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The prolonged circulation of the SARS‐CoV‐2 virus resulted in the emergence of several viral variants, with different spreading features. Moreover, the increased number of recovered and/or vaccinated people introduced a selective pressure toward variants able to evade the immune system, developed against the former viral versions. This process results in reinfections. Aiming to study the latter process, we first collected a large structural dataset of antibodies in complex with the original version of SARS‐CoV‐2 Spike protein. We characterized the peculiarities of such antibodies population with respect to a control dataset of antibody‐protein complexes, highlighting some statistically significant differences between these two sets of antibodies. Thus, moving our attention to the Spike side of the complexes, we identify the Spike region most prone to interaction with antibodies, describing in detail also the energetic mechanisms used by antibodies to recognize different epitopes. In this framework, fast protocols able to assess the effect of novel mutations on the cohort of developed antibodies would help establish the impact of the variants on the population. Performing a molecular dynamics simulation of the trimeric form of the SARS‐CoV‐2 Spike protein for the wild type and two variants of concern, that is, the Delta and Omicron variants, we described the physicochemical features and the conformational changes experienced locally by the variants with respect to the original version. Hence, combining the dynamical information with the structural study on the antibody‐spike dataset, we quantitatively explain why the Omicron variant has a higher capability of escaping the immune system than the Delta variant, due to the higher conformational variability of the most immunogenic regions. Overall, our results shed light on the molecular mechanism behind the different responses the SARS‐CoV‐2 variants display against the immune response induced by either vaccines or previous infections. Moreover, our analysis proposes an approach that can be easily extended to both other SARS‐CoV‐2 variants or different molecular systems. [ABSTRACT FROM AUTHOR]

Understanding the complexities of the human brain and its associated disorders poses a significant challenge in neuroscience. Traditional research methods have limitations in replicating its intricacies, necessitating the development of in vitro models that can simulate its structure and function. Three-dimensional in vitro models, including organoids, cerebral organoids, bioprinted brain models, and functionalized brain organoids, offer promising platforms for studying human brain development, physiology, and disease. These models accurately replicate key aspects of human brain anatomy, gene expression, and cellular behavior, enabling drug discovery and toxicology studies while providing insights into human-specific phenomena not easily studied in animal models. The use of human-induced pluripotent stem cells has revolutionized the generation of 3D brain structures, with various techniques developed to generate specific brain regions. These advancements facilitate the study of brain structure development and function, overcoming previous limitations due to the scarcity of human brain samples. This technical review provides an overview of current 3D in vitro models of the human cortex, their development, characterization, and limitations, and explores the state of the art and future directions in the field, with a specific focus on their applications in studying neurodevelopmental and neurodegenerative disorders. [ABSTRACT FROM AUTHOR]

Most proteins perform their biological function by interacting with one or more molecular partners. In this respect, characterizing local features of the molecular surface, that can potentially be involved in the interaction with other molecules, represents a step forward in the investigation of the mechanisms of recognition and binding between molecules. Predictive methods often rely on extensive samplings of molecular patches with the aim to identify hot spots on the surface. In this framework, analysis of large proteins and/or many molecular dynamics frames is often unfeasible due to the high computational cost. Thus, finding optimal ways to reduce the number of points to be sampled maintaining the biological information (including the surface shape) carried by the molecular surface is pivotal. In this perspective, we here present a new theoretical and computational algorithm with the aim of defining a set of molecular surfaces composed of points not uniformly distributed in space, in such a way as to maximize the information of the overall shape of the molecule by minimizing the number of total points. We test our procedure's ability in recognizing hot-spots by describing the local shape properties of portions of molecular surfaces through a recently developed method based on the formalism of 2D Zernike polynomials. The results of this work show the ability of the proposed algorithm to preserve the key information of the molecular surface using a reduced number of points compared to the complete surface, where all points of the surface are used for the description. In fact, the methodology shows a significant gain of the information stored in the sampling procedure compared to uniform random sampling. [ABSTRACT FROM AUTHOR]

Many of the molecular mechanisms underlying the pathological aggregation of proteins observed in neurodegenerative diseases are still not fully understood. Among the aggregate-associated diseases, Amyotrophic Lateral Sclerosis (ALS) is of relevant importance. In fact, although understanding the processes that cause the disease is still an open challenge, its relationship with protein aggregation is widely known. In particular, human TDP-43, an RNA/DNA binding protein, is a major component of the pathological cytoplasmic inclusions observed in ALS patients. Indeed, the deposition of the phosphorylated full-length TDP-43 in spinal cord cells has been widely studied. Moreover, it has also been shown that the brain cortex presents an accumulation of phosphorylated C-terminal fragments (CTFs). Even if it is debated whether the aggregation of CTFs represents a primary cause of ALS, it is a hallmark of TDP-43 related neurodegeneration in the brain. Here, we investigate the CTFs aggregation process, providing a computational model of interaction based on the evaluation of shape complementarity at the molecular interfaces. To this end, extensive Molecular Dynamics (MD) simulations were conducted for different types of protein fragments, with the aim of exploring the equilibrium conformations. Adopting a newly developed approach based on Zernike polynomials, able to find complementary regions in the molecular surface, we sampled a large set of solvent-exposed portions of CTFs structures as obtained from MD simulations. Our analysis proposes and assesses a set of possible association mechanisms between the CTFs, which could drive the aggregation process of the CTFs. To further evaluate the structural details of such associations, we perform molecular docking and additional MD simulations to propose possible complexes and assess their stability, focusing on complexes whose interacting regions are both characterized by a high shape complementarity and involve β 3 and β 5 strands at their interfaces. [ABSTRACT FROM AUTHOR]

Investigating protein-protein interactions is crucial for understanding cellular biological processes because proteins often function within molecular complexes rather than in isolation. While experimental and computational methods have provided valuable insights into these interactions, they often overlook a critical factor: the crowded cellular environment. This environment significantly impacts protein behavior, including structural stability, diffusion, and ultimately the nature of binding. In this review, we discuss theoretical and computational approaches that allow the modeling of biological systems to guide and complement experiments and can thus significantly advance the investigation, and possibly the predictions, of protein-protein interactions in the crowded environment of cell cytoplasm. We explore topics such as statistical mechanics for lattice simulations, hydrodynamic interactions, diffusion processes in high-viscosity environments, and several methods based on molecular dynamics simulations. By synergistically leveraging methods from biophysics and computational biology, we review the state of the art of computational methods to study the impact of molecular crowding on protein-protein interactions and discuss its potential revolutionizing effects on the characterization of the human interactome.

The continuous emergence of novel variants represents one of the major problems in dealing with the SARS-CoV-2 virus. Indeed, also due to its prolonged circulation, more than ten variants of concern emerged, each time rapidly overgrowing the current viral version due to improved spreading features. As, up to now, all variants carry at least one mutation on the spike Receptor Binding Domain, the stability of the binding between the SARS-CoV-2 spike protein and the human ACE2 receptor seems one of the molecular determinants behind the viral spreading potential. In this framework, a better understanding of the interplay between spike mutations and complex stability can help to assess the impact of novel variants. Here, we characterize the peculiarities of the most representative variants of concern in terms of the molecular interactions taking place between the residues of the spike RBD and those of the ACE2 receptor. To do so, we performed molecular dynamics simulations of the RBD-ACE2 complexes of the seven variants of concern in comparison with a large set of complexes with different single mutations taking place on the RBD solvent-exposed residues and for which the experimental binding affinity was available. Analyzing the strength and spatial organization of the intermolecular interactions of the binding region residues, we found that (i) mutations producing an increase of the complex stability mainly rely on instaurating more favorable van der Waals optimization at the cost of Coulombic ones. In particular, (ii) an anti-correlation is observed between the shape and electrostatic complementarities of the binding regions. Finally, (iii) we showed that combining a set of dynamical descriptors is possible to estimate the outcome of point mutations on the complex binding region with a performance of 0.7. Overall, our results introduce a set of dynamical observables that can be rapidly evaluated to probe the effects of novel isolated variants or different molecular systems., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Miotto, Di Rienzo, Grassmann, Desantis, Cidonio, Gosti, Leonetti, Ruocco and Milanetti.)