Current Protein and Peptide Science - Volume 21, Issue 12, 2020

Decades of research has stunned us with the very distinctive anatomy and physiology of our brain, and on the other hand, its complexity has always posed great difficulty in treating its dysfunction or damage. Understanding the brain under normal and, particularly in the diseased state, has always been very challenging and would have been impossible without proteomics. Neuroproteomic techniques have been extensively used for unraveling both dynamics and content of the proteome of our nervous system. This modern-day investigation and quantification of protein concentration and expression have given us a platform that enhances our knowledge on disease-associated processes and pathways modification and also leads to the identification of possible biomarkers that can be therapeutically targeted. With an increased interest in identifying and targeting possible biomarkers, this article focuses on describing applications of the much discussed neuroproteomics, with a significant role in the disease pathogenesis of some very common neurological disorders. This article will collectively discuss the use and relevance of neuroproteomics in a range of neurological diseases, such as Alzheimer’s disease, Parkinson’s disease, multiple sclerosis, epilepsy, and psychiatric disorders. We have also attempted to present the current successes and failures of the neuroproteomics approach on the results obtained from different clinical studies that targeted biomarkers associated with any particular neurological disorder.

Alzheimer’s disease (AD) is a progressive brain disorder and one of the most common causes of dementia and death. AD can be of two types; early-onset and late-onset, where late-onset AD occurs sporadically while early-onset AD results from a mutation in any of the three genes that include amyloid precursor protein (APP), presenilin 1 (PSEN 1) and presenilin 2 (PSEN 2). Biologically, AD is defined by the presence of the distinct neuropathological profile that consists of the extracellular β-amyloid (Aβ) deposition in the form of diffuse neuritic plaques, intraneuronal neurofibrillary tangles (NFTs) and neuropil threads; in dystrophic neuritis, consisting of aggregated hyperphosphorylated tau protein. Elevated levels of (Aβ), total tau (t-tau) and phosphorylated tau (ptau) in cerebrospinal fluid (CSF) have become an important biomarker for the identification of this neurodegenerative disease. The aggregation of Aβ peptide derived from amyloid precursor protein initiates a series of events that involve inflammation, tau hyperphosphorylation and its deposition, in addition to synaptic dysfunction and neurodegeneration, ultimately resulting in dementia. The current review focuses on the role of proteomes in the pathogenesis of AD.

Changes in protein structure and function, alteration in protein-protein interaction, and significant difference in protein concentration inside the body could play an important role in indicating the pathological evidence of abnormalities before the development of clinical symptoms and act as a critical detection and diagnostic tool commonly known as biomarkers. Biomarkers play important roles in the diagnosis of various chronic diseases, including cancer. Neurodegenerative disorders, including Parkinson's, Alzheimer's, Huntington's, prion, and multiple sclerosis, are well characterized by neuronal deterioration, resulting in precise modifications of neuronal proteins. Nowadays, the diagnosis of neurological disorders is based on proteins or biomarkers. These biomarkers may be found in the cerebrospinal fluid, blood, serum, plasma, saliva, or urine sample. Early diagnosis is urgently needed to prevent further damage. For early diagnosis, identifying the changes in novel protein levels and their functions under the disease conditions is necessary. These can be used as specific proteomic biomarkers for diseases, and they can be possibly identified using neuroproteomics. Neuroproteomics is an emerging tool to corroborate disease-associated protein profiles. It also gives an idea about how these proteins interact with other proteins and undergo post-translational modifications. Neuroproteomics is based on bioinformatics, which provides functional characteristics and advances in technology such as mass spectroscopy, and can help in the discovery of various disease-specific biomarkers. This review gives a complete idea about the types of biomarkers, sources of biomarkers, and techniques involved in the discovery of biomarkers for early diagnosis of neurodegenerative diseases.

Alzheimer’s disease (AD) is characterized by deposition of amyloid-β protein aggregates and an appropriate treatment strategy is urgently needed, as the number of diagnosed cases continues to increase. The management of AD and other brain-associated diseases is limited by the blood brain barrier and its selective control of drug passage. In fact, most of the promising drugs have restricted curative effects on AD owing to their lower bioavailability. Gold nanoparticles (AuNPs) have emerged as attractive therapeutic agents and have distinctive properties that could contribute to the development of a novel treatment strategy for neurodegenerative disorders. In this review article, we attempt to identify promising ways of developing competent AD therapeutic agents from anti-amyloid aggregating AuNPs. Initially, we discuss the current status of anti-amyloid inhibitors, the abilities of AuNPs to inhibit amyloid aggregation, and mechanistic aspects, and then describe plausible modifications that could aid the translation of AuNP-based therapeutics into neuromedicines. The review highlights some interesting characteristics that might effectively bridge the gap between laboratory and bedside treatments.

The ubiquitin (Ub)-proteasome system (UPS) targets various cellular proteins for degradation. It has been found that defects in the UPS play a crucial role in the pathogenesis of Alzheimer's disease (AD), as the existence of Ub immunoreactivity in AD-linked neuronal inclusions, including neurofibrillary tangles, is observed in all types of AD cases. Current investigations have shown that components of the UPS can be connected with the early stage of AD, which is characterized by synaptic dysfunction, and to the late phases of the disease, marked by neurodegeneration. Although the significance of UPS in the pathogenesis of AD has been emphasized, targeted treatment at the main components of these pathways has a great perspective in advancing new therapeutic interventions for AD. In this review, we emphasize the relationship between UPS and AD pathology. We also represent the recent therapeutic advancements targeting UPS components in AD.

Neurodegenerative diseases are caused by progressive lesions or loss of specific nerve cells, which endanger human health. However, the mechanism by which neurodegeneration manifests remains unclear. Proteomics can shed light on this question as well as help establish diagnostic standards and discover new drug targets. The power of proteomics for understanding neurodegenerative diseases has increased substantially with the application of iTRAQ and TMT labeling techniques. This review focuses on progress in these labeling techniques and their applications in neurodegeneration research.

Huntington’s disease (HD) is a neurodegenerative disease that is genetically inherited through an autosomal dominant gene located on chromosome 4. HD is caused by DNA mutation (generally 37 or more repetition of CAG nucleotides) that leads to an excessive stretch of glutamine residues. However, the main pathogenesis pathway resulted by polyglutamine expansion in mutant HD is unknown. The characteristics of this disease mostly appear in adults. Patients who suffer from this disease have shown an inability to control physical movements, emotional problems, speech disturbance, dementia, loss of thinking ability and death occurs between 15-20 years from the time of symptomatic onset. This review article suggested that investigation of mutation in the HD gene can be done by proteomic analysis such as mass spectroscopy, gel electrophoresis, western blotting, chromatographic based technology, and X-ray crystallography. The primary aim of proteomics is to focus on the molecular changes occurring in HD, there by enhancing the effectiveness of treatment.

Background: Parkinson´s disease (PD) and depression have an interplay at multiple cellular levels, a phenomenon which is translated into clinical data showing that depressive patients presented an enhanced risk for developing PD. The pathogenesis of both diseases is under intensive debate as correlated to dysregulations related to Ca2+ signaling. Objective: Then, revealing this interplay between these diseases may provide novel insights into the pathogenesis of them. Methods: Publications involving Ca2+ signaling, PD and depression (alone or combined) were collected by searching PubMed and EMBASE. Results: Not surprisingly, calcium (Ca2+) channel blockers (CCBs), classical antihypertensive medicines, have been demonstrated off-label effects, such as alleviating both PD and depression symptoms. Discussion: A mechanism under debate for the antiparkinsonism and antidepressant effects associated to CCBs is focused on the restoration of Ca2+ signaling dysregulations. In addition, previous studies have observed that CCBs can affect Ca2+/cAMP signaling. Conclusion: Thus, this article discussed the role of Ca2+/cAMP signaling in the interplay between depression and PD, including the implications for the pharmacotherapy involving CCBs.

The chloroplast is a type of subcellular organelle of green plants and eukaryotic algae, which plays an important role in the photosynthesis process. Since the function of a protein correlates with its location, knowing its subchloroplast localization is helpful for elucidating its functions. However, due to a large number of chloroplast proteins, it is costly and time-consuming to design biological experiments to recognize subchloroplast localizations of these proteins. To address this problem, during the past ten years, twelve computational prediction methods have been developed to predict protein subchloroplast localization. This review summarizes the research progress in this area. We hope the review could provide important guide for further computational study on protein subchloroplast localization.

Peptides act as promising anticancer agents due to their ease of synthesis and modifications, enhanced tumor penetration, and less systemic toxicity. However, only limited success has been achieved so far, as experimental design and synthesis of anticancer peptides (ACPs) are prohibitively costly and time-consuming. Furthermore, the sequential increase in the protein sequence data via highthroughput sequencing makes it difficult to identify ACPs only through experimentation, which often involves months or years of speculation and failure. All these limitations could be overcome by applying machine learning (ML) approaches, which is a field of artificial intelligence that automates analytical model building for rapid and accurate outcome predictions. Recently, ML approaches hold great promise in the rapid discovery of ACPs, which could be witnessed by the growing number of MLbased anticancer prediction tools. In this review, we aim to provide a comprehensive view on the existing ML approaches for ACP predictions. Initially, we will briefly discuss the currently available ACP databases. This is followed by the main text, where state-of-the-art ML approaches working principles and their performances based on the ML algorithms are reviewed. Lastly, we discuss the limitations and future directions of the ML methods in the prediction of ACPs.