1
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Wei Z, Chen M, Lu X, Liu Y, Peng G, Yang J, Tang C, Yu P. A New Advanced Approach: Design and Screening of Affinity Peptide Ligands Using Computer Simulation Techniques. Curr Top Med Chem 2024; 24:667-685. [PMID: 38549525 DOI: 10.2174/0115680266281358240206112605] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2023] [Revised: 01/14/2024] [Accepted: 01/26/2024] [Indexed: 05/31/2024]
Abstract
Peptides acquire target affinity based on the combination of residues in their sequences and the conformation formed by their flexible folding, an ability that makes them very attractive biomaterials in therapeutic, diagnostic, and assay fields. With the development of computer technology, computer-aided design and screening of affinity peptides has become a more efficient and faster method. This review summarizes successful cases of computer-aided design and screening of affinity peptide ligands in recent years and lists the computer programs and online servers used in the process. In particular, the characteristics of different design and screening methods are summarized and categorized to help researchers choose between different methods. In addition, experimentally validated sequences are listed, and their applications are described, providing directions for the future development and application of computational peptide screening and design.
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Affiliation(s)
- Zheng Wei
- Xiangya School of Pharmacy, Central South University, Changsha, Hunan, 410013, China
| | - Meilun Chen
- Xiangya School of Pharmacy, Central South University, Changsha, Hunan, 410013, China
| | - Xiaoling Lu
- Xiangya School of Pharmacy, Central South University, Changsha, Hunan, 410013, China
| | - Yijie Liu
- Xiangya School of Pharmacy, Central South University, Changsha, Hunan, 410013, China
| | - Guangnan Peng
- School of Life Science, Central South University, Changsha, Hunan, 410013, China
| | - Jie Yang
- Xiangya School of Pharmacy, Central South University, Changsha, Hunan, 410013, China
| | - Chunhua Tang
- Xiangya School of Pharmacy, Central South University, Changsha, Hunan, 410013, China
| | - Peng Yu
- Xiangya School of Pharmacy, Central South University, Changsha, Hunan, 410013, China
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2
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Ramirez-Acosta K, Rosales-Fuerte IA, Perez-Sanchez JE, Nuñez-Rivera A, Juarez J, Cadena-Nava RD. Design and selection of peptides to block the SARS-CoV-2 receptor binding domain by molecular docking. BEILSTEIN JOURNAL OF NANOTECHNOLOGY 2022; 13:699-711. [PMID: 35957673 PMCID: PMC9344557 DOI: 10.3762/bjnano.13.62] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Accepted: 07/12/2022] [Indexed: 05/05/2023]
Abstract
The novel Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) is currently one of the most contagious viruses in existence and the cause of the worst pandemic in this century, COVID-19. SARS-CoV-2 infection begins with the recognition of the cellular receptor angiotensin converting enzyme-2 by its spike glycoprotein receptor-binding domain (RBD). Thus, the use of small peptides to neutralize the infective mechanism of SARS-CoV-2 through the RBD is an interesting strategy. The binding ability of 104 peptides (University of Nebraska Medical Center's Antimicrobial Peptide Database) to the RBD was assessed using molecular docking. Based on the molecular docking results, peptides with great affinity to the RBD were selected. The most common amino acids involved in the recognition of the RBD were identified to design novel peptides based on the number of hydrogen bonds that were formed. At physiological pH, these peptides are almost neutral and soluble in aqueous media. Interestingly, several peptides showed the capability to bind to the active surface area of the RBD of the Wuhan strain, as well as to the RBD of the Delta variant and other SARS-Cov-2 variants. Therefore, these peptides have promising potential in the treatment of the COVID-19 disease caused by different variants of SARS-CoV-2. This research work will be focused on the molecular docking of peptides by molecular dynamics, in addition to an analysis of the possible interaction of these peptides with physiological proteins. This methodology could be extended to design peptides that are active against other viruses.
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Affiliation(s)
- Kendra Ramirez-Acosta
- Centro de Nanociencias y Nanotecnología - Universidad Nacional Autónoma de México (UNAM) – Ensenada, Baja California, México
- Centro de Investigación Científica y de Educación Superior de Ensenada, Baja California, (CICESE), Ensenada, Baja California, México
| | - Ivan A Rosales-Fuerte
- Centro de Nanociencias y Nanotecnología - Universidad Nacional Autónoma de México (UNAM) – Ensenada, Baja California, México
- Centro de Investigación Científica y de Educación Superior de Ensenada, Baja California, (CICESE), Ensenada, Baja California, México
| | - J Eduardo Perez-Sanchez
- Centro de Nanociencias y Nanotecnología - Universidad Nacional Autónoma de México (UNAM) – Ensenada, Baja California, México
- Centro de Investigación Científica y de Educación Superior de Ensenada, Baja California, (CICESE), Ensenada, Baja California, México
| | - Alfredo Nuñez-Rivera
- Centro de Nanociencias y Nanotecnología - Universidad Nacional Autónoma de México (UNAM) – Ensenada, Baja California, México
- Centro de Investigación Científica y de Educación Superior de Ensenada, Baja California, (CICESE), Ensenada, Baja California, México
| | - Josue Juarez
- Departamento de Física, Universidad de Sonora, Blvd. Luis Encinas y Rosales, Hermosillo, Sonora, México
| | - Ruben D Cadena-Nava
- Centro de Nanociencias y Nanotecnología - Universidad Nacional Autónoma de México (UNAM) – Ensenada, Baja California, México
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3
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Swanson S, Sivaraman V, Grigoryan G, Keating AE. Tertiary motifs as building blocks for the design of protein‐binding peptides. Protein Sci 2022; 31:e4322. [PMID: 35634780 PMCID: PMC9088223 DOI: 10.1002/pro.4322] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 04/12/2022] [Accepted: 04/14/2022] [Indexed: 11/07/2022]
Affiliation(s)
- Sebastian Swanson
- Department of Biology Massachusetts Institute of Technology Cambridge Massachusetts USA
| | - Venkatesh Sivaraman
- Department of Biology Massachusetts Institute of Technology Cambridge Massachusetts USA
| | - Gevorg Grigoryan
- Department of Computer Science Dartmouth College Hanover New Hampshire USA
| | - Amy E. Keating
- Department of Biology Massachusetts Institute of Technology Cambridge Massachusetts USA
- Department of Biological Engineering Massachusetts Institute of Technology Cambridge Massachusetts USA
- Koch Center for Integrative Cancer Research Massachusetts Institute of Technology Cambridge Massachusetts USA
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4
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Matching protein surface structural patches for high-resolution blind peptide docking. Proc Natl Acad Sci U S A 2022; 119:e2121153119. [PMID: 35482919 PMCID: PMC9170164 DOI: 10.1073/pnas.2121153119] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Modeling interactions between short peptides and their receptors is a challenging docking problem due to the peptide flexibility, resulting in a formidable sampling problem of peptide conformation in addition to its orientation. Alternatively, the peptide can be viewed as a piece that complements the receptor monomer structure. Here, we show that the peptide conformation can be determined based on the receptor backbone only and sampled using local structural motifs found in solved protein monomers and interfaces, independent of sequence similarity. This approach outperforms current peptide docking protocols and promotes new directions for peptide interface design. Peptide docking can be perceived as a subproblem of protein–protein docking. However, due to the short length and flexible nature of peptides, many do not adopt one defined conformation prior to binding. Therefore, to tackle a peptide docking problem, not only the relative orientation, but also the bound conformation of the peptide needs to be modeled. Traditional peptide-centered approaches use information about peptide sequences to generate representative conformer ensembles, which can then be rigid-body docked to the receptor. Alternatively, one may look at this problem from the viewpoint of the receptor, namely, that the protein surface defines the peptide-bound conformation. Here, we present PatchMAN (Patch-Motif AligNments), a global peptide-docking approach that uses structural motifs to map the receptor surface with backbone scaffolds extracted from protein structures. On a nonredundant set of protein–peptide complexes, starting from free receptor structures, PatchMAN successfully models and identifies near-native peptide–protein complexes in 58%/84% within 2.5 Å/5 Å interface backbone RMSD, with corresponding sampling in 81%/100% of the cases, outperforming other approaches. PatchMAN leverages the observation that structural units of peptides with their binding pocket can be found not only within interfaces, but also within monomers. We show that the bound peptide conformation is sampled based on the structural context of the receptor only, without taking into account any sequence information. Beyond peptide docking, this approach opens exciting new avenues to study principles of peptide–protein association, and to the design of new peptide binders. PatchMAN is available as a server at https://furmanlab.cs.huji.ac.il/patchman/.
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5
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Xu X, Xiaoqin Zou. Predicting Protein-Peptide Complex Structures by Accounting for Peptide Flexibility and the Physicochemical Environment. J Chem Inf Model 2022; 62:27-39. [PMID: 34931833 PMCID: PMC9020583 DOI: 10.1021/acs.jcim.1c00836] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
Predicting protein-peptide complex structures is crucial to the understanding of a vast variety of peptide-mediated cellular processes and to peptide-based drug development. Peptide flexibility and binding mode ranking are the two major challenges for protein-peptide complex structure prediction. Peptides are highly flexible molecules, and therefore, brute-force modeling of peptide conformations of interest in protein-peptide docking is beyond current computing power. Inspired by the fact that the protein-peptide binding process is like protein folding, we developed a novel strategy, named MDockPeP2, which tries to address these challenges using physicochemical information embedded in abundant monomeric proteins with an exhaustive search strategy, in combination with an integrated global search and a local flexible minimization method. Only the peptide sequence and the protein crystal structure are required. The method was systemically assessed using a newly constructed structural database of 89 nonredundant protein-peptide complexes with the peptide sequence length ranging from 5 to 29 in which about half of the peptides are longer than 15 residues. MDockPeP2 yielded a total success rate of 58.4% (70.8, 79.8%) for the bound docking (i.e., with the bound receptor and fully flexible peptides) and 19.0% (44.8, 70.7%) for the challenging unbound docking when top 10 (100, 1000) models were considered for each prediction. MDockPeP2 achieved significantly higher success rates on two other datasets, peptiDB and LEADS-PEP, which contain only short- and medium-size peptides (≤ 15 residues). For peptiDB, our method obtained a success rate of 62.0% for the bound docking and 35.9% for the unbound docking when the top 10 models were considered. For LEADS-PEP, MDockPeP2 achieved a success rate of 69.8% when the top 10 models were considered. The program is available at https://zougrouptoolkit.missouri.edu/mdockpep2/download.html.
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6
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Narykov O, Johnson NT, Korkin D. Predicting protein interaction network perturbation by alternative splicing with semi-supervised learning. Cell Rep 2021; 37:110045. [PMID: 34818539 DOI: 10.1016/j.celrep.2021.110045] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 07/21/2021] [Accepted: 11/02/2021] [Indexed: 10/19/2022] Open
Abstract
Alternative splicing introduces an additional layer of protein diversity and complexity in regulating cellular functions that can be specific to the tissue and cell type, physiological state of a cell, or disease phenotype. Recent high-throughput experimental studies have illuminated the functional role of splicing events through rewiring protein-protein interactions; however, the extent to which the macromolecular interactions are affected by alternative splicing has yet to be fully understood. In silico methods provide a fast and cheap alternative to interrogating functional characteristics of thousands of alternatively spliced isoforms. Here, we develop an accurate feature-based machine learning approach that predicts whether a protein-protein interaction carried out by a reference isoform is perturbed by an alternatively spliced isoform. Our method, called the alternatively spliced interactions prediction (ALT-IN) tool, is compared with the state-of-the-art PPI prediction tools and shows superior performance, achieving 0.92 in precision and recall values.
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Affiliation(s)
- Oleksandr Narykov
- Department of Computer Science, and Bioinformatics and Computational Biology Program, Worcester Polytechnic Institute, Worcester, MA, USA
| | - Nathan T Johnson
- Department of Computer Science, and Bioinformatics and Computational Biology Program, Worcester Polytechnic Institute, Worcester, MA, USA; Harvard Program in Therapeutic Sciences, Harvard Medical School, and Breast Tumor Immunology Laboratory, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Dmitry Korkin
- Department of Computer Science, and Bioinformatics and Computational Biology Program, Worcester Polytechnic Institute, Worcester, MA, USA.
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7
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Abstract
Biological processes are often mediated by complexes formed between proteins and various biomolecules. The 3D structures of such protein-biomolecule complexes provide insights into the molecular mechanism of their action. The structure of these complexes can be predicted by various computational methods. Choosing an appropriate method for modelling depends on the category of biomolecule that a protein interacts with and the availability of structural information about the protein and its interacting partner. We intend for the contents of this chapter to serve as a guide as to what software would be the most appropriate for the type of data at hand and the kind of 3D complex structure required. Particularly, we have dealt with protein-small molecule ligand, protein-peptide, protein-protein, and protein-nucleic acid interactions.Most, if not all, model building protocols perform some sampling and scoring. Typically, several alternate conformations and configurations of the interactors are sampled. Each such sample is then scored for optimization. To boost the confidence in these predicted models, their assessment using other independent scoring schemes besides the inbuilt/default ones would prove to be helpful. This chapter also lists such software and serves as a guide to gauge the fidelity of modelled structures of biomolecular complexes.
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8
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Kurpe SR, Grishin SY, Surin AK, Panfilov AV, Slizen MV, Chowdhury SD, Galzitskaya OV. Antimicrobial and Amyloidogenic Activity of Peptides. Can Antimicrobial Peptides Be Used against SARS-CoV-2? Int J Mol Sci 2020; 21:E9552. [PMID: 33333996 PMCID: PMC7765370 DOI: 10.3390/ijms21249552] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Revised: 12/07/2020] [Accepted: 12/12/2020] [Indexed: 02/07/2023] Open
Abstract
At present, much attention is paid to the use of antimicrobial peptides (AMPs) of natural and artificial origin to combat pathogens. AMPs have several points that determine their biological activity. We analyzed the structural properties of AMPs, as well as described their mechanism of action and impact on pathogenic bacteria and viruses. Recently published data on the development of new AMP drugs based on a combination of molecular design and genetic engineering approaches are presented. In this article, we have focused on information on the amyloidogenic properties of AMP. This review examines AMP development strategies from the perspective of the current high prevalence of antibiotic-resistant bacteria, and the potential prospects and challenges of using AMPs against infection caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).
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Affiliation(s)
- Stanislav R. Kurpe
- Institute of Protein Research, Russian Academy of Sciences, 142290 Pushchino, Russia; (S.R.K.); (S.Y.G.); (A.K.S.); (A.V.P.); (M.V.S.)
| | - Sergei Yu. Grishin
- Institute of Protein Research, Russian Academy of Sciences, 142290 Pushchino, Russia; (S.R.K.); (S.Y.G.); (A.K.S.); (A.V.P.); (M.V.S.)
| | - Alexey K. Surin
- Institute of Protein Research, Russian Academy of Sciences, 142290 Pushchino, Russia; (S.R.K.); (S.Y.G.); (A.K.S.); (A.V.P.); (M.V.S.)
- The Branch of the Institute of Bioorganic Chemistry, Russian Academy of Sciences, 142290 Pushchino, Russia
- State Research Center for Applied Microbiology and Biotechnology, 142279 Obolensk, Russia
| | - Alexander V. Panfilov
- Institute of Protein Research, Russian Academy of Sciences, 142290 Pushchino, Russia; (S.R.K.); (S.Y.G.); (A.K.S.); (A.V.P.); (M.V.S.)
| | - Mikhail V. Slizen
- Institute of Protein Research, Russian Academy of Sciences, 142290 Pushchino, Russia; (S.R.K.); (S.Y.G.); (A.K.S.); (A.V.P.); (M.V.S.)
| | - Saikat D. Chowdhury
- Department of Biological Sciences, Indian Institute of Science Education and Research Kolkata, Mohanpur 741246, West Bengal, India;
| | - Oxana V. Galzitskaya
- Institute of Protein Research, Russian Academy of Sciences, 142290 Pushchino, Russia; (S.R.K.); (S.Y.G.); (A.K.S.); (A.V.P.); (M.V.S.)
- Institute of Theoretical and Experimental Biophysics, Russian Academy of Sciences, 142290 Pushchino, Russia
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9
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Xu X, Zou X. MDockPeP: A Web Server for Blind Prediction of Protein-Peptide Complex Structures. Methods Mol Biol 2020; 2165:259-272. [PMID: 32621230 DOI: 10.1007/978-1-0716-0708-4_15] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/13/2023]
Abstract
Protein-peptide interactions mediate a wide range of important cellular tasks. In silico prediction of protein-peptide complex structure is highly desirable for mechanistic investigation of these processes and for therapeutic design. Recently, we developed a docking-based method for predicting protein-peptide complex structures, which starts with the peptide sequence and globally docks the all-atom, flexible peptide onto the protein structure. The produced modes are then evaluated with a statistical potential-based scoring function. The method has been implemented into an online server, MDockPeP server, which is freely available at http://zougrouptoolkit.missouri.edu/mdockpep . The server can be used for protein-peptide complex structure prediction. The server can also be used for initial-stage sampling of the protein-peptide binding modes for computational-demanding simulation or docking methods.
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Affiliation(s)
- Xianjin Xu
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, MO, USA.,Department of Physics and Astronomy, University of Missouri, Columbia, MO, USA.,Department of Biochemistry, University of Missouri, Columbia, MO, USA.,Institute for Data Science and Informatics, University of Missouri, Columbia, MO, USA
| | - Xiaoqin Zou
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, MO, USA. .,Department of Physics and Astronomy, University of Missouri, Columbia, MO, USA. .,Department of Biochemistry, University of Missouri, Columbia, MO, USA. .,Institute for Data Science and Informatics, University of Missouri, Columbia, MO, USA.
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10
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Maia EHB, Assis LC, de Oliveira TA, da Silva AM, Taranto AG. Structure-Based Virtual Screening: From Classical to Artificial Intelligence. Front Chem 2020; 8:343. [PMID: 32411671 PMCID: PMC7200080 DOI: 10.3389/fchem.2020.00343] [Citation(s) in RCA: 209] [Impact Index Per Article: 52.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2019] [Accepted: 04/01/2020] [Indexed: 12/15/2022] Open
Abstract
The drug development process is a major challenge in the pharmaceutical industry since it takes a substantial amount of time and money to move through all the phases of developing of a new drug. One extensively used method to minimize the cost and time for the drug development process is computer-aided drug design (CADD). CADD allows better focusing on experiments, which can reduce the time and cost involved in researching new drugs. In this context, structure-based virtual screening (SBVS) is robust and useful and is one of the most promising in silico techniques for drug design. SBVS attempts to predict the best interaction mode between two molecules to form a stable complex, and it uses scoring functions to estimate the force of non-covalent interactions between a ligand and molecular target. Thus, scoring functions are the main reason for the success or failure of SBVS software. Many software programs are used to perform SBVS, and since they use different algorithms, it is possible to obtain different results from different software using the same input. In the last decade, a new technique of SBVS called consensus virtual screening (CVS) has been used in some studies to increase the accuracy of SBVS and to reduce the false positives obtained in these experiments. An indispensable condition to be able to utilize SBVS is the availability of a 3D structure of the target protein. Some virtual databases, such as the Protein Data Bank, have been created to store the 3D structures of molecules. However, sometimes it is not possible to experimentally obtain the 3D structure. In this situation, the homology modeling methodology allows the prediction of the 3D structure of a protein from its amino acid sequence. This review presents an overview of the challenges involved in the use of CADD to perform SBVS, the areas where CADD tools support SBVS, a comparison between the most commonly used tools, and the techniques currently used in an attempt to reduce the time and cost in the drug development process. Finally, the final considerations demonstrate the importance of using SBVS in the drug development process.
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Affiliation(s)
- Eduardo Habib Bechelane Maia
- Laboratory of Pharmaceutical Medicinal Chemistry, Federal University of São João Del Rei, Divinópolis, Brazil.,Federal Center for Technological Education of Minas Gerais-CEFET-MG, Belo Horizonte, Brazil
| | - Letícia Cristina Assis
- Laboratory of Pharmaceutical Medicinal Chemistry, Federal University of São João Del Rei, Divinópolis, Brazil
| | | | | | - Alex Gutterres Taranto
- Laboratory of Pharmaceutical Medicinal Chemistry, Federal University of São João Del Rei, Divinópolis, Brazil
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11
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Xu X, Zou X. PepPro: A Nonredundant Structure Data Set for Benchmarking Peptide-Protein Computational Docking. J Comput Chem 2019; 41:362-369. [PMID: 31793016 DOI: 10.1002/jcc.26114] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2019] [Revised: 09/20/2019] [Accepted: 11/03/2019] [Indexed: 12/19/2022]
Abstract
We present a nonredundant benchmark, coined PepPro, for testing peptide-protein docking algorithms. Currently, PepPro contains 89 nonredundant experimentally determined peptide-protein complex structures, with peptide sequence lengths ranging from 5 to 30 amino acids. The benchmark covers peptides with distinct secondary structures, including helix, partial helix, a mixture of helix and β-sheet, β-sheet formed through binding, β-sheet formed through self-folding, and coil. In addition, unbound proteins' structures are provided for 58 complexes and can be used for testing the ability of a docking algorithm handling the conformational changes of proteins during the binding process. PepPro should benefit the docking community for the development and improvement of peptide docking algorithms. The benchmark is available at http://zoulab.dalton.missouri.edu/PepPro_benchmark. © 2019 Wiley Periodicals, Inc.
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Affiliation(s)
- Xianjin Xu
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri, 65211.,Department of Physics and Astronomy, University of Missouri, Columbia, Missouri, 65211.,Department of Biochemistry, University of Missouri, Columbia, Missouri, 65211.,Informatics Institute, University of Missouri, Columbia, Missouri, 65211
| | - Xiaoqin Zou
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri, 65211.,Department of Physics and Astronomy, University of Missouri, Columbia, Missouri, 65211.,Department of Biochemistry, University of Missouri, Columbia, Missouri, 65211.,Informatics Institute, University of Missouri, Columbia, Missouri, 65211
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12
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Blaszczyk M, Ciemny MP, Kolinski A, Kurcinski M, Kmiecik S. Protein-peptide docking using CABS-dock and contact information. Brief Bioinform 2019; 20:2299-2305. [PMID: 30247502 PMCID: PMC6954405 DOI: 10.1093/bib/bby080] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2018] [Revised: 08/09/2018] [Accepted: 08/10/2018] [Indexed: 12/11/2022] Open
Abstract
CABS-dock is a computational method for protein-peptide molecular docking that does not require predefinition of the binding site. The peptide is treated as fully flexible, while the protein backbone undergoes small fluctuations and, optionally, large-scale rearrangements. Here, we present a specific CABS-dock protocol that enhances the docking procedure using fragmentary information about protein-peptide contacts. The contact information is used to narrow down the search for the binding peptide pose to the proximity of the binding site. We used information on a single-chosen and randomly chosen native protein-peptide contact to validate the protocol on the peptiDB benchmark. The contact information significantly improved CABS-dock performance. The protocol has been made available as a new feature of the CABS-dock web server (at http://biocomp.chem.uw.edu.pl/CABSdock/). SHORT ABSTRACT CABS-dock is a tool for flexible docking of peptides to proteins. In this article, we present a protocol for CABS-dock docking driven by information about protein-peptide contact(s). Using information on individual protein-peptide contacts allows to improve the accuracy of CABS-dock docking.
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13
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Protein-assisted RNA fragment docking (RnaX) for modeling RNA-protein interactions using ModelX. Proc Natl Acad Sci U S A 2019; 116:24568-24573. [PMID: 31732673 PMCID: PMC6900601 DOI: 10.1073/pnas.1910999116] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Protein–RNA interactions, key in biological processes, remained refractory to prediction algorithms. Here we present a new extension of the ModelX tool suite designed for this purpose. RNA–protein complexes in the Protein Data Bank were decomposed into small peptide–oligonucleotide interacting fragment pairs and used as building blocks to assemble big scaffolds representing complex RNA–protein interactions. This method has already been successful for designing DNA–protein and protein–protein interfaces. Areas under the curve up to 0.86 were achieved on binding site prediction showing the accuracy and coverage of our approach over established and in-house benchmarking sets. Together with FoldX protein design tool suite we were able to engineer backbone- and side chain-compatible interfaces using naked protein structures as input. RNA–protein interactions are crucial for such key biological processes as regulation of transcription, splicing, translation, and gene silencing, among many others. Knowing where an RNA molecule interacts with a target protein and/or engineering an RNA molecule to specifically bind to a protein could allow for rational interference with these cellular processes and the design of novel therapies. Here we present a robust RNA–protein fragment pair-based method, termed RnaX, to predict RNA-binding sites. This methodology, which is integrated into the ModelX tool suite (http://modelx.crg.es), takes advantage of the structural information present in all released RNA–protein complexes. This information is used to create an exhaustive database for docking and a statistical forcefield for fast discrimination of true backbone-compatible interactions. RnaX, together with the protein design forcefield FoldX, enables us to predict RNA–protein interfaces and, when sufficient crystallographic information is available, to reengineer the interface at the sequence-specificity level by mimicking those conformational changes that occur on protein and RNA mutagenesis. These results, obtained at just a fraction of the computational cost of methods that simulate conformational dynamics, open up perspectives for the engineering of RNA–protein interfaces.
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14
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Wen Z, He J, Tao H, Huang SY. PepBDB: a comprehensive structural database of biological peptide-protein interactions. Bioinformatics 2019; 35:175-177. [PMID: 29982280 DOI: 10.1093/bioinformatics/bty579] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2017] [Accepted: 07/04/2018] [Indexed: 02/01/2023] Open
Abstract
Summary A structural database of peptide-protein interactions is important for drug discovery targeting peptide-mediated interactions. Although some peptide databases, especially for special types of peptides, have been developed, a comprehensive database of cleaned peptide-protein complex structures is still not available. Such cleaned structures are valuable for docking and scoring studies in structure-based drug design. Here, we have developed PepBDB-a curated Peptide Binding DataBase of biological complex structures from the Protein Data Bank (PDB). PepBDB presents not only cleaned structures but also extensive information about biological peptide-protein interactions, and allows users to search the database with a variety of options and interactively visualize the search results. Availability and implementation PepBDB is available at http://huanglab.phys.hust.edu.cn/pepbdb/.
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Affiliation(s)
- Zeyu Wen
- Institute of Biophysics, School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Jiahua He
- Institute of Biophysics, School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Huanyu Tao
- Institute of Biophysics, School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Sheng-You Huang
- Institute of Biophysics, School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China
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15
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Bálint M, Horváth I, Mészáros N, Hetényi C. Towards Unraveling the Histone Code by Fragment Blind Docking. Int J Mol Sci 2019; 20:ijms20020422. [PMID: 30669446 PMCID: PMC6358888 DOI: 10.3390/ijms20020422] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2018] [Revised: 01/11/2019] [Accepted: 01/16/2019] [Indexed: 12/02/2022] Open
Abstract
Histones serve as protein spools for winding the DNA in the nucleosome. High variability of their post-translational modifications result in a unique code system often responsible for the pathomechanisms of epigenetics-based diseases. Decoding is performed by reader proteins via complex formation with the N-terminal peptide tails of histones. Determination of structures of histone-reader complexes would be a key to unravel the histone code and the design of new drugs. However, the large number of possible histone complex variations imposes a true challenge for experimental structure determination techniques. Calculation of such complexes is difficult due to considerable size and flexibility of peptides and the shallow binding surfaces of the readers. Moreover, location of the binding sites is often unknown, which requires a blind docking search over the entire surface of the target protein. To accelerate the work in this field, a new approach is presented for prediction of the structure of histone H3 peptide tails docked to their targets. Using a fragmenting protocol and a systematic blind docking method, a collection of well-positioned fragments of the H3 peptide is produced. After linking the fragments, reconstitution of anchoring regions of the target-bound H3 peptide conformations was possible. As a first attempt of combination of blind and fragment docking approaches, our new method is named fragment blind docking (FBD).
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Affiliation(s)
- Mónika Bálint
- Department of Pharmacology and Pharmacotherapy, Medical School, University of Pécs, Szigeti út 12, 7624 Pécs, Hungary.
| | - István Horváth
- Chemistry Doctoral School, University of Szeged, Dugonics tér 13, 6720 Szeged, Hungary.
| | - Nikolett Mészáros
- Department of Biochemistry, Eötvös Loránd University, Pázmány Péter sétány 1/C, 1117 Budapest, Hungary.
| | - Csaba Hetényi
- Department of Pharmacology and Pharmacotherapy, Medical School, University of Pécs, Szigeti út 12, 7624 Pécs, Hungary.
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16
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Ochoa R, Soler MA, Laio A, Cossio P. Assessing the capability of in silico mutation protocols for predicting the finite temperature conformation of amino acids. Phys Chem Chem Phys 2018; 20:25901-25909. [PMID: 30289133 DOI: 10.1039/c8cp03826k] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Mutation protocols are a key tool in computational biophysics for modelling unknown side chain conformations. In particular, these protocols are used to generate the starting structures for molecular dynamics simulations. The accuracy of the initial side chain and backbone placement is crucial to obtain a stable and quickly converging simulation. In this work, we assessed the performance of several mutation protocols in predicting the most probable conformer observed in finite temperature molecular dynamics simulations for a set of protein-peptide crystals differing only by single-point mutations in the peptide sequence. Our results show that several programs which predict well the crystal conformations fail to predict the most probable finite temperature configuration. Methods relying on backbone-dependent rotamer libraries have, in general, a better performance, but even the best protocol fails in predicting approximately 30% of the mutations.
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Affiliation(s)
- Rodrigo Ochoa
- Biophysics of Tropical Diseases, Max Planck Tandem Group, University of Antioquia, Medellin, Colombia.
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17
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Xu X, Yan C, Zou X. MDockPeP: An ab-initio protein-peptide docking server. J Comput Chem 2018; 39:2409-2413. [PMID: 30368849 DOI: 10.1002/jcc.25555] [Citation(s) in RCA: 47] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2018] [Revised: 07/13/2018] [Accepted: 07/22/2018] [Indexed: 01/29/2023]
Abstract
Protein-peptide interactions play a crucial role in a variety of cellular processes. The protein-peptide complex structure is a key to understand the mechanisms underlying protein-peptide interactions and is critical for peptide therapeutic development. We present a user-friendly protein-peptide docking server, MDockPeP. Starting from a peptide sequence and a protein receptor structure, the MDockPeP Server globally docks the all-atom, flexible peptide to the protein receptor. The produced modes are then evaluated with a statistical potential-based scoring function, ITScorePeP. This method was systematically validated using the peptiDB benchmarking database. At least one near-native peptide binding mode was ranked among top 10 (or top 500) in 59% (85%) of the bound cases, and in 40.6% (71.9%) of the challenging unbound cases. The server can be used for both protein-peptide complex structure prediction and initial-stage sampling of the protein-peptide binding modes for other docking or simulation methods. MDockPeP Server is freely available at http://zougrouptoolkit.missouri.edu/mdockpep. © 2018 Wiley Periodicals, Inc.
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Affiliation(s)
- Xianjin Xu
- Dalton Cardiovascular Research Center, Department of Physics and Astronomy, Department of Biochemistry, Informatics Institute, University of Missouri, Columbia, Missouri, 65211
| | - Chengfei Yan
- Dalton Cardiovascular Research Center, Department of Physics and Astronomy, Department of Biochemistry, Informatics Institute, University of Missouri, Columbia, Missouri, 65211
| | - Xiaoqin Zou
- Dalton Cardiovascular Research Center, Department of Physics and Astronomy, Department of Biochemistry, Informatics Institute, University of Missouri, Columbia, Missouri, 65211
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18
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Abstract
During the last two decades, the pharmaceutical industry has progressed from detecting small molecules to designing biologic-based therapeutics. Amino acid-based drugs are a group of biologic-based therapeutics that can effectively combat the diseases caused by drug resistance or molecular deficiency. Computational techniques play a key role to design and develop the amino acid-based therapeutics such as proteins, peptides and peptidomimetics. In this study, it was attempted to discuss the various elements for computational design of amino acid-based therapeutics. Protein design seeks to identify the properties of amino acid sequences that fold to predetermined structures with desirable structural and functional characteristics. Peptide drugs occupy a middle space between proteins and small molecules and it is hoped that they can target "undruggable" intracellular protein-protein interactions. Peptidomimetics, the compounds that mimic the biologic characteristics of peptides, present refined pharmacokinetic properties compared to the original peptides. Here, the elaborated techniques that are developed to characterize the amino acid sequences consistent with a specific structure and allow protein design are discussed. Moreover, the key principles and recent advances in currently introduced computational techniques for rational peptide design are spotlighted. The most advanced computational techniques developed to design novel peptidomimetics are also summarized.
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Affiliation(s)
- Tayebeh Farhadi
- Chronic Respiratory Diseases Research Center (CRDRC), National Research Institute of Tuberculosis and Lung Diseases (NRITLD), Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Seyed MohammadReza Hashemian
- Chronic Respiratory Diseases Research Center (CRDRC), National Research Institute of Tuberculosis and Lung Diseases (NRITLD), Shahid Beheshti University of Medical Sciences, Tehran, Iran
- Clinical Tuberculosis and Epidemiology Research Center, National Research Institute of Tuberculosis and Lung Disease, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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19
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Ciemny M, Kurcinski M, Kamel K, Kolinski A, Alam N, Schueler-Furman O, Kmiecik S. Protein-peptide docking: opportunities and challenges. Drug Discov Today 2018; 23:1530-1537. [PMID: 29733895 DOI: 10.1016/j.drudis.2018.05.006] [Citation(s) in RCA: 153] [Impact Index Per Article: 25.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2018] [Revised: 03/20/2018] [Accepted: 05/02/2018] [Indexed: 12/31/2022]
Abstract
Peptides have recently attracted much attention as promising drug candidates. Rational design of peptide-derived therapeutics usually requires structural characterization of the underlying protein-peptide interaction. Given that experimental characterization can be difficult, reliable computational tools are needed. In recent years, a variety of approaches have been developed for 'protein-peptide docking', that is, predicting the structure of the protein-peptide complex, starting from the protein structure and the peptide sequence, including variable degrees of information about the peptide binding site and/or conformation. In this review, we provide an overview of protein-peptide docking methods and outline their capabilities, limitations, and applications in structure-based drug design. Key challenges are also briefly discussed, such as modeling of large-scale conformational changes upon binding, scoring of predicted models, and optimal inclusion of varied types of experimental data and theoretical predictions into an integrative modeling process.
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Affiliation(s)
- Maciej Ciemny
- Biological and Chemical Research Center, Faculty of Chemistry, University of Warsaw, Warsaw, Poland; Faculty of Physics, University of Warsaw, Warsaw, Poland
| | - Mateusz Kurcinski
- Biological and Chemical Research Center, Faculty of Chemistry, University of Warsaw, Warsaw, Poland
| | - Karol Kamel
- Institute of Bioorganic Chemistry, Polish Academy of Sciences, Poznan, Poland
| | - Andrzej Kolinski
- Biological and Chemical Research Center, Faculty of Chemistry, University of Warsaw, Warsaw, Poland
| | - Nawsad Alam
- Department of Microbiology and Molecular Genetics, Institute for Medical Research Israel-Canada, Faculty of Medicine, The Hebrew University, Jerusalem, Israel
| | - Ora Schueler-Furman
- Department of Microbiology and Molecular Genetics, Institute for Medical Research Israel-Canada, Faculty of Medicine, The Hebrew University, Jerusalem, Israel
| | - Sebastian Kmiecik
- Biological and Chemical Research Center, Faculty of Chemistry, University of Warsaw, Warsaw, Poland.
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20
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Peptide Derivatives of Erythropoietin in the Treatment of Neuroinflammation and Neurodegeneration. THERAPEUTIC PROTEINS AND PEPTIDES 2018; 112:309-357. [DOI: 10.1016/bs.apcsb.2018.01.007] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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21
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Taherzadeh G, Zhou Y, Liew AWC, Yang Y. Structure-based prediction of protein– peptide binding regions using Random Forest. Bioinformatics 2017; 34:477-484. [DOI: 10.1093/bioinformatics/btx614] [Citation(s) in RCA: 46] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2017] [Accepted: 09/25/2017] [Indexed: 11/12/2022] Open
Affiliation(s)
- Ghazaleh Taherzadeh
- School of Information and Communication Technology, Griffith University, Parklands Drive, Southport, QLD, Australia
| | - Yaoqi Zhou
- School of Information and Communication Technology, Griffith University, Parklands Drive, Southport, QLD, Australia
- Institute for Glycomics, Griffith University, Parklands Drive, Southport, QLD, Australia
| | - Alan Wee-Chung Liew
- School of Information and Communication Technology, Griffith University, Parklands Drive, Southport, QLD, Australia
| | - Yuedong Yang
- School of Information and Communication Technology, Griffith University, Parklands Drive, Southport, QLD, Australia
- Institute for Glycomics, Griffith University, Parklands Drive, Southport, QLD, Australia
- School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China
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22
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Kim Y, Kim H, Park D, Lee H, Lee YS, Choe J, Kim YM, Jeon D, Jeoung D. The pentapeptide Gly-Thr-Gly-Lys-Thr confers sensitivity to anti-cancer drugs by inhibition of CAGE binding to GSK3β and decreasing the expression of cyclinD1. Oncotarget 2017; 8:13632-13651. [PMID: 28099142 PMCID: PMC5355126 DOI: 10.18632/oncotarget.14621] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2016] [Accepted: 01/03/2017] [Indexed: 12/27/2022] Open
Abstract
We previously reported the role of cancer/testis antigen CAGE in the response to anti-cancer drugs. CAGE increased the expression of cyclinD1, and pGSK3βSer9, an inactive GSK3β, while decreasing the expression of phospho-cyclinD1Thr286. CAGE showed binding to GSK3β and the domain of CAGE (amino acids 231-300) necessary for binding to GSK3β and for the expression regulation of cyclinD1 was determined. 269GTGKT273 peptide, corresponding to the DEAD box helicase domain of CAGE, decreased the expression of cyclinD1 and pGSK3βSer9 while increasing the expression of phospho-cyclinD1Thr286. GTGKT peptide showed the binding to CAGE and prevented CAGE from binding to GSK3β. GTGKT peptide changed the localization of CAGE and inhibited the binding of CAGE to the promoter sequences of cyclin D1. GTGKT peptide enhanced the apoptotic effects of anti-cancer drugs and decreased the migration, invasion, angiogenic, tumorigenic and metastatic potential of anti-cancer drug-resistant cancer cells. We found that Lys272 of GTGKT peptide was necessary for conferring anti-cancer activity. Peptides corresponding to the DEAD box helicase domain of CAGE, such as AQTGTGKT, QTGTGKT and TGTGKT, also showed anti-cancer activity by preventing CAGE from binding to GSK3β. GTGKT peptide showed ex vivo tumor homing potential. Thus, peptides corresponding to the DEAD box helicase domain of CAGE can be developed as anti-cancer drugs in cancer patients expressing CAGE.
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Affiliation(s)
- Youngmi Kim
- Department of Biochemistry, College of Natural Sciences, Kangwon National University, Chunchon 24341, Korea
| | - Hyuna Kim
- Department of Biochemistry, College of Natural Sciences, Kangwon National University, Chunchon 24341, Korea
| | - Deokbum Park
- Department of Biochemistry, College of Natural Sciences, Kangwon National University, Chunchon 24341, Korea
| | - Hansoo Lee
- Department of Biological Sciences, College of Natural Sciences, Kangwon National University, Chunchon 24341, Korea
| | - Yun Sil Lee
- College of Pharmacy, Ewha Womans University, Seoul 03760, Korea
| | - Jongseon Choe
- Graduate School of Medicine, Kangwon National University, Chunchon 24341, Korea
| | - Young Myeong Kim
- Graduate School of Medicine, Kangwon National University, Chunchon 24341, Korea
| | | | - Dooil Jeoung
- Department of Biochemistry, College of Natural Sciences, Kangwon National University, Chunchon 24341, Korea
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23
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Kurcinski M, Blaszczyk M, Ciemny MP, Kolinski A, Kmiecik S. A protocol for CABS-dock protein-peptide docking driven by side-chain contact information. Biomed Eng Online 2017; 16:73. [PMID: 28830545 PMCID: PMC5568604 DOI: 10.1186/s12938-017-0363-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023] Open
Abstract
Background The characterization of protein–peptide interactions is a challenge for computational molecular docking. Protein–peptide docking tools face at least two major difficulties: (1) efficient sampling of large-scale conformational changes induced by binding and (2) selection of the best models from a large set of predicted structures. In this paper, we merge an efficient sampling technique with external information about side-chain contacts to sample and select the best possible models. Methods In this paper we test a new protocol that uses information about side-chain contacts in CABS-dock protein–peptide docking. As shown in our recent studies, CABS-dock enables efficient modeling of large-scale conformational changes without knowledge about the binding site. However, the resulting set of binding sites and poses is in many cases highly diverse and difficult to score. Results As we demonstrate here, information about a single side-chain contact can significantly improve the prediction accuracy. Importantly, the imposed constraints for side-chain contacts are quite soft. Therefore, the developed protocol does not require precise contact information and ensures large-scale peptide flexibility in the broad contact area. Conclusions The demonstrated protocol provides the extension of the CABS-dock method that can be practically used in the structure prediction of protein–peptide complexes guided by the knowledge of the binding interface.
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Affiliation(s)
- Mateusz Kurcinski
- Faculty of Chemistry, Biological and Chemical Research Center, University of Warsaw, Żwirki i Wigury 101, 02-089, Warsaw, Poland
| | - Maciej Blaszczyk
- Faculty of Chemistry, Biological and Chemical Research Center, University of Warsaw, Żwirki i Wigury 101, 02-089, Warsaw, Poland
| | - Maciej Pawel Ciemny
- Faculty of Chemistry, Biological and Chemical Research Center, University of Warsaw, Żwirki i Wigury 101, 02-089, Warsaw, Poland.,Faculty of Physics, University of Warsaw, Pasteura 5, Warsaw, 02-093, Poland
| | - Andrzej Kolinski
- Faculty of Chemistry, Biological and Chemical Research Center, University of Warsaw, Żwirki i Wigury 101, 02-089, Warsaw, Poland
| | - Sebastian Kmiecik
- Faculty of Chemistry, Biological and Chemical Research Center, University of Warsaw, Żwirki i Wigury 101, 02-089, Warsaw, Poland.
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24
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Ciemny MP, Kurcinski M, Blaszczyk M, Kolinski A, Kmiecik S. Modeling EphB4-EphrinB2 protein-protein interaction using flexible docking of a short linear motif. Biomed Eng Online 2017; 16:71. [PMID: 28830442 PMCID: PMC5568603 DOI: 10.1186/s12938-017-0362-7] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
Background Many protein–protein interactions are mediated by a short linear motif. Usually, amino acid sequences of those motifs are known or can be predicted. It is much harder to experimentally characterize or predict their structure in the bound form. In this work, we test a possibility of using flexible docking of a short linear motif to predict the interaction interface of the EphB4-EphrinB2 complex (a system extensively studied for its significance in tumor progression). Methods In the modeling, we only use knowledge about the motif sequence and experimental structures of EphB4-EphrinB2 complex partners. The proposed protocol enables efficient modeling of significant conformational changes in the short linear motif fragment during molecular docking simulation. For the docking simulations, we use the CABS-dock method for docking fully flexible peptides to flexible protein receptors (available as a server at http://biocomp.chem.uw.edu.pl/CABSdock/). Based on the docking result, the protein–protein complex is reconstructed and refined. Results Using this novel protocol, we obtained an accurate EphB4-EphrinB2 interaction model. Conclusions The results show that the CABS-dock method may be useful as the primary docking tool in specific protein–protein docking cases similar to EphB4-EphrinB2 complex—that is, where a short linear motif fragment can be identified.
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Affiliation(s)
- Maciej Pawel Ciemny
- Faculty of Chemistry, Biological and Chemical Research Centre, University of Warsaw, Żwirki i Wigury 101, 02-089, Warsaw, Poland.,Faculty of Physics, University of Warsaw, Pasteura 5, Warsaw, Poland
| | - Mateusz Kurcinski
- Faculty of Chemistry, Biological and Chemical Research Centre, University of Warsaw, Żwirki i Wigury 101, 02-089, Warsaw, Poland
| | - Maciej Blaszczyk
- Faculty of Chemistry, Biological and Chemical Research Centre, University of Warsaw, Żwirki i Wigury 101, 02-089, Warsaw, Poland
| | - Andrzej Kolinski
- Faculty of Chemistry, Biological and Chemical Research Centre, University of Warsaw, Żwirki i Wigury 101, 02-089, Warsaw, Poland
| | - Sebastian Kmiecik
- Faculty of Chemistry, Biological and Chemical Research Centre, University of Warsaw, Żwirki i Wigury 101, 02-089, Warsaw, Poland.
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25
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Mackenzie CO, Grigoryan G. Protein structural motifs in prediction and design. Curr Opin Struct Biol 2017; 44:161-167. [PMID: 28460216 PMCID: PMC5513761 DOI: 10.1016/j.sbi.2017.03.012] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2016] [Revised: 03/18/2017] [Accepted: 03/28/2017] [Indexed: 01/11/2023]
Abstract
The Protein Data Bank (PDB) has been an integral resource for shaping our fundamental understanding of protein structure and for the advancement of such applications as protein design and structure prediction. Over the years, information from the PDB has been used to generate models ranging from specific structural mechanisms to general statistical potentials. With accumulating structural data, it has become possible to mine for more complete and complex structural observations, deducing more accurate generalizations. Motif libraries, which capture recurring structural features along with their sequence preferences, have exposed modularity in the structural universe and found successful application in various problems of structural biology. Here we summarize recent achievements in this arena, focusing on subdomain level structural patterns and their applications to protein design and structure prediction, and suggest promising future directions as the structural database continues to grow.
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Affiliation(s)
- Craig O Mackenzie
- Institute for Quantitative Biomedical Sciences, Dartmouth College, Hanover, NH 03755, United States
| | - Gevorg Grigoryan
- Institute for Quantitative Biomedical Sciences, Dartmouth College, Hanover, NH 03755, United States; Department of Computer Science, Dartmouth College, Hanover, NH 03755, United States.
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26
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Sequence statistics of tertiary structural motifs reflect protein stability. PLoS One 2017; 12:e0178272. [PMID: 28552940 PMCID: PMC5446159 DOI: 10.1371/journal.pone.0178272] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2017] [Accepted: 05/10/2017] [Indexed: 11/19/2022] Open
Abstract
The Protein Data Bank (PDB) has been a key resource for learning general rules of sequence-structure relationships in proteins. Quantitative insights have been gained by defining geometric descriptors of structure (e.g., distances, dihedral angles, solvent exposure, etc.) and observing their distributions and sequence preferences. Here we argue that as the PDB continues to grow, it may become unnecessary to reduce structure into a set of elementary descriptors. Instead, it could be possible to deduce quantitative sequence-structure relationships in the context of precisely-defined complex structural motifs by mining the PDB for closely matching backbone geometries. To validate this idea, we turned to the the task of predicting changes in protein stability upon amino-acid substitution—a difficult problem of broad significance. We defined non-contiguous tertiary motifs (TERMs) around a protein site of interest and extracted sequence preferences from ensembles of closely-matching substructures in the PDB to predict mutational stability changes at the site, ΔΔGm. We demonstrate that these ensemble statistics predict ΔΔGm on par with state-of-the-art statistical and machine-learning methods on large thermodynamic datasets, and outperform these, along with a leading structure-based modeling approach, when tested in the context of unbiased diverse mutations. Further, we show that the performance of the TERM-based method is directly related to the amount of available relevant structural data, automatically improving with the growing PDB. This enables a means of estimating prediction accuracy. Our results clearly demonstrate that: 1) statistics of non-contiguous structural motifs in the PDB encode fundamental sequence-structure relationships related to protein thermodynamic stability, and 2) the PDB is now large enough that such statistics are already useful in practice, with their accuracy expected to continue increasing as the database grows. These observations suggest new ways of using structural data towards addressing problems of computational structural biology.
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Peterson LX, Roy A, Christoffer C, Terashi G, Kihara D. Modeling disordered protein interactions from biophysical principles. PLoS Comput Biol 2017; 13:e1005485. [PMID: 28394890 PMCID: PMC5402988 DOI: 10.1371/journal.pcbi.1005485] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2016] [Revised: 04/24/2017] [Accepted: 03/29/2017] [Indexed: 12/12/2022] Open
Abstract
Disordered protein-protein interactions (PPIs), those involving a folded protein and an intrinsically disordered protein (IDP), are prevalent in the cell, including important signaling and regulatory pathways. IDPs do not adopt a single dominant structure in isolation but often become ordered upon binding. To aid understanding of the molecular mechanisms of disordered PPIs, it is crucial to obtain the tertiary structure of the PPIs. However, experimental methods have difficulty in solving disordered PPIs and existing protein-protein and protein-peptide docking methods are not able to model them. Here we present a novel computational method, IDP-LZerD, which models the conformation of a disordered PPI by considering the biophysical binding mechanism of an IDP to a structured protein, whereby a local segment of the IDP initiates the interaction and subsequently the remaining IDP regions explore and coalesce around the initial binding site. On a dataset of 22 disordered PPIs with IDPs up to 69 amino acids, successful predictions were made for 21 bound and 18 unbound receptors. The successful modeling provides additional support for biophysical principles. Moreover, the new technique significantly expands the capability of protein structure modeling and provides crucial insights into the molecular mechanisms of disordered PPIs.
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Affiliation(s)
- Lenna X. Peterson
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana, United States of America
| | - Amitava Roy
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana, United States of America
- Department of Medicinal Chemistry and Molecular Pharmacology, Purdue University, West Lafayette, Indiana, United States of America
- Bioinformatics and Computational Biosciences Branch, Rocky Mountain Laboratories, NIAID, National Institutes of Health, Hamilton, Montana, United States of America
| | - Charles Christoffer
- Department of Computer Science, Purdue University, West Lafayette, Indiana, United States of America
| | - Genki Terashi
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana, United States of America
- School of Pharmacy, Kitasato University, Tokyo, Japan
| | - Daisuke Kihara
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana, United States of America
- Department of Computer Science, Purdue University, West Lafayette, Indiana, United States of America
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28
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Application of the ATTRACT Coarse-Grained Docking and Atomistic Refinement for Predicting Peptide-Protein Interactions. Methods Mol Biol 2017. [PMID: 28236233 DOI: 10.1007/978-1-4939-6798-8_5] [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
Peptide-protein interactions are abundant in the cell and form an important part of the interactome. Large-scale modeling of peptide-protein complexes requires a fully blind approach; i.e., simultaneously predicting the peptide-binding site and the peptide conformation to high accuracy. Here, we present one of the first fully blind peptide-protein docking protocols, pepATTRACT. It combines a coarse-grained ensemble docking search of the entire protein surface with two stages of atomistic flexible refinement. pepATTRACT yields high-quality predictions for 70 % of the cases when tested on a large benchmark of peptide-protein complexes. This performance in fully blind mode is similar to state-of-the-art local docking approaches that use information on the location of the binding site. Limiting the search to the peptide-binding region, the resulting pepATTRACT-local approach further improves the performance. Docking scripts for pepATTRACT and pepATTRACT-local can be generated via a web interface at www.attract.ph.tum.de/peptide.html . Here, we explain how to set up a docking run with the pepATTRACT web interface and demonstrate its usage by an application on binding of disordered regions from tumor suppressor p53 to a partner protein.
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29
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OncoBinder facilitates interpretation of proteomic interaction data by capturing coactivation pairs in cancer. Oncotarget 2017; 7:17608-15. [PMID: 26872056 PMCID: PMC4951236 DOI: 10.18632/oncotarget.7305] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2015] [Accepted: 01/29/2016] [Indexed: 11/25/2022] Open
Abstract
High-throughput methods such as co-immunoprecipitationmass spectrometry (coIP-MS) and yeast 2 hybridization (Y2H) have suggested a broad range of unannotated protein-protein interactions (PPIs), and interpretation of these PPIs remains a challenging task. The advancements in cancer genomic researches allow for the inference of "coactivation pairs" in cancer, which may facilitate the identification of PPIs involved in cancer. Here we present OncoBinder as a tool for the assessment of proteomic interaction data based on the functional synergy of oncoproteins in cancer. This decision tree-based method combines gene mutation, copy number and mRNA expression information to infer the functional status of protein-coding genes. We applied OncoBinder to evaluate the potential binders of EGFR and ERK2 proteins based on the gastric cancer dataset of The Cancer Genome Atlas (TCGA). As a result, OncoBinder identified high confidence interactions (annotated by Kyoto Encyclopedia of Genes and Genomes (KEGG) or validated by low-throughput assays) more efficiently than co-expression based method. Taken together, our results suggest that evaluation of gene functional synergy in cancer may facilitate the interpretation of proteomic interaction data. The OncoBinder toolbox for Matlab is freely accessible online.
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30
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Liao JM, Wang YT, Lin CLS. A fragment-based docking simulation for investigating peptide–protein bindings. Phys Chem Chem Phys 2017; 19:10436-10442. [DOI: 10.1039/c6cp07136h] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
We developed a fragment-based docking strategy for long peptide docking simulations, which separates a long peptide into halves for docking, and then recombined to rebuild whole-peptide docking conformations. With further screening, optimizations and MM/GBSA scoring, our method was capable of efficiently predicting the near-native peptide binding conformations.
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Affiliation(s)
- Jun-min Liao
- Graduate School of Medicine
- Kaohsiung Medical University
- Taiwan
| | - Yeng-Tseng Wang
- Department of Biochemistry
- Kaohsiung Medical University
- Taiwan
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31
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Ciemny MP, Kurcinski M, Kozak KJ, Kolinski A, Kmiecik S. Highly Flexible Protein-Peptide Docking Using CABS-Dock. Methods Mol Biol 2017; 1561:69-94. [PMID: 28236234 DOI: 10.1007/978-1-4939-6798-8_6] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Protein-peptide molecular docking is a difficult modeling problem. It is even more challenging when significant conformational changes that may occur during the binding process need to be predicted. In this chapter, we demonstrate the capabilities and features of the CABS-dock server for flexible protein-peptide docking. CABS-dock allows highly efficient modeling of full peptide flexibility and significant flexibility of a protein receptor. During CABS-dock docking, the peptide folding and binding process is explicitly simulated and no information about the peptide binding site or its structure is used. This chapter presents a successful CABS-dock use for docking a potentially therapeutic peptide to a protein target. Moreover, simulation contact maps, a new CABS-dock feature, are described and applied to the docking test case. Finally, a tutorial for running CABS-dock from the command line or command line scripts is provided. The CABS-dock web server is available from http://biocomp.chem.uw.edu.pl/CABSdock/ .
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Affiliation(s)
- Maciej Paweł Ciemny
- Faculty of Chemistry, University of Warsaw, Pasteura 1, 02-093, Warsaw, Poland
| | - Mateusz Kurcinski
- Faculty of Chemistry, University of Warsaw, Pasteura 1, 02-093, Warsaw, Poland
| | - Konrad Jakub Kozak
- Faculty of Chemistry, University of Warsaw, Pasteura 1, 02-093, Warsaw, Poland
| | - Andrzej Kolinski
- Faculty of Chemistry, University of Warsaw, Pasteura 1, 02-093, Warsaw, Poland
| | - Sebastian Kmiecik
- Faculty of Chemistry, University of Warsaw, Pasteura 1, 02-093, Warsaw, Poland.
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32
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Chen J, Xie ZR, Wu Y. Understand protein functions by comparing the similarity of local structural environments. BIOCHIMICA ET BIOPHYSICA ACTA-PROTEINS AND PROTEOMICS 2016; 1865:142-152. [PMID: 27884635 DOI: 10.1016/j.bbapap.2016.11.008] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/17/2016] [Revised: 11/03/2016] [Accepted: 11/17/2016] [Indexed: 12/20/2022]
Abstract
The three-dimensional structures of proteins play an essential role in regulating binding between proteins and their partners, offering a direct relationship between structures and functions of proteins. It is widely accepted that the function of a protein can be determined if its structure is similar to other proteins whose functions are known. However, it is also observed that proteins with similar global structures do not necessarily correspond to the same function, while proteins with very different folds can share similar functions. This indicates that function similarity is originated from the local structural information of proteins instead of their global shapes. We assume that proteins with similar local environments prefer binding to similar types of molecular targets. In order to testify this assumption, we designed a new structural indicator to define the similarity of local environment between residues in different proteins. This indicator was further used to calculate the probability that a given residue binds to a specific type of structural neighbors, including DNA, RNA, small molecules and proteins. After applying the method to a large-scale non-redundant database of proteins, we show that the positive signal of binding probability calculated from the local structural indicator is statistically meaningful. In summary, our studies suggested that the local environment of residues in a protein is a good indicator to recognize specific binding partners of the protein. The new method could be a potential addition to a suite of existing template-based approaches for protein function prediction.
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Affiliation(s)
- Jiawen Chen
- Department of Systems and Computational Biology, Albert Einstein College of Medicine of Yeshiva University, 1300 Morris Park Avenue, Bronx, NY 10461, United States
| | - Zhong-Ru Xie
- Department of Systems and Computational Biology, Albert Einstein College of Medicine of Yeshiva University, 1300 Morris Park Avenue, Bronx, NY 10461, United States
| | - Yinghao Wu
- Department of Systems and Computational Biology, Albert Einstein College of Medicine of Yeshiva University, 1300 Morris Park Avenue, Bronx, NY 10461, United States.
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Abstract
Here, we systematically decompose the known protein structural universe into its basic elements, which we dub tertiary structural motifs (TERMs). A TERM is a compact backbone fragment that captures the secondary, tertiary, and quaternary environments around a given residue, comprising one or more disjoint segments (three on average). We seek the set of universal TERMs that capture all structure in the Protein Data Bank (PDB), finding remarkable degeneracy. Only ∼600 TERMs are sufficient to describe 50% of the PDB at sub-Angstrom resolution. However, more rare geometries also exist, and the overall structural coverage grows logarithmically with the number of TERMs. We go on to show that universal TERMs provide an effective mapping between sequence and structure. We demonstrate that TERM-based statistics alone are sufficient to recapitulate close-to-native sequences given either NMR or X-ray backbones. Furthermore, sequence variability predicted from TERM data agrees closely with evolutionary variation. Finally, locations of TERMs in protein chains can be predicted from sequence alone based on sequence signatures emergent from TERM instances in the PDB. For multisegment motifs, this method identifies spatially adjacent fragments that are not contiguous in sequence-a major bottleneck in structure prediction. Although all TERMs recur in diverse proteins, some appear specialized for certain functions, such as interface formation, metal coordination, or even water binding. Structural biology has benefited greatly from previously observed degeneracies in structure. The decomposition of the known structural universe into a finite set of compact TERMs offers exciting opportunities toward better understanding, design, and prediction of protein structure.
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34
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Raghavender US. Analysis of residue conformations in peptides in Cambridge structural database and protein-peptide structural complexes. Chem Biol Drug Des 2016; 89:428-442. [DOI: 10.1111/cbdd.12862] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2016] [Revised: 07/27/2016] [Accepted: 08/25/2016] [Indexed: 01/29/2023]
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Yan C, Xu X, Zou X. Fully Blind Docking at the Atomic Level for Protein-Peptide Complex Structure Prediction. Structure 2016; 24:1842-1853. [PMID: 27642160 DOI: 10.1016/j.str.2016.07.021] [Citation(s) in RCA: 69] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2016] [Revised: 07/13/2016] [Accepted: 07/29/2016] [Indexed: 02/05/2023]
Abstract
Protein-peptide interactions play an important role in many cellular processes. In silico prediction of protein-peptide complex structure is highly desirable for mechanistic investigation of these processes and for therapeutic design. However, predicting all-atom structures of protein-peptide complexes without any knowledge about the peptide binding site and the bound peptide conformation remains a big challenge. Here, we present a docking-based method for predicting protein-peptide complex structures, referred to as MDockPeP, which starts with the peptide sequence and globally docks the all-atom, flexible peptide onto the protein structure. MDockPeP was tested on the peptiDB benchmarking database using both bound and unbound protein structures. The results show that MDockPeP successfully generated near-native peptide binding modes in 95.0% of the bound docking cases and in 92.2% of the unbound docking cases. The performance is significantly better than other existing docking methods. MDockPeP is computationally efficient and suitable for large-scale applications.
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Affiliation(s)
- Chengfei Yan
- Department of Physics and Astronomy, Dalton Cardiovascular Research Center, University of Missouri, Columbia, MO 65211, USA; Department of Biochemistry, Informatics Institute, University of Missouri, Columbia, MO 65211, USA
| | - Xianjin Xu
- Department of Physics and Astronomy, Dalton Cardiovascular Research Center, University of Missouri, Columbia, MO 65211, USA; Department of Biochemistry, Informatics Institute, University of Missouri, Columbia, MO 65211, USA
| | - Xiaoqin Zou
- Department of Physics and Astronomy, Dalton Cardiovascular Research Center, University of Missouri, Columbia, MO 65211, USA; Department of Biochemistry, Informatics Institute, University of Missouri, Columbia, MO 65211, USA.
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Taherzadeh G, Yang Y, Zhang T, Liew AW, Zhou Y. Sequence‐based prediction of protein–peptide binding sites using support vector machine. J Comput Chem 2016; 37:1223-9. [DOI: 10.1002/jcc.24314] [Citation(s) in RCA: 56] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2015] [Accepted: 01/06/2016] [Indexed: 11/06/2022]
Affiliation(s)
- Ghazaleh Taherzadeh
- School of Information and Communication TechnologyGriffith UniversityParklands DriveSouthport Queensland4215 Australia
| | - Yuedong Yang
- School of Information and Communication TechnologyGriffith UniversityParklands DriveSouthport Queensland4215 Australia
- Institute for Glycomics, Griffith UniversityParklands DrSouthport Queensland4215 Australia
| | - Tuo Zhang
- Weill Cornell Medical College1300 York AvenueNew York, New York10065
| | - Alan Wee‐Chung Liew
- School of Information and Communication TechnologyGriffith UniversityParklands DriveSouthport Queensland4215 Australia
| | - Yaoqi Zhou
- School of Information and Communication TechnologyGriffith UniversityParklands DriveSouthport Queensland4215 Australia
- Institute for Glycomics, Griffith UniversityParklands DrSouthport Queensland4215 Australia
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37
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Rational, computer-enabled peptide drug design: principles, methods, applications and future directions. Future Med Chem 2015; 7:2173-93. [PMID: 26510691 DOI: 10.4155/fmc.15.142] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Abstract
Peptides provide promising templates for developing drugs to occupy a middle space between small molecules and antibodies and for targeting 'undruggable' intracellular protein-protein interactions. Importantly, rational or in cerebro design, especially when coupled with validated in silico tools, can be used to efficiently explore chemical space and identify islands of 'drug-like' peptides to satisfy diverse drug discovery program objectives. Here, we consider the underlying principles of and recent advances in rational, computer-enabled peptide drug design. In particular, we consider the impact of basic physicochemical properties, potency and ADME/Tox opportunities and challenges, and recently developed computational tools for enabling rational peptide drug design. Key principles and practices are spotlighted by recent case studies. We close with a hypothetical future case study.
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38
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de Souza Rocha T, Hernandez LMR, Mojica L, Johnson MH, Chang YK, González de Mejía E. Germination of Phaseolus vulgaris and alcalase hydrolysis of its proteins produced bioactive peptides capable of improving markers related to type-2 diabetes in vitro. Food Res Int 2015. [DOI: 10.1016/j.foodres.2015.04.041] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
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39
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Design of an electroactive peptide probe for sensing of a protein. Anal Chim Acta 2015; 890:143-9. [PMID: 26347176 DOI: 10.1016/j.aca.2015.07.052] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2015] [Revised: 07/12/2015] [Accepted: 07/30/2015] [Indexed: 12/29/2022]
Abstract
We designed a new electroactive peptide probe that has a molecular recognition function for the sensing of a protein. Ovalbumin (OVA) was the model protein, and when RNRCKGTDVQAW interacted with OVA, it conjugated with a tyrosine-rich peptide (Y4C). This peptide is electroactive, has a high degree of biocompatibility, and offers the possibility of gene expression. To measure the effect of a number of the tyrosine residues, voltammetric measurements were conducted using a series of tyrosine-rich peptides (YnC, n = 3-7) with sensitivities that ranged from 10(-9) to 10(-8) M. The electrode response of Y5C was the maximum value in the series. However, the peak current did not increase when the number of tyrosine residues was increased in a linear fashion. This may have been due to the micelles that are formed by a tyrosine-rich surfactant peptide. Thus, Y4C was suitable as an electroactive label for the construction of the peptide probe. The electrode response of Y4CRNRCKGTDVQAW obtained by a glassy carbon electrode was 100-fold that of tyrosine alone. The measurement of OVA via the peptide probe resulted in a detection on the order of 10(-12) M. In contrast, the sensitivity of OVA using RCKGTDVQAWY4C probe was at the 10(-11) M level, because the hydrophobic moiety gave it a molecular recognition function. The recoveries of the OVA using Y4CRNRCKGTDVQAW in a solution containing fetal bovine serum ranged between 98 and 101%. Consequently, the combination of a specific peptide and an electroactive element could be a powerful probe for the sensing of proteins.
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40
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Modeling of protein-peptide interactions using the CABS-dock web server for binding site search and flexible docking. Methods 2015; 93:72-83. [PMID: 26165956 DOI: 10.1016/j.ymeth.2015.07.004] [Citation(s) in RCA: 114] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2015] [Revised: 07/06/2015] [Accepted: 07/08/2015] [Indexed: 11/22/2022] Open
Abstract
Protein-peptide interactions play essential functional roles in living organisms and their structural characterization is a hot subject of current experimental and theoretical research. Computational modeling of the structure of protein-peptide interactions is usually divided into two stages: prediction of the binding site at a protein receptor surface, and then docking (and modeling) the peptide structure into the known binding site. This paper presents a comprehensive CABS-dock method for the simultaneous search of binding sites and flexible protein-peptide docking, available as a user's friendly web server. We present example CABS-dock results obtained in the default CABS-dock mode and using its advanced options that enable the user to increase the range of flexibility for chosen receptor fragments or to exclude user-selected binding modes from docking search. Furthermore, we demonstrate a strategy to improve CABS-dock performance by assessing the quality of models with classical molecular dynamics. Finally, we discuss the promising extensions and applications of the CABS-dock method and provide a tutorial appendix for the convenient analysis and visualization of CABS-dock results. The CABS-dock web server is freely available at http://biocomp.chem.uw.edu.pl/CABSdock/.
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41
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Schindler CEM, de Vries SJ, Zacharias M. Fully Blind Peptide-Protein Docking with pepATTRACT. Structure 2015; 23:1507-1515. [PMID: 26146186 DOI: 10.1016/j.str.2015.05.021] [Citation(s) in RCA: 87] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2015] [Revised: 05/21/2015] [Accepted: 05/25/2015] [Indexed: 02/02/2023]
Abstract
Peptide-protein interactions are ubiquitous in the cell and form an important part of the interactome. Computational docking methods can complement experimental characterization of these complexes, but current protocols are not applicable on the proteome scale. Here, we present a new fully blind flexible peptide-protein docking protocol, pepATTRACT, which combines a rapid coarse-grained global peptide docking search of the entire protein surface with a two-stage atomistic flexible refinement. Global unbound-unbound docking yielded near-native models for 70% of the docking cases when testing the protocol on the largest benchmark of peptide-protein complexes available to date. This performance is similar to that of state-of-the-art local docking protocols that rely on information about the binding site. Upon restricting the search to the peptide binding region, the resulting pepATTRACT-local approach outperformed existing methods. Docking scripts for pepATTRACT and pepATTRACT-local can be generated via a web interface at www.attract.ph.tum.de/peptide.html.
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Affiliation(s)
- Christina E M Schindler
- Physics Department T38, Technische Universität München, James-Franck-Straße 1, 85748 Garching, Germany
| | - Sjoerd J de Vries
- Physics Department T38, Technische Universität München, James-Franck-Straße 1, 85748 Garching, Germany
| | - Martin Zacharias
- Physics Department T38, Technische Universität München, James-Franck-Straße 1, 85748 Garching, Germany.
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42
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Ben-Shimon A, Niv MY. AnchorDock: Blind and Flexible Anchor-Driven Peptide Docking. Structure 2015; 23:929-940. [PMID: 25914054 DOI: 10.1016/j.str.2015.03.010] [Citation(s) in RCA: 53] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2014] [Revised: 03/20/2015] [Accepted: 03/22/2015] [Indexed: 12/18/2022]
Abstract
The huge conformational space stemming from the inherent flexibility of peptides is among the main obstacles to successful and efficient computational modeling of protein-peptide interactions. Current peptide docking methods typically overcome this challenge using prior knowledge from the structure of the complex. Here we introduce AnchorDock, a peptide docking approach, which automatically targets the docking search to the most relevant parts of the conformational space. This is done by precomputing the free peptide's structure and by computationally identifying anchoring spots on the protein surface. Next, a free peptide conformation undergoes anchor-driven simulated annealing molecular dynamics simulations around the predicted anchoring spots. In the challenging task of a completely blind docking test, AnchorDock produced exceptionally good results (backbone root-mean-square deviation ≤ 2.2Å, rank ≤15) for 10 of 13 unbound cases tested. The impressive performance of AnchorDock supports a molecular recognition pathway that is driven via pre-existing local structural elements.
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Affiliation(s)
- Avraham Ben-Shimon
- Institute of Biochemistry, Food Science and Nutrition, The Robert H. Smith Faculty of Agriculture, Food and Environment and The Fritz Haber Center for Molecular Dynamics, The Hebrew University, Rehovot 76100, Israel
| | - Masha Y Niv
- Institute of Biochemistry, Food Science and Nutrition, The Robert H. Smith Faculty of Agriculture, Food and Environment and The Fritz Haber Center for Molecular Dynamics, The Hebrew University, Rehovot 76100, Israel.
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43
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Sellers MS, Hurley MM. XPairIt Docking Protocolfor peptide docking and analysis. MOLECULAR SIMULATION 2015. [DOI: 10.1080/08927022.2015.1025267] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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44
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Rentzsch R, Renard BY. Docking small peptides remains a great challenge: an assessment using AutoDock Vina. Brief Bioinform 2015; 16:1045-56. [PMID: 25900849 DOI: 10.1093/bib/bbv008] [Citation(s) in RCA: 92] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2014] [Indexed: 02/03/2023] Open
Abstract
There is a growing interest in the mechanisms and the prediction of how flexible peptides bind proteins, often in a highly selective and conserved manner. While both existing small-molecule docking methods and custom protocols can be used, even short peptides make difficult targets owing to their high torsional flexibility. Any benchmarking should therefore start with those. We compiled a meta-data set of 47 complexes with peptides up to five residues, based on 11 related studies from the past decade. Although their highly varying strategies and constraints preclude direct, quantitative comparisons, we still provide a comprehensive overview of the reported results, using a simple yet stringent measure: the quality of the top-scoring peptide pose. Using the entire data set, this is augmented by our own benchmark of AutoDock Vina, a freely available, fast and widely used docking tool. It particularly addresses non-expert users and was therefore implemented in a highly integrated manner. Guidelines addressing important issues such as the amount of sampling required for result reproducibility are so far lacking. Using peptide docking as an example, this is the first study to address these issues in detail. Finally, to encourage further, standardized benchmarking efforts, the compiled data set is made available in an accessible, transparent and extendable manner.
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45
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Yang C, Zhang S, He P, Wang C, Huang J, Zhou P. Self-Binding Peptides: Folding or Binding? J Chem Inf Model 2015; 55:329-42. [DOI: 10.1021/ci500522v] [Citation(s) in RCA: 116] [Impact Index Per Article: 12.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Affiliation(s)
- Chao Yang
- Center of Bioinformatics (COBI), Key Laboratory for Neuroinformation of the Ministry of Education, Center for Information in BioMedicine, School of Life Science and Technology, University of Electronic Science and Technology of China (UESTC), Chengdu 610054, China
| | - Shilei Zhang
- Center of Bioinformatics (COBI), Key Laboratory for Neuroinformation of the Ministry of Education, Center for Information in BioMedicine, School of Life Science and Technology, University of Electronic Science and Technology of China (UESTC), Chengdu 610054, China
| | - Ping He
- Department of Cardiothoracic Surgery, Southwest Hospital, Third Military Medical University, Chongqing 400038, China
| | - Congcong Wang
- Center of Bioinformatics (COBI), Key Laboratory for Neuroinformation of the Ministry of Education, Center for Information in BioMedicine, School of Life Science and Technology, University of Electronic Science and Technology of China (UESTC), Chengdu 610054, China
| | - Jian Huang
- Center of Bioinformatics (COBI), Key Laboratory for Neuroinformation of the Ministry of Education, Center for Information in BioMedicine, School of Life Science and Technology, University of Electronic Science and Technology of China (UESTC), Chengdu 610054, China
| | - Peng Zhou
- Center of Bioinformatics (COBI), Key Laboratory for Neuroinformation of the Ministry of Education, Center for Information in BioMedicine, School of Life Science and Technology, University of Electronic Science and Technology of China (UESTC), Chengdu 610054, China
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46
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Petrey D, Chen TS, Deng L, Garzon JI, Hwang H, Lasso G, Lee H, Silkov A, Honig B. Template-based prediction of protein function. Curr Opin Struct Biol 2015; 32:33-8. [PMID: 25678152 DOI: 10.1016/j.sbi.2015.01.007] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2014] [Revised: 01/13/2015] [Accepted: 01/19/2015] [Indexed: 12/11/2022]
Abstract
We discuss recent approaches for structure-based protein function annotation. We focus on template-based methods where the function of a query protein is deduced from that of a template for which both the structure and function are known. We describe the different ways of identifying a template. These are typically based on sequence analysis but new methods based on purely structural similarity are also being developed that allow function annotation based on structural relationships that cannot be recognized by sequence. The growing number of available structures of known function, improved homology modeling techniques and new developments in the use of structure allow template-based methods to be applied on a proteome-wide scale and in many different biological contexts. This progress significantly expands the range of applicability of structural information in function annotation to a level that previously was only achievable by sequence comparison.
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Affiliation(s)
- Donald Petrey
- Howard Hughes Medical Institute, Department of Biochemistry and Molecular Biophysics, Department of Systems Biology, Center for Computational Biology and Bioinformatics, 1130 St. Nicholas Avenue, Room 815, New York, NY 10032, United States.
| | - T Scott Chen
- Howard Hughes Medical Institute, Department of Biochemistry and Molecular Biophysics, Department of Systems Biology, Center for Computational Biology and Bioinformatics, 1130 St. Nicholas Avenue, Room 815, New York, NY 10032, United States
| | - Lei Deng
- Howard Hughes Medical Institute, Department of Biochemistry and Molecular Biophysics, Department of Systems Biology, Center for Computational Biology and Bioinformatics, 1130 St. Nicholas Avenue, Room 815, New York, NY 10032, United States
| | - Jose Ignacio Garzon
- Howard Hughes Medical Institute, Department of Biochemistry and Molecular Biophysics, Department of Systems Biology, Center for Computational Biology and Bioinformatics, 1130 St. Nicholas Avenue, Room 815, New York, NY 10032, United States
| | - Howook Hwang
- Howard Hughes Medical Institute, Department of Biochemistry and Molecular Biophysics, Department of Systems Biology, Center for Computational Biology and Bioinformatics, 1130 St. Nicholas Avenue, Room 815, New York, NY 10032, United States
| | - Gorka Lasso
- Howard Hughes Medical Institute, Department of Biochemistry and Molecular Biophysics, Department of Systems Biology, Center for Computational Biology and Bioinformatics, 1130 St. Nicholas Avenue, Room 815, New York, NY 10032, United States
| | - Hunjoong Lee
- Howard Hughes Medical Institute, Department of Biochemistry and Molecular Biophysics, Department of Systems Biology, Center for Computational Biology and Bioinformatics, 1130 St. Nicholas Avenue, Room 815, New York, NY 10032, United States
| | - Antonina Silkov
- Howard Hughes Medical Institute, Department of Biochemistry and Molecular Biophysics, Department of Systems Biology, Center for Computational Biology and Bioinformatics, 1130 St. Nicholas Avenue, Room 815, New York, NY 10032, United States
| | - Barry Honig
- Howard Hughes Medical Institute, Department of Biochemistry and Molecular Biophysics, Department of Systems Biology, Center for Computational Biology and Bioinformatics, 1130 St. Nicholas Avenue, Room 815, New York, NY 10032, United States
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47
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Zhou J, Grigoryan G. Rapid search for tertiary fragments reveals protein sequence-structure relationships. Protein Sci 2014; 24:508-24. [PMID: 25420575 DOI: 10.1002/pro.2610] [Citation(s) in RCA: 47] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2014] [Accepted: 11/21/2014] [Indexed: 12/31/2022]
Abstract
Finding backbone substructures from the Protein Data Bank that match an arbitrary query structural motif, composed of multiple disjoint segments, is a problem of growing relevance in structure prediction and protein design. Although numerous protein structure search approaches have been proposed, methods that address this specific task without additional restrictions and on practical time scales are generally lacking. Here, we propose a solution, dubbed MASTER, that is both rapid, enabling searches over the Protein Data Bank in a matter of seconds, and provably correct, finding all matches below a user-specified root-mean-square deviation cutoff. We show that despite the potentially exponential time complexity of the problem, running times in practice are modest even for queries with many segments. The ability to explore naturally plausible structural and sequence variations around a given motif has the potential to synthesize its design principles in an automated manner; so we go on to illustrate the utility of MASTER to protein structural biology. We demonstrate its capacity to rapidly establish structure-sequence relationships, uncover the native designability landscapes of tertiary structural motifs, identify structural signatures of binding, and automatically rewire protein topologies. Given the broad utility of protein tertiary fragment searches, we hope that providing MASTER in an open-source format will enable novel advances in understanding, predicting, and designing protein structure.
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Affiliation(s)
- Jianfu Zhou
- Department of Computer Science, Dartmouth College, Hanover, New Hampshire, 03755
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48
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Chen H, Zhao Y, Li H, Zhang D, Huang Y, Shen Q, Van Duyne R, Kashanchi F, Zeng C, Liu S. Break CDK2/Cyclin E1 interface allosterically with small peptides. PLoS One 2014; 9:e109154. [PMID: 25290691 PMCID: PMC4188581 DOI: 10.1371/journal.pone.0109154] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2014] [Accepted: 09/03/2014] [Indexed: 02/07/2023] Open
Abstract
Most inhibitors of Cyclin-dependent kinase 2 (CDK2) target its ATP-binding pocket. It is difficult, however, to use this pocket to design very specific inhibitors because this catalytic pocket is highly conserved in the protein family of CDKs. Here we report some short peptides targeting a noncatalytic pocket near the interface of the CDK2/Cyclin complex. Docking and molecular dynamics simulations were used to select the peptides, and detailed dynamical network analysis revealed that these peptides weaken the complex formation via allosteric interactions. Our experiments showed that upon binding to the noncatalytic pocket, these peptides break the CDK2/Cyclin complex partially and diminish its kinase activity in vitro. The binding affinity of these peptides measured by Surface Plasmon Resonance can reach as low as 0.5 µM.
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Affiliation(s)
- Hao Chen
- Department of Physics, The George Washington University, Washington, D. C., United States of America
| | - Yunjie Zhao
- Department of Physics, The George Washington University, Washington, D. C., United States of America
| | - Haotian Li
- Department of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Dongyan Zhang
- Department of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Yanzhao Huang
- Department of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Qi Shen
- BNLMS, Center for Quantitative Biology, Peking University, Beijing, China
| | - Rachel Van Duyne
- George Mason University, National Center for Biodefense & Infectious Diseases, Manassas, Virginia, United States of America
- The George Washington University Medical Center, Department of Microbiology, Immunology, and Tropical Medicine, Washington, D. C., United States of America
| | - Fatah Kashanchi
- George Mason University, National Center for Biodefense & Infectious Diseases, Manassas, Virginia, United States of America
| | - Chen Zeng
- Department of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China
- Department of Physics, The George Washington University, Washington, D. C., United States of America
| | - Shiyong Liu
- Department of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China
- * E-mail:
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Saladin A, Rey J, Thévenet P, Zacharias M, Moroy G, Tufféry P. PEP-SiteFinder: a tool for the blind identification of peptide binding sites on protein surfaces. Nucleic Acids Res 2014; 42:W221-6. [PMID: 24803671 PMCID: PMC4086095 DOI: 10.1093/nar/gku404] [Citation(s) in RCA: 61] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Peptide-protein interactions are important to many processes of life, particularly for signal transmission or regulatory mechanisms. When no information is known about the interaction between a protein and a peptide, it is of interest to propose candidate sites of interaction at the protein surface, to assist the design of biological experiments to probe the interaction, or to serve as a starting point for more focused in silico approaches. PEP-SiteFinder is a tool that will, given the structure of a protein and the sequence of a peptide, identify protein residues predicted to be at peptide-protein interface. PEP-SiteFinder relies on the 3D de novo generation of peptide conformations given its sequence. These conformations then undergo a fast blind rigid docking on the complete protein surface, and we have found, as the result of a benchmark over 41 complexes, that the best poses overlap to some extent the experimental patch of interaction for close to 90% complexes. In addition, PEP-SiteFinder also returns a propensity index we have found informative about the confidence of the prediction. The PEP-SiteFinder web server is available at http://bioserv.rpbs.univ-paris-diderot.fr/PEP-SiteFinder.
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Affiliation(s)
- Adrien Saladin
- INSERM U973, MTi, F-75205 Paris, France Université Paris Diderot, Sorbonne Paris Cité, F-75205 Paris, France
| | - Julien Rey
- INSERM U973, MTi, F-75205 Paris, France Université Paris Diderot, Sorbonne Paris Cité, F-75205 Paris, France Ressource Parisienne en Bioinformatique Structurale, F-75205 Paris, France
| | - Pierre Thévenet
- INSERM U973, MTi, F-75205 Paris, France Université Paris Diderot, Sorbonne Paris Cité, F-75205 Paris, France
| | | | - Gautier Moroy
- INSERM U973, MTi, F-75205 Paris, France Université Paris Diderot, Sorbonne Paris Cité, F-75205 Paris, France
| | - Pierre Tufféry
- INSERM U973, MTi, F-75205 Paris, France Université Paris Diderot, Sorbonne Paris Cité, F-75205 Paris, France Ressource Parisienne en Bioinformatique Structurale, F-75205 Paris, France
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Dhanik A, McMurray JS, Kavraki LE. DINC: a new AutoDock-based protocol for docking large ligands. BMC STRUCTURAL BIOLOGY 2013; 13 Suppl 1:S11. [PMID: 24564952 PMCID: PMC3952135 DOI: 10.1186/1472-6807-13-s1-s11] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Background Using the popular program AutoDock, computer-aided docking of small ligands with 6 or fewer rotatable bonds, is reasonably fast and accurate. However, docking large ligands using AutoDock's recommended standard docking protocol is less accurate and computationally slow. Results In our earlier work, we presented a novel AutoDock-based incremental protocol (DINC) that addresses the limitations of AutoDock's standard protocol by enabling improved docking of large ligands. Instead of docking a large ligand to a target protein in one single step as done in the standard protocol, our protocol docks the large ligand in increments. In this paper, we present three detailed examples of docking using DINC and compare the docking results with those obtained using AutoDock's standard protocol. We summarize the docking results from an extended docking study that was done on 73 protein-ligand complexes comprised of large ligands. We demonstrate not only that DINC is up to 2 orders of magnitude faster than AutoDock's standard protocol, but that it also achieves the speed-up without sacrificing docking accuracy. We also show that positional restraints can be applied to the large ligand using DINC: this is useful when computing a docked conformation of the ligand. Finally, we introduce a webserver for docking large ligands using DINC. Conclusions Docking large ligands using DINC is significantly faster than AutoDock's standard protocol without any loss of accuracy. Therefore, DINC could be used as an alternative protocol for docking large ligands. DINC has been implemented as a webserver and is available at http://dinc.kavrakilab.org. Applications such as therapeutic drug design, rational vaccine design, and others involving large ligands could benefit from DINC and its webserver implementation.
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