A fruitful fly forward : the role of the fly in drug discovery for neurodegeneration

AD, Alzheimer’s disease; APP, amyloid precursor protein; BBB, blood brain barrier; GFP, green fluorescent protein; HTS, high-throughput screening; HD, Huntington’s disease; LB, Lewy bodies; PD, Parkinson’s disease; PolyQ, Polyglutamine; RNAi, RNA interference; SNCA, α-synuclein gene; UAS, Upstream Activating Sequence.peer-reviewe

Combining Computational And Experimental Approaches To Study Disordered And Aggregation Prone Proteins

Over the past two decades disordered proteins have become more widely recognized, challenging the canonical structure-function paradigm associated with proteins. These highly dynamic proteins have been identified across a wide range of species and play a variety of functional roles. Furthermore, the structural plasticity of these proteins gives way to their increased aggregation susceptibility, compared to canonical, well-folded proteins, placing disordered proteins at the center of many neurodegenerative diseases. Despite the increased recognition of the abundance and complexity of disordered proteins, their structural features and the mechanisms by which they transit between functional and pathological roles remains elusive. The efforts described herein focus on leveraging both experimental and computational approaches to study the structure and dynamics of these proteins. Fluorescence-based experiment have proven useful for studying these systems as the intrinsic heterogeneity of this class of proteins, which precludes the use of many traditional structural biochemistry techniques, can be accommodated. Therefore, initial efforts focused on developing new minimally perturbing fluorescence probes and coupling these probes with site-selective labeling strategies. Subsequent efforts focused on identifying methods which could predict where these probes would be tolerated to boost protein yield and avoid structural perturbation. These and other fluorescence probes were employed in Förster Resonance Energy Transfer (FRET) experiments, to study the conformational ensemble of α-synuclein, a disordered protein whose aggregation is implicated in Parkinson’s Disease pathogenesis. Experimental FRET data was paired with molecular modeling in PyRosetta to simulate the conformational ensembles of α-synuclein in the presence and absence of 2 M TMAO. The accuracy of the resultant ensembles was corroborated by comparison to other experimental data. Following this initial success using experimentally constrained simulations, attention was directed towards the development of algorithms capable of generating accurate structural representations of both disordered and ordered proteins de novo. Lastly, this work showcases the utility of a high-throughput in-silico screening approach in identifying a compound that binds selectively to α-synuclein fibrils with nanomolar affinity. Overall this work highlights several computational and experimental approaches which are broadly applicable to the study of disordered and aggregation prone protein

Step-by-step design of proteins for small molecule interaction: a review on recent milestones

Protein design is the field of synthetic biology that aims at developing de-novo custom made proteins and peptides for specific applications. Despite exploring an ambitious goal, recent computational advances in both hardware and software technologies have paved the way to high-throughput screening and detailed design of novel folds and improved functionalities. Modern advances in the field of protein design for small molecule targeting are described in this review, organized in a step-by-step fashion: from the conception of a new or upgraded active binding site, to scaffold design, sequence optimization and experimental expression of the custom protein. In each step, contemporary examples are described, and state-of-the art software is briefly explored.publishe

Integrating computational methods to predict mutagenicity of aromatic azo compounds

Azo dyes have several industrial uses. However, these azo dyes and their degradation products showed mutagenicity, inducing damage in environmental and human systems. Computational methods are proposed as cheap and rapid alternatives to predict the toxicity of azo dyes. A benchmark dataset of Ames data for 354 azo dyes was employed to develop three classification strategies using knowledge-based methods and docking simulations. Results were compared and integrated with three models from the literature, developing a series of consensus strategies. The good results confirm the usefulness of in silico methods as a support for experimental methods to predict the mutagenicity of azo compounds