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Li X, Chen S, Shao J, Bai M, Zhang Z, Song P, Jiang S, Li J. From waste to strength: Tailor-made enzyme activation design transformation of denatured soy meal into high-performance all-biomass adhesive. Int J Biol Macromol 2024; 273:133054. [PMID: 38862054 DOI: 10.1016/j.ijbiomac.2024.133054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Revised: 05/26/2024] [Accepted: 06/07/2024] [Indexed: 06/13/2024]
Abstract
Given the severe protein denaturation and self-aggregation during the high-temperature desolubilization, denatured soy meal (DSM) is limited by its low reactivity, high viscosity, and poor water solubility. Preparing low-cost and high-performance adhesives with DSM as the key feedstock is still challenging. Herein, this study reveals a double-enzyme co-activation method targeting DSM with the glycosidic bonds in protein-carbohydrate complexes and partial amide bonds in protein, increasing the protein dispersion index from 10.2 % to 75.1 % improves the reactivity of DSM. The green crosslinker transglutaminase (TGase) constructs a robust adhesive isopeptide bond network with high water-resistant bonding strength comparable to chemical crosslinkers. The adhesive has demonstrated high dry/wet shear strength (2.56 and 0.93 MPa) for plywood. After molecular recombination by enzyme strategy, the adhesive had the proper viscosity, high reactivity, and strong water resistance. This research showcases a novel perspective on developing a DSM-based adhesive and blazes new avenues for changes in protein structural function and adhesive performance.
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Affiliation(s)
- Xinyi Li
- State Key Laboratory of Efficient Production of Forest Resources, MOE Key Laboratory of Wood Material Science and Application & Beijing Key Laboratory of Wood Science and Engineering, Beijing Forestry University, Beijing 100083, China
| | - Shiqing Chen
- State Key Laboratory of Efficient Production of Forest Resources, MOE Key Laboratory of Wood Material Science and Application & Beijing Key Laboratory of Wood Science and Engineering, Beijing Forestry University, Beijing 100083, China
| | - Jiawei Shao
- State Key Laboratory of Efficient Production of Forest Resources, MOE Key Laboratory of Wood Material Science and Application & Beijing Key Laboratory of Wood Science and Engineering, Beijing Forestry University, Beijing 100083, China
| | - Mingyang Bai
- State Key Laboratory of Efficient Production of Forest Resources, MOE Key Laboratory of Wood Material Science and Application & Beijing Key Laboratory of Wood Science and Engineering, Beijing Forestry University, Beijing 100083, China
| | - Zhicheng Zhang
- State Key Laboratory of Efficient Production of Forest Resources, MOE Key Laboratory of Wood Material Science and Application & Beijing Key Laboratory of Wood Science and Engineering, Beijing Forestry University, Beijing 100083, China
| | - Pingan Song
- Centre for Future Materials, School of Agriculture and Environmental Science, University of Southern Queensland, Springfield 4300, Australia
| | - Shuaicheng Jiang
- State Key Laboratory of Efficient Production of Forest Resources, MOE Key Laboratory of Wood Material Science and Application & Beijing Key Laboratory of Wood Science and Engineering, Beijing Forestry University, Beijing 100083, China.
| | - Jianzhang Li
- State Key Laboratory of Efficient Production of Forest Resources, MOE Key Laboratory of Wood Material Science and Application & Beijing Key Laboratory of Wood Science and Engineering, Beijing Forestry University, Beijing 100083, China.
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Shanmugam NRS, Kulandaisamy A, Veluraja K, Gromiha MM. CarbDisMut: database on neutral and disease-causing mutations in human carbohydrate-binding proteins. Glycobiology 2024; 34:cwae011. [PMID: 38335248 DOI: 10.1093/glycob/cwae011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 01/03/2024] [Indexed: 02/12/2024] Open
Abstract
Protein-carbohydrate interactions are involved in several cellular and biological functions. Integrating structure and function of carbohydrate-binding proteins with disease-causing mutations help to understand the molecular basis of diseases. Although databases are available for protein-carbohydrate complexes based on structure, binding affinity and function, no specific database for mutations in human carbohydrate-binding proteins is reported in the literature. We have developed a novel database, CarbDisMut, a comprehensive integrated resource for disease-causing mutations with sequence and structural features. It has 1.17 million disease-associated mutations and 38,636 neutral mutations from 7,187 human carbohydrate-binding proteins. The database is freely available at https://web.iitm.ac.in/bioinfo2/carbdismut. The web-site is implemented using HTML, PHP and JavaScript and supports recent versions of all major browsers, such as Firefox, Chrome and Opera.
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Affiliation(s)
- N R Siva Shanmugam
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, India
| | - A Kulandaisamy
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, India
- Basic and Translational Research, Department of Cardiology, Boston Children's Hospital, Boston, MA 02115, United States
| | - K Veluraja
- PSN College of Engineering and Technology, Melathediyoor, Tirunelveli, Tamil Nadu 627451, India
| | - M Michael Gromiha
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, India
- Department of Computer Science, Tokyo Tech World Research Hub Initiative (WRHI), Institute of Innovative Research, Tokyo Institute of Technology, 4259 Nagatsutacho, Midori-ku, Yokohama, Kanagawa 226-8501, Japan
- Department of Computer Science, National University of Singapore, 117417, Singapore
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Sharma D, Rawat P, Greiff V, Janakiraman V, Gromiha MM. Predicting the immune escape of SARS-CoV-2 neutralizing antibodies upon mutation. Biochim Biophys Acta Mol Basis Dis 2024; 1870:166959. [PMID: 37967796 DOI: 10.1016/j.bbadis.2023.166959] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 10/25/2023] [Accepted: 11/07/2023] [Indexed: 11/17/2023]
Abstract
COVID-19 has resulted in millions of deaths and severe impact on economies worldwide. Moreover, the emergence of SARS-CoV-2 variants presented significant challenges in controlling the pandemic, particularly their potential to avoid the immune system and evade vaccine immunity. This has led to a growing need for research to predict how mutations in SARS-CoV-2 reduces the ability of antibodies to neutralize the virus. In this study, we assembled a set of 1813 mutations from the interface of SARS-CoV-2 spike protein's receptor binding domain (RBD) and neutralizing antibody complexes and developed a machine learning model to classify high or low escape mutations using interaction energy, inter-residue contacts and predicted binding free energy change. Our approach achieved an Area under the Receiver Operating Characteristics (ROC) Curve (AUC) of 0.91 using the Random Forest classifier on the test dataset with 217 mutations. The model was further utilized to predict the escape mutations on a dataset of 29,165 mutations located at the interface of 83 RBD-neutralizing antibody complexes. A small subset of this dataset was also validated based on available experimental data. We found that top 10 % high escape mutations were dominated by charged to nonpolar mutations whereas low escape mutations were dominated by polar to nonpolar mutations. We believe that the present method will allow prioritization of high/low escape mutations in the context of neutralizing antibodies targeting SARS-CoV-2 RBD region and assist antibody design for current and emerging variants.
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Affiliation(s)
- Divya Sharma
- Protein Bioinformatics Lab, Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, Tamil Nadu 600036, India
| | - Puneet Rawat
- University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Victor Greiff
- University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Vani Janakiraman
- Infection Biology Lab, Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, Tamil Nadu 600036, India
| | - M Michael Gromiha
- Protein Bioinformatics Lab, Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, Tamil Nadu 600036, India; International Research Frontiers Initiative, School of Computing, Tokyo Institute of Technology, Yokohama 226-8501, Japan; Department of Computer Science, National University of Singapore, Singapore.
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Harini K, Sekijima M, Gromiha MM. PRA-Pred: Structure-based prediction of protein-RNA binding affinity. Int J Biol Macromol 2024; 259:129490. [PMID: 38224813 DOI: 10.1016/j.ijbiomac.2024.129490] [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: 12/10/2023] [Revised: 01/10/2024] [Accepted: 01/12/2024] [Indexed: 01/17/2024]
Abstract
Understanding crucial factors that affect the binding affinity of protein-RNA complexes is vital for comprehending their recognition mechanisms. This study involved compiling experimentally measured binding affinity (ΔG) values of 217 protein-RNA complexes and extracting numerous structure-based features, considering RNA, protein, and interactions between protein and RNA. Our findings indicate the significance of RNA base-step parameters, interaction energies, number of atomic contacts in the complex, hydrogen bonds, and contact potentials in understanding the binding affinity. Further, we observed that these factors are influenced by the type of RNA strand and the function of the protein in a protein-RNA complex. Multiple regression equations were developed for different classes of complexes to perform the prediction of the binding affinity between the protein and RNA. We evaluated the models using the jack-knife test and achieved an overall correlation 0.77 between the experimental and predicted binding affinities with a mean absolute error of 1.02 kcal/mol. Furthermore, we introduced a web server, PRA-Pred, intended for the prediction of protein-RNA binding affinity, and it is freely accessible through https://web.iitm.ac.in/bioinfo2/prapred/. We propose that our approach could function as a potential resource for investigating protein-RNA recognitions and developing therapeutic strategies.
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Affiliation(s)
- K Harini
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, India
| | - M Sekijima
- Department of Computer Science, Tokyo Institute of Technology, Yokohama, Japan
| | - M Michael Gromiha
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, India; International Research Frontiers Initiative, School of Computing, Tokyo Institute of Technology, Yokohama, 226-8501, Japan; Department of Computer Science, National University of Singapore, Singapore.
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Waseem T, Ahmed M, Rajput TA, Babar MM. Molecular implications of glycosaminoglycans in diabetes pharmacotherapy. Int J Biol Macromol 2023; 247:125821. [PMID: 37467830 DOI: 10.1016/j.ijbiomac.2023.125821] [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: 02/05/2023] [Revised: 06/26/2023] [Accepted: 07/11/2023] [Indexed: 07/21/2023]
Abstract
Diabetes mellitus causes a wide range of metabolic derangements with multiple organ damage. The microvascular and macrovascular complications of diabetes result partly from the damage to the glycosaminoglycans (GAG) in the basement membrane. GAGs are negatively charged polysaccharides with repeating disaccharide units. They play a significant role in cellular proliferation and signal transduction. Destruction of extracellular matrix results in diseases in various organs including myocardial fibrosis, retinal damage and nephropathy. To substitute the natural GAGs pharmacotherapeutically, they have been synthesized by using basic disaccharide units. Among the four classes of GAGs, heparin is the most widely studied. Recent studies have revealed multiple significant GAG-protein interactions suggesting their use for the management of diabetic complications. Moreover, they can act as biomarkers for assessing the disease progression. A number of GAG-based therapeutic agents are being evaluated for managing diabetic complications. The current review provides an outline of the role of GAGs in diabetes while covering their interaction with different molecular players that can serve as targets for the diagnosis, management and prevention of diabetes and its complications. The medicinal chemistry and clinical pharmacotherapeutics aspects have are covered to aid in the establishment of GAG-based therapies as a possible avenue for diabetes.
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Affiliation(s)
- Tanya Waseem
- Department of Pharmaceutical Chemistry, Shifa College of Pharmaceutical Sciences, Shifa Tameer-e-Millat University, Islamabad 44000, Pakistan
| | - Madiha Ahmed
- Department of Pharmaceutical Chemistry, Shifa College of Pharmaceutical Sciences, Shifa Tameer-e-Millat University, Islamabad 44000, Pakistan
| | - Tausif Ahmed Rajput
- Department of Pharmaceutical Chemistry, Shifa College of Pharmaceutical Sciences, Shifa Tameer-e-Millat University, Islamabad 44000, Pakistan
| | - Mustafeez Mujtaba Babar
- Department of Basic Medical Sciences, Shifa College of Pharmaceutical Sciences, Shifa Tameer-e-Millat University, Islamabad 44000, Pakistan.
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Harini K, Kihara D, Michael Gromiha M. PDA-Pred: Predicting the binding affinity of protein-DNA complexes using machine learning techniques and structural features. Methods 2023; 213:10-17. [PMID: 36924867 PMCID: PMC10563387 DOI: 10.1016/j.ymeth.2023.03.002] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 02/17/2023] [Accepted: 03/11/2023] [Indexed: 03/17/2023] Open
Abstract
Protein-DNA interactions play an important role in various biological processes such as gene expression, replication, and transcription. Understanding the important features that dictate the binding affinity of protein-DNA complexes and predicting their affinities is important for elucidating their recognition mechanisms. In this work, we have collected the experimental binding free energy (ΔG) for a set of 391 Protein-DNA complexes and derived several structure-based features such as interaction energy, contact potentials, volume and surface area of binding site residues, base step parameters of the DNA and contacts between different types of atoms. Our analysis on relationship between binding affinity and structural features revealed that the important factors mainly depend on the number of DNA strands as well as functional and structural classes of proteins. Specifically, binding site properties such as number of atom contacts between the DNA and protein, volume of protein binding sites and interaction-based features such as interaction energies and contact potentials are important to understand the binding affinity. Further, we developed multiple regression equations for predicting the binding affinity of protein-DNA complexes belonging to different structural and functional classes. Our method showed an average correlation and mean absolute error of 0.78 and 0.98 kcal/mol, respectively, between the experimental and predicted binding affinities on a jack-knife test. We have developed a webserver, PDA-PreD (Protein-DNA Binding affinity predictor), for predicting the affinity of protein-DNA complexes and it is freely available at https://web.iitm.ac.in/bioinfo2/pdapred/.
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Affiliation(s)
- K Harini
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, India
| | - Daisuke Kihara
- Department of Biological Sciences, Purdue University, West Lafayette, IN, United States; Department of Computer Science, Purdue University, West Lafayette, IN, United States
| | - M Michael Gromiha
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, India; International Research Frontiers Initiative, School of Computing, Tokyo Institute of Technology, Yokohama 226-8501, Japan.
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Pandey M, Anoosha P, Yesudhas D, Gromiha MM. Identification of potential driver mutations in glioblastoma using machine learning. Brief Bioinform 2022; 23:6764546. [PMID: 36266243 DOI: 10.1093/bib/bbac451] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 09/13/2022] [Accepted: 09/22/2022] [Indexed: 12/14/2022] Open
Abstract
Glioblastoma is a fast and aggressively growing tumor in the brain and spinal cord. Mutation of amino acid residues in targets proteins, which are involved in glioblastoma, alters the structure and function and may lead to disease. In this study, we collected a set of 9386 disease-causing (drivers) mutations based on the recurrence in patient samples and experimentally annotated as pathogenic and 8728 as neutral (passenger) mutations. We observed that Arg is highly preferred at the mutant sites of drivers, whereas Met and Ile showed preferences in passengers. Inspecting neighboring residues at the mutant sites revealed that the motifs YP, CP and GRH, are preferred in drivers, whereas SI, IQ and TVI are dominant in neutral. In addition, we have computed other sequence-based features such as conservation scores, Position Specific Scoring Matrices (PSSM) and physicochemical properties, and developed a machine learning-based method, GBMDriver (GlioBlastoma Multiforme Drivers), for distinguishing between driver and passenger mutations. Our method showed an accuracy and AUC of 73.59% and 0.82, respectively, on 10-fold cross-validation and 81.99% and 0.87 in a blind set of 1809 mutants. The tool is available at https://web.iitm.ac.in/bioinfo2/GBMDriver/index.html. We envisage that the present method is helpful to prioritize driver mutations in glioblastoma and assist in identifying therapeutic targets.
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Affiliation(s)
- Medha Pandey
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, India
| | - P Anoosha
- Division of Medical Oncology, Department of Internal Medicine, Comprehensive Cancer Center, The Ohio State University, Columbus, Ohio, USA
| | - Dhanusha Yesudhas
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, India
| | - M Michael Gromiha
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, India
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PCA-MutPred: Prediction of binding free energy change upon missense mutation in protein-carbohydrate complexes. J Mol Biol 2022; 434:167526. [DOI: 10.1016/j.jmb.2022.167526] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Revised: 02/26/2022] [Accepted: 03/01/2022] [Indexed: 11/22/2022]
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