Gene/Protein Disease Symptom Drug Enzyme Compound
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Query: EC:1.6.5.3 (complex I)
 8,901 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

Pathogenic variants in mitochondrial aminoacyl-tRNA synthetases result in a broad range of mitochondrial respiratory chain disorders despite their shared role in mitochondrial protein synthesis. LARS2 encodes the mitochondrial leucyl-tRNA synthetase, which attaches leucine to its cognate tRNA. Sequence variants in LARS2 have previously been associated with Perrault syndrome, characterized by premature ovarian failure and hearing loss (OMIM #615300). In this study, we report variants in LARS2 that are associated with a severe multisystem metabolic disorder. The proband was born prematurely with severe lactic acidosis, hydrops, and sideroblastic anemia. She had multisystem complications with hyaline membrane disease, impaired cardiac function, a coagulopathy, pulmonary hypertension, and progressive renal disease and succumbed at 5 days of age. Whole exome sequencing of patient DNA revealed compound heterozygous variants in LARS2 (c.1289C>T; p.Ala430Val and c.1565C>A; p.Thr522Asn). The c.1565C>A (p.Thr522Asn) LARS2 variant has previously been associated with Perrault syndrome and both identified variants are predicted to be damaging (SIFT, PolyPhen). Muscle and liver samples from the proband did not display marked mitochondrial respiratory chain enzyme deficiency. Immunoblotting of patient muscle and liver showed LARS2 levels were reduced in liver and complex I protein levels were reduced in patient muscle and liver. Aminoacylation assays revealed p.Ala430Val LARS2 had an 18-fold loss of catalytic efficiency and p.Thr522Asn a 9-fold loss compared to wild-type LARS2. We suggest that the identified LARS2 variants are responsible for the severe multisystem clinical phenotype seen in this baby and that mutations in LARS2 can result in variable phenotypes.
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PMID:LARS2 Variants Associated with Hydrops, Lactic Acidosis, Sideroblastic Anemia, and Multisystem Failure. 2653 77

Molecular interactions are essential for regulation of cellular processes from the formation of multi-protein complexes to the allosteric activation of enzymes. Identifying the essential residues and molecular features that regulate such interactions is paramount for understanding the biochemical process in question, allowing for suppression of a reaction through drug interventions or optimization of a chemical process using bioengineered molecules. In order to identify important residues and information pathways within molecular complexes, the dynamical network analysis method was developed and has since been broadly applied in the literature. However, in the dawn of exascale computing, this method is frequently limited to relatively small biomolecular systems. In this work, we provide an evolution of the method, application, and interface. All data processing and analysis are conducted through Jupyter notebooks, providing automatic detection of important solvent and ion residues, an optimized and parallel generalized correlation implementation that is linear with respect to the number of nodes in the system, and subsequent community clustering, calculation of betweenness of contacts, and determination of optimal paths. Using the popular visualization program visual molecular dynamics (VMD), high-quality renderings of the networks over the biomolecular structures can be produced. Our new implementation was employed to investigate three different systems, with up to 2.5M atoms, namely, the OMP-decarboxylase, the leucyl-tRNA synthetase complexed with its cognate tRNA and adenylate, and respiratory complex I in a membrane environment. Our enhanced and updated protocol provides the community with an intuitive and interactive interface, which can be easily applied to large macromolecular complexes.
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PMID:Generalized correlation-based dynamical network analysis: a new high-performance approach for identifying allosteric communications in molecular dynamics trajectories. 3303 27