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1.
Patterns ; : 100551, 2022.
Article in English | ScienceDirect | ID: covidwho-1966986

ABSTRACT

Summary Prediction and understanding of virus-host protein-protein interactions (PPIs) have relevance for the development of novel therapeutic interventions. In addition, virus-like particles open novel opportunities to deliver therapeutics to targeted cell types and tissues. Given our incomplete knowledge of PPIs on the one hand and the cost and time associated with experimental procedures on the other, we here propose a deep learning approach to predict virus-host PPIs. Our method (Siamese Tailored deep sequence Embedding of Proteins [STEP]) is based on recent deep protein sequence embedding techniques, which we integrate into a Siamese neural network. After showing the state-of-the-art performance of STEP on external datasets, we apply it to two use cases, severe acute respiratory syndrome coronavirus 2 and John Cunningham polyomavirus, to predict virus-host PPIs. Altogether our work highlights the potential of deep sequence embedding techniques originating from the field of NLP as well as explainable artificial intelligence methods for the analysis of biological sequences.

2.
10th International Conference on Bioinformatics and Computational Biology, ICBCB 2022 ; : 6-12, 2022.
Article in English | Scopus | ID: covidwho-1961388

ABSTRACT

Cytokine storms, an overaggressive immune response due to the overexpression of pro-inflammatory cytokines, have been identified to play a significant role in COVID-19 infections. Studies have shown that TNF-α and IFN-γ are integral to the process, however, its genetic mechanisms have yet to be fully elucidated. Herein, the key changes in the gene expression of TNF-α and IFN-γ induced cytokine storms are identified through differential gene analysis on the publicly available GEO GSE160163 dataset. GO and KEGG enrichment were used to annotate identified DEGs, and a PPI network was constructed based on the STRING database. A total of 446 differentially expressed genes were identified. Up-regulated genes and downregulated genes were enriched in viral immune response and infection pathways, and steroid biosynthesis and metabolic pathways, respectively. PPI construction revealed 1,834 interactions between 428 proteins, indicating their biological connectivity. Module analysis identified nine (9) hub genes: STAT1, CXCL10, CD274, CXCL9, IRF1, PSMB9, CD86, STAT3, and CXCR4, involved in viral immune response and three (3) significant modules involved in NOD-like receptor signaling, steroid biosynthesis, and viral infections. These identified DEGs, hub genes, and their respective enriched pathways aid us in understanding the molecular mechanisms of cytokine storms, as well as provide potential gene targets and druggable receptors for the treatment of cytokine storms. © 2022 IEEE.

3.
Chembiochem ; : e202200372, 2022 Jul 04.
Article in English | MEDLINE | ID: covidwho-1929772

ABSTRACT

During viral cell entry, the spike protein of SARS-CoV-2 binds to the α1-helix motif of human angiotensin-converting enzyme 2 (ACE2). Thus, alpha-helical peptides mimicking this motif may serve as inhibitors of viral cell entry. For this purpose, we employed the rigidified diproline-derived module ProM-5 to induce α-helicity in short peptide sequences inspired by the ACE2 α1-helix. Starting with Ac-QAKTFLDKFNHEAEDLFYQ-NH2 as a relevant section of α1, a series of peptides, N-capped with either Ac-ßHAsp-[ProM-5] or Ac-ßHAsp-PP, were prepared and their α-helicities were investigated. While ProM-5 clearly showed a pronounced effect, an even increased degree of helicity (up to 63 %) was observed in sequences in which non-binding amino acids were replaced by alanine. The binding affinities of the peptides towards the spike protein, as determined by means of microscale thermophoresis (MST), revealed only a subtle influence of the α-helical content and, noteworthy, led to the identification of an Ac-ßHAsp-PP-capped peptide displaying a very strong binding affinity (KD =62 nM).

4.
J Biomol Struct Dyn ; : 1-21, 2022 Jul 09.
Article in English | MEDLINE | ID: covidwho-1927169

ABSTRACT

SARS-CoV-2 remains a health threat with the continuous emergence of new variants. This work aims to expand the knowledge about the SARS-CoV-2 receptor-binding domain (RBD) interactions with cell receptors and monoclonal antibodies (mAbs). By using constant-pH Monte Carlo simulations, the free energy of interactions between the RBD from different variants and several partners (Angiotensin-Converting Enzyme-2 (ACE2) polymorphisms and various mAbs) were predicted. Computed RBD-ACE2-binding affinities were higher for two ACE2 polymorphisms (rs142984500 and rs4646116) typically found in Europeans which indicates a genetic susceptibility. This is amplified for Omicron (BA.1) and its sublineages BA.2 and BA.3. The antibody landscape was computationally investigated with the largest set of mAbs so far in the literature. From the 32 studied binders, groups of mAbs were identified from weak to strong binding affinities (e.g. S2K146). These mAbs with strong binding capacity and especially their combination are amenable to experimentation and clinical trials because of their high predicted binding affinities and possible neutralization potential for current known virus mutations and a universal coronavirus.Communicated by Ramaswamy H. Sarma.

5.
Comput Struct Biotechnol J ; 20: 1244-1253, 2022.
Article in English | MEDLINE | ID: covidwho-1926351

ABSTRACT

The protein-protein interactions (PPIs) between human and viruses play important roles in viral infection and host immune responses. Rapid accumulation of experimentally validated human-virus PPIs provides an unprecedented opportunity to investigate the regulatory pattern of viral infection. However, we are still lack of knowledge about the regulatory patterns of human-virus interactions. We collected 27,293 experimentally validated human-virus PPIs, covering 8 virus families, 140 viral proteins and 6059 human proteins. Functional enrichment analysis revealed that the viral interacting proteins were likely to be enriched in cell cycle and immune-related pathways. Moreover, we analysed the topological features of the viral interacting proteins and found that they were likely to locate in central regions of human PPI network. Based on network proximity analyses of diseases genes and human-virus interactions in the human interactome, we revealed the associations between complex diseases and viral infections. Network analysis also implicated potential antiviral drugs that were further validated by text mining. Finally, we presented the Human-Virus Protein-Protein Interaction database (HVPPI, http://bio-bigdata.hrbmu.edu.cn/HVPPI), that provides experimentally validated human-virus PPIs as well as seamlessly integrates online functional analysis tools. In summary, comprehensive understanding the regulatory pattern of human-virus interactome will provide novel insights into fundamental infectious mechanism discovery and new antiviral therapy development.

6.
J Mol Biol ; 434(13): 167637, 2022 07 15.
Article in English | MEDLINE | ID: covidwho-1907327

ABSTRACT

After two years since the outbreak, the COVID-19 pandemic remains a global public health emergency. SARS-CoV-2 variants with substitutions on the spike (S) protein emerge increasing the risk of immune evasion and cross-species transmission. Here, we analyzed the evolution of the S protein as recorded in 276,712 samples collected before the start of vaccination efforts. Our analysis shows that most variants destabilize the S protein trimer, increase its conformational heterogeneity and improve the odds of the recognition by the host cell receptor. Most frequent substitutions promote overall hydrophobicity by replacing charged amino acids, reducing stabilizing local interactions in the unbound S protein trimer. Moreover, our results identify "forbidden" regions that rarely show any sequence variation, and which are related to conformational changes occurring upon fusion. These results are significant for understanding the structure and function of SARS-CoV-2 related proteins which is a critical step in vaccine development and epidemiological surveillance.


Subject(s)
COVID-19 , Spike Glycoprotein, Coronavirus , Angiotensin-Converting Enzyme 2 , COVID-19/epidemiology , Humans , Pandemics/prevention & control , Protein Binding , SARS-CoV-2/genetics , Spike Glycoprotein, Coronavirus/chemistry , Spike Glycoprotein, Coronavirus/genetics
7.
1st International Conference on Computing, Communication and Green Engineering, CCGE 2021 ; 2021.
Article in English | Scopus | ID: covidwho-1901427

ABSTRACT

The immense pressure and tension has created in the worldwide healthcare systems by disease. Various existing system has defined drug prediction system based on current patient evaluation. In this research we proposed a drug prediction for COVID-19 patient based on protein to protein reactions and availability. In order to evaluate the protein-protein interactions (PPIs) between some of the virus and individual receptors that are also confirmed utilizing biomedical simulations, the framework also defines machine learning models. The classification techniques are consistent with the predictions of separate physical material sequence-based characteristics such as classification of amino acids, distribution of pseudo amino acids and conjoint triads. Finally we will evaluate the system with numerous machine learning algorithm and show the effectiveness of propose systems. © 2021 IEEE.

8.
Lett Appl Microbiol ; 74(6): 992-1000, 2022 Jun.
Article in English | MEDLINE | ID: covidwho-1868679

ABSTRACT

Chikungunya is a fast-mutating virus causing Chikungunya virus disease (ChikvD) with a significant load of disability-adjusted life years (DALY) around the world. The outbreak of this virus is significantly higher in the tropical countries. Several experiments have identified crucial viral-host protein-protein interactions (PPIs) between Chikungunya Virus (Chikv) and the human host. However, no standard database that catalogs this PPI information exists. Here we develop a Chikv-Human PPI database, ChikvInt, to facilitate understanding ChikvD disease pathogenesis and the progress of vaccine studies. ChikvInt consists of 109 interactions and is available at www.chikvint.com.


Subject(s)
Chikungunya Fever , Chikungunya virus , Chikungunya Fever/pathology , Humans
9.
Adv Protein Chem Struct Biol ; 131: 261-276, 2022.
Article in English | MEDLINE | ID: covidwho-1866754

ABSTRACT

Numerous viruses have evolved mechanisms to inhibit or alter the host cell's apoptotic response as part of their coevolution with their hosts. The analysis of virus-host protein interactions require an in-depth understanding of both the viral and host protein structures and repertoires, as well as evolutionary mechanisms and pertinent biological facts. Throughout the course of a viral infection, there is constant battle for binding between virus and cellular proteins. Exogenous interfaces facilitating viral-host interactions are well known for constantly targeting and suppressing endogenous interfaces mediating intraspecific interactions, such as viral-viral and host-host connections. In these interactions, the protein-protein interactions (PPIs), are mostly shown as networks (protein interaction networks, PINs), with proteins represented as nodes and their interactions represented as edges. Host proteins with a higher degree of connectivity are more likely to interact with viral proteins. Due to technical advancements, three-dimensional interactions may now be visualized computationally utilizing molecular modeling and cryo-EM approaches. The uniqueness of viral domain repertoires, their evolution, and their activities during viral infection make viruses fascinating models for research. This chapter aims to provide readers a complete picture of the viral hijacking mechanism in protein-protein interactions.


Subject(s)
Host Microbial Interactions , Viral Proteins , Humans , Viral Proteins/chemistry
10.
Methods Mol Biol ; 2452: 197-212, 2022.
Article in English | MEDLINE | ID: covidwho-1844268

ABSTRACT

As the knowledge of biomolecules is increasing from the last decades, it is helping the researchers to understand the unsolved issues regarding virology. Recent technologies in high-throughput sequencing are providing the swift generation of SARS-CoV-2 genomic data with the basic inside of viral infection. Owing to various virus-host protein interactions, high-throughput technologies are unable to provide complete details of viral pathogenesis. Identifying the virus-host protein interactions using bioinformatics approaches can assist in understanding the mechanism of SARS-CoV-2 infection and pathogenesis. In this chapter, recent integrative bioinformatics approaches are discussed to help the virologists and computational biologists in the identification of structurally similar proteins of human and SARS-CoV-2 virus, and to predict the potential of virus-host interactions. Considering experimental and time limitations for effective viral drug development, computational aided drug design (CADD) can reduce the gap between drug prediction and development. More research with respect to evolutionary solutions could be helpful to make a new pipeline for virus-host protein-protein interactions and provide more understanding to disclose the cases of host switch, and also expand the virulence of the pathogen and host range in developing viral infections.


Subject(s)
COVID-19 , Computational Biology , Host Microbial Interactions , Host-Pathogen Interactions/genetics , Humans , Proteins , SARS-CoV-2/genetics
11.
Front Microbiol ; 13: 849781, 2022.
Article in English | MEDLINE | ID: covidwho-1834460

ABSTRACT

Viral infections are one of the major causes of human diseases that cause yearly millions of deaths and seriously threaten global health, as we have experienced with the COVID-19 pandemic. Numerous approaches have been adopted to understand viral diseases and develop pharmacological treatments. Among them, the study of virus-host protein-protein interactions is a powerful strategy to comprehend the molecular mechanisms employed by the virus to infect the host cells and to interact with their components. Experimental protein-protein interactions described in the scientific literature have been systematically captured into several molecular interaction databases. These data are organized in structured formats and can be easily downloaded by users to perform further bioinformatic and network studies. Network analysis of available virus-host interactomes allow us to understand how the host interactome is perturbed upon viral infection and what are the key host proteins targeted by the virus and the main cellular pathways that are subverted. In this review, we give an overview of publicly available viral-human protein-protein interactions resources and the community standards, curation rules and adopted ontologies. A description of the main virus-human interactome available is provided, together with the main network analyses that have been performed. We finally discuss the main limitations and future challenges to assess the quality and reliability of protein-protein interaction datasets and resources.

12.
J Infect Public Health ; 15(4): 437-447, 2022 Apr.
Article in English | MEDLINE | ID: covidwho-1828917

ABSTRACT

BACKGROUND: COVID-19 is a new coronavirus that constitutes a great challenge to human health. At this stage, there are still cases of COVID-19 infection in some countries and regions, in which ischemic stroke (IS) is a risk factor for new coronavirus pneumonia, and patients with COVID-19 infection have a dramatically elevated risk of stroke. At the same time, patients with long-term IS are vulnerable to COVID-19 infection and have more severe disease, and carotid atherosclerosis is an early lesion in IS. METHODS: This study used human induced pluripotent stem cell (hiPSC)-derived monolayer brain cell dataset and human carotid atherosclerosis genome-wide dataset to analyze COVID-19 infection and carotid atherosclerosis patients to determine the synergistic effect of new coronavirus infection on carotid atherosclerosis patients, to clarify the common genes of both, and to identify common pathways and potential drugs for carotid atherosclerosis in patients with COVID-19 infection RESULTS: Using several advanced bioinformatics tools, we present the causes of COVID-19 infection leading to increased mortality in carotid atherosclerosis patients and the susceptibility of carotid atherosclerosis patients to COVID-19. Potential therapeutic agents for COVID-19 -infected patients with carotid atherosclerosis are also proposed. CONCLUSIONS: With COVID-19 being a relatively new disease, associations have been proposed for its connections with several ailments and conditions, including IS and carotid atherosclerosis. More patient-based data-sets and studies are needed to fully explore and understand the relationship.


Subject(s)
COVID-19 , Carotid Artery Diseases , Induced Pluripotent Stem Cells , Carotid Artery Diseases/complications , Computational Biology , Humans , SARS-CoV-2
13.
5th International Conference on Smart Computing and Informatics, SCI 2021 ; 282:1-15, 2022.
Article in English | Scopus | ID: covidwho-1826285

ABSTRACT

COrona VIrus Disease abbreviated as COVID-19 is a novel virus which is initially identified in Wuhan of China in December of 2019, and now, this deadly disease has spread all over the world. According to World Health Organization (WHO), a total of 3,124,905 people died from 2019 to 2021, April. In this case, many methods, AI-based techniques, and machine learning algorithms, have been researched and are being used to save people from this pandemic. The SARS-CoV and the 2019-nCoV, SARS-CoV-2 virus invade our bodies, causing some differences in the structure of cell proteins. Protein–protein interaction (PPI) is an essential process in our cells and plays a very important role in the development of medicines and gives ideas about the disease. In this study, we performed clustering on PPI networks generated from 92 genes of the COVID-19 dataset. We have used three graph-based clustering algorithms to give intuition to the analysis of clusters. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

14.
Front Microbiol ; 13: 842976, 2022.
Article in English | MEDLINE | ID: covidwho-1825493

ABSTRACT

Identifying human-virus protein-protein interactions (PPIs) is an essential step for understanding viral infection mechanisms and antiviral response of the human host. Recent advances in high-throughput experimental techniques enable the significant accumulation of human-virus PPI data, which have further fueled the development of machine learning-based human-virus PPI prediction methods. Emerging as a very promising method to predict human-virus PPIs, deep learning shows the powerful ability to integrate large-scale datasets, learn complex sequence-structure relationships of proteins and convert the learned patterns into final prediction models with high accuracy. Focusing on the recent progresses of deep learning-powered human-virus PPI predictions, we review technical details of these newly developed methods, including dataset preparation, deep learning architectures, feature engineering, and performance assessment. Moreover, we discuss the current challenges and potential solutions and provide future perspectives of human-virus PPI prediction in the coming post-AlphaFold2 era.

15.
Comput Biol Med ; 146: 105575, 2022 07.
Article in English | MEDLINE | ID: covidwho-1814283

ABSTRACT

SARS-CoV-2, the causal agent of COVID-19, is primarily a pulmonary virus that can directly or indirectly infect several organs. Despite many studies carried out during the current COVID-19 pandemic, some pathological features of SARS-CoV-2 have remained unclear. It has been recently attempted to address the current knowledge gaps on the viral pathogenicity and pathological mechanisms via cellular-level tropism of SARS-CoV-2 using human proteomics, visualization of virus-host protein-protein interactions (PPIs), and enrichment analysis of experimental results. The synergistic use of models and methods that rely on graph theory has enabled the visualization and analysis of the molecular context of virus/host PPIs. We review current knowledge on the SARS-COV-2/host interactome cascade involved in the viral pathogenicity through the graph theory concept and highlight the hub proteins in the intra-viral network that create a subnet with a small number of host central proteins, leading to cell disintegration and infectivity. Then we discuss the putative principle of the "gene-for-gene and "network for network" concepts as platforms for future directions toward designing efficient anti-viral therapies.


Subject(s)
COVID-19 , SARS-CoV-2 , Antiviral Agents/pharmacology , Antiviral Agents/therapeutic use , COVID-19/drug therapy , Humans , Pandemics , Proteins/metabolism
16.
Front Microbiol ; 13: 844447, 2022.
Article in English | MEDLINE | ID: covidwho-1785371

ABSTRACT

The ongoing SARS-CoV-2 pandemic has tested the capabilities of public health and scientific community. Since the dawn of the twenty-first century, viruses have caused several outbreaks, with coronaviruses being responsible for 2: SARS-CoV in 2007 and MERS-CoV in 2013. As the border between wildlife and the urban population continue to shrink, it is highly likely that zoonotic viruses may emerge more frequently. Furthermore, it has been shown repeatedly that these viruses are able to efficiently evade the innate immune system through various strategies. The strong and abundant antiviral innate immunity evasion strategies shown by SARS-CoV-2 has laid out shortcomings in our approach to quickly identify and modulate these mechanisms. It is thus imperative that there be a systematic framework for the study of the immune evasion strategies of these viruses, to guide development of therapeutics and curtail transmission. In this review, we first provide a brief overview of general viral evasion strategies against the innate immune system. Then, we utilize SARS-CoV-2 as a case study to highlight the methods used to identify the mechanisms of innate immune evasion, and pinpoint the shortcomings in the current paradigm with its focus on overexpression and protein-protein interactions. Finally, we provide a recommendation for future work to unravel viral innate immune evasion strategies and suitable methods to aid in the study of virus-host interactions. The insights provided from this review may then be applied to other viruses with outbreak potential to remain ahead in the arms race against viral diseases.

17.
Mol Divers ; 2022 Mar 03.
Article in English | MEDLINE | ID: covidwho-1782875

ABSTRACT

In India, during the second wave of the COVID-19 pandemic, the breakthrough infections were mainly caused by the SARS-COV-2 delta variant (B.1.617.2). It was reported that, among majority of the infections due to the delta variant, only 9.8% percent cases required hospitalization, whereas only 0.4% fatality was observed. Sudden dropdown in COVID-19 infections cases were observed within a short timeframe, suggesting better host adaptation with evolved delta variant. Downregulation of host immune response against SARS-CoV-2 by ORF8 induced MHC-I degradation has been reported earlier. The Delta variant carried mutations (deletion) at Asp119 and Phe120 amino acids which are critical for ORF8 dimerization. The deletions of amino acids Asp119 and Phe120 in ORF8 of delta variant resulted in structural instability of ORF8 dimer caused by disruption of hydrogen bonds and salt bridges as revealed by structural analysis and MD simulation studies. Further, flexible docking of wild type and mutant ORF8 dimer revealed reduced interaction of mutant ORF8 dimer with MHC-I as compared to wild-type ORF8 dimer with MHC-1, thus implicating its possible role in MHC-I expression and host immune response against SARS-CoV-2. We thus propose that mutant ORF8 of SARS-CoV-2 delta variant may not be hindering the MHC-I expression thereby resulting in a better immune response against the SARS-CoV-2 delta variant, which partly explains the possible reason for sudden drop of SARS-CoV-2 infection rate in the second wave of SARS-CoV-2 predominated by delta variant in India.

18.
Viruses ; 14(2)2022 02 17.
Article in English | MEDLINE | ID: covidwho-1786043

ABSTRACT

Various adenoviruses are being used as viral vectors for the generation of vaccines against chronic and emerging diseases (e.g., AIDS, COVID-19). Here, we report the improved capsid structure for one of these vectors, human adenovirus D26 (HAdV-D26), at 3.4 Å resolution, by reprocessing the previous cryo-electron microscopy dataset and obtaining a refined model. In addition to overall improvements in the model, the highlights of the structure include (1) locating a segment of the processed peptide of VIII that was previously believed to be released from the mature virions, (2) reorientation of the helical appendage domain (APD) of IIIa situated underneath the vertex region relative to its counterpart observed in the cleavage defective (ts1) mutant of HAdV-C5 that resulted in the loss of interactions between the APD and hexon bases, and (3) the revised conformation of the cleaved N-terminal segments of pre-protein VI (pVIn), located in the hexon cavities, is highly conserved, with notable stacking interactions between the conserved His13 and Phe18 residues. Taken together, the improved model of HAdV-D26 capsid provides a better understanding of protein-protein interactions in HAdV capsids and facilitates the efforts to modify and/or design adenoviral vectors with altered properties. Last but not least, we provide some insights into clotting factors (e.g., FX and PF4) binding to AdV vectors.


Subject(s)
Adenoviruses, Human/chemistry , Capsid/chemistry , Capsid/ultrastructure , Cryoelectron Microscopy/methods , Adenoviruses, Human/genetics , Capsid Proteins/genetics , Humans , Models, Molecular , Protein Conformation , Protein Interaction Domains and Motifs , Virus Assembly , Virus Internalization
19.
Non-conventional in English | National Technical Information Service, Grey literature | ID: grc-753728

ABSTRACT

Heat shock proteins Hsp40, Hsp70 and Hsp90 are molecular chaperones required for stabilization/activation of nuclear receptors, including full-length androgen receptor (AR) and glucocorticoid receptor (GR). Although ligand-binding domain (LBD) targeted (LBDT) therapy initially inhibits AR function and improves patient survival, this treatment almost invariably leads to emergence of castration-resistant prostate cancer (CRPC). CRPC is frequently characterized by elevated expression of alternative nuclear receptors able to at least partially maintain the AR transcriptional program. These alternative receptors include GR, which is expressed in approximately 30% of LBDT therapy-sensitive prostate cancer, but is expressed at a much higher frequency in CRPC and in those patients with a poor response to LBDT therapy. Additionally, elevated expression of a number of constitutively active AR splice variants lacking the LBD (ARv, particularly ARv7, which correlates with poor prognosis, reduced survival, and resistance to LBDT therapy, and ARv567es) is a frequent occurrence in CRPC. While full length GR and AR depend on the Hsp40/Hsp70/Hsp90 chaperone axis for activity, the chaperone requirements of ARv are not known. Because Hsp90 interacts with the LBD, ARv are insensitive to Hsp90 inhibitors. However, based on strong preliminary data, we believe that ARv, like GR and AR, retain dependence on Hsp40/Hsp70 and we will test this hypothesis using combined biophysical, genetic, biochemical and pharmacological approaches, including novel small molecules able to bind and inhibit both Hsp40 and Hsp70. We envision a synergistic group of studies that will result in a detailed and comprehensive picture of the specific chaperone dependence of these individual nuclear receptors.

20.
Non-conventional in English | National Technical Information Service, Grey literature | ID: grc-753560

ABSTRACT

The purpose of this study was to characterize a mutant mouse that bears an NF1 mutation that is identical to one found in humans. No human samples exist to determine the nature of how this particular mutation affects the NF1 gene and protein. This mutation is particularly important to understand as it does not lead to the neurofibroma formation found in most patients, but it can lead to learning challenges, developmental delay and in some cases epilepsy. This study was able to determine that this particular mutant NF1 protein neither affects the most common pathways associated with the function of the NF1 protein nor leads to some inflammatory aspects of the disease. These are critical findings as they reveal that the protein has additional functions that are linked to a very specific region of the protein and that this mutation may be acting in very distinct ways from the effects of complete loss of the NF1 protein. This study also found a link between this mutation and programmed cell death in a region of the brain critical for learning and memory. This information will allow new studies to more clearly define the molecular basis for the neurological aspects of Neurofibromatosis with this new understanding bringing the potential for new therapeutic targets.

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