ArticlePloS one2015
Prediction of protein structural features from sequence data based on Shannon entropy and Kolmogorov complexity.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Deep learning-driven de novo discovery of binders targeting the transmembrane domain of BamA.Archives of microbiology · 2026Article
- From Energy Deprivation Stress to Catalytic Adaptation of Reductive Dehalogenase TmrA for 1,1-DCA Dechlorination: An Experimental and in Silico Study.Microbial biotechnology · 2026Article
- PyTEA-O: a Python implementation of Two-Entropies Analysis for protein sequence variation analysis.Bioinformatics (Oxford, England) · 2026Article
- In Silico Evaluation, Phylogenetic Analysis, and Structural Modeling of the Class II Hydrophobin Family from Different Fungal Phytopathogens.Microorganisms · 2023Article
- Entropy and Variability: A Second Opinion by Deep Learning.Biomolecules · 2022Article
- The complexity landscape of viral genomes.GigaScience · 2022Article
- Computational design of a synthetic PD-1 agonist.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- AC2: An Efficient Protein Sequence Compression Tool Using Artificial Neural Networks and Cache-Hash Models.Entropy (Basel, Switzerland) · 2021Article
- Mutually exclusive locales for N-linked glycans and disorder in human glycoproteins.Scientific reports · 2020Article
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- Comparison of Algorithms for Prediction of Protein Structural Features from Evolutionary Data.PloS one · 2016Article
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1 author.
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No grant is acknowledged in the PubMed record.
Abstract
While the genome for a given organism stores the information necessary for the organism to function and flourish it is the proteins that are encoded by the genome that perhaps more than anything else characterize the phenotype for that organism. It is therefore not surprising that one of the many approaches to understanding and predicting protein folding and properties has come from genomics and more specifically from multiple sequence alignments. In this work I explore ways in which data derived from sequence alignment data can be used to investigate in a predictive way three different aspects of protein structure: secondary structures, inter-residue contacts and the dynamics of switching between different states of the protein. In particular the use of Kolmogorov complexity has identified a novel pathway towards achieving these goals.
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