Self-adaptive multiscaling algorithm for efficient simulations of many-protein systems in crowded conditions

Modulation of free energy landscapes as a strategy for the design of antimicrobial peptides

Computational design of antimicrobial peptides (AMPs) is a promising area of research for developing novel agents against drug-resistant bacteria. AMPs are present naturally in many organisms, from bacteria to humans, a time-tested mechanism that makes them attractive as effective antibiotics. Depending on the environment, AMPs can exhibit α-helical or β-sheet conformations, a mix of both, or lack secondary structure; they can be linear or cyclic. Prediction of their structures is challenging but critical for rational design. Promising AMP leads can be developed using essentially two approaches: traditional modeling of the physicochemical mechanisms that determine peptide behavior in aqueous and membrane environments and knowledge-based, e.g., machine learning (ML) techniques, that exploit ever-growing AMP databases. Here, we explore the conformational landscapes of two recently ML-designed AMPs, characterize the dependence of these landscapes on the medium conditions, and identify features in peptide and membrane landscapes that mediate protein-membrane association. For both peptides, we observe greater conformational diversity in an aqueous solvent than in a less polar solvent, and one peptide is seen to alter its conformation more dramatically than the other upon the change of solvent. Our results support the view that structural rearrangement in response to environmental changes is central to the mechanism of membrane-structure disruption by linear peptides. We expect that the design of AMPs by ML will benefit from the incorporation of peptide conformational substates as quantified here with molecular simulations.

Biomolecular interactions of ultrasmall metallic nanoparticles and nanoclusters

The use of nanoparticles (NPs) in biomedicine has made a gradual transition from proof-of-concept to clinical applications, with several NP types meeting regulatory approval or undergoing clinical trials. A new type of metallic nanostructures called ultrasmall nanoparticles (usNPs) and nanoclusters (NCs), while retaining essential properties of the larger (classical) NPs, have features common to bioactive proteins. This combination expands the potential use of usNPs and NCs to areas of diagnosis and therapy traditionally reserved for small-molecule medicine. Their distinctive physicochemical properties can lead to unique in vivo behaviors, including improved renal clearance and tumor distribution. Both the beneficial and potentially deleterious outcomes (cytotoxicity, inflammation) can, in principle, be controlled through a judicious choice of the nanocore shape and size, as well as the chemical ligands attached to the surface. At present, the ability to control the behavior of usNPs is limited, partly because advances are still needed in nanoengineering and chemical synthesis to manufacture and characterize ultrasmall nanostructures and partly because our understanding of their interactions in biological environments is incomplete. This review addresses the second limitation. We review experimental and computational methods currently available to understand molecular mechanisms, with particular attention to usNP-protein complexation, and highlight areas where further progress is needed. We discuss approaches that we find most promising to provide relevant molecular-level insight for designing usNPs with specific behaviors and pave the way to translational applications.

Artificial neural networks for the inverse design of nanoparticles with preferential nano-bio behaviors

Safe and efficient use of ultrasmall nanoparticles (NPs) in biomedicine requires numerous independent conditions to be met, including colloidal stability, selectivity for proteins and membranes, binding specificity, and low affinity for plasma proteins. The ability of a NP to satisfy one or more of these requirements depends on its physicochemical characteristics, such as size, shape, and surface chemistry. Multiscale and pattern recognition techniques are here integrated to guide the design of NPs with preferential nano-bio behaviors. Data systematically collected from simulations (or experiments, if available) are first used to train one or more artificial neural networks, each optimized for a specific kind of nano-bio interaction; the trained networks are then interconnected in suitable arrays to obtain the NP core morphology and layer composition that best satisfy all the nano-bio interactions underlying more complex behaviors. This reverse engineering approach is illustrated in the case of NP-membrane interactions, using binding modes and affinities and early stage membrane penetrations as training data. Adaptations for designing NPs with preferential nano-protein interactions and for optimizing solution conditions in the test tube are discussed.

Strong dependence of the nano-bio interactions on core morphology and layer composition of ultrasmall nanostructures

The interactions between nanoparticles (NPs) and proteins, cells, and tissues, broadly known as nano-bio interactions, depend on the NP size and shape and on the characteristics of the NP coating layer, such as density, thickness, and chemical makeup. The dependence of nano-membrane interactions on the design parameters of ultrasmall nanostructures is studied by computer simulations. Considered here are spheres, plates, rings, rods, tubes, and helices made up of either bare magnetite or passivated gold, interacting with charged or zwitterionic membranes. The analysis reveals a strong dependence on shape, size, and layer composition of various quantities that characterize the nano-bio behavior, including binding modes and affinities. This sensitivity can be exploited to design nanostructures that bind preferentially to membranes or that stabilize or disrupt membrane structural integrity. The method used here is general and not limited to the ultrasmall regime, so it can be adopted to study other nano-bio interactions systematically. The implications for the distribution of NPs in cells and tissues (biodistribution) and for passive and active transmembrane transport are discussed, both important processes in biomedicine.

Development of R7BP inhibitors through cross-linking coupled mass spectrometry and integrated modeling

Protein-protein interaction (PPI) networks are known to be valuable targets for therapeutic intervention; yet the development of PPI modulators as next-generation drugs to target specific vertices, edges, and hubs has been impeded by the lack of structural information of many of the proteins and complexes involved. Building on recent advancements in cross-linking mass spectrometry (XL-MS), we describe an effective approach to obtain relevant structural data on R7BP, a master regulator of itch sensation, and its interfaces with other proteins in its network. This approach integrates XL-MS with a variety of modeling techniques to successfully develop antibody inhibitors of the R7BP and RGS7/Gβ5 duplex interaction. Binding and inhibitory efficiency are studied by surface plasmon resonance spectroscopy and through an R7BP-derived dominant negative construct. This approach may have broader applications as a tool to facilitate the development of PPI modulators in the absence of crystal structures or when structural information is limited.