ArticleInternational journal of molecular sciences2019
Novel Descriptors and Digital Signal Processing- Based Method for Protein Sequence Activity Relationship Study.
Article in International journal of molecular sciences, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- A protein fitness predictive framework based on feature combination and intelligent searching.Protein science : a publication of the Protein Society · 2024Article
- Prediction of Thermostability of Enzymes Based on the Amino Acid Index (AAindex) Database and Machine Learning.Molecules (Basel, Switzerland) · 2023Article
- Article
- In silico prediction of Severe Acute Respiratory Syndrome Coronavirus 2 main protease cleavage sites.Proteins · 2022Article
- Machine Learning Enables Selection of Epistatic Enzyme Mutants for Stability Against Unfolding and Detrimental Aggregation.Chembiochem : a European journal of chemical biology · 2021Article
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3 authors.
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Abstract
The work aiming to unravel the correlation between protein sequence and function in the absence of structural information can be highly rewarding. We present a new way of considering descriptors from the amino acids index database for modeling and predicting the fitness value of a polypeptide chain. This approach includes the following steps: (i) Calculating Q elementary numerical sequences (Ele_SEQ) depending on the encoding of the amino acid residues, (ii) determining an extended numerical sequence (Ext_SEQ) by concatenating the Q elementary numerical sequences, wherein at least one elementary numerical sequence is a protein spectrum obtained by applying fast Fourier transformation (FFT), and (iii) predicting a value of fitness for polypeptide variants (train and/or validation set). These new descriptors were tested on four sets of proteins of different lengths (GLP-2, TNF alpha, cytochrome P450, and epoxide hydrolase) and activities (cAMP activation, binding affinity, thermostability and enantioselectivity). We show that the use of multiple physicochemical descriptors coupled with the implementation of the FFT, taking into account the interactions between residues of amino amides within the protein sequence, could lead to very significant improvement in the quality of models and predictions. The choice of the descriptor or of the combination of descriptors and/or FFT is dependent on the couple protein/fitness. This approach can provide potential users with value added to existing mutant libraries where screening efforts have so far been unsuccessful in finding improved polypeptide mutants for useful applications.
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