Biology subjects

Schafer, N.

Publications and source records attributed to Schafer, N..

2 recordsLinked to original sources

bioRxiv2018

PAGE4 and Conformational Switching: Insights from Molecular Dynamics Simulations and Implications for Prostate Cancer

Prostate-Associated Gene 4 (PAGE4) is a disordered protein implicated in the progression of prostate cancer. PAGE4 can be phosphorylated at two residue sites by Homeodomain-Interacting Protein Kinase 1 (HIPK1) to facilitate its binding to the Activator Protein-1 (AP-1) transcription factor. In contrast, a further hyperphosphorylation of PAGE4 by CDC-Like Kinase 2 (CLK2) reduces its binding affinity to AP-1, thus affecting the androgen receptor (AR) activity. Both SAXS and smFRET experiments have shown a structural expansion of PAGE4 upon hyperphosphorylation and a significant increase in size at its N-terminal half than that at its C-terminus. To understand the molecular mechanism underlying this structural transition, we performed a series of constant temperature molecular dynamics simulations using Atomistic AWSEM -- a multi-scale molecular model combining detailed atomistic and coarse-grained simulation approaches. Our simulations show that electrostatic interaction drives a transient formation of an N-terminal loop, which causes the change in size for different phosphorylated forms of PAGE4. Phosphorylation also changes the preference of secondary structure formation of PAGE4, which signifies a transition between states that display different degree of disorder. Finally, we construct a mechanism-based mathematical model that allows us to capture the interactions of different forms of PAGE4 with AP-1 and AR, a key therapeutic target in prostate cancer. Our model predicts intracellular oscillatory dynamics of HIPK1-PAGE4, CLK2-PAGE4 and AR activity, indicating phenotypic heterogeneity in an isogenic cell population. Thus, conformational switching among different forms of PAGE4 may potentially affect the efficiency of therapeutic targeting of AR.

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↗