Prediction of Protein–Protein Interactions Between Alsin DH/PH and Rac1 and Resulting Protein Dynamics

Alsin is a protein of 1,657 amino acids known for its crucial role in vesicular trafficking in neurons thanks to its ability to interact with two guanosine triphosphatases, Rac1 and Rab5. Evidence suggests that Rac1 can bind Alsin central region, composed by a Dbl Homology (DH) domain followed by a Pleckstrin Homology (PH) domain, leading to Alsin relocalization. However, Alsin three-dimensional structure and its relationship with known biological functions of this protein are still unknown. In this work, a homology model of the Alsin DH/PH domain was developed and studied through molecular dynamics both in the presence and in the absence of its binding partner, Rac1. Due to different conformations of DH domain, the presence of Rac1 seems to stabilize an open state of the protein, while the absence of its binding partner results in closed conformations. Furthermore, Rac1 interaction was able to reduce the fluctuations in the second conserved region of DH motif, which may be involved in the formation of a homodimer. Moreover, the dynamics of DH/PH was described through a Markov State Model to study the pathways linking the open and closed states. In conclusion, this work provided an all-atom model for the DH/PH domain of Alsin protein; moreover, molecular dynamics investigations suggested underlying molecular mechanisms in the signal transduction between Rac1 and Alsin, providing the basis for a deeper understanding of the whole structure–function relationship for Alsin protein

Alteration of lipid bilayer mechanics by volatile anesthetics: insights from μs-long molecular dynamics simulations

Very few drugs in clinical practice feature the chemical diversity, narrow therapeutic window, unique route of administration, and reversible cognitive effects of volatile anesthetics. The correlation between their hydrophobicity and their potency and the increasing amount of evidence suggesting that anesthetics exert their action on transmembrane proteins, justifies the investigation of their effects on phospholipid bilayers at the molecular level, given the strong functional and structural link between transmembrane proteins and the surrounding lipid matrix. Molecular dynamics simulations of a model lipid bilayer in the presence of ethylene, desflurane, methoxyflurane, and the nonimmobilizer 1,2-dichlorohexafluorocyclobutane (also called F6 or 2N) at different concentrations highlight the structural consequences of VA partitioning in the lipid phase, with a decrease of lipid order and bilayer thickness, an increase in overall lipid lateral mobility and area-per-lipid, and a marked reduction in the mechanical stiffness of the membrane, that strongly correlates with the compounds' hydrophobicity

In silico investigation of molecular interactions of Volatile Anesthetics: Effects on phospholipid membranes and subcellular targets

The ability of anesthetics to reversibly suppress consciousness must reside in the effects exerted onto specific molecular tar- gets. Interactions between Volatile Anesthetics and the phospholipid mem- brane as well as intracellular tubulin, were investigated using Computational Molecular Modelling, which showed rapid ligand partitioning inside the membrane and significant effects on the mechanical char- acteristics thereof, while transient binding locations have been found on the tubulin dimer

Insights into the interaction dynamics between volatile anesthetics and tubulin through computational molecular modelling

General anesthetics, able to reversibly suppress all conscious brain activity, have baffled medical science for decades, and little is known about their exact molecular mechanism of action. Given the recent scientific interest in the exploration of microtubules as putative functional targets of anesthetics, and the involvement thereof in neurodegenerative disorders, the present work focuses on the investigation of the interaction between human tubulin and four volatile anesthetics: ethylene, desflurane, halothane and methoxyflurane. Interaction sites on different tubulin isotypes are predicted through docking, along with an estimate of the binding affinity ranking. The analysis is expanded by Molecular Dynamics simulations, where the dimers are allowed to freely interact with anesthetics in the surrounding medium. This allowed for the determination of interaction hotspots on tubulin dimers, which could be linked to different functional consequences on the microtubule architecture, and confirmed the weak, Van der Waals-type interaction, occurring within hydrophobic pockets on the dimer. Both docking and MD simulations highlighted significantly weaker interactions of ethylene, consistent with its far lower potency as a general anesthetic. Overall, simulations suggest a transient interaction between anesthetics and microtubules in general anesthesia, and contact probability analysis shows interaction strengths consistent with the potencies of the four compounds

Elucidating molecular connetion between IAHSP onset and Alsin protein by means of Homology Modelling and Molecular Dynamics

The Infantile-onset Ascending Hereditary Spastic Paralysis (IAHSP) is an incurable rare neurodegerative disease related to a mutation-driven aberrant behaviour of the Alsin protein. The lack of information on Alsin atomic structure limits a complete understanding on pathology mechanisms. In this work, molecular modelling techniques have been applied to shed lights on Alsin folding dynamics and misfunction induced by aberrant mutations

LightCPPgen: An Explainable Machine Learning Pipeline for Rational Design of Cell Penetrating Peptides

Cell-penetrating peptides (CPPs) are powerful vectors for the intracellular delivery of a diverse array of therapeutic molecules. Despite their potential, the rational design of CPPs remains a challenging task that often requires extensive experimental efforts and iterations. In this study, we introduce an innovative approach for the de novo design of CPPs, leveraging the strengths of machine learning (ML) and optimization algorithms. Our strategy, named LightCPPgen, integrates a LightGBM-based predictive model with a genetic algorithm (GA), enabling the systematic generation and optimization of CPP sequences. At the core of our methodology is the development of an accurate, efficient, and interpretable predictive model, which utilizes 20 explainable features to shed light on the critical factors influencing CPP translocation capacity. The CPP predictive model works synergistically with an optimization algorithm, which is tuned to enhance computational efficiency while maintaining optimization performance. The GA solutions specifically target the candidate sequences' penetrability score, while trying to maximize similarity with the original non-penetrating peptide in order to retain its original biological and physicochemical properties. By prioritizing the synthesis of only the most promising CPP candidates, LightCPPgen can drastically reduce the time and cost associated with wet lab experiments. In summary, our research makes a substantial contribution to the field of CPP design, offering a robust framework that combines ML and optimization techniques to facilitate the rational design of penetrating peptides, by enhancing the explainability and interpretability of the design process

Informed classification of sweeteners/bitterants compounds via explainable machine learning

Perception of taste is an emergent phenomenon arising from complex molecular interactions between chemical compounds and specific taste receptors. Among all the taste perceptions, the dichotomy of sweet and bitter tastes has been the subject of several machine learning studies for classification purposes. While previous studies have provided accurate sweeteners/bitterants classifiers, there is ample scope to enhance these models by enriching the understanding of the molecular basis of bitter-sweet tastes. Towards these goals, our study focuses on the development and testing of several machine learning strategies coupled with the novel SHapley Additive exPlanations (SHAP) for a rational sweetness/bitterness classification. This allows the identification of the chemical descriptors of interest by allowing a more informed approach toward the rational design and screening of sweeteners/bitterants. To support future research in this field, we make all datasets and machine learning models publicly available and present an easy-to-use code for bitter-sweet taste prediction

PAMAM and PPI dendrimers as potential anti-cancer drug carriers: a computational investigation

Photodynamic therapy (PDT) is a promising technique for several types of anti-cancer therapy, exploiting a photosensitizer, a light source and oxygen. The present work computationally investigates the properties of poly(amidoamine) (PAMAM) and poly(propyleneimine) (PPI) dendrimers of generation 3 and 4 as potential nanoscale drug delivery systems for Rose Bengal (RB), a candidate photosensitizer for PDT

Thermodynamic and kinetic stability of the Josephin Domain closed arrangement: evidences from replica exchange molecular dynamics

Abstract Background Molecular phenomena driving pathological aggregation in neurodegenerative diseases are not completely understood yet. Peculiar is the case of Spinocerebellar Ataxia 3 (SCA3) where the conformational properties of the AT-3 N-terminal region, also called Josephin Domain (JD), play a key role in the first step of aggregation, having the JD an amyloidogenic propensity itself. For this reason, unraveling the intimate relationship between JD structural features and aggregation tendency may lead to a step forward in understanding the pathology and rationally design a cure. In this connection, computational modeling has demonstrated to be helpful in exploring the protein molecular dynamics and mechanism of action. Results Conformational dynamics of the JD is here finely investigated by replica exchange molecular dynamics simulations able to sample the microsecond time scale and to provide both a thermodynamic and kinetic description of the protein conformational changes. Accessible structural conformations of the JD have been identified in: open, intermediate and closed like arrangement. Data indicated the closed JD arrangement as the most likely protein arrangement. The protein transition from closed toward intermediate/open states was characterized by a rate constant higher than 700\ua0ns. This result also explains the inability of classical molecular dynamics to explore transitions from closed to open JD configuration on a time scale of hundreds of nanoseconds. Conclusion This work provides the first kinetic estimation of the JD transition pathway from open-like to closed-like arrangement and vice-versa, indicating the closed-like arrangement as the most likely configuration for a JD in water environment. More widely, the importance of our results is also underscored considering that the ability to provide a kinetic description of the protein conformational changes is a scientific challenge for both experimental and theoretical approaches to date. Reviewers This article was reviewed by Oliviero Carugo, Bojan Zagrovic