Biology subjects

Misiura, M.

Publications and source records attributed to Misiura, M..

4 recordsLinked to original sources

bioRxiv2024

Heterotrimeric Collagen Helix with High Specificity of Assembly Results in a Rapid Rate of Folding

The most abundant natural collagens form heterotrimeric triple helices. Synthetic mimics of collagen heterotrimers have been found to fold slowly, even compared to the already slow rates of homotrimeric helices. These prolonged folding rates are not understood and have not been studied. This work compares three heterotrimeric collagen mimics stabilities, specificities and folding rates. One of these was designed through a computational-assisted approach, resulting in a well-controlled composition and register, in addition to providing increased amino acid diversity and excellent specificity. The crystal structure of this heterotrimer elucidates the composition, register and geometry of pairwise cation-{pi} and axial and lateral salt bridges. Complementary experimental methods of circular dichroism and NMR suggest the folding paradigm is frustrated by unproductive, competing heterotrimer species and these species must completely unwind to the monomeric state before refolding into the thermodynamically favored assembly. This collagen heterotrimer, which displays the best reported thermal specificity, was also found to fold much faster (hours vs days) than comparable, well-designed systems. The heterotrimeric collagen folding rate was observed to be both concentration and temperature-independent, suggesting a complex, multi-step mechanism. These results suggest heterotrimer folding kinetics are dominated by frustration of the energy landscape caused by competing triple helices.

biophysics↗

bioRxiv2021

DLPacker: Deep Learning for Prediction of Amino Acid Side Chain Conformations in Proteins

AO_SCPLOWBSTRACTC_SCPLOWPrediction of side chain conformations of amino acids in proteins (also termed packing) is an important and challenging part of protein structure prediction with many interesting applications in protein design. A variety of methods for packing have been developed but more accurate ones are still needed. Machine learning (ML) methods have recently become a powerful tool for solving various problems in diverse areas of science, including structural biology. In this work we evaluate the potential of Deep Neural Networks (DNNs) for prediction of amino acid side chain conformations. We formulate the problem as image-to-image transformation and train a U-net style DNN to solve the problem. We show that our method outperforms other physics-based methods by a significant margin: reconstruction RMSDs for most amino acids are about 20% smaller compared to SCWRL4 and Rosetta Packer with RMSDs for bulky hydrophobic amino acids Phe, Tyr and Trp being up to 50% smaller.

biophysics↗

bioRxiv2018

Race modifies default mode network connectivity in Alzheimer’s disease

ObjectiveTo determine if resting state functional MRI biomarkers of Alzheimers disease (AD) differ between older African Americans and Caucasians.\n\nMethodsWe analyzed MRI profiles from 78 individuals (31 African Americans, 47 Caucasians) with normal cognition (n=36) or mild cognitive impairment/mild AD dementia (MCI/AD, n=42). We compared AD-associated intra-network functional connectivity of the default mode network (DMN) according to race, and correlated domain-specific cognitive functions with functional connectivity which differed between the racial groups.\n\nResultsWe identified key differences in DMN functional connectivity associated with AD between the races. Whereas MCI/AD was associated with decreased functional connectivity within the midline core DMN subsystem in older Caucasians, MCI/AD was instead associated with increased functional connectivity within the same subsystem of older African Americans. This is despite decreased functional connectivity in the medial temporal lobe DMN subsystem in both races. Memory function was also positively associated with connectivity between the precuneus and the posterior cingulate/inferior parietal lobule within the midline core subsystem, in keeping with a less amnestic-profile in older African Americans with MCI/AD.\n\nConclusionsThese findings provide structural support that race modifies the AD phenotypes downstream from cerebral amyloid deposition, and suggests a rsf-MRI correlate of African Americans less amnestic neuropsychological profile in MCI/AD.

neuroscience↗

bioRxiv2018

Kinetic network model to explain gain-of-function mutations in ERK2 enzymes

ERK2 is a kinase protein that belongs to a Ras/Raf/MEK/ERK signalling pathway, which is activated in response to a range of extracellular signals. Malfunctioning of this cascade leads to variety of serious diseases, including cancers. This is often caused by mutations in proteins belonging to the cascade, frequently leading to abnormally high activity of the cascade even in the absence of external signal. One such gain-of-function mutation in ERK2 protein, called a sevenmaker mutation (D319N), was discovered in 1994 in Drosophila. This mutation leads to disruption of interactions of other proteins with D-site of ERK2 and results, contrary to expectations, in increase of its activity in vivo. However, no molecular mechanism to explain this effect has been presented so far. The difficulty is that this mutation should equally negatively affect interactions of ERK2 with all substrates, activators and deactivators. In this paper, we present a quantitative kinetic network model that gives a possible explanation of the increased activity of mutant ERK2 species. A simplified biochemical network for ERK2, viewed as a system of coupled Michaelis-Menten processes, is presented. Its dynamic properties are calculated explicitly using the method of first-passage processes. The effect of mutation is associated with changes in the strength of interaction energy between the enzyme and the substrates. It is found that the dependence of kinetic properties of the protein on the interaction energy is non-monotonic, suggesting that some mutations might lead to more efficient catalytic properties, despite weakening inter-molecular interactions. Our theoretical predictions agree with experimental observations for the sevenmaker mutation in ERK2. It is also argued that the effect of mutations might depend on the concentrations of substrates.

biophysics↗