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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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52
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Shamakhi A, Kordbacheh E. Immunoinformatic design of an epitope-based immunogen candidate against Bacillus anthracis. INFORMATICS IN MEDICINE UNLOCKED 2021. [DOI: 10.1016/j.imu.2021.100574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022] Open
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53
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Abstract
Molecular dynamics (MD) simulations have become increasingly useful in the modern drug development process. In this review, we give a broad overview of the current application possibilities of MD in drug discovery and pharmaceutical development. Starting from the target validation step of the drug development process, we give several examples of how MD studies can give important insights into the dynamics and function of identified drug targets such as sirtuins, RAS proteins, or intrinsically disordered proteins. The role of MD in antibody design is also reviewed. In the lead discovery and lead optimization phases, MD facilitates the evaluation of the binding energetics and kinetics of the ligand-receptor interactions, therefore guiding the choice of the best candidate molecules for further development. The importance of considering the biological lipid bilayer environment in the MD simulations of membrane proteins is also discussed, using G-protein coupled receptors and ion channels as well as the drug-metabolizing cytochrome P450 enzymes as relevant examples. Lastly, we discuss the emerging role of MD simulations in facilitating the pharmaceutical formulation development of drugs and candidate drugs. Specifically, we look at how MD can be used in studying the crystalline and amorphous solids, the stability of amorphous drug or drug-polymer formulations, and drug solubility. Moreover, since nanoparticle drug formulations are of great interest in the field of drug delivery research, different applications of nano-particle simulations are also briefly summarized using multiple recent studies as examples. In the future, the role of MD simulations in facilitating the drug development process is likely to grow substantially with the increasing computer power and advancements in the development of force fields and enhanced MD methodologies.
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54
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Allam L, Ghrifi F, Mohammed H, El Hafidi N, El Jaoudi R, El Harti J, Lmimouni B, Belyamani L, Ibrahimi A. Targeting the GRP78-Dependant SARS-CoV-2 Cell Entry by Peptides and Small Molecules. Bioinform Biol Insights 2020; 14:1177932220965505. [PMID: 33149560 PMCID: PMC7585878 DOI: 10.1177/1177932220965505] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2020] [Accepted: 09/19/2020] [Indexed: 12/28/2022] Open
Abstract
The global burden of infections and the rapid spread of viral diseases show the need for new approaches in the prevention and development of effective therapies. To this end, we aimed to explore novel inhibitor compounds that can stop replication or decrease the viral load of the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), for which there is currently no approved treatment. Besides using the angiotensin-converting enzyme (ACE2) receptor as a main gate, the CoV-2 can bind to the glucose-regulating protein 78 (GRP78) receptor to get into the cells to start an infection. Here, we report potential inhibitors comprising small molecules and peptides that could interfere with the interaction of SARS-CoV-2 and its target cells by blocking the recognition of the GRP78 cellular receptor by the viral Spike protein. These inhibitors were discovered through an approach of in silico screening of available databases of bioactive peptides and polyphenolic compounds and the analysis of their docking modes. This process led to the selection of 9 compounds with optimal binding affinities to the target sites. The peptides (satpdb18674, satpdb18446, satpdb12488, satpdb14438, and satpdb28899) act on regions III and IV of the viral Spike protein and on its binding sites in GRP78. However, 4 polyphenols such as epigallocatechin gallate (EGCG), homoeriodictyol, isorhamnetin, and curcumin interact, in addition to the Spike protein and its binding sites in GRP78, with the ATPase domain of GRP78. Our work demonstrates that there are at least 2 approaches to block the spread of SARS-CoV-2 by preventing its fusion with the host cells via GRP78.
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Affiliation(s)
- Loubna Allam
- Medical Biotechnology Laboratory (MedBiotech), Bioinova Research Center, Rabat Medical & Pharmacy School, Mohammed Vth University in Rabat, Rabat, Morocco
| | - Fatima Ghrifi
- Medical Biotechnology Laboratory (MedBiotech), Bioinova Research Center, Rabat Medical & Pharmacy School, Mohammed Vth University in Rabat, Rabat, Morocco
| | - Hakmi Mohammed
- Medical Biotechnology Laboratory (MedBiotech), Bioinova Research Center, Rabat Medical & Pharmacy School, Mohammed Vth University in Rabat, Rabat, Morocco
| | - Naima El Hafidi
- Medical Biotechnology Laboratory (MedBiotech), Bioinova Research Center, Rabat Medical & Pharmacy School, Mohammed Vth University in Rabat, Rabat, Morocco
| | - Rachid El Jaoudi
- Medical Biotechnology Laboratory (MedBiotech), Bioinova Research Center, Rabat Medical & Pharmacy School, Mohammed Vth University in Rabat, Rabat, Morocco
| | - Jaouad El Harti
- Therapeutic Chemistry Laboratory, Medical Biotechnology Laboratory (MedBiotech), Rabat Medical & Pharmacy School, Mohammed Vth University in Rabat, Rabat, Morocco
| | - Badreddine Lmimouni
- Parasitology and Mycology Department, Military Hospital Mohammed V, Rabat Medical & Pharmacy School, Mohammed Vth University in Rabat, Rabat, Morocco
| | - Lahcen Belyamani
- Emergency Department, Military Hospital Mohammed V, Rabat Medical & Pharmacy School, Mohammed Vth University in Rabat, Rabat, Morocco
| | - Azeddine Ibrahimi
- Medical Biotechnology Laboratory (MedBiotech), Bioinova Research Center, Rabat Medical & Pharmacy School, Mohammed Vth University in Rabat, Rabat, Morocco
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55
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Cyclic Peptide Inhibitors of the Tsg101 UEV Protein Interactions Refined through Global Docking and Gaussian Accelerated Molecular Dynamics Simulations. Polymers (Basel) 2020; 12:polym12102235. [PMID: 32998394 PMCID: PMC7650771 DOI: 10.3390/polym12102235] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Revised: 09/26/2020] [Accepted: 09/27/2020] [Indexed: 01/08/2023] Open
Abstract
Tsg101 UEV domain proteins are potential targets for virus infection therapy, especially for HIV and Ebola viruses. Peptides are key in curbing virus transmission, and cyclic peptides have a greater survival time than their linear peptides. To date, the accurate prediction of cyclic peptide-protein receptors binding conformations still is challenging because of high peptide flexibility. Here, a useful approach combined the global peptide docking, Gaussian accelerated molecular dynamics (GaMD), two-dimensional (2D) potential of mean force (PMF), normal molecular dynamics (cMD), and solvated interaction energy (SIE) techniques. Then we used this approach to investigate the binding conformations of UEV domain proteins with three cyclic peptides inhibitors. We reported the possible cyclic peptide-UEV domain protein binding conformations via 2D PMF free energy profiles and SIE free energy calculations. The residues Trp145, Tyr147, and Trp148 of the native cyclic peptide (CP1) indeed play essential roles in the cyclic peptides-UEV domain proteins interactions. Our findings might increase the accuracy of cyclic peptide-protein conformational prediction, which may facilitate cyclic peptide inhibitor design. Our approach is expected to further aid in addressing the challenges in cyclic peptide inhibitor design.
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56
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Lazim R, Suh D, Choi S. Advances in Molecular Dynamics Simulations and Enhanced Sampling Methods for the Study of Protein Systems. Int J Mol Sci 2020; 21:E6339. [PMID: 32882859 PMCID: PMC7504087 DOI: 10.3390/ijms21176339] [Citation(s) in RCA: 80] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Revised: 08/10/2020] [Accepted: 08/11/2020] [Indexed: 12/12/2022] Open
Abstract
Molecular dynamics (MD) simulation is a rigorous theoretical tool that when used efficiently could provide reliable answers to questions pertaining to the structure-function relationship of proteins. Data collated from protein dynamics can be translated into useful statistics that can be exploited to sieve thermodynamics and kinetics crucial for the elucidation of mechanisms responsible for the modulation of biological processes such as protein-ligand binding and protein-protein association. Continuous modernization of simulation tools enables accurate prediction and characterization of the aforementioned mechanisms and these qualities are highly beneficial for the expedition of drug development when effectively applied to structure-based drug design (SBDD). In this review, current all-atom MD simulation methods, with focus on enhanced sampling techniques, utilized to examine protein structure, dynamics, and functions are discussed. This review will pivot around computer calculations of protein-ligand and protein-protein systems with applications to SBDD. In addition, we will also be highlighting limitations faced by current simulation tools as well as the improvements that have been made to ameliorate their efficiency.
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Affiliation(s)
- Raudah Lazim
- College of Pharmacy and Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul 03760, Korea
| | - Donghyuk Suh
- College of Pharmacy and Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul 03760, Korea
| | - Sun Choi
- College of Pharmacy and Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul 03760, Korea
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57
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Kardani K, Bolhassani A, Namvar A. An overview of in silico vaccine design against different pathogens and cancer. Expert Rev Vaccines 2020; 19:699-726. [PMID: 32648830 DOI: 10.1080/14760584.2020.1794832] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
INTRODUCTION Due to overcome the hardness of the vaccine design, computational vaccinology is emerging widely. Prediction of T cell and B cell epitopes, antigen processing analysis, antigenicity analysis, population coverage, conservancy analysis, allergenicity assessment, toxicity prediction, and protein-peptide docking are important steps in the process of designing and developing potent vaccines against various viruses and cancers. In order to perform all of the analyses, several bioinformatics tools and online web servers have been developed. Scientists must take the decision to apply more suitable and precise servers for each part based on their accuracy. AREAS COVERED In this review, a wide-range list of different bioinformatics tools and online web servers has been provided. Moreover, some studies were proposed to show the importance of various bioinformatics tools for predicting and developing efficient vaccines against different pathogens including viruses, bacteria, parasites, and fungi as well as cancer. EXPERT OPINION Immunoinformatics is the best way to find potential vaccine candidates against different pathogens. Thus, the selection of the most accurate tools is necessary to predict and develop potent preventive and therapeutic vaccines. To further evaluation of the computational and in silico vaccine design, in vitro/in vivo analyses are required to develop vaccine candidates.
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Affiliation(s)
- Kimia Kardani
- Department of Pharmaceutical Biotechnology, School of Pharmacy, Shahid Beheshti University of Medical Sciences , Tehran, Iran.,Department of Hepatitis and AIDS, Pasteur Institute of Iran , Tehran, Iran
| | - Azam Bolhassani
- Department of Hepatitis and AIDS, Pasteur Institute of Iran , Tehran, Iran
| | - Ali Namvar
- Iranian Comprehensive Hemophilia Care Center , Tehran, Iran
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58
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Khramushin A, Marcu O, Alam N, Shimony O, Padhorny D, Brini E, Dill KA, Vajda S, Kozakov D, Schueler-Furman O. Modeling beta-sheet peptide-protein interactions: Rosetta FlexPepDock in CAPRI rounds 38-45. Proteins 2020; 88:1037-1049. [PMID: 31891416 PMCID: PMC7539656 DOI: 10.1002/prot.25871] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2019] [Revised: 12/17/2019] [Accepted: 12/26/2019] [Indexed: 01/09/2023]
Abstract
Peptide-protein docking is challenging due to the considerable conformational freedom of the peptide. CAPRI rounds 38-45 included two peptide-protein interactions, both characterized by a peptide forming an additional beta strand of a beta sheet in the receptor. Using the Rosetta FlexPepDock peptide docking protocol we generated top-performing, high-accuracy models for targets 134 and 135, involving an interaction between a peptide derived from L-MAG with DLC8. In addition, we were able to generate the only medium-accuracy models for a particularly challenging target, T121. In contrast to the classical peptide-mediated interaction, in which receptor side chains contact both peptide backbone and side chains, beta-sheet complementation involves a major contribution to binding by hydrogen bonds between main chain atoms. To establish how binding affinity and specificity are established in this special class of peptide-protein interactions, we extracted PeptiDBeta, a benchmark of solved structures of different protein domains that are bound by peptides via beta-sheet complementation, and tested our protocol for global peptide-docking PIPER-FlexPepDock on this dataset. We find that the beta-strand part of the peptide is sufficient to generate approximate and even high resolution models of many interactions, but inclusion of adjacent motif residues often provides additional information necessary to achieve high resolution model quality.
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Affiliation(s)
- Alisa Khramushin
- Department of Microbiologyand Molecular Genetics, Institute
for Medical Research Israel-Canada, Faculty of Medicine, The Hebrew University,
Jerusalem, Israel
| | - Orly Marcu
- Department of Microbiologyand Molecular Genetics, Institute
for Medical Research Israel-Canada, Faculty of Medicine, The Hebrew University,
Jerusalem, Israel
| | - Nawsad Alam
- Department of Microbiologyand Molecular Genetics, Institute
for Medical Research Israel-Canada, Faculty of Medicine, The Hebrew University,
Jerusalem, Israel
| | - Orly Shimony
- Department of Microbiologyand Molecular Genetics, Institute
for Medical Research Israel-Canada, Faculty of Medicine, The Hebrew University,
Jerusalem, Israel
| | - Dzmitry Padhorny
- Department of Applied Mathematics and Statistics, Stony
Brook University, New York, New York
- Laufer Center for Physical and Quantitative Biology, Stony
Brook University, New York, New York
| | - Emiliano Brini
- Laufer Center for Physical and Quantitative Biology, Stony
Brook University, New York, New York
| | - Ken A. Dill
- Laufer Center for Physical and Quantitative Biology, Stony
Brook University, New York, New York
- Department of Physics and Astronomy, Stony Brook
University, New York, New York
- Department of Chemistry, Stony Brook University, New York,
New York
| | - Sandor Vajda
- Department of Biomedical Engineering, Boston University,
Boston, Massachusetts
- Department of Chemistry, Boston University, Boston,
Massachusetts
| | - Dima Kozakov
- Department of Applied Mathematics and Statistics, Stony
Brook University, New York, New York
- Laufer Center for Physical and Quantitative Biology, Stony
Brook University, New York, New York
| | - Ora Schueler-Furman
- Department of Microbiologyand Molecular Genetics, Institute
for Medical Research Israel-Canada, Faculty of Medicine, The Hebrew University,
Jerusalem, Israel
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59
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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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60
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Kim SJ, Ju JS, Kang MH, Won JE, Kim YH, Raninga PV, Khanna KK, Győrffy B, Pack CG, Han HD, Lee HJ, Gong G, Shin Y, Mills GB, Eyun SI, Park YY. RNA-binding protein NONO contributes to cancer cell growth and confers drug resistance as a theranostic target in TNBC. Theranostics 2020; 10:7974-7992. [PMID: 32724453 PMCID: PMC7381744 DOI: 10.7150/thno.45037] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2020] [Accepted: 06/15/2020] [Indexed: 12/22/2022] Open
Abstract
Breast cancer (BC) is one of the most common cancers in women. TNBC (Triple-negative breast cancer) has limited treatment options and still lacks viable molecular targets, leading to poor outcomes. Recently, RNA-binding proteins (RBPs) have been shown to play crucial roles in human cancers, including BC, by modulating a number of oncogenic phenotypes. This suggests that RBPs represent potential molecular targets for BC therapy. Methods: We employed genomic data to identify RBPs specifically expressed in TNBC. NONO was silenced in TNBC cell lines to examine cell growth, colony formation, invasion, and migration. Gene expression profiles in NONO-silenced cells were generated and analyzed. A high-throughput screening for NONO-targeted drugs was performed using an FDA-approved library. Results: We found that the NONO RBP is highly expressed in TNBC and is associated with poor patient outcomes. NONO binds to STAT3 mRNA, increasing STAT3 mRNA levels in TNBC. Surprisingly, NONO directly interacts with STAT3 protein increasing its stability and transcriptional activity, thus contributing to its oncogenic function. Importantly, high-throughput drug screening revealed that auranofin is a potential NONO inhibitor and inhibits cell growth in TNBC. Conclusions: NONO is an RBP upstream regulator of both STAT3 RNA and protein levels and function. It represents an important and clinically relevant promoter of growth and resistance of TNBCs. NONO is also therefore a potential therapeutic target in TNBC.
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61
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Wang YT, Cheng TL. Computational modeling of cyclic peptide inhibitor-MDM2/MDMX binding through global docking and Gaussian accelerated molecular dynamics simulations. J Biomol Struct Dyn 2020; 39:4005-4014. [PMID: 32448094 DOI: 10.1080/07391102.2020.1773317] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
MDM2 and MDMX are potential targets for p53-dependent cancer therapy. Peptides are key in cellular immunology and oncology, and cyclic peptides generally have higher half-life than their linear counterparts. However, prediction of cyclic peptide-protein binding is challenging with normal molecular simulation approaches because of high peptide flexibility. Here, we used global peptide docking, normal molecular dynamics, Gaussian accelerated molecular dynamics (GaMD), two-dimensional (2D) potential of mean force (PMF) profiles, and solvated interaction energy (SIE) techniques to investigate the interactions of MDM2/MDMX with three N-to-C-terminal cyclic peptide-based inhibitors. We determined the possible cyclic peptide-MDM2/MDMX complex structures via 2D PMF profiles and SIE calculations. Our findings increase the accuracy of peptide-protein structural prediction, which may facilitate cyclic peptide drug design. Advancements in the computational methods and computing power may further aid in addressing the challenges in cyclic peptide drug design. Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Yeng-Tseng Wang
- Department of Biochemistry, School of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan.,Drug Development and Value Creation Research Center, Kaohsiung Medical University, Kaohsiung, Taiwan.,Graduate Institute of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan.,Department of Medical Research, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan
| | - Tian-Lu Cheng
- Drug Development and Value Creation Research Center, Kaohsiung Medical University, Kaohsiung, Taiwan.,Graduate Institute of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan.,Department of Biomedical Science and Environment Biology, Kaohsiung Medical University, Kaohsiung, Taiwan
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62
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Guven-Maiorov E, Hakouz A, Valjevac S, Keskin O, Tsai CJ, Gursoy A, Nussinov R. HMI-PRED: A Web Server for Structural Prediction of Host-Microbe Interactions Based on Interface Mimicry. J Mol Biol 2020; 432:3395-3403. [PMID: 32061934 PMCID: PMC7261632 DOI: 10.1016/j.jmb.2020.01.025] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2019] [Revised: 11/28/2019] [Accepted: 01/14/2020] [Indexed: 02/07/2023]
Abstract
Microbes, commensals, and pathogens, control the numerous functions in the host cells. They can alter host signaling and modulate immune surveillance by interacting with the host proteins. For shedding light on the contribution of microbes to health and disease, it is vital to discern how microbial proteins rewire host signaling and through which host proteins they do this. Host-Microbe Interaction PREDictor (HMI-PRED) is a user-friendly web server for structural prediction of protein-protein interactions (PPIs) between the host and a microbial species, including bacteria, viruses, fungi, and protozoa. HMI-PRED relies on "interface mimicry" through which the microbial proteins hijack host binding surfaces. Given the structure of a microbial protein of interest, HMI-PRED will return structural models of potential host-microbe interaction (HMI) complexes, the list of host endogenous and exogenous PPIs that can be disrupted, and tissue expression of the microbe-targeted host proteins. The server also allows users to upload homology models of microbial proteins. Broadly, it aims at large-scale, efficient identification of HMIs. The prediction results are stored in a repository for community access. HMI-PRED is free and available at https://interactome.ku.edu.tr/hmi.
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Affiliation(s)
- Emine Guven-Maiorov
- Computational Structural Biology Section, Basic Science Program, Frederick National Laboratory for Cancer Research, Frederick, MD, 21702, USA.
| | - Asma Hakouz
- Department of Computer Engineering, Koc University, Istanbul, 34450, Turkey.
| | - Sukejna Valjevac
- Department of Computer Engineering, Koc University, Istanbul, 34450, Turkey.
| | - Ozlem Keskin
- Department of Chemical and Biological Engineering, Koc University, Istanbul, 34450, Turkey.
| | - Chung-Jung Tsai
- Computational Structural Biology Section, Basic Science Program, Frederick National Laboratory for Cancer Research, Frederick, MD, 21702, USA.
| | - Attila Gursoy
- Department of Computer Engineering, Koc University, Istanbul, 34450, Turkey.
| | - Ruth Nussinov
- Computational Structural Biology Section, Basic Science Program, Frederick National Laboratory for Cancer Research, Frederick, MD, 21702, USA; Sackler Inst. of Molecular Medicine, Department of Human Genetics and Molecular Medicine, Sackler School of Medicine, Tel Aviv University, Tel Aviv, 69978, Israel.
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63
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Weng G, Gao J, Wang Z, Wang E, Hu X, Yao X, Cao D, Hou T. Comprehensive Evaluation of Fourteen Docking Programs on Protein–Peptide Complexes. J Chem Theory Comput 2020; 16:3959-3969. [DOI: 10.1021/acs.jctc.9b01208] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Affiliation(s)
- Gaoqi Weng
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Junbo Gao
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Zhe Wang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Ercheng Wang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Xueping Hu
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Xiaojun Yao
- State Key Laboratory of Quality Research in Chinese Medicine, Macau Institute for Applied Research in Medicine and Health, Macau University of Science and Technology, Avenida Wai Long, Taipa, Macau (SAR), China
| | - Dongsheng Cao
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410013, Hunan, China
| | - Tingjun Hou
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
- State Key Lab of CAD&CG, Zhejiang University, Hangzhou 310058, Zhejiang, China
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64
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Padhorny D, Porter KA, Ignatov M, Alekseenko A, Beglov D, Kotelnikov S, Ashizawa R, Desta I, Alam N, Sun Z, Brini E, Dill K, Schueler-Furman O, Vajda S, Kozakov D. ClusPro in rounds 38 to 45 of CAPRI: Toward combining template-based methods with free docking. Proteins 2020; 88:1082-1090. [PMID: 32142178 DOI: 10.1002/prot.25887] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2019] [Revised: 02/27/2020] [Accepted: 03/04/2020] [Indexed: 01/01/2023]
Abstract
Targets in the protein docking experiment CAPRI (Critical Assessment of Predicted Interactions) generally present new challenges and contribute to new developments in methodology. In rounds 38 to 45 of CAPRI, most targets could be effectively predicted using template-based methods. However, the server ClusPro required structures rather than sequences as input, and hence we had to generate and dock homology models. The available templates also provided distance restraints that were directly used as input to the server. We show here that such an approach has some advantages. Free docking with template-based restraints using ClusPro reproduced some interfaces suggested by weak or ambiguous templates while not reproducing others, resulting in correct server predicted models. More recently we developed the fully automated ClusPro TBM server that performs template-based modeling and thus can use sequences rather than structures of component proteins as input. The performance of the server, freely available for noncommercial use at https://tbm.cluspro.org, is demonstrated by predicting the protein-protein targets of rounds 38 to 45 of CAPRI.
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Affiliation(s)
- Dzmitry Padhorny
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York, USA.,Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York, USA
| | - Kathryn A Porter
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA
| | - Mikhail Ignatov
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York, USA.,Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York, USA
| | - Andrey Alekseenko
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York, USA.,Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York, USA.,Institute of Computer Aided Design of the Russian Academy of Sciences, Moscow, Russia
| | - Dmitri Beglov
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA.,Acpharis Inc., Holliston, Massachusetts, USA
| | - Sergei Kotelnikov
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York, USA.,Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York, USA.,Innopolis University, Innopolis, Russia
| | - Ryota Ashizawa
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York, USA.,Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York, USA
| | - Israel Desta
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA
| | - Nawsad Alam
- Department of Microbiology and Molecular Genetics, Institute for Medical Research Israel-Canada, Faculty of Medicine, The Hebrew University, Jerusalem, Israel
| | - Zhuyezi Sun
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA
| | - Emiliano Brini
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York, USA
| | - Ken Dill
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York, USA.,Department of Physics and Astronomy, Stony Brook University, Stony Brook, New York, USA.,Department of Chemistry, Stony Brook University, Stony Brook, New York, USA
| | - Ora Schueler-Furman
- Department of Microbiology and Molecular Genetics, Institute for Medical Research Israel-Canada, Faculty of Medicine, The Hebrew University, Jerusalem, Israel
| | - Sandor Vajda
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA.,Department of Chemistry, Boston University, Boston, Massachusetts, USA
| | - Dima Kozakov
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York, USA.,Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York, USA
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65
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Kurcinski M, Badaczewska‐Dawid A, Kolinski M, Kolinski A, Kmiecik S. Flexible docking of peptides to proteins using CABS-dock. Protein Sci 2020; 29:211-222. [PMID: 31682301 PMCID: PMC6933849 DOI: 10.1002/pro.3771] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2019] [Revised: 10/30/2019] [Accepted: 10/31/2019] [Indexed: 12/12/2022]
Abstract
Molecular docking of peptides to proteins can be a useful tool in the exploration of the possible peptide binding sites and poses. CABS-dock is a method for protein-peptide docking that features significant conformational flexibility of both the peptide and the protein molecules during the peptide search for a binding site. The CABS-dock has been made available as a web server and a standalone package. The web server is an easy to use tool with a simple web interface. The standalone package is a command-line program dedicated to professional users. It offers a number of advanced features, analysis tools and support for large-sized systems. In this article, we outline the current status of the CABS-dock method, its recent developments, applications, and challenges ahead.
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Affiliation(s)
- Mateusz Kurcinski
- Faculty of Chemistry, Biological and Chemical Research CenterUniversity of WarsawWarsawPoland
| | | | - Michal Kolinski
- Bioinformatics Laboratory, Mossakowski Medical Research CentrePolish Academy of SciencesWarsawPoland
| | - Andrzej Kolinski
- Faculty of Chemistry, Biological and Chemical Research CenterUniversity of WarsawWarsawPoland
| | - Sebastian Kmiecik
- Faculty of Chemistry, Biological and Chemical Research CenterUniversity of WarsawWarsawPoland
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66
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Chong LC, Khan AM. Identification of highly conserved, serotype-specific dengue virus sequences: implications for vaccine design. BMC Genomics 2019; 20:921. [PMID: 31874646 PMCID: PMC6929274 DOI: 10.1186/s12864-019-6311-z] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2019] [Accepted: 11/19/2019] [Indexed: 11/24/2022] Open
Abstract
Background The sequence diversity of dengue virus (DENV) is one of the challenges in developing an effective vaccine against the virus. Highly conserved, serotype-specific (HCSS), immune-relevant DENV sequences are attractive candidates for vaccine design, and represent an alternative to the approach of selecting pan-DENV conserved sequences. The former aims to limit the number of possible cross-reactive epitope variants in the population, while the latter aims to limit the cross-reactivity between the serotypes to favour a serotype-specific response. Herein, we performed a large-scale systematic study to map and characterise HCSS sequences in the DENV proteome. Methods All reported DENV protein sequence data for each serotype was retrieved from the NCBI Entrez Protein (nr) Database (txid: 12637). The downloaded sequences were then separated according to the individual serotype proteins by use of BLASTp search, and subsequently removed for duplicates and co-aligned across the serotypes. Shannon’s entropy and mutual information (MI) analyses, by use of AVANA, were performed to measure the diversity within and between the serotype proteins to identify HCSS nonamers. The sequences were evaluated for the presence of promiscuous T-cell epitopes by use of NetCTLpan 1.1 and NetMHCIIpan 3.2 server for human leukocyte antigen (HLA) class I and class II supertypes, respectively. The predicted epitopes were matched to reported epitopes in the Immune Epitope Database. Results A total of 2321 nonamers met the HCSS selection criteria of entropy < 0.25 and MI > 0.8. Concatenating these resulted in a total of 337 HCSS sequences. DENV4 had the most number of HCSS nonamers; NS5, NS3 and E proteins had among the highest, with none in the C and only one in prM. The HCSS sequences were immune-relevant; 87 HCSS sequences were both reported T-cell epitopes/ligands in human and predicted epitopes, supporting the accuracy of the predictions. A number of the HCSS clustered as immunological hotspots and exhibited putative promiscuity beyond a single HLA supertype. The HCSS sequences represented, on average, ~ 40% of the proteome length for each serotype; more than double of pan-DENV sequences (conserved across the four serotypes), and thus offer a larger choice of sequences for vaccine target selection. HCSS sequences of a given serotype showed significant amino acid difference to all the variants of the other serotypes, supporting the notion of serotype-specificity. Conclusion This work provides a catalogue of HCSS sequences in the DENV proteome, as candidates for vaccine target selection. The methodology described herein provides a framework for similar application to other pathogens.
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Affiliation(s)
- Li Chuin Chong
- Centre for Bioinformatics, School of Data Sciences, Perdana University, Jalan MAEPS Perdana, 43400, Serdang, Selangor Darul Ehsan, Malaysia
| | - Asif M Khan
- Centre for Bioinformatics, School of Data Sciences, Perdana University, Jalan MAEPS Perdana, 43400, Serdang, Selangor Darul Ehsan, Malaysia.
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67
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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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68
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Wang J, Alekseenko A, Kozakov D, Miao Y. Improved Modeling of Peptide-Protein Binding Through Global Docking and Accelerated Molecular Dynamics Simulations. Front Mol Biosci 2019; 6:112. [PMID: 31737642 PMCID: PMC6835073 DOI: 10.3389/fmolb.2019.00112] [Citation(s) in RCA: 49] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2019] [Accepted: 10/09/2019] [Indexed: 01/31/2023] Open
Abstract
Peptides mediate up to 40% of known protein-protein interactions in higher eukaryotes and play a key role in cellular signaling, protein trafficking, immunology, and oncology. However, it is challenging to predict peptide-protein binding with conventional computational modeling approaches, due to slow dynamics and high peptide flexibility. Here, we present a prototype of the approach which combines global peptide docking using ClusPro PeptiDock and all-atom enhanced simulations using Gaussian accelerated molecular dynamics (GaMD). For three distinct model peptides, the lowest backbone root-mean-square deviations (RMSDs) of their bound conformations relative to X-ray structures obtained from PeptiDock were 3.3–4.8 Å, being medium quality predictions according to the Critical Assessment of PRediction of Interactions (CAPRI) criteria. GaMD simulations refined the peptide-protein complex structures with significantly reduced peptide backbone RMSDs of 0.6–2.7 Å, yielding two high quality (sub-angstrom) and one medium quality models. Furthermore, the GaMD simulations identified important low-energy conformational states and revealed the mechanism of peptide binding to the target proteins. Therefore, PeptiDock+GaMD is a promising approach for exploring peptide-protein interactions.
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Affiliation(s)
- Jinan Wang
- Center for Computational Biology and Department of Molecular Biosciences, University of Kansas, Lawrence, KS, United States
| | - Andrey Alekseenko
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, United States.,Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, United States
| | - Dima Kozakov
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, United States.,Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, United States
| | - Yinglong Miao
- Center for Computational Biology and Department of Molecular Biosciences, University of Kansas, Lawrence, KS, United States
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69
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Ansar S, Vetrivel U. PepVis: An integrated peptide virtual screening pipeline for ensemble and flexible docking protocols. Chem Biol Drug Des 2019; 94:2041-2050. [DOI: 10.1111/cbdd.13607] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2019] [Revised: 08/06/2019] [Accepted: 08/10/2019] [Indexed: 01/06/2023]
Affiliation(s)
- Samdani Ansar
- Centre for Bioinformatics Kamalnayan Bajaj Institute for Research in Vision and Ophthalmology Vision Research Foundation Sankara Nethralaya Chennai India
- School of Chemical and Biotechnology SASTRA Deemed University Thanjavur India
| | - Umashankar Vetrivel
- Centre for Bioinformatics Kamalnayan Bajaj Institute for Research in Vision and Ophthalmology Vision Research Foundation Sankara Nethralaya Chennai India
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70
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Weng G, Wang E, Chen F, Sun H, Wang Z, Hou T. Assessing the performance of MM/PBSA and MM/GBSA methods. 9. Prediction reliability of binding affinities and binding poses for protein-peptide complexes. Phys Chem Chem Phys 2019; 21:10135-10145. [PMID: 31062799 DOI: 10.1039/c9cp01674k] [Citation(s) in RCA: 84] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
A significant number of protein-protein interactions (PPIs) are mediated through the interactions between proteins and peptide segments, and therefore determination of protein-peptide interactions (PpIs) is critical to gain an in-depth understanding of the PPI network and even design peptides or small molecules capable of modulating PPIs. Computational approaches, especially molecular docking, provide an efficient way to model PpIs, and a reliable scoring function that can recognize the correct binding conformations for protein-peptide complexes is one of the most important components in protein-peptide docking. The end-point binding free energy calculation methods, such as MM/GBSA and MM/PBSA, are theoretically more rigorous than most empirical and semi-empirical scoring functions designed for protein-peptide docking, but their performance in predicting binding affinities and binding poses for protein-peptide systems has not been systematically assessed. In this study, we first evaluated the capability of MM/GBSA and MM/PBSA with different solvation models, interior dielectric constants (εin) and force fields to predict the binding affinities for 53 protein-peptide complexes. For the 19 short peptides with 5-12 residues, MM/PBSA based on the minimized structures in explicit solvent with the ff99 force field and εin = 2 yields the best correlation between the predicted binding affinities and the experimental data (rp = 0.748), while for the 34 medium-size peptides with 20-25 residues, MM/GBSA based on 1 ns of molecular dynamics (MD) simulations in implicit solvent with the ff03 force field, the GBOBC1 model and a low interior dielectric constant (εin = 1) yields the best accuracy (rp = 0.735). Then, we assessed the rescoring capability of MM/PBSA and MM/GBSA to distinguish the correct binding conformations from the decoys for 112 protein-peptide systems. The results illustrate that MM/PBSA based on the minimized structures with the ff99 or ff14SB force field and MM/GBSA based on the minimized structures with the ff03 force field show excellent capability to recognize the near-native binding poses for the short and medium-size peptides, respectively, and they outperform the predictions given by two popular protein-peptide docking algorithms (pepATTRACT and HPEPDOCK). Therefore, MM/PBSA and MM/GBSA are powerful tools to predict the binding affinities and identify the correct binding poses for protein-peptide systems.
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Affiliation(s)
- Gaoqi Weng
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China.
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71
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Lee ACL, Harris JL, Khanna KK, Hong JH. A Comprehensive Review on Current Advances in Peptide Drug Development and Design. Int J Mol Sci 2019; 20:ijms20102383. [PMID: 31091705 PMCID: PMC6566176 DOI: 10.3390/ijms20102383] [Citation(s) in RCA: 363] [Impact Index Per Article: 72.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2019] [Revised: 05/09/2019] [Accepted: 05/10/2019] [Indexed: 11/16/2022] Open
Abstract
Protein-protein interactions (PPIs) execute many fundamental cellular functions and have served as prime drug targets over the last two decades. Interfering intracellular PPIs with small molecules has been extremely difficult for larger or flat binding sites, as antibodies cannot cross the cell membrane to reach such target sites. In recent years, peptides smaller size and balance of conformational rigidity and flexibility have made them promising candidates for targeting challenging binding interfaces with satisfactory binding affinity and specificity. Deciphering and characterizing peptide-protein recognition mechanisms is thus central for the invention of peptide-based strategies to interfere with endogenous protein interactions, or improvement of the binding affinity and specificity of existing approaches. Importantly, a variety of computation-aided rational designs for peptide therapeutics have been developed, which aim to deliver comprehensive docking for peptide-protein interaction interfaces. Over 60 peptides have been approved and administrated globally in clinics. Despite this, advances in various docking models are only on the merge of making their contribution to peptide drug development. In this review, we provide (i) a holistic overview of peptide drug development and the fundamental technologies utilized to date, and (ii) an updated review on key developments of computational modeling of peptide-protein interactions (PepPIs) with an aim to assist experimental biologists exploit suitable docking methods to advance peptide interfering strategies against PPIs.
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Affiliation(s)
- Andy Chi-Lung Lee
- QIMR Berghofer Medical Research Institute, Brisbane, QLD 4006, Australia.
- Radiation Biology Research Center, Institute for Radiological Research, Chang Gung Memorial Hospital, Chang Gung University, Taoyuan 333, Taiwan.
- Department of Radiation Oncology, Chang Gung Memorial Hospital, Linkou 333, Taiwan.
| | | | - Kum Kum Khanna
- QIMR Berghofer Medical Research Institute, Brisbane, QLD 4006, Australia.
| | - Ji-Hong Hong
- Radiation Biology Research Center, Institute for Radiological Research, Chang Gung Memorial Hospital, Chang Gung University, Taoyuan 333, Taiwan.
- Department of Radiation Oncology, Chang Gung Memorial Hospital, Linkou 333, Taiwan.
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72
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Fouladi M, Sarhadi S, Tohidkia M, Fahimi F, Samadi N, Sadeghi J, Barar J, Omidi Y. Selection of a fully human single domain antibody specific to Helicobacter pylori urease. Appl Microbiol Biotechnol 2019; 103:3407-3420. [DOI: 10.1007/s00253-019-09674-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2018] [Revised: 01/29/2019] [Accepted: 01/30/2019] [Indexed: 12/14/2022]
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73
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Inhibition of protein interactions: co-crystalized protein-protein interfaces are nearly as good as holo proteins in rigid-body ligand docking. J Comput Aided Mol Des 2018; 32:769-779. [PMID: 30003468 DOI: 10.1007/s10822-018-0124-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2017] [Accepted: 05/22/2018] [Indexed: 12/15/2022]
Abstract
Modulating protein interaction pathways may lead to the cure of many diseases. Known protein-protein inhibitors bind to large pockets on the protein-protein interface. Such large pockets are detected also in the protein-protein complexes without known inhibitors, making such complexes potentially druggable. The inhibitor-binding site is primary defined by the side chains that form the largest pocket in the protein-bound conformation. Low-resolution ligand docking shows that the success rate for the protein-bound conformation is close to the one for the ligand-bound conformation, and significantly higher than for the apo conformation. The conformational change on the protein interface upon binding to the other protein results in a pocket employed by the ligand when it binds to that interface. This proof-of-concept study suggests that rather than using computational pocket-opening procedures, one can opt for an experimentally determined structure of the target co-crystallized protein-protein complex as a starting point for drug design.
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74
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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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75
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Lamothe G, Malliavin TE. re-TAMD: exploring interactions between H3 peptide and YEATS domain using enhanced sampling. BMC STRUCTURAL BIOLOGY 2018; 18:4. [PMID: 29615024 PMCID: PMC5883362 DOI: 10.1186/s12900-018-0083-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/20/2017] [Accepted: 03/04/2018] [Indexed: 01/05/2023]
Abstract
BACKGROUND Analysis of preferred binding regions of a ligand on a protein is important for detecting cryptic binding pockets and improving the ligand selectivity. RESULT The enhanced sampling approach TAMD has been adapted to allow a ligand to unbind from its native binding site and explore the protein surface. This so-called re-TAMD procedure was then used to explore the interaction between the N terminal peptide of histone H3 and the YEATS domain. Depending on the length of the peptide, several regions of the protein surface were explored. The peptide conformations sampled during the re-TAMD correspond to peptide free diffusion around the protein surface. CONCLUSIONS The re-TAMD approach permitted to get information on the relative influence of different regions of the N terminal peptide of H3 on the interaction between H3 and YEATS.
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Affiliation(s)
- Gilles Lamothe
- Unité de Bioinformatique Structurale, UMR CNRS 3528 and Institut Pasteur, Paris, France.,Université Denis Diderot Paris 7, Paris, France
| | - Thérèse E Malliavin
- Unité de Bioinformatique Structurale, UMR CNRS 3528 and Institut Pasteur, Paris, France.
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76
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Padhorny D, Hall DR, Mirzaei H, Mamonov AB, Moghadasi M, Alekseenko A, Beglov D, Kozakov D. Protein-ligand docking using FFT based sampling: D3R case study. J Comput Aided Mol Des 2018; 32:225-230. [PMID: 29101520 PMCID: PMC5767528 DOI: 10.1007/s10822-017-0069-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2017] [Accepted: 09/16/2017] [Indexed: 12/15/2022]
Abstract
Fast Fourier transform (FFT) based approaches have been successful in application to modeling of relatively rigid protein-protein complexes. Recently, we have been able to adapt the FFT methodology to treatment of flexible protein-peptide interactions. Here, we report our latest attempt to expand the capabilities of the FFT approach to treatment of flexible protein-ligand interactions in application to the D3R PL-2016-1 challenge. Based on the D3R assessment, our FFT approach in conjunction with Monte Carlo minimization off-grid refinement was among the top performing methods in the challenge. The potential advantage of our method is its ability to globally sample the protein-ligand interaction landscape, which will be explored in further applications.
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Affiliation(s)
- Dzmitry Padhorny
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, 11794, USA
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, 11794, USA
| | | | - Hanieh Mirzaei
- Department of Biomedical Engineering, Boston University, Boston, MA, 02215, USA
| | - Artem B Mamonov
- Department of Biomedical Engineering, Boston University, Boston, MA, 02215, USA
| | - Mohammad Moghadasi
- Department of Biomedical Engineering, Boston University, Boston, MA, 02215, USA
| | - Andrey Alekseenko
- Moscow Institute of Physics and Technology (State University), Institutskii per. 9, Dolgoprudny, Moscow Oblast, Russia, 141700
- Institute of Computer Aided Design of the Russian Academy of Sciences, 19/18, 2-nd Brestskaya St, Moscow, Russia, 123056
| | - Dmitri Beglov
- Department of Biomedical Engineering, Boston University, Boston, MA, 02215, USA.
| | - Dima Kozakov
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, 11794, USA.
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, 11794, USA.
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77
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Alam N, Goldstein O, Xia B, Porter KA, Kozakov D, Schueler-Furman O. High-resolution global peptide-protein docking using fragments-based PIPER-FlexPepDock. PLoS Comput Biol 2017; 13:e1005905. [PMID: 29281622 PMCID: PMC5760072 DOI: 10.1371/journal.pcbi.1005905] [Citation(s) in RCA: 87] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2017] [Revised: 01/09/2018] [Accepted: 11/29/2017] [Indexed: 11/24/2022] Open
Abstract
Peptide-protein interactions contribute a significant fraction of the protein-protein interactome. Accurate modeling of these interactions is challenging due to the vast conformational space associated with interactions of highly flexible peptides with large receptor surfaces. To address this challenge we developed a fragment based high-resolution peptide-protein docking protocol. By streamlining the Rosetta fragment picker for accurate peptide fragment ensemble generation, the PIPER docking algorithm for exhaustive fragment-receptor rigid-body docking and Rosetta FlexPepDock for flexible full-atom refinement of PIPER docked models, we successfully addressed the challenge of accurate and efficient global peptide-protein docking at high-resolution with remarkable accuracy, as validated on a small but representative set of peptide-protein complex structures well resolved by X-ray crystallography. Our approach opens up the way to high-resolution modeling of many more peptide-protein interactions and to the detailed study of peptide-protein association in general. PIPER-FlexPepDock is freely available to the academic community as a server at http://piperfpd.furmanlab.cs.huji.ac.il. Peptide-protein interactions are crucial components of various important biological processes in living cells. High-resolution structural information of such interactions provides insight about the underlying biophysical principles governing the interactions, and a starting point for their targeted manipulations. Accurate docking algorithms can help fill the gap between the vast number of these interactions and the small number of experimentally solved structures. However, the accuracies of the existing protocols have been limited, in particular for ab initio docking when no information about the peptide beyond its sequence is available. Here we introduce PIPER-FlexPepDock, a fragment-based global docking protocol for high-resolution modeling of peptide-protein interactions. Integration of accurate and efficient representation of the peptide using fragment ensembles, their fast and exhaustive rigid-body docking, and their subsequent accurate flexible refinement, enables peptide-protein docking of remarkable accuracy. The validation on a representative benchmark set of crystallographically solved high-resolution peptide-protein complexes demonstrates significantly improved performance over all existing docking protocols. This opens up the way to the modeling of many more peptide-protein interactions, and to a more detailed study of peptide-protein association in general.
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Affiliation(s)
- Nawsad Alam
- Department of Microbiology and Molecular Genetics, Institute for Medical Research Israel-Canada, Faculty of Medicine, The Hebrew University, Jerusalem, Israel
| | - Oriel Goldstein
- School of Computer Sciences and Engineering, The Hebrew University, Jerusalem, Israel
| | - Bing Xia
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, United States of America
| | - Kathryn A. Porter
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, United States of America
| | - Dima Kozakov
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York, United States of America
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York, United States of America
- Institute for Advanced Computational Sciences, Stony Brook University, Stony Brook, New York, United States of America
- * E-mail: (OSF); (DK)
| | - Ora Schueler-Furman
- Department of Microbiology and Molecular Genetics, Institute for Medical Research Israel-Canada, Faculty of Medicine, The Hebrew University, Jerusalem, Israel
- * E-mail: (OSF); (DK)
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