Discussion of "EQUI-energy sampler" by Kou, Zhou and Wong

Novel sampling algorithms can significantly impact open questions in computational biology, most notably the in silico protein folding problem. By using computational methods, protein folding aims to find the three-dimensional structure of a protein chain given the sequence of its amino acid building blocks. The complexity of the problem strongly depends on the protein representation and its energy function. The more detailed the model, the more complex its corresponding energy function and the more challenge it sets for sampling algorithms. Kou, Zhou and Wong [math.ST/0507080] have introduced a novel sampling method, which could contribute significantly to the field of structural prediction.Comment: Published at http://dx.doi.org/10.1214/009053606000000470 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Training-free Measures Based on Algorithmic Probability Identify High Nucleosome Occupancy in DNA Sequences

We introduce and study a set of training-free methods of information-theoretic and algorithmic complexity nature applied to DNA sequences to identify their potential capabilities to determine nucleosomal binding sites. We test our measures on well-studied genomic sequences of different sizes drawn from different sources. The measures reveal the known in vivo versus in vitro predictive discrepancies and uncover their potential to pinpoint (high) nucleosome occupancy. We explore different possible signals within and beyond the nucleosome length and find that complexity indices are informative of nucleosome occupancy. We compare against the gold standard (Kaplan model) and find similar and complementary results with the main difference that our sequence complexity approach. For example, for high occupancy, complexity-based scores outperform the Kaplan model for predicting binding representing a significant advancement in predicting the highest nucleosome occupancy following a training-free approach.Comment: 8 pages main text (4 figures), 12 total with Supplementary (1 figure

HLA-DM Stabilizes the Empty MHCII Binding Groove:A Model Using Customized Natural Move Monte Carlo

MHC class II molecules bind peptides derived from extracellular proteins that have been ingested by antigen-presenting cells and display them to the immune system. Peptide loading occurs within the antigen-presenting cell and is facilitated by HLA-DM. HLA-DM stabilises the open conformation of the MHCII binding groove when no peptide is bound. While a structure of the MHCII/HLA-DM complex exists, the mechanism of stabilisation is still largely unknown. Here, we applied customised Natural Move Monte Carlo to investigate this interaction. We found a possible long range mechanism that implicates the configuration of the membrane-proximal globular domains in stabilising the open state of the empty MHCII binding groove

M (2010) Conformational optimization with natural degrees of freedom: a novel stochastic chain closure algorithm

The present article introduces a set of novel methods that facilitate the use of ‘‘natural moves’ ’ or arbitrary degrees of freedom that can give rise to collective rearrangements in the structure of biological macromolecules. While such ‘‘natural moves’ ’ may spoil the stereochemistry and even break the bonded chain at multiple locations, our new method restores the correct chain geometry by adjusting bond and torsion angles in an arbitrary defined molten zone. This is done by successive stages of partial closure that propagate the location of the chain break backwards along the chain. At the end of these stages, the size of the chain break is generally reduced so much that it can be repaired by adjusting the position of a single atom. Our chain closure method is efficient with a computational complexity of O(Nd), where Nd is the number of degrees of freedom used to repair the chain break. The new method facilitates the use of arbitrary degrees of freedom including the ‘‘natural’ ’ degrees of freedom inferred from analyzing experimental (X-ray crystallography and nuclear magnetic resonance [NMR]) structures of nucleic acids and proteins. In terms of its ability to generate large conformational moves and its effectiveness in locating low energy states, the new method is robust and computationally efficient

Conformational Optimization with Natural Degrees of Freedom: A Novel Stochastic Chain Closure Algorithm

The present article introduces a set of novel methods that facilitate the use of “natural moves” or arbitrary degrees of freedom that can give rise to collective rearrangements in the structure of biological macromolecules. While such “natural moves” may spoil the stereochemistry and even break the bonded chain at multiple locations, our new method restores the correct chain geometry by adjusting bond and torsion angles in an arbitrary defined molten zone. This is done by successive stages of partial closure that propagate the location of the chain break backwards along the chain. At the end of these stages, the size of the chain break is generally reduced so much that it can be repaired by adjusting the position of a single atom. Our chain closure method is efficient with a computational complexity of O(Nd), where Nd is the number of degrees of freedom used to repair the chain break. The new method facilitates the use of arbitrary degrees of freedom including the “natural” degrees of freedom inferred from analyzing experimental (X-ray crystallography and nuclear magnetic resonance [NMR]) structures of nucleic acids and proteins. In terms of its ability to generate large conformational moves and its effectiveness in locating low energy states, the new method is robust and computationally efficient