Predicting the structure of a protein from its amino acid sequence is a long-standing unsolved problem in computational biology. Its solution would be of both fundamental and practical importance as the gap between the number of known sequences and the number of experimentally solved structures widens rapidly. Currently, the most successful approaches are based on fragment/template reassembly. Lacking progress in template-free structure prediction calls for novel ideas and approaches. This article reviews trends in the development of physical and specific knowledge-based energy functions as well as sampling techniques for fragment-free structure prediction. Recent physical- and knowledge-based studies demonstrated that it is possible to sample and predict highly accurate protein structures without borrowing native fragments from known protein structures. These emerging approaches with fully flexible sampling have the potential to move the field forward.

Sequence-programmable transcription activator-like effector (TALE) proteins have emerged as a highly efficient tool for genome engineering. Recent crystal structures depict a transition between an open unbound solenoid and more compact DNA-bound solenoid formed by the 34 amino acid repeats. How TALEs switch conformation between these two forms without substantial energetic compensation, and how the repeat-variable di-residues (RVDs) discriminate between the cognate base and other bases still remain unclear. Computational analysis on these two aspects of TALE-DNA interaction mechanism has been conducted in order to achieve a better understanding of the energetics. High elasticity was observed in the molecular dynamics simulations of DNA-free TALE structure that started from the bound conformation where it sampled a wide range of conformations including the experimentally determined apo and bound conformations. This elastic feature was also observed in the simulations starting from the apo form which suggests low free energy barrier between the two conformations and small compensation required upon binding. To analyze binding specificity, we performed free energy calculations of various combinations of RVDs and bases using Poisson-Boltzmann surface area (PBSA) and other approaches. The PBSA calculations indicated that the native RVD-base structures had lower binding free energy than mismatched structures for most of the RVDs examined. Our theoretical analyses provided new insight on the dynamics and energetics of TALE-DNA binding mechanism.

Background

Defining the RNA transcriptome in Alzheimer Disease (AD) will help understand the disease mechanisms and provide biomarkers. Though the AD blood transcriptome has been studied, effects of white matter hyperintensities (WMH) were not considered. This study investigated the AD blood transcriptome and accounted for WMH.

Methods

RNA from whole blood was processed on whole-genome microarrays.

Results

A total of 293 probe sets were differentially expressed in AD versus controls, 5 of which were significant for WMH status. The 288 AD-specific probe sets classified subjects with 87.5% sensitivity and 90.5% specificity. They represented 188 genes of which 29 have been reported in prior AD blood and 89 in AD brain studies. Regulated blood genes included MMP9, MME (Neprilysin), TGFβ1, CA4, OCLN, ATM, TGM3, IGFR2, NOV, RNF213, BMX, LRRN1, CAMK2G, INSR, CTSD, SORCS1, SORL1, and TANC2.

Conclusions

RNA expression is altered in AD blood irrespective of WMH status. Some genes are shared with AD brain.