Biology subjects

Wolynes, P. G.

Publications and source records attributed to Wolynes, P. G..

2 recordsLinked to original sources

bioRxiv2017

De Novo Prediction of Human Chromosome Structures: Epigenetic Marking Patterns Encode Genome Architecture

Inside the cell nucleus, genomes fold into organized structures that are characteristic of cell type. Here, we show that this chromatin architecture can be predicted de novo using epigenetic data derived from ChIP-Seq. We exploit the idea that chromosomes encode a one-dimensional sequence of chromatin structural types. Interactions between these chromatin types determine the three-dimensional (3D) structural ensemble of chromosomes through a process similar to phase separation. First, a recurrent neural network is used to infer the relation between the epigenetic marks present at a locus, as assayed by ChIP-Seq, and the genomic compartment in which those loci reside, as measured by DNA-DNA proximity ligation (Hi-C). Next, types inferred from this neural network are used as an input to an energy landscape model for chromatin organization (MiChroM) in order to generate an ensemble of 3D chromosome conformations. After training the model, dubbed MEGABASE (Maximum Entropy Genomic Annotation from Biomarkers Associated to Structural Ensembles), on odd numbered chromosomes, we predict the chromatin type sequences and the subsequent 3D conformational ensembles for the even chromosomes. We validate these structural ensembles by using ChIP-Seq tracks alone to predict Hi-C maps as well as distances measured using 3D FISH experiments. Both sets of experiments support the hypothesis of phase separation being the driving process behind compartmentalization. These findings strongly suggest that epigenetic marking patterns encode sufficient information to determine the global architecture of chromosomes and that de novo structure prediction for whole genomes may be increasingly possible.

biophysics↗

bioRxiv2017

The AWSEM-Amylometer: Predicting Amyloid Propensity And Fibril Topology Using An Optimized Folding Landscape Model

Amyloids are fibrillar protein aggregates with simple repeated structural motifs in their cores, usually {beta}-strands but sometimes -helices. Identifying the amyloid-prone regions within protein sequences is important both for understanding the mechanisms of amyloid-associated diseases and for understanding functional amyloids. Based on the crystal structures of seven cross-{beta} amyloidogenic peptides with different topologies and one recently solved cross- fiber structure, we have developed a computational approach for identifying amyloidogenic segments in protein sequences using the Associative memory, Water mediated, Structure and Energy Model. The AWSEM-Amylometer performs favorably in comparison with other predictors in predicting aggregation-prone sequences in multiple datasets. The method also predicts the specific topologies (the relative arrangement of {beta}-strands in the core) of the amyloid fibrils well. An important advantage of the AWSEM-Amylometer over other existing methods is its direct connection with an efficient, optimized protein folding simulation model, AWSEM. This connection allows one to combine efficient and accurate search of protein sequences for amyloidogenic segments with the detailed study of the thermodynamic and kinetic roles that these segments play in folding and aggregation in the context of the entire protein sequence. We present new simulation results that highlight the free energy landscapes of peptides that can take on multiple fibril topologies. We also demonstrate how the Amylometer methodology can be straightforwardly extended to the study of functional amyloids that have the recently discovered cross- fibril architecture.

biophysics↗