  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.
  Some structural properties of proteins that are closely linked to their
function may be easier (or much faster) to predict from sequence than the
complete tertiary structure; for example, secondary structure, surface
accessibility, flexibility, disorder, interface regions or hydrophobic patches.
Serving as building blocks for the native protein fold, these structural
properties also contain important structural and functional information not
apparent from the amino acid sequence. Here, we will first give an introduction
into the application of machine learning for structural property prediction,
and explain the concepts of cross-validation and benchmarking. Next, we will
review various methods that incorporate knowledge of these concepts to predict
those structural properties, such as secondary structure, surface
accessibility, disorder and flexibility, and aggregation.
