients remains poor, despite the combination of new preoperative and intraoperative neuroimaging, radical surgery, and recent advances in radiotherapy and chemotherapy. To improve GBM therapy and patient outcome, sustained drug delivery to glioma cells is needed, while minimizing toxicity to adjacent neurons and glia cells. This might be achieved through an anti-proteomic approach based on nanobodies, the single-domain antigen-binding fragments of heavy-chain antibodies of the camelid adaptive immune system. We report here on the validation and quantification of a nanobody raised against mitochondrial translation elongation factor (TUFM). Differential expression of TUFM was examined in different GBM cell lines and GBM tissue at the protein and mRNA levels, as compared to their expression in neural stem cells and normal brain tissue. We further used in-silico modelling and immunocytochemistry to define the specificity of anti-TUFM nanobody (Nb206) towards GBM stem cells, as compared to GBM cell lines (U251MG and U87MG cells). Due to its specificity and pronounced inhibitory effect on GBM stem cell growth, we propose the use of this anti-TUFM nanobody for GBM in vitro immunoimaging and potentially also cancer stem cell targeting.
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Affiliation(s)
- Neja Samec
- Medical Centre for Molecular Biology, Institute of Biochemistry, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Ivana Jovcevska
- Medical Centre for Molecular Biology, Institute of Biochemistry, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Jure Stojan
- Medical Centre for Molecular Biology, Institute of Biochemistry, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Alja Zottel
- Medical Centre for Molecular Biology, Institute of Biochemistry, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Mirjana Liovic
- Medical Centre for Molecular Biology, Institute of Biochemistry, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Michael P Myers
- International Centre for Genetic Engineering and Biotechnology (ICGEB), Trieste, Italy
| | - Serge Muyldermans
- Cellular and Molecular Immunology, Bioengineering Sciences Department, Vrije Universiteit Brussel, Brussels, Belgium
| | - Jernej Šribar
- Department of Molecular and Biomedical Sciences, Jožef Stefan Institute, Ljubljana, Slovenia
| | - Igor Križaj
- Department of Molecular and Biomedical Sciences, Jožef Stefan Institute, Ljubljana, Slovenia
| | - Radovan Komel
- Medical Centre for Molecular Biology, Institute of Biochemistry, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
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Ranking Enzyme Structures in the PDB by Bound Ligand Similarity to Biological Substrates. Structure 2018; 26:565-571.e3. [PMID: 29551288 PMCID: PMC5890617 DOI: 10.1016/j.str.2018.02.009] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2017] [Revised: 01/26/2018] [Accepted: 02/09/2018] [Indexed: 11/22/2022]
Abstract
There are numerous applications that use the structures of protein-ligand complexes from the PDB, such as 3D pharmacophore identification, virtual screening, and fragment-based drug design. The structures underlying these applications are potentially much more informative if they contain biologically relevant bound ligands, with high similarity to the cognate ligands. We present a study of ligand-enzyme complexes that compares the similarity of bound and cognate ligands, enabling the best matches to be identified. We calculate the molecular similarity scores using a method called PARITY (proportion of atoms residing in identical topology), which can conveniently be combined to give a similarity score for all cognate reactants or products in the reaction. Thus, we generate a rank-ordered list of related PDB structures, according to the biological similarity of the ligands bound in the structures. We present PARITY, matching atoms in identical topology to gauge ligand similarity Bound-cognate ligand similarity is a useful metric for ranking PDB structures Only 26% of enzyme structures in the PDB have bound-cognate ligand similarity ≥0.7 We provide rank-ordered lists of PDBs with the most biologically relevant ligands
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38
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Škrlj B, Kunej T, Konc J. Insights from Ion Binding Site Network Analysis into Evolution and Functions of Proteins. Mol Inform 2018; 37:e1700144. [PMID: 29418080 DOI: 10.1002/minf.201700144] [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: 11/27/2017] [Accepted: 02/01/2018] [Indexed: 01/05/2023]
Abstract
Many biological phenomena can be represented as complex networks. Using a protein binding site comparison approach, we generated a network of ion binding sites on the scale of all known protein structures from the Protein Data Bank. We found that this ion binding site similarity network is scale-free, indicating a network in which a few ion binding site scaffolds are the network hubs, and these are connected to hundreds of nodes, whereas the vast majority of nodes have only a few neighbors. Enrichment and statistical analysis of the network components and communities yielded insights into underlying processes from the functional and the structural perspective. Largest components and communities were observed to be closely related to basic metabolic processes and some of the most common structural folds, which, from the evolutionary point of view, indicates that they may be the oldest ones. Further, we derived the first comprehensive map of ion interchangeability, based on binding site similarity. Several highly interchangeable protein-ion binding site pairs emerged (e.g., Ca2+ and Mg2+ ), as well as structurally distinct ones. The constructed network of ion binding site similarities will aid in understanding the general principles of protein-ion binding sites structure, function and evolution. We demonstrate potential uses of the network on proteins involved in cancer development and immune response, where individual ions play prominent roles in disease development.
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Affiliation(s)
- Blaž Škrlj
- Department of molecular modeling, National Institute of Chemistry, Hajdrihova 19, Ljubljana, Slovenia.,Jožef Stefan International Postgraduate School, Jamova cesta 39, 1000, Ljubljana, Slovenia
| | - Tanja Kunej
- Department of Animal Science, Biotechnical Faculty, University of Ljubljana, Slovenia
| | - Janez Konc
- Department of molecular modeling, National Institute of Chemistry, Hajdrihova 19, Ljubljana, Slovenia
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BoBER: web interface to the base of bioisosterically exchangeable replacements. J Cheminform 2017; 9:62. [PMID: 29234984 PMCID: PMC5727005 DOI: 10.1186/s13321-017-0251-x] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2017] [Accepted: 12/04/2017] [Indexed: 11/10/2022] Open
Abstract
We describe a novel freely available web server Base of Bioisosterically Exchangeable Replacements (BoBER), which implements an interface to a database of bioisosteric and scaffold hopping replacements. Bioisosterism and scaffold hopping are key concepts in drug design and optimization, and can be defined as replacements of biologically active compound's fragments with other fragments to improve activity, reduce toxicity, change bioavailability or to diversify the scaffold space. Our web server enables fast and user-friendly searches for bioisosteric and scaffold replacements which were obtained by mining the whole Protein Data Bank. The working of the web server is presented on an existing MurF inhibitor as example. BoBER web server enables medicinal chemists to quickly search for and get new and unique ideas about possible bioisosteric or scaffold hopping replacements that could be used to improve hit or lead drug-like compounds.
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Jukič M, Konc J, Gobec S, Janežič D. Identification of Conserved Water Sites in Protein Structures for Drug Design. J Chem Inf Model 2017; 57:3094-3103. [DOI: 10.1021/acs.jcim.7b00443] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Affiliation(s)
- Marko Jukič
- Faculty of Pharmacy, University of Ljubljana, Aškerčeva 7, SI−1000, Ljubljana, Slovenia
| | - Janez Konc
- National Institute of Chemistry, Hajdrihova 19, SI−1000, Ljubljana, Slovenia
- Faculty of
Mathematics, Natural Sciences and Information Technologies, University of Primorska, Glagoljaška 8, SI−6000 Koper, Slovenia
| | - Stanislav Gobec
- Faculty of Pharmacy, University of Ljubljana, Aškerčeva 7, SI−1000, Ljubljana, Slovenia
| | - Dušanka Janežič
- Faculty of
Mathematics, Natural Sciences and Information Technologies, University of Primorska, Glagoljaška 8, SI−6000 Koper, Slovenia
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Lee J, Konc J, Janežič D, Brooks BR. Global organization of a binding site network gives insight into evolution and structure-function relationships of proteins. Sci Rep 2017; 7:11652. [PMID: 28912495 PMCID: PMC5599562 DOI: 10.1038/s41598-017-10412-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2017] [Accepted: 08/07/2017] [Indexed: 01/06/2023] Open
Abstract
The global organization of protein binding sites is analyzed by constructing a weighted network of binding sites based on their structural similarities and detecting communities of structurally similar binding sites based on the minimum description length principle. The analysis reveals that there are two central binding site communities that play the roles of the network hubs of smaller peripheral communities. The sizes of communities follow a power-law distribution, which indicates that the binding sites included in larger communities may be older and have been evolutionary structural scaffolds of more recent ones. Structurally similar binding sites in the same community bind to diverse ligands promiscuously and they are also embedded in diverse domain structures. Understanding the general principles of binding site interplay will pave the way for improved drug design and protein design.
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Affiliation(s)
- Juyong Lee
- Department of Chemistry, Kangwon National University, 1 Kangwondaehak-gil, Chuncheon, 24341, Republic of Korea. .,Laboratory of Computational Biology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland, 20892, United States.
| | - Janez Konc
- Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska, Glagoljaška 8, SI-6000, Koper, Slovenia.,National Institute of Chemistry, Hajdrihova 19, SI-1000, Ljubljana, Slovenia
| | - Dušanka Janežič
- Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska, Glagoljaška 8, SI-6000, Koper, Slovenia
| | - Bernard R Brooks
- Laboratory of Computational Biology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland, 20892, United States
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ProBiS tools (algorithm, database, and web servers) for predicting and modeling of biologically interesting proteins. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2017; 128:24-32. [PMID: 28212856 DOI: 10.1016/j.pbiomolbio.2017.02.005] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/26/2016] [Revised: 12/14/2016] [Accepted: 02/07/2017] [Indexed: 01/30/2023]
Abstract
ProBiS (Protein Binding Sites) Tools consist of algorithm, database, and web servers for prediction of binding sites and protein ligands based on the detection of structurally similar binding sites in the Protein Data Bank. In this article, we review the operations that ProBiS Tools perform, provide comments on the evolution of the tools, and give some implementation details. We review some of its applications to biologically interesting proteins. ProBiS Tools are freely available at http://probis.cmm.ki.si and http://probis.nih.gov.
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Abstract
The dramatic increase in the number of protein sequences and structures deposited in biological databases has led to the development of many bioinformatics tools and programs to manage, validate, compare, and interpret this large volume of data. In addition, powerful tools are being developed to use this sequence and structural data to facilitate protein classification and infer biological function of newly identified proteins. This chapter covers freely available bioinformatics resources on the World Wide Web that are commonly used for protein structure analysis.
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Affiliation(s)
- Jason J Paxman
- Department of Biochemistry and Genetics, La Trobe Institute for Molecular Science, La Trobe University, Rm 521, LIMS1, Kingsbury Drive, Bundoora, Melbourne, VIC, 3086, Australia
| | - Begoña Heras
- Department of Biochemistry and Genetics, La Trobe Institute for Molecular Science, La Trobe University, Rm 521, LIMS1, Kingsbury Drive, Bundoora, Melbourne, VIC, 3086, Australia.
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Štular T, Lešnik S, Rožman K, Schink J, Zdouc M, Ghysels A, Liu F, Aldrich CC, Haupt VJ, Salentin S, Daminelli S, Schroeder M, Langer T, Gobec S, Janežič D, Konc J. Discovery of Mycobacterium tuberculosis InhA Inhibitors by Binding Sites Comparison and Ligands Prediction. J Med Chem 2016; 59:11069-11078. [PMID: 27936766 DOI: 10.1021/acs.jmedchem.6b01277] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Drug discovery is usually focused on a single protein target; in this process, existing compounds that bind to related proteins are often ignored. We describe ProBiS plugin, extension of our earlier ProBiS-ligands approach, which for a given protein structure allows prediction of its binding sites and, for each binding site, the ligands from similar binding sites in the Protein Data Bank. We developed a new database of precalculated binding site comparisons of about 290000 proteins to allow fast prediction of binding sites in existing proteins. The plugin enables advanced viewing of predicted binding sites, ligands' poses, and their interactions in three-dimensional graphics. Using the InhA query protein, an enoyl reductase enzyme in the Mycobacterium tuberculosis fatty acid biosynthesis pathway, we predicted its possible ligands and assessed their inhibitory activity experimentally. This resulted in three previously unrecognized inhibitors with novel scaffolds, demonstrating the plugin's utility in the early drug discovery process.
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Affiliation(s)
- Tanja Štular
- National Institute of Chemistry , Hajdrihova 19, SI-1000 Ljubljana, Slovenia
| | - Samo Lešnik
- National Institute of Chemistry , Hajdrihova 19, SI-1000 Ljubljana, Slovenia
| | - Kaja Rožman
- Faculty of Pharmacy, University of Ljubljana , Aškerčeva cesta 7, SI-1000 Ljubljana, Slovenia
| | - Julia Schink
- Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska , Glagoljaška 8, SI-6000 Koper, Slovenia
| | - Mitja Zdouc
- Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska , Glagoljaška 8, SI-6000 Koper, Slovenia
| | - An Ghysels
- Center for Molecular Modeling, Ghent University , Technologiepark 903, 9052 Zwijnaarde, Belgium
| | - Feng Liu
- AAT Bioquest, Inc. , 520 Mercury Drive, Sunnyvale, California 94085, United States
| | - Courtney C Aldrich
- Department of Medicinal Chemistry, University of Minnesota , 308 Harvard Street Southeast, Minneapolis, Minnesota 55455, United States
| | - V Joachim Haupt
- Biotechnology Center (BIOTEC), Technische Universität Dresden , 01307 Dresden, Germany
| | - Sebastian Salentin
- Biotechnology Center (BIOTEC), Technische Universität Dresden , 01307 Dresden, Germany
| | - Simone Daminelli
- Biotechnology Center (BIOTEC), Technische Universität Dresden , 01307 Dresden, Germany
| | - Michael Schroeder
- Biotechnology Center (BIOTEC), Technische Universität Dresden , 01307 Dresden, Germany
| | - Thierry Langer
- Department of Pharmaceutical Chemistry, Faculty of Life Sciences, University of Vienna , Althanstrasse 14, A-1090 Vienna, Austria
| | - Stanislav Gobec
- Faculty of Pharmacy, University of Ljubljana , Aškerčeva cesta 7, SI-1000 Ljubljana, Slovenia
| | - Dušanka Janežič
- Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska , Glagoljaška 8, SI-6000 Koper, Slovenia
| | - Janez Konc
- National Institute of Chemistry , Hajdrihova 19, SI-1000 Ljubljana, Slovenia.,Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska , Glagoljaška 8, SI-6000 Koper, Slovenia
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Skrlj B, Kunej T. Computational identification of non-synonymous polymorphisms within regions corresponding to protein interaction sites. Comput Biol Med 2016; 79:30-35. [PMID: 27744178 DOI: 10.1016/j.compbiomed.2016.10.003] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2016] [Revised: 10/02/2016] [Accepted: 10/03/2016] [Indexed: 11/24/2022]
Abstract
BACKGROUND Protein-protein interactions (PPI) play an important role in function of all organisms and enable understanding of underlying metabolic processes. Computational predictions of PPIs are an important aspect in proteomics, as experimental methods may result in high degree of false positive results and are more expensive. Although there are many databases collecting predicted PPIs, exploration of genetics information underlying PPI interactions has not been investigated thoroughly. The aim of the present study was to identify genomic locations corresponding to regions involved in predicted PPIs and to collect non-synonymous polymorphisms (nsSNPs) located within those regions; which we termed PPI-SNPs. METHODS Predicted PPIs were obtained from PiSITE database (http://pisite.hgc.jp). Non-synonymous SNPs mapped on protein structural data (PDBs) were obtained from the UCSC server. Polymorphism locations on protein structures were mapped to predicted PPI regions. DAVID tool was used for pathway enrichment and gene cluster analysis (https://david.ncifcrf.gov/). RESULTS We collected 544 polymorphisms located within predicted PPI sites that map to 197 genes. We identified 9 SNPs, previously associated with diseases, but not yet associated with PPI sites. We also found examples in which polymorphisms located within predicted PPI regions are also occurring within previously experimentally validated PPIs and within experimentally determined functional domains. CONCLUSIONS Our study provides the first catalog of nsSNPs located within predicted PPIs. These prioritized SNPs present the basis for planning experimental validation of SNPs that cause gain or loss of PPIs. Our implementation is expandable, as datasets used are constantly updated.
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Affiliation(s)
- Blaz Skrlj
- Department of Animal Science, Biotechnical Faculty, University of Ljubljana, Groblje 3, Slovenia.
| | - Tanja Kunej
- Department of Animal Science, Biotechnical Faculty, University of Ljubljana, Groblje 3, Slovenia.
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46
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Hu X, Wang K, Dong Q. Protein ligand-specific binding residue predictions by an ensemble classifier. BMC Bioinformatics 2016; 17:470. [PMID: 27855637 PMCID: PMC5114821 DOI: 10.1186/s12859-016-1348-3] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2016] [Accepted: 11/10/2016] [Indexed: 11/22/2022] Open
Abstract
BACKGROUND Prediction of ligand binding sites is important to elucidate protein functions and is helpful for drug design. Although much progress has been made, many challenges still need to be addressed. Prediction methods need to be carefully developed to account for chemical and structural differences between ligands. RESULTS In this study, we present ligand-specific methods to predict the binding sites of protein-ligand interactions. First, a sequence-based method is proposed that only extracts features from protein sequence information, including evolutionary conservation scores and predicted structure properties. An improved AdaBoost algorithm is applied to address the serious imbalance problem between the binding and non-binding residues. Then, a combined method is proposed that combines the current template-free method and four other well-established template-based methods. The above two methods predict the ligand binding sites along the sequences using a ligand-specific strategy that contains metal ions, acid radical ions, nucleotides and ferroheme. Testing on a well-established dataset showed that the proposed sequence-based method outperformed the profile-based method by 4-19% in terms of the Matthews correlation coefficient on different ligands. The combined method outperformed each of the individual methods, with an improvement in the average Matthews correlation coefficients of 5.55% over all ligands. The results also show that the ligand-specific methods significantly outperform the general-purpose methods, which confirms the necessity of developing elaborate ligand-specific methods for ligand binding site prediction. CONCLUSIONS Two efficient ligand-specific binding site predictors are presented. The standalone package is freely available for academic usage at http://dase.ecnu.edu.cn/qwdong/TargetCom/TargetCom_standalone.tar.gz or request upon the corresponding author.
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Affiliation(s)
- Xiuzhen Hu
- College of Sciences, Inner Mongolia University of Technology, Hohhot, 010051 People’s Republic of China
| | - Kai Wang
- College of Animal Science and Technology, Jilin Agricultural University, Changchun, 130118 People’s Republic of China
| | - Qiwen Dong
- Institute for Data Science and Engineering, East China Normal University, Shanghai, 200062 People’s Republic of China
- Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong 518055 People’s Republic of China
- Present Address: School of Computer Science and Software Engineering, East China Normal University, #3663, North Zhongshan RD, Shanghai, 200062 China
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Tibaut T, Borišek J, Novič M, Turk D. Comparison of in silico tools for binding site prediction applied for structure-based design of autolysin inhibitors. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2016; 27:573-587. [PMID: 27686112 DOI: 10.1080/1062936x.2016.1217271] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/01/2016] [Accepted: 07/22/2016] [Indexed: 06/06/2023]
Abstract
Autolysin E (AtlE) is a bacteriolytic enzyme which plays an important role in division and growth of bacterial cells and therefore represents a promising potential drug target. Its 3D structure has been recently elucidated. We used in silico prediction tools to study substrate or ligand (inhibitor) binding regions of AtlE. We applied several freely available tools and a commercial tool for binding site identification and compared results of the prediction. Calculation time, number of predictions and output data provided by specific software vary according to the different approaches utilized by specific method categories. Despite different approaches, binding sites in similar locations on the protein were predicted. Specific amino acid residues that form these binding sites were predicted as binding residues. The predicted residues, especially those with predicted highest conservation score, could theoretically have catalytic and binding properties. According to our results, we assume that E138, which has the highest conservation score, is the catalytic residue and F161, G162 and Y224, which are also highly conserved, are responsible for substrate binding. Ligands developed with binding specificity towards these residues could inhibit the catalysis and binding of the substrate of AtlE. The molecules with inhibitory potency could therefore represent potential new antibacterial agents.
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Affiliation(s)
- T Tibaut
- a Laboratory of Chemometrics , National Institute of Chemistry , Ljubljana , Slovenia
| | - J Borišek
- a Laboratory of Chemometrics , National Institute of Chemistry , Ljubljana , Slovenia
| | - M Novič
- a Laboratory of Chemometrics , National Institute of Chemistry , Ljubljana , Slovenia
| | - D Turk
- b Department of Biochemistry and Molecular and Structural Biology , Institute Jozef Stefan , Ljubljana , Slovenia
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Mhashilkar AS, Vankayala SL, Liu C, Kearns F, Mehrotra P, Tzertzinis G, Palli SR, Woodcock HL, Unnasch TR. Identification of Ecdysone Hormone Receptor Agonists as a Therapeutic Approach for Treating Filarial Infections. PLoS Negl Trop Dis 2016; 10:e0004772. [PMID: 27300294 PMCID: PMC4907521 DOI: 10.1371/journal.pntd.0004772] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2016] [Accepted: 05/21/2016] [Indexed: 11/27/2022] Open
Abstract
Background A homologue of the ecdysone receptor has previously been identified in human filarial parasites. As the ecdysone receptor is not found in vertebrates, it and the regulatory pathways it controls represent attractive potential chemotherapeutic targets. Methodology/ Principal Findings Administration of 20-hydroxyecdysone to gerbils infected with B. malayi infective larvae disrupted their development to adult stage parasites. A stable mammalian cell line was created incorporating the B. malayi ecdysone receptor ligand-binding domain, its heterodimer partner and a secreted luciferase reporter in HEK293 cells. This was employed to screen a series of ecdysone agonist, identifying seven agonists active at sub-micromolar concentrations. A B. malayi ecdysone receptor ligand-binding domain was developed and used to study the ligand-receptor interactions of these agonists. An excellent correlation between the virtual screening results and the screening assay was observed. Based on both of these approaches, steroidal ecdysone agonists and the diacylhydrazine family of compounds were identified as a fruitful source of potential receptor agonists. In further confirmation of the modeling and screening results, Ponasterone A and Muristerone A, two compounds predicted to be strong ecdysone agonists stimulated expulsion of microfilaria and immature stages from adult parasites. Conclusions The studies validate the potential of the B. malayi ecdysone receptor as a drug target and provide a means to rapidly evaluate compounds for development of a new class of drugs against the human filarial parasites. The human filarial parasites are the causative agents of two neglected tropical diseases targeted for elimination by the international community. The current elimination programs rely upon the mass distribution of a limited number of drugs, leaving the programs open to failure in the event that resistance develops. Thus, there is a critical need for novel chemotherapeutic agents to supplement the current arsenal. The filarial parasites are ecdysozoans, whose developmental processes are controlled by a master regulator, the ecdysone receptor. Here we validate the potential of the filarial ecdysone receptor as a chemotherapeutic target and report the development of high throughput and virtual screening assays that may be used to compounds that target it.
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Affiliation(s)
- Amruta S. Mhashilkar
- Department of Global Health, College of Public Health, University of South Florida, Tampa, Florida, United States of America
| | - Sai L. Vankayala
- Department of Chemistry, University of South Florida, Tampa, Florida, United States of America
| | - Canhui Liu
- Department of Global Health, College of Public Health, University of South Florida, Tampa, Florida, United States of America
| | - Fiona Kearns
- Department of Chemistry, University of South Florida, Tampa, Florida, United States of America
| | - Priyanka Mehrotra
- Department of Chemistry, University of South Florida, Tampa, Florida, United States of America
| | - George Tzertzinis
- New England Biolabs, Ipswich, Massachusetts, United States of America
| | - Subba R. Palli
- Department of Entomology, University of Kentucky, Lexington, Kentucky, United States of America
| | - H. Lee Woodcock
- Department of Chemistry, University of South Florida, Tampa, Florida, United States of America
| | - Thomas R. Unnasch
- Department of Global Health, College of Public Health, University of South Florida, Tampa, Florida, United States of America
- * E-mail:
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Minimal Functional Sites in Metalloproteins and Their Usage in Structural Bioinformatics. Int J Mol Sci 2016; 17:ijms17050671. [PMID: 27153067 PMCID: PMC4881497 DOI: 10.3390/ijms17050671] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2016] [Revised: 04/18/2016] [Accepted: 04/28/2016] [Indexed: 12/12/2022] Open
Abstract
Metal ions play a functional role in numerous biochemical processes and cellular pathways. Indeed, about 40% of all enzymes of known 3D structure require a metal ion to be able to perform catalysis. The interactions of the metals with the macromolecular framework determine their chemical properties and reactivity. The relevant interactions involve both the coordination sphere of the metal ion and the more distant interactions of the so-called second sphere, i.e., the non-bonded interactions between the macromolecule and the residues coordinating the metal (metal ligands). The metal ligands and the residues in their close spatial proximity define what we call a minimal functional site (MFS). MFSs can be automatically extracted from the 3D structures of metal-binding biological macromolecules deposited in the Protein Data Bank (PDB). They are 3D templates that describe the local environment around a metal ion or metal cofactor and do not depend on the overall macromolecular structure. MFSs provide a different view on metal-binding proteins and nucleic acids, completely focused on the metal. Here we present different protocols and tools based upon the concept of MFS to obtain deeper insight into the structural and functional properties of metal-binding macromolecules. We also show that structure conservation of MFSs in metalloproteins relates to local sequence similarity more strongly than to overall protein similarity.
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Fu X, Zhang G, Liu R, Wei J, Zhang-Negrerie D, Jian X, Gao Q. Mechanistic Study of Human Glucose Transport Mediated by GLUT1. J Chem Inf Model 2016; 56:517-26. [PMID: 26821218 DOI: 10.1021/acs.jcim.5b00597] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
The glucose transporter 1 (GLUT1) belongs to the major facilitator superfamily (MFS) and is responsible for the constant uptake of glucose. However, the molecular mechanism of sugar transport remains obscure. In this study, homology modeling and molecular dynamics (MD) simulations in lipid bilayers were performed to investigate the combination of the alternate and multisite transport mechanism of glucose with GLUT1 in atomic detail. To explore the substrate recognition mechanism, the outward-open state human GLUT1 homology model was generated based on the template of xylose transporter XylE (PDB ID: 4GBZ), which shares up to 29% sequence identity and 49% similarity with GLUT1. Through the MD simulation study of glucose across lipid bilayer with both the outward-open GLUT1 and the GLUT1 inward-open crystal structure, we investigated six different conformational states and identified four key binding sites in both exofacial and endofacial loops that are essential for glucose recognition and transport. The study further revealed that four flexible gates consisting of W65/Y292/Y293-M420/TM10b-W388 might play important roles in the transport cycle. The study showed that some side chains close to the central ligand binding site underwent larger position changes. These conformational interchanges formed gated networks within an S-shaped central channel that permitted staged ligand diffusion across the transporter. This study provides new inroads for the understanding of GLUT1 ligand recognition paradigm and configurational features which are important for molecular, structural, and physiological research of the MFS members, especially for GLUT1-targeted drug design and discovery.
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Affiliation(s)
- Xuegang Fu
- Tianjin Key Laboratory for Modern Drug Delivery & High-Efficiency, School of Pharmaceutical Science and Technology, Tianjin University , Tianjin, 300072, P. R. China
| | - Gang Zhang
- Tianjin Key Laboratory for Modern Drug Delivery & High-Efficiency, School of Pharmaceutical Science and Technology, Tianjin University , Tianjin, 300072, P. R. China
| | - Ran Liu
- Tianjin Key Laboratory for Modern Drug Delivery & High-Efficiency, School of Pharmaceutical Science and Technology, Tianjin University , Tianjin, 300072, P. R. China
| | - Jing Wei
- Tianjin Key Laboratory for Modern Drug Delivery & High-Efficiency, School of Pharmaceutical Science and Technology, Tianjin University , Tianjin, 300072, P. R. China
| | - Daisy Zhang-Negrerie
- Concordia International School , 999 Mingyue Road, Shanghai, 201206, P. R. China
| | - Xiaodong Jian
- National Supercomputing Center in Tianjin , TEDA Service Outsourcing Industrial Park, Binhai New Area, Tianjin, 300457, P. R. China
| | - Qingzhi Gao
- Tianjin Key Laboratory for Modern Drug Delivery & High-Efficiency, School of Pharmaceutical Science and Technology, Tianjin University , Tianjin, 300072, P. R. China.,Tianjin University Collaborative Innovation Center of Chemical Science and Engineering , Tianjin, 300072, P. R. China
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