Protein signatures using electrostatic molecular surfaces in harmonic space

Comparative effects of phosphorylation and acetylation on glycolysis and myofibrillar proteins degradation in postmortem muscle

The study investigated the different effects between protein phosphorylation and acetylation on glycolytic enzyme activity and myofibrillar protein degradation. Lamb longissimus thoracis lumborum muscles were homogenized and then inhibitors were added for incubation at 4 °C. Phosphatase inhibitor was added to produce a high phosphorylation level (PI group) and lysine deacetylase inhibitor was added to produce a high acetylation level (DI group). The lactate and ATP content in the PI group was inhibited compared with that in the DI group (P < 0.05). Phosphofructokinase (PFK) activity was negatively related with the phosphorylation level and was positively related with the acetylation level in the DI group (P < 0.05). The degradation of troponin T and desmin of the DI group were restrained when compared to that in the PI group (P < 0.05). Compared with initial PFK and desmin, the simulation of phosphorylation and acetylation of PFK and desmin showed different electrostatic potential at the surface and a more unstable structure. The phosphorylation level of the DI group was increased, suggesting that the changes of protein acetylation altered protein phosphorylation. In conclusion, compared with protein phosphorylation, protein acetylation had a greater effect on promoting glycolysis and inhibiting protein degradation.

Drugena: A Fully Automated Immunoinformatics Platform for the Design of Antibody-Drug Conjugates Against Neurodegenerative Diseases

Antibodies are proteins that are the first line of defense in the adaptive immune response of vertebrates. Thereby, they are involved in a multitude of biochemical mechanisms and clinical manifestations with significant medical interest, such as autoimmunity, the regulation of infection, and cancer. An emerging field in antibody science that is of huge medicinal interest is the development of novel antibody-interacting drugs. Such entities are the antibody-drug conjugates (ADCs), which are a new type of targeted therapy, which consist of an antibody linked to a payload drug. Overall, the underlying principle of ADCs is the discerning delivery of a drug to a target, hoping to increase the potency of the original drug. Drugena suite is a pioneering platform that employs state-of-the-art computational biology methods in the fight against neurodegenerative diseases using ADCs. Drugena encompasses an up-to-date structural database of specialized antibodies for neurological disorders and the NCI database with over 96 million entities for the in silico development of ADCs. The pipeline of the Drugena suite has been divided into several steps and modules that are closely related with a synergistic fashion under a user-friendly graphical user interface.

Structural Study of the DNA: Clock/Bmal1 Complex Provides Insights for the Role of Cortisol, hGR, and HPA Axis in Stress Management and Sleep Disorders

Herein, we deploy an in silico pipeline of structural bioinformatics, thermodynamics, and molecular dynamics to investigate the role of cortisol in circadian rhythms, biorhythms, stress response, and even sleep disorders. Our study shows that high concentrations of cortisol intercalate in the minor groove of DNA. This phenomenon widens the adjacent major grooves and provides the Clock/Bmal1 complex with more space to dock and interact with DNA. Then, the strong charges of cortisol pull the alpha helices of the Clock/Bmal1 complex and bend it inward, thus establishing stronger interactions and prolonged signaling. Our results indicate that elevated cortisol levels play an important role in stress, inflammation, and sleep disorders as a result of prolonged and stronger dsDNA - Clock/Bmal1 interactions.

Antibody Clustering Using a Machine Learning Pipeline that Fuses Genetic, Structural, and Physicochemical Properties

Antibody V domain clustering is of paramount importance to a repertoire of immunology-related areas. Although several approaches have been proposed for antibody clustering, still no consensus has been reached. Numerous attempts use information from genes, protein sequences, 3D structures, and 3D surfaces in an effort to elucidate unknown action mechanisms directly related to their function and to either link them directly to diseases or drive the discovery of new medicines, such as antibody drug conjugates (ADC). Herein, we describe a new V domain antibody clustering method based on the comparison of the interaction sites between each antibody and its antigen. A more specific clustering analysis of the antibody's V domain was provided using deep learning and data mining techniques. The multidimensional information was extracted from the structural resolved antibodies when they were captured to interact with other proteins. The available 3D structures of protein antigen-antibody (Ag-Ab) interfaces contain information about how antibody V domains recognize antigens as well as about which amino acids are involved in the recognition. As such, the antibody surface holds information about antigens' folding that reside with the Ab-Ag interface residues and how they interact. In order to gain insight into the nature of such interactions, we propose a new simple philosophy to transform the conserved framework (fragment regions, complementarity-determining regions) of antibody V domain in a binary form using structural features of antibody-antigen interactions, toward identifying new antibody signatures in V domain binding activity. Finally, an advanced three-level hybrid classification scheme has been set for clustering antibodies in subgroups, which can combine the information from the protein sequences, the three-dimensional structures, and specific "key patterns" of recognized interactions. The clusters provide multilevel information about antibodies and antibody-antigen complexes.

Olive Oil Polyphenols in Neurodegenerative Pathologies

Neurodegenerative diseases lead to the death of nerve cells in the brain or the spinal cord. A wide range of diseases are included within the group of neurodegenerative disorders, with the most common ones being dementia, Alzheimer's, and Parkinson's diseases. Millions of older people are suffering from such pathologies. The global increase of life expectancy unavoidably leads to a consequent increase in the number of people who will be at some degree affected by neurodegenerative-related diseases. At this moment, there is no effective therapy or treatment that can reverse the loss of neurons. A growing number of studies highlight the value of the consumption of medical foods, and in particular olive oil, as one of the most important components of the Mediterranean diet. A diet based on extra virgin olive oil seems to contribute toward the lowering of risk of age-related pathologies due to high phenol concentration. The link of a polyphenol found in extra virgin olive oil, namely, tyrosol, with the protein tyrosinase, associated to Parkinson's disease is underlined as a paradigm of affiliation between polyphenols and neurodegenerative disorders.

3D structural analysis of proteins using electrostatic surfaces based on image segmentation

Herein, we present a novel strategy to analyse and characterize proteins using protein molecular electro-static surfaces. Our approach starts by calculating a series of distinct molecular surfaces for each protein that are subsequently flattened out, thus reducing 3D information noise. RGB images are appropriately scaled by means of standard image processing techniques whilst retaining the weight information of each protein's molecular electrostatic surface. Then homogeneous areas in the protein surface are estimated based on unsupervised clustering of the 3D images, while performing similarity searches. This is a computationally fast approach, which efficiently highlights interesting structural areas among a group of proteins. Multiple protein electrostatic surfaces can be combined together and in conjunction with their processed images, they can provide the starting material for protein structural similarity and molecular docking experiments.