In Silico Exploration of Metabolically Active Peptides as Potential Therapeutic Agents against Amyotrophic Lateral Sclerosis.

Amyotrophic lateral sclerosis (ALS) is regarded as a fatal neurodegenerative disease that is featured by progressive damage of the upper and lower motor neurons. To date, over 45 genes have been found to be connected with ALS pathology. The aim of this work was to computationally identify unique sets of protein hydrolysate peptides that could serve as therapeutic agents against ALS. Computational methods which include target prediction, protein-protein interaction, and peptide-protein molecular docking were used. The results showed that the network of critical ALS-associated genes consists of ATG16L2, SCFD1, VAC15, VEGFA, KEAP1, KIF5A, FIG4, TUBA4A, SIGMAR1, SETX, ANXA11, HNRNPL, NEK1, C9orf72, VCP, RPSA, ATP5B, and SOD1 together with predicted kinases such as AKT1, CDK4, DNAPK, MAPK14, and ERK2 in addition to transcription factors such as MYC, RELA, ZMIZ1, EGR1, TRIM28, and FOXA2. The identified molecular targets of the peptides that support multi-metabolic components in ALS pathogenesis include cyclooxygenase-2, angiotensin I-converting enzyme, dipeptidyl peptidase IV, X-linked inhibitor of apoptosis protein 3, and endothelin receptor ET-A. Overall, the results showed that AGL, APL, AVK, IIW, PVI, and VAY peptides are promising candidates for further study. Future work would be needed to validate the therapeutic properties of these hydrolysate peptides by in vitro and in vivo approaches.

Deletion of neural tube defect-associated gene Mthfd1l causes reduced cranial mesenchyme density.

BACKGROUND: Periconceptional intake of supplemental folic acid can reduce the incidence of neural tube defects by as much as 70%, but the mechanisms by which folic acid supports cellular processes during neural tube closure are unknown. The mitochondrial 10-formyl-tetrahydrofolate synthetase MTHFD1L catalyzes production of formate, thus generating one-carbon units for cytoplasmic processes. Deletion of Mthfd1l causes embryonic lethality, developmental delay, and neural tube defects in mice. METHODS: To investigate the role of mitochondrial one-carbon metabolism during cranial neural tube closure, we have analyzed cellular morphology and function in neural tissues in Mthfd1l knockout embryos. RESULTS: The head mesenchyme showed significantly lower cellular density in Mthfd1l nullizygous embryos compared to wildtype embryos during the process of neural tube closure. Apoptosis and neural crest cell specification were not affected by deletion of Mthfd1l. Sections from the cranial region of Mthfd1l knockout embryos exhibited decreased cellular proliferation, but only after completion of neural tube closure. Supplementation of pregnant dams with formate improved mesenchymal density and corrected cell proliferation in the nullizygous embryos. CONCLUSIONS: Deletion of Mthfd1l causes decreased density in the cranial mesenchyme and this defect is improved with formate supplementation. This study reveals a mechanistic link between folate-dependent mitochondrially produced formate, head mesenchyme formation and neural tube defects.

Classifying Multifunctional Enzymes by Incorporating Three Different Models into Chou's General Pseudo Amino Acid Composition.

With the avalanche of the newly found protein sequences in the post-genomic epoch, there is an increasing trend for annotating a number of newly discovered enzyme sequences. Among the various proteins, enzyme was considered as the one of the largest kind of proteins. It takes part in most of the biochemical reactions and plays a key role in metabolic pathways. Multifunctional enzyme is enzyme that plays multiple physiological roles. Given a multifunctional enzyme sequence, how can we identify its class? Especially, how can we deal with the multi-classes problem since an enzyme may simultaneously belong to two or more functional classes? To address these problems, which are obviously very important both to basic research and drug development, a multi-label classifier was developed via three different prediction models with multi-label K-nearest algorithm. Experimental results obtained on a stringent benchmark dataset of enzymes by jackknife cross-validation test show that the predicting results were exciting, indicating that the current method could be an effective and promising high throughput method in the enzyme research. We hope it could play an important complementary role to the existing predictors in identifying the classes of enzymes.