scholarly journals Evolutionary model of protein secondary structure capable of revealing new biological relationships

2019 ◽  
Author(s):  
Jhih-Siang Lai ◽  
Burkhard Rost ◽  
Bostjan Kobe ◽  
Mikael Bodén
Keyword(s):  
Amino Acid ◽  
Secondary Structure ◽  
Predictive Accuracy ◽  
Protein Structures ◽  
Protein Secondary Structure ◽  
Evolutionary Model ◽  
Ancestral Sequences ◽  
Recent Success ◽  
Protein Functions ◽  
Sequence Reconstruction

AbstractAncestral sequence reconstruction has had recent success in decoding the origins and the determinants of complex protein functions. However, phylo­genetic analyses of remote homologues must handle extreme amino-acid se­quence diversity resulting from extended periods of evolutionary change. We exploited the wealth of protein structures to develop an evolutionary model based on protein secondary structure. The approach follows the differences between discrete secondary structure states observed in modern proteins and those hypothesised in their immediate ancestors. We implemented maximum likelihood-based phylogenetic inference to reconstruct ancestral secondary structure. The predictive accuracy from the use of the evolutionary model surpasses that of comparative modelling and sequence-based prediction; the reconstruction extracts information not available from modern structures or the ancestral sequences alone. Based on a phylogenetic analysis of multiple protein families, we showed that the model can highlight relationships that are evolutionarily rooted in structure and not evident in amino acid-based analysis.

scholarly journals A thermodynamic model of protein structure evolution explains empirical amino acid rate matrices

2020 ◽  
Author(s):  
Christoffer Norn ◽  
Ingemar André ◽  
Douglas L. Theobald
Keyword(s):  
Amino Acid ◽  
Amino Acid Substitution ◽  
Evolutionary Dynamics ◽  
Fitness Function ◽  
Protein Structures ◽  
Evolutionary Model ◽  
Structure Evolution ◽  
Ancestral Sequences ◽  
Substitution Matrices ◽  
Nucleotide Mutation

AbstractProteins evolve under a myriad of biophysical selection pressures that collectively control the patterns of amino acid substitutions. Averaged over time and across proteins, these evolutionary pressures are sufficiently consistent to produce global substitution patterns that can be used to successfully find homologues, infer phylogenies, and reconstruct ancestral sequences. Although the factors which govern the variation of protein substitution rates has received much attention, the influence of thermodynamic stability constraints remains unresolved. Here we develop a simple model to calculate amino acid rate matrices from evolutionary dynamics controlled by a fitness function that reports on the thermodynamic effects of amino acid mutations in protein structures. This hybrid biophysical and evolutionary model accounts for nucleotide transition/transversion rate bias, multi-nucleotide codon changes, the number of codons per amino acid, and thermodynamic protein stability. We find that our theoretical model accurately recapitulates the complex pattern of empirical rates observed in common global amino acid substitution matrices used in phylogenetics. These results suggest that selection for thermodynamically stable proteins, coupled with nucleotide mutation bias filtered by the structure of the genetic code, is the primary global driver behind the amino acid substitution patterns observed in proteins throughout the tree of life.

CLUSTERING OF AMINO ACIDS FOR PROTEIN SECONDARY STRUCTURE PREDICTION

2004 ◽  
Vol 02 (02) ◽  
pp. 333-342 ◽  
Author(s):  
WEI-MOU ZHENG
Keyword(s):  
Amino Acid ◽  
Secondary Structure ◽  
Structure Prediction ◽  
Markov Models ◽  
Secondary Structure Prediction ◽  
Length Distribution ◽  
Protein Secondary Structure ◽  
Coarse Graining ◽  
High Prediction ◽  
Duration Effect

Simple hidden Markov models are proposed for predicting secondary structure of a protein from its amino acid sequence. Since the length of protein conformation segments varies in a narrow range, we ignore the duration effect of length distribution, and focus on inclusion of short range correlations of residues and of conformation states in the models. Conformation-independent and -dependent amino acid coarse-graining schemes are designed for the models by means of proper mutual information. We compare models of different level of complexity, and establish a practical model with a high prediction accuracy.

scholarly journals Hermes: an ensemble machine learning architecture for protein secondary structure prediction

2019 ◽  
Author(s):  
Larry Bliss ◽  
Ben Pascoe ◽  
Samuel K Sheppard
Keyword(s):  
Machine Learning ◽  
Protein Structure ◽  
Secondary Structure ◽  
Structure Prediction ◽  
Cross Validation ◽  
Secondary Structure Prediction ◽  
Protein Structures ◽  
Lower Boundary ◽  
Protein Secondary Structure ◽  
Homologous Proteins

AbstractMotivationProtein structure predictions, that combine theoretical chemistry and bioinformatics, are an increasingly important technique in biotechnology and biomedical research, for example in the design of novel enzymes and drugs. Here, we present a new ensemble bi-layered machine learning architecture, that directly builds on ten existing pipelines providing rapid, high accuracy, 3-State secondary structure prediction of proteins.ResultsAfter training on 1348 solved protein structures, we evaluated the model with four independent datasets: JPRED4 - compiled by the authors of the successful predictor with the same name, and CASP11, CASP12 & CASP13 - assembled by the Critical Assessment of protein Structure Prediction consortium who run biannual experiments focused on objective testing of predictors. These rigorous, pre-established protocols included 7-fold cross-validation and blind testing. This led to a mean Hermes accuracy of 95.5%, significantly (p<0.05) better than the ten previously published models analysed in this paper. Furthermore, Hermes yielded a reduction in standard deviation, lower boundary outliers, and reduced dependency on solved structures of homologous proteins, as measured by NEFF score. This architecture provides advantages over other pipelines, while remaining accessible to users at any level of bioinformatics experience.Availability and ImplementationThe source code for Hermes is freely available at: https://github.com/HermesPrediction/Hermes. This page also includes the cross-validation with corresponding models, and all training/testing data presented in this study with predictions and accuracy.

scholarly journals Literature Survey of Protein Secondary Structure Prediction

2012 ◽  
Author(s):  
Satya Nanda Vel Arjunan ◽  
Safaai Deris ◽  
Rosli Md Illias
Keyword(s):  
Protein Structure ◽  
Secondary Structure ◽  
Structure Prediction ◽  
Large Scale ◽  
Secondary Structure Prediction ◽  
Protein Structures ◽  
Protein Secondary Structure ◽  
Fundamental Theory ◽  
Protein Secondary Structure Prediction ◽  
General Guide

Dengan wujudnya projek jujukan DNA secara besar-besaran, teknik yang tepat untuk meramalkan struktur protein diperlukan. Masalah meramalkan struktur protein daripada jujukan DNA pada dasarnya masih belum dapat diselesaikan walaupun kajian intensif telah dilakukan selama lebih daripada tiga dekad. Dalam kertas kerja ini, teori asas struktur protein akan dibincangkan sebagai panduan umum bagi kajian peramalan struktur protein sekunder. Analisis jujukan terkini serta prinsi p yang digunakan dalam teknik-teknik tersebut akan diterangkan. Kata kunci: peramalan stuktur sekunder protein; rangkaian neural. In the wake of large-scale DNA sequencing projects, accurate tools are needed to predict protein structures. The problem of predicting protein structure from DNA sequence remains fundamentally unsolved even after more than three decades of intensive research. In this paper, fundamental theory of the protein structure of the protein structure will be presented as a general guide to protein secondary structure prediction research. An overview of the state-of-theart in sequence analysis and some princi ples of the methods invloved wil be described. Key words: protein secondary structure prediction;neural networks.

Prediction of Protein Secondary Structure

2012 ◽  
Author(s):  
Satya Nanda Vel Arjunan ◽  
Safaai Deris ◽  
Rosli Md Illias
Keyword(s):  
Protein Structure ◽  
Secondary Structure ◽  
Structure Prediction ◽  
Large Scale ◽  
Secondary Structure Prediction ◽  
State Of The Art ◽  
Protein Structures ◽  
Protein Secondary Structure ◽  
Protein Secondary Structure Prediction ◽  
General Guide

Dengan wujudnya projek jujukan DNA secara besar–besaran, teknik yang tepat untuk meramalkan struktur protein diperlukan. Masalah meramalkan struktur protein daripada jujukan DNA pada dasarnya masih belum dapat diselesaikan walaupun kajian intensif telah dilakukan selama lebih daripada tiga dekad. Dalam kertas kerja ini, teori asas struktur protein akan dibincangkan sebagai panduan umum bagi kajian peramalan struktur protein sekunder. Analisis jujukan terkini serta prinsip yang digunakan dalam teknik–teknik tersebut akan diterangkan. Kata kunci: Peramalan struktur sekunder protein; Rangkaian Neural In the wake of large-scale DNA sequencing projects, accurate tools are needed to predict protein structures. The problem of predicting protein structure from DNA sequence remains fundamentally unsolved even after more than three decades of intensive research. In this paper, fundamental theory of the protein structure will be presented as a general guide to protein secondary structure prediction research. An overview of the state–of–the–art in sequence analysis and some principles of the methods involved wil be described. Key words: Protein secondary structure prediction; Neural networks

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