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[Display omitted] • The polymorphic selection of three cocrystals via four techniques was investigated. • Solvent selection showed little control via solution evaporation and LAG. • Larger flow rates favour stable polymorphs due to droplet size upon atomization. • DFT computed electron density maps further rationalised polymorphic predominance. Polymorphism can be a valuable tool as well as an impediment in the development and approval of pharmaceuticals, providing an opportunity to tune active pharmaceutical ingredient (API) physicochemical properties. The control of polymorphism in cocrystalline systems and other multicomponent forms remains underexplored. The study herein aims to investigate the potential of several techniques, liquid-assisted grinding (LAG), solvent evaporation (SE), supercritical enhanced atomization (SEA) and electrospraying, to control the cocrystal polymorphic outcome of three cocrystals: isonicotinamide-citric acid (IsoCa), ethenzamide-saccharin (EthSac) and ethenzamide-gentisic acid (EthGa). Solvent selection employing LAG and SE showed little effect on polymorphic outcome. Electrospraying and SEA primarily produced the α form of IsoCa, with process parameter variations leading to the β form during SEA, and a mixture of α and γ from electrospraying. Electrospraying led to the stable form I of EthSac, while SEA could produce pure form II, and a mixture. Electrospraying produced the form I of EthGa while SEA could produce form II, with an unknown polymorphic impurity. Density functional theory (DFT) computed electron density (ED) maps of cocrystal polymorph binary systems further rationalised the polymorphic predominance observed through the electrospraying. Ultimately this study provides a general road map for polymorph selection via atomization-based methodologies. [ABSTRACT FROM AUTHOR]

Assembly of monomeric α-synuclein (αS) into aggregation-resistant helically folded tetramers and related multimers is a key target for Parkinson's disease (PD). Protein dynamics hampers experimental characterization of the polymorphism of these structures and so computational modeling and simulation is providing a complementary approach to obtain high-resolution structural information on the assembly of αS and interactions with biological surfaces. These computational techniques are particularly valuable for intrinsically disordered proteins (IDPs) and short-lived peptide and protein assemblies with as yet undetermined 3D structures. Experimental observables such as NMR J-coupling constants and chemical shifts can be predicted directly from simulation data, and compared with available experimental data to generate the most physically realistic atomic-resolution structure. For appropriately validated and benchmarked computational models, macroscopic aggregation properties can be related to the calculated thermodynamic properties at an atomic level. In this chapter, we describe a useful protocol for designing helical αS multimers, especially tetramers, and scanning the peptide-membrane interface for cell-bound αS tetramers. These computationally modeled structures are validated by comparison with the range of available known experimental parameters at time of writing in early 2020, and used to generate predictive design rules to motivate and guide experiments., (© 2022. Springer Science+Business Media, LLC, part of Springer Nature.)

Prototypical amyloidogenic peptides amyloid-β (Aβ) and α-synuclein (αS) can undergo helix-helix associations via partially folded helical conformers, which may influence pathological progression to Alzheimer's (AD) and Parkinson's disease (PD), respectively. At the other extreme, stable folded helical conformers have been reported to resist self-assembly and amyloid formation. Experimental characterisation of such disparities in aggregation profiles due to subtle differences in peptide stabilities is precluded by the conformational heterogeneity of helical subspace. The diverse physical models used in molecular simulations allow sampling distinct regions of the phase space and are extensive in capturing the ensemble of rich helical subspace. Robust and powerful computational predictive methods utilizing network theory and free energy mapping can model the origin of helical population shifts in amyloidogenic peptides, which highlight their inherent aggregability. In this chapter, we discuss computational models, methods, design rules, and strategies to identify the driving force behind helical self-assembly and the molecular origin of aggregation resistance in helical intermediates of Aβ42 and αS. By extensive multiscale mapping of intrapeptide interactions, we show that the computational models can capture features that are otherwise imperceptible to experiments. Our models predict that targeting terminal residues may allow modulation and control of initial pathogenic aggregability of amyloidogenic peptides., (© 2022. Springer Science+Business Media, LLC, part of Springer Nature.)