Combining prediction of secondary structure and solvent accessibility in proteins

  title={Combining prediction of secondary structure and solvent accessibility in proteins},
  author={Rafal Adamczak and Aleksey A. Porollo and Jaroslaw Meller},
  journal={Proteins: Structure},
Owing to the use of evolutionary information and advanced machine learning protocols, secondary structures of amino acid residues in proteins can be predicted from the primary sequence with more than 75% per‐residue accuracy for the 3‐state (i.e., helix, β‐strand, and coil) classification problem. In this work we investigate whether further progress may be achieved by incorporating the relative solvent accessibility (RSA) of an amino acid residue as a fingerprint of the overall topology of the… 

Prediction of Protein Secondary Structure Using Feature Selection and Analysis Approach

A novel method that uses binomial distribution to optimize tetrapeptide structural words and increment of diversity with quadratic discriminant to perform prediction for protein three-state secondary structure is proposed and results suggest that the feature selection technique can detect the optimized tetrapeptic structural words which affect the accuracy of predicted secondary structures.

Computational Prediction of Protein Secondary Structure from Sequence

This unit summarizes several recent third‐generation predictors of secondary structure from protein sequences, discussing their inputs and outputs, availability, and predictive performance and explaining how to perform and interpret their predictions.

Enhanced recognition of protein transmembrane domains with prediction-based structural profiles

It is demonstrated that membrane domain prediction methods based on a compact representation of an amino acid residue and its environment outperform approaches that utilize explicitly evolutionary profiles and multiple alignments.

Secondary protein structure prediction combining protein structural class, relative surface accessibility, and contact number

Experimental results indicate that accuracy improves the most when incorporating contact number, relative surface accessibility or any combination that includes at least one of the two into the prediction process.

Protein secondary structure prediction through a novel framework of secondary structure transition sites and new encoding schemes

  • Masood ZamaniS. C. Kremer
  • Computer Science
    2016 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)
  • 2016
An ab initio two-stage protein secondary structure (PSS) prediction model through a novel framework of PSS transition site prediction by using Artificial Neural Networks (ANNs) and Genetic Programming (GP).


A two-stage PSS prediction model based on Artificial Neural Networks and Genetic Programming is developed through a novel framework of PSS transition sites, and new amino acid encoding schemes derived from the genetic Codon mappings, Clustering and Information theory are developed.

Impact of residue accessible surface area on the prediction of protein secondary structures

The success of applying the RSA information on different secondary structure prediction methods suggest that prediction accuracy can be improved independent of prediction approaches, and solvent accessibility can be considered as a rich source of information to help the improvement of these methods.

Improved Sequence-Based Prediction of Strand residues

A sequence-based predictor, BETArPRED, which improves prediction of strand residues and β-strand segments and findsβ-strands that were missed by the other methods and is compared with the ZHANG-server.

New evolutionary approaches to protein structure prediction

This thesis work includes a compilation of the soft computing techniques for the protein structure prediction problem (secondary and tertiary structures) and proposes a multi-objective evolutionary approach for contact map prediction based on physico-chemical properties of amino acids.

SPINE X: Improving protein secondary structure prediction by multistep learning coupled with prediction of solvent accessible surface area and backbone torsion angles

A multistep neural‐network algorithm was developed by coupling secondary structure prediction with prediction of solvent accessibility and backbone torsion angles in an iterative manner by applying SPINE X to a dataset of 2640 proteins and achieving 82.0% accuracy based on 10‐fold cross validation.



Conservation and prediction of solvent accessibility in protein families

A neural network system that predicts relative solvent accessibility of each residue using evolutionary profiles of amino acid substitutions derived from multiple sequence alignments is introduced, and the most reliably predicted fraction of the residues (50%) is predicted as accurately as by automatic homology modeling.

Linear Regression Models for Solvent Accessibility Prediction in Proteins

This work investigates several regression models for RSA prediction using linear L1-support vector regression (SVR) approaches as well as standard linear least squares (LS) regression, and compares the performance of the SVR with that of LS regression and NN-based methods.

Prediction of protein secondary structure at 80% accuracy

Secondary structure prediction involving up to 800 neural network predictions has been developed, by use of novel methods such as output expansion and a unique balloting procedure, and with respect to blind prediction, this work is preliminary and awaits evaluation by CASP4.

Prediction of protein secondary structure at better than 70% accuracy.

A two-layered feed-forward neural network is trained on a non-redundant data base to predict the secondary structure of water-soluble proteins with a new key aspect is the use of evolutionary information in the form of multiple sequence alignments that are used as input in place of single sequences.

Prediction of coordination number and relative solvent accessibility in proteins

Ensembles of bidirectional recurrent neural network architectures are developed to improve the state of the art in both contact and accessibility prediction, leveraging a large corpus of curated data together with evolutionary information.

Review: protein secondary structure prediction continues to rise.

  • B. Rost
  • Computer Science
    Journal of structural biology
  • 2001
Methods predicting protein secondary structure improved substantially in the 1990s through the use of evolutionary information taken from the divergence of proteins in the same structural family.

NETASA: neural network based prediction of solvent accessibility

A server, NETASA is implemented for predicting solvent accessibility of amino acids using a newly optimized neural network algorithm, and applicability of neural networks for ASA prediction has been confirmed with a larger data set and wider range of state thresholds.

The use of amino acid patterns of classified helices and strands in secondary structure prediction.

This method predicts not only position of a secondary structure in a protein sequence but also the orientation of the secondary structure with respect to the core of the protein tertiary structure.

Exploiting the past and the future in protein secondary structure prediction

A family of novel architectures which can learn to make predictions based on variable ranges of dependencies are introduced, extending recurrent neural networks and introducing non-causal bidirectional dynamics to capture both upstream and downstream information.

Improving the prediction of protein secondary structure in three and eight classes using recurrent neural networks and profiles

Ensembles of bidirectional recurrent neural network architectures, PSI‐BLAST‐derived profiles, and a large nonredundant training set are used to derive two new predictors for secondary structure predictions, and confusion matrices are reported.