Effect of Social Experience on Dopamine-Stimulated Adenylyl Cyclase Activity and G Protein Composition in Chick Forebrain

The stimulation of adenylyl cyclase (AC) by dopamine was investigated in membrane fractions of the forebrain areas mediorostral neostriatum/hyperstriatum ventrale (MNH) and lobus parolfactorius (LPO) of 8-day-old domestic chicks that had been raised under different social conditions: group A, socially isolated; group B, imprinted on an acoustic stimulus; group C, trained but nonimprinted; and group D, reared in small groups. Only in the brain of the socially experienced groups could cyclic AMP (cAMP) synthesis be stimulated by dopamine, but not in the socially isolated animals (group A). Ligand binding studies of dopamine D1- and D2-type receptors in membrane fractions did not reveal differences between socially experienced and isolated animals. Forskolin stimulation of total AC in MNH and LPO membrane fractions revealed a significantly enhanced AC stimulation in the socially reared but not in the imprinted group compared with isolated controls. Stimulation of AC by the G protein activator guanylylimidodiphosphate was significantly increased in the MNH and the LPO of socially reared chicks compared with isolated control animals. These results suggest that early postnatal social experience modulates the rate of cAMP synthesis and that these lasting changes are not due to changes of dopamine receptors but are related to increased AC activities and to increased sensitivity of Gs protein.     

Are learning and attention related to the sequence of amino acids in ACTH/MSH peptides?

Learning and attention were examined in rats after injections of one of several molecules related to adrenocorticotropic hormone (ACTH) and melanocyte-stimulating hormone (MSH). The initial phase of the learning process was linearly related to the length of the peptide with the smallest fragment (MSH/ACTH 4–10) improving learning the most and the largest molecule (ACTH 1–24) exerting no effect. Later stages of the learning problem, which were sensitive to the attentional state of the organism, resulted in U-shaped relations with the length of the same peptides. Enhancement of attention was significant only for the MSH compounds. These data indicate that some behaviors may be influenced as a function of the redundant information contained in the molecule while other behaviors may be discretely related to the specific conformation of the molecule.     

Smart de novo Macromolecular Structure Modeling from Cryo-EM Maps

The study of macromolecular structures has expanded our understanding of the amazing cell machinery and such knowledge has changed how the pharmaceutical industry develops new vaccines in recent years. Traditionally, X-ray crystallography has been the main method for structure determination, however, cryogenic electron microscopy (cryo-EM) has increasingly become more popular due to recent advancements in hardware and software. The number of cryo-EM maps deposited in the EMDataResource (formerly EMDatabase) since 2002 has been dramatically increasing and it continues to do so. De novo macromolecular complex modeling is a labor-intensive process, therefore, it is highly desirable to develop software that can automate this process. Here we discuss our automated, data-driven, and artificial intelligence approaches including map processing, feature extraction, modeling building, and target identification. Recently, we have enabled DNA/RNA modeling in our deep learning-based prediction tool, DeepTracer. We have also developed DeepTracer-ID, a tool that can identify proteins solely based on the cryo-EM map. In this paper, we will present our accumulated experiences in developing deep learning-based methods surrounding macromolecule modeling applications.