A new age in protein design empowered by deep learning

International audienceThe rapid progress in the field of deep learning has had a significant impact on protein design. Deep learning methods have recently produced a breakthrough in protein structure prediction, leading to the availability of high-quality models for millions of proteins. Along with novel architectures for generative modeling and sequence analysis, they have revolutionized the protein design field in the past few years remarkably by improving the accuracy and ability to identify novel protein sequences and structures. Deep neural networks can now learn and extract the fundamental features of protein structures, predict how they interact with other biomolecules, and have the potential to create new effective drugs for treating disease. As their applicability in protein design is rapidly growing, we review the recent developments and technology in deep learning methods and provide examples of their performance to generate novel functional proteins

ART: iatrogenic multiple pregnancy?

Assisted reproductive technologies (ART) are now widely accepted as effective treatment for most causes of infertility. With improving success rates, attention has turned to the problem of multiple pregnancies, which are associated with a poor perinatal outcome, maternal complications and significant financial consequences. The challenge is to reduce multigestational pregnancies while maintaining good treatment outcomes. Methods to prevent multiple pregnancy include restrictive use of ART in couples with a good chance of spontaneous pregnancy, cautious use of gonadotrophins, and increased use of natural-cycle intra-uterine insemination and elective single embryo transfer in in-vitro fertilization and intracytoplasmic sperm injection. The aim of this article is to review the contribution of fertility treatment to multiple pregnancies and strategies for reducing multiples in ART