Solving the protein folding problem

The list author says:
	"Very accessible, insightful book by two pioneers of the field. Very good illustrations too."

The list author says:
	"Standard work on the statistics of angles, axes and orientations. Not easy, but a treasure trove of probabilistic solutions to common problems in structure prediction, waiting to be uncovered!"

The list author says:
	"A great pictorial overview of protein geometry and structure."

The list author says:
	"Classic reference on proteins (enzyme mechanism, protein stability, kinetics of folding,...). Concise, lots of references."

The list author says:
	"Nice introduction to thermodynamics & statistical mechanics. Lots of relevant protein-related topics (like the Zimm-Bragg model of helix-coil transition)."

The list author says:
	"From KD-trees to Voronoi diagrams. A pleasure to read. Plenty of clear pseudo code."

The list author says:
	"Probabilistic models are getting more and more important in macromolecular 3D structure prediction. Great reference, and some good tutorial chapters too. Also deals with approximative algorithms."

The list author says:
	"Thorough covering of Bayesian Networks. Emphasis on deterministic, exact algorithms. Start with this, and then proceed to Jordan's book."

The list author says:
	"A classic on probabilistic applications of MCMC methods."

The list author says:
	"A Bayesian, probabilistic view on science. Provides many relevant general insights. Jaynes pointed out the importance of Bayesian methods for protein structure prediction long ago."

The list author says:
	"For all those matrix problems (starting with optimal RMSD calculation of course!)"

The list author says:
	"The first book you need on Bioinformatics. Emphasis on probabilistic models and associated algorithms. Very clearly written."

The list author says:
	"The second book you need on Bioinformatics. More theoretical. Also based on probabilistic models. Covers a lot of ground, but can be quite cryptic."

The list author says:
	"Could do with an update, but still a great, accessible reference for protein structure and properties."

The list author says:
	"Simple, concise introduction to Bayesian statistics."

The list author says:
	"Fantastic, thorough overview of machine learning from a Bayesian viewpoint. Beautifully illustrated and an example of clarity, yet rigorous. A gem."

The list author says:
	"Modern treatment of statistical mechanics, including Monte Carlo simulations, Ising models and renormalization group theory.  A bit terse, but very clear and well organized."

The list author says:
	"Read this for deeper insights into the history and development of Bayesian networks. BTW: you'll find the justification of the ROSETTA pairwise energy function in paragraph 3.2.4 on page 110."

The list author says:
	"Picking the best probabilistic model out of a set of possible models is a key problem. Methods based on the Akaike Information Criterion are both conceptually elegant and computationally tractable."