Collective influencers in protein interaction networks

Structures of calmodulin-melittin complexes show multiple binding modes lacking classical anchoring interactions

Calmodulin (CaM) is a Ca²⁺ sensor protein found in all eukaryotic cells that regulates a large number of target proteins in a Ca²⁺ concentration-dependent manner. As a transient type hub protein, it recognizes linear motifs of its targets, though for the Ca²⁺-dependent binding no consensus sequence was identified. Its complex with melittin, a major component of bee venom, is often used as a model system of protein - protein complexes. Yet, the structural aspects of the binding are not well understood, as only diverse, low-resolution data are available concerning the association. We present the crystal structure of melittin in complex with Ca²⁺-saturated calmodulins from two, evolutionarily distant species, Homo sapiens and Plasmodium falciparum representing three binding modes of the peptide. Results - augmented by molecular dynamics simulations - indicate that multiple binding modes can exist for CaM-melittin complexes, as an intrinsic characteristic of the binding. While the helical structure of melittin remains, swapping of its salt bridges and partial unfolding of its C-terminal segment can occur. In contrast to the classical way of target recognition by CaM, we found that different sets of residues can anchor at the hydrophobic pockets of CaM, which were considered as main recognition sites. Finally, the nanomolar binding affinity of the CaM-melittin complex is created by an ensemble of arrangements of similar stability - tight binding is achieved not by optimized specific interactions but by simultaneously satisfying less optimal interaction patterns in co-existing different conformers.

Resistance Training Modulates Reticulum Endoplasmic Stress, Independent of Oxidative and Inflammatory Responses, in Elderly People

Aging is related to changes in the redox status, low-grade inflammation, and decreased endoplasmic reticulum unfolded protein response (UPR). Exercise has been shown to regulate the inflammatory response, balance redox homeostasis, and ameliorate the UPR. This work aimed to investigate the effects of resistance training on changes in the UPR, oxidative status, and inflammatory responses in peripheral blood mononuclear cells of elderly subjects. Thirty elderly subjects volunteered to participate in an 8-week resistance training program, and 11 youth subjects were included for basal assessments. Klotho, heat shock protein 60 (HSP60), oxidative marker expression (catalase, glutathione, lipid peroxidation, nuclear factor erythroid 2-related factor 2, protein carbonyls, reactive oxygen species, and superoxide dismutase 1 and 2), the IRE1 arm of UPR, and TLR4/TRAF6/pIRAK1 pathway activation were evaluated before and following training. No changes in the HSP60 and Klotho protein content, oxidative status markers, and TLR4/TRAF6/pIRAK1 pathway activation were found with exercise. However, an attenuation of the reduced pIRE1/IRE1 ratio was observed following training. Systems biology analysis showed that a low number of proteins (RPS27A, SYVN1, HSPA5, and XBP1) are associated with IRE1, where XBP1 and RPS27A are essential nodes according to the centrality analysis. Additionally, a gene ontology analysis confirms that endoplasmic reticulum stress is a key mechanism modulated by IRE1. These findings might partially support the modulatory effect of resistance training on the endoplasmic reticulum in the elderly.

Computational Network Inference for Bacterial Interactomics

Since the large-scale experimental characterization of protein–protein interactions (PPIs) is not possible for all species, several computational PPI prediction methods have been developed that harness existing data from other species. While PPI network prediction has been extensively used in eukaryotes, microbial network inference has lagged behind. However, bacterial interactomes can be built using the same principles and techniques; in fact, several methods are better suited to bacterial genomes. These predicted networks allow systems-level analyses in species that lack experimental interaction data. This review describes the current network inference and analysis techniques and summarizes the use of computationally-predicted microbial interactomes to date.

Tracing the footsteps of autophagy in computational biology

Autophagy plays a crucial role in maintaining cellular homeostasis through the degradation of unwanted materials like damaged mitochondria and misfolded proteins. However, the contribution of autophagy toward a healthy cell environment is not only limited to the cleaning process. It also assists in protein synthesis when the system lacks the amino acids’ inflow from the extracellular environment due to diet consumptions. Reduction in the autophagy process is associated with diseases like cancer, diabetes, non-alcoholic steatohepatitis, etc., while uncontrolled autophagy may facilitate cell death. We need a better understanding of the autophagy processes and their regulatory mechanisms at various levels (molecules, cells, tissues). This demands a thorough understanding of the system with the help of mathematical and computational tools. The present review illuminates how systems biology approaches are being used for the study of the autophagy process. A comprehensive insight is provided on the application of computational methods involving mathematical modeling and network analysis in the autophagy process. Various mathematical models based on the system of differential equations for studying autophagy are covered here. We have also highlighted the significance of network analysis and machine learning in capturing the core regulatory machinery governing the autophagy process. We explored the available autophagic databases and related resources along with their attributes that are useful in investigating autophagy through computational methods. We conclude the article addressing the potential future perspective in this area, which might provide a more in-depth insight into the dynamics of autophagy.