Advances in protein chemistry and structural biology最新文献

Osteoporosis, a progressive skeletal disorder characterized by decreased bone mass and increased fracture risk, has traditionally been treated with pharmacological agents targeting bone remodeling. However, emerging research highlights the critical role of immune system in regulating bone metabolism, introducing the concept of Osteoimmunology. Chronic low-grade inflammation is now recognized as a significant contributor to osteoporosis, particularly in postmenopausal women and the elderly. Immune cells, such as T cells and B cells, and their secreted cytokines directly influence bone resorption and formation, tipping the balance toward net bone loss in inflammatory environments. Immunotherapy, a treatment modality traditionally associated with cancer and autoimmune diseases, is now gaining attention in the management of osteoporosis. By targeting immune dysregulation and reducing inflammatory bone loss, immunotherapies offer a novel approach to treating osteoporosis that goes beyond merely inhibiting bone resorption or promoting bone formation. This therapeutic strategy includes monoclonal antibodies targeting inflammatory cytokines, cell-based therapies to enhance the function of regulatory T and B cells, and interventions aimed at modulating immune pathways linked to bone health. This chapter reviews the emerging role of immunotherapy in addressing inflammatory bone loss in osteoporosis. Present chapter also explores the underlying immune mechanisms contributing to bone degradation, current immunotherapeutic strategies under investigation, and the potential of these approaches to revolutionize the management of osteoporosis.

Lamins, which are crucial type V intermediate filament proteins found in the nuclear lamina, are essential for maintaining the stability and function of the nucleus in higher vertebrates. They are classified into A- and B-types, and their distinct expression patterns contribute to cellular survival, development, and functionality. Lamins emerged during the transition from open to closed mitosis, with their complexity increasing alongside organism evolution. Derived from the LMNA, LMNB1, and LMNB2 genes, lamins undergo alternative splicing to produce seven variants, influencing cellular processes such as stiffness, chromatin condensation, and cell cycle regulation. The lamin network interacts with the cytoskeleton via Linkers of the nucleoskeleton to the cytoskeleton (LINC) complexes, playing a critical role in cellular stability and mechanotransduction. Lamins also regulate active transport into and out of the nucleus, affecting nuclear integrity, positioning, DNA maintenance, and gene expression. Genetic mutations in lamin genes lead to laminopathies, highlighting their functional significance and organizational roles. Changes in lamin subtype composition within the nuclear lamina have significant implications for cancer development, impacting cellular stiffness, mobility, and the Epithelial-to-Mesenchymal Transition (EMT). Lamin A/C, in particular, plays multifaceted roles in cancer biology, influencing progression, metastasis, and therapy response through interactions with various proteins and pathways. Dysregulated lamin expression is commonly observed in cancers, suggesting their potential as diagnostic and prognostic markers. This chapter underscores the pivotal roles of lamins in nuclear architecture and cancer biology, emphasizing their impact on cellular functions and disease pathology. Understanding lamin behavior and regulation mechanisms holds promise for developing novel diagnostic tools and targeted therapies in cancer treatment.

Oncolytic virus (OV) mediated immunotherapy is one of the recent techniques used to treat higher grade cancers where conventional therapies like chemotherapy, radiation fail. OVs as a therapeutic tool show high efficacy and fewer side effects than conventional methods as supported by multiple preclinical and clinical studies since they are engineered to target tumours. In this chapter, we discuss the modifications in viruses to make them oncolytic, types of strains commonly administered, mechanisms employed by viruses to specifically target and eradicate malignancy and progress achieved as reported in case studies (preclinical and clinical trials). OVs also face some unique challenges with respect to the malignancy being treated and the varied pathogen exposure of the patients, which is also highlighted here. Since pathogen exposure varies according to population dynamics worldwide, chances of generating a non-specific recall response to an OV cannot be negated. Lastly, the future perspectives and ongoing practises of combination therapies are discussed as they provide a leading edge over monotherapies in terms of tumour clearance, blocking metastasis and enhancing patient survival. Efforts undertaken to overcome current challenges are also highlighted.

Three-dimensional protein structure prediction is one of the fundamental problems of Structural Bioinformatics. The use of problem information through fragment insertion, secondary structure, and contact maps can help explore the search space better. An evolutionary algorithm is proposed in this work, which uses this problem information for protein structure prediction. In the proposed method, a dynamic speciation technique and fragment insertion are used to promote the diversity of the population. The fragment library is generated based on the Rosetta Quota protocol to provide fragments with increased diversity. The information from contact maps and secondary structure are used in two selection strategies to better explore the conformational search space. The results of an experiment with nine proteins are presented. Results obtained are competitive with the literature and are compared in terms of RMSD, GDT, and processing time.

Variants in the ABCA4 gene are a fundamental cause of several inherited retinal degenerations (IRDs), including Stargardt macular dystrophy, retinitis pigmentosa, and cone-rod dystrophy. These three ABCA4-driven diseases are estimated to cause blindness in 1.4 million people worldwide. As a result, genetic testing of ABCA4 is increasingly common in clinical settings. Of the 4111 identified variants in ABCA4, 1668 are missense, of which 47 % are of unknown pathogenicity (variants of unknown significance, VUS). This genetic uncertainty leads to three fundamental problems: (i) for IRD patients with multiple unclassified ABCA4 mutations, it is impossible to predict which variant will cause disease in relatives who have not yet developed it; (ii) development of variant-specific therapies remains limited; and (iii) these variants cannot be used to predict disease prospectively, which is essential for life-planning decisions and for directing patients to new clinical trials. This chapter describes approaches to deciphering the impact of ABCA4 genetic variants of unknown significance (VUS) using a combination of in silico and in vitro analyses. By leveraging complementary fields-protein biochemistry and computational biology-to create a "sequence-structure-function" workflow, where in silico 3D protein structural analysis of ABCA4 sequence variants serves as a tool to predict disease severity and clinical pathogenicity in conjunction with first-line bioinformatic tools and functional analysis. This approach represents a helpful step forward in understanding how ABCA4 variants affect structure and function and in evaluating their potential to cause inherited retinal diseases.

Alzheimer's disease (AD) is the most common type of dementia. It is characterized by chronic memory defects, alterations in behavior, and cognitive decline. AD is histopathologically characterized by two hallmarks: intracellular accumulation of Tau protein as neurofibrillary tangles (NFTs) and extracellular deposition of amyloid beta. In this book chapter, we highlighted the microtubule-associated protein Tau, exploring its structural diversity and its distinct isoforms. It is an intrinsically disordered protein which lacks three-dimensional structure that are defined by their vast structural segments that undergo rapid and prolonged conformational alterations. It has not been possible to analyze the structure of disordered proteins since they often have different conformations and are very flexible. Tau proteins comprise various domains that significantly participate in physiology in neurons, including stabilizing microtubule structure and dynamics and axonal cargo transport. In its physiological state, Tau interacts with various molecules and proteins. By various post-translational modifications at specific sites in Tau protein, including phosphorylation, acetylation, and methylation. Tau protein undergo pathological structural confirmation by hyperphosphorylation, forming insoluble oligomers, and developing as paired helical filaments. Finally, as the disease progressed, it accumulated inside the neurons as NFTs.

Solid tumors are characterized by chaotic architecture and abnormal vasculature, which trigger rapid cell proliferation leading to steep oxygen gradients, and render the tumor core highly hypoxic or anoxic. These hypoxic regions within a tumor profoundly drive cancer progression by stabilizing key transcription factors, Hypoxia-Inducible Factors, HIF-1 and HIF-2. In addition to the well-established HIF pathways, hypoxic areas in tumors are being increasingly examined for their capacity to disrupt proteostasis, specifically influencing oxygen-dependent protein folding in the endoplasmic reticulum. Hypoxia acts as a key stressor, leading to the accumulation of misfolded proteins, triggering Unfolded Protein Response as a compensatory mechanism, mediated by the three main ER sensors: PKR-like ER kinase, Inositol-Requiring enzyme 1, and Activating Transcription factor 6. In a healthy cell, UPR typically seeks to induce cell death, reestablishing cellular equilibrium. Cancer cells subvert this response by utilizing it to their advantage, enhancing metabolic flexibility, evading immune surveillance, and establishing resistance. There is growing evidence that these hypoxia-induced misfolded proteins contribute to the progression of tumors by causing genomic instability and dysregulating oncogenic signaling. This chapter details how hypoxia regulates protein misfolding, leading to cancer cell adaptation, and outlines relevant therapeutic targets.

Protein-protein interactions are fundamental to the cell function, but some of them are slow processes happening in time scales in the microsecond to millisecond range, therefore inaccessible for standard atomistic molecular dynamics (MD) simulations. A way to reduce the computational cost demanded by the simulation of long timescale phenomena is to use coarse-grained (CG) models to reduce the number of particles included in the simulation. In this Review we provide an overview of CG models for the study of protein dynamics and interactions. The majority of protein CG models have been designed to describe accurately the structure of folded, stable proteins, but recently new CG models and force fields have been designed to study disordered proteins. The difficulty of finding a force field fully transferable between stable and disordered proteins hinders the computational study of the intracellular environment in its most complex case, where protein-protein interactions occur in multiprotein systems constituted by both stable and disordered proteins. In this Review we overview several existing CG protein models, focusing on its applicability to the study of multiprotein systems including both stable and disordered proteins. We also discuss the utility of implicit solvent models, which accelerate the conformational sampling of protein solutions, to explore a broader configurational space of the system in shorter simulation times, and analyze the inaccuracies inherent to this approximation.

Nuclear protein transcription factors (TFs) regulate all biological processes in plants and are necessary for gene regulation. The transcription of genes during plant growth and development and their response to environmental cues are regulated by TF binding to specific promoter regions in the genomic DNA. Polyploid plants with several sets of chromosomes frequently display intricate genomic biases concerning TF expression. One or more subgenomes may dominate in terms of gene expression, leading to subgenome biases or dominance. These biases can influence various aspects of the crop's biology, including its growth, development, and adaptation. Advances in genomics have speed up the improvement of many important agricultural diploid crops, yet comparable endeavours in polyploid crops have been more challenging. This challenge primarily stems from the large size and intricate nature of the complex genome in polyploid crops, along with the need for comprehensive genome assembly data for such crop varieties as bread wheat, cotton and sugarcane. Several studies have evaluated the biased/asymmetric gene expression patterns, including TFs, within the polyploid crop genomes. In many polyploid crops, not all homologues of TF genes contribute equally to the phenotype. Here, we have examined polyploid crop plants for homeolog gene expression, emphasizing TFs. It is observed that the polyploids retain many gene alleles as functional homeologs that define important features involved in stress response, sugar metabolism, and fibre formation. The possible molecular mechanism describing the structural and epigenetic basis of differential subgenomic TF expression in polyploids is discussed.

Immunotherapy, harnessing components like antibodies, cells, and cytokines, has become a cornerstone in treating diseases such as cancer and autoimmune disorders. Therapeutic antibodies, in particular, have transformed modern medicine, providing a targeted approach that destroys disease-causing cells while sparing healthy tissues, thereby reducing the side effects commonly associated with chemotherapy. Beyond oncology, these antibodies also hold promise in addressing chronic infections where conventional therapeutics may fall short. However, antibodies identified through in vivo or in vitro methods often require extensive engineering to enhance their therapeutic potential. This optimization process, aimed at improving affinity, specificity, and reducing immunogenicity, is both challenging and costly, often involving trade-offs between critical properties. Traditional methods of antibody development, such as hybridoma technology and display techniques, are resource-intensive and time-consuming. In contrast, computational approaches offer a faster, more efficient alternative, enabling the precise design and analysis of therapeutic antibodies. These methods include sequence and structural bioinformatics approaches, next-generation sequencing-based data mining, machine learning algorithms, systems biology, immuno-informatics, and integrative approaches. These approaches are advancing the field by providing new insights and enhancing the accuracy of antibody design and analysis. In conclusion, computational approaches are essential in the development of therapeutic antibodies, significantly improving the precision and speed of discovery, optimization, and validation. Integrating these methods with experimental approaches accelerates therapeutic antibody development, paving the way for innovative strategies and treatments for various diseases ranging from cancers to autoimmune and infectious diseases.