Analysis of mutations in APP1 protein associated with development and protection against Alzheimer's disease - an In silico approach / Análise de mutações na proteína APP1 associadas ao desenvolvimento e proteção contra a doença de Alzheimer - uma abordagem In silico

Introduction: Alzheimer's disease (AD) is the dementia with the highest number of cases worldwide, causing great social and economic impact. The amyloid cascade is the most accepted hypothesis to explain the beginning of AD. According to it, neurodegeneration is caused by the accumulation of Aβ peptides and the formation of amyloid plaques in the brain. In familial cases of Alzheimer's, mutations in the APP1 protein lead to increased production of Aβ plaques. Objectives: Analyze in silico the structural and functional impact of missense mutations in APP1 and construct a complete model for the protein. Methods: We generated and validated a theoretical structure of APP1 protein using structural modeling and quality assessment algorithms. We further analyzed the effects of AD-related mutations on APP1 protein and also the neuroprotective mutation A673T by performing functional predictions, evolutionary conservation analysis, and molecular dynamics (MD). Results: The predictive analysis indicated that most mutations occur in conserved regions of APP1 and also present an elevated rate of deleterious predictions, pointing to their harmful effects. The computational modeling generated an unprecedented, accurate and complete model of human APP1, whose quality was corroborated by validation algorithms and structural alignment. The MD simulations of codon 673 variants pointed to flexibility and essential dynamics alterations at the AICD and Aβ domains, which could have strong and non-intuitive consequences on APP1 interactions, including those involved in β-secretase cleavage and, consequently, aβ peptide formation. Conclusions: Flexibility and essential dynamics alterations upon codon 673 variants may have functional implications for APP1, influencing the generation of Aβ peptide, the main responsible for APP1 toxicity in AD

Predictive analysis of Tryptophan Hydroxylase 2 (TPH2) missense mutations in psychiatric disorders: Análise preditiva das mutações missense da Triptofano Hidroxilase 2 (TPH2)

Psychiatric disorders are syndromes characterized by cognitive disturbance and behavioral dysfunction, which affect over 800 million people worldwide. It is considered a major public health problem responsible for severe distress with significant impairment in social and working relationships. In the United States and Canada, psychiatric disorders are considered the main cause of disability in young individuals, in addition to being a key factor underlying suicide. Missense mutations in tryptophan hydroxylase 2 enzyme (TPH2) are associated with the development of psychiatric disorders. TPH2 catalyzes the first step of serotonin biosynthesis, a neurotransmitter that plays a central role in the regulation of emotional behavior and cognition. These mutations lead to TPH2 dysfunction with impaired enzymatic activity, which ultimately results in abnormally low levels of serotonin in the brain. Despite the importance of missense mutations in TPH2 to the development of psychiatric disorders, most of them have not yet been characterized, so their effects are still unknown. In this study, we characterized the impact of missense mutations in TPH2 using prediction algorithms and evolutionary conservation analysis. We also used a penalty system to prioritize the most likely harmful mutations of TPH2 by combining the predictive analyses, evolutionary conservation, literature review, and alterations in physicochemical properties upon amino acid substitution. Three hundred and eighty-four missense mutations of TPH2 were compiled from the literature and databases. Our predictive analysis pointed to a high rate of deleterious and destabilizing predictions for the TPH2 mutations. These mutations mainly affect conserved and, possibly, functionally important residues. Among the uncharacterized mutations of TPH2, variants V295E, R441C T311P, Y281C, R441S, S383F, P308S, Y281H, and E363G received the highest penalties, thus, being the most likely deleterious and, consequently, important targets for future investigation. Our findings may guide the design of clinical and laboratory experiments, optimizing time and resources

Comprehensive in silico analysis of the TDP-43 protein variants related to Amyotrophic Lateral Sclerosis and Frontotemporal Dementia: Abrangente na análise silicoanalítica das variantes proteicas TDP-43 relacionadas à Esclerose Lateral Amiotrófica e Demência Frontotempor

Amyotrophic lateral sclerosis (ALS) is a highly disabling neurodegenerative disorder characterized by the progressive loss of voluntary motor activity. ALS is currently the most frequent adult-onset motor neuron disorder, which is associated with a major economic burden. Two drugs have already been approved to treat ALS, but they confer a limited survival benefit. In turn, frontotemporal dementia (FTD) is an early-onset and fatal dementia characterized by deficits in behavior, language, and executive function. FTD is the most frequent cause of pre-senile dementia after Alzheimer's. Currently, FTD has no cure and the available treatments are merely symptomatic. Missense mutations in TDP-43, a nuclear RNA/DNA-binding protein, are among the main causes associated with ALS and FTD. Nonetheless, most of these mutations are not yet characterized. To date, no complete three-dimensional structure has already been determined for TDP-43. In this work, we characterized the impact of missense mutations in TDP-43 using prediction algorithms, evolutionary conservation analysis, and molecular dynamics simulations (MD). We also performed structural modeling and validation of the TDP-43 protein. Two hundred and seven TDP-43 mutations were compiled from the databases and literature. The predictive analysis pointed to a moderate rate of deleterious and destabilizing mutations. Furthermore, most mutations occur at evolutionarily variable positions. Combining the predictive analyses into a penalty system, our findings suggested that the uncharacterized mutations Y43C, D201Y, F211S, I222T, K224N, A260D, P262T, and A321D are considered the most-likely deleterious, thus being important targets for future investigation. This work also provided an accurate, complete, and unprecedented three-dimensional structure for TDP-43 that can be used to identify and optimize potential drug candidates. At last, our MD findings pointed to a noticeable flexibility increase in functional domains upon K263E, G335D, M337V, and Q343R variants, which may cause non-native interactions and impaired TDP-43 recognition, ultimately leading to protein aggregation

Amyotrophic Lateral Sclerosis Type 20 - In Silico Analysis and Molecular Dynamics Simulation of hnRNPA1.

Amyotrophic Lateral Sclerosis (ALS) is a fatal neurodegenerative disease that affects the upper and lower motor neurons. 5-10% of cases are genetically inherited, including ALS type 20, which is caused by mutations in the hnRNPA1 gene. The goals of this work are to analyze the effects of non-synonymous single nucleotide polymorphisms (nsSNPs) on hnRNPA1 protein function, to model the complete tridimensional structure of the protein using computational methods and to assess structural and functional differences between the wild type and its variants through Molecular Dynamics simulations. nsSNP, PhD-SNP, Polyphen2, SIFT, SNAP, SNPs&GO, SNPeffect and PROVEAN were used to predict the functional effects of nsSNPs. Ab initio modeling of hnRNPA1 was made using Rosetta and refined using KoBaMIN. The structure was validated by PROCHECK, Rampage, ERRAT, Verify3D, ProSA and Qmean. TM-align was used for the structural alignment. FoldIndex, DICHOT, ELM, D2P2, Disopred and DisEMBL were used to predict disordered regions within the protein. Amino acid conservation analysis was assessed by Consurf, and the molecular dynamics simulations were performed using GROMACS. Mutations D314V and D314N were predicted to increase amyloid propensity, and predicted as deleterious by at least three algorithms, while mutation N73S was predicted as neutral by all the algorithms. D314N and D314V occur in a highly conserved amino acid. The Molecular Dynamics results indicate that all mutations increase protein stability when compared to the wild type. Mutants D314N and N319S showed higher overall dimensions and accessible surface when compared to the wild type. The flexibility level of the C-terminal residues of hnRNPA1 is affected by all mutations, which may affect protein function, especially regarding the protein ability to interact with other proteins

Structural modeling and in silico analysis of human superoxide dismutase 2.

Aging in the world population has increased every year. Superoxide dismutase 2 (Mn-SOD or SOD2) protects against oxidative stress, a main factor influencing cellular longevity. Polymorphisms in SOD2 have been associated with the development of neurodegenerative diseases, such as Alzheimer's and Parkinson's disease, as well as psychiatric disorders, such as schizophrenia, depression and bipolar disorder. In this study, all of the described natural variants (S10I, A16V, E66V, G76R, I82T and R156W) of SOD2 were subjected to in silico analysis using eight different algorithms: SNPeffect, PolyPhen-2, PhD-SNP, PMUT, SIFT, SNAP, SNPs&GO and nsSNPAnalyzer. This analysis revealed disparate results for a few of the algorithms. The results showed that, from at least one algorithm, each amino acid substitution appears to harmfully affect the protein. Structural theoretical models were created for variants through comparative modelling performed using the MHOLline server (which includes MODELLER and PROCHECK) and ab initio modelling, using the I-Tasser server. The predicted models were evaluated using TM-align, and the results show that the models were constructed with high accuracy. The RMSD values of the modelled mutants indicated likely pathogenicity for all missense mutations. Structural phylogenetic analysis using ConSurf revealed that human SOD2 is highly conserved. As a result, a human-curated database was generated that enables biologists and clinicians to explore SOD2 nsSNPs, including predictions of their effects and visualisation of the alignment of both the wild-type and mutant structures. The database is freely available at http://bioinfogroup.com/database and will be regularly updated

Validation of the <i>in silico</i> modeled hnRNPA1 structure.

<p>The modeled structure was validated by PROCHECK, Rampage and ERRAT. (A) PROCHECK’s Ramachandran plot indicates that 89.9% of residues lie in most favored regions, 8.2% in additional allowed regions, 1.5% in generously allowed regions and 0.4% in disallowed regions. (B) Rampage’s Ramachandran plot shows 95.7% of residues in favored regions, 2.7% in allowed regions and 1.6% in outlier regions. (C) According to ERRAT, the structure obtained an 89.011 overall quality factor.</p

Representation of hnRNPA1 colored according to B-factor.

<p>Warm colors indicate high B-factor values, whereas cold colors indicate low B-factor values. (A) Wild type protein. (B) Mutant N73S. (C) Mutant D314V. (D) Mutant D314N. (E) Mutant N319S.</p

Radius of gyration (Rg) of Cα atoms as a function of time.

<p>The Radius of gyration of Cα atoms of the wild type and the mutants during the MD trajectory is shown. (A) The wild type is represented in black, and mutant N73S in red. (B) The wild type is represented in black, and mutant D314V in green. (C) The wild type is represented in black, and mutant D314N in purple. (D) The wild type is represented in black, and mutant N319S in pink.</p

Functional effect prediction of hnRNPA1 natural variants by different SNP prediction algorithms.

<p>Functional effect prediction of hnRNPA1 natural variants by different SNP prediction algorithms.</p

Schematic representation of the domains found on hnRNPA1.

<p>The two RNA recognition motifs (RRM 1 and 2) are represented in blue, the glycine-rich domain is represented in purple, the RNA-binding box is represented in green, and the nuclear localization signal M9 is represented in pink. The red arrows indicate the location where the four known mutations occur: position 73 (mutation N73S), position 314 (mutations D314V and D314N) and position 319 (mutation N319S).</p