Advances in protein chemistry and structural biology最新文献

For decades, antibodies have remained the archetypal binding proteins that can be rapidly produced with high affinity and specificity against virtually any target. A conventional antibody is still considered the prototype of a binding molecule. It is therefore not surprising that antibodies are routinely used in basic scientific and biomedical research, analytical workflows, molecular diagnostics etc. and represent the fastest growing sector in the field of biotechnology. However, several limitations associated with conventional antibodies, including stringent requirement of animal immunizations, mammalian cells for expression, issues on stability and aggregation, bulkier size and the overall time and cost of production has propelled evolution of concepts along alternative antigen binders. Rapidly evolving protein engineering approaches and high throughput screening platforms have further complemented the development of myriads of classes of non-conventional protein binders including antibody derived as well as non-antibody based molecular scaffolds. These non-canonical binders are finding use across disciplines of which diagnostics and therapeutics are the most noteworthy.

Major histocompatibility complex (MHC) tetramers stand as formidable tools within T cell biology, facilitating the exploration and comprehension of immune responses. These artificial molecules, comprising four bound MHC molecules, typically with a specified peptide and a fluorescent label, play a pivotal role in characterizing T cell subsets, monitoring clonal expansion, and unraveling T cell dynamics during responses to infections or immunotherapies. Beyond their applications in T cell biology, MHC tetramers prove valuable in investigating a spectrum of diseases such as infectious diseases, autoimmune disorders, and cancers. Their instrumental role extends to vaccine research and development. Notably, when appropriately configured, tetramers transcend T cell biology research and find utility in exploring natural killer T cells and contributing to specific T cell clonal deletions.

This chapter traces a route through Proteomics from its origins to the present day. The different proteomics applications are discussed with a focus on microarray technology. Analytical microarrays, functional microarrays and reverse phase microarrays and their different applications are discussed. Several studies are mentioned where the great versatility of this approach is shown. Finally, the advantages and future challenges of microarray technology are outlined.

Traditional medicines are impactful in treating a cluster of respiratory-related illnesses. This paper demonstrates screening active, druggable phytoconstituents from a classical Siddha-based poly-herbal formulation called Swasa Kudori Tablet to treat asthma. The phytoconstituents of Swasa Kudori are identified as Calotropis gigantea, Piper nigrum, and (Co-drug) Abies webbiana. Active chemical compounds are extracted with the Chemical Entities of Biological Interest (ChEBI) database. The gene targets of each compound are identified based on the pharmacological activity using the DIGEP-Pred database. Thirty-two genes showing P_a> 0.7 is screened, and the target markers are selected after performing gene overlap evaluation with the asthma genes reported in GeneCards and DisGeNET database. Ten markers are identified, such as ADIPOQ, CASP8, CAT, CCL2, CD86, FKBP5, HMOX1, NFE2L2, TIMP1, VDR, in common, listed as molecular targets. Pharmacokinetic assessment (ADME) revealed five natural drug compounds 2-5-7-trihydroxy-2-(4-hydroxyphenyl)-2,3-dihydro-4H-chromen-4-one, (+)-catechin-3'-methyl ether, futoenone, 5-hydroxy-4',7-dimethoxyflavanone, and pinocembrin showing better druggability. Further screening delineates the target (HMOX1) and drug (pinocembrin) for molecular docking evaluation. When docked with HO-1, Pinocembrin showed a binding affinity of -8.0 kcal/mol. MD simulation studies substantiate the docking studies as HO-1 in complex with pinocembrin remains stable in the simulated trajectory. The current findings exhibit the significance of traditional medicines as potential drug candidates against asthma.

Advancements in genome sequencing have expanded the scope of investigating mutations in proteins across different diseases. Amino acid mutations in a protein alter its structure, stability and function and some of them lead to diseases. Identification of disease-causing mutations is a challenging task and it will be helpful for designing therapeutic strategies. Hence, mutation data available in the literature have been curated and stored in several databases, which have been effectively utilized for developing computational methods to identify deleterious mutations (drivers), using sequence and structure-based properties of proteins. In this chapter, we describe the contents of specific databases that have information on disease-causing and neutral mutations followed by sequence and structure-based properties. Further, characteristic features of disease-causing mutations will be discussed along with computational methods for identifying cancer hotspot residues and disease-causing mutations in proteins.

Antimicrobial resistance (AMR) is a growing global concern with significant implications for infectious disease control and therapeutics development. This chapter presents a comprehensive overview of computational methods in the study of AMR. We explore the prevalence and statistics of AMR, underscoring its alarming impact on public health. The role of AMR in infectious disease outbreaks and its impact on therapeutics development are discussed, emphasizing the need for novel strategies. Resistance mutations are pivotal in AMR, enabling pathogens to evade antimicrobial treatments. We delve into their importance and contribution to the spread of AMR. Experimental methods for quantitatively evaluating resistance mutations are described, along with their limitations. To address these challenges, computational methods provide promising solutions. We highlight the advantages of computational approaches, including rapid analysis of large datasets and prediction of resistance profiles. A comprehensive overview of computational methods for studying AMR is presented, encompassing genomics, proteomics, structural bioinformatics, network analysis, and machine learning algorithms. The strengths and limitations of each method are briefly outlined. Additionally, we introduce ResScan-design, our own computational method, which employs a protein (re)design protocol to identify potential resistance mutations and adaptation signatures in pathogens. Case studies are discussed to showcase the application of ResScan in elucidating hotspot residues, understanding underlying mechanisms, and guiding the design of effective therapies. In conclusion, we emphasize the value of computational methods in understanding and combating AMR. Integration of experimental and computational approaches can expedite the discovery of innovative antimicrobial treatments and mitigate the threat posed by AMR.

Metalloproteins represents more than one third of human proteome, with huge variation in physiological functions and pathological implications, depending on the metal/metals involved and tissue context. Their functions range from catalysis, bioenergetics, redox, to DNA repair, cell proliferation, signaling, transport of vital elements, and immunity. The human metalloproteomic studies revealed that many families of metalloproteins along with individual metalloproteins are dysregulated under several clinical conditions. Also, several sorts of interaction between redox- active or redox- inert metalloproteins are observed in health and disease. Metalloproteins profiling shows distinct alterations in neurodegenerative diseases, cancer, inflammation, infection, diabetes mellitus, among other diseases. This makes metalloproteins -either individually or as families- a promising target for several therapeutic approaches. Inhibitors and activators of metalloenzymes, metal chelators, along with artificial metalloproteins could be versatile in diagnosis and treatment of several diseases, in addition to other biomedical and industrial applications.

Breast cancer (BC) is the most common cancer among women and a major cause of death from cancer. The role of estrogen and progestins, including synthetic hormones like R5020, in the development of BC has been highlighted in numerous studies. In our study, we employed machine learning and advanced bioinformatics to identify genes that could serve as diagnostic markers for BC. We thoroughly analyzed the transcriptomic data of two BC cell lines, T47D and UDC4, and performed differential gene expression analysis. We also conducted functional enrichment analysis to understand the biological functions influenced by these genes. Our study identified several diagnostic genes strongly associated with BC, including MIR6728, ENO1-IT1, ENO1-AS1, RNU6-304P, HMGN2P17, RP3-477M7.5, RP3-477M7.6, and CA6. The genes MIR6728, ENO1-IT1, ENO1-AS1, and HMGN2P17 are involved in cancer control, glycolysis, and DNA-related processes, while CA6 is associated with apoptosis and cancer development. These genes could potentially serve as predictors for BC, paving the way for more precise diagnostic methods and personalized treatment plans. This research enhances our understanding of BC and offers promising avenues for improving patient care in the future.

Host-pathogen interactions are complex associations which evolve over long co-evolutionary histories. Pathogens exhibit different mechanisms to gain advantage over their host. Mimicry of host factors is an influential tool in subverting host mechanisms to ensure pathogenesis. This chapter discusses such molecular mimicry exhibited during viral infections. Understanding the evolutionary relationships, shared identity and functional impact of the virus encoded mimics is critical. With a particular emphasis on viral mimics and their association with cancer and autoimmune diseases, this chapter highlights the importance of molecular mimicry in virus biology.

We recently identified TMEM230 as a master regulator of the endomembrane system of cells. TMEM230 expression is necessary for promoting motor protein dependent intracellular trafficking of metalloproteins for cellular energy production in mitochondria. TMEM230 is also required for transport and secretion of metalloproteinases for autophagy and phagosome dependent clearance of misfolded proteins, defective RNAs and damaged cells, activities that decline with aging. This suggests that aberrant levels of TMEM230 may contribute to aging and regain of proper levels may have therapeutic applications. The components of the endomembrane system include the Golgi complex, other membrane bound organelles, and secreted vesicles and factors. Secreted cellular components modulate immune response and tissue regeneration in aging. Upregulation of intracellular packaging, trafficking and secretion of endosome components while necessary for tissue homeostasis and normal wound healing, also promote secretion of pro-inflammatory and pro-senescence factors. We recently determined that TMEM230 is co-regulated with trafficked cargo of the endomembrane system, including lysosome factors such as RNASET2. Normal tissue regeneration (in aging), repair (following injury) and aberrant destructive tissue remodeling (in cancer or autoimmunity) likely are regulated by TMEM230 activities of the endomembrane system, mitochondria and autophagosomes. The role of TMEM230 in aging is supported by its ability to regulate the pro-inflammatory secretome and senescence-associated secretory phenotype in tissue cells of patients with advanced age and chronic disease. Identifying secreted factors regulated by TMEM230 in young patients and patients of advanced age will facilitate identification of aging associated targets that aberrantly promote, inhibit or reverse aging. Ex situ culture of patient derived cells for identifying secreted factors in tissue regeneration and aging provides opportunities in developing therapeutic and personalized medicine strategies. Identification and validation of human secreted factors in tissue regeneration requires long-term stabile scaffold culture conditions that are different from those previously reported for cell lines used as cell models for aging. We describe a 3 dimensional (3D) platform utilizing non-biogenic and non-labile poly ε-caprolactone scaffolds that supports maintenance of long-term continuous cultures of human stem cells, in vitro generated 3D organoids and patient derived tissue. Combined with animal component free culture media, non-biogenic scaffolds are suitable for proteomic and glycobiological analyses to identify human factors in aging. Applications of electrospun nanofiber technologies in 3D cell culture allow for ex situ screening and the development of patient personalized therapeutic strategies and predicting their effectiveness in mitigating or promoting aging.