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Shad M, Rehman HM, Akhtar MW, Sajjad M. Structural and functional insights of starch processing α-amylase from hyperthermophilic archaeon Pyrococcusabyssi. Carbohydr Res 2024; 539:109122. [PMID: 38657354 DOI: 10.1016/j.carres.2024.109122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Revised: 03/26/2024] [Accepted: 04/16/2024] [Indexed: 04/26/2024]
Abstract
The genomic screening of hyper-thermophilic Pyrococcus abyssi showed uncharacterized novel α-amylase sequences. Homology modelling analysis revealed that the α-amylase from P. abyssi consists of an N-terminal GH57 catalytic domain, α-amylase central, and C-terminal domain. Current studies emphasize in-silico structural and functional analysis, recombinant expression, characterization, structural studies through CD spectroscopy, and ligand binding studies of the novel α-amylase from P. abyssi. The soluble expression of PaAFG was observed in the E. coli Rosetta™ (DE3) pLysS strain upon incubation overnight at 18 °C in an orbital shaker. The optimum temperature and pH of the PaAFG were observed at 90 °C in 50 mM phosphate buffer pH 6. The Km value for PaAFG against wheat starch was determined as 0.20 ± 0.053 mg while the corresponding Vmax value was 25.00 ± 0.67 μmol min-1 mg-1 in the presence of 2 mM CaCl2 and 12.5 % glycerol. The temperature ramping experiments through CD spectroscopy reveal no significant change in the secondary structures and positive and negative ellipticities of the CD spectra showing the proper folding and optimal temperature of PaAFG protein. The RMSD and RMSF of the PaAFG enzyme determined through molecular dynamic simulation show the significant protein's stability and mobility. The soluble production, thermostability and broad substrate specificity make this enzyme a promising choice for various industrial applications.
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Affiliation(s)
- Mohsin Shad
- School of Biological Sciences, University of the Punjab, Quaid-e-Azam Campus, P.O. 54590, Lahore, Pakistan; Structural Biology, The Rosalind Franklin Institute, Harwell Science & Innovation Campus, Didcot, OX11 0QS, United Kingdom
| | - Hafiz Muzzammel Rehman
- School of Biochemistry and Biotechnology, University of the Punjab, Quaid-e-Azam Campus, P.O. 54590, Lahore, Pakistan
| | - Muhammad Waheed Akhtar
- School of Biological Sciences, University of the Punjab, Quaid-e-Azam Campus, P.O. 54590, Lahore, Pakistan
| | - Muhammad Sajjad
- School of Biological Sciences, University of the Punjab, Quaid-e-Azam Campus, P.O. 54590, Lahore, Pakistan.
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2
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Shad M, Nazir A, Usman M, Akhtar MW, Sajjad M. Investigating the effect of SUMO fusion on solubility and stability of amylase-catalytic domain from Pyrococcus abyssi. Int J Biol Macromol 2024; 266:131310. [PMID: 38569986 DOI: 10.1016/j.ijbiomac.2024.131310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2023] [Revised: 02/09/2024] [Accepted: 03/30/2024] [Indexed: 04/05/2024]
Abstract
Alpha amylase belonging to starch hydrolyzing enzymes has significant contributions to different industrial processes. The enzyme production through recombinant DNA technology faces certain challenges related to their expression, solubility and purification, which can be overcome through fusion tags. This study explored the influence of SUMO, a protein tag reported to enhance the solubility and stability of target proteins when fused to the N-terminal of the catalytic domain of amylase from Pyrococcus abyssi (PaAD). The insoluble expression of PaAD in E. coli was overcome when the enzyme was expressed in a fusion state (S-PaAD) and culture was cultivated at 18 °C. Moreover, the activity of S-PaAD increased by 1.5-fold as compared to that of PaAD. The ligand binding and enzyme activity assays against different substrates demonstrated that it was more active against 1 % glycogen and amylopectin. The analysis of the hydrolysates through HPLC demonstrated that the enzyme activity is mainly amylolytic, producing longer oligosaccharides as the major end product. The secondary structure analyses by temperature ramping in CD spectroscopy and MD simulation demonstrated the enzymes in the free, as well as fusion state, were stable at 90 °C. The soluble production, thermostability and broad substrate specificity make this enzyme a promising choice for various foods, feed, textiles, detergents, pharmaceuticals, and many industrial applications.
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Affiliation(s)
- Mohsin Shad
- School of Biological Sciences, University of the Punjab, Quaid-e-Azam Campus, Lahore, P.O. 54590, Pakistan; Structural Biology, The Rosalind Franklin Institute, Harwell Science & Innovation Campus, Didcot OX11 0QS, United Kingdom
| | - Arshia Nazir
- School of Biological Sciences, University of the Punjab, Quaid-e-Azam Campus, Lahore, P.O. 54590, Pakistan
| | - Muhammad Usman
- Department of Plant Pathology, University of Agriculture, Faisalabad, P.O. 38000, Pakistan
| | - Muhammad Waheed Akhtar
- School of Biological Sciences, University of the Punjab, Quaid-e-Azam Campus, Lahore, P.O. 54590, Pakistan
| | - Muhammad Sajjad
- School of Biological Sciences, University of the Punjab, Quaid-e-Azam Campus, Lahore, P.O. 54590, Pakistan.
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3
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Tripp A, Braun M, Wieser F, Oberdorfer G, Lechner H. Click, Compute, Create: A Review of Web-based Tools for Enzyme Engineering. Chembiochem 2024:e202400092. [PMID: 38634409 DOI: 10.1002/cbic.202400092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 04/14/2024] [Accepted: 04/15/2024] [Indexed: 04/19/2024]
Abstract
Enzyme engineering, though pivotal across various biotechnological domains, is often plagued by its time-consuming and labor-intensive nature. This review aims to offer an overview of supportive in silico methodologies for this demanding endeavor. Starting from methods to predict protein structures, to classification of their activity and even the discovery of new enzymes we continue with describing tools used to increase thermostability and production yields of selected targets. Subsequently, we discuss computational methods to modulate both, the activity as well as selectivity of enzymes. Last, we present recent approaches based on cutting-edge machine learning methods to redesign enzymes. With exception of the last chapter, there is a strong focus on methods easily accessible via web-interfaces or simple Python-scripts, therefore readily useable for a diverse and broad community.
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Affiliation(s)
- Adrian Tripp
- Institute of Biochemistry, Graz University of Technology, Petersgasse 12/2, 8010, Graz, Austria
| | - Markus Braun
- Institute of Biochemistry, Graz University of Technology, Petersgasse 12/2, 8010, Graz, Austria
| | - Florian Wieser
- Institute of Biochemistry, Graz University of Technology, Petersgasse 12/2, 8010, Graz, Austria
| | - Gustav Oberdorfer
- Institute of Biochemistry, Graz University of Technology, Petersgasse 12/2, 8010, Graz, Austria
- BioTechMed, Graz, Austria
| | - Horst Lechner
- Institute of Biochemistry, Graz University of Technology, Petersgasse 12/2, 8010, Graz, Austria
- BioTechMed, Graz, Austria
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Ganji M, Bakhshi S, Ahmadi K, Shoari A, Moeini S, Ghaemi A. Rational design of B-cell and T-cell multi epitope-based vaccine against Zika virus, an in silico study. J Biomol Struct Dyn 2024; 42:3426-3440. [PMID: 37190978 DOI: 10.1080/07391102.2023.2213339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Accepted: 05/06/2023] [Indexed: 05/17/2023]
Abstract
The Zika virus (ZKV) is a single-stranded positive-sense, enveloped RNA virus. Zika infection during pregnancy can cause congenital microcephaly, Guillain-Barré syndrome, miscarriage, and other CNS abnormalities. The world needs safe and effective vaccinations to fight against ZIKV infection since vaccination is generally regarded as one of the most effective ways to prevent infectious diseases. In the present work, we used immunoinformatics and docking studies to construct a vaccine containing multi-epitopes using the structural and non-structural proteins of ZKV. The structural models of ZKV proteins (PrE, PrM, NS1, and NS2A) were constructed using Pyre2 and RaptorX servers. The epitopes of B-cell, T-cell (HTL and CTL), and IFN-γ were predicted, and each epitope's immunogenic nature and physiochemical properties were confirmed. As an adjuvant, the CPG-Oligodeoxynucleotide, an agonist of Toll-like receptor 9 (TLR9), is associated to cytotoxic T-lymphocytes (CTL) epitopes via PAPAP linker. To assess the binding affinity and the tendency of the designed vaccine to induce an immune response through TLR9, molecular docking was done. In the next step, molecular dynamics (MD) simulation to 100 nanoseconds (ns) was used to evaluate the stability of the interaction of the designed vaccine with TLR9. The designed vaccine is predicted to be highly antigenic, non-toxic, soluble, and stable with low flexibility in MD simulation. MD studies indicated that the finalized vaccine-TLR9 docked complex was stable during simulation time. The vaccine construct is able to stimulate both humoral and cellular immune responses. We suppose that our constructed model of the vaccine may have the ability to induce the host immune response against ZKV. Further studies, including in vitro and in vivo experimental analyses, are needed to prove the constructed vaccine's efficacy with multi-epitopes.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Mahmoud Ganji
- Department of Medical Biotechnology, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran
| | - Shohreh Bakhshi
- Faculty of Pharmacy, Tehran University of Medical Sciences, Tehran, Iran
| | - Khadijeh Ahmadi
- Infectious and Tropical Diseases Research Center, Hormozgan Health Institute, Hormozgan University of Medical Sciences, Bandar Abbas, Iran
| | - Alireza Shoari
- Department of Cancer Biology, Mayo Clinic, Jacksonville, Florida, USA
| | - Soheila Moeini
- Department of Surgery, Faculty of Medicine, McGill University, Montréal, Québec, Canada
| | - Amir Ghaemi
- Department of Influenza and Other Respiratory Viruses, Pasteur Institute of Iran, Tehran, Iran
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Banesh S, Patil N, Chethireddy VR, Bhukmaria A, Saudagar P. Design and evaluation of a multiepitope vaccine for pancreatic cancer using immune-dominant epitopes derived from the signature proteome in expression datasets. Med Oncol 2024; 41:90. [PMID: 38522058 DOI: 10.1007/s12032-024-02334-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Accepted: 02/14/2024] [Indexed: 03/25/2024]
Abstract
Pancreatic cancer is a highly aggressive and often lethal malignancy with limited treatment options. Its late-stage diagnosis and resistance to conventional therapies make it a significant challenge in oncology. Immunotherapy, particularly cancer vaccines, has emerged as a promising avenue for treating pancreatic cancer. Multi-epitope vaccines, designed to target multiple epitopes derived from various antigens associated with pancreatic cancer, have gained attention as potential candidates for improving therapeutic outcomes. In this study, we have explored transcriptomics and protein expression databases to identify potential upregulated proteins in pancreatic cancer cells. After examining a total of 21,054 proteins from various databases, it was discovered that 143 proteins expressed differently in malignant and healthy cells. The CTL, HTL and BCE epitopes were predicted for the shortlisted proteins. 51,840 vaccine constructs were created by concatenating CTL, HTL, and B-cell epitopes in the respective sequences. The best 86 structures were selected from a set of 51,840 designs after they were analyzed for vaxijenicity, allergenicity, toxicity, and antigenicity scores. In further simulation of the immune response using constructs, it was found that 41417, 37961, and 40841 constructs could produce a strong immune response when injected. Further, it was found that construct 37961 showed stronger interaction and stability with TLR-9 as determined from the large-scale molecular dynamics simulations. Moreover, the 37961 construct has shown interactions with TLR-9 suggests its potential in inducing immune response. In addition, construct 37961 has shown 100% predicted solubility in the E. coli expression system. Overall, the study indicates the designed construct 37961 has the potential to induce an anti-tumor immune response and long-standing protection pending further studies.
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Affiliation(s)
- Sooram Banesh
- Department of Biotechnology, National Institute of Technology-Warangal, Warangal, Telangana, 506004, India
| | - Nupoor Patil
- Department of Biotechnology, National Institute of Technology-Warangal, Warangal, Telangana, 506004, India
| | - Vihadhar Reddy Chethireddy
- Department of Biotechnology, National Institute of Technology-Warangal, Warangal, Telangana, 506004, India
| | - Arnav Bhukmaria
- Department of Biotechnology, National Institute of Technology-Warangal, Warangal, Telangana, 506004, India
| | - Prakash Saudagar
- Department of Biotechnology, National Institute of Technology-Warangal, Warangal, Telangana, 506004, India.
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Ishaq Z, Zaheer T, Waseem M, Shahwar Awan H, Ullah N, AlAsmari AF, AlAsmari F, Ali A. Immunoinformatics aided designing of a next generation poly-epitope vaccine against uropathogenic Escherichia coli to combat urinary tract infections. J Biomol Struct Dyn 2023:1-21. [PMID: 37811774 DOI: 10.1080/07391102.2023.2266018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2023] [Accepted: 09/27/2023] [Indexed: 10/10/2023]
Abstract
Urinary tract infections (UTIs) are the second most prevalent bacterial infections and uropathogenic Escherichia coli (UPEC) stands among the primary causative agents of UTIs. The usage of antibiotics is the routine therapy being used in various countries to treat UTIs but becoming ineffective because of increasing antibiotic resistance among UPEC strains. Thus, there must be the development of some alternative treatment strategies such as vaccine development against UPEC. In the following study, pan-genomics along with reverse vaccinology approaches is used under the framework of bioinformatics for the identification of core putative vaccine candidates, employing 307 UPEC genomes (complete and draft), available publicly. A total of nine T-cell epitopes (derived from B-cells) of both MHC classes (I and II), were prioritized among three potential protein candidates. These epitopes were then docked together by using linkers (GPGPG and AAY) and an adjuvant (Cholera Toxin B) to form a poly-valent vaccine construct. The chimeric vaccine construct was undergone by molecular modelling, further refinement and energy minimization. We predicted positive results of the vaccine construct in immune simulations with significantly high levels of immune cells. The protein-protein docking analysis of vaccine construct with toll-like receptors predicted efficient binding, which was further validated by molecular dynamics simulation of vaccine construct with TLR-2 and TLR-4 at 120 ns, resulting in stable complexes' conformation throughout the simulation run. Overall, the vaccine construct demonstrated positive antigenic response. In future, this chimeric vaccine construct or the identified epitopes could be experimentally validated for the development of UPEC vaccines against UTIs.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Zaara Ishaq
- Department of Industrial Biotechnology, Atta-ur-Rahman School of Applied Biosciences (ASAB), National University of Sciences and Technology (NUST), Islamabad, Pakistan
| | - Tahreem Zaheer
- Department of Industrial Biotechnology, Atta-ur-Rahman School of Applied Biosciences (ASAB), National University of Sciences and Technology (NUST), Islamabad, Pakistan
- Department of Biology, Indiana University Bloomington, Bloomington, Indiana, USA
| | - Maaz Waseem
- Department of Industrial Biotechnology, Atta-ur-Rahman School of Applied Biosciences (ASAB), National University of Sciences and Technology (NUST), Islamabad, Pakistan
| | - Hayeqa Shahwar Awan
- Department of Industrial Biotechnology, Atta-ur-Rahman School of Applied Biosciences (ASAB), National University of Sciences and Technology (NUST), Islamabad, Pakistan
- Shifa International Hospitals Ltd, Islamabad, Pakistan
| | - Nimat Ullah
- Department of Industrial Biotechnology, Atta-ur-Rahman School of Applied Biosciences (ASAB), National University of Sciences and Technology (NUST), Islamabad, Pakistan
- NYU Langone Health, New York, United States
| | - Abdullah F AlAsmari
- Department of Pharmacology and Toxicology, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia
| | - Fawaz AlAsmari
- Department of Pharmacology and Toxicology, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia
| | - Amjad Ali
- Department of Industrial Biotechnology, Atta-ur-Rahman School of Applied Biosciences (ASAB), National University of Sciences and Technology (NUST), Islamabad, Pakistan
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Mashhadi Abolghasem Shirazi M, Sadat SM, Haghighat S, Roohvand F, Arashkia A. Alum and a TLR7 agonist combined with built-in TLR4 and 5 agonists synergistically enhance immune responses against HPV RG1 epitope. Sci Rep 2023; 13:16801. [PMID: 37798448 PMCID: PMC10556035 DOI: 10.1038/s41598-023-43965-3] [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: 11/16/2022] [Accepted: 09/30/2023] [Indexed: 10/07/2023] Open
Abstract
To relieve the limitations of the human papillomavirus (HPV) vaccines based on L1 capsid protein, vaccine formulations based on RG1 epitope of HPV L2 using various built-in adjuvants are under study. Herein, we describe design and construction of a rejoined peptide (RP) harboring HPV16 RG1 epitope fused to TLR4/5 agonists and a tetanus toxoid epitope, which were linked by the (GGGS)3 linker in tandem. In silico analyses indicated the proper physicochemical, immunogenic and safety profile of the RP. Docking analyses on predicted 3D model suggested the effective interaction of TLR4/5 agonists within RP with their corresponding TLRs. Expressing the 1206 bp RP-coding DNA in E. coli produced a 46 kDa protein, and immunization of mice by natively-purified RP in different adjuvant formulations indicated the crucial role of the built-in adjuvants for induction of anti-RG1 responses that could be further enhanced by combination of TLR7 agonist/alum adjuvants. While the TLR4/5 agonists contributed in the elicitation of the Th2-polarized immune responses, combination with TLR7 agonist changed the polarization to the balanced Th1/Th2 immune responses. Indeed, RP + TLR7 agonist/alum adjuvants induced the strongest immune responses that could efficiently neutralize the HPV pseudoviruses, and thus might be a promising formulation for an inexpensive and cross-reactive HPV vaccine.
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Affiliation(s)
| | - Seyed Mehdi Sadat
- Department of Hepatitis, AIDS and Blood borne Diseases, Pasteur Institute of Iran, Tehran, Iran
| | - Setareh Haghighat
- Department of Microbiology, Faculty of Advanced Sciences and Technology, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
| | - Farzin Roohvand
- Department of Molecular Virology, Pasteur Institute of Iran, No. 69, Pasteur Ave, Tehran, Iran.
| | - Arash Arashkia
- Department of Molecular Virology, Pasteur Institute of Iran, No. 69, Pasteur Ave, Tehran, Iran.
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Biala G, Kedzierska E, Kruk-Slomka M, Orzelska-Gorka J, Hmaidan S, Skrok A, Kaminski J, Havrankova E, Nadaska D, Malik I. Research in the Field of Drug Design and Development. Pharmaceuticals (Basel) 2023; 16:1283. [PMID: 37765091 PMCID: PMC10536713 DOI: 10.3390/ph16091283] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Revised: 08/28/2023] [Accepted: 09/07/2023] [Indexed: 09/29/2023] Open
Abstract
The processes used by academic and industrial scientists to discover new drugs have recently experienced a true renaissance, with many new and exciting techniques being developed over the past 5-10 years alone. Drug design and discovery, and the search for new safe and well-tolerated compounds, as well as the ineffectiveness of existing therapies, and society's insufficient knowledge concerning the prophylactics and pharmacotherapy of the most common diseases today, comprise a serious challenge. This can influence not only the quality of human life, but also the health of whole societies, which became evident during the COVID-19 pandemic. In general, the process of drug development consists of three main stages: drug discovery, preclinical development using cell-based and animal models/tests, clinical trials on humans and, finally, forward moving toward the step of obtaining regulatory approval, in order to market the potential drug. In this review, we will attempt to outline the first three most important consecutive phases in drug design and development, based on the experience of three cooperating and complementary academic centers of the Visegrád group; i.e., Medical University of Lublin, Poland, Masaryk University of Brno, Czech Republic, and Comenius University Bratislava, Slovak Republic.
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Affiliation(s)
- Grazyna Biala
- Chair and Department of Pharmacology with Pharmacodynamics, Medical University of Lublin, Chodźki 4A, 20-093 Lublin, Poland; (E.K.); (M.K.-S.); (J.O.-G.)
| | - Ewa Kedzierska
- Chair and Department of Pharmacology with Pharmacodynamics, Medical University of Lublin, Chodźki 4A, 20-093 Lublin, Poland; (E.K.); (M.K.-S.); (J.O.-G.)
| | - Marta Kruk-Slomka
- Chair and Department of Pharmacology with Pharmacodynamics, Medical University of Lublin, Chodźki 4A, 20-093 Lublin, Poland; (E.K.); (M.K.-S.); (J.O.-G.)
| | - Jolanta Orzelska-Gorka
- Chair and Department of Pharmacology with Pharmacodynamics, Medical University of Lublin, Chodźki 4A, 20-093 Lublin, Poland; (E.K.); (M.K.-S.); (J.O.-G.)
| | - Sara Hmaidan
- Chair and Department of Pharmacology with Pharmacodynamics, Medical University of Lublin, Chodźki 4A, 20-093 Lublin, Poland; (E.K.); (M.K.-S.); (J.O.-G.)
| | - Aleksandra Skrok
- Chair and Department of Pharmacology with Pharmacodynamics, Medical University of Lublin, Chodźki 4A, 20-093 Lublin, Poland; (E.K.); (M.K.-S.); (J.O.-G.)
| | - Jakub Kaminski
- Chair and Department of Pharmacology with Pharmacodynamics, Medical University of Lublin, Chodźki 4A, 20-093 Lublin, Poland; (E.K.); (M.K.-S.); (J.O.-G.)
| | - Eva Havrankova
- Department of Chemical Drugs, Faculty of Pharmacy, Masaryk University of Brno, 601 77 Brno, Czech Republic;
| | - Dominika Nadaska
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Comenius University Bratislava, 832 32 Bratislava, Slovakia (I.M.)
| | - Ivan Malik
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Comenius University Bratislava, 832 32 Bratislava, Slovakia (I.M.)
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Barman P, Joshi S, Sharma S, Preet S, Sharma S, Saini A. Strategic Approaches to Improvise Peptide Drugs as Next Generation Therapeutics. Int J Pept Res Ther 2023; 29:61. [PMID: 37251528 PMCID: PMC10206374 DOI: 10.1007/s10989-023-10524-3] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/20/2023] [Indexed: 05/31/2023]
Abstract
In recent years, the occurrence of a wide variety of drug-resistant diseases has led to an increase in interest in alternate therapies. Peptide-based drugs as an alternate therapy hold researchers' attention in various therapeutic fields such as neurology, dermatology, oncology, metabolic diseases, etc. Previously, they had been overlooked by pharmaceutical companies due to certain limitations such as proteolytic degradation, poor membrane permeability, low oral bioavailability, shorter half-life, and poor target specificity. Over the last two decades, these limitations have been countered by introducing various modification strategies such as backbone and side-chain modifications, amino acid substitution, etc. which improve their functionality. This has led to a substantial interest of researchers and pharmaceutical companies, moving the next generation of these therapeutics from fundamental research to the market. Various chemical and computational approaches are aiding the production of more stable and long-lasting peptides guiding the formulation of novel and advanced therapeutic agents. However, there is not a single article that talks about various peptide design approaches i.e., in-silico and in-vitro along with their applications and strategies to improve their efficacy. In this review, we try to bring different aspects of peptide-based therapeutics under one article with a clear focus to cover the missing links in the literature. This review draws emphasis on various in-silico approaches and modification-based peptide design strategies. It also highlights the recent progress made in peptide delivery methods important for their enhanced clinical efficacy. The article would provide a bird's-eye view to researchers aiming to develop peptides with therapeutic applications. Graphical Abstract
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Affiliation(s)
- Panchali Barman
- Institute of Forensic Science and Criminology (UIEAST), Panjab University, Sector 14, Chandigarh, 160014 India
| | - Shubhi Joshi
- Energy Research Centre, Panjab University, Sector 14, Chandigarh, 160014 India
| | - Sheetal Sharma
- Department of Biophysics, Panjab University, Sector 25, Chandigarh, U.T 160014 India
| | - Simran Preet
- Department of Biophysics, Panjab University, Sector 25, Chandigarh, U.T 160014 India
| | - Shweta Sharma
- Institute of Forensic Science and Criminology (UIEAST), Panjab University, Sector 14, Chandigarh, 160014 India
| | - Avneet Saini
- Department of Biophysics, Panjab University, Sector 25, Chandigarh, U.T 160014 India
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10
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Wang C, Zou Q. Prediction of protein solubility based on sequence physicochemical patterns and distributed representation information with DeepSoluE. BMC Biol 2023; 21:12. [PMID: 36694239 PMCID: PMC9875434 DOI: 10.1186/s12915-023-01510-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 01/05/2023] [Indexed: 01/25/2023] Open
Abstract
BACKGROUND Protein solubility is a precondition for efficient heterologous protein expression at the basis of most industrial applications and for functional interpretation in basic research. However, recurrent formation of inclusion bodies is still an inevitable roadblock in protein science and industry, where only nearly a quarter of proteins can be successfully expressed in soluble form. Despite numerous solubility prediction models having been developed over time, their performance remains unsatisfactory in the context of the current strong increase in available protein sequences. Hence, it is imperative to develop novel and highly accurate predictors that enable the prioritization of highly soluble proteins to reduce the cost of actual experimental work. RESULTS In this study, we developed a novel tool, DeepSoluE, which predicts protein solubility using a long-short-term memory (LSTM) network with hybrid features composed of physicochemical patterns and distributed representation of amino acids. Comparison results showed that the proposed model achieved more accurate and balanced performance than existing tools. Furthermore, we explored specific features that have a dominant impact on the model performance as well as their interaction effects. CONCLUSIONS DeepSoluE is suitable for the prediction of protein solubility in E. coli; it serves as a bioinformatics tool for prescreening of potentially soluble targets to reduce the cost of wet-experimental studies. The publicly available webserver is freely accessible at http://lab.malab.cn/~wangchao/softs/DeepSoluE/ .
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Affiliation(s)
- Chao Wang
- grid.411307.00000 0004 1790 5236School of Software Engineering, Chengdu University of Information Technology, Chengdu, China
| | - Quan Zou
- grid.54549.390000 0004 0369 4060Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
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Jade D, Gupta S, Mohan S, Ponnambalam S, Harrison M, Bhatnagar R. Homology modelling and molecular simulation approach to prediction of B-cell and T-cell epitopes in an OMP25 peptide vaccine against Brucella abortus. MOLECULAR SIMULATION 2023. [DOI: 10.1080/08927022.2023.2165126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Affiliation(s)
- Dhananjay Jade
- Laboratory of Molecular Biology and Genetic Engineering, School of Biotechnology, JNU, New Delhi India
- School of Biomedical Sciences, University of Leeds School of Molecular and Cellular Biology, Leeds, UK
- School of Molecular & Cellular Biology, University of Leeds, Leeds, UK
| | - Sonal Gupta
- Laboratory of Molecular Biology and Genetic Engineering, School of Biotechnology, JNU, New Delhi India
- Department of Bacteriology, University of Wisconsin–Madison, Madison, WI, USA
| | - Surender Mohan
- Laboratory of Molecular Biology and Genetic Engineering, School of Biotechnology, JNU, New Delhi India
| | | | - Michael Harrison
- School of Biomedical Sciences, University of Leeds School of Molecular and Cellular Biology, Leeds, UK
| | - Rakesh Bhatnagar
- Laboratory of Molecular Biology and Genetic Engineering, School of Biotechnology, JNU, New Delhi India
- Banaras Hindu University, Banaras, India
- Amity University Jaipur, Jaipur, India
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12
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Sharma L, Bisht GS. Short Antimicrobial Peptides: Therapeutic Potential and Recent Advancements. Curr Pharm Des 2023; 29:3005-3017. [PMID: 38018196 DOI: 10.2174/0113816128248959231102114334] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 09/28/2023] [Accepted: 10/11/2023] [Indexed: 11/30/2023]
Abstract
There has been a lot of interest in antimicrobial peptides (AMPs) as potential next-generation antibiotics. They are components of the innate immune system. AMPs have broad-spectrum action and are less prone to resistance development. They show potential applications in various fields, including medicine, agriculture, and the food industry. However, despite the good activity and safety profiles, AMPs have had difficulty finding success in the clinic due to their various limitations, such as production cost, proteolytic susceptibility, and oral bioavailability. To overcome these flaws, a number of solutions have been devised, one of which is developing short antimicrobial peptides. Short antimicrobial peptides do have an advantage over longer peptides as they are more stable and do not collapse during absorption. They have generated a lot of interest because of their evolutionary success and advantageous properties, such as low molecular weight, selective targets, cell or organelles with minimal toxicity, and enormous therapeutic potential. This article provides an overview of the development of short antimicrobial peptides with an emphasis on those with ≤ 30 amino acid residues as a potential therapeutic agent to fight drug-resistant microorganisms. It also emphasizes their applications in many fields and discusses their current state in clinical trials.
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Affiliation(s)
- Lalita Sharma
- Department of Biotechnology and Bioinformatics, Jaypee University of Information Technology, Waknaghat, Himachal Pradesh, India
| | - Gopal Singh Bisht
- Department of Biotechnology and Bioinformatics, Jaypee University of Information Technology, Waknaghat, Himachal Pradesh, India
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13
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Hashemzaei M, Nezafat N, Ghoshoon MB, Negahdaripour M. In-silico selection of appropriate signal peptides for romiplostim secretory production in Escherichia coli. INFORMATICS IN MEDICINE UNLOCKED 2022. [DOI: 10.1016/j.imu.2022.101146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
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14
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Current insights into protein solubility: A review of its importance for alternative proteins. Food Hydrocoll 2022. [DOI: 10.1016/j.foodhyd.2022.108416] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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15
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Rahmatabadi SS, Mobini K, Askari S, Najafian J, Karami K, Soleymani B, Mostafaie A. In silico characterization of fructosyl peptide oxidase properties from Eupenicillium terrenum. J Mol Recognit 2022; 35:e2980. [PMID: 35657361 DOI: 10.1002/jmr.2980] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Revised: 05/23/2022] [Accepted: 06/01/2022] [Indexed: 12/24/2022]
Abstract
Fructosyl peptide oxidase (FPOX) enzyme from Eupenicillium terrenum has a high potential to be applied as a diagnostic enzyme. The aim of the present study is the characterization of FPOX from E. terrenum using different bioinformatics tools. The computational prediction of the RNA and protein secondary structures of FPOX, solubility profile in Escherichia coli, stability, domains, and functional properties were performed. In the FPOX protein, six motifs were detected. The d-amino acid oxidase motif was found as the most important motif that is a FAD-dependent oxidoreductase. The cysteines including 97, 154, 234, 280, and 360 showed a lower score than -10 that have a low possibility for participitation in the formation of the SS bond. The 56.52% of FPOX amino acids are nonpolar. Random coils are dominant in the FPOX sequence, followed by alpha-helix and extended strand. The fpox gene is capable of generating a stable RNA secondary structure (-423.90 kcal/mol) in E. coli. FPOX has a large number of hydrophobic amino acids. FPOX showed a low solubility in E. coli which has several aggregation-prone sites in its 3-D structure. According to the scores, the best mutation candidate for increasing solubility was the conversion of methionine 302 to arginine. The melting temperature of FPOX based on its amino acid sequence was 55°C to 65°C. The amounts of thermodynamic parameters for the FPOX enzyme were -137.4 kcal/mol, -3.59 kcal/(mol K), and -6.8 kcal/mol for standard folding enthalpy, heat capacity, and folding free energy, respectively. In conclusion, the in silico study of proteins can provide a valuable method for better understanding the protein properties and functions for use in our purposes.
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Affiliation(s)
| | - Keivan Mobini
- Department of Hematology, Faculty of Allied Medical Science, Bushehr University of Medical Sciences, Bushehr, Iran
| | - Soudabeh Askari
- Department Biotechnolgy, Applied Razi Biotechnology, Kermanshah, Iran
| | - Javad Najafian
- Department of Biology, Faculty of Basic Science, University of Mazandaran, Baboulsar, Iran
| | - Keyvan Karami
- Medical Biology Research Center, Health Technology Institute, Kermanshah University of Medical Sciences, Kermanshah, Iran
| | - Bijan Soleymani
- Medical Biology Research Center, Health Technology Institute, Kermanshah University of Medical Sciences, Kermanshah, Iran
| | - Ali Mostafaie
- Medical Biology Research Center, Health Technology Institute, Kermanshah University of Medical Sciences, Kermanshah, Iran
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16
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Qing R, Hao S, Smorodina E, Jin D, Zalevsky A, Zhang S. Protein Design: From the Aspect of Water Solubility and Stability. Chem Rev 2022; 122:14085-14179. [PMID: 35921495 PMCID: PMC9523718 DOI: 10.1021/acs.chemrev.1c00757] [Citation(s) in RCA: 54] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Indexed: 12/13/2022]
Abstract
Water solubility and structural stability are key merits for proteins defined by the primary sequence and 3D-conformation. Their manipulation represents important aspects of the protein design field that relies on the accurate placement of amino acids and molecular interactions, guided by underlying physiochemical principles. Emulated designer proteins with well-defined properties both fuel the knowledge-base for more precise computational design models and are used in various biomedical and nanotechnological applications. The continuous developments in protein science, increasing computing power, new algorithms, and characterization techniques provide sophisticated toolkits for solubility design beyond guess work. In this review, we summarize recent advances in the protein design field with respect to water solubility and structural stability. After introducing fundamental design rules, we discuss the transmembrane protein solubilization and de novo transmembrane protein design. Traditional strategies to enhance protein solubility and structural stability are introduced. The designs of stable protein complexes and high-order assemblies are covered. Computational methodologies behind these endeavors, including structure prediction programs, machine learning algorithms, and specialty software dedicated to the evaluation of protein solubility and aggregation, are discussed. The findings and opportunities for Cryo-EM are presented. This review provides an overview of significant progress and prospects in accurate protein design for solubility and stability.
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Affiliation(s)
- Rui Qing
- State
Key Laboratory of Microbial Metabolism, School of Life Sciences and
Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
- Media
Lab, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
- The
David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Shilei Hao
- Media
Lab, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
- Key
Laboratory of Biorheological Science and Technology, Ministry of Education, College of Bioengineering, Chongqing University, Chongqing 400030, China
| | - Eva Smorodina
- Department
of Immunology, University of Oslo and Oslo
University Hospital, Oslo 0424, Norway
| | - David Jin
- Avalon GloboCare
Corp., Freehold, New Jersey 07728, United States
| | - Arthur Zalevsky
- Laboratory
of Bioinformatics Approaches in Combinatorial Chemistry and Biology, Shemyakin−Ovchinnikov Institute of Bioorganic
Chemistry RAS, Moscow 117997, Russia
| | - Shuguang Zhang
- Media
Lab, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
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17
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Wang H, Kwong CF, Liu Q, Liu Z, Chen Z. A Novel Artificial Intelligence System in Formulation Dissolution Prediction. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:8640115. [PMID: 35978897 PMCID: PMC9377879 DOI: 10.1155/2022/8640115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/01/2022] [Revised: 06/20/2022] [Accepted: 06/24/2022] [Indexed: 11/29/2022]
Abstract
Artificial neural network (ANN) techniques are widely used to screen the data and predict the experimental result in pharmaceutical studies. In this study, a novel dissolution result prediction and screen system with a backpropagation network and regression methods was modeled. For this purpose, 21 groups of dissolution data were used to train and verify the ANN model. Based on the design of input data, the related data were still available to train the ANN model when the formulation composition was changed. Two regression methods, the effective data regression method (EDRM) and the reference line regression method (RLRM), make this system predict dissolution results with a high accuracy rate but use less database than the orthogonal experiment. Based on the decision tree, a data screen function is also realized in this system. This ANN model provides a novel drug prediction system with a decrease in time and cost and also easily facilitates the design of new formulation.
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Affiliation(s)
- Haoyu Wang
- Department of Electrical and Electronic Engineering, University of Nottingham Ningbo China, Ningbo, China
| | - Chiew Foong Kwong
- Department of Electrical and Electronic Engineering, University of Nottingham Ningbo China, Ningbo, China
| | - Qianyu Liu
- International Doctoral Innovation Centre, NingboTech University, Ningbo, China
| | - Zhixin Liu
- Department of Outpatient, Liaoning Thrombus Treatment Center of Integrated Chinese and Western Medicine, Shenyang, China
| | - Zhiyuan Chen
- Department of Mechanical, Materials and Manufacture, University of Nottingham Ningbo China, Ningbo, China
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18
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Karaiyan P, Chang CCH, Chan ES, Tey BT, Ramanan RN, Ooi CW. In silico screening and heterologous expression of soluble dimethyl sulfide monooxygenases of microbial origin in Escherichia coli. Appl Microbiol Biotechnol 2022; 106:4523-4537. [PMID: 35713659 PMCID: PMC9259527 DOI: 10.1007/s00253-022-12008-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2022] [Revised: 05/30/2022] [Accepted: 06/01/2022] [Indexed: 11/28/2022]
Abstract
Abstract Sequence-based screening has been widely applied in the discovery of novel microbial enzymes. However, majority of the sequences in the genomic databases were annotated using computational approaches and lacks experimental characterization. Hence, the success in obtaining the functional biocatalysts with improved characteristics requires an efficient screening method that considers a wide array of factors. Recombinant expression of microbial enzymes is often hampered by the undesirable formation of inclusion body. Here, we present a systematic in silico screening method to identify the proteins expressible in soluble form and with the desired biological properties. The screening approach was adopted in the recombinant expression of dimethyl sulfide (DMS) monooxygenase in Escherichia coli. DMS monooxygenase, a two-component enzyme consisting of DmoA and DmoB subunits, was used as a model protein. The success rate of producing soluble and active DmoA is 71% (5 out of 7 genes). Interestingly, the soluble recombinant DmoA enzymes exhibited the NADH:FMN oxidoreductase activity in the absence of DmoB (second subunit), and the cofactor FMN, suggesting that DmoA is also an oxidoreductase. DmoA originated from Janthinobacterium sp. AD80 showed the maximum NADH oxidation activity (maximum reaction rate: 6.6 µM/min; specific activity: 133 µM/min/mg). This novel finding may allow DmoA to be used as an oxidoreductase biocatalyst for various industrial applications. The in silico gene screening methodology established from this study can increase the success rate of producing soluble and functional enzymes while avoiding the laborious trial and error involved in the screening of a large pool of genes available. Key points • A systematic gene screening method was demonstrated. • DmoA is also an oxidoreductase capable of oxidizing NADH and reducing FMN. • DmoA oxidizes NADH in the absence of external FMN. Supplementary Information The online version contains supplementary material available at 10.1007/s00253-022-12008-8.
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Affiliation(s)
- Prasanth Karaiyan
- Chemical Engineering Discipline, School of Engineering, Monash University Malaysia, Jalan Lagoon Selatan, 47500, Bandar Sunway, Selangor, Malaysia
| | - Catherine Ching Han Chang
- Arkema Thiochemicals Sdn. Bhd., Jalan PJU 1A/7A OASIS Ara Damansara, 47301, Petaling Jaya, Selangor Darul Ehsan, Malaysia
| | - Eng-Seng Chan
- Chemical Engineering Discipline, School of Engineering, Monash University Malaysia, Jalan Lagoon Selatan, 47500, Bandar Sunway, Selangor, Malaysia
| | - Beng Ti Tey
- Chemical Engineering Discipline, School of Engineering, Monash University Malaysia, Jalan Lagoon Selatan, 47500, Bandar Sunway, Selangor, Malaysia.,Advanced Engineering Platform, Monash University Malaysia, Jalan Lagoon Selatan, 47500, Bandar Sunway, Selangor, Malaysia
| | - Ramakrishnan Nagasundara Ramanan
- Chemical Engineering Discipline, School of Engineering, Monash University Malaysia, Jalan Lagoon Selatan, 47500, Bandar Sunway, Selangor, Malaysia. .,Arkema Thiochemicals Sdn. Bhd., Jalan PJU 1A/7A OASIS Ara Damansara, 47301, Petaling Jaya, Selangor Darul Ehsan, Malaysia.
| | - Chien Wei Ooi
- Chemical Engineering Discipline, School of Engineering, Monash University Malaysia, Jalan Lagoon Selatan, 47500, Bandar Sunway, Selangor, Malaysia. .,Advanced Engineering Platform, Monash University Malaysia, Jalan Lagoon Selatan, 47500, Bandar Sunway, Selangor, Malaysia.
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19
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Packiam KAR, Ooi CW, Li F, Mei S, Tey BT, Fang Ong H, Song J, Ramanan RN. PERISCOPE-Opt: Machine learning-based prediction of optimal fermentation conditions and yields of recombinant periplasmic protein expressed in Escherichia coli. Comput Struct Biotechnol J 2022; 20:2909-2920. [PMID: 35765650 PMCID: PMC9201004 DOI: 10.1016/j.csbj.2022.06.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2022] [Revised: 06/01/2022] [Accepted: 06/01/2022] [Indexed: 11/26/2022] Open
Abstract
The ensemble model considered both fermentation conditions and protein properties. Optimal fermentation conditions and periplasmic recombinant protein yield can be predicted. Predictor’s accuracy and Pearson correlation coefficient are 75% and 0.91, respectively.
Optimization of the fermentation process for recombinant protein production (RPP) is often resource-intensive. Machine learning (ML) approaches are helpful in minimizing the experimentations and find vast applications in RPP. However, these ML-based tools primarily focus on features with respect to amino-acid-sequence, ruling out the influence of fermentation process conditions. The present study combines the features derived from fermentation process conditions with that from amino acid-sequence to construct an ML-based model that predicts the maximal protein yields and the corresponding fermentation conditions for the expression of target recombinant protein in the Escherichia coli periplasm. Two sets of XGBoost classifiers were employed in the first stage to classify the expression levels of the target protein as high (>50 mg/L), medium (between 0.5 and 50 mg/L), or low (<0.5 mg/L). The second-stage framework consisted of three regression models involving support vector machines and random forest to predict the expression yields corresponding to each expression-level-class. Independent tests showed that the predictor achieved an overall average accuracy of 75% and a Pearson coefficient correlation of 0.91 for the correctly classified instances. Therefore, our model offers a reliable substitution of numerous trial-and-error experiments to identify the optimal fermentation conditions and yield for RPP. It is also implemented as an open-access webserver, PERISCOPE-Opt (http://periscope-opt.erc.monash.edu).
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20
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Martiny HM, Armenteros JJA, Johansen AR, Salomon J, Nielsen H. Deep protein representations enable recombinant protein expression prediction. Comput Biol Chem 2021; 95:107596. [PMID: 34775287 DOI: 10.1016/j.compbiolchem.2021.107596] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Revised: 10/21/2021] [Accepted: 10/21/2021] [Indexed: 11/19/2022]
Abstract
A crucial process in the production of industrial enzymes is recombinant gene expression, which aims to induce enzyme overexpression of the genes in a host microbe. Current approaches for securing overexpression rely on molecular tools such as adjusting the recombinant expression vector, adjusting cultivation conditions, or performing codon optimizations. However, such strategies are time-consuming, and an alternative strategy would be to select genes for better compatibility with the recombinant host. Several methods for predicting soluble expression are available; however, they are all optimized for the expression host Escherichia coli and do not consider the possibility of an expressed protein not being soluble. We show that these tools are not suited for predicting expression potential in the industrially important host Bacillus subtilis. Instead, we build a B. subtilis-specific machine learning model for expressibility prediction. Given millions of unlabelled proteins and a small labeled dataset, we can successfully train such a predictive model. The unlabeled proteins provide a performance boost relative to using amino acid frequencies of the labeled proteins as input. On average, we obtain a modest performance of 0.64 area-under-the-curve (AUC) and 0.2 Matthews correlation coefficient (MCC). However, we find that this is sufficient for the prioritization of expression candidates for high-throughput studies. Moreover, the predicted class probabilities are correlated with expression levels. A number of features related to protein expression, including base frequencies and solubility, are captured by the model.
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Affiliation(s)
- Hannah-Marie Martiny
- Research Group for Genomic Epidemiology, National Food Institute, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark.
| | - Jose Juan Almagro Armenteros
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen, Denmark
| | | | - Jesper Salomon
- Enzyme Research, Novozymes A/S, Krogshøjvej 36, 2880 Bagsværd, Denmark
| | - Henrik Nielsen
- Department of Health Technology, Section for Bioinformatics, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
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21
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Beiranvand S, Doosti A, Mirzaei SA. Putative novel B-cell vaccine candidates identified by reverse vaccinology and genomics approaches to control Acinetobacter baumannii serotypes. INFECTION GENETICS AND EVOLUTION 2021; 96:105138. [PMID: 34793968 DOI: 10.1016/j.meegid.2021.105138] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Revised: 10/12/2021] [Accepted: 11/09/2021] [Indexed: 11/26/2022]
Abstract
In the last decade, Multi-drug resistance (MDR)-associated infections of Acinetobacter baumannii have grown worldwide. A cost-effective preventative strategy against this bacterium is vaccination. This study has presented five novel vaccine candidates against A. baumannii produced using the reverse vaccinology method. BLASTn was done to identify the most conserved antigens. PSORTb 3.0.2 was run to predict the subcellular localization of the proteins. The initial screening and antigenicity evaluation were performed using Vaxign. The ccSOL omics was also employed to predict protein solubility. The cross-membrane localization of the protein was predicted using PRED-TMBB. B cell epitope prediction was made for immunogenicity using the IEDB and BepiPred-2.0 database. Eventually, BLASTp was done to verify the extent of similarity to the human proteome to exclude the possibility of autoimmunity. Proteins failing to comply with the set parameters were filtered at each step. In silico, potential vaccines against 21 A. baumannii strains were identified using reverse vaccinology and subtractive genomic techniques. Based on the above criteria, out of the initial 15 A. baumannii proteins selected for screening, nine exposed/secreted/membrane proteins, i.e., Pfsr, LptE, OmpH, CarO, CsuB, CdiB, MlaA, FhuE, and were the most promising candidates. Their solubility and antigenicity were also examined and found to be more than 0.45 and 0.6, respectively. Based on the results, LptE was selected with the highest average antigenic score of 1.043 as the best protein, followed by FimF and Pfsr with scores of 1.022 and 1.014, respectively. In the end, five proteins were verified as promising candidates. Overall, the targets identified herein may be utilized in future strategies to control A. baumannii worldwide.
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Affiliation(s)
- Sheida Beiranvand
- Department of Biology, Faculty of Basic Sciences, Shahrekord Branch, Islamic Azad University, Shahrekord, Iran
| | - Abbas Doosti
- Biotechnology Research Center, Shahrekord Branch, Islamic Azad University, Shahrekord, Iran.
| | - Seyed Abbas Mirzaei
- Department of Biology, Faculty of Basic Sciences, Shahrekord Branch, Islamic Azad University, Shahrekord, Iran; Cellular and Molecular Research Center, Basic Health Sciences Institute, Shahrekord University of Medical Sciences, Shahrekord, Iran
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22
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Batool S, Bin-T-Abid D, Batool H, Shahid S, Saleem M, Khan AU, Hamid A, Mahmood MS, Ashraf NM. Development of multi-epitope vaccine constructs for non-small cell lung cancer (NSCLC) against USA human leukocyte antigen background: an immunoinformatic approach toward future vaccine designing. Expert Opin Biol Ther 2021; 21:1525-1533. [PMID: 34547976 DOI: 10.1080/14712598.2021.1981285] [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: 10/20/2022]
Abstract
OBJECTIVES The design of peptide-based vaccines for cancer is a promising immunotherapy that can induce a cancer-specific cytotoxic response in tumor cells. METHODS Herein, we used the immunoinformatic approach in designing a multi-epitope vaccine targeting G-protein coupled receptor 87 (GPCR-87), cystine/glutamate transporter (SLC7A11), Immunoglobulin binding protein 1 (IGBP1), and thioredoxin domain-containing protein 5 (TXNDC5), which can potentially contribute to NSCLC. The MHC-I and MHC-II epitopes selected for the fusion construct were evaluated for their antigenic and non-allergenic natures via VaxiJen and AllerTop. RESULTS A total of five epitopes, four class-I (FIFYLKNIV, CRYTSVLFY, RYLKVVKPF, and RQAKIQRYK), and one class-II (NQVRGYPTLLWFRDG), having combined USA population coverage of 100%, were used to make ten possible multi-epitope fusion constructs. In these constructs, PADRE, a universal T-helper epitope, and RSO9, a TLR4 agonist, were fused as adjuvants. The molecular docking analysis revealed that two constructs were showing significant binding affinities toward HLA-A*02:01, the most prevalent HLA allele in USA. Moreover, MD simulations marked one construct as a promising therapeutic candidate. CONCLUSION The multi-epitope vaccine constructs designed using immunogenic, and non-allergenic peptides of NSCLS tumor-associated proteins are likely to pose significant therapeutic efficacies in cancer immunotherapy due to their high binding affinities toward HLA molecules.
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Affiliation(s)
- Sana Batool
- School of Biological Sciences, University of the Punjab, Lahore, Pakistan.,School of Biochemistry and Biotechnology, University of the Punjab, Lahore, Pakistan
| | - Duaa Bin-T-Abid
- School of Biochemistry and Biotechnology, University of the Punjab, Lahore, Pakistan
| | - Hina Batool
- Department of Life Science, School of Science, University of Management Technology, Lahore, Pakistan
| | - Saher Shahid
- School of Biological Sciences, University of the Punjab, Lahore, Pakistan
| | - Mahjabeen Saleem
- School of Biochemistry and Biotechnology, University of the Punjab, Lahore, Pakistan
| | - Azmat Ullah Khan
- Department of Biochemistry and Biotechnology, University of Gujrat, Gujrat Pakistan
| | | | - Malik Siddique Mahmood
- School of Biochemistry and Biotechnology, University of the Punjab, Lahore, Pakistan.,Department of Biochemistry, Nur International University, Lahore, Pakistan
| | - Naeem Mahmood Ashraf
- Department of Biochemistry and Biotechnology, University of Gujrat, Gujrat Pakistan
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23
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Akhtar N, Joshi A, Singh J, Kaushik V. Design of a novel and potent multivalent epitope based human cytomegalovirus peptide vaccine: An immunoinformatics approach. J Mol Liq 2021. [DOI: 10.1016/j.molliq.2021.116586] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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24
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Prediction of Protein Solubility Based on Sequence Feature Fusion and DDcCNN. Interdiscip Sci 2021; 13:703-716. [PMID: 34236625 DOI: 10.1007/s12539-021-00456-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2021] [Revised: 06/21/2021] [Accepted: 06/23/2021] [Indexed: 10/20/2022]
Abstract
BACKGROUND Prediction of protein solubility is an indispensable prerequisite for pharmaceutical research and production. The general and specific objective of this work is to design a new model for predicting protein solubility by using protein sequence feature fusion and deep dual-channel convolutional neural networks (DDcCNN) to improve the performance of existing prediction models. METHODS The redundancy of raw protein is reduced by CD-HIT. The four subsequences are built from protein sequence: one global and three locals. The global subsequence is the entire protein sequence, and these local subsequences are obtained by moving a sliding window with some rules. Using G-gap to extract the features of the above four subsequences, a mixed matrix is constructed as the input of one channel which is composed of three-layer convolutional operating. Additional features are extracted by SCRATCH tool as input of another channel, which is consist of a single convolution in order to find hidden relationships and improve the accuracy of predictor. The outputs of two parallel channels are concatenated as the input of the hidden layer. And the prediction of protein solubility is obtained in the output layer. The best protein solubility prediction model is obtained by doing some comparative experiments of different frameworks. RESULTS The performance indicators of DDcCNN model (our designed) are as follows: accuracy of 77.82%, Matthew's correlation coefficient of 0.57, sensitivity of 76.13% and specificity of 79.32%. The results of some comparative experiments show that the overall performance of DDcCNN model is better than existing models (GCNN, LCNN and PCNN). The related models and data are publicly deposited at http://www.ddccnn.wang . CONCLUSION The satisfactory performance of DDcCNN model reveals that these features and flexible computational methodologies can reinforce the existing prediction models for better prediction of protein solubility could be applied in several applications, such as to preselect initial targets that are soluble or to alter solubility of target proteins, thus can help to reduce the production cost.
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25
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Bhandari BK, Lim CS, Gardner PP. TISIGNER.com: web services for improving recombinant protein production. Nucleic Acids Res 2021; 49:W654-W661. [PMID: 33744969 PMCID: PMC8265118 DOI: 10.1093/nar/gkab175] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Revised: 02/17/2021] [Accepted: 03/03/2021] [Indexed: 12/25/2022] Open
Abstract
Experiments that are planned using accurate prediction algorithms will mitigate failures in recombinant protein production. We have developed TISIGNER (https://tisigner.com) with the aim of addressing technical challenges to recombinant protein production. We offer three web services, TIsigner (Translation Initiation coding region designer), SoDoPE (Soluble Domain for Protein Expression) and Razor, which are specialised in synonymous optimisation of recombinant protein expression, solubility and signal peptide analysis, respectively. Importantly, TIsigner, SoDoPE and Razor are linked, which allows users to switch between the tools when optimising genes of interest.
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Affiliation(s)
- Bikash K Bhandari
- Department of Biochemistry, School of Biomedical Sciences, University of Otago, Dunedin 9054, New Zealand
| | - Chun Shen Lim
- Department of Biochemistry, School of Biomedical Sciences, University of Otago, Dunedin 9054, New Zealand
| | - Paul P Gardner
- Department of Biochemistry, School of Biomedical Sciences, University of Otago, Dunedin 9054, New Zealand
- Biomolecular Interaction Centre, University of Canterbury, Christchurch 8140, New Zealand
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26
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Khodakarami A, Dabirmanesh B, Asad S, Khaledi M. Enhanced Solubility and One-Step Purification of Functional Dimeric Carboxypeptidase G2. BIOCHEMISTRY (MOSCOW) 2021; 86:190-196. [PMID: 33832417 DOI: 10.1134/s0006297921020073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Carboxypeptidase G2 is a bacterial enzyme that catalyzes methotrexate conversion to its inactive forms which are then eliminated via a non-renal pathway in patients with renal disorders during a high-dose methotrexate administration. Due to the increasing demand of this enzyme, it was of interest to simplify its production process. For this reason, we developed a method for production and one-step purification of this enzyme using an intein-mediated system with a chitin-binding affinity tag. The carboxypeptidase G2 gene from Pseudomonas RS16 was optimized, synthesized, cloned into the pTXB1 expression vector and finally transformed into Escherichia coli BL21 (DE3) cells. The optimal condition for the enzyme soluble expression was achieved in 2×YT medium containing 1% glucose at 25°C for 30 h with 0.5 mM IPTG. The enzyme without intein was expressed as inclusion bodies indicating the importance of intein for the protein solubility. The expressed homodimer protein was purified to homogeneity on a chitin affinity column. The Km and kcat values of 6.5 µM and 4.57 s-1, respectively, were obtained for the purified enzyme. Gel filtration analysis indicated that the resulting recombinant protein was a dimer of 83 kDa. Fluorescence and circular dichroism spectroscopy confirmed the enzyme tertiary and secondary structures, respectively. The use of intein-mediated system provided the possibility of the one-step carboxypeptidase G2 purification, paving the way to the application of this enzyme in pharmaceutics.
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Affiliation(s)
- Atefeh Khodakarami
- Department of Biochemistry, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, 14115, Iran
| | - Bahareh Dabirmanesh
- Department of Biochemistry, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, 14115, Iran.
| | - Sedigheh Asad
- Department of Biotechnology, College of Science, University of Tehran, Tehran, 14155, Iran
| | - Mohammad Khaledi
- Department of Biochemistry, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, 14115, Iran
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27
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Prabakaran R, Rawat P, Kumar S, Gromiha MM. Evaluation of in silico tools for the prediction of protein and peptide aggregation on diverse datasets. Brief Bioinform 2021; 22:6309925. [PMID: 34181000 DOI: 10.1093/bib/bbab240] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Revised: 05/18/2021] [Accepted: 06/02/2021] [Indexed: 01/09/2023] Open
Abstract
Several prediction algorithms and tools have been developed in the last two decades to predict protein and peptide aggregation. These in silico tools aid to predict the aggregation propensity and amyloidogenicity as well as the identification of aggregation-prone regions. Despite the immense interest in the field, it is of prime importance to systematically compare these algorithms for their performance. In this review, we have provided a rigorous performance analysis of nine prediction tools using a variety of assessments. The assessments were carried out on several non-redundant datasets ranging from hexapeptides to protein sequences as well as amyloidogenic antibody light chains to soluble protein sequences. Our analysis reveals the robustness of the current prediction tools and the scope for improvement in their predictive performances. Insights gained from this work provide critical guidance to the scientific community on advantages and limitations of different aggregation prediction methods and make informed decisions about their research needs.
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Affiliation(s)
| | | | - Sandeep Kumar
- Department of Biotherapeutics Discovery in Boehringer-Ingelheim Pharmaceutical Inc., Ridgefield, CT, USA
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28
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Monti M, Armaos A, Fantini M, Pastore A, Tartaglia GG. Aggregation is a Context-Dependent Constraint on Protein Evolution. Front Mol Biosci 2021; 8:678115. [PMID: 34222334 PMCID: PMC8249573 DOI: 10.3389/fmolb.2021.678115] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Accepted: 05/13/2021] [Indexed: 12/27/2022] Open
Abstract
Solubility is a requirement for many cellular processes. Loss of solubility and aggregation can lead to the partial or complete abrogation of protein function. Thus, understanding the relationship between protein evolution and aggregation is an important goal. Here, we analysed two deep mutational scanning experiments to investigate the role of protein aggregation in molecular evolution. In one data set, mutants of a protein involved in RNA biogenesis and processing, human TAR DNA binding protein 43 (TDP-43), were expressed in S. cerevisiae. In the other data set, mutants of a bacterial enzyme that controls resistance to penicillins and cephalosporins, TEM-1 beta-lactamase, were expressed in E. coli under the selective pressure of an antibiotic treatment. We found that aggregation differentiates the effects of mutations in the two different cellular contexts. Specifically, aggregation was found to be associated with increased cell fitness in the case of TDP-43 mutations, as it protects the host from aberrant interactions. By contrast, in the case of TEM-1 beta-lactamase mutations, aggregation is linked to a decreased cell fitness due to inactivation of protein function. Our study shows that aggregation is an important context-dependent constraint of molecular evolution and opens up new avenues to investigate the role of aggregation in the cell.
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Affiliation(s)
- Michele Monti
- Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology, Barcelona, Spain.,RNA System Biology Lab, Centre for Human Technologies, Istituto Italiano di Tecnologia (IIT), Genoa, Italy
| | - Alexandros Armaos
- Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology, Barcelona, Spain.,RNA System Biology Lab, Centre for Human Technologies, Istituto Italiano di Tecnologia (IIT), Genoa, Italy
| | - Marco Fantini
- Department of Chemistry, Columbia University, New York, NY, United States
| | - Annalisa Pastore
- 3UK-DRI Centre at the Maurice Wohl Institute, Department of Clinical and Basic Neuroscience, King's College London, London, United Kingdom
| | - Gian Gaetano Tartaglia
- Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology, Barcelona, Spain.,RNA System Biology Lab, Centre for Human Technologies, Istituto Italiano di Tecnologia (IIT), Genoa, Italy.,Centre for Genomic Regulation (CRG) and ICREA, The Barcelona Institute for Science and Technology, Barcelona, Spain.,Dipartimento di Biologia e Biotecnologie, Sapienza University, Rome, Italy
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29
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Ptak-Kaczor M, Banach M, Stapor K, Fabian P, Konieczny L, Roterman I. Solubility and Aggregation of Selected Proteins Interpreted on the Basis of Hydrophobicity Distribution. Int J Mol Sci 2021; 22:ijms22095002. [PMID: 34066830 PMCID: PMC8125953 DOI: 10.3390/ijms22095002] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Revised: 05/03/2021] [Accepted: 05/06/2021] [Indexed: 11/30/2022] Open
Abstract
Protein solubility is based on the compatibility of the specific protein surface with the polar aquatic environment. The exposure of polar residues to the protein surface promotes the protein’s solubility in the polar environment. The aquatic environment also influences the folding process by favoring the centralization of hydrophobic residues with the simultaneous exposure to polar residues. The degree of compatibility of the residue distribution, with the model of the concentration of hydrophobic residues in the center of the molecule, with the simultaneous exposure of polar residues is determined by the sequence of amino acids in the chain. The fuzzy oil drop model enables the quantification of the degree of compatibility of the hydrophobicity distribution observed in the protein to a form fully consistent with the Gaussian 3D function, which expresses an idealized distribution that meets the preferences of the polar water environment. The varied degrees of compatibility of the distribution observed with the idealized one allow the prediction of preferences to interactions with molecules of different polarity, including water molecules in particular. This paper analyzes a set of proteins with different levels of hydrophobicity distribution in the context of the solubility of a given protein and the possibility of complex formation.
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Affiliation(s)
- Magdalena Ptak-Kaczor
- Department of Bioinformatics and Telemedicine, Jagiellonian University—Medical College, Medyczna 7, 30-688 Kraków, Poland; (M.P.-K.); (M.B.)
- Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, Łojasiewicza 11, 30-348 Kraków, Poland
| | - Mateusz Banach
- Department of Bioinformatics and Telemedicine, Jagiellonian University—Medical College, Medyczna 7, 30-688 Kraków, Poland; (M.P.-K.); (M.B.)
| | - Katarzyna Stapor
- Institute of Computer Science, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland; (K.S.); (P.F.)
| | - Piotr Fabian
- Institute of Computer Science, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland; (K.S.); (P.F.)
| | - Leszek Konieczny
- Chair of Medical Biochemistry—Jagiellonian University—Medical College, Kopernika 7, 31-034 Kraków, Poland;
| | - Irena Roterman
- Department of Bioinformatics and Telemedicine, Jagiellonian University—Medical College, Medyczna 7, 30-688 Kraków, Poland; (M.P.-K.); (M.B.)
- Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, Łojasiewicza 11, 30-348 Kraków, Poland
- Correspondence:
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30
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Bocanegra-Jiménez FY, Montero-Morán GM, Lara-González S. Purification and characterization of an Fe II- and α-ketoglutarate-dependent xanthine hydroxylase from Aspergillus oryzae. Protein Expr Purif 2021; 183:105862. [PMID: 33716123 DOI: 10.1016/j.pep.2021.105862] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2020] [Revised: 02/13/2021] [Accepted: 02/28/2021] [Indexed: 11/29/2022]
Abstract
XanA is an FeII- and α-ketoglutarate-dependent enzyme responsible for the conversion of xanthine to uric acid. It is unique to fungi and it was first described in Aspergillus nidulans. In this work, we present the preliminary characterization of the XanA enzyme from Aspergillus oryzae, a relevant fungus in food production in Japan. The XanA protein (GenBank BAE56701.1) was expressed as a recombinant protein in Escherichia coli BL21 (DE3) Arctic cells. Initial purification assays showed low protein solubility; therefore, the buffer composition was optimized using a fluorescence-based thermal shift assay. The protein was stabilized in solution in the presence of either 600 μM xanthine, 1 M NaCl, 600 μM α-ketoglutarate or 20% glycerol, which increases the melting temperature (Tm) by 2, 4, 5 and 6 °C respectively. The XanA protein was purified by following a three-step purification protocol. The nickel affinity purified protein was subjected to ion-exchange chromatography once the N-terminal 6XHis-tag had been successfully removed, followed by size-exclusion purification. Dynamic light scattering experiments showed that the purified protein was monodisperse and behaved as a monomer in solution. Preliminary activity assays in the presence of xanthine, α-ketoglutarate, and iron suggest that the enzyme is an iron- and α-ketoglutarate-dependent xanthine dioxygenase. Furthermore, the enzyme's optimum activity conditions were determined to be 25 °C, pH of 7.2, HEPES buffer, and 1% of glycerol. In conclusion, we established the conditions to purify the XanA enzyme from A. oryzae in its active form from E. coli bacteria and determined the optimal activity conditions.
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Affiliation(s)
- Fitzya Y Bocanegra-Jiménez
- IPICYT, División de Biología Molecular, Instituto Potosino de Investigación Científica y Tecnológica A. C., San Luis Potosí, SLP, Mexico
| | - Gabriela M Montero-Morán
- Facultad de Ciencias Químicas, Laboratorio IBCM, Universidad Autónoma de San Luis Potosí, San Luis Potosí, SLP, Mexico
| | - Samuel Lara-González
- IPICYT, División de Biología Molecular, Instituto Potosino de Investigación Científica y Tecnológica A. C., San Luis Potosí, SLP, Mexico.
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31
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Bhandari BK, Gardner PP, Lim CS. Solubility-Weighted Index: fast and accurate prediction of protein solubility. Bioinformatics 2021; 36:4691-4698. [PMID: 32559287 PMCID: PMC7750957 DOI: 10.1093/bioinformatics/btaa578] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Revised: 05/05/2020] [Accepted: 06/12/2020] [Indexed: 12/14/2022] Open
Abstract
Motivation Recombinant protein production is a widely used technique in the biotechnology and biomedical industries, yet only a quarter of target proteins are soluble and can therefore be purified. Results We have discovered that global structural flexibility, which can be modeled by normalized B-factors, accurately predicts the solubility of 12 216 recombinant proteins expressed in Escherichia coli. We have optimized these B-factors, and derived a new set of values for solubility scoring that further improves prediction accuracy. We call this new predictor the ‘Solubility-Weighted Index’ (SWI). Importantly, SWI outperforms many existing protein solubility prediction tools. Furthermore, we have developed ‘SoDoPE’ (Soluble Domain for Protein Expression), a web interface that allows users to choose a protein region of interest for predicting and maximizing both protein expression and solubility. Availability and implementation The SoDoPE web server and source code are freely available at https://tisigner.com/sodope and https://github.com/Gardner-BinfLab/TISIGNER-ReactJS, respectively. The code and data for reproducing our analysis can be found at https://github.com/Gardner-BinfLab/SoDoPE_paper_2020. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Bikash K Bhandari
- Department of Biochemistry, School of Biomedical Sciences, University of Otago, Dunedin, New Zealand
| | - Paul P Gardner
- Department of Biochemistry, School of Biomedical Sciences, University of Otago, Dunedin, New Zealand.,Biomolecular Interaction Centre, University of Canterbury, Christchurch, New Zealand
| | - Chun Shen Lim
- Department of Biochemistry, School of Biomedical Sciences, University of Otago, Dunedin, New Zealand
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32
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Planas-Iglesias J, Marques SM, Pinto GP, Musil M, Stourac J, Damborsky J, Bednar D. Computational design of enzymes for biotechnological applications. Biotechnol Adv 2021; 47:107696. [PMID: 33513434 DOI: 10.1016/j.biotechadv.2021.107696] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Revised: 01/12/2021] [Accepted: 01/13/2021] [Indexed: 12/14/2022]
Abstract
Enzymes are the natural catalysts that execute biochemical reactions upholding life. Their natural effectiveness has been fine-tuned as a result of millions of years of natural evolution. Such catalytic effectiveness has prompted the use of biocatalysts from multiple sources on different applications, including the industrial production of goods (food and beverages, detergents, textile, and pharmaceutics), environmental protection, and biomedical applications. Natural enzymes often need to be improved by protein engineering to optimize their function in non-native environments. Recent technological advances have greatly facilitated this process by providing the experimental approaches of directed evolution or by enabling computer-assisted applications. Directed evolution mimics the natural selection process in a highly accelerated fashion at the expense of arduous laboratory work and economic resources. Theoretical methods provide predictions and represent an attractive complement to such experiments by waiving their inherent costs. Computational techniques can be used to engineer enzymatic reactivity, substrate specificity and ligand binding, access pathways and ligand transport, and global properties like protein stability, solubility, and flexibility. Theoretical approaches can also identify hotspots on the protein sequence for mutagenesis and predict suitable alternatives for selected positions with expected outcomes. This review covers the latest advances in computational methods for enzyme engineering and presents many successful case studies.
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Affiliation(s)
- Joan Planas-Iglesias
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, Kamenice 5/A13, 625 00 Brno, Czech Republic; International Clinical Research Center, St. Anne's University Hospital Brno, Pekarska 53, 656 91 Brno, Czech Republic
| | - Sérgio M Marques
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, Kamenice 5/A13, 625 00 Brno, Czech Republic; International Clinical Research Center, St. Anne's University Hospital Brno, Pekarska 53, 656 91 Brno, Czech Republic
| | - Gaspar P Pinto
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, Kamenice 5/A13, 625 00 Brno, Czech Republic; International Clinical Research Center, St. Anne's University Hospital Brno, Pekarska 53, 656 91 Brno, Czech Republic
| | - Milos Musil
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, Kamenice 5/A13, 625 00 Brno, Czech Republic; International Clinical Research Center, St. Anne's University Hospital Brno, Pekarska 53, 656 91 Brno, Czech Republic; IT4Innovations Centre of Excellence, Faculty of Information Technology, Brno University of Technology, 61266 Brno, Czech Republic
| | - Jan Stourac
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, Kamenice 5/A13, 625 00 Brno, Czech Republic; International Clinical Research Center, St. Anne's University Hospital Brno, Pekarska 53, 656 91 Brno, Czech Republic
| | - Jiri Damborsky
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, Kamenice 5/A13, 625 00 Brno, Czech Republic; International Clinical Research Center, St. Anne's University Hospital Brno, Pekarska 53, 656 91 Brno, Czech Republic.
| | - David Bednar
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, Kamenice 5/A13, 625 00 Brno, Czech Republic.
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33
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Hon J, Marusiak M, Martinek T, Kunka A, Zendulka J, Bednar D, Damborsky J. SoluProt: Prediction of Soluble Protein Expression in Escherichia coli. Bioinformatics 2021; 37:23-28. [PMID: 33416864 PMCID: PMC8034534 DOI: 10.1093/bioinformatics/btaa1102] [Citation(s) in RCA: 55] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2020] [Revised: 12/05/2020] [Accepted: 12/28/2020] [Indexed: 12/17/2022] Open
Abstract
Motivation Poor protein solubility hinders the production of many therapeutic and industrially useful proteins. Experimental efforts to increase solubility are plagued by low success rates and often reduce biological activity. Computational prediction of protein expressibility and solubility in Escherichia coli using only sequence information could reduce the cost of experimental studies by enabling prioritization of highly soluble proteins. Results A new tool for sequence-based prediction of soluble protein expression in E.coli, SoluProt, was created using the gradient boosting machine technique with the TargetTrack database as a training set. When evaluated against a balanced independent test set derived from the NESG database, SoluProt’s accuracy of 58.5% and AUC of 0.62 exceeded those of a suite of alternative solubility prediction tools. There is also evidence that it could significantly increase the success rate of experimental protein studies. SoluProt is freely available as a standalone program and a user-friendly webserver at https://loschmidt.chemi.muni.cz/soluprot/. Availability and implementation https://loschmidt.chemi.muni.cz/soluprot/. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Jiri Hon
- Loschmidt Laboratories, Centre for Toxic Compounds in the Environment RECETOX and Department of Experimental Biology, Faculty of Science, Masaryk University, 625 00 Brno, Czech Republic.,International Clinical Research Center, St. Anne's University Hospital Brno, 656 91 Brno, Czech Republic.,IT4Innovations Centre of Excellence, Faculty of Information Technology, Brno University of Technology, 612 66 Brno, Czech Republic
| | - Martin Marusiak
- IT4Innovations Centre of Excellence, Faculty of Information Technology, Brno University of Technology, 612 66 Brno, Czech Republic
| | - Tomas Martinek
- IT4Innovations Centre of Excellence, Faculty of Information Technology, Brno University of Technology, 612 66 Brno, Czech Republic
| | - Antonin Kunka
- Loschmidt Laboratories, Centre for Toxic Compounds in the Environment RECETOX and Department of Experimental Biology, Faculty of Science, Masaryk University, 625 00 Brno, Czech Republic.,International Clinical Research Center, St. Anne's University Hospital Brno, 656 91 Brno, Czech Republic
| | - Jaroslav Zendulka
- IT4Innovations Centre of Excellence, Faculty of Information Technology, Brno University of Technology, 612 66 Brno, Czech Republic
| | - David Bednar
- Loschmidt Laboratories, Centre for Toxic Compounds in the Environment RECETOX and Department of Experimental Biology, Faculty of Science, Masaryk University, 625 00 Brno, Czech Republic.,International Clinical Research Center, St. Anne's University Hospital Brno, 656 91 Brno, Czech Republic
| | - Jiri Damborsky
- Loschmidt Laboratories, Centre for Toxic Compounds in the Environment RECETOX and Department of Experimental Biology, Faculty of Science, Masaryk University, 625 00 Brno, Czech Republic.,International Clinical Research Center, St. Anne's University Hospital Brno, 656 91 Brno, Czech Republic
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34
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Armaos A, Zacco E, Sanchez de Groot N, Tartaglia GG. RNA-protein interactions: Central players in coordination of regulatory networks. Bioessays 2020; 43:e2000118. [PMID: 33284474 DOI: 10.1002/bies.202000118] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2020] [Revised: 09/30/2020] [Accepted: 10/01/2020] [Indexed: 12/12/2022]
Abstract
Changes in the abundance of protein and RNA molecules can impair the formation of complexes in the cell leading to toxicity and death. Here we exploit the information contained in protein, RNA and DNA interaction networks to provide a comprehensive view of the regulation layers controlling the concentration-dependent formation of assemblies in the cell. We present the emerging concept that RNAs can act as scaffolds to promote the formation ribonucleoprotein complexes and coordinate the post-transcriptional layer of gene regulation. We describe the structural and interaction network properties that characterize the ability of protein and RNA molecules to interact and phase separate in liquid-like compartments. Finally, we show that presence of structurally disordered regions in proteins correlate with the propensity to undergo liquid-to-solid phase transitions and cause human diseases. Also see the video abstract here https://youtu.be/kfpqibsNfS0.
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Affiliation(s)
- Alexandros Armaos
- Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology, Universitat Pompeu Fabra (UPF), Barcelona, Spain.,Center for Human Technologies, Istituto Italiano di Tecnologia, Genova, Italy
| | - Elsa Zacco
- Center for Human Technologies, Istituto Italiano di Tecnologia, Genova, Italy
| | - Natalia Sanchez de Groot
- Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology, Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Gian Gaetano Tartaglia
- Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology, Universitat Pompeu Fabra (UPF), Barcelona, Spain.,Center for Human Technologies, Istituto Italiano di Tecnologia, Genova, Italy.,Department of Biology 'Charles Darwin', Sapienza University of Rome, Rome, Italy.,Institucio Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain
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35
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Vandelli A, Monti M, Milanetti E, Armaos A, Rupert J, Zacco E, Bechara E, Delli Ponti R, Tartaglia GG. Structural analysis of SARS-CoV-2 genome and predictions of the human interactome. Nucleic Acids Res 2020. [PMID: 33068416 DOI: 10.1101/2020.03.28.013789] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/07/2023] Open
Abstract
Specific elements of viral genomes regulate interactions within host cells. Here, we calculated the secondary structure content of >2000 coronaviruses and computed >100 000 human protein interactions with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The genomic regions display different degrees of conservation. SARS-CoV-2 domain encompassing nucleotides 22 500-23 000 is conserved both at the sequence and structural level. The regions upstream and downstream, however, vary significantly. This part of the viral sequence codes for the Spike S protein that interacts with the human receptor angiotensin-converting enzyme 2 (ACE2). Thus, variability of Spike S is connected to different levels of viral entry in human cells within the population. Our predictions indicate that the 5' end of SARS-CoV-2 is highly structured and interacts with several human proteins. The binding proteins are involved in viral RNA processing, include double-stranded RNA specific editases and ATP-dependent RNA-helicases and have strong propensity to form stress granules and phase-separated assemblies. We propose that these proteins, also implicated in viral infections such as HIV, are selectively recruited by SARS-CoV-2 genome to alter transcriptional and post-transcriptional regulation of host cells and to promote viral replication.
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Affiliation(s)
- Andrea Vandelli
- Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology, Dr. Aiguader 88, 08003 Barcelona, Spain and Universitat Pompeu Fabra (UPF), 08003 Barcelona, Spain
- Systems Biology of Infection Lab, Department of Biochemistry and Molecular Biology, Biosciences Faculty, Universitat Autònoma de Barcelona, 08193 Cerdanyola del Vallès, Spain
| | - Michele Monti
- Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology, Dr. Aiguader 88, 08003 Barcelona, Spain and Universitat Pompeu Fabra (UPF), 08003 Barcelona, Spain
- Center for Human Technologies, Istituto Italiano di Tecnologia, Via Enrico Melen 83, 16152 Genoa, Italy
| | - Edoardo Milanetti
- Department of Physics, Sapienza University, Piazzale Aldo Moro 5, 00185 Rome, Italy
- Center for Life Nanoscience, Istituto Italiano di Tecnologia, Viale Regina Elena 291, 00161 Rome, Italy
| | - Alexandros Armaos
- Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology, Dr. Aiguader 88, 08003 Barcelona, Spain and Universitat Pompeu Fabra (UPF), 08003 Barcelona, Spain
- Center for Human Technologies, Istituto Italiano di Tecnologia, Via Enrico Melen 83, 16152 Genoa, Italy
| | - Jakob Rupert
- Center for Human Technologies, Istituto Italiano di Tecnologia, Via Enrico Melen 83, 16152 Genoa, Italy
- Department of Biology 'Charles Darwin', Sapienza University of Rome, P.le A. Moro 5, Rome 00185, Italy
| | - Elsa Zacco
- Center for Human Technologies, Istituto Italiano di Tecnologia, Via Enrico Melen 83, 16152 Genoa, Italy
| | - Elias Bechara
- Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology, Dr. Aiguader 88, 08003 Barcelona, Spain and Universitat Pompeu Fabra (UPF), 08003 Barcelona, Spain
- Center for Human Technologies, Istituto Italiano di Tecnologia, Via Enrico Melen 83, 16152 Genoa, Italy
| | - Riccardo Delli Ponti
- School of Biological Sciences, Nanyang Technological University, 60 Nanyang Drive, Singapore 637551, Singapore
| | - Gian Gaetano Tartaglia
- Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology, Dr. Aiguader 88, 08003 Barcelona, Spain and Universitat Pompeu Fabra (UPF), 08003 Barcelona, Spain
- Center for Human Technologies, Istituto Italiano di Tecnologia, Via Enrico Melen 83, 16152 Genoa, Italy
- Department of Biology 'Charles Darwin', Sapienza University of Rome, P.le A. Moro 5, Rome 00185, Italy
- Institucio Catalana de Recerca i Estudis Avançats (ICREA), 23 Passeig Lluis Companys, 08010 Barcelona, Spain
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36
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Vandelli A, Monti M, Milanetti E, Armaos A, Rupert J, Zacco E, Bechara E, Delli Ponti R, Tartaglia G. Structural analysis of SARS-CoV-2 genome and predictions of the human interactome. Nucleic Acids Res 2020; 48:11270-11283. [PMID: 33068416 PMCID: PMC7672441 DOI: 10.1093/nar/gkaa864] [Citation(s) in RCA: 65] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Revised: 09/15/2020] [Accepted: 09/25/2020] [Indexed: 12/17/2022] Open
Abstract
Specific elements of viral genomes regulate interactions within host cells. Here, we calculated the secondary structure content of >2000 coronaviruses and computed >100 000 human protein interactions with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The genomic regions display different degrees of conservation. SARS-CoV-2 domain encompassing nucleotides 22 500-23 000 is conserved both at the sequence and structural level. The regions upstream and downstream, however, vary significantly. This part of the viral sequence codes for the Spike S protein that interacts with the human receptor angiotensin-converting enzyme 2 (ACE2). Thus, variability of Spike S is connected to different levels of viral entry in human cells within the population. Our predictions indicate that the 5' end of SARS-CoV-2 is highly structured and interacts with several human proteins. The binding proteins are involved in viral RNA processing, include double-stranded RNA specific editases and ATP-dependent RNA-helicases and have strong propensity to form stress granules and phase-separated assemblies. We propose that these proteins, also implicated in viral infections such as HIV, are selectively recruited by SARS-CoV-2 genome to alter transcriptional and post-transcriptional regulation of host cells and to promote viral replication.
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Affiliation(s)
- Andrea Vandelli
- Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology, Dr. Aiguader 88, 08003 Barcelona, Spain and Universitat Pompeu Fabra (UPF), 08003 Barcelona, Spain
- Systems Biology of Infection Lab, Department of Biochemistry and Molecular Biology, Biosciences Faculty, Universitat Autònoma de Barcelona, 08193 Cerdanyola del Vallès, Spain
| | - Michele Monti
- Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology, Dr. Aiguader 88, 08003 Barcelona, Spain and Universitat Pompeu Fabra (UPF), 08003 Barcelona, Spain
- Center for Human Technologies, Istituto Italiano di Tecnologia, Via Enrico Melen 83, 16152 Genoa, Italy
| | - Edoardo Milanetti
- Department of Physics, Sapienza University, Piazzale Aldo Moro 5, 00185 Rome, Italy
- Center for Life Nanoscience, Istituto Italiano di Tecnologia, Viale Regina Elena 291, 00161 Rome, Italy
| | - Alexandros Armaos
- Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology, Dr. Aiguader 88, 08003 Barcelona, Spain and Universitat Pompeu Fabra (UPF), 08003 Barcelona, Spain
- Center for Human Technologies, Istituto Italiano di Tecnologia, Via Enrico Melen 83, 16152 Genoa, Italy
| | - Jakob Rupert
- Center for Human Technologies, Istituto Italiano di Tecnologia, Via Enrico Melen 83, 16152 Genoa, Italy
- Department of Biology ‘Charles Darwin’, Sapienza University of Rome, P.le A. Moro 5, Rome 00185, Italy
| | - Elsa Zacco
- Center for Human Technologies, Istituto Italiano di Tecnologia, Via Enrico Melen 83, 16152 Genoa, Italy
| | - Elias Bechara
- Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology, Dr. Aiguader 88, 08003 Barcelona, Spain and Universitat Pompeu Fabra (UPF), 08003 Barcelona, Spain
- Center for Human Technologies, Istituto Italiano di Tecnologia, Via Enrico Melen 83, 16152 Genoa, Italy
| | - Riccardo Delli Ponti
- School of Biological Sciences, Nanyang Technological University, 60 Nanyang Drive, Singapore 637551, Singapore
| | - Gian Gaetano Tartaglia
- Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology, Dr. Aiguader 88, 08003 Barcelona, Spain and Universitat Pompeu Fabra (UPF), 08003 Barcelona, Spain
- Center for Human Technologies, Istituto Italiano di Tecnologia, Via Enrico Melen 83, 16152 Genoa, Italy
- Department of Biology ‘Charles Darwin’, Sapienza University of Rome, P.le A. Moro 5, Rome 00185, Italy
- Institucio Catalana de Recerca i Estudis Avançats (ICREA), 23 Passeig Lluis Companys, 08010 Barcelona, Spain
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In Silico Design of a Poly-epitope Vaccine for Urinary Tract Infection Based on Conserved Antigens by Modern Vaccinology. Int J Pept Res Ther 2020. [DOI: 10.1007/s10989-020-10137-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
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38
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Hou Q, Kwasigroch JM, Rooman M, Pucci F. SOLart: a structure-based method to predict protein solubility and aggregation. Bioinformatics 2020; 36:1445-1452. [PMID: 31603466 DOI: 10.1093/bioinformatics/btz773] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2019] [Revised: 08/31/2019] [Accepted: 10/08/2019] [Indexed: 12/12/2022] Open
Abstract
MOTIVATION The solubility of a protein is often decisive for its proper functioning. Lack of solubility is a major bottleneck in high-throughput structural genomic studies and in high-concentration protein production, and the formation of protein aggregates causes a wide variety of diseases. Since solubility measurements are time-consuming and expensive, there is a strong need for solubility prediction tools. RESULTS We have recently introduced solubility-dependent distance potentials that are able to unravel the role of residue-residue interactions in promoting or decreasing protein solubility. Here, we extended their construction by defining solubility-dependent potentials based on backbone torsion angles and solvent accessibility, and integrated them, together with other structure- and sequence-based features, into a random forest model trained on a set of Escherichia coli proteins with experimental structures and solubility values. We thus obtained the SOLart protein solubility predictor, whose most informative features turned out to be folding free energy differences computed from our solubility-dependent statistical potentials. SOLart performances are very good, with a Pearson correlation coefficient between experimental and predicted solubility values of almost 0.7 both in cross-validation on the training dataset and in an independent set of Saccharomyces cerevisiae proteins. On test sets of modeled structures, only a limited drop in performance is observed. SOLart can thus be used with both high-resolution and low-resolution structures, and clearly outperforms state-of-art solubility predictors. It is available through a user-friendly webserver, which is easy to use by non-expert scientists. AVAILABILITY AND IMPLEMENTATION The SOLart webserver is freely available at http://babylone.ulb.ac.be/SOLART/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Qingzhen Hou
- Computational Biology and Bioinformatics, Université Libre de Bruxelles, Avenue Roosevelt 50, 1050 Brussels, Belgium.,Interuniversity Institute of Bioinformatics in Brussels, Boulevard du Triomphe, 1050 Brussels, Belgium
| | - Jean Marc Kwasigroch
- Computational Biology and Bioinformatics, Université Libre de Bruxelles, Avenue Roosevelt 50, 1050 Brussels, Belgium.,Interuniversity Institute of Bioinformatics in Brussels, Boulevard du Triomphe, 1050 Brussels, Belgium
| | - Marianne Rooman
- Computational Biology and Bioinformatics, Université Libre de Bruxelles, Avenue Roosevelt 50, 1050 Brussels, Belgium.,Interuniversity Institute of Bioinformatics in Brussels, Boulevard du Triomphe, 1050 Brussels, Belgium
| | - Fabrizio Pucci
- Computational Biology and Bioinformatics, Université Libre de Bruxelles, Avenue Roosevelt 50, 1050 Brussels, Belgium.,Interuniversity Institute of Bioinformatics in Brussels, Boulevard du Triomphe, 1050 Brussels, Belgium.,John von Neumann Institute for Computing, Jülich Supercomputer Centre, Forschungszentrum Jülich, 52428 Jülich, Germany
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39
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Yazdani Z, Rafiei A, Yazdani M, Valadan R. Design an Efficient Multi-Epitope Peptide Vaccine Candidate Against SARS-CoV-2: An in silico Analysis. Infect Drug Resist 2020; 13:3007-3022. [PMID: 32943888 PMCID: PMC7459237 DOI: 10.2147/idr.s264573] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Accepted: 07/28/2020] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND To date, no specific vaccine or drug has been proven to be effective against SARS-CoV-2 infection. Therefore, we implemented an immunoinformatic approach to design an efficient multi-epitopes vaccine against SARS-CoV-2. RESULTS The designed-vaccine construct consists of several immunodominant epitopes from structural proteins of spike, nucleocapsid, membrane, and envelope. These peptides promote cellular and humoral immunity and interferon-gamma responses. Also, these epitopes have a high antigenic capacity and are not likely to cause allergies. To enhance the vaccine immunogenicity, we used three potent adjuvants: Flagellin of Salmonella enterica subsp. enterica serovar Dublin, a driven peptide from high mobility group box 1 as HP-91, and human beta-defensin 3 protein. The physicochemical and immunological properties of the vaccine structure were evaluated. The tertiary structure of the vaccine protein was predicted and refined by Phyre2 and Galaxi refine and validated using RAMPAGE and ERRAT. Results of ElliPro showed 246 sresidues from vaccine might be conformational B-cell epitopes. Docking of the vaccine with toll-like receptors (TLR) 3, 5, 8, and angiotensin-converting enzyme 2 approved an appropriate interaction between the vaccine and receptors. Prediction of mRNA secondary structure and in silico cloning demonstrated that the vaccine can be efficiently expressed in Escherichia coli. CONCLUSION Our results demonstrated that the multi-epitope vaccine might be potentially antigenic and induce humoral and cellular immune responses against SARS-CoV-2. This vaccine can interact appropriately with the TLR3, 5, and 8. Also, it has a high-quality structure and suitable characteristics such as high stability and potential for expression in Escherichia coli .
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Affiliation(s)
- Zahra Yazdani
- Department of Immunology, Molecular and Cell Biology Research Center, School of Medicine, Mazandaran University of Medical Sciences, Sari, Iran
| | - Alireza Rafiei
- Department of Immunology, Molecular and Cell Biology Research Center, School of Medicine, Mazandaran University of Medical Sciences, Sari, Iran
| | - Mohammadreza Yazdani
- Department of Chemistry, Isfahan University of Technology, Isfahan84156-83111, Iran
| | - Reza Valadan
- Department of Immunology, Molecular and Cell Biology Research Center, School of Medicine, Mazandaran University of Medical Sciences, Sari, Iran
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40
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Lathwal A, Kumar R, Raghava GP. Computer-aided designing of oncolytic viruses for overcoming translational challenges of cancer immunotherapy. Drug Discov Today 2020; 25:1198-1205. [DOI: 10.1016/j.drudis.2020.04.008] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Revised: 04/05/2020] [Accepted: 04/15/2020] [Indexed: 12/26/2022]
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41
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Raimondi D, Orlando G, Fariselli P, Moreau Y. Insight into the protein solubility driving forces with neural attention. PLoS Comput Biol 2020; 16:e1007722. [PMID: 32352965 PMCID: PMC7217484 DOI: 10.1371/journal.pcbi.1007722] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2019] [Revised: 05/12/2020] [Accepted: 02/10/2020] [Indexed: 12/29/2022] Open
Abstract
Protein solubility is a key aspect for many biotechnological, biomedical and industrial processes, such as the production of active proteins and antibodies. In addition, understanding the molecular determinants of the solubility of proteins may be crucial to shed light on the molecular mechanisms of diseases caused by aggregation processes such as amyloidosis. Here we present SKADE, a novel Neural Network protein solubility predictor and we show how it can provide novel insight into the protein solubility mechanisms, thanks to its neural attention architecture. First, we show that SKADE positively compares with state of the art tools while using just the protein sequence as input. Then, thanks to the neural attention mechanism, we use SKADE to investigate the patterns learned during training and we analyse its decision process. We use this peculiarity to show that, while the attention profiles do not correlate with obvious sequence aspects such as biophysical properties of the aminoacids, they suggest that N- and C-termini are the most relevant regions for solubility prediction and are predictive for complex emergent properties such as aggregation-prone regions involved in beta-amyloidosis and contact density. Moreover, SKADE is able to identify mutations that increase or decrease the overall solubility of the protein, allowing it to be used to perform large scale in-silico mutagenesis of proteins in order to maximize their solubility. The solubility of proteins is a crucial biophysical aspect when it comes to understanding many human diseases and to improve the industrial processes for protein production. Due to its relevance, computational methods have been devised in order to study and possibly optimize the solubility of proteins. In this work we apply a deep-learning technique, called neural attention to predict protein solubility while “opening” the model itself to interpretability, even though Machine Learning models are usually considered black boxes. Thank to the attention mechanism, we show that i) our model implicitly learns complex patterns related to emergent, protein folding-related, aspects such as to recognize β-amyloidosis regions and that ii) the N-and C-termini are the regions with the highes signal fro solubility prediction. When it comes to enhancing the solubility of proteins, we, for the first time, propose to investigate the synergistic effects of tandem mutations instead of “single” mutations, suggesting that this could minimize the number of required proposed mutations.
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Affiliation(s)
| | | | | | - Yves Moreau
- ESAT-STADIUS, KU Leuven, Leuven, Belgium
- * E-mail:
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42
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Yazdani Z, Rafiei A, Valadan R, Ashrafi H, Pasandi M, Kardan M. Designing a potent L1 protein-based HPV peptide vaccine: A bioinformatics approach. Comput Biol Chem 2020; 85:107209. [DOI: 10.1016/j.compbiolchem.2020.107209] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2019] [Revised: 01/08/2020] [Accepted: 01/16/2020] [Indexed: 12/29/2022]
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43
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Stepwise optimization of recombinant protein production in Escherichia coli utilizing computational and experimental approaches. Appl Microbiol Biotechnol 2020; 104:3253-3266. [DOI: 10.1007/s00253-020-10454-w] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2019] [Revised: 01/28/2020] [Accepted: 02/07/2020] [Indexed: 12/14/2022]
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44
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Bordbar A, Bagheri KP, Ebrahimi S, Parvizi P. Bioinformatics analyses of immunogenic T-cell epitopes of LeIF and PpSP15 proteins from Leishmania major and sand fly saliva used as model antigens for the design of a multi-epitope vaccine to control leishmaniasis. INFECTION GENETICS AND EVOLUTION 2020; 80:104189. [PMID: 31931259 DOI: 10.1016/j.meegid.2020.104189] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/22/2019] [Revised: 01/05/2020] [Accepted: 01/08/2020] [Indexed: 11/17/2022]
Abstract
Leishmaniasis is caused by protozoan parasites belonging to 20 Leishmania species. This infectious disease is transmitted by bites of infected phlebotomine sandflies, and is widespread in 97 countries throughout the world. No preventive or effective vaccine has been developed yet. In this study, diverse computational methods were integrated to calculate evolutionary divergence, immunogenicity, IFN-γ production, epitope conservancy, and population coverage of protein fusion models of LeIF-SP15 namely SaLeish. Immunogenicity of LeIF of Leishmania species and SP15 of sandfly saliva has not been investigated in-silico in fusion form. A complete set of 9-mer MHC class I and 15-mer MHC class II peptides were identified with a high affinity for the antigenic epitopes of SaLeish inducing specific responses of CD8+ and CD4+ T cells from BALB/c and human. Our preferred approach was determining truncated fragment of SaLeish rather than a whole length bearing the capacity to trigger specific immune response. Phylogenetic analysis showed that LeIF protein is under balancing selection and is conserved between different Leishmania species. Selected SaLeish model contained 19 and 35 antigenic peptides for MHC class I and II, respectively, with strong binding affinity to both highly frequent HLA-I and HLA-II alleles. Analysis of class I CTL epitopes showed that promiscuous peptides of KSLKADIRK, MSCIPHCKY, LQAGVIVAV, and YQYYGFVAM have greater affinity to interact with HLA-A*01:01, HLA-A*02 (03, 06), HLA-A*30:02, HLA-B*40:01, and HLA-B*52:01 molecules. Population coverage with a range of 78-85% confirmed SaLeish-Model4 as an appropriate vaccine candidate among Persian, South Asia, Europe, and North America population. Also, predicted antigenic epitopes of AKPEIRTFSNVLIKY, TRVQDDLRKLQAGVI, and VALFSATMPEEVLEL corresponding to MHC class II were found to provide strong ability to produce IFNγ toward TH(1)-biased-DTH responses. Findings of the current investigation warrant the future experimental assessment of promising SaLeish prophylaxis vaccine that is capable to enhance both innate and specific cellular immune responses.
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Affiliation(s)
- Ali Bordbar
- Molecular Systematics Laboratory, Parasitology Department, Microbiology Research Center, Pasteur Institute of Iran, Tehran, Iran.
| | - Kamran Pooshang Bagheri
- Venom and Biotherapeutics Molecules Lab., Biotechnology Dept., Biotechnology Research Center, Pasteur Institute of Iran, Tehran, Iran
| | - Sahar Ebrahimi
- Molecular Systematics Laboratory, Parasitology Department, Microbiology Research Center, Pasteur Institute of Iran, Tehran, Iran
| | - Parviz Parvizi
- Molecular Systematics Laboratory, Parasitology Department, Microbiology Research Center, Pasteur Institute of Iran, Tehran, Iran.
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45
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In Silico Study of Different Signal Peptides to Express Recombinant Glutamate Decarboxylase in the Outer Membrane of Escherichia coli. Int J Pept Res Ther 2019. [DOI: 10.1007/s10989-019-09986-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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46
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Schlee S, Straub K, Schwab T, Kinateder T, Merkl R, Sterner R. Prediction of quaternary structure by analysis of hot spot residues in protein-protein interfaces: the case of anthranilate phosphoribosyltransferases. Proteins 2019; 87:815-825. [PMID: 31134642 DOI: 10.1002/prot.25744] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2019] [Revised: 05/06/2019] [Accepted: 05/22/2019] [Indexed: 12/13/2022]
Abstract
It is an important goal of computational biology to correctly predict the association state of a protein based on its amino acid sequence and the structures of known homologues. We have pursued this goal on the example of anthranilate phosphoribosyltransferase (AnPRT), an enzyme that is involved in the biosynthesis of the amino acid tryptophan. Firstly, known crystal structures of naturally occurring homodimeric AnPRTs were analyzed using the Protein Interfaces, Surfaces, and Assemblies (PISA) service of the European Bioinformatics Institute (EBI). This led to the identification of two hydrophobic "hot spot" amino acids in the protein-protein interface that were predicted to be essential for self-association. Next, in a comprehensive multiple sequence alignment (MSA), naturally occurring AnPRT variants with hydrophilic or charged amino acids in place of hydrophobic residues in the two hot spot positions were identified. Representative variants were characterized in terms of thermal stability, enzymatic activity, and quaternary structure. We found that AnPRT variants with charged residues in both hot spot positions exist exclusively as monomers in solution. Variants with hydrophilic amino acids in one hot spot position occur in both forms, monomer and dimer. The results of the present study provide a detailed characterization of the determinants of the AnPRT monomer-dimer equilibrium and show that analysis of hot spots in combination with MSAs can be a valuable tool in prediction of protein quaternary structures.
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Affiliation(s)
- Sandra Schlee
- Institute of Biophysics and Physical Biochemistry, University of Regensburg, Regensburg, Germany
| | - Kristina Straub
- Institute of Biophysics and Physical Biochemistry, University of Regensburg, Regensburg, Germany
| | - Thomas Schwab
- Institute of Biophysics and Physical Biochemistry, University of Regensburg, Regensburg, Germany
| | - Thomas Kinateder
- Institute of Biophysics and Physical Biochemistry, University of Regensburg, Regensburg, Germany
| | - Rainer Merkl
- Institute of Biophysics and Physical Biochemistry, University of Regensburg, Regensburg, Germany
| | - Reinhard Sterner
- Institute of Biophysics and Physical Biochemistry, University of Regensburg, Regensburg, Germany
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47
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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: 344] [Impact Index Per Article: 68.8] [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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48
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Abstract
Production of fuels and chemicals from renewable lignocellulosic feedstocks is a promising alternative to petroleum-derived compounds. Due to the complexity of lignocellulosic feedstocks, microbial conversion of all potential substrates will require substantial metabolic engineering. Non-model microbes offer desirable physiological traits, but also increase the difficulty of heterologous pathway engineering and optimization. The development of modular design principles that allow metabolic pathways to be used in a variety of novel microbes with minimal strain-specific optimization will enable the rapid construction of microbes for commercial production of biofuels and bioproducts. In this review, we discuss variability of lignocellulosic feedstocks, pathways for catabolism of lignocellulose-derived compounds, challenges to heterologous engineering of catabolic pathways, and opportunities to apply modular pathway design. Implementation of these approaches will simplify the process of modifying non-model microbes to convert diverse lignocellulosic feedstocks.
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49
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Amin SA, Endalur Gopinarayanan V, Nair NU, Hassoun S. Establishing synthesis pathway-host compatibility via enzyme solubility. Biotechnol Bioeng 2019; 116:1405-1416. [PMID: 30802311 DOI: 10.1002/bit.26959] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2018] [Revised: 12/18/2018] [Accepted: 02/21/2019] [Indexed: 12/12/2022]
Abstract
Current pathway synthesis tools identify possible pathways that can be added to a host to produce the desired target molecule through the exploration of abstract metabolic and reaction network space. However, not many of these tools explore gene-level information required to physically realize the identified synthesis pathways, and none explore enzyme-host compatibility. Developing tools that address this disconnect between abstract reactions/metabolic design space and physical genetic sequence design space will enable expedited experimental efforts that avoid exploring unprofitable synthesis pathways. This work describes a workflow, termed Probabilistic Pathway Assembly with Solubility Confidence Scores (ProPASS), which links synthesis pathway construction with the exploration of the physical design space as imposed by the availability of enzymes with predicted characterized activities within the host. Predicted protein solubility propensity scores are used as a confidence level to quantify the compatibility of each pathway enzyme with the host Escherichia coli (E. coli). This study also presents a database, termed Protein Solubility Database (ProSol DB), which provides solubility confidence scores in E. coli for 240,016 characterized enzymes obtained from UniProtKB/Swiss-Prot. The utility of ProPASS is demonstrated by generating genetic implementations of heterologous synthesis pathways in E. coli that target several commercially useful biomolecules.
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Affiliation(s)
- Sara A Amin
- Department of Computer Science, Tufts University, Medford, Massachusetts
| | | | - Nikhil U Nair
- Department of Chemical and Biological Engineering, Tufts University, Medford, Massachusetts
| | - Soha Hassoun
- Department of Computer Science, Tufts University, Medford, Massachusetts.,Department of Chemical and Biological Engineering, Tufts University, Medford, Massachusetts
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50
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Musil M, Konegger H, Hon J, Bednar D, Damborsky J. Computational Design of Stable and Soluble Biocatalysts. ACS Catal 2018. [DOI: 10.1021/acscatal.8b03613] [Citation(s) in RCA: 56] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Affiliation(s)
- Milos Musil
- Loschmidt Laboratories, Centre for Toxic Compounds in the Environment (RECETOX), and Department of Experimental Biology, Faculty of Science, Masaryk University, 625 00 Brno, Czech Republic
- IT4Innovations Centre of Excellence, Faculty of Information Technology, Brno University of Technology, 612 66 Brno, Czech Republic
- International Clinical Research Center, St. Anne’s University Hospital, Pekarska 53, 656 91 Brno, Czech Republic
| | - Hannes Konegger
- Loschmidt Laboratories, Centre for Toxic Compounds in the Environment (RECETOX), and Department of Experimental Biology, Faculty of Science, Masaryk University, 625 00 Brno, Czech Republic
- International Clinical Research Center, St. Anne’s University Hospital, Pekarska 53, 656 91 Brno, Czech Republic
| | - Jiri Hon
- Loschmidt Laboratories, Centre for Toxic Compounds in the Environment (RECETOX), and Department of Experimental Biology, Faculty of Science, Masaryk University, 625 00 Brno, Czech Republic
- IT4Innovations Centre of Excellence, Faculty of Information Technology, Brno University of Technology, 612 66 Brno, Czech Republic
- International Clinical Research Center, St. Anne’s University Hospital, Pekarska 53, 656 91 Brno, Czech Republic
| | - David Bednar
- Loschmidt Laboratories, Centre for Toxic Compounds in the Environment (RECETOX), and Department of Experimental Biology, Faculty of Science, Masaryk University, 625 00 Brno, Czech Republic
- International Clinical Research Center, St. Anne’s University Hospital, Pekarska 53, 656 91 Brno, Czech Republic
| | - Jiri Damborsky
- Loschmidt Laboratories, Centre for Toxic Compounds in the Environment (RECETOX), and Department of Experimental Biology, Faculty of Science, Masaryk University, 625 00 Brno, Czech Republic
- International Clinical Research Center, St. Anne’s University Hospital, Pekarska 53, 656 91 Brno, Czech Republic
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