An Explainable Model Using Graph-Wavelet for Predicting Biophysical Properties of Proteins and Measuring Mutational Effects
Proteins hold multispectral patterns of different kinds of physicochemical features of amino acids in their structures, which can help understand proteins’ behavior. Here, we propose a method based on the graph-wavelet transform of signals of features of amino acids in protein residue net...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10329327/ |
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| author | Shreya Mishra Neetesh Pandey Atul Rawat Divyanshu Srivastava Arjun Ray Vibhor Kumar |
| author_facet | Shreya Mishra Neetesh Pandey Atul Rawat Divyanshu Srivastava Arjun Ray Vibhor Kumar |
| author_sort | Shreya Mishra |
| collection | DOAJ |
| description | Proteins hold multispectral patterns of different kinds of physicochemical features of amino acids in their structures, which can help understand proteins’ behavior. Here, we propose a method based on the graph-wavelet transform of signals of features of amino acids in protein residue networks derived from their structures to achieve their abstract numerical representations. Such abstract representations of protein structures hand in hand with amino-acid features can be used for different purposes, such as modelling the biophysical property of proteins. Our method outperformed graph-Fourier and convolutional neural-network-based methods in predicting the biophysical properties of proteins. Even though our method does not predict deleterious mutations, it can summarize the effect of an amino acid based on its location and neighbourhood in protein-structure using graph-wavelet to estimate its influence on the biophysical property of proteins. Such an estimate of the influence of amino-acid has the potential to explain the mechanism of the effect of deleterious non-synonymous mutations. Thus, our approach can reveal patterns of distribution of amino-acid properties in the structure of the protein in the context of a biophysical property for better classification and more insightful understanding. |
| format | Article |
| id | doaj-art-d7cffc01d8924d1d8771d4d2bcdecf39 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-d7cffc01d8924d1d8771d4d2bcdecf392025-08-20T03:21:34ZengIEEEIEEE Access2169-35362023-01-011113522213523410.1109/ACCESS.2023.333726010329327An Explainable Model Using Graph-Wavelet for Predicting Biophysical Properties of Proteins and Measuring Mutational EffectsShreya Mishra0Neetesh Pandey1Atul Rawat2Divyanshu Srivastava3https://orcid.org/0000-0001-6287-9127Arjun Ray4Vibhor Kumar5https://orcid.org/0000-0002-9523-9422Department of Computational Biology, Indraprastha Institute of Information Technology Delhi, New Delhi, IndiaDepartment of Computational Biology, Indraprastha Institute of Information Technology Delhi, New Delhi, IndiaDepartment of Computational Biology, Indraprastha Institute of Information Technology Delhi, New Delhi, IndiaDepartment of Computational Biology, Indraprastha Institute of Information Technology Delhi, New Delhi, IndiaDepartment of Computational Biology, Indraprastha Institute of Information Technology Delhi, New Delhi, IndiaDepartment of Computational Biology, Indraprastha Institute of Information Technology Delhi, New Delhi, IndiaProteins hold multispectral patterns of different kinds of physicochemical features of amino acids in their structures, which can help understand proteins’ behavior. Here, we propose a method based on the graph-wavelet transform of signals of features of amino acids in protein residue networks derived from their structures to achieve their abstract numerical representations. Such abstract representations of protein structures hand in hand with amino-acid features can be used for different purposes, such as modelling the biophysical property of proteins. Our method outperformed graph-Fourier and convolutional neural-network-based methods in predicting the biophysical properties of proteins. Even though our method does not predict deleterious mutations, it can summarize the effect of an amino acid based on its location and neighbourhood in protein-structure using graph-wavelet to estimate its influence on the biophysical property of proteins. Such an estimate of the influence of amino-acid has the potential to explain the mechanism of the effect of deleterious non-synonymous mutations. Thus, our approach can reveal patterns of distribution of amino-acid properties in the structure of the protein in the context of a biophysical property for better classification and more insightful understanding.https://ieeexplore.ieee.org/document/10329327/Amino acidsgraph signal processinggraph waveletprotein propertyresidue interaction graph |
| spellingShingle | Shreya Mishra Neetesh Pandey Atul Rawat Divyanshu Srivastava Arjun Ray Vibhor Kumar An Explainable Model Using Graph-Wavelet for Predicting Biophysical Properties of Proteins and Measuring Mutational Effects IEEE Access Amino acids graph signal processing graph wavelet protein property residue interaction graph |
| title | An Explainable Model Using Graph-Wavelet for Predicting Biophysical Properties of Proteins and Measuring Mutational Effects |
| title_full | An Explainable Model Using Graph-Wavelet for Predicting Biophysical Properties of Proteins and Measuring Mutational Effects |
| title_fullStr | An Explainable Model Using Graph-Wavelet for Predicting Biophysical Properties of Proteins and Measuring Mutational Effects |
| title_full_unstemmed | An Explainable Model Using Graph-Wavelet for Predicting Biophysical Properties of Proteins and Measuring Mutational Effects |
| title_short | An Explainable Model Using Graph-Wavelet for Predicting Biophysical Properties of Proteins and Measuring Mutational Effects |
| title_sort | explainable model using graph wavelet for predicting biophysical properties of proteins and measuring mutational effects |
| topic | Amino acids graph signal processing graph wavelet protein property residue interaction graph |
| url | https://ieeexplore.ieee.org/document/10329327/ |
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