Proteins rarely function in isolation but they form part of complex networks of interactions with other proteins within or among cells. The importance of a particular protein for cell viability is directly dependent upon the number of interactions where it participates and the function it performs: the larger the number of interactions of a protein the greater its functional importance is for the cell. With the advent of genome sequencing and "omics" technologies it became feasible conducting large-scale searches for protein interacting partners. Unfortunately, the accuracy of such analyses has been underwhelming owing to methodological limitations and to the inherent complexity of protein interactions. In addition to these experimental approaches, many computational methods have been developed to identify protein-protein interactions by assuming that interacting proteins coevolve resulting from the coadaptation dynamics between the amino acids of their interacting faces. We review the main technological advances made in the field of interactomics and discuss the feasibility of computational methods to identify protein-protein interactions based on the estimation of coevolution. As proof-of-concept, we present a classical case study: the interactions of cell surface proteins (receptors) and their ligands. Finally, we take this discussion one step forward to include interactions between organisms and species to understand the generation of biological complexity. Development of technologies for accurate detection of protein-protein interactions may shed light on processes that go from the fine-tuning of pathways and metabolic networks to the emergence of biological complexity.
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http://dx.doi.org/10.1002/iub.455 | DOI Listing |
J Integr Neurosci
December 2024
Department of Neurology, Hainan West Central Hospital, 571799 Danzhou, Hainan, China.
Background: Ischemic stroke (IS) is the leading cause of mortality worldwide. Herein, we aimed to identify novel biomarkers and explore the role of C-type lectin domain family 7 member A () in IS.
Methods: Differentially expressed genes (DEGs) were screened using the GSE106680, GSE97537, and GSE61616 datasets, and hub genes were identified through construction of protein-protein interaction networks.
JACS Au
December 2024
State Key Laboratory of Chemical Biology, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, No. 345 Lingling Road, Shanghai 200032, China.
Macrocyclization is a compelling strategy for conventional drug design for improving biological activity, target specificity, and metabolic stability, but it was rarely applied to the design of PROTACs possibly due to the mechanism and structural complexity. Herein, we report the rational design of the first series of "Head-to-Tail" macrocyclic PROTACs. The resulting molecule exhibited pronounced Brd4 protein degradation with low nM DC values while almost totally dismissing the "hook effect", which is a general character and common concern of a PROTAC, in multiple cancer cell lines.
View Article and Find Full Text PDFJ Inflamm Res
December 2024
Department of Internal and Emergency Medicine, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China.
Purpose: Septic cardiomyopathy (SCM) is a significant global public health concern characterized by substantial morbidity and mortality, which has not been improved for decades due to lack of early diagnosis and effective therapies. This study aimed to identify hub biomarkers in SCM and explore their potential mechanisms.
Methods: We utilized the GSE53007 and GSE207363 datasets for transcriptome analysis of normal and SCM mice.
Oncol Res
December 2024
Department of Biotherapy, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, 610041, China.
Background: Triple-negative breast cancer (TNBC), characterized by its lack of traditional hormone receptors and HER2, presents a significant challenge in oncology due to its poor response to conventional therapies. Autophagy is an important process for maintaining cellular homeostasis, and there are currently autophagy biomarkers that play an effective role in the clinical treatment of tumors. In contrast to targeting protein activity, intervention with protein-protein interaction (PPI) can avoid unrelated crosstalk and regulate the autophagy process with minimal interference pathways.
View Article and Find Full Text PDFBioinform Adv
December 2024
Laboratory of Experimental Biophysics, Center for Advanced Technologies, Tashkent, 100174, Uzbekistan.
Motivation: Understanding the conformational landscape of protein-ligand interactions is critical for elucidating the binding mechanisms that govern these interactions. Traditional methods like molecular dynamics (MD) simulations are computationally intensive, leading to a demand for more efficient approaches. This study explores how multiple sequence alignment (MSA) clustering enhance AF-Multimer's ability to predict conformational landscapes, particularly for proteins with multiple conformational states.
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