A calpain-2 selective inhibitor enhances learning & memory by prolonging ERK activation

While calpain-1 activation is required for LTP induction by theta burst stimulation (TBS), calpain-2 activation limits its magnitude during the consolidation period. A selective calpain-2 inhibitor applied either before or shortly after TBS enhanced the degree of potentiation. In the present study, we tested whether the selective calpain-2 inhibitor, Z-Leu-Abu-CONH-CH2-C6H3 (3, 5-(OMe)2 (C2I), could enhance learning and memory in wild-type (WT) and calpain-1 knock-out (C1KO) mice. We first showed that C2I could reestablish TBS-LTP in hippocampal slices from C1KO mice, and this effect was blocked by PD98059, an inhibitor of ERK. TBS resulted in PTEN degradation in hippocampal slices from both WT and C1KO mice, and C2I treatment blocked this effect in both mouse genotypes. Systemic injection of C2I 30 min before training in the fear-conditioning paradigm resulted in a biphasic dose-response curve, with low doses enhancing and high doses inhibiting freezing behavior. The difference between the doses needed to enhance and inhibit learning matches the difference in concentrations producing inhibition of calpain-2 and calpain-1. A low dose of C2I also restored normal learning in a novel object recognition task in C1KO mice. Levels of SCOP, a ERK phosphatase known to be cleaved by calpain-1, were decreased in dorsal hippocampus early but not late following training in WT mice; C2I treatment did not affect the early decrease in SCOP levels but prevented its recovery at the later time-point and prolonged ERK activation. The results indicate that calpain-2 activation limits the extent of learning, an effect possibly due to temporal limitation of ERK activation, as a result of SCOP synthesis induced by calpain-2-mediated PTEN degradation.

Identification of Capsid/Coat Related Protein Folds and Their Utility for Virus Classification

The viral supergroup includes the entire collection of known and unknown viruses that roam our planet and infect life forms. The supergroup is remarkably diverse both in its genetics and morphology and has historically remained difficult to study and classify. The accumulation of protein structure data in the past few years now provides an excellent opportunity to re-examine the classification and evolution of viruses. Here we scan completely sequenced viral proteomes from all genome types and identify protein folds involved in the formation of viral capsids and virion architectures. Viruses encoding similar capsid/coat related folds were pooled into lineages, after benchmarking against published literature. Remarkably, the in silico exercise reproduced all previously described members of known structure-based viral lineages, along with several proposals for new additions, suggesting it could be a useful supplement to experimental approaches and to aid qualitative assessment of viral diversity in metagenome samples.

55 Years of the Rossmann Fold

The Rossmann fold is one of the most commonly observed structural domains in proteins. The fold is composed of consecutive alternating β-strands and α-helices that form a layer of β-sheet with one (or two) layer(s) of α-helices. Here, we will discuss the Rossmann fold starting from its discovery 55 years ago, then overview entries of the fold in the major protein classification databases, SCOP and CATH, as well as the number of the occurrences of the fold in genomes. We also discuss the Rossmann fold as an interesting target of protein engineering as the site-directed mutagenesis of the fold can alter the ligand-binding specificity of the structure.

Postnatal calpeptin treatment causes hippocampal neurodevelopmental defects in neonatal rats

Our previous studies showed that the early use of calpain inhibitors reduces calpain activity in multiple brain regions, and that postnatal treatment with calpeptin may lead to cerebellar motor dysfunction. However, it remains unclear whether postnatal calpeptin application affects hippocampus-related behaviors. In this study, Sprague-Dawley rats were purchased from the Animal Center of Anhui Medical University of China. For the experiments in the adult stage, rats were intraperitoneally injected with calpeptin, 2 mg/kg, once a day, on postnatal days 7-14. Then on postnatal day 60, the Morris water maze test was used to evaluate spatial learning and memory abilities. The open field test was carried out to assess anxiety-like activities. Phalloidin staining was performed to observe synaptic morphology in the hippocampus. Immunohistochemistry was used to count the number of NeuN-positive cells in the hippocampal CA1 region. DiI was applied to label dendritic spines. Calpeptin administration impaired spatial memory, caused anxiety-like behavior in adulthood, reduced the number and area of apical dendritic spines, and decreased actin polymerization in the hippocampus, but did not affect the number of NeuN-positive cells in the hippocampal CA1 region. For the neonatal experiments, neonatal rats were intraperitoneally injected with calpeptin, 2 mg/kg, on postnatal days 7 and 8. Western blot assay was performed to analyze the protein levels of Akt, Erk, p-Akt, p-Erk1/2, Erk1/2, SCOP, PTEN, mTOR, p-mTOR, CREB and p-CREB in the hippocampus. SCOP expression was increased, and the phosphorylation levels of Akt, mTOR and CREB were reduced in the hippocampus. These findings show that calpeptin administration after birth affects synaptic development in neonatal rats by inhibiting the Akt/mTOR signaling pathway, thereby perturbing hippocampal function. Therefore, calpeptin administration after birth is a risk factor for neurodevelopmental defects.

The value of protein structure classification information-Surveying the scientific literature

The Structural Classification of Proteins (SCOP) and Class, Architecture, Topology, Homology (CATH) databases have been valuable resources for protein structure classification for over 20 years. Development of SCOP (version 1) concluded in June 2009 with SCOP 1.75. The SCOPe (SCOP-extended) database offers continued development of the classic SCOP hierarchy, adding over 33,000 structures. We have attempted to assess the impact of these two decade old resources and guide future development. To this end, we surveyed recent articles to learn how structure classification data are used. Of 571 articles published in 2012-2013 that cite SCOP, 439 actually use data from the resource. We found that the type of use was fairly evenly distributed among four top categories: A) study protein structure or evolution (27% of articles), B) train and/or benchmark algorithms (28% of articles), C) augment non-SCOP datasets with SCOP classification (21% of articles), and D) examine the classification of one protein/a small set of proteins (22% of articles). Most articles described computational research, although 11% described purely experimental research, and a further 9% included both. We examined how CATH and SCOP were used in 158 articles that cited both databases: while some studies used only one dataset, the majority used data from both resources. Protein structure classification remains highly relevant for a diverse range of problems and settings.

Protein Structure Classification and Loop Modeling Using Multiple Ramachandran Distributions

Recently, the study of protein structures using angular representations has attracted much attention among structural biologists. The main challenge is how to efficiently model the continuous conformational space of the protein structures based on the differences and similarities between different Ramachandran plots. Despite the presence of statistical methods for modeling angular data of proteins, there is still a substantial need for more sophisticated and faster statistical tools to model the large-scale circular datasets. To address this need, we have developed a nonparametric method for collective estimation of multiple bivariate density functions for a collection of populations of protein backbone angles. The proposed method takes into account the circular nature of the angular data using trigonometric spline which is more efficient compared to existing methods. This collective density estimation approach is widely applicable when there is a need to estimate multiple density functions from different populations with common features. Moreover, the coefficients of adaptive basis expansion for the fitted densities provide a low-dimensional representation that is useful for visualization, clustering, and classification of the densities. The proposed method provides a novel and unique perspective to two important and challenging problems in protein structure research: structure-based protein classification and angular-sampling-based protein loop structure prediction.

A Dynamic Model for the Evolution of Protein Structure

Domains are folded structures and evolutionary building blocks of protein molecules. Their three-dimensional atomic conformations, which define biological functions, can be coarse-grained into levels of a hierarchy. Here we build global dynamical models for the evolution of domains at fold and fold superfamily (FSF) levels. We fit the models with data from phylogenomic trees of domain structures and evaluate the distributions of the resulting parameters and their implications. The trees were inferred from a census of domain structures in hundreds of genomes from all three superkingdoms of life. The models used birth-death differential equations with the global abundances of structures as state variables, with one set of equations for folds and another for FSFs. Only the transitions present in the tree are assumed possible. Each fold or FSF diversifies in variants, eventually producing a new fold or FSF. The parameters specify rates of generation of variants and of new folds or FSFs. The equations were solved for the parameters by simplifying the trees to a comb-like topology, treating branches as emerging directly from a trunk. We found that the rate constants for folds and FSFs evolved similarly. These parameters showed a sharp transient change at about 1.5 Gyrs ago. This time coincides with a period in which domains massively combined in proteins and their arrangements distributed in novel lineages during the rise of organismal diversification. Our simulations suggest that exploration of protein structure space occurs through coarse-grained discoveries that undergo fine-grained elaboration.

Effects of Scoparone on differentiation, adhesion, migration, autophagy and mineralization through the osteogenic signalling pathways

Scoparone (SCOP), an active and efficient coumarin compound derived from Artemisia capillaris Thunb, has been used as a traditional Chinese herbal medicine. Herein, we investigated the effects of SCOP on the osteogenic processes using MC3T3‐E1 pre‐osteoblasts in in vitro cell systems. SCOP (C₁₁H₁₀O₄, > 99.17%) was purified and identified from A. capillaries. SCOP (0.1 to 100 μM concentrations) did not have cytotoxic effects in pre‐osteoblasts; however, it promoted alkaline phosphatase (ALP) staining and activity, and mineralized nodule formation under early and late osteogenic induction. SCOP elevated osteogenic signals through the bone morphogenetic protein 2 (BMP2)‐Smad1/5/8 pathway, leading to the increased expression of runt‐related transcription factor 2 (RUNX2) with its target protein, matrix metallopeptidase 13 (MMP13). SCOP also induced the non‐canonical BMP2‐MAPKs pathway, but not the Wnt3a‐β‐catenin pathway. Moreover, SCOP promoted autophagy, migration and adhesion under the osteogenic induction. Overall, the findings of this study demonstrated that SCOP has osteogenic effects associated with cell differentiation, adhesion, migration, autophagy and mineralization.

BoMiProt 2.0: An update of the bovine milk protein database

Milk is a biofluid with various functions, containing carbohydrates, lipids, proteins, vitamins, and minerals. Owing to its importance and availability of vast proteomics information, our research group designed a database for bovine milk proteins (N = 3159) containing the primary and secondary information called BoMiProt. Due to the gaining interest and intensively published literature in the last three years, BoMiProt has been upgraded with newer identified proteins (N = 7459) from peer-reviewed journals, significantly expanding the database from different milk fractions (e.g., whey, fat globule membranes, and exosomes). Additionally, class, architecture, topology, and homology, structural classification of proteins, known and predicted disorder, predicted transmembrane helices, and structures have been included. Each protein entry in the database is thoroughly cross-referenced, including 1392 BoMiProt defined proteins provided with secondary information, such as protein function, biochemical properties, post-translational modifications, significance in milk, domains, fold, AlphaFold predicted models and crystal structures. The proteome data in the database can be retrieved using several search parameters using protein name, accession IDs, and FASTA sequence. Overall, BoMiProt represents an extensive compilation of newer proteins, including structural, functional, and hierarchical information, to help researchers better understand mammary gland pathophysiology, including their potential application in improving the nutritional quality of dairy products.

The Genome3D Consortium for Structural Annotations of Selected Model Organisms

Genome3D consortium is a collaborative project involving protein structure prediction and annotation resources developed by six world-leading structural bioinformatics groups, based in the United Kingdom (namely Blundell, Murzin, Gough, Sternberg, Orengo, and Jones). The main objective of Genome3D serves as a common portal to provide both predicted models and annotations of proteins in model organisms, using several resources developed by these labs such as CATH-Gene3D, DOMSERF, pDomTHREADER, PHYRE, SUPERFAMILY, FUGUE/TOCATTA, and VIVACE. These resources primarily use SCOP- and/or CATH-based protein domain assignments. Another objective of Genome3D is to compare structural classifications of protein domains in CATH and SCOP databases and to provide a consensus mapping of CATH and SCOP protein superfamilies. CATH/SCOP mapping analyses led to the identification of total of 1429 consensus superfamilies.Currently, Genome3D provides structural annotations for ten model organisms, including Homo sapiens, Arabidopsis thaliana, Mus musculus, Escherichia coli, Saccharomyces cerevisiae, Caenorhabditis elegans, Drosophila melanogaster, Plasmodium falciparum, Staphylococcus aureus, and Schizosaccharomyces pombe. Thus, Genome3D serves as a common gateway to each structure prediction/annotation resource and allows users to perform comparative assessment of the predictions. It, thus, assists researchers to broaden their perspective on structure/function predictions of their query protein of interest in selected model organisms.

Protein Structure Databases

Web-based protein structure databases come in a wide variety of types and levels of information content. Those having the most general interest are the various atlases that describe each experimentally determined protein structure and provide useful links, analyses, and schematic diagrams relating to its 3D structure and biological function. Also of great interest are the databases that classify 3D structures by their folds as these can reveal evolutionary relationships which may be hard to detect from sequence comparison alone. Related to these are the numerous servers that compare folds-particularly useful for newly solved structures, and especially those of unknown function. Beyond these are a vast number of databases for the more specialized user, dealing with specific families, diseases, structural features, and so on.

Machine learning-based prediction of proteins' architecture using sequences of amino acids and structural alphabets

In addition to the growth of protein structures generated through wet laboratory experiments and deposited in the PDB repository, AlphaFold predictions have significantly contributed to the creation of a much larger database of protein structures. Annotating such a vast number of structures has become an increasingly challenging task. CATH is widely recognized as one the most common platforms for addressing this challenge, as it classifies proteins based on their structural and evolutionary relationships, offering the scientific community an invaluable resource for uncovering various properties, including functional annotations. While CATH annotation involves - to some extent - human intervention, keeping up with the classification of the rapidly expanding repositories of protein structures has become exceedingly difficult. Therefore, there is a pressing need for a fully automated approach. On the other hand, the abundance of protein sequences stemming from next generation sequencing technologies, lacking structural annotations, presents an additional challenge to the scientific community. Consequently, 'pre-annotating' protein sequences with structural features, ensuring a high level of precision, could prove highly advantageous. In this paper, after a thorough investigation, we introduce a novel machine-learning model capable of classifying any protein domain, whether it has a known structure or not, into one of the 40 main CATH Architectures. We achieve an F1 Score of 0.92 using only the amino acid sequence and a score of 0.94 using both the sequence of amino acids and the sequence of structural alphabets.