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Protein structure Prediction
article from 1998
Protein structure prediction
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instance of
scholarly article
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title
Protein structure prediction
(English)
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main subject
protein structure prediction
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protein structure
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author
David R. Westhead
object named as
David R Westhead
series ordinal
1
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Janet Thornton
object named as
Janet M Thornton
series ordinal
2
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author name string
D R Westhead
series ordinal
1
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J M Thornton
series ordinal
2
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language of work or name
English
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publication date
August 1998
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published in
Current Opinion in Biotechnology
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volume
9
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issue
4
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page(s)
383-389
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383-9
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cites work
The use of amino acid patterns of classified helices and strands in secondary structure prediction.
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Improving prediction of protein secondary structure using structured neural networks and multiple sequence alignments.
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Using Evolutionary Trees in Protein Secondary Structure Prediction and Other Comparative Sequence Analyses
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Identification and application of the concepts important for accurate and reliable protein secondary structure prediction
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Improvement of protein secondary structure prediction using binary word encoding
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Improved method for prediction of protein backbone U-turn positions and major secondary structural elements between U-turns
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Incorporation of non-local interactions in protein secondary structure prediction from the amino acid sequence.
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Seventy-five percent accuracy in protein secondary structure prediction
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The future of protein secondary structure prediction accuracy
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Protein secondary structure prediction using local alignments
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Exploring the limits of nearest neighbour secondary structure prediction.
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Secondary structure prediction using segment similarity
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Predicting protein secondary structure with probabilistic schemata of evolutionarily derived information
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Three-dimensional structures and contexts associated with recurrent amino acid sequence patterns
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Prediction of protein secondary structure and active sites using the alignment of homologous sequences
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Combining evolutionary information and neural networks to predict protein secondary structure
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Turn prediction in proteins using a pattern-matching approach
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Recognition of analogous and homologous protein folds: analysis of sequence and structure conservation 1 1Edited by F. E. Cohen
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Multiple sequence information for threading algorithms
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Multiple sequence threading: an analysis of alignment quality and stability
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Protein structure prediction by threading methods: evaluation of current techniques
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Protein fold recognition by mapping predicted secondary structures.
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Protein fold recognition by prediction-based threading
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Protein fold recognition using sequence-derived predictions
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A 3D-1D substitution matrix for protein fold recognition that includes predicted secondary structure of the sequence
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Protein topology recognition from secondary structure sequences: application of the hidden Markov models to the alpha class proteins.
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Distant homology recognition using structural classification of proteins
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7 January 2021
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Protein fold recognition and dynamics in the space of contact maps
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Factors affecting the ability of energy functions to discriminate correct from incorrect folds
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Statistical significance of hierarchical multi-body potentials based on Delaunay tessellation and their application in sequence-structure alignment
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Protein sequence-structure compatibility criteria in terms of statistical hypothesis testing.
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Self-consistently optimized statistical mechanical energy functions for sequence structure alignment
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Evaluation of threading specificity and accuracy
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Similarities and differences between nonhomologous proteins with similar folds: evaluation of threading strategies.
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Residue-residue mean-force potentials for protein structure recognition.
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Protein fold recognition without Boltzmann statistics or explicit physical basis
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The current state of the art in protein structure prediction
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Advances in comparative protein-structure modelling
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Knowledge-Based Protein Modeling
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7 January 2021
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Protein model building using structural homology
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Comparative modeling of homologous proteins
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Protein Modeling by E-mail
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7 January 2021
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ProMod and Swiss-Model: Internet-based tools for automated comparative protein modelling
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Conformational analysis and clustering of short and medium size loops connecting regular secondary structures: a database for modeling and prediction
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An automated classification of the structure of protein loops.
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Predicting the conformational class of short and medium size loops connecting regular secondary structures: application to comparative modelling
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PDB-based protein loop prediction: parameters for selection and methods for optimization.
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Structural families in loops of homologous proteins: automatic classification, modelling and application to antibodies
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Creation and characterization of a new, non-redundant fragment data bank
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All in one: a highly detailed rotamer library improves both accuracy and speed in the modelling of sidechains by dead-end elimination
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Prediction of protein side-chain rotamers from a backbone-dependent rotamer library: a new homology modeling tool
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Bayesian statistical analysis of protein side-chain rotamer preferences
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Prediction of protein side-chain conformations by principal component analysis for fixed main-chain atoms.
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Prediction of protein side chain conformations: a study on the influence of backbone accuracy on conformation stability in the rotamer space.
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Homology modelling by distance geometry
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Distance geometry based comparative modelling.
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7 January 2021
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Homology modeling using simulated annealing of restrained molecular dynamics and conformational search calculations with CONGEN: application in predicting the three-dimensional structure of murine homeodomain Msx-1.
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Alignment algorithm for homology modeling and threading
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7 January 2021
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Contact area difference (CAD): a robust measure to evaluate accuracy of protein models
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Marrying structure and genomics
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7 January 2021
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The Argonne Structural Genomics Workshop: Lamaze class for the birth of a new science
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Identifiers
DOI
10.1016/S0958-1669(98)80012-8
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PubMed publication ID
9751638
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ResearchGate publication ID
13533624
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