Decrypting Allostery in Membrane-Bound K-Ras4B Using Complementary In Silico Approaches Based on Unbiased Molecular Dynamics Simulations.

Protein functions are dynamically regulated by allostery, which enables conformational communication even between faraway residues, and expresses itself in many forms, akin to different "languages": allosteric control pathways predominating in an unperturbed protein are often unintuitively reshaped whenever biochemical perturbations arise (e.g., mutations). To accurately model allostery, unbiased molecular dynamics (MD) simulations require integration with a reliable method able to, e.g., detect incipient allosteric changes or likely perturbation pathways; this is because allostery can operate at longer time scales than those accessible by plain MD. Such methods are typically applied singularly, but we here argue their joint application─as a "multilingual" approach─could work significantly better. We successfully prove this through unbiased MD simulations (∼100 µs) of the widely studied, allosterically active oncotarget K-Ras4B, solvated and embedded in a phospholipid membrane, from which we decrypt allostery using four showcase "languages": Distance Fluctuation analysis and the Shortest Path Map capture allosteric hotspots at equilibrium; Anisotropic Thermal Diffusion and Dynamical Non-Equilibrium MD simulations assess perturbations upon, respectively, either superheating or hydrolyzing the GTP that oncogenically activates K-Ras4B. Chosen "languages" work synergistically, providing an articulate, mutually coherent, experimentally consistent picture of K-Ras4B allostery, whereby distinct traits emerge at equilibrium and upon GTP cleavage. At equilibrium, combined evidence confirms prominent allosteric communication from the membrane-embedded hypervariable region, through a hub comprising helix α5 and sheet ß5, and up to the active site, encompassing allosteric "switches" I and II (marginally), and two proposed pockets. Upon GTP cleavage, allosteric perturbations mostly accumulate on the switches and documented interfaces.

2G-lactic acid from olive oil supply chain waste: olive leaves upcycling via Lactobacillus casei fermentation.

The transition towards a sustainable model, particularly the circular economy, emphasizes the importance of redefining waste as a valuable resource, paving the way for innovative upcycling strategies. The olive oil industry, with its significant output of agricultural waste, offers a promising avenue for high-value biomass conversion into useful products through microbial processes. This study focuses on exploring new, high-value applications for olive leaves waste, utilizing a biotechnological approach with Lactobacillus casei for the production of second-generation lactic acid. Contrary to initial expectations, the inherent high polyphenol content and low fermentable glucose levels in olive leaves posed challenges for fermentation. Addressing this, an enzymatic hydrolysis step, following a preliminary extraction process, was implemented to increase glucose availability. Subsequent small-scale fermentation tests were conducted with and without nutrient supplements, identifying the medium that yielded the highest lactic acid production for scale-up. The scaled-up batch fermentation process achieved an enhanced conversion rate (83.58%) and specific productivity (0.26 g/L·h). This research confirms the feasibility of repurposing olive waste leaves for the production of lactic acid, contributing to the advancement of a greener economy through the valorization of agricultural waste. KEY POINTS: • Olive leaves slurry as it did not allow L. casei to ferment. • High concentrations of polyphenols inhibit fermentation of L. casei. • Enzymatic hydrolysis combined to organosolv extraction is the best pretreatment for lactic acid production starting from leaves and olive pruning waste.

SARS-CoV-2 Spike Protein Mutations and Escape from Antibodies: A Computational Model of Epitope Loss in Variants of Concern.

The SARS-CoV-2 spike (S) protein is exposed on the viral surface and is the first point of contact between the virus and the host. For these reasons it represents the prime target for Covid-19 vaccines. In recent months, variants of this protein have started to emerge. Their ability to reduce or evade recognition by S-targeting antibodies poses a threat to immunological treatments and raises concerns for their consequences on vaccine efficacy. To develop a model able to predict the potential impact of S-protein mutations on antibody binding sites, we performed unbiased multi-microsecond molecular dynamics of several glycosylated S-protein variants and applied a straightforward structure-dynamics-energy based strategy to predict potential changes in immunogenic regions on each variant. We recover known epitopes on the reference D614G sequence. By comparing our results, obtained on isolated S-proteins in solution, to recently published data on antibody binding and reactivity in new S variants, we directly show that modifications in the S-protein consistently translate into the loss of potentially immunoreactive regions. Our findings can thus be qualitatively reconnected to the experimentally characterized decreased ability of some of the Abs elicited against the dominant S-sequence to recognize variants. While based on the study of SARS-CoV-2 spike variants, our computational epitope-prediction strategy is portable and could be applied to study immunoreactivity in mutants of proteins of interest whose structures have been characterized, helping the development/selection of vaccines and antibodies able to control emerging variants.

SAGE: A Fast Computational Tool for Linear Epitope Grafting onto a Foreign Protein Scaffold.

Computational design is becoming a driving force of structural vaccinology, whereby protein antigens are engineered to generate new biomolecules with optimized immunological properties. In particular, the design of new proteins that contain multiple, different epitopes can potentially provide novel highly efficient vaccine candidates. In this context, epitope grafting, which entails the transplantation of an antibody recognition motif from one protein onto a different protein scaffold (possibly containing other immunoreactive sequences) holds great promise for the realization of superantigens. Herein, we present SAGE (strategy for alignment and grafting of epitopes), an automated computational tool for the implantation of immunogenic epitopes onto a given scaffold. It is based on the comparison between the expected secondary structures of the candidates to be grafted with all the secondary structures in the target scaffold. Evaluating the differences both in sequence and in structure between the epitope and the scaffold returns a ranking of most probable molecules containing the new antigenic sequence. We validate this approach identifying the grafting positions obtained in previous works by experimental and computational methods, proving an efficient, flexible, and fast tool to perform the initial scanning for epitope grafting. This approach is fully general and may be applied to any target antigen and candidate epitopes with known 3D structures.

How likely are oscillations in a genetic feedback loop with delay?

Some genetic control networks display temporal oscillations as a result of delays in their homeostatic control. A relevant question about these systems is whether the oscillating regime is a rare feature, or it corresponds to a sizeable volume of the space of parameters. The answer is not trivial mainly due to the large number of parameters controlling the rate equations which describe the network. We have developed an efficient sampling scheme of the parameter space, based on a Monte Carlo algorithm, and applied it to a two-node system with delay, characterised by a 8-dimension parameter space. The result is that the volume fraction of the parameter space associated with oscillations is small but not negligible, and it is weakly dependent on the duration of the delay. The most critical parameter to control oscillations is the coupling production rates, which must have opposite sign, giving rise to a negative feedback loop. The oscillating regions are connected except along the equilibrium constants between the two species, not allowing neutral evolution along this parameter.

Conformational Behavior of SARS-Cov-2 Spike Protein Variants: Evolutionary Jumps in Sequence Reverberate in Structural Dynamic Differences.

SARS-CoV-2 has evolved rapidly in the first 3 years of pandemic diffusion. The initial evolution of the virus appeared to proceed through big jumps in sequence changes rather than through the stepwise accumulation of point mutations on already established variants. Here, we examine whether this nonlinear mutational process reverberates in variations of the conformational dynamics of the SARS-CoV-2 Spike protein (S-protein), the first point of contact between the virus and the human host. We run extensive microsecond-scale molecular dynamics simulations of seven distinct variants of the protein in their fully glycosylated state and set out to elucidate possible links between the mutational spectrum of the S-protein and the structural dynamics of the respective variant, at global and local levels. The results reveal that mutation-dependent structural and dynamic modulations mostly consist of increased coordinated motions in variants that acquire stability and in an increased internal flexibility in variants that are less stable. Importantly, a limited number of functionally important substructures (the receptor binding domain, in particular) share the same time of movements in all variants, indicating efficient preorganization for functional regions dedicated to host interactions. Our results support a model in which the internal dynamics of the S-proteins from different strains varies in a way that reflects the observed random and non-stepwise jumps in sequence evolution, while conserving the functionally oriented traits of conformational dynamics necessary to support productive interactions with host receptors.

Protein Allostery and Ligand Design: Computational Design Meets Experiments to Discover Novel Chemical Probes.

Herein we examine the determinants of the allosteric inhibition of the mitochondrial chaperone TRAP1 by a small molecule ligand. The knowledge generated is harnessed into the design of novel derivatives with interesting biological properties. TRAP1 is a member of the Hsp90 family of proteins, which work through sequential steps of ATP processing coupled to client-protein remodeling. Isoform selective inhibition of TRAP1 can provide novel information on the biomolecular mechanisms of molecular chaperones, as well as new insights into the development of small molecules with therapeutic potential. Our analysis of the interactions between an active first-generation allosteric ligand and TRAP1 shows how the small molecule induces long-range perturbations that influence the attainment of reactive poses in the active site. At the same time, the dynamic adaptation of the allosteric binding pocket to the presence of the first-generation compound sets the stage for the design of a set of second-generation ligands: the characterization of the formation/disappearance of pockets around the allosteric site that is used to guide optimize the ligands' fit for the allosteric site and improve inhibitory activities. The effects of the newly designed molecules are validated experimentally in vitro and in vivo. We discuss the implications of our approach as a promising strategy towards understanding the molecular determinants of allosteric regulation in chemical and molecular biology, and towards speeding up the design of allosteric small molecule modulators.

Machine Learning Prediction of Allosteric Drug Activity from Molecular Dynamics.

Allosteric drugs have been attracting increasing interest over the past few years. In this context, it is common practice to use high-throughput screening for the discovery of non-natural allosteric drugs. While the discovery stage is supported by a growing amount of biological information and increasing computing power, major challenges still remain in selecting allosteric ligands and predicting their effect on the target protein's function. Indeed, allosteric compounds can act both as inhibitors and activators of biological responses. Computational approaches to the problem have focused on variations on the theme of molecular docking coupled to molecular dynamics with the aim of recovering information on the (long-range) modulation typical of allosteric proteins.

Exploiting Folding and Degradation Machineries To Target Undruggable Proteins: What Can a Computational Approach Tell Us?

Advances in genomics and proteomics have unveiled an ever-growing number of key proteins and provided mechanistic insights into the genesis of pathologies. This wealth of data showed that changes in expression levels of specific proteins, mutations, and post-translational modifications can result in (often subtle) perturbations of functional protein-protein interaction networks, which ultimately determine disease phenotypes. Although many such validated pathogenic proteins have emerged as ideal drug targets, there are also several that escape traditional pharmacological regulation; these proteins have thus been labeled "undruggable". The challenges posed by undruggable targets call for new sorts of molecular intervention. One fascinating solution is to perturb a pathogenic protein's expression levels, rather than blocking its activities. In this Concept paper, we shall discuss chemical interventions aimed at recruiting undruggable proteins to the ubiquitin proteasome system, or aimed at disrupting protein-protein interactions in the chaperone-mediated cellular folding machinery: both kinds of intervention lead to a decrease in the amount of active pathogenic protein expressed. Specifically, we shall discuss the role of computational strategies in understanding the molecular determinants characterizing the function of synthetic molecules typically designed for either type of intervention. Finally, we shall provide our perspectives and views on the current limitations and possibilities to expand the scope of rational approaches to the design of chemical regulators of protein levels.

New perspectives in cancer drug development: computational advances with an eye to design.

Computational chemistry has come of age in drug discovery. Indeed, most pharmaceutical development programs rely on computer-based data and results at some point. Herein, we discuss recent applications of advanced simulation techniques to difficult challenges in drug discovery. These entail the characterization of allosteric mechanisms and the identification of allosteric sites or cryptic pockets determined by protein motions, which are not immediately evident in the experimental structure of the target; the study of ligand binding mechanisms and their kinetic profiles; and the evaluation of drug-target affinities. We analyze different approaches to tackle challenging and emerging biological targets. Finally, we discuss the possible perspectives of future application of computation in drug discovery.

The Answer Lies in the Energy: How Simple Atomistic Molecular Dynamics Simulations May Hold the Key to Epitope Prediction on the Fully Glycosylated SARS-CoV-2 Spike Protein.

SARS-CoV-2 is a health threat with dire socioeconomical consequences. As the crucial mediator of infection, the viral glycosylated spike protein (S) has attracted the most attention and is at the center of efforts to develop therapeutics and diagnostics. Herein, we use an original decomposition approach to identify energetically uncoupled substructures as antibody binding sites on the fully glycosylated S. Crucially, all that is required are unbiased MD simulations; no prior knowledge of binding properties or ad hoc parameter combinations is needed. Our results are validated by experimentally confirmed structures of S in complex with anti- or nanobodies. We identify poorly coupled subdomains that are poised to host (several) epitopes and potentially involved in large functional conformational transitions. Moreover, we detect two distinct behaviors for glycans: those with stronger energetic coupling are structurally relevant and protect underlying peptidic epitopes, and those with weaker coupling could themselves be prone to antibody recognition.

The Subtle Trade-Off between Evolutionary and Energetic Constraints in Protein-Protein Interactions.

Life machinery, although overwhelmingly complex, is rooted on a rather limited number of molecular processes. One of the most important is protein-protein interaction. Metabolic regulation, protein folding control, and cellular motility are examples of processes based on the fine-tuned interaction of several protein partners. The region on the protein surface devoted to the recognition of a specific partner is essential for the function of the protein and is, therefore, likely to be conserved during evolution. On the other hand, the physical chemistry of amino acids underlies the mechanism of interactions. Both evolutionary and energetic constraints can then be used to build scoring functions capable of recognizing interaction sites. Our working hypothesis is that residues within the interaction interface tend at the same time to be evolutionarily conserved (to preserve their function) and to provide little contribution to the internal stabilization of the structure of their cognate protein, to facilitate conformational adaptation to the partner. Here, we show that for some classes of protein partners (for example, those involved in signal transduction and in enzymes) evolutionary constraints play the key role in defining the interaction surface. In contrast, energetic constraints emerge as more important in protein partners involved in immune response, in inhibitor proteins, and in structural proteins. Our results indicate that a general-purpose scoring function for protein-protein interaction should not be agnostic of the biological function of the partners.

The Interplay between Structural Stability and Plasticity Determines Mutation Profiles and Chaperone Dependence in Protein Kinases.

We present a novel comparative analysis of representative protein kinases to characterize the main dynamic and energetic determinants of functional regulation shared among different families. The relationships between stability and plasticity are also used to rationalize kinase tendencies to interact with the molecular chaperone Hsp90. These questions are tackled through newly developed molecular-dynamics-based methods of analysis of internal energy and dynamics applied to a total of 37 different systems, which represent wild-type and mutated proteins, including active and inactive states. Energetic decomposition analysis is coupled to multiple structural alignments and dynamic decomposition methods and identifies, across different families, common elements that underlie fold stabilization and conformational regulation. This analysis also exposes which substructures play a key role in determining chaperone dependence. Overall, the results highlight common interaction networks that underpin kinase stabilization, are modulated by mutations (even if located at a distance), and underlie their tendencies to act as clients or nonclients of Hsp90.

Formulation and Characterization of Native and Crosslinked Hyaluronic Acid Microspheres for Dermal Delivery of Sodium Ascorbyl Phosphate: A Comparative Study.

The present work evaluates for the first time the use of urea-crosslinked hyaluronic acid (HA-CL), a novel derivative of native hyaluronic acid (HA), to produce microspheres (MS) by emulsification-solvent evaporation, for dermal delivery of sodium ascorbyl phosphate (SAP). As the term of comparison, HA MS were prepared. A pre-formulation study-investigation of the effects of polymers solutions properties (pH, viscosity) and working conditions-led to the - production of optimized HA-CL MS and HA-CL-SAP MS with: almost unimodal size distributions; mean diameter of 13.0 ± 0.7 and 9.9 ± 0.8 µm, respectively; spherical shape and rough surface; high yield, similar to HA MS and HA⁻SAP MS (≈ 85%). SAP was more efficiently encapsulated into HA-CL MS (78.8 ± 2.6%) compared to HA MS (69.7 ± 4.6%). Physical state, thermal properties, relative moisture stability of HA-CL MS and HA-CL⁻SAP MS were comparable to those of HA MS and HA⁻SAP MS. However, HA-CL⁻SAP MS exhibited an extended drug release compared to HA⁻SAP MS, despite the same kinetic mechanism-contemporaneous drug diffusion and polymer swelling/dissolution. Therefore, HA-CL formulation showed a greater potential as microcarrier (for encapsulation efficiency and release kinetic), that could be improved, in future, using suitable excipients.