Advances in protein chemistry and structural biology最新文献

This book chapter highlights a comprehensive exploration of the transformative innovations in the field of cancer immunotherapy. CAR (Chimeric Antigen Receptor) T-cell therapy represents a groundbreaking approach to treat cancer by reprogramming a patient immune cells to recognize and destroy cancer cells. This chapter underscores the critical role of synthetic biology in enhancing the safety and effectiveness of CAR T-cell therapies. It begins by emphasizing the growing importance of personalized medicine in cancer treatment, emphasizing the shift from one-size-fits-all approaches to patient-specific solutions. Synthetic biology, a multidisciplinary field, has been instrumental in customizing CAR T-cell therapies, allowing for fine-tuned precision and minimizing unwanted side effects. The chapter highlights recent advances in gene editing, synthetic gene circuits, and molecular engineering, showcasing how these technologies are optimizing CAR T-cell function. In summary, this book chapter sheds light on the remarkable progress made in the development of CAR T-cell therapies using synthetic biology, providing hope for cancer patients and hinting at a future where highly personalized and effective cancer treatments are the norm.

Antibodies play a crucial role in host defense against various diseases. Antibody engineering is a multidisciplinary field that seeks to improve the quality of life of humans. In the context of disease, antibodies are highly specialized proteins that form a critical line of defense against pathogens and the disease caused by them. These infections trigger the innate arm of immunity by presenting on antigen-presenting cells such as dendritic cells. This ultimately links to the adaptive arm, where antibody production and maturation occur against that particular antigen. Upon binding with their specific antigens, antibodies trigger various immune responses to eliminate pathogens in a process called complement-dependent cytotoxicity and phagocytosis of invading microorganisms by immune cells or induce antibody-dependent cellular cytotoxicity is done by antibodies. These engineered antibodies are being used for various purposes, such as therapeutics, diagnostics, and biotechnology research. Cutting-edge techniques that include hybridoma technology, transgenic mice, display techniques like phage, yeast and ribosome displays, and next-generation sequencing are ways to engineer antibodies and mass production for the use of humankind. Considering the importance of antibodies in protecting from a diverse array of pathogens, investing in research holds great promise to develop future therapeutic targets to combat various diseases.

Intrinsically disordered proteins (IDPs), which are functional proteins without stable tertiary structure, and hybrid proteins containing ordered domains and intrinsically disordered regions (IDRs) constitute prominent parts of all proteomes collectively known as unfoldomes. IDPs/IDRs exist as highly dynamic structural ensembles of rapidly interconverting conformations and are characterized by the exceptional structural heterogeneity, where their different parts are (dis)ordered to different degree, and their overall structure represents a complex mosaic of foldons, inducible foldons, inducible morphing foldons, non-foldons, semifoldons, and even unfoldons. Despite their lack of unique 3D structures, IDPs/IDRs play crucial roles in the control of various biological processes and the regulation of different cellular pathways and are commonly involved in recognition and signaling, indicating that the disorder-based functional repertoire is complementary to the functions of ordered proteins. Furthermore, IDPs/IDRs are frequently multifunctional, and this multifunctionality is defined by their structural flexibility and heterogeneity. Intrinsic disorder phenomenon is at the roots of the structure-function continuum model, where the structure continuum is defined by the presence of differently (dis)ordered regions, and the function continuum arises from the ability of all these differently (dis)ordered parts to have different functions. In their everyday life, IDPs/IDRs utilize a broad spectrum of interaction mechanisms thereby acting as interaction specialists. They are crucial for the biogenesis of numerous proteinaceous membrane-less organelles driven by the liquid-liquid phase separation. This review introduces functional unfoldomics by representing some aspects of the intrinsic disorder-based functionality.

The arylsulfatase A (ARSA) gene is observed to be deficient in patients with metachromatic leukodystrophy (MLD), a type of lysosomal storage disease. MLD is a severe neurodegenerative disorder characterized by an autosomal recessive inheritance pattern. This study aimed to map the most deleterious mutations at the metal binding sites of ARSA and the amino acids in proximity to the mutated positions. We utilized an array of computational tools, including PredictSNP, MAPP, PhD-SNP, PolyPhen-1, PolyPhen-2, SIFT, SNAP, and ConSurf, to identify the most detrimental mutations potentially implicated in MLD collected from UniProt, ClinVar, and HGMD. Two mutations, D29N and D30H, as being extremely deleterious based on assessments of pathogenicity, conservation, biophysical characteristics, and stability analysis. The D29 and D30 are located at the metal-interacting regions of ARSA and found to undergo post-translational modification, specifically phosphorylation. Henceforth, the in-depth effect of metal binding upon mutation was examined using molecular dynamics simulations (MDS) before and after phosphorylation. The MDS results exhibited high deviation for the D29N and D30H mutations in comparison to the native, and the same was confirmed by significant residue fluctuation and reduced compactness. These structural alterations suggest that such mutations may influence protein functionality, offering potential avenues for personalized therapeutic and providing a basis for potential mutation-specific treatments for severe MLD patients.

The word 'cancer' encompasses a heterogenous group of distinct disease types characterized by a spectrum of pathological features, genetic alterations and response to therapies. According to the World Health Organization, cancer is the second leading cause of death worldwide, responsible for one in six deaths and hence imposes a significant burden on global healthcare systems. High-throughput omics technologies combined with advanced imaging tools, have revolutionized our ability to interrogate the molecular landscape of tumors and has provided unprecedented understanding of the disease. Yet, there is a gap between basic research discoveries and their translation into clinically meaningful therapies for improving patient care. To bridge this gap, there is a need to analyse the vast amounts of high dimensional datasets from multi-omics platforms. The integration of multi-omics data with clinical information like patient history, histological examination and imaging has led to the novel concept of clinicomics and may expedite the bench-to-bedside transition in cancer. The journey from omics to clinicomics has gained momentum with development of radiomics which involves extracting quantitative features from medical imaging data with the help of deep learning and artificial intelligence (AI) tools. These features capture detailed information about the tumor's shape, texture, intensity, and spatial distribution. Together, the related fields of multiomics, translational bioinformatics, radiomics and clinicomics may provide evidence-based recommendations tailored to the individual cancer patient's molecular profile and clinical characteristics. In this chapter, we summarize multiomics studies in solid cancers with a specific focus on breast cancer. We also review machine learning and AI based algorithms and their use in cancer diagnosis, subtyping, prognosis and predicting treatment resistance and relapse.

The integration of computational resources and chemoinformatics has revolutionized translational health research. It has offered a powerful set of tools for accelerating drug discovery. This chapter overviews the computational resources and chemoinformatics methods used in translational health research. The resources and methods can be used to analyze large datasets, identify potential drug candidates, predict drug-target interactions, and optimize treatment regimens. These resources have the potential to transform the drug discovery process and foster personalized medicine research. We discuss insights into their various applications in translational health and emphasize the need for addressing challenges, promoting collaboration, and advancing the field to fully realize the potential of these tools in transforming healthcare.

In the past three decades, interest in using carbon-based nanomaterials (CBNs) in biomedical application has witnessed remarkable growth. Despite the rapid advancement, the translation of laboratory experimentation to clinical applications of nanomaterials is one of the major challenges. This might be attributed to poor understanding of bio-nano interface. Arguably, the most significant barrier is the complexity that arises by interplay of several factors like properties of nanomaterial (shape, size, surface chemistry), its interaction with suspending media (surface hydration and dehydration, surface reconstruction and release of free surface energy) and the interaction with biomolecules (conformational change in biomolecules, interaction with membrane and receptor). Tailoring a nanomaterial that minimally interacts with protein and lipids in the medium while effectively acts on target site in biological milieu has been very difficult. Computational methods and artificial intelligence techniques have displayed potential in effectively addressing this problem. Through predictive modelling and deep learning, computer-based methods have demonstrated the capability to create accurate models of interactions between nanoparticles and cell membranes, as well as the uptake of nanomaterials by cells. Computer-based simulations techniques enable these computational models to forecast how making particular alterations to a material's physical and chemical properties could enhance functional aspects, such as the retention of drugs, the process of cellular uptake and biocompatibility. We review the most recent progress regarding the bio-nano interface studies between the plasma proteins and CBNs with a special focus on computational simulations based on molecular dynamics and density functional theory.

The proteome is complex, dynamic, and functionally diverse. Functional proteomics aims to characterize the functions of proteins in biological systems. However, there is a delay in annotating the function of proteins, even in model organisms. This gap is even greater in other organisms, including Trypanosoma cruzi, the causative agent of the parasitic, systemic, and sometimes fatal disease called Chagas disease. About 99.8% of Trypanosoma cruzi proteome is not manually annotated (unreviewed), among which>25% are conserved hypothetical proteins (CHPs), calling attention to the knowledge gap on the protein content of this organism. CHPs are conserved proteins among different species of various evolutionary lineages; however, they lack functional validation. This study describes a bioinformatics pipeline applied to public proteomic data to infer possible biological functions of conserved hypothetical Trypanosoma cruzi proteins. Here, the adopted strategy consisted of collecting differentially expressed proteins between the epimastigote and metacyclic trypomastigotes stages of Trypanosoma cruzi; followed by the functional characterization of these CHPs applying a manifold learning technique for dimension reduction and 3D structure homology analysis (Spalog). We found a panel of 25 and 26 upregulated proteins in the epimastigote and metacyclic trypomastigote stages, respectively; among these, 18 CHPs (8 in the epimastigote stage and 10 in the metacyclic stage) were characterized. The data generated corroborate the literature and complement the functional analyses of differentially regulated proteins at each stage, as they attribute potential functions to CHPs, which are frequently identified in Trypanosoma cruzi proteomics studies. However, it is important to point out that experimental validation is required to deepen our understanding of the CHPs.

Cytoskeletal motor proteins are biological nanomachines that convert chemical energy into mechanical work to carry out various functions such as cell division, cell motility, cargo transport, muscle contraction, beating of cilia and flagella, and ciliogenesis. Most of these processes are driven by the collective operation of several motors in the crowded viscous intracellular environment. Imaging and manipulation of the motors with powerful experimental probes have been complemented by mathematical analysis and computer simulations of the corresponding theoretical models. In this article, we illustrate some of the key theoretical approaches used to understand how coordination, cooperation and competition of multiple motors in the crowded intra-cellular environment drive the processes that are essential for biological function of a cell. In spite of the focus on theory, experimentalists will also find this article as an useful summary of the progress made so far in understanding multiple motor systems.

Considering the importance, bone physiology has long been studied to understand what systematic and cellular impact its cells and functions have. Exploring more questions is a substantially solid way to improve the understanding of bone physiological functions in/out sides. In adult bone, osteocytes (Ots) form about 95% of bone cells and live the longest lifespan inside their mineralized surroundings. Ots are the endocrine cells and originate from blood vessel's endothelial cells. In this work, we discussed the vital role of the "Ots". To determine the association between osteocytes' network with metabolic parameters in healthy mice, the experiments were performed on ten (10) adult C57BL6 male mice. Fasting blood and bone samples were collected weekly from mice for measurement of metabolic parameters and bone morphology. Scanning electron microscopy (SEM) revealed a 2D fine morphology of the bone which indicates a strong functional interconnection with bone nano/micro, and macro components of the organs. The long-branched canaliculi look like neurocytes in structure. The morphology and quantitative measurements of the osteocyte lacunal-canalicular system showed its wide spectrum spatial resolution of the positive and negative relationship within this system or metabolite parameters, confirming a strong cross connection between osteocyte lacunal-canalicular system and metabolism. We believe that the findings of this study can deliver a strategy about the potential roles of metabolic relation among osteocytes, insulin, and lipid in management of bone and metabolic diseases.