Interactome, Network, and Enrichment Motif Analysis Reveals Possible Linkage Between Human Mental Disorders, Emotions, and Coronavirus: In-Silico Insight (preprint)

The pandemic of COVID-19 has caused a global crisis. Today, everybody focuses on COVID-19 infection prevention, preparation, and discussion of physical health effects issues. It is important to understand, however, that a few will face life-threatening negative effects on physical health, but that all people will face the negative impact of the pandemic on mental health. COVID-19 hospitals are established in different locations to address the physical health implications of the pandemic. However, it is necessary to understand the effects of infections on mental health more effectively to prevent the negative consequences of infection. Here, we try to find out how the infection could affect mental health. We identify motifs in SARS-CoV-2 that are predicted to interact with human transcription factors (TF). Those TFs regulating behavior and mental health. Our results show that SARS-CoV-2 infection may lead to overactivation or inhibition of critical genes already known to affect behavior and mental health. This study is still limited to in silico limits so, clinical investigation needs to be addressed to assess our hypothesis.

Learning from COVID-19 Pandemic: In Silico Vaccine and Cloning Design Against Nipah Virus by Studying and Analyzing the Whole Nipah Virus Proteome. (preprint)

Nipah virus (NiV) is a zoonotic paramyxovirus of the Henipavirus genus first identified in Malaysia in 1998. Henipavirus have bat reservoir hosts and have been isolated from fruit bats found across Oceania, Asia, and Africa. Bat-to-human transmission is thought to be the primary mode of human NiV infection, although multiple intermediate hosts are described. Human infections with NiV were originally described as a syndrome of fever and rapid neurological decline following contact with swine. More recent outbreaks describe a syndrome with prominent respiratory symptoms and human-to-human transmission. Nearly annual outbreaks have been described since 1998 with case fatality rates reaching greater than 90%. To prevent the spreading of the Nipah virus and turning it into a new pandemic, we must be armed with a ready-made vaccine to save the time consuming that vaccine takes until production. Here we in this paper, we analyzed the whole Nipah virus proteome to find out the most antigenic, non-allergic, and immune inducing epitopes to construct different vaccines that undergone deep investigation to reveal the most appropriate vaccine to immunize humanity from this probably pandemic.

COVATOR: A Software for Chimeric Coronavirus Identification (preprint)

The term chimeric virus was not popular in the last decades. Recently, according to current sequencing efforts in discovering COVID-19 Secrets, the generated information assumed the presence of 6 Coronavirus main strains, but coronavirus diverges into hundreds of sub-strains. the bottleneck is the mutation rate. With two mutation/month, humanity will meet a new sub-strain every month. Tracking new sequenced viruses is urgently needed because of the pathogenic effect of the new sub-strains. here we introduce COVATOR, A user-friendly and python-based software that identifies viral chimerism. COVATOR aligns input genome and protein that has no known source, against genomes and protein with known source, then gives the user a graphical summary.

COVIDier: A Deep-learning Tool For Coronaviruses Genome And Virulence Proteins Classification (preprint)

COVID-19, caused by SARS-CoV-2 infection, has already reached pandemic proportions in a matter of a few weeks. At the time of writing this manuscript, the unprecedented public health crisis caused more than 2.5 million cases with a mortality range of 5-7%. The SARS-CoV-2, also called novel Coronavirus, is related to both SARS-CoV and bat SARS. Great efforts have been spent to control the pandemic that has become a significant burden on the health systems in a short time. Since the emergence of the crisis, a great number of researchers started to use the AI tools to identify drugs, diagnosing using CT scan images, scanning body temperature, and classifying the severity of the disease. The emergence of variants of the SARS-CoV-2 genome is a challenging problem with expected serious consequences on the management of the disease. Here, we introduce COVIDier, a deep learning-based software that is enabled to classify the different genomes of Alpha coronavirus, Beta coronavirus, MERS, SARS-CoV-1, SARS-CoV-2, and bronchitis-CoV. COVIDier was trained on 1925 genomes, belonging to the three families of SARS retrieved from NCBI Database to propose a new method to train deep learning model trained on genome data using Multi-layer Perceptron Classifier (MLPClassifier), a deep learning algorithm, that could blindly predict the virus family name from the genome of by predicting the statistically similar genome from training data to the given genome. COVIDier able to predict how close the emerging novel genomes of SARS to the known genomes with accuracy 99%. COVIDier can replace tools like BLAST that consume higher CPU and time.