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Zhang J, Lin L, Wei W, Wei D. Identification, Characterization, and Computer-Aided Rational Design of a Novel Thermophilic Esterase from Geobacillus subterraneus, and Application in the Synthesis of Cinnamyl Acetate. Appl Biochem Biotechnol 2024; 196:3553-3575. [PMID: 37713064 DOI: 10.1007/s12010-023-04697-2] [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] [Accepted: 08/16/2023] [Indexed: 09/16/2023]
Abstract
Investigation of a novel thermophilic esterase gene from Geobacillus subterraneus DSMZ 13552 indicated a high amino acid sequence similarity of 25.9% to a reported esterase from Geobacillus sp. A strategy that integrated computer-aided rational design tools was developed to select mutation sites. Six mutants were selected from four criteria based on the simulated saturation mutation (including 19 amino acid residues) results. Of these, the mutants Q78Y and G119A were found to retain 87% and 27% activity after incubation at 70 °C for 20 min, compared with the 19% activity for the wild type. Subsequently, a double-point mutant (Q78Y/G119A) was obtained and identified with optimal temperature increase from 65 to 70 °C and a 41.51% decrease in Km. The obtained T1/2 values of 42.2 min (70 °C) and 16.9 min (75 °C) for Q78Y/G119A showed increases of 340% and 412% compared with that in the wild type. Q78Y/G119A was then employed as a biocatalyst to synthesize cinnamyl acetate, for which the conversion rate reached 99.40% with 0.3 M cinnamyl alcohol at 60 °C. The results validated the enhanced enzymatic properties of the mutant and indicated better prospects for industrial application as compared to that in the wild type. This study reported a method by which an enzyme could evolve to achieve enhanced thermostability, thereby increasing its potential for industrial applications, which could also be expanded to other esterases.
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Affiliation(s)
- Jin Zhang
- State Key Laboratory of Bioreactor Engineering, Newworld Institute of Biotechnology, East China University of Science and Technology, Shanghai, 200237, People's Republic of China
| | - Lin Lin
- School of Chemical and Environmental Engineering, Shanghai Institute of Technology, Shanghai, 201418, People's Republic of China
- Research Laboratory for Functional Nanomaterial, National Engineering Research Center for Nanotechnology, Shanghai, 200241, People's Republic of China
| | - Wei Wei
- State Key Laboratory of Bioreactor Engineering, Newworld Institute of Biotechnology, East China University of Science and Technology, Shanghai, 200237, People's Republic of China.
| | - Dongzhi Wei
- State Key Laboratory of Bioreactor Engineering, Newworld Institute of Biotechnology, East China University of Science and Technology, Shanghai, 200237, People's Republic of China
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2
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Myrtollari K, Calderini E, Kracher D, Schöngaßner T, Galušić S, Slavica A, Taden A, Mokos D, Schrüfer A, Wirnsberger G, Gruber K, Daniel B, Kourist R. Stability Increase of Phenolic Acid Decarboxylase by a Combination of Protein and Solvent Engineering Unlocks Applications at Elevated Temperatures. ACS SUSTAINABLE CHEMISTRY & ENGINEERING 2024; 12:3575-3584. [PMID: 38456190 PMCID: PMC10915792 DOI: 10.1021/acssuschemeng.3c06513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Revised: 12/16/2023] [Accepted: 01/25/2024] [Indexed: 03/09/2024]
Abstract
Enzymatic decarboxylation of biobased hydroxycinnamic acids gives access to phenolic styrenes for adhesive production. Phenolic acid decarboxylases are proficient enzymes that have been applied in aqueous systems, organic solvents, biphasic systems, and deep eutectic solvents, which makes stability a key feature. Stabilization of the enzyme would increase the total turnover number and thus reduce the energy consumption and waste accumulation associated with biocatalyst production. In this study, we used ancestral sequence reconstruction to generate thermostable decarboxylases. Investigation of a set of 16 ancestors resulted in the identification of a variant with an unfolding temperature of 78.1 °C and a half-life time of 45 h at 60 °C. Crystal structures were determined for three selected ancestors. Structural attributes were calculated to fit different regression models for predicting the thermal stability of variants that have not yet been experimentally explored. The models rely on hydrophobic clusters, salt bridges, hydrogen bonds, and surface properties and can identify more stable proteins out of a pool of candidates. Further stabilization was achieved by the application of mixtures of natural deep eutectic solvents and buffers. Our approach is a straightforward option for enhancing the industrial application of the decarboxylation process.
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Affiliation(s)
- Kamela Myrtollari
- Institute
of Molecular Biotechnology, Graz University
of Technology, Petersgasse
14, 8010 Graz, Austria
- Austrian
Centre of Industrial Biotechnology, ACIB GmbH, Petersgasse 14/1, 8010 Graz, Austria
- Adhesive
Technologies, Henkel AG & Co. KGaA, Henkelstr. 67, 40191 Düsseldorf, Germany
| | - Elia Calderini
- Institute
of Molecular Biotechnology, Graz University
of Technology, Petersgasse
14, 8010 Graz, Austria
| | - Daniel Kracher
- Institute
of Molecular Biotechnology, Graz University
of Technology, Petersgasse
14, 8010 Graz, Austria
- BioTechMed-Graz, Mozartgasse
12/II, 8010 Graz, Austria
| | - Tobias Schöngaßner
- Institute
of Molecular Biotechnology, Graz University
of Technology, Petersgasse
14, 8010 Graz, Austria
| | - Stela Galušić
- Institute
of Molecular Biotechnology, Graz University
of Technology, Petersgasse
14, 8010 Graz, Austria
| | - Anita Slavica
- Faculty
of Food Technology and Biotechnology, Department of Biochemical Engineering, University of Zagreb, Pierottijeva 6, HR-10000 Zagreb, Croatia
| | - Andreas Taden
- Adhesive
Technologies, Henkel AG & Co. KGaA, Henkelstr. 67, 40191 Düsseldorf, Germany
| | - Daniel Mokos
- Institute
of Molecular Biosciences, University of
Graz, NAWI Graz, Humboldtstraße
50/3, 8010 Graz, Austria
| | - Anna Schrüfer
- Institute
of Molecular Biosciences, University of
Graz, NAWI Graz, Humboldtstraße
50/3, 8010 Graz, Austria
| | - Gregor Wirnsberger
- Institute
of Molecular Biosciences, University of
Graz, NAWI Graz, Humboldtstraße
50/3, 8010 Graz, Austria
| | - Karl Gruber
- BioTechMed-Graz, Mozartgasse
12/II, 8010 Graz, Austria
- Institute
of Molecular Biosciences, University of
Graz, NAWI Graz, Humboldtstraße
50/3, 8010 Graz, Austria
| | - Bastian Daniel
- BioTechMed-Graz, Mozartgasse
12/II, 8010 Graz, Austria
- Institute
of Molecular Biosciences, University of
Graz, NAWI Graz, Humboldtstraße
50/3, 8010 Graz, Austria
| | - Robert Kourist
- Institute
of Molecular Biotechnology, Graz University
of Technology, Petersgasse
14, 8010 Graz, Austria
- Austrian
Centre of Industrial Biotechnology, ACIB GmbH, Petersgasse 14/1, 8010 Graz, Austria
- BioTechMed-Graz, Mozartgasse
12/II, 8010 Graz, Austria
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3
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Ma Z, Mu K, Zhu J, Xiao M, Wang L, Jiang X. Molecular dynamics simulations identify the topological weak spots of a protease CN2S8A. J Mol Graph Model 2023; 124:108571. [PMID: 37487372 DOI: 10.1016/j.jmgm.2023.108571] [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: 05/10/2023] [Revised: 07/06/2023] [Accepted: 07/19/2023] [Indexed: 07/26/2023]
Abstract
Thermophilic enzymes are highly desired in industrial applications due to their efficient catalytic activity at high temperature. However, most enzymes exhibit inferior thermostability and it remains challenging to identify the optimal sites for designing mutations to improve protein stability. To tackle this issue, we integrated topological analysis and all-atom molecular dynamics simulations to efficiently pinpoint the thermally-unstable regions in protein structures. Using a protease CN2S8A as the model, we analyzed the intramolecular hydrogen bonding interactions between adjacent secondary structure elements, and then identified the topological weak spots of CN2S8A where weak hydrogen bonding interactions were formed. To examine the role of these sites in protein structural stability, we designed three virtual mutations at different weak spots and characterized the effects of these mutations on the structural properties of CN2S8A. The results showed that all three mutations increased the protein structural stability. In conclusion, these findings provide a novel method to identify the topological weak spots of proteins, with implications in the rational design of biocatalysts with superior thermostability.
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Affiliation(s)
- Zhenyu Ma
- National Glycoengineering Research Center, Shandong University, Qingdao, 266237, China
| | - Kaijie Mu
- Biomedicine Discovery Institute, Monash University, Melbourne, 3500, Australia
| | - Jingyi Zhu
- State Key Laboratory of Microbial Technology, Shandong University, Qingdao, 266237, China
| | - Min Xiao
- National Glycoengineering Research Center, Shandong University, Qingdao, 266237, China
| | - Lushan Wang
- State Key Laboratory of Microbial Technology, Shandong University, Qingdao, 266237, China
| | - Xukai Jiang
- National Glycoengineering Research Center, Shandong University, Qingdao, 266237, China.
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4
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Pandey P, Panday SK, Rimal P, Ancona N, Alexov E. Predicting the Effect of Single Mutations on Protein Stability and Binding with Respect to Types of Mutations. Int J Mol Sci 2023; 24:12073. [PMID: 37569449 PMCID: PMC10418460 DOI: 10.3390/ijms241512073] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Revised: 07/24/2023] [Accepted: 07/26/2023] [Indexed: 08/13/2023] Open
Abstract
The development of methods and algorithms to predict the effect of mutations on protein stability, protein-protein interaction, and protein-DNA/RNA binding is necessitated by the needs of protein engineering and for understanding the molecular mechanism of disease-causing variants. The vast majority of the leading methods require a database of experimentally measured folding and binding free energy changes for training. These databases are collections of experimental data taken from scientific investigations typically aimed at probing the role of particular residues on the above-mentioned thermodynamic characteristics, i.e., the mutations are not introduced at random and do not necessarily represent mutations originating from single nucleotide variants (SNV). Thus, the reported performance of the leading algorithms assessed on these databases or other limited cases may not be applicable for predicting the effect of SNVs seen in the human population. Indeed, we demonstrate that the SNVs and non-SNVs are not equally presented in the corresponding databases, and the distribution of the free energy changes is not the same. It is shown that the Pearson correlation coefficients (PCCs) of folding and binding free energy changes obtained in cases involving SNVs are smaller than for non-SNVs, indicating that caution should be used in applying them to reveal the effect of human SNVs. Furthermore, it is demonstrated that some methods are sensitive to the chemical nature of the mutations, resulting in PCCs that differ by a factor of four across chemically different mutations. All methods are found to underestimate the energy changes by roughly a factor of 2.
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Affiliation(s)
- Preeti Pandey
- Department of Physics and Astronomy, Clemson University, Clemson, SC 29634, USA; (P.P.); (S.K.P.); (P.R.)
| | - Shailesh Kumar Panday
- Department of Physics and Astronomy, Clemson University, Clemson, SC 29634, USA; (P.P.); (S.K.P.); (P.R.)
| | - Prawin Rimal
- Department of Physics and Astronomy, Clemson University, Clemson, SC 29634, USA; (P.P.); (S.K.P.); (P.R.)
| | - Nicolas Ancona
- Department of Biological Sciences, Clemson University, Clemson, SC 29634, USA;
| | - Emil Alexov
- Department of Physics and Astronomy, Clemson University, Clemson, SC 29634, USA; (P.P.); (S.K.P.); (P.R.)
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5
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Vega-Rodríguez MAD, Rodríguez-González JA, Armendáriz-Ruiz MA, Asaff-Torres A, Sotelo-Mundo RR, Velasco-Lozano S, Mateos-Díaz JC. Feruloyl Esterases Protein Engineering to Enhance Their Performance as Biocatalysts: A Review. Chembiochem 2022; 23:e202200354. [PMID: 35781918 DOI: 10.1002/cbic.202200354] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 07/01/2022] [Indexed: 02/03/2023]
Abstract
Feruloyl esterases (FAEs) are versatile enzymes able to release hydroxycinnamic acids or synthesize their ester derivatives, both molecules with interesting biological activities such as: antioxidants, antifungals, antivirals, antifibrotic, anti-inflammatory, among others. The importance of these molecules in medicine, food or cosmetic industries provides FAEs with several biotechnological applications as key industrial biocatalysts. However, FAEs have some operational limitations that must be overcome, which can be addressed through different protein engineering approaches to enhance their thermal stability, catalytic efficiencies, and selectivity. This review aims to present a brief historical tour through the mutagenesis strategies employed to improve enzymes performance and analyze the current protein engineering strategies applied to FAEs as interesting biocatalysts. Finally, an outlook of the future of FAEs protein engineering approaches to achieve successful industrial biocatalysts is given.
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Affiliation(s)
- Ms Ana Daniela Vega-Rodríguez
- Unidad de Biotecnología Industrial, Centro de Investigación y Asistencia en Tecnología y Diseño del Estado de Jalisco (CIATEJ), Camino Arenero No. 1227 Colonia El Bajío del Arenal, 45019, Zapopan, Jalisco, Mexico
| | - Jorge Alberto Rodríguez-González
- Unidad de Biotecnología Industrial, Centro de Investigación y Asistencia en Tecnología y Diseño del Estado de Jalisco (CIATEJ), Camino Arenero No. 1227 Colonia El Bajío del Arenal, 45019, Zapopan, Jalisco, Mexico
| | | | - Ali Asaff-Torres
- Unidad de Biotecnología Industrial, Centro de Investigación en Alimentación y Desarrollo (CIAD), Carretera a la Victoria Km 0.6, 83304, Hermosillo, Sonora, Mexico
| | - Rogerio R Sotelo-Mundo
- Laboratorio de Estructura Biomolecular, Centro de Investigación en Alimentación y Desarrollo (CIAD), Carretera a la Victoria Km 0.6, 83304, Hermosillo, Sonora (Mexico
| | - Susana Velasco-Lozano
- Heterogeneous Biocatalysis Laboratory, Center for Cooperative Research in Biomaterials (CIC biomaGUNE), Miramon Pasealekua, 182, 20014, Donostia, Spain
| | - Juan Carlos Mateos-Díaz
- Unidad de Biotecnología Industrial, Centro de Investigación y Asistencia en Tecnología y Diseño del Estado de Jalisco (CIATEJ), Camino Arenero No. 1227 Colonia El Bajío del Arenal, 45019, Zapopan, Jalisco, Mexico
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6
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Avery C, Patterson J, Grear T, Frater T, Jacobs DJ. Protein Function Analysis through Machine Learning. Biomolecules 2022; 12:1246. [PMID: 36139085 PMCID: PMC9496392 DOI: 10.3390/biom12091246] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2022] [Revised: 08/22/2022] [Accepted: 08/31/2022] [Indexed: 11/16/2022] Open
Abstract
Machine learning (ML) has been an important arsenal in computational biology used to elucidate protein function for decades. With the recent burgeoning of novel ML methods and applications, new ML approaches have been incorporated into many areas of computational biology dealing with protein function. We examine how ML has been integrated into a wide range of computational models to improve prediction accuracy and gain a better understanding of protein function. The applications discussed are protein structure prediction, protein engineering using sequence modifications to achieve stability and druggability characteristics, molecular docking in terms of protein-ligand binding, including allosteric effects, protein-protein interactions and protein-centric drug discovery. To quantify the mechanisms underlying protein function, a holistic approach that takes structure, flexibility, stability, and dynamics into account is required, as these aspects become inseparable through their interdependence. Another key component of protein function is conformational dynamics, which often manifest as protein kinetics. Computational methods that use ML to generate representative conformational ensembles and quantify differences in conformational ensembles important for function are included in this review. Future opportunities are highlighted for each of these topics.
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Affiliation(s)
- Chris Avery
- Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
| | - John Patterson
- Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
| | - Tyler Grear
- Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
- Department of Physics and Optical Science, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
| | - Theodore Frater
- Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
| | - Donald J. Jacobs
- Department of Physics and Optical Science, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
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7
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Perciballi E, Bovio F, Rosati J, Arrigoni F, D’Anzi A, Lattante S, Gelati M, De Marchi F, Lombardi I, Ruotolo G, Forcella M, Mazzini L, D’Alfonso S, Corrado L, Sabatelli M, Conte A, De Gioia L, Martino S, Vescovi AL, Fusi P, Ferrari D. Characterization of the p.L145F and p.S135N Mutations in SOD1: Impact on the Metabolism of Fibroblasts Derived from Amyotrophic Lateral Sclerosis Patients. Antioxidants (Basel) 2022; 11:antiox11050815. [PMID: 35624679 PMCID: PMC9137766 DOI: 10.3390/antiox11050815] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 04/13/2022] [Accepted: 04/20/2022] [Indexed: 12/24/2022] Open
Abstract
Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease characterized by the loss of the upper and lower motor neurons (MNs). About 10% of patients have a family history (familial, fALS); however, most patients seem to develop the sporadic form of the disease (sALS). SOD1 (Cu/Zn superoxide dismutase-1) is the first studied gene among the ones related to ALS. Mutant SOD1 can adopt multiple misfolded conformation, lose the correct coordination of metal binding, decrease structural stability, and form aggregates. For all these reasons, it is complicated to characterize the conformational alterations of the ALS-associated mutant SOD1, and how they relate to toxicity. In this work, we performed a multilayered study on fibroblasts derived from two ALS patients, namely SOD1L145F and SOD1S135N, carrying the p.L145F and the p.S135N missense variants, respectively. The patients showed diverse symptoms and disease progression in accordance with our bioinformatic analysis, which predicted the different effects of the two mutations in terms of protein structure. Interestingly, both mutations had an effect on the fibroblast energy metabolisms. However, while the SOD1L145F fibroblasts still relied more on oxidative phosphorylation, the SOD1S135N fibroblasts showed a metabolic shift toward glycolysis. Our study suggests that SOD1 mutations might lead to alterations in the energy metabolism.
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Affiliation(s)
- Elisa Perciballi
- Department of Biotechnology and Biosciences, University of Milano-Bicocca, P.zza della Scienza, 2, 20126 Milan, Italy; (E.P.); (F.B.); (F.A.); (I.L.); (M.F.); (L.D.G.); (A.L.V.)
| | - Federica Bovio
- Department of Biotechnology and Biosciences, University of Milano-Bicocca, P.zza della Scienza, 2, 20126 Milan, Italy; (E.P.); (F.B.); (F.A.); (I.L.); (M.F.); (L.D.G.); (A.L.V.)
| | - Jessica Rosati
- Cellular Reprogramming Unit, Fondazione IRCCS Casa Sollievo della Sofferenza, Viale dei Cappuccini 1, 71013 San Giovanni Rotondo, Italy; (J.R.); (A.D.); (G.R.)
| | - Federica Arrigoni
- Department of Biotechnology and Biosciences, University of Milano-Bicocca, P.zza della Scienza, 2, 20126 Milan, Italy; (E.P.); (F.B.); (F.A.); (I.L.); (M.F.); (L.D.G.); (A.L.V.)
| | - Angela D’Anzi
- Cellular Reprogramming Unit, Fondazione IRCCS Casa Sollievo della Sofferenza, Viale dei Cappuccini 1, 71013 San Giovanni Rotondo, Italy; (J.R.); (A.D.); (G.R.)
| | - Serena Lattante
- Section of Genomic Medicine, Department of Life Sciences and Public Health, Università Cattolica del Sacro Cuore, Largo Francesco Vito, 1, 00168 Rome, Italy;
- Unit of Medical Genetics, Department of Laboratory and Infectious Disease Sciences, Fondazione Policlinico Universitario A. Gemelli IRCCS, Largo A. Gemelli 8, 00168 Rome, Italy
| | - Maurizio Gelati
- UPTA Unit, Fondazione IRCCS Casa Sollievo della Sofferenza, Viale dei Cappuccini 1, 71013 San Giovanni Rotondo, Italy;
| | - Fabiola De Marchi
- ALS Centre Maggiore della Carità Hospital and Università del Piemonte Orientale, 28100 Novara, Italy; (F.D.M.); (L.M.)
| | - Ivan Lombardi
- Department of Biotechnology and Biosciences, University of Milano-Bicocca, P.zza della Scienza, 2, 20126 Milan, Italy; (E.P.); (F.B.); (F.A.); (I.L.); (M.F.); (L.D.G.); (A.L.V.)
| | - Giorgia Ruotolo
- Cellular Reprogramming Unit, Fondazione IRCCS Casa Sollievo della Sofferenza, Viale dei Cappuccini 1, 71013 San Giovanni Rotondo, Italy; (J.R.); (A.D.); (G.R.)
| | - Matilde Forcella
- Department of Biotechnology and Biosciences, University of Milano-Bicocca, P.zza della Scienza, 2, 20126 Milan, Italy; (E.P.); (F.B.); (F.A.); (I.L.); (M.F.); (L.D.G.); (A.L.V.)
| | - Letizia Mazzini
- ALS Centre Maggiore della Carità Hospital and Università del Piemonte Orientale, 28100 Novara, Italy; (F.D.M.); (L.M.)
| | - Sandra D’Alfonso
- Department of Health Sciences, Center on Autoimmune and Allergic Diseases (CAAD), UPO, University of Eastern Piedmont, 28100 Novara, Italy; (S.D.); (L.C.)
| | - Lucia Corrado
- Department of Health Sciences, Center on Autoimmune and Allergic Diseases (CAAD), UPO, University of Eastern Piedmont, 28100 Novara, Italy; (S.D.); (L.C.)
| | - Mario Sabatelli
- Adult NEMO Clinical Center, Unit of Neurology, Department of Aging, Neurological, Orthopedic and Head-Neck Sciences, Fondazione Policlinico Universitario A. Gemelli IRCCS, Largo A. Gemelli 8, 00168 Rome, Italy; (M.S.); (A.C.)
- Section of Neurology, Department of Neuroscience, Faculty of Medicine and Surgery, Università Cattolica del Sacro Cuore, Largo Francesco Vito, 1, 00168 Rome, Italy
| | - Amelia Conte
- Adult NEMO Clinical Center, Unit of Neurology, Department of Aging, Neurological, Orthopedic and Head-Neck Sciences, Fondazione Policlinico Universitario A. Gemelli IRCCS, Largo A. Gemelli 8, 00168 Rome, Italy; (M.S.); (A.C.)
- Section of Neurology, Department of Neuroscience, Faculty of Medicine and Surgery, Università Cattolica del Sacro Cuore, Largo Francesco Vito, 1, 00168 Rome, Italy
| | - Luca De Gioia
- Department of Biotechnology and Biosciences, University of Milano-Bicocca, P.zza della Scienza, 2, 20126 Milan, Italy; (E.P.); (F.B.); (F.A.); (I.L.); (M.F.); (L.D.G.); (A.L.V.)
| | - Sabata Martino
- Department of Chemistry, Biology and Biotechnology, University of Perugia, Via del Giochetto, 06123 Perugia, Italy;
| | - Angelo Luigi Vescovi
- Department of Biotechnology and Biosciences, University of Milano-Bicocca, P.zza della Scienza, 2, 20126 Milan, Italy; (E.P.); (F.B.); (F.A.); (I.L.); (M.F.); (L.D.G.); (A.L.V.)
- Fondazione IRCCS Casa Sollievo della Sofferenza, Viale dei Cappuccini 1, 71013 San Giovanni Rotondo, Italy
| | - Paola Fusi
- Department of Biotechnology and Biosciences, University of Milano-Bicocca, P.zza della Scienza, 2, 20126 Milan, Italy; (E.P.); (F.B.); (F.A.); (I.L.); (M.F.); (L.D.G.); (A.L.V.)
- Correspondence: (P.F.); (D.F.); Tel.: +39-348-004-6641 (D.F.)
| | - Daniela Ferrari
- Department of Biotechnology and Biosciences, University of Milano-Bicocca, P.zza della Scienza, 2, 20126 Milan, Italy; (E.P.); (F.B.); (F.A.); (I.L.); (M.F.); (L.D.G.); (A.L.V.)
- Correspondence: (P.F.); (D.F.); Tel.: +39-348-004-6641 (D.F.)
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8
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Kebabci N, Timucin AC, Timucin E. Toward Compilation of Balanced Protein Stability Data Sets: Flattening the ΔΔ G Curve through Systematic Enrichment. J Chem Inf Model 2022; 62:1345-1355. [PMID: 35201762 DOI: 10.1021/acs.jcim.2c00054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Often studies analyzing stability data sets and/or predictors ignore neutral mutations and use a binary classification scheme labeling only destabilizing and stabilizing mutations. Recognizing that highly concentrated neutral mutations interfere with data set quality, we have explored three protein stability data sets: S2648, PON-tstab, and the symmetric Ssym that differ in size and quality. A characteristic leptokurtic shape in the ΔΔG distributions of all three data sets including the curated and symmetric ones was reported due to concentrated neutral mutations. To further investigate the impact of neutral mutations on ΔΔG predictions, we have comprehensively assessed the performance of 11 predictors on the PON-tstab data set. Correlation and error analyses showed that all of the predictors performed the best on the neutral mutations, while their performance became gradually worse as the ΔΔG of the mutations departed further from the neutral zone regardless of the direction, implying a bias toward dense mutations. To this end, after unraveling the role of concentrated neutral mutations in biases of stability data sets, we described a systematic enrichment approach to balance the ΔΔG distributions. Before enrichment, mutations were clustered based on their biochemical and/or structural features, and then three mutations were selected from every 2 kcal/mol of each cluster. Upon implementation of this approach by distinct clustering schemes, we generated five subsets varying in size and ΔΔG distributions. All subsets showed improved ΔΔG and frequency distributions. We ultimately reported that the errors toward enriched subsets were higher than those toward the parent data sets, confirming the enrichment of difficult-to-predict mutations in the subsets. In summary, we elaborated the prediction bias toward a concentrated neutral zone and also implemented a rational strategy to tackle this and other forms of biases. Ultimately, this study equipping us with an extended view of shortcomings of stability data sets is a step taken toward development of an unbiased predictor.
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Affiliation(s)
- Narod Kebabci
- Department of Biostatistics and Bioinformatics, Institute of Health Sciences, Acibadem University, Istanbul 34752, Turkey
| | - Ahmet Can Timucin
- Department of Molecular Biology and Genetics, Faculty of Arts and Sciences, Acibadem University, Istanbul 34752, Turkey
| | - Emel Timucin
- Department of Biostatistics and Medical Informatics, School of Medicine, Acibadem University, Istanbul 34752, Turkey
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9
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Holland J, Grigoryan G. Structure‐conditioned amino‐acid couplings: how contact geometry affects pairwise sequence preferences. Protein Sci 2022; 31:900-917. [PMID: 35060221 PMCID: PMC8927866 DOI: 10.1002/pro.4280] [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: 11/04/2021] [Revised: 01/06/2022] [Accepted: 01/12/2022] [Indexed: 11/11/2022]
Abstract
Relating a protein's sequence to its conformation is a central challenge for both structure prediction and sequence design. Statistical contact potentials, as well as their more descriptive versions that account for side‐chain orientation and other geometric descriptors, have served as simplistic but useful means of representing second‐order contributions in sequence–structure relationships. Here we ask what happens when a pairwise potential is conditioned on the fully defined geometry of interacting backbones fragments. We show that the resulting structure‐conditioned coupling energies more accurately reflect pair preferences as a function of structural contexts. These structure‐conditioned energies more reliably encode native sequence information and more highly correlate with experimentally determined coupling energies. Clustering a database of interaction motifs by structure results in ensembles of similar energies and clustering them by energy results in ensembles of similar structures. By comparing many pairs of interaction motifs and showing that structural similarity and energetic similarity go hand‐in‐hand, we provide a tangible link between modular sequence and structure elements. This link is applicable to structural modeling, and we show that scoring CASP models with structured‐conditioned energies results in substantially higher correlation with structural quality than scoring the same models with a contact potential. We conclude that structure‐conditioned coupling energies are a good way to model the impact of interaction geometry on second‐order sequence preferences.
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Affiliation(s)
- Jack Holland
- Department of Computer Science Dartmouth College Hanover New Hampshire USA
| | - Gevorg Grigoryan
- Department of Computer Science Dartmouth College Hanover New Hampshire USA
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10
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Baek KT, Kepp KP. Data set and fitting dependencies when estimating protein mutant stability: Toward simple, balanced, and interpretable models. J Comput Chem 2022; 43:504-518. [PMID: 35040492 DOI: 10.1002/jcc.26810] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Revised: 12/13/2021] [Accepted: 01/03/2022] [Indexed: 12/27/2022]
Abstract
Accurate prediction of protein stability changes upon mutation (ΔΔG) is increasingly important to evolution studies, protein engineering, and screening of disease-causing gene variants but is challenged by biases in training data. We investigated 45 linear regression models trained on data sets that account systematically for destabilization bias and mutation-type bias BM . The models were externally validated on three test data sets probing different pathologies and for internal consistency (symmetry and neutrality). Model structure and performance substantially depended on training data and even fitting method. We developed two final models: SimBa-IB for typical natural mutations and SimBa-SYM for situations where stabilizing and destabilizing mutations occur to a similar extent. SimBa-SYM, despite is simplicity, is essentially non-biased (vs. the Ssym data set) while still performing well for all data sets (R ~ 0.46-0.54, MAE = 1.16-1.24 kcal/mol). The simple models provide advantage in terms of interpretability, use and future improvement, and are freely available on GitHub.
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Affiliation(s)
| | - Kasper P Kepp
- DTU Chemistry, Technical University of Denmark, Lyngby, Denmark
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11
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Pham NTH, Létourneau M, Fortier M, Bégin G, Al-Abdul-Wahid MS, Pucci F, Folch B, Rooman M, Chatenet D, St-Pierre Y, Lagüe P, Calmettes C, Doucet N. Perturbing dimer interactions and allosteric communication modulates the immunosuppressive activity of human galectin-7. J Biol Chem 2021; 297:101308. [PMID: 34673030 PMCID: PMC8592873 DOI: 10.1016/j.jbc.2021.101308] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Revised: 10/10/2021] [Accepted: 10/11/2021] [Indexed: 11/16/2022] Open
Abstract
The design of allosteric modulators to control protein function is a key objective in drug discovery programs. Altering functionally essential allosteric residue networks provides unique protein family subtype specificity, minimizes unwanted off-target effects, and helps avert resistance acquisition typically plaguing drugs that target orthosteric sites. In this work, we used protein engineering and dimer interface mutations to positively and negatively modulate the immunosuppressive activity of the proapoptotic human galectin-7 (GAL-7). Using the PoPMuSiC and BeAtMuSiC algorithms, mutational sites and residue identity were computationally probed and predicted to either alter or stabilize the GAL-7 dimer interface. By designing a covalent disulfide bridge between protomers to control homodimer strength and stability, we demonstrate the importance of dimer interface perturbations on the allosteric network bridging the two opposite glycan-binding sites on GAL-7, resulting in control of induced apoptosis in Jurkat T cells. Molecular investigation of G16X GAL-7 variants using X-ray crystallography, biophysical, and computational characterization illuminates residues involved in dimer stability and allosteric communication, along with discrete long-range dynamic behaviors involving loops 1, 3, and 5. We show that perturbing the protein-protein interface between GAL-7 protomers can modulate its biological function, even when the overall structure and ligand-binding affinity remains unaltered. This study highlights new avenues for the design of galectin-specific modulators influencing both glycan-dependent and glycan-independent interactions.
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Affiliation(s)
- N T Hang Pham
- Centre Armand-Frappier Santé Biotechnologie, Institut National de la Recherche Scientifique (INRS), Université du Québec, Laval, Quebec, Canada
| | - Myriam Létourneau
- Centre Armand-Frappier Santé Biotechnologie, Institut National de la Recherche Scientifique (INRS), Université du Québec, Laval, Quebec, Canada
| | - Marlène Fortier
- Centre Armand-Frappier Santé Biotechnologie, Institut National de la Recherche Scientifique (INRS), Université du Québec, Laval, Quebec, Canada
| | - Gabriel Bégin
- Département de Biochimie, de Microbiologie et de Bio-informatique and Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Québec, Quebec, Canada; PROTEO, the Québec Network for Research on Protein Function, Engineering, and Applications, Université Laval, Québec, Quebec, Canada
| | | | - Fabrizio Pucci
- Computational Biology and Bioinformatics, Université Libre de Bruxelles, Brussels, Belgium
| | - Benjamin Folch
- Centre Armand-Frappier Santé Biotechnologie, Institut National de la Recherche Scientifique (INRS), Université du Québec, Laval, Quebec, Canada
| | - Marianne Rooman
- Computational Biology and Bioinformatics, Université Libre de Bruxelles, Brussels, Belgium
| | - David Chatenet
- Centre Armand-Frappier Santé Biotechnologie, Institut National de la Recherche Scientifique (INRS), Université du Québec, Laval, Quebec, Canada
| | - Yves St-Pierre
- Centre Armand-Frappier Santé Biotechnologie, Institut National de la Recherche Scientifique (INRS), Université du Québec, Laval, Quebec, Canada
| | - Patrick Lagüe
- Département de Biochimie, de Microbiologie et de Bio-informatique and Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Québec, Quebec, Canada; PROTEO, the Québec Network for Research on Protein Function, Engineering, and Applications, Université Laval, Québec, Quebec, Canada
| | - Charles Calmettes
- Centre Armand-Frappier Santé Biotechnologie, Institut National de la Recherche Scientifique (INRS), Université du Québec, Laval, Quebec, Canada; PROTEO, the Québec Network for Research on Protein Function, Engineering, and Applications, Université Laval, Québec, Quebec, Canada
| | - Nicolas Doucet
- Centre Armand-Frappier Santé Biotechnologie, Institut National de la Recherche Scientifique (INRS), Université du Québec, Laval, Quebec, Canada; PROTEO, the Québec Network for Research on Protein Function, Engineering, and Applications, Université Laval, Québec, Quebec, Canada.
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12
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Aprigliano R, Aksu ME, Bradamante S, Mihaljevic B, Wang W, Rian K, Montaldo NP, Grooms KM, Fordyce Martin SL, Bordin DL, Bosshard M, Peng Y, Alexov E, Skinner C, Liabakk NB, Sullivan GJ, Bjørås M, Schwartz CE, van Loon B. Increased p53 signaling impairs neural differentiation in HUWE1-promoted intellectual disabilities. Cell Rep Med 2021; 2:100240. [PMID: 33948573 PMCID: PMC8080178 DOI: 10.1016/j.xcrm.2021.100240] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2020] [Revised: 01/18/2021] [Accepted: 03/16/2021] [Indexed: 02/06/2023]
Abstract
Essential E3 ubiquitin ligase HUWE1 (HECT, UBA, and WWE domain containing 1) regulates key factors, such as p53. Although mutations in HUWE1 cause heterogenous neurodevelopmental X-linked intellectual disabilities (XLIDs), the disease mechanisms common to these syndromes remain unknown. In this work, we identify p53 signaling as the central process altered in HUWE1-promoted XLID syndromes. By focusing on Juberg-Marsidi syndrome (JMS), one of the severest XLIDs, we show that increased p53 signaling results from p53 accumulation caused by HUWE1 p.G4310R destabilization. This further alters cell-cycle progression and proliferation in JMS cells. Modeling of JMS neurodevelopment reveals majorly impaired neural differentiation accompanied by increased p53 signaling. The neural differentiation defects can be successfully rescued by reducing p53 levels and restoring the expression of p53 target genes, in particular CDKN1A/p21. In summary, our findings suggest that increased p53 signaling underlies HUWE1-promoted syndromes and impairs XLID JMS neural differentiation.
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Affiliation(s)
- Rossana Aprigliano
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology (NTNU), 7049 Trondheim, Norway
- Department of Molecular Mechanisms of Disease, University of Zurich, 8057 Zürich, Switzerland
| | - Merdane Ezgi Aksu
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology (NTNU), 7049 Trondheim, Norway
| | - Stefano Bradamante
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology (NTNU), 7049 Trondheim, Norway
- Department of Pathology and Medical Genetics, St. Olavs University Hospital, 7049 Trondheim, Norway
| | - Boris Mihaljevic
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology (NTNU), 7049 Trondheim, Norway
| | - Wei Wang
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology (NTNU), 7049 Trondheim, Norway
| | - Kristin Rian
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology (NTNU), 7049 Trondheim, Norway
| | - Nicola P. Montaldo
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology (NTNU), 7049 Trondheim, Norway
| | - Kayla Mae Grooms
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology (NTNU), 7049 Trondheim, Norway
| | - Sarah L. Fordyce Martin
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology (NTNU), 7049 Trondheim, Norway
| | - Diana L. Bordin
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology (NTNU), 7049 Trondheim, Norway
| | - Matthias Bosshard
- Department of Molecular Mechanisms of Disease, University of Zurich, 8057 Zürich, Switzerland
| | - Yunhui Peng
- Computational Biophysics and Bioinformatics, Department of Physics and Astronomy, Clemson University, Clemson, SC 29631, USA
| | - Emil Alexov
- Computational Biophysics and Bioinformatics, Department of Physics and Astronomy, Clemson University, Clemson, SC 29631, USA
| | | | - Nina-Beate Liabakk
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology (NTNU), 7049 Trondheim, Norway
| | - Gareth J. Sullivan
- Department of Molecular Medicine, Institute of Basic Medical Sciences, University of Oslo, 0315 Oslo, Norway
- Hybrid Technology Hub, Centre of Excellence, Institute of Basic Medical Sciences, University of Oslo, 0315 Oslo, Norway
| | - Magnar Bjørås
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology (NTNU), 7049 Trondheim, Norway
- Department of Pathology and Medical Genetics, St. Olavs University Hospital, 7049 Trondheim, Norway
- Department of Microbiology, Oslo University Hospital, Department of Medical Biochemistry, Oslo University Hospital and University of Oslo, 0372 Oslo, Norway
| | | | - Barbara van Loon
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology (NTNU), 7049 Trondheim, Norway
- Department of Molecular Mechanisms of Disease, University of Zurich, 8057 Zürich, Switzerland
- Department of Pathology and Medical Genetics, St. Olavs University Hospital, 7049 Trondheim, Norway
- Corresponding author
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13
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Caldararu O, Blundell TL, Kepp KP. A base measure of precision for protein stability predictors: structural sensitivity. BMC Bioinformatics 2021; 22:88. [PMID: 33632133 PMCID: PMC7908712 DOI: 10.1186/s12859-021-04030-w] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Accepted: 02/15/2021] [Indexed: 01/17/2023] Open
Abstract
BACKGROUND Prediction of the change in fold stability (ΔΔG) of a protein upon mutation is of major importance to protein engineering and screening of disease-causing variants. Many prediction methods can use 3D structural information to predict ΔΔG. While the performance of these methods has been extensively studied, a new problem has arisen due to the abundance of crystal structures: How precise are these methods in terms of structure input used, which structure should be used, and how much does it matter? Thus, there is a need to quantify the structural sensitivity of protein stability prediction methods. RESULTS We computed the structural sensitivity of six widely-used prediction methods by use of saturated computational mutagenesis on a diverse set of 87 structures of 25 proteins. Our results show that structural sensitivity varies massively and surprisingly falls into two very distinct groups, with methods that take detailed account of the local environment showing a sensitivity of ~ 0.6 to 0.8 kcal/mol, whereas machine-learning methods display much lower sensitivity (~ 0.1 kcal/mol). We also observe that the precision correlates with the accuracy for mutation-type-balanced data sets but not generally reported accuracy of the methods, indicating the importance of mutation-type balance in both contexts. CONCLUSIONS The structural sensitivity of stability prediction methods varies greatly and is caused mainly by the models and less by the actual protein structural differences. As a new recommended standard, we therefore suggest that ΔΔG values are evaluated on three protein structures when available and the associated standard deviation reported, to emphasize not just the accuracy but also the precision of the method in a specific study. Our observation that machine-learning methods deemphasize structure may indicate that folded wild-type structures alone, without the folded mutant and unfolded structures, only add modest value for assessing protein stability effects, and that side-chain-sensitive methods overstate the significance of the folded wild-type structure.
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Affiliation(s)
- Octav Caldararu
- DTU Chemistry, Technical University of Denmark, Building 206, 2800, Kgs. Lyngby, Denmark
| | - Tom L Blundell
- Department of Biochemistry, University of Cambridge, Cambridge, CB2 1GA, UK
| | - Kasper P Kepp
- DTU Chemistry, Technical University of Denmark, Building 206, 2800, Kgs. Lyngby, Denmark.
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14
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Hou Q, Pucci F, Ancien F, Kwasigroch JM, Bourgeas R, Rooman M. SWOTein: a structure-based approach to predict stability Strengths and Weaknesses of prOTEINs. Bioinformatics 2021; 37:1963–1971. [PMID: 33471089 DOI: 10.1093/bioinformatics/btab034] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Revised: 12/05/2020] [Accepted: 01/15/2021] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION Although structured proteins adopt their lowest free energy conformation in physiological conditions, the individual residues are generally not in their lowest free energy conformation. Residues that are stability weaknesses are often involved in functional regions, whereas stability strengths ensure local structural stability. The detection of strengths and weaknesses provides key information to guide protein engineering experiments aiming to modulate folding and various functional processes. RESULTS We developed the SWOTein predictor which identifies strong and weak residues in proteins on the basis of three types of statistical energy functions describing local interactions along the chain, hydrophobic forces and tertiary interactions. The large-scale analysis of the different types of strengths and weaknesses demonstrated their complementarity and the enhancement of the information they provide. Moreover, a good average correlation was observed between predicted and experimental strengths and weaknesses obtained from native hydrogen exchange data. SWOTein application to three test cases further showed its suitability to predict and interpret strong and weak residues in the context of folding, conformational changes and protein-protein binding. In summary, SWOTein is both fast and accurate and can be applied at small and large scale to analyze and modulate folding and molecular recognition processes. AVAILABILITY The SWOTein webserver provides the list of predicted strengths and weaknesses and a protein structure visualization tool that facilitates the interpretation of the predictions. It is freely available for academic use at http://babylone.ulb.ac.be/SWOTein/.
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Affiliation(s)
- Qingzhen Hou
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Shandong 250002, P. R. China.,National Institute of Health Data Science of China, Shandong University, Shandong 250002, P. R. China.,Computational Biology and Bioinformatics, Université Libre de Bruxelles, Brussels 1050, Belgium
| | - Fabrizio Pucci
- Computational Biology and Bioinformatics, Université Libre de Bruxelles, Brussels 1050, Belgium.,Interuniversity Institute of Bioinformatics in Brussels, Boulevard du Triomphe, 1050 Brussels, Belgium
| | - François Ancien
- Computational Biology and Bioinformatics, Université Libre de Bruxelles, Brussels 1050, Belgium.,Interuniversity Institute of Bioinformatics in Brussels, Boulevard du Triomphe, 1050 Brussels, Belgium
| | - Jean-Marc Kwasigroch
- Computational Biology and Bioinformatics, Université Libre de Bruxelles, Brussels 1050, Belgium
| | - Raphaël Bourgeas
- Computational Biology and Bioinformatics, Université Libre de Bruxelles, Brussels 1050, Belgium
| | - Marianne Rooman
- Computational Biology and Bioinformatics, Université Libre de Bruxelles, Brussels 1050, Belgium.,Interuniversity Institute of Bioinformatics in Brussels, Boulevard du Triomphe, 1050 Brussels, Belgium
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15
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Roda S, Santiago G, Guallar V. Mapping enzyme-substrate interactions: its potential to study the mechanism of enzymes. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2020; 122:1-31. [PMID: 32951809 DOI: 10.1016/bs.apcsb.2020.06.001] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
With the increase of the need to use more sustainable processes for the industry in our society, the modeling of enzymes has become crucial to fully comprehend their mechanism of action and use this knowledge to enhance and design their properties. A lot of methods to study enzymes computationally exist and they have been classified on sequence-based, structure-based, and the more new artificial intelligence-based ones. Albeit the abundance of methods to help predict the function of an enzyme, molecular modeling is crucial when trying to understand the enzyme mechanism, as they aim to correlate atomistic information with experimental data. Among them, methods that simulate the system dynamics at a molecular mechanics level of theory (classical force fields) have shown to offer a comprehensive study. In this book chapter, we will analyze these techniques, emphasizing the importance of precise modeling of enzyme-substrate interactions. In the end, a brief explanation of the transference of the information from research studies to the industry is given accompanied with two examples of family enzymes where their modeling has helped their exploitation.
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Affiliation(s)
- Sergi Roda
- Barcelona Supercomputing Center (BSC), Barcelona, Spain
| | | | - Victor Guallar
- Barcelona Supercomputing Center (BSC), Barcelona, Spain; Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain
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16
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Caldararu O, Mehra R, Blundell TL, Kepp KP. Systematic Investigation of the Data Set Dependency of Protein Stability Predictors. J Chem Inf Model 2020; 60:4772-4784. [DOI: 10.1021/acs.jcim.0c00591] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Affiliation(s)
- Octav Caldararu
- DTU Chemistry, Technical University of Denmark, Building 206, 2800 Kgs. Lyngby, Denmark
| | - Rukmankesh Mehra
- DTU Chemistry, Technical University of Denmark, Building 206, 2800 Kgs. Lyngby, Denmark
| | - Tom L. Blundell
- Department of Biochemistry, University of Cambridge, Cambridge CB2 1GA, United Kingdom
| | - Kasper P. Kepp
- DTU Chemistry, Technical University of Denmark, Building 206, 2800 Kgs. Lyngby, Denmark
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17
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Zhu C, Miller M, Zeng Z, Wang Y, Mahlich Y, Aptekmann A, Bromberg Y. Computational Approaches for Unraveling the Effects of Variation in the Human Genome and Microbiome. Annu Rev Biomed Data Sci 2020. [DOI: 10.1146/annurev-biodatasci-030320-041014] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
The past two decades of analytical efforts have highlighted how much more remains to be learned about the human genome and, particularly, its complex involvement in promoting disease development and progression. While numerous computational tools exist for the assessment of the functional and pathogenic effects of genome variants, their precision is far from satisfactory, particularly for clinical use. Accumulating evidence also suggests that the human microbiome's interaction with the human genome plays a critical role in determining health and disease states. While numerous microbial taxonomic groups and molecular functions of the human microbiome have been associated with disease, the reproducibility of these findings is lacking. The human microbiome–genome interaction in healthy individuals is even less well understood. This review summarizes the available computational methods built to analyze the effect of variation in the human genome and microbiome. We address the applicability and precision of these methods across their possible uses. We also briefly discuss the exciting, necessary, and now possible integration of the two types of data to improve the understanding of pathogenicity mechanisms.
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Affiliation(s)
- Chengsheng Zhu
- Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, New Jersey 08873, USA;,
| | - Maximilian Miller
- Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, New Jersey 08873, USA;,
| | - Zishuo Zeng
- Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, New Jersey 08873, USA;,
| | - Yanran Wang
- Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, New Jersey 08873, USA;,
| | - Yannick Mahlich
- Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, New Jersey 08873, USA;,
| | - Ariel Aptekmann
- Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, New Jersey 08873, USA;,
| | - Yana Bromberg
- Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, New Jersey 08873, USA;,
- Department of Genetics, Rutgers University, Piscataway, New Jersey 08854, USA
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18
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Chen CW, Lin MH, Liao CC, Chang HP, Chu YW. iStable 2.0: Predicting protein thermal stability changes by integrating various characteristic modules. Comput Struct Biotechnol J 2020; 18:622-630. [PMID: 32226595 PMCID: PMC7090336 DOI: 10.1016/j.csbj.2020.02.021] [Citation(s) in RCA: 46] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Revised: 02/25/2020] [Accepted: 02/27/2020] [Indexed: 11/15/2022] Open
Abstract
Protein mutations can lead to structural changes that affect protein function and result in disease occurrence. In protein engineering, drug design or and optimization industries, mutations are often used to improve protein stability or to change protein properties while maintaining stability. To provide possible candidates for novel protein design, several computational tools for predicting protein stability changes have been developed. Although many prediction tools are available, each tool employs different algorithms and features. This can produce conflicting prediction results that make it difficult for users to decide upon the correct protein design. Therefore, this study proposes an integrated prediction tool, iStable 2.0, which integrates 11 sequence-based and structure-based prediction tools by machine learning and adds protein sequence information as features. Three coding modules are designed for the system, an Online Server Module, a Stand-alone Module and a Sequence Coding Module, to improve the prediction performance of the previous version of the system. The final integrated structure-based classification model has a higher Matthews correlation coefficient than that of the single prediction tool (0.708 vs 0.547, respectively), and the Pearson correlation coefficient of the regression model likewise improves from 0.669 to 0.714. The sequence-based model not only successfully integrates off-the-shelf predictors but also improves the Matthews correlation coefficient of the best single prediction tool by at least 0.161, which is better than the individual structure-based prediction tools. In addition, both the Sequence Coding Module and the Stand-alone Module maintain performance with only a 5% decrease of the Matthews correlation coefficient when the integrated online tools are unavailable. iStable 2.0 is available at http://ncblab.nchu.edu.tw/iStable2.
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Affiliation(s)
- Chi-Wei Chen
- Department of Computer Science and Engineering, National Chung-Hsing University, 145 Xingda Rd., South Dist., Taichung City 402, Taiwan
- Institute of Genomics and Bioinformatics, National Chung Hsing University, 145 Xingda Rd., South Dist., Taichung City 402, Taiwan
| | - Meng-Han Lin
- Institute of Genomics and Bioinformatics, National Chung Hsing University, 145 Xingda Rd., South Dist., Taichung City 402, Taiwan
| | - Chi-Chou Liao
- Institute of Genomics and Bioinformatics, National Chung Hsing University, 145 Xingda Rd., South Dist., Taichung City 402, Taiwan
- Institute of Molecular Biology, National Chung Hsing University, 145 Xingda Rd., South Dist., Taichung City 402, Taiwan
| | - Hsung-Pin Chang
- Department of Computer Science and Engineering, National Chung-Hsing University, 145 Xingda Rd., South Dist., Taichung City 402, Taiwan
| | - Yen-Wei Chu
- Institute of Genomics and Bioinformatics, National Chung Hsing University, 145 Xingda Rd., South Dist., Taichung City 402, Taiwan
- Institute of Molecular Biology, National Chung Hsing University, 145 Xingda Rd., South Dist., Taichung City 402, Taiwan
- Agricultural Biotechnology Center, National Chung Hsing University, 145 Xingda Rd., South Dist., Taichung City 402, Taiwan
- Biotechnology Center, National Chung Hsing University, 145 Xingda Rd., South Dist., Taichung City 402, Taiwan
- Ph.D. Program in Translational Medicine, National Chung Hsing University, 145 Xingda Rd., South Dist., Taichung City 402, Taiwan
- Rong Hsing Research Center for Translational Medicine, National Chung Hsing University, 145 Xingda Rd., South Dist., Taichung City 402, Taiwan
- Corresponding author at: Institute of Genomics and Bioinformatics, National Chung Hsing University, 145 Xingda Rd., South Dist., Taichung City 402, Taiwan.
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19
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Alemasov NA, Ivanisenko NV, Ivanisenko VA. Learning the changes of barnase mutants thermostability from structural fluctuations obtained using anisotropic network modeling. J Mol Graph Model 2020; 97:107572. [PMID: 32114079 DOI: 10.1016/j.jmgm.2020.107572] [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: 11/11/2019] [Revised: 01/29/2020] [Accepted: 02/19/2020] [Indexed: 11/17/2022]
Abstract
In biotechnology applications, rational design of new proteins with improved physico-chemical properties includes a number of important tasks. One of the greatest practical and fundamental challenges is the design of highly thermostable protein enzymes that maintain catalytic activity at high temperatures. This problem may be solved by introducing mutations into the wild-type enzyme protein. In this work, to predict the impact of such mutations in barnase protein we applied the anisotropic network modeling approach, revealing atomic fluctuations in structural regions that are changed in mutants compared to the wild-type protein. A regression model was constructed based on these structural features that can allow one to predict the thermal stability of new barnase mutants. Moreover, the analysis of regression model provides a mechanistic explanation of how the structural features can contribute to the thermal stability of barnase mutants.
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Affiliation(s)
- Nikolay A Alemasov
- The Federal Research Center Institute of Cytology and Genetics, The Siberian Branch of the Russian Academy of Sciences, 630090, Prospekt Lavrentyeva 10, Novosibirsk, Russia; The Kurchatov's Genomics Center of the Institute of Cytology and Genetics, The Siberian Branch of the Russian Academy of Sciences, 630090, Prospekt Lavrentyeva 10, Novosibirsk, Russia.
| | - Nikita V Ivanisenko
- The Federal Research Center Institute of Cytology and Genetics, The Siberian Branch of the Russian Academy of Sciences, 630090, Prospekt Lavrentyeva 10, Novosibirsk, Russia; The Kurchatov's Genomics Center of the Institute of Cytology and Genetics, The Siberian Branch of the Russian Academy of Sciences, 630090, Prospekt Lavrentyeva 10, Novosibirsk, Russia
| | - Vladimir A Ivanisenko
- The Federal Research Center Institute of Cytology and Genetics, The Siberian Branch of the Russian Academy of Sciences, 630090, Prospekt Lavrentyeva 10, Novosibirsk, Russia; The Kurchatov's Genomics Center of the Institute of Cytology and Genetics, The Siberian Branch of the Russian Academy of Sciences, 630090, Prospekt Lavrentyeva 10, Novosibirsk, Russia
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20
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Computational analysis of Alzheimer-causing mutations in amyloid precursor protein and presenilin 1. Arch Biochem Biophys 2019; 678:108168. [DOI: 10.1016/j.abb.2019.108168] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2019] [Revised: 10/25/2019] [Accepted: 11/02/2019] [Indexed: 12/13/2022]
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21
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Torktaz I, Hemmat J, Karkhane AA, Rigi G, Rostami A, Khezri J, Behroozi R. Molecular Engineering of the Geobacillus stearothermophilus α-Amylase and Cel5E from Chlostridium thermocellim; In Silico Approach. IRANIAN JOURNAL OF BIOTECHNOLOGY 2019; 16:e1284. [PMID: 31457020 PMCID: PMC6697822 DOI: 10.15171/ijb.1284] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/06/2015] [Revised: 10/24/2017] [Accepted: 10/25/2017] [Indexed: 11/23/2022]
Abstract
Background Considering natural thermal stability, Geobacillus stearothermophilus amylase and Cel5E from Clostridium thermocellum are good candidates for industrial applications. To be compatible with the industrial applications, this enzyme should be stable in the high temperatures, so any improvement in their thermal stability is valuable. Objectives Using in silico approach and identifying point mutations in the structure amylase of G. stearothermophilus and Cel5E from C. termocellum we tried to increase thermal stability of the enzymes along with their catalytic activity to reach a new industrial amylase with higher thermostability and an improved function. Materials and Methods In this study we predicted the 3D structure of the enzymes, then simulated the molecular docking study using MolDock, PLANTS, and Lamarkian genetic algorithm as scoring functions for the docking and in silico engineering of the protein aiming to increase the thermal stability and catalytic activity. Results A series of thermal stability increasing point mutations were exerted around the active site of the enzyme, then by docking procedure, the binding affinity was measured and finally a list of mutations which theoretically improved the increased thermal stability as well as catalytic activity were proposed. Conclusions Based on the in silico results obtained the modified enzymes seems to be suitable candidates for considering in both laboratory and industrial scales.
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Affiliation(s)
- Ibrahim Torktaz
- National Institute of Genetic Engineering and Biotechnology, Tehran, Iran.,Biotechnology Department. Iranian Research Organization for Science and Technology (IROST) Tehran-Iran
| | - Jafar Hemmat
- Biotechnology Department. Iranian Research Organization for Science and Technology (IROST) Tehran-Iran
| | | | - Garshasb Rigi
- Department of Genetics, Faculty of Basic Science, University of Shahrekord, Shahrekord, Iran
| | - Amin Rostami
- Department of Basic Sciences, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Jafar Khezri
- National Institute of Genetic Engineering and Biotechnology, Tehran, Iran
| | - Reza Behroozi
- National Institute of Genetic Engineering and Biotechnology, Tehran, Iran
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22
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Tajielyato N, Alexov E. Modeling pKas of unfolded proteins to probe structural models of unfolded state. JOURNAL OF THEORETICAL & COMPUTATIONAL CHEMISTRY 2019. [DOI: 10.1142/s0219633619500202] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Modeling unfolded states of proteins has implications for protein folding and stability. Since in unfolded state proteins adopt multiple conformations, any experimentally measured quantity is ensemble averaged, therefore the computed quantity should be ensemble averaged as well. Here, we investigate the possibility that one can model an unfolded state ensemble with the coil model approach, algorithm such as “flexible-meccano” [Ozenne V et al., Flexible-meccano: A tool for the generation of explicit ensemle descriptions of intrinsically disordered proteins and their associated experimental observables, Bioinformatics 28:1463–1470, 2012], developed to generate structures for intrinsically disordered proteins. We probe such a possibility by using generated structures to calculate pKas of titratable groups and compare with experimental data. It is demonstrated that even with a small number of representative structures of unfolded state, the average calculated pKas are in very good agreement with experimentally measured pKas. Also, predictions are made for titratable groups for which there is no experimental data available. This suggests that the coil model approach is suitable for generating 3D structures of unfolded state of proteins. To make the approach suitable for large-scale modeling, which requires limited number of structures, we ranked the structures according to their solvent accessible surface area (SASA). It is shown that in the majority of cases, the top structures with smallest SASA are enough to represent unfolded state.
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Affiliation(s)
- Nayere Tajielyato
- Department of Physics and Astronomy, Clemson University, Clemson, SC 29630, USA
| | - Emil Alexov
- Department of Physics and Astronomy, Clemson University, Clemson, SC 29630, USA
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23
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Usmanova DR, Bogatyreva NS, Ariño Bernad J, Eremina AA, Gorshkova AA, Kanevskiy GM, Lonishin LR, Meister AV, Yakupova AG, Kondrashov FA, Ivankov DN. Self-consistency test reveals systematic bias in programs for prediction change of stability upon mutation. Bioinformatics 2018; 34:3653-3658. [PMID: 29722803 PMCID: PMC6198859 DOI: 10.1093/bioinformatics/bty340] [Citation(s) in RCA: 46] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2017] [Revised: 03/15/2018] [Accepted: 04/30/2018] [Indexed: 11/12/2022] Open
Abstract
Motivation Computational prediction of the effect of mutations on protein stability is used by researchers in many fields. The utility of the prediction methods is affected by their accuracy and bias. Bias, a systematic shift of the predicted change of stability, has been noted as an issue for several methods, but has not been investigated systematically. Presence of the bias may lead to misleading results especially when exploring the effects of combination of different mutations. Results Here we use a protocol to measure the bias as a function of the number of introduced mutations. It is based on a self-consistency test of the reciprocity the effect of a mutation. An advantage of the used approach is that it relies solely on crystal structures without experimentally measured stability values. We applied the protocol to four popular algorithms predicting change of protein stability upon mutation, FoldX, Eris, Rosetta and I-Mutant, and found an inherent bias. For one program, FoldX, we manage to substantially reduce the bias using additional relaxation by Modeller. Authors using algorithms for predicting effects of mutations should be aware of the bias described here. Availability and implementation All calculations were implemented by in-house PERL scripts. Supplementary information Supplementary data are available at Bioinformatics online. Note The article 10.1093/bioinformatics/bty348, published alongside this paper, also addresses the problem of biases in protein stability change predictions.
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Affiliation(s)
- Dinara R Usmanova
- Department of Systems Biology, Columbia University Medical Center, New York, NY, USA
| | - Natalya S Bogatyreva
- Bioinformatics and Genomics Programme, Centre for Genomic Regulation (CRG), Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- Laboratory of Protein Physics, Institute of Protein Research of the Russian Academy of Sciences, Pushchino, Moscow Region, Russia
| | - Joan Ariño Bernad
- Centre de Formació Interdisciplinària Superior, Universitat Politècnica de Catalunya, Barcelona, Spain
| | - Aleksandra A Eremina
- School of Biological Sciences, College of Science and Engineering, University of Edinburgh, Edinburgh, UK
| | | | - German M Kanevskiy
- Higher Chemical College of the Russian Academy of Sciences, Moscow, Russia
| | - Lyubov R Lonishin
- Faculty of Technical Physics, Institute of Physics, Nanotechnology and Telecommunications, Peter the Great Saint-Petersburg Polytechnic University, Saint-Petersburg, Russia
| | | | - Alisa G Yakupova
- Biological Faculty, Lomonosov Moscow State University, Moscow, Russia
| | | | - Dmitry N Ivankov
- Laboratory of Protein Physics, Institute of Protein Research of the Russian Academy of Sciences, Pushchino, Moscow Region, Russia
- Institute of Science and Technology, Klosterneuburg, Austria
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24
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Jimenez-Rosales A, Flores-Merino MV. Tailoring Proteins to Re-Evolve Nature: A Short Review. Mol Biotechnol 2018; 60:946-974. [DOI: 10.1007/s12033-018-0122-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
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25
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Thompson HN, Thompson CE, Andrade Caceres R, Dardenne LE, Netz PA, Stassen H. Prion protein conversion triggered by acidic condition: a molecular dynamics study through different force fields. J Comput Chem 2018; 39:2000-2011. [DOI: 10.1002/jcc.25380] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2018] [Revised: 05/15/2018] [Accepted: 05/26/2018] [Indexed: 11/06/2022]
Affiliation(s)
- Helen Nathalia Thompson
- Departamento de Físico-Química, Instituto de Química; Universidade Federal do Rio Grande do Sul; 91501-970 Porto Alegre Rio Grande do Sul Brazil
| | - Claudia Elizabeth Thompson
- Departamento de Farmacociências; Universidade Federal de Ciências da Saúde de Porto Alegre; 90050-170 Porto Alegre Rio Grande do Sul Brazil
| | - Rafael Andrade Caceres
- Departamento de Farmacociências; Universidade Federal de Ciências da Saúde de Porto Alegre; 90050-170 Porto Alegre Rio Grande do Sul Brazil
| | | | - Paulo Augusto Netz
- Departamento de Físico-Química, Instituto de Química; Universidade Federal do Rio Grande do Sul; 91501-970 Porto Alegre Rio Grande do Sul Brazil
| | - Hubert Stassen
- Departamento de Físico-Química, Instituto de Química; Universidade Federal do Rio Grande do Sul; 91501-970 Porto Alegre Rio Grande do Sul Brazil
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26
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Bigman LS, Levy Y. Stability Effects of Protein Mutations: The Role of Long-Range Contacts. J Phys Chem B 2018; 122:11450-11459. [DOI: 10.1021/acs.jpcb.8b07379] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Lavi S. Bigman
- Department of Structural Biology, Weizmann Institute of Science, Rehovot 76100, Israel
| | - Yaakov Levy
- Department of Structural Biology, Weizmann Institute of Science, Rehovot 76100, Israel
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27
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Rigoldi F, Donini S, Redaelli A, Parisini E, Gautieri A. Review: Engineering of thermostable enzymes for industrial applications. APL Bioeng 2018; 2:011501. [PMID: 31069285 PMCID: PMC6481699 DOI: 10.1063/1.4997367] [Citation(s) in RCA: 166] [Impact Index Per Article: 27.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2017] [Accepted: 11/14/2017] [Indexed: 01/19/2023] Open
Abstract
The catalytic properties of some selected enzymes have long been exploited to carry out efficient and cost-effective bioconversions in a multitude of research and industrial sectors, such as food, health, cosmetics, agriculture, chemistry, energy, and others. Nonetheless, for several applications, naturally occurring enzymes are not considered to be viable options owing to their limited stability in the required working conditions. Over the years, the quest for novel enzymes with actual potential for biotechnological applications has involved various complementary approaches such as mining enzyme variants from organisms living in extreme conditions (extremophiles), mimicking evolution in the laboratory to develop more stable enzyme variants, and more recently, using rational, computer-assisted enzyme engineering strategies. In this review, we provide an overview of the most relevant enzymes that are used for industrial applications and we discuss the strategies that are adopted to enhance enzyme stability and/or activity, along with some of the most relevant achievements. In all living species, many different enzymes catalyze fundamental chemical reactions with high substrate specificity and rate enhancements. Besides specificity, enzymes also possess many other favorable properties, such as, for instance, cost-effectiveness, good stability under mild pH and temperature conditions, generally low toxicity levels, and ease of termination of activity. As efficient natural biocatalysts, enzymes provide great opportunities to carry out important chemical reactions in several research and industrial settings, ranging from food to pharmaceutical, cosmetic, agricultural, and other crucial economic sectors.
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Affiliation(s)
- Federica Rigoldi
- Biomolecular Engineering Lab, Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milano, Italy
| | - Stefano Donini
- Center for Nano Science and Technology at Polimi, Istituto Italiano di Tecnologia, Via G. Pascoli 70/3, 20133 Milano, Italy
| | - Alberto Redaelli
- Biomolecular Engineering Lab, Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milano, Italy
| | - Emilio Parisini
- Center for Nano Science and Technology at Polimi, Istituto Italiano di Tecnologia, Via G. Pascoli 70/3, 20133 Milano, Italy
| | - Alfonso Gautieri
- Biomolecular Engineering Lab, Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milano, Italy
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28
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Key apoptotic genes APAF1 and CASP9 implicated in recurrent folate-resistant neural tube defects. Eur J Hum Genet 2018; 26:420-427. [PMID: 29358613 PMCID: PMC5838979 DOI: 10.1038/s41431-017-0025-y] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2017] [Revised: 09/29/2017] [Accepted: 10/10/2017] [Indexed: 12/25/2022] Open
Abstract
Neural tube defects (NTDs) remain one of the most serious birth defects, and although genes in several pathways have been implicated as risk factors for neural tube defects via knockout mouse models, very few molecular causes in humans have been identified. Whole exome sequencing identified deleterious variants in key apoptotic genes in two families with recurrent neural tube defects. Functional studies in fibroblasts indicate that these variants are loss-of-function, as apoptosis is significantly reduced. This is the first report of variants in apoptotic genes contributing to neural tube defect risk in humans.
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29
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Thermal stabilization of the deglycating enzyme Amadoriase I by rational design. Sci Rep 2018; 8:3042. [PMID: 29445091 PMCID: PMC5813194 DOI: 10.1038/s41598-018-19991-x] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2017] [Accepted: 01/03/2018] [Indexed: 11/16/2022] Open
Abstract
Amadoriases are a class of FAD-dependent enzymes that are found in fungi, yeast and bacteria and that are able to hydrolyze glycated amino acids, cleaving the sugar moiety from the amino acidic portion. So far, engineered Amadoriases have mostly found practical application in the measurement of the concentration of glycated albumin in blood samples. However, these engineered forms of Amadoriases show relatively low absolute activity and stability levels, which affect their conditions of use. Therefore, enzyme stabilization is desirable prior to function-altering molecular engineering. In this work, we describe a rational design strategy based on a computational screening method to evaluate a library of potentially stabilizing disulfide bonds. Our approach allowed the identification of two thermostable Amadoriase I mutants (SS03 and SS17) featuring a significantly higher T50 (55.3 °C and 60.6 °C, respectively) compared to the wild-type enzyme (52.4 °C). Moreover, SS17 shows clear hyperstabilization, with residual activity up to 95 °C, whereas the wild-type enzyme is fully inactive at 55 °C. Our computational screening method can therefore be considered as a promising approach to expedite the design of thermostable enzymes.
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30
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Maddock DJ, Gerth ML, Patrick WM. An Engineered Glycerol Dehydratase With Improved Activity for the Conversion ofmeso-2,3-butanediol to Butanone. Biotechnol J 2017; 12. [DOI: 10.1002/biot.201700480] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2017] [Revised: 08/30/2017] [Indexed: 11/09/2022]
Affiliation(s)
| | - Monica L. Gerth
- Department of Biochemistry; University of Otago; Dunedin 9054 New Zealand
| | - Wayne M. Patrick
- Department of Biochemistry; University of Otago; Dunedin 9054 New Zealand
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31
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Spatial distribution of disease-associated variants in three-dimensional structures of protein complexes. Oncogenesis 2017; 6:e380. [PMID: 28945216 PMCID: PMC5623905 DOI: 10.1038/oncsis.2017.79] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2016] [Revised: 07/26/2017] [Accepted: 08/06/2017] [Indexed: 01/06/2023] Open
Abstract
Next-generation sequencing enables simultaneous analysis of hundreds of human genomes
associated with a particular phenotype, for example, a disease. These genomes
naturally contain a lot of sequence variation that ranges from single-nucleotide
variants (SNVs) to large-scale structural rearrangements. In order to establish a
functional connection between genotype and disease-associated phenotypes, one needs
to distinguish disease drivers from neutral passenger variants. Functional annotation
based on experimental assays is feasible only for a limited number of candidate
mutations. Thus alternative computational tools are needed. A possible approach to
annotating mutations functionally is to consider their spatial location relative to
functionally relevant sites in three-dimensional (3D) structures of the harboring
proteins. This is impeded by the lack of available protein 3D structures.
Complementing experimentally resolved structures with reliable computational models
is an attractive alternative. We developed a structure-based approach to
characterizing comprehensive sets of non-synonymous single-nucleotide variants
(nsSNVs): associated with cancer, non-cancer diseases and putatively functionally
neutral. We searched experimentally resolved protein 3D structures for potential
homology-modeling templates for proteins harboring corresponding mutations. We found
such templates for all proteins with disease-associated nsSNVs, and 51 and 66%
of proteins carrying common polymorphisms and annotated benign variants. Many
mutations caused by nsSNVs can be found in protein–protein,
protein–nucleic acid or protein–ligand complexes. Correction for the
number of available templates per protein reveals that protein–protein
interaction interfaces are not enriched in either cancer nsSNVs, or nsSNVs associated
with non-cancer diseases. Whereas cancer-associated mutations are enriched in
DNA-binding proteins, they are rarely located directly in DNA-interacting interfaces.
In contrast, mutations associated with non-cancer diseases are in general rare in
DNA-binding proteins, but enriched in DNA-interacting interfaces in these proteins.
All disease-associated nsSNVs are overrepresented in ligand-binding pockets, and
nsSNVs associated with non-cancer diseases are additionally enriched in protein core,
where they probably affect overall protein stability.
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32
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Schomburg KT, Nittinger E, Meyder A, Bietz S, Schneider N, Lange G, Klein R, Rarey M. Prediction of protein mutation effects based on dehydration and hydrogen bonding - A large-scale study. Proteins 2017; 85:1550-1566. [DOI: 10.1002/prot.25315] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2017] [Revised: 04/29/2017] [Accepted: 05/02/2017] [Indexed: 11/11/2022]
Affiliation(s)
- Karen T. Schomburg
- Universität Hamburg, ZBH - Center for Bioinformatics; Bundestrasse 43 Hamburg 20146 Germany
| | - Eva Nittinger
- Universität Hamburg, ZBH - Center for Bioinformatics; Bundestrasse 43 Hamburg 20146 Germany
| | - Agnes Meyder
- Universität Hamburg, ZBH - Center for Bioinformatics; Bundestrasse 43 Hamburg 20146 Germany
| | - Stefan Bietz
- Universität Hamburg, ZBH - Center for Bioinformatics; Bundestrasse 43 Hamburg 20146 Germany
| | - Nadine Schneider
- Universität Hamburg, ZBH - Center for Bioinformatics; Bundestrasse 43 Hamburg 20146 Germany
| | - Gudrun Lange
- Bayer CropScience AG, Industriepark Hoechst; G836 Frankfurt am Main 65926 Germany
| | - Robert Klein
- Bayer CropScience AG, Industriepark Hoechst; G836 Frankfurt am Main 65926 Germany
| | - Matthias Rarey
- Universität Hamburg, ZBH - Center for Bioinformatics; Bundestrasse 43 Hamburg 20146 Germany
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Mate DM, Palomino MA, Molina-Espeja P, Martin-Diaz J, Alcalde M. Modification of the peroxygenative:peroxidative activity ratio in the unspecific peroxygenase from Agrocybe aegerita by structure-guided evolution. Protein Eng Des Sel 2017; 30:189-196. [PMID: 28044007 DOI: 10.1093/protein/gzw073] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2016] [Accepted: 12/12/2016] [Indexed: 11/14/2022] Open
Abstract
Unspecific peroxygenase (UPO) is a heme-thiolate peroxidase capable of performing with high-selectivity C-H oxyfunctionalizations of great interest in organic synthesis through its peroxygenative activity. However, the convergence of such activity with an unwanted peroxidative activity encumbers practical applications. In this study, we have modified the peroxygenative:peroxidative activity ratio (P:p ratio) of UPO from Agrocybe aegerita by structure-guided evolution. Several flexible loops (Glu1-Pro35, Gly103-Asp131, Ser226-Gly243, Gln254-Thr276 and Ty293-Arg327) were selected on the basis on their B-factors and ΔΔG values. The full ensemble of segments (43% of UPO sequence) was subjected to focused evolution by the Mutagenic Organized Recombination Process by Homologous IN vivo Grouping (MORPHING) method in Saccharomyces cerevisiae. Five independent mutant libraries were screened in terms of P:p ratio and thermostability. We identified several variants that harbored substitutions at positions 120 and 320 with a strong enhancement in the P:p ratio albeit at the cost of stability. The most thermostable mutant of this process (S226G with an increased T50 of 2°C) was subjected to further combinatorial saturation mutagenesis on Thr120 and Thr320 yielding a collection of variants with modified P:p ratio and recovered stability. Our results seem to indicate the coexistence of several oxidation sites for peroxidative and peroxygenative activities in UPO.
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Affiliation(s)
- Diana M Mate
- Department of Biocatalysis, Institute of Catalysis, CSIC, Marie Curie 2, 28049 Madrid, Spain
| | - Miguel A Palomino
- Department of Biocatalysis, Institute of Catalysis, CSIC, Marie Curie 2, 28049 Madrid, Spain
| | - Patricia Molina-Espeja
- Department of Biocatalysis, Institute of Catalysis, CSIC, Marie Curie 2, 28049 Madrid, Spain
| | - Javier Martin-Diaz
- Department of Biocatalysis, Institute of Catalysis, CSIC, Marie Curie 2, 28049 Madrid, Spain
| | - Miguel Alcalde
- Department of Biocatalysis, Institute of Catalysis, CSIC, Marie Curie 2, 28049 Madrid, Spain
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Applying Bioinformatic Tools for Modeling and Modifying Type II E. coli l-Asparginase to Present a Better Therapeutic Agent/Drug for Acute Lymphoblastic Leukemia. INTERNATIONAL JOURNAL OF CANCER MANAGEMENT 2017. [DOI: 10.5812/ijcm.5785] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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35
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Yang Y, Sun J, Wu J, Zhang L, Du L, Matsukawa S, Xie J, Wei D. Characterization of a Novel α-l-Arabinofuranosidase from Ruminococcus albus 7 and Rational Design for Its Thermostability. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2016; 64:7546-7554. [PMID: 27633043 DOI: 10.1021/acs.jafc.6b02482] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
An α-l-arabinofuranosidase (Abf) encoding gene was obtained via genomic mining from a Ruminococcus albus strain. The specific activity of this GH 51 Abf was 73.3 U/mg at pH 6.0 and 50 °C. The modification of Abf, aimed at improving thermostability, was performed through different strategies. Structure-based rational design using the PoPMuSiC and the Enzyme Thermal Stability System (ETSS) predicted thermal stability of Abf and enhanced the half-life of thermal inactivation (t1/2) at 50 °C for K208W more than 11.1 times versus the wild-type (WT). Sequence-based rational design was also conducted by substituting histidine with lysine at various sites. Among eight mutants, the t1/2 at 50 °C of H337K was prolonged by 5.0-fold, and the specific activity of this mutant was increased to 121.8 U/mg. In addition, the mutant H337K was utilized with some enzymes to extract pectin from apple pomace. The enzymatically produced pectin got less moisture and ash, milder pH, and higher viscosity than its acid-extracted counterpart, indicating that Abf has an application prospect in pectin production.
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Affiliation(s)
- Ying Yang
- State Key Laboratory of Bioreactor Engineering, Department of Food Science and Technology, School of Biotechnology, East China University of Science and Technology , Shanghai 200237, People's Republic of China
| | - Jiaqi Sun
- State Key Laboratory of Bioreactor Engineering, Department of Food Science and Technology, School of Biotechnology, East China University of Science and Technology , Shanghai 200237, People's Republic of China
| | - Junjie Wu
- State Key Laboratory of Bioreactor Engineering, Department of Food Science and Technology, School of Biotechnology, East China University of Science and Technology , Shanghai 200237, People's Republic of China
| | - Lujia Zhang
- State Key Laboratory of Bioreactor Engineering, Department of Food Science and Technology, School of Biotechnology, East China University of Science and Technology , Shanghai 200237, People's Republic of China
| | - Lei Du
- Department of Food Science and Technology, Tokyo University of Marine Science and Technology , Tokyo 108-8477, Japan
| | - Shingo Matsukawa
- Department of Food Science and Technology, Tokyo University of Marine Science and Technology , Tokyo 108-8477, Japan
| | - Jingli Xie
- State Key Laboratory of Bioreactor Engineering, Department of Food Science and Technology, School of Biotechnology, East China University of Science and Technology , Shanghai 200237, People's Republic of China
- Shanghai Collaborative Innovation Center for Biomanufacturing (SCICB) , Shanghai 200237, People's Republic of China
| | - Dongzhi Wei
- State Key Laboratory of Bioreactor Engineering, Department of Food Science and Technology, School of Biotechnology, East China University of Science and Technology , Shanghai 200237, People's Republic of China
- Shanghai Collaborative Innovation Center for Biomanufacturing (SCICB) , Shanghai 200237, People's Republic of China
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Singh P, Dass JFP. A multifaceted computational report on the variants effect on KIR2DL3 and IFNL3 candidate gene in HCV clearance. Mol Biol Rep 2016; 43:1101-17. [PMID: 27461217 DOI: 10.1007/s11033-016-4044-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2015] [Accepted: 07/14/2016] [Indexed: 12/15/2022]
Abstract
HCV infection causes acute and chronic liver diseases including, cirrhosis and hepatocellular carcinoma. Following HCV infection, spontaneous clearance occurs in approximately 20 % of the population dependant upon HCV genotype. In this study, functional and non-functional variant analysis was executed for the classical and the latest HCV clearance candidate genes namely, KIR2DL3 and IFNL3. Initially, the functional effects of non-synonymous SNPs were assigned on exposing to homology based tools, SIFT, PolyPhen-2 and PROVEAN. Further, UTR and splice sites variants were scanned for the gene expression and regulation changes. Subsequently, the haplotype and CNV were also identified. The mutation H77Y of KIR2DL3 and R157Q, H156Y, S63L, R157W, F179V, H128R, T101M, R180C, and F176I of IFNL3 results in conservation, RMSD, total energy, stability, and secondary structures revealed a negative impact on the structural fitness. UTRscan and the splice site result indicate functional change, which may affect gene regulation and expression. The graphical display of selected population shows alleles like rs270779, rs2296370, rs10423751, rs12982559, rs9797797, and rs35987710 of KIR2DL3 and rs12972991, rs12980275, rs4803217, rs8109886, and rs8099917 of IFNL3 are in high LD with a measure of [Formula: see text] broadcasting its protective effect in HCV clearance. Similarly, CNV report suggests major DNA fragment loss that could have a profound impact on the gene expression affecting the overall phenotype. This roundup report specifies the effect of NK cell receptor, KIR2DL3 and IFNL3 variants that can have a better prospect in GWAS and immunogenetic studies leading to better understanding of HCV clearance and progression.
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Affiliation(s)
- Pratichi Singh
- Bioinformatics Division, School of Biosciences and Technology, VIT University, Vellore, Tamil Nadu, 632014, India
| | - J Febin Prabhu Dass
- Bioinformatics Division, School of Biosciences and Technology, VIT University, Vellore, Tamil Nadu, 632014, India.
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37
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Niroula A, Vihinen M. Variation Interpretation Predictors: Principles, Types, Performance, and Choice. Hum Mutat 2016; 37:579-97. [DOI: 10.1002/humu.22987] [Citation(s) in RCA: 90] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2015] [Accepted: 03/07/2016] [Indexed: 12/18/2022]
Affiliation(s)
- Abhishek Niroula
- Department of Experimental Medical Science; Lund University; BMC B13 Lund SE-22184 Sweden
| | - Mauno Vihinen
- Department of Experimental Medical Science; Lund University; BMC B13 Lund SE-22184 Sweden
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38
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SAAFEC: Predicting the Effect of Single Point Mutations on Protein Folding Free Energy Using a Knowledge-Modified MM/PBSA Approach. Int J Mol Sci 2016; 17:512. [PMID: 27070572 PMCID: PMC4848968 DOI: 10.3390/ijms17040512] [Citation(s) in RCA: 60] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2016] [Accepted: 03/28/2016] [Indexed: 11/16/2022] Open
Abstract
Folding free energy is an important biophysical characteristic of proteins that reflects the overall stability of the 3D structure of macromolecules. Changes in the amino acid sequence, naturally occurring or made in vitro, may affect the stability of the corresponding protein and thus could be associated with disease. Several approaches that predict the changes of the folding free energy caused by mutations have been proposed, but there is no method that is clearly superior to the others. The optimal goal is not only to accurately predict the folding free energy changes, but also to characterize the structural changes induced by mutations and the physical nature of the predicted folding free energy changes. Here we report a new method to predict the Single Amino Acid Folding free Energy Changes (SAAFEC) based on a knowledge-modified Molecular Mechanics Poisson-Boltzmann (MM/PBSA) approach. The method is comprised of two main components: a MM/PBSA component and a set of knowledge based terms delivered from a statistical study of the biophysical characteristics of proteins. The predictor utilizes a multiple linear regression model with weighted coefficients of various terms optimized against a set of experimental data. The aforementioned approach yields a correlation coefficient of 0.65 when benchmarked against 983 cases from 42 proteins in the ProTherm database. Availability: the webserver can be accessed via http://compbio.clemson.edu/SAAFEC/.
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39
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Jiang W, Wang S, Wang Y, Fang B. Key enzymes catalyzing glycerol to 1,3-propanediol. BIOTECHNOLOGY FOR BIOFUELS 2016; 9:57. [PMID: 26966462 PMCID: PMC4785665 DOI: 10.1186/s13068-016-0473-6] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/10/2015] [Accepted: 02/24/2016] [Indexed: 05/27/2023]
Abstract
Biodiesel can replace petroleum diesel as it is produced from animal fats and vegetable oils, and it produces about 10 % (w/w) glycerol, which is a promising new industrial microbial carbon, as a major by-product. One of the most potential applications of glycerol is its biotransformation to high value chemicals such as 1,3-propanediol (1,3-PD), dihydroxyacetone (DHA), succinic acid, etc., through microbial fermentation. Glycerol dehydratase, 1,3-propanediol dehydrogenase (1,3-propanediol-oxydoreductase), and glycerol dehydrogenase, which were encoded, respectively, by dhaB, dhaT, and dhaD and with DHA kinase are encompassed by the dha regulon, are the three key enzymes in glycerol bioconversion into 1,3-PD and DHA, and these are discussed in this review article. The summary of the main research direction of these three key enzyme and methods of glycerol bioconversion into 1,3-PD and DHA indicates their potential application in future enzymatic research and industrial production, especially in biodiesel industry.
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Affiliation(s)
- Wei Jiang
- />Department of Chemical and Biochemical Engineering, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005 China
- />The Key Lab for Synthetic Biotechnology of Xiamen City, Xiamen University, Xiamen, 361005 China
| | - Shizhen Wang
- />Department of Chemical and Biochemical Engineering, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005 China
- />The Key Lab for Synthetic Biotechnology of Xiamen City, Xiamen University, Xiamen, 361005 China
| | - Yuanpeng Wang
- />Department of Chemical and Biochemical Engineering, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005 China
| | - Baishan Fang
- />Department of Chemical and Biochemical Engineering, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005 China
- />The Key Lab for Synthetic Biotechnology of Xiamen City, Xiamen University, Xiamen, 361005 China
- />The Key Laboratory for Chemical Biology of Fujian Province, Xiamen University, Xiamen, 361005 Fujian China
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40
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Abstract
Using structure and sequence based analysis we can engineer proteins to increase their thermal stability.
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Affiliation(s)
- H. Pezeshgi Modarres
- Molecular Cell Biomechanics Laboratory
- Departments of Bioengineering and Mechanical Engineering
- University of California Berkeley
- Berkeley
- USA
| | - M. R. Mofrad
- Molecular Cell Biomechanics Laboratory
- Departments of Bioengineering and Mechanical Engineering
- University of California Berkeley
- Berkeley
- USA
| | - A. Sanati-Nezhad
- BioMEMS and Bioinspired Microfluidic Laboratory
- Department of Mechanical and Manufacturing Engineering
- University of Calgary
- Calgary
- Canada
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41
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De Laet M, Gilis D, Rooman M. Stability strengths and weaknesses in protein structures detected by statistical potentials: Application to bovine seminal ribonuclease. Proteins 2015; 84:143-58. [DOI: 10.1002/prot.24962] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2015] [Revised: 10/27/2015] [Accepted: 11/09/2015] [Indexed: 11/10/2022]
Affiliation(s)
- Marie De Laet
- 3BIO-BioInfo Department; Université Libre De Bruxelles; Avenue F. Roosevelt 50 CP 165/61 Brussels 1050 Belgium
| | - Dimitri Gilis
- 3BIO-BioInfo Department; Université Libre De Bruxelles; Avenue F. Roosevelt 50 CP 165/61 Brussels 1050 Belgium
| | - Marianne Rooman
- 3BIO-BioInfo Department; Université Libre De Bruxelles; Avenue F. Roosevelt 50 CP 165/61 Brussels 1050 Belgium
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42
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Thermal stabilization of dihydrofolate reductase using monte carlo unfolding simulations and its functional consequences. PLoS Comput Biol 2015; 11:e1004207. [PMID: 25905910 PMCID: PMC4407897 DOI: 10.1371/journal.pcbi.1004207] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2014] [Accepted: 02/20/2015] [Indexed: 12/28/2022] Open
Abstract
Design of proteins with desired thermal properties is important for scientific and biotechnological applications. Here we developed a theoretical approach to predict the effect of mutations on protein stability from non-equilibrium unfolding simulations. We establish a relative measure based on apparent simulated melting temperatures that is independent of simulation length and, under certain assumptions, proportional to equilibrium stability, and we justify this theoretical development with extensive simulations and experimental data. Using our new method based on all-atom Monte-Carlo unfolding simulations, we carried out a saturating mutagenesis of Dihydrofolate Reductase (DHFR), a key target of antibiotics and chemotherapeutic drugs. The method predicted more than 500 stabilizing mutations, several of which were selected for detailed computational and experimental analysis. We find a highly significant correlation of r = 0.65–0.68 between predicted and experimentally determined melting temperatures and unfolding denaturant concentrations for WT DHFR and 42 mutants. The correlation between energy of the native state and experimental denaturation temperature was much weaker, indicating the important role of entropy in protein stability. The most stabilizing point mutation was D27F, which is located in the active site of the protein, rendering it inactive. However for the rest of mutations outside of the active site we observed a weak yet statistically significant positive correlation between thermal stability and catalytic activity indicating the lack of a stability-activity tradeoff for DHFR. By combining stabilizing mutations predicted by our method, we created a highly stable catalytically active E. coli DHFR mutant with measured denaturation temperature 7.2°C higher than WT. Prediction results for DHFR and several other proteins indicate that computational approaches based on unfolding simulations are useful as a general technique to discover stabilizing mutations. All-atom molecular simulations have provided valuable insight into the workings of molecular machines and the folding and unfolding of proteins. However, commonly employed molecular dynamics simulations suffer from a limitation in accessible time scale, making it difficult to model large-scale unfolding events in a realistic amount of simulation time without employing unrealistically high temperatures. Here, we describe a rapid all-atom Monte Carlo simulation approach to simulate unfolding of the essential bacterial enzyme Dihydrofolate Reductase (DHFR) and all possible single point-mutants. We use these simulations to predict which mutants will be more thermodynamically stable (i.e., reside more often in the native folded state vs. the unfolded state) than the wild-type protein, and we confirm our predictions experimentally, creating several highly stable and catalytically active mutants. Thermally stable active engineered proteins can be used as a starting point in directed evolution experiments to evolve new functions on the background of this additional “reservoir of stability.” The stabilized enzyme may be able to accumulate a greater number of destabilizing yet functionally important mutations before unfolding, protease digestion, and aggregation abolish its activity.
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43
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Yang H, Liu L, Li J, Chen J, Du G. Rational Design to Improve Protein Thermostability: Recent Advances and Prospects. CHEMBIOENG REVIEWS 2015. [DOI: 10.1002/cben.201400032] [Citation(s) in RCA: 47] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
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44
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Heselpoth RD, Yin Y, Moult J, Nelson DC. Increasing the stability of the bacteriophage endolysin PlyC using rationale-based FoldX computational modeling. Protein Eng Des Sel 2015; 28:85-92. [DOI: 10.1093/protein/gzv004] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
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45
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Thermostability enhancement of an endo-1,4-β-galactanase from Talaromyces stipitatus by site-directed mutagenesis. Appl Microbiol Biotechnol 2014; 99:4245-53. [DOI: 10.1007/s00253-014-6244-z] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2014] [Revised: 11/14/2014] [Accepted: 11/17/2014] [Indexed: 11/25/2022]
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46
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Bhattacherjee A, Mallik S, Kundu S. Compensatory mutations occur within the electrostatic interaction range of deleterious mutations in protein structure. J Mol Evol 2014; 80:10-2. [PMID: 25399321 DOI: 10.1007/s00239-014-9654-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2014] [Accepted: 11/07/2014] [Indexed: 10/24/2022]
Abstract
A compensatory mutation (CM) counter balances lethal effects of a deleterious mutation (DM), ensuring the persistence of both through natural selection. However, little is known about the biological aspects of CMs those restore the structural alterations of proteins caused by slightly DMs. Here, by analyzing the evolution of the UDP-glycosyltransferase 73B4 protein among monocot-dicot plants, we investigate the occurrence of CMs around slightly DMs in 3D space. Our results illustrate that CMs exhibit significantly higher tendency to occur within the range of electrostatic interaction around the slightly DMs, compared to occurring randomly in the protein.
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Affiliation(s)
- Amrita Bhattacherjee
- Department of Biophysics, Molecular Biology and Bioinformatics, University of Calcutta, 92, Acharya Prafulla Chandra Road, Kolkata, 700009, India
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47
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Giollo M, Martin AJM, Walsh I, Ferrari C, Tosatto SCE. NeEMO: a method using residue interaction networks to improve prediction of protein stability upon mutation. BMC Genomics 2014; 15 Suppl 4:S7. [PMID: 25057121 PMCID: PMC4083412 DOI: 10.1186/1471-2164-15-s4-s7] [Citation(s) in RCA: 71] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The rapid growth of un-annotated missense variants poses challenges requiring novel strategies for their interpretation. From the thermodynamic point of view, amino acid changes can lead to a change in the internal energy of a protein and induce structural rearrangements. This is of great relevance for the study of diseases and protein design, justifying the development of prediction methods for variant-induced stability changes. RESULTS Here we propose NeEMO, a tool for the evaluation of stability changes using an effective representation of proteins based on residue interaction networks (RINs). RINs are used to extract useful features describing interactions of the mutant amino acid with its structural environment. Benchmarking shows NeEMO to be very effective, allowing reliable predictions in different parts of the protein such as β-strands and buried residues. Validation on a previously published independent dataset shows that NeEMO has a Pearson correlation coefficient of 0.77 and a standard error of 1 Kcal/mol, outperforming nine recent methods. The NeEMO web server can be freely accessed from URL: http://protein.bio.unipd.it/neemo/. CONCLUSIONS NeEMO offers an innovative and reliable tool for the annotation of amino acid changes. A key contribution are RINs, which can be used for modeling proteins and their interactions effectively. Interestingly, the approach is very general, and can motivate the development of a new family of RIN-based protein structure analyzers. NeEMO may suggest innovative strategies for bioinformatics tools beyond protein stability prediction.
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48
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Kepp KP. Computing stability effects of mutations in human superoxide dismutase 1. J Phys Chem B 2014; 118:1799-812. [PMID: 24472010 DOI: 10.1021/jp4119138] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Protein stability is affected in several diseases and is of substantial interest in efforts to correlate genotypes to phenotypes. Superoxide dismutase 1 (SOD1) is a suitable test case for such correlations due to its abundance, stability, available crystal structures and thermochemical data, and physiological importance. In this work, stability changes of SOD1 mutations were computed with five methods, CUPSAT, I-Mutant2.0, I-Mutant3.0, PoPMuSiC, and SDM, with emphasis on structural sensitivity as a potential issue in structure-based protein calculation. The large correlation between experimental literature data of SOD1 dimers and monomers (r = 0.82) suggests that mutations in separate protein monomers are mostly additive. PoPMuSiC was most accurate (typical MAE ~ 1 kcal/mol, r ~ 0.5). The relative performance of the methods was not very structure-dependent, and the more accurate methods also displayed less structural sensitivity, with the standard deviation from different high-resolution structures down to ~0.2 kcal/mol. Structures of variable resolution and number of protein copies locally affected specific sites, emphasizing the use of state-relevant crystal structures when such sites are of interest, but had little impact on overall batch estimates. Protein-interaction effects (as a mimic of crystal packing) were small for the more accurate methods. Thus, batch computations, relevant to, e.g., comparisons of disease/nondisease mutant sets or different clades in phylogenetic trees, are much more significant than single mutant calculations and may be the only meaningful way to computationally bridge the genotype-phenotype gap of proteomics. Finally, mutations involving glycine were most difficult to model, of relevance to future method improvement. This could be due to structure changes (glycine has a low structural propensity) or water colocalization with glycine.
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Affiliation(s)
- Kasper P Kepp
- DTU Chemistry, Technical University of Denmark , DK 2800 Kongens Lyngby, Denmark
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49
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C. KN, Y. S, P. SR. Computational analysis of molt-inhibiting hormone from selected crustaceans. COMPARATIVE BIOCHEMISTRY AND PHYSIOLOGY D-GENOMICS & PROTEOMICS 2013; 8:292-9. [DOI: 10.1016/j.cbd.2013.08.004] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/14/2013] [Revised: 08/21/2013] [Accepted: 08/21/2013] [Indexed: 11/16/2022]
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50
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Silva IR, Larsen DM, Jers C, Derkx P, Meyer AS, Mikkelsen JD. Enhancing RGI lyase thermostability by targeted single point mutations. Appl Microbiol Biotechnol 2013; 97:9727-35. [DOI: 10.1007/s00253-013-5184-3] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2013] [Revised: 08/07/2013] [Accepted: 08/10/2013] [Indexed: 11/25/2022]
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