Computational approaches for identifying disease-causing mutations in proteins
Copyright © 2024. Published by Elsevier Inc..
Advancements in genome sequencing have expanded the scope of investigating mutations in proteins across different diseases. Amino acid mutations in a protein alter its structure, stability and function and some of them lead to diseases. Identification of disease-causing mutations is a challenging task and it will be helpful for designing therapeutic strategies. Hence, mutation data available in the literature have been curated and stored in several databases, which have been effectively utilized for developing computational methods to identify deleterious mutations (drivers), using sequence and structure-based properties of proteins. In this chapter, we describe the contents of specific databases that have information on disease-causing and neutral mutations followed by sequence and structure-based properties. Further, characteristic features of disease-causing mutations will be discussed along with computational methods for identifying cancer hotspot residues and disease-causing mutations in proteins.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:139 |
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| Enthalten in: |
Advances in protein chemistry and structural biology - 139(2024) vom: 05., Seite 141-171 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Pandey, Medha [VerfasserIn] |
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| Links: |
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| Themen: |
Cancer hotspots |
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| Anmerkungen: |
Date Completed 08.03.2024 Date Revised 08.03.2024 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/bs.apcsb.2023.11.007 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 520 | |a Advancements in genome sequencing have expanded the scope of investigating mutations in proteins across different diseases. Amino acid mutations in a protein alter its structure, stability and function and some of them lead to diseases. Identification of disease-causing mutations is a challenging task and it will be helpful for designing therapeutic strategies. Hence, mutation data available in the literature have been curated and stored in several databases, which have been effectively utilized for developing computational methods to identify deleterious mutations (drivers), using sequence and structure-based properties of proteins. In this chapter, we describe the contents of specific databases that have information on disease-causing and neutral mutations followed by sequence and structure-based properties. Further, characteristic features of disease-causing mutations will be discussed along with computational methods for identifying cancer hotspot residues and disease-causing mutations in proteins | ||
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| 700 | 1 | |a Gromiha, M Michael |e verfasserin |4 aut | |
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