GENETICS & GENOMICS IN THE CLINIC
predicts the structure of human proteins
Proteins fold their string of amino acids into specific three-dimen- sional structures to carry out their biological function. Knowing that three-dimensional structure allows scientists to understand how the protein works, identify what goes wrong when it’s been altered and design medications to boost or silence its activity. Unfortunately, predicting protein structure is hard work and can take months of computer-based simulations and modeling. Less than ⅓ of all human proteins (collectively known as the proteome) have a known structure. DeepMind, a sister company to Google, is an artificial intelli- gence laboratory based in the UK. They developed AlphaFold2, a machine-learning platform that predicts protein structures with a very high degree of accuracy. The platform uses a repetitive system of model refinement, applied millions of times to improve predictions based on prior experiences. With a relatively high level of confi- dence, AlphaFold2 predicted structures for nearly the entire human proteome, as well as the proteome of 20 model organisms such as mouse, fruit fly and E. coli . DeepMind has predicted more than 350,000 protein structure and plans to submit up to 130 million more by the end of 2021, nearly half of all known proteins. Protein predictions are freely available and academic research teams can use AlphaFold2 at no charge. Even though these predictions must still be experimentally verified, the sudden availability of so many protein structures will likely transform many aspects of biology and human health. n REFERENCES: Tunyasuvunakool K. et al. Highly accurate protein structure prediction for the human proteome. Nature (2021) 596:590-596. DOI: 10.1038/s41586- 021-03828-1. And AlphaFold protein structure database: https://alphafold.ebi.ac.uk/ Online resource helps healthcare workers treat genetic diseases With the advent of whole genome sequencing and other genom- ic testing, the cause for many genetic disorders are being rapidly discovered. One geneticist acknowledged this and set out to create a resource that serves as a convenient, readily available starting point for health care providers looking for treatment information for genetic disorders. The resource, called Rx-Genes (Rx-genes.com), provides information about current treatments and treatments that are in clinical trials for genetic disorders. The website and corresponding mobile app currently contain more than 630 disease entries that include references to disease information and treatment guidance, a brief summary of treatments, the inheritance pattern, disease frequency, nonmolecular confirmato- ry testing, and a link to experimental treatments. Existing entries are continuously updated, and new entries are added as new treatments appear in the literature. Rx-Genes is a promising tool that helps healthcare providers more easily and efficiently access the newest information about genetic disorders. n REFERENCE: Bick D. et al., An online compendium of treatable genetic disorders. Am J Med Genet . (2021) 187C:48-54. DOI: 10.1002/ajmg.c.31874. Rx-genes.com
Long-read sequencing identifies “missed” disease-causing variants
Many neurodevelopmental diseases are genetic in nature. Despite advances in genome sequencing technology, specific diagnoses for these disorders remain elusive. This is likely because certain disease-causing genetic variants are challenging to detect with typical sequencing approaches. Traditionally, genome sequencing is performed by gen- erating millions of “short” sequences, called reads, generally around 150 base pairs long. These short-reads are pieced back together like a puzzle using a human reference genome as a template. However, it is hard to accurately map certain types of short reads, especially regions containing highly repetitive stretches of DNA. These portions of the genome often go unanalyzed. One approach to overcome this limitation is to use a sequencing platform that produces longer reads. “Long- read” sequencers generate sequences up to 1,000 times longer than short-read systems. Fewer, bigger puzzle pieces means fewer gaps in the assembled sequence. Greater ge- nome coverage lets researchers and clinicians more accurately detect DNA variants. Recently, scientists used long-read sequencing to rean- alyze the genomes of six families with children suspected of having a genetic neurodevelopmental disorder. The families had previously been sequenced using short-read technology, but no disease-causing genetic variant had been identified. Long-read sequencing found multiple genetic variants in each family that had previously been missed. Among these newly detected variants, disease-causing DNA changes were identified in two of the six children. If these findings are extended to larger populations, long-read sequencing may supplement or even replace short-read analysis pipelines, improving the rare disease genetic discovery rates. n REFERENCE: Hiatt S.M. et al. Long-read genome sequencing for the molecular diagnosis of neurodevelopmental disorders. HGC Advances (2021) 2:100023. DOI: 10.1016/j.xhgg.2021.100023.
The laboratories of HudsonAlpha faculty researchers Jane Grimwood PhD, Jeremy Schmutz and Greg Cooper PhD contributed to this work.
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