scholarly journals Data Mining of Molecular Simulations Suggest Key Amino Acid Residues for Aggregation, Signaling and Drug Action

Biomolecules ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 1541
Author(s):  
Vaibhav Gurunathan ◽  
John Hamre Hamre ◽  
Dmitri K. Klimov ◽  
Mohsin Saleet Jafri
Keyword(s):  
Alzheimer’S Disease ◽  
Data Mining ◽  
Alzheimer's Disease ◽  
Molecular Simulation ◽  
Confusion Matrix ◽  
Amyloid Β ◽  
Md Simulations ◽  
Machine Learning Techniques ◽  
Data Set ◽  
Specificity And Sensitivity

Alzheimer’s disease, the most common form of dementia, currently has no cure. There are only temporary treatments that reduce symptoms and the progression of the disease. Alzheimer’s disease is characterized by the prevalence of plaques of aggregated amyloid β (Aβ) peptide. Recent treatments to prevent plaque formation have provided little to relieve disease symptoms. Although there have been numerous molecular simulation studies on the mechanisms of Aβ aggregation, the signaling role has been less studied. In this study, a total of over 38,000 simulated structures, generated from molecular dynamics (MD) simulations, exploring different conformations of the Aβ42 mutants and wild-type peptides were used to examine the relationship between Aβ torsion angles and disease measures. Unique methods characterized the data set and pinpointed residues that were associated in aggregation and others associated with signaling. Machine learning techniques were applied to characterize the molecular simulation data and classify how much each residue influenced the predicted variant of Alzheimer’s Disease. Orange3 data mining software provided the ability to use these techniques to generate tables and rank the data. The test and score module coupled with the confusion matrix module analyzed data with calculations of specificity and sensitivity. These methods evaluating frequency and rank allowed us to analyze and predict important residues associated with different phenotypic measures. This research has the potential to help understand which specific residues of Aβ should be targeted for drug development.

scholarly journals RGS2 expression predicts amyloid-β sensitivity, MCI and Alzheimer’s disease: genome-wide transcriptomic profiling and bioinformatics data mining

2016 ◽  
Vol 6 (10) ◽  
pp. e909-e909 ◽  
Author(s):  
A Hadar ◽  
E Milanesi ◽  
A Squassina ◽  
P Niola ◽  
C Chillotti ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Data Mining ◽  
Alzheimer's Disease ◽  
G Protein ◽  
Amyloid Β ◽  
Healthy Individuals ◽  
Transcriptomic Profiling ◽  
Expression Levels ◽  
Genome Wide ◽  
Lower Expression

Abstract Alzheimer's disease (AD) is the most frequent cause of dementia. Misfolded protein pathological hallmarks of AD are brain deposits of amyloid-β (Aβ) plaques and phosphorylated tau neurofibrillary tangles. However, doubts about the role of Aβ in AD pathology have been raised as Aβ is a common component of extracellular brain deposits found, also by in vivo imaging, in non-demented aged individuals. It has been suggested that some individuals are more prone to Aβ neurotoxicity and hence more likely to develop AD when aging brains start accumulating Aβ plaques. Here, we applied genome-wide transcriptomic profiling of lymphoblastoid cells lines (LCLs) from healthy individuals and AD patients for identifying genes that predict sensitivity to Aβ. Real-time PCR validation identified 3.78-fold lower expression of RGS2 (regulator of G-protein signaling 2; P=0.0085) in LCLs from healthy individuals exhibiting high vs low Aβ sensitivity. Furthermore, RGS2 showed 3.3-fold lower expression (P=0.0008) in AD LCLs compared with controls. Notably, RGS2 expression in AD LCLs correlated with the patients’ cognitive function. Lower RGS2 expression levels were also discovered in published expression data sets from postmortem AD brain tissues as well as in mild cognitive impairment and AD blood samples compared with controls. In conclusion, Aβ sensitivity phenotyping followed by transcriptomic profiling and published patient data mining identified reduced peripheral and brain expression levels of RGS2, a key regulator of G-protein-coupled receptor signaling and neuronal plasticity. RGS2 is suggested as a novel AD biomarker (alongside other genes) toward early AD detection and future disease modifying therapeutics.

scholarly journals d-Amino Acid Pseudopeptides as Potential Amyloid-Beta Aggregation Inhibitors

Molecules ◽  
2018 ◽  
Vol 23 (9) ◽  
pp. 2387 ◽  
Author(s):  
Banafsheh Mehrazma ◽  
Stanley Opare ◽  
Anahit Petoyan ◽  
Arvi Rauk
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Molecular Dynamics ◽  
Amino Acid ◽  
Amyloid Β ◽  
Md Simulations ◽  
Causative Factor ◽  
Umbrella Sampling ◽  
Aggregation Inhibitors ◽  
Dynamics Simulations

A causative factor for neurotoxicity associated with Alzheimer’s disease is the aggregation of the amyloid-β (Aβ) peptide into soluble oligomers. Two all d-amino acid pseudo-peptides, SGB1 and SGD1, were designed to stop the aggregation. Molecular dynamics (MD) simulations have been carried out to study the interaction of the pseudo-peptides with both Aβ13–23 (the core recognition site of Aβ) and full-length Aβ1–42. Umbrella sampling MD calculations have been used to estimate the free energy of binding, ∆G, of these peptides to Aβ13–23. The highest ∆Gbinding is found for SGB1. Each of the pseudo-peptides was also docked to Aβ1–42 and subjected up to seven microseconds of all atom molecular dynamics simulations. The resulting structures lend insight into how the dynamics of Aβ1–42 are altered by complexation with the pseudo-peptides and confirmed that SGB1 may be a better candidate for developing into a drug to prevent Alzheimer’s disease.

scholarly journals Inter-Cohort Validation of SuStaIn Model for Alzheimer’s Disease

2021 ◽  
Vol 4 ◽  
Author(s):  
Damiano Archetti ◽  
Alexandra L. Young ◽  
Neil P. Oxtoby ◽  
Daniel Ferreira ◽  
Gustav Mårtensson ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Clinical Setting ◽  
Neurodegenerative Disorder ◽  
Amyloid Β ◽  
Research Data ◽  
Training Set ◽  
Cross Sectional ◽  
Data Set ◽  
Test Sets

Alzheimer’s disease (AD) is a neurodegenerative disorder which spans several years from preclinical manifestations to dementia. In recent years, interest in the application of machine learning (ML) algorithms to personalized medicine has grown considerably, and a major challenge that such models face is the transferability from the research settings to clinical practice. The objective of this work was to demonstrate the transferability of the Subtype and Stage Inference (SuStaIn) model from well-characterized research data set, employed as training set, to independent less-structured and heterogeneous test sets representative of the clinical setting. The training set was composed of MRI data of 1043 subjects from the Alzheimer’s disease Neuroimaging Initiative (ADNI), and the test set was composed of data from 767 subjects from OASIS, Pharma-Cog, and ViTA clinical datasets. Both sets included subjects covering the entire spectrum of AD, and for both sets volumes of relevant brain regions were derived from T1-3D MRI scans processed with Freesurfer v5.3 cross-sectional stream. In order to assess the predictive value of the model, subpopulations of subjects with stable mild cognitive impairment (MCI) and MCIs that progressed to AD dementia (pMCI) were identified in both sets. SuStaIn identified three disease subtypes, of which the most prevalent corresponded to the typical atrophy pattern of AD. The other SuStaIn subtypes exhibited similarities with the previously defined hippocampal sparing and limbic predominant atrophy patterns of AD. Subject subtyping proved to be consistent in time for all cohorts and the staging provided by the model was correlated with cognitive performance. Classification of subjects on the basis of a combination of SuStaIn subtype and stage, mini mental state examination and amyloid-β1-42 cerebrospinal fluid concentration was proven to predict conversion from MCI to AD dementia on par with other novel statistical algorithms, with ROC curves that were not statistically different for the training and test sets and with area under curve respectively equal to 0.77 and 0.76. This study proves the transferability of a SuStaIn model for AD from research data to less-structured clinical cohorts, and indicates transferability to the clinical setting.

scholarly journals Novel blood test for early biomarkers of preeclampsia and Alzheimer’s disease

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Shibin Cheng ◽  
Sayani Banerjee ◽  
Lori A. Daiello ◽  
Akitoshi Nakashima ◽  
Sukanta Jash ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Blood Test ◽  
Late Onset ◽  
Early Stage ◽  
Amyloid Β ◽  
Protein Aggregates ◽  
Disease Diagnosis ◽  
Early Stage Disease ◽  
Specificity And Sensitivity

AbstractA non-invasive and sensitive blood test has long been a goal for early stage disease diagnosis and treatment for Alzheimer’s disease (AD) and other proteinopathy diseases. We previously reported that preeclampsia (PE), a severe pregnancy complication, is another proteinopathy disorder with impaired autophagy. We hypothesized that induced autophagy deficiency would promote accumulation of pathologic protein aggregates. Here, we describe a novel, sensitive assay that detects serum protein aggregates from patients with PE (n = 33 early onset and 33 late onset) and gestational age-matched controls (n = 77) as well as AD in both dementia and prodromal mild cognitive impairment (MCI, n = 24) stages with age-matched controls (n = 19). The assay employs exposure of genetically engineered, autophagy-deficient human trophoblasts (ADTs) to serum from patients. The aggregated protein complexes and their individual components, including transthyretin, amyloid β-42, α-synuclein, and phosphorylated tau231, can be detected and quantified by co-staining with ProteoStat, a rotor dye with affinity to aggregated proteins, and respective antibodies. Detection of protein aggregates in ADTs was not dependent on transcriptional upregulation of these biomarkers. The ROC curve analysis validated the robustness of the assay for its specificity and sensitivity (PE; AUC: 1, CI: 0.949–1.00; AD; AUC: 0.986, CI: 0.832–1.00). In conclusion, we have developed a novel, noninvasive diagnostic and predictive assay for AD, MCI and PE.

Enhanced Detection Specificity and Sensitivity of Alzheimer’s Disease Using Amyloid-β-Targeted Quantum Dots

2016 ◽  
Vol 27 (3) ◽  
pp. 809-814 ◽  
Author(s):  
Li Quan ◽  
Jiangxiao Wu ◽  
Lucas A. Lane ◽  
Jianquan Wang ◽  
Qian Lu ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Quantum Dots ◽  
Amyloid Β ◽  
Specificity And Sensitivity ◽  
Detection Specificity

scholarly journals Computational Analysis of the Interactions between the S100B Extracellular Chaperone and Its Amyloid β Peptide Client

2021 ◽  
Vol 22 (7) ◽  
pp. 3629
Author(s):  
Filipe E. P. Rodrigues ◽  
António J. Figueira ◽  
Cláudio M. Gomes ◽  
Miguel Machuqueiro
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Amyloid Β ◽  
Structural Data ◽  
Md Simulations ◽  
Binding Mode ◽  
Inhibitory Effect ◽  
Amyloid Β Peptide ◽  
Coulombic Interactions ◽  
Aggregation Activity

S100B is an astrocytic extracellular Ca2+-binding protein implicated in Alzheimer’s disease, whose role as a holdase-type chaperone delaying Aβ42 aggregation and toxicity was recently uncovered. Here, we employ computational biology approaches to dissect the structural details and dynamics of the interaction between S100B and Aβ42. Driven by previous structural data, we used the Aβ25–35 segment, which recapitulates key aspects of S100B activity, as a starting guide for the analysis. We used Haddock to establish a preferred binding mode, which was studied with the full length Aβ using long (1 μs) molecular dynamics (MD) simulations to investigate the structural dynamics and obtain representative interaction complexes. From the analysis, Aβ-Lys28 emerged as a key candidate for stabilizing interactions with the S100B binding cleft, in particular involving a triad composed of Met79, Thr82 and Glu86. Binding constant calculations concluded that coulombic interactions, presumably implicating the Lys28(Aβ)/Glu86(S100B) pair, are very relevant for the holdase-type chaperone activity. To confirm this experimentally, we examined the inhibitory effect of S100B over Aβ aggregation at high ionic strength. In agreement with the computational predictions, we observed that electrostatic perturbation of the Aβ-S100B interaction decreases anti-aggregation activity. Altogether, these findings unveil features relevant in the definition of selectivity of the S100B chaperone, with implications in Alzheimer’s disease.

Amyloid β-peptide and Alzheimer's disease

2014 ◽  
Vol 56 ◽  
pp. 99-110 ◽  
Author(s):  
David Allsop ◽  
Jennifer Mayes
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Senile Plaques ◽  
Strong Support ◽  
Amyloid Β ◽  
Amyloid Β Peptide ◽  
Amyloid Cascade Hypothesis ◽  
Sequence Of Events ◽  
Aβ Amyloid ◽  
Amyloid Cascade

One of the hallmarks of AD (Alzheimer's disease) is the formation of senile plaques in the brain, which contain fibrils composed of Aβ (amyloid β-peptide). According to the ‘amyloid cascade’ hypothesis, the aggregation of Aβ initiates a sequence of events leading to the formation of neurofibrillary tangles, neurodegeneration, and on to the main symptom of dementia. However, emphasis has now shifted away from fibrillar forms of Aβ and towards smaller and more soluble ‘oligomers’ as the main culprit in AD. The present chapter commences with a brief introduction to the disease and its current treatment, and then focuses on the formation of Aβ from the APP (amyloid precursor protein), the genetics of early-onset AD, which has provided strong support for the amyloid cascade hypothesis, and then on the development of new drugs aimed at reducing the load of cerebral Aβ, which is still the main hope for providing a more effective treatment for AD in the future.

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