Variance of Atomic Coordinates as a Dynamical Metric to Distinguish Proteins and Protein-Protein Interactions in Molecular Dynamics Simulations

A self-assembling cytotoxic nanotherapeutic strategy for high drug loading and synergistic delivery of molecularly targeted therapies

Despite significant advancements in anticancer nanotherapeutics, the efficient encapsulation of multiple therapeutic modalities within single nanocarriers remains challenging due to the complex requirements of supramolecular self-assembly and/or chemical modification. These intricate synthesis procedures often impede the clinical translation of promising nanomedicines. In this study, we introduce a cost-effective and straightforward self-assembling cytotoxic nanotherapeutic strategy that enables the noncovalent incorporation of water-insoluble anticancer molecular inhibitors with high drug loading. This was achieved through the lipid conjugation of camptothecin, enabling nanoassembly in aqueous solutions devoid of excipients. These nanoassemblies were further developed into nanovehicles capable of encapsulating a high capacity of structurally diverse cargos, including molecularly targeted agents. Notably, nanoassemblies composed of linoleic acid-conjugated camptothecin and sorafenib demonstrated stability and sustained release of their payloads. The combination nanoparticles exhibited synergistic effects and effectively overcame ABCG2-mediated drug resistance in hepatocellular carcinoma (HCC). Systemic administration of these nanotherapeutics led to sustained tumor growth inhibition in various HCC xenograft-bearing mouse models, including a chemically induced orthotopic HCC model. This innovative supramolecular assembly strategy, which allows a single vehicle to deliver multimodal therapies, shows promise in overcoming drug resistance in human HCC and could be adapted for the development of other injectable nanomedicines, warranting further investigation. STATEMENT OF SIGNIFICANCE: This study advances anticancer nanotherapy by developing a simple and cost-effective self-assembling strategy that enables high loading of multiple water-insoluble chemotherapeutics. Using lipid-conjugated camptothecin, we created stable nanoassemblies capable of synergistically delivering diverse molecularly targeted agents. This combinatory platform effectively overcame therapeutic resistance and demonstrated sustained tumor inhibition in hepatocellular carcinoma-bearing mouse models. This new self-assembling cytotoxic nanotherapeutic strategy has potential applications for the development of other injectable nanomedicines.

A Mode Evolution Metric to Extract Reaction Coordinates for Biomolecular Conformational Transitions

The complex, multidimensional energy landscape of biomolecules makes the extraction of suitable, nonintuitive collective variables (CVs) that describe their conformational transitions challenging. At present, dimensionality reduction approaches and machine learning (ML) schemes are employed to obtain CVs from molecular dynamics (MD)/Monte Carlo (MC) trajectories or structural databanks for biomolecules. However, minimum sampling conditions to generate reliable CVs that accurately describe the underlying energy landscape remain unclear. Here, we address this issue by developing a Mode evolution Metric (MeM) to extract CVs that can pinpoint new states and describe local transitions in the vicinity of a reference minimum from nonequilibrated MD/MC trajectories. We present a general mathematical formulation of MeM for both statistical dimensionality reduction and machine learning approaches. Application of MeM to MC trajectories of model potential energy landscapes and MD trajectories of solvated alanine dipeptide reveals that the principal components which locate new states in the vicinity of a reference minimum emerge well before the trajectories locally equilibrate between the associated states. Finally, we demonstrate a possible application of MeM in designing efficient biased sampling schemes to construct accurate energy landscape slices that link transitions between states. MeM can help speed up the search for new minima around a biomolecular conformational state and enable the accurate estimation of thermodynamics for states lying on the energy landscape and the description of associated transitions.

Modeling the Dynamics of Protein-Protein Interfaces, How and Why?

Protein-protein assemblies act as a key component in numerous cellular processes. Their accurate modeling at the atomic level remains a challenge for structural biology. To address this challenge, several docking and a handful of deep learning methodologies focus on modeling protein-protein interfaces. Although the outcome of these methods has been assessed using static reference structures, more and more data point to the fact that the interaction stability and specificity is encoded in the dynamics of these interfaces. Therefore, this dynamics information must be taken into account when modeling and assessing protein interactions at the atomistic scale. Expanding on this, our review initially focuses on the recent computational strategies aiming at investigating protein-protein interfaces in a dynamic fashion using enhanced sampling, multi-scale modeling, and experimental data integration. Then, we discuss how interface dynamics report on the function of protein assemblies in globular complexes, in fuzzy complexes containing intrinsically disordered proteins, as well as in active complexes, where chemical reactions take place across the protein-protein interface.

Estimating the Directional Flexibility of Proteins from Equilibrium Thermal Fluctuations

The directional flexibility of proteins is an equilibrium molecular property which is accessible to both experiment and computation. Single molecule force spectroscopy (SMFS) experiments report effective directional spring constants to describe the collective anisotropic response of a protein structure to mechanical pulling forces applied along selected axes. On the other hand, computational methods have thus far employed either indirect force based nonequilibrium simulations or coarse-grained elastic network models (ENM) to predict protein directional spring constants. Here, we examine the ability of equilibrium atomistic Molecular Dynamics (MD) simulations to estimate the directional flexibility and mechanical anisotropy of proteins. MD-derived effective directional spring constants are found to correlate well with SMFS spring constants (ρ² = 0.97-0.99; Adj R² = 0.92-0.99) and unfolding forces (ρ² = 0.85-0.97; Adj R² = 0.63-0.91) for five different globular proteins. Specifically, the computed spring constants reproduce the mechanical anisotropy reported by SMFS along five different directions of green fluorescence protein (GFP) and six directions of the immunoglobulin-binding B1 domain of streptococcal protein G (GB1). Further, protein dynamics as captured in MD can be translated into spring constants which can distinguish the N-C directional flexibility of ubiquitin (Ub) from two structurally homologous small ubiquitin-like modifier (SUMO1 and SUMO2) isoforms. We apply our computational framework to study the mechanical anisotropy of Ub along the seven lysine-C-term directions which are functionally relevant. We show that Ub possesses two distinct flexibility scales along these directions which roughly differ by an order of magnitude. Further, our studies reveal that the mechanical anisotropy of Ub is modified in contrasting ways by the binding of two partner proteins (UBCH5A and UEV) which attach and recognize these biomolecular tag proteins. On the basis of equilibrium MD benchmarks for flexibility along 2485 bond vectors in Ub, we propose and validate a new covariance-propagation scheme to extract spring constants from ENM normal modes. We also critically examine the ability of ENM to predict directional flexibility of proteins and suggest modifications to improve these intuitive and scalable descriptions.