Leveraging Machine Learning-Guided Molecular Simulations Coupled with Experimental Data to Decipher Membrane Binding Mechanisms of Aminosterols.

Understanding the molecular mechanisms of the interactions between specific compounds and cellular membranes is essential for numerous biotechnological applications, including targeted drug delivery, elucidation of the drug mechanism of action, pathogen identification, and novel antibiotic development. However, estimation of the free energy landscape associated with solute binding to realistic biological systems is still a challenging task. In this work, we leverage the Time-lagged Independent Component Analysis (TICA) in combination with neural networks (NN) through the Deep-TICA approach for determining the free energy associated with the membrane insertion processes of two natural aminosterol compounds, trodusquemine (TRO), and squalamine (SQ).

VirtuousPocketome: a computational tool for screening protein-ligand complexes to identify similar binding sites.

Protein residues within binding pockets play a critical role in determining the range of ligands that can interact with a protein, influencing its structure and function. Identifying structural similarities in proteins offers valuable insights into their function and activation mechanisms, aiding in predicting protein-ligand interactions, anticipating off-target effects, and facilitating the development of therapeutic agents. Numerous computational methods assessing global or local similarity in protein cavities have emerged, but their utilization is impeded by complexity, impractical automation for amino acid pattern searches, and an inability to evaluate the dynamics of scrutinized protein-ligand systems.

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.

Pain Extent Is Not Associated with Sensory-Associated Symptoms, Cognitive or Psychological Variables in COVID-19 Survivors Suffering from Post-COVID Pain.

We aimed to investigate the relationship between pain extent, as a sign of sensitization, and sensory-related, cognitive and psychological variables in hospitalized COVID-19 survivors with post-COVID pain. One hundred and forty-six (67 males, 79 females) previously hospitalized COVID-19 survivors with post-COVID pain completed demographic (age, sex, height, weight), sensory-related (Central Sensitization Inventory, Self-Report Leeds Assessment of Neuropathic Symptoms), cognitive (Pain Catastrophizing Scale, Tampa Scale for Kinesiophobia) and psychological (Hospital Anxiety and Depression Scale, Pittsburgh Sleep Quality Index) variables. Pain extent and frequency maps were calculated from pain drawings using customized software.

A survey on computational taste predictors.

Unlabelled: Taste is a sensory modality crucial for nutrition and survival, since it allows the discrimination between healthy foods and toxic substances thanks to five tastes, i.e., sweet, bitter, umami, salty, and sour, associated with distinct nutritional or physiological needs.

An annotated dataset for extracting gene-melanoma relations from scientific literature.

Background: Melanoma is one of the least common but the deadliest of skin cancers. This cancer begins when the genes of a cell suffer damage or fail, and identifying the genes involved in melanoma is crucial for understanding the melanoma tumorigenesis. Thousands of publications about human melanoma appear every year.