147 research outputs found
Introduction to Protein Structure Prediction
This chapter gives a graceful introduction to problem of protein three-
dimensional structure prediction, and focuses on how to make structural sense
out of a single input sequence with unknown structure, the 'query' or 'target'
sequence. We give an overview of the different classes of modelling techniques,
notably template-based and template free. We also discuss the way in which
structural predictions are validated within the global com- munity, and
elaborate on the extent to which predicted structures may be trusted and used
in practice. Finally we discuss whether the concept of a sin- gle fold
pertaining to a protein structure is sustainable given recent insights. In
short, we conclude that the general protein three-dimensional structure
prediction problem remains unsolved, especially if we desire quantitative
predictions. However, if a homologous structural template is available in the
PDB model or reasonable to high accuracy may be generated
Preface to Introduction to Structural Bioinformatics
While many good textbooks are available on Protein Structure, Molecular
Simulations, Thermodynamics and Bioinformatics methods in general, there is no
good introductory level book for the field of Structural Bioinformatics. This
book aims to give an introduction into Structural Bioinformatics, which is
where the previous topics meet to explore three dimensional protein structures
through computational analysis. We provide an overview of existing
computational techniques, to validate, simulate, predict and analyse protein
structures. More importantly, it will aim to provide practical knowledge about
how and when to use such techniques. We will consider proteins from three major
vantage points: Protein structure quantification, Protein structure prediction,
and Protein simulation & dynamics
Disordered Flanks Prevent Peptide Aggregation
Natively unstructured or disordered regions appear to be abundant in eukaryotic proteins. Many such regions have been found alongside small linear binding motifs. We report a Monte Carlo study that aims to elucidate the role of disordered regions adjacent to such binding motifs. The coarse-grained simulations show that small hydrophobic peptides without disordered flanks tend to aggregate under conditions where peptides embedded in unstructured peptide sequences are stable as monomers or as part of small micelle-like clusters. Surprisingly, the binding free energy of the motif is barely decreased by the presence of disordered flanking regions, although it is sensitive to the loss of entropy of the motif itself upon binding. This latter effect allows for reversible binding of the signalling motif to the substrate. The work provides insights into a mechanism that prevents the aggregation of signalling peptides, distinct from the general mechanism of protein folding, and provides a testable hypothesis to explain the abundance of disordered regions in proteins
Strategies for protein structure model generation
This chapter deals with approaches for protein three-dimensional structure
prediction, starting out from a single input sequence with unknown struc- ture,
the 'query' or 'target' sequence. Both template based and template free
modelling techniques are treated, and how resulting structural models may be
selected and refined. We give a concrete flowchart for how to de- cide which
modelling strategy is best suited in particular circumstances, and which steps
need to be taken in each strategy. Notably, the ability to locate a suitable
structural template by homology or fold recognition is crucial; without this
models will be of low quality at best. With a template avail- able, the quality
of the query-template alignment crucially determines the model quality. We also
discuss how other, courser, experimental data may be incorporated in the
modelling process to alleviate the problem of missing template structures.
Finally, we discuss measures to predict the quality of models generated
Using Phylogeny to Improve Genome-Wide Distant Homology Recognition
The gap between the number of known protein sequences and structures continues to widen, particularly as a result of sequencing projects for entire genomes. Recently there have been many attempts to generate structural assignments to all genes on sets of completed genomes using fold-recognition methods. We developed a method that detects false positives made by these genome-wide structural assignment experiments by identifying isolated occurrences. The method was tested using two sets of assignments, generated by SUPERFAMILY and PSI-BLAST, on 150 completed genomes. A phylogeny of these genomes was built and a parsimony algorithm was used to identify isolated occurrences by detecting occurrences that cause a gain at leaf level. Isolated occurrences tend to have high e-values, and in both sets of assignments, a sudden increase in isolated occurrences is observed for e-values >10(−8) for SUPERFAMILY and >10(−4) for PSI-BLAST. Conditions to predict false positives are based on these results. Independent tests confirm that the predicted false positives are indeed more likely to be incorrectly assigned. Evaluation of the predicted false positives also showed that the accuracy of profile-based fold-recognition methods might depend on secondary structure content and sequence length. We show that false positives generated by fold-recognition methods can be identified by considering structural occurrence patterns on completed genomes; occurrences that are isolated within the phylogeny tend to be less reliable. The method provides a new independent way to examine the quality of fold assignments and may be used to improve the output of any genome-wide fold assignment method
Consistent treatment of hydrophobicity in protein lattice models accounts for cold denaturation
The hydrophobic effect stabilizes the native structure of proteins by
minimizing the unfavourable interactions between hydrophobic residues and water
through the formation of a hydrophobic core. Here we include the entropic and
enthalpic contributions of the hydrophobic effect explicitly in an implicit
solvent model. This allows us to capture two important effects: a length-scale
dependence and a temperature dependence for the solvation of a hydrophobic
particle. This consistent treatment of the hydrophobic effect explains cold
denaturation and heat capacity measurements of solvated proteins.Comment: Added and corrected references for design procedure in main text (p.
2) and in Supplemental Information (p. 8
Data Resources for Structural Bioinformatics
While many good textbooks are available on Protein Structure, Molecular Simulations, Thermodynamics and Bioinformatics methods in general, there is no good introductory level book for the field of Structural Bioinformatics. This book aims to give an introduction into Structural Bioinformatics, which is where the previous topics meet to explore three dimensional protein structures through computational analysis. We provide an overview of existing computational techniques, to validate, simulate, predict and analyse protein structures. More importantly, it will aim to provide practical knowledge about how and when to use such techniques. We will consider proteins from three major vantage points: Protein structure quantification, Protein structure prediction, and Protein simulation & dynamics. Structural bioinformatics involves a variety of computational methods, all of which require input data. Typical inputs include protein structures and sequences, which are usually retrieved from a public or private database. This chapter introduces several key resources that make such data available, as well as a handful of tools that derive additional information from experimentally determined or computationally predicted protein structures and sequences
Data Resources for Structural Bioinformatics
While many good textbooks are available on Protein Structure, Molecular
Simulations, Thermodynamics and Bioinformatics methods in general, there is no
good introductory level book for the field of Structural Bioinformatics. This
book aims to give an introduction into Structural Bioinformatics, which is
where the previous topics meet to explore three dimensional protein structures
through computational analysis. We provide an overview of existing
computational techniques, to validate, simulate, predict and analyse protein
structures. More importantly, it will aim to provide practical knowledge about
how and when to use such techniques. We will consider proteins from three major
vantage points: Protein structure quantification, Protein structure prediction,
and Protein simulation & dynamics.
Structural bioinformatics involves a variety of computational methods, all of
which require input data. Typical inputs include protein structures and
sequences, which are usually retrieved from a public or private database. This
chapter introduces several key resources that make such data available, as well
as a handful of tools that derive additional information from experimentally
determined or computationally predicted protein structures and sequences.Comment: editorial responsability: Sanne Abeln, K. Anton Feenstra, Halima
Mouhib. This chapter is part of the book "Introduction to Protein Structural
Bioinformatics". The Preface arXiv:1801.09442 contains links to all the
(published) chapters. The update adds available arxiv hyperlinks for the
chapter
Structure Alignment
While many good textbooks are available on Protein Structure, Molecular Simulations, Thermodynamics and Bioinformatics methods in general, there is no good introductory level book for the field of Structural Bioinformatics. This book aims to give an introduction into Structural Bioinformatics, which is where the previous topics meet to explore three dimensional protein structures through computational analysis. We provide an overview of existing computational techniques, to validate, simulate, predict and analyse protein structures. More importantly, it will aim to provide practical knowledge about how and when to use such techniques. We will consider proteins from three major vantage points: Protein structure quantification, Protein structure prediction, and Protein simulation & dynamics. The Protein DataBank (PDB) contains a wealth of structural information. In order to investigate the similarity between different proteins in this database, one can compare the primary sequence through pairwise alignment and calculate the sequence identity (or similarity) over the two sequences. This strategy will work particularly well if the proteins you want to compare are close homologs. However, in this chapter we will explain that a structural comparison through structural alignment will give you much more valuable information, that allows you to investigate similarities between proteins that cannot be discovered by comparing the sequences alone
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