Alpha-Synuclein-Nanoparticle Interactions: Understanding, Controlling and Exploiting Conformational Plasticity

Alpha-synuclein (alpha S) is an extensively studied protein due to its involvement in a group of neurodegenerative disorders, including Parkinson ' s disease, and its documented ability to undergo aberrant self-aggregation resulting in the formation of amyloid-like fibrils. In dilute solution, the protein is intrinsically disordered but can adopt multiple alternative conformations under given conditions, such as upon adsorption to nanoscale surfaces. The study of alpha S-nanoparticle interactions allows us to better understand the behavior of the protein and provides the basis for developing systems capable of mitigating the formation of toxic aggregates as well as for designing hybrid nanomaterials with novel functionalities for applications in various research areas. In this review, we summarize current progress on alpha S-nanoparticle interactions with an emphasis on the conformational plasticity of the biomolecule

Protein Structure

Since the dawn of recorded history, and probably even before, men and women have been grasping at the mechanisms by which they themselves exist. Only relatively recently, did this grasp yield anything of substance, and only within the last several decades did the proteins play a pivotal role in this existence. In this expose on the topic of protein structure some of the current issues in this scientific field are discussed. The aim is that a non-expert can gain some appreciation for the intricacies involved, and in the current state of affairs. The expert meanwhile, we hope, can gain a deeper understanding of the topic

Computational strategies for a system-level understanding of metabolism

Cell metabolism is the biochemical machinery that provides energy and building blocks to sustain life. Understanding its fine regulation is of pivotal relevance in several fields, from metabolic engineering applications to the treatment of metabolic disorders and cancer. Sophisticated computational approaches are needed to unravel the complexity of metabolism. To this aim, a plethora of methods have been developed, yet it is generally hard to identify which computational strategy is most suited for the investigation of a specific aspect of metabolism. This review provides an up-to-date description of the computational methods available for the analysis of metabolic pathways, discussing their main advantages and drawbacks. In particular, attention is devoted to the identification of the appropriate scale and level of accuracy in the reconstruction of metabolic networks, and to the inference of model structure and parameters, especially when dealing with a shortage of experimental measurements. The choice of the proper computational methods to derive in silico data is then addressed, including topological analyses, constraint-based modeling and simulation of the system dynamics. A description of some computational approaches to gain new biological knowledge or to formulate hypotheses is finally provided

Ion Mobility-Mass Spectrometry and Collision Induced Unfolding of Multi-Protein Ligand Complexes.

Mass spectrometry (MS) serves as an indispensable technology for modern pharmaceutical drug discovery and development processes, where it is used to assess ligand binding to target proteins and to search for biomarkers that can be used to gauge disease progression and drug action. However, MS is rarely treated as a screening technology for the structural consequences of drug binding. Instead, more time-consuming technologies capable of projecting atomic models of protein-drug interactions are utilized. In this thesis, ion mobility-mass spectrometry (IM-MS) methods are developed in order to fill these technology gaps. Principle among these is collision induced unfolding (CIU), which leverages the ability of IM to separate ions according to their size and charge, in order to fingerprint gas-phase unfolding pathways for non-covalent protein complexes. Following a comprehensive introductory chapter, we demonstrate the consequences of sugar binding on the CIU of Concanavalin A (Con A) in Chapter 2. Our CIU assay reveals cooperative stabilization upon small molecule binding, and such effect cannot be easily detected by solution phase assays, or by MS alone. In Chapter 3, the underlying mechanism of multi-protein unfolding is systematically investigated by IM-MS and molecular modeling approaches. Our results show a strong positive correlation between monomeric Coulombic unfolding and the tetrameric CIU process. This provides strong evidence that multi-protein unfolding events are initiated primarily by charge migration from the complex to a single monomer. In Chapter 4, the interactions between human histone deacetylase 8 (HDAC8) and poly-r(C)-binding protein 1 (PCBP1) are investigated by IM-MS. Our data suggest that these proteins interact with each other in a specific manner, a fact revealed by our optimized ESI-MS workflow for quantifying binding affinity (KD) for weakly-associated hetero-protein complexes. In Chapter 5, the translocator protein (TSPO) dimer from Rhodobacter sphaeroides, as well as its disease-associated variant forms, is analyzed by IM-MS and CIU assays. By utilizing a combination of CIU and collision induced dissociation (CID) stability data, an unknown endogenous ligand bound to TSPO is detected and identified.PHDChemistryUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/116693/1/shuainiu_1.pd

2020 IMSAloquium

Welcome to IMSAloquium 2020. This is IMSA’s 33rd year of leading in educational innovation, and the 32nd year of the IMSA Student Inquiry and Research (SIR) Program.https://digitalcommons.imsa.edu/archives_sir/1030/thumbnail.jp

Identifying the molecular components that matter: a statistical modelling approach to linking functional genomics data to cell physiology

Functional genomics technologies, in which thousands of mRNAs, proteins, or metabolites can be measured in single experiments, have contributed to reshape biological investigations. One of the most important issues in the analysis of the generated large datasets is the selection of relatively small sub-sets of variables that are predictive of the physiological state of a cell or tissue. In this thesis, a truly multivariate variable selection framework using diverse functional genomics data has been developed, characterized, and tested. This framework has also been used to prove that it is possible to predict the physiological state of the tumour from the molecular state of adjacent normal cells. This allows us to identify novel genes involved in cell to cell communication. Then, using a network inference technique networks representing cell-cell communication in prostate cancer have been inferred. The analysis of these networks has revealed interesting properties that suggests a crucial role of directional signals in controlling the interplay between normal and tumour cell to cell communication. Experimental verification performed in our laboratory has provided evidence that one of the identified genes could be a novel tumour suppressor gene. In conclusion, the findings and methods reported in this thesis have contributed to further understanding of cell to cell interaction and multivariate variable selection not only by applying and extending previous work, but also by proposing novel approaches that can be applied to any functional genomics data