CRISPR as a Potential Technique to Reduce Suicide Risk

As suicide is the nineteenth leading cause of death worldwide, it is important to focus on discovering ways to reduce the risk of suicide-related death as much as possible. With CRISPR starting to become increasingly popular over the past few years, this gene editing technique has been used to study how to edit, turn off, or knock out multiple parts of the genome. However, research on genes related to diseases as cystic fibrosis or Alzheimer’s disease has been mainly prioritized and, even though they are of high importance as well, important issues such as suicide have been left into oblivion. Four genes have been proven to be key in influencing suicide risk, showing that not only environmental factors account for an increased possibility of death by this cause. Therefore, gene editing techniques such as CRISPR could be applied in order to knock out those genes and reduce this risk. This research used Synthego’s guide RNA design tool to predict how the use of CRISPR can be helpful in knocking out those four suicide-related genes and, consequently, in preventing suicide. The top-ranked guide RNAs for each gene were used, showing the best results possible and with the least number of off-targets, which, in turn, demonstrates the effectiveness of CRISPR as a potential technique to reduce the number of suicide-related deaths worldwide.

Individual and collective human intelligence in drug design: evaluating the search strategy

In recent years, individual and collective human intelligence, defined as the knowledge, skills, reasoning and intuition of individuals and groups, have been used in combination with computer algorithms to solve complex scientific problems. Such approach was successfully used in different research fields such as: structural biology, comparative genomics, macromolecular crystallography and RNA design. Herein we describe an attempt to use a similar approach in small-molecule drug discovery, specifically to drive search strategies of de novo drug design. This is assessed with a case study that consists of a series of public experiments in which participants had to explore the huge chemical space in silico to find predefined compounds by designing molecules and analyzing the score associate with them. Such a process may be seen as an instantaneous surrogate of the classical design-make-test cycles carried out by medicinal chemists during the drug discovery hit to lead phase but not hindered by long synthesis and testing times. We present first findings on (1) assessing human intelligence in chemical space exploration, (2) comparing individual and collective human intelligence performance in this task and (3) contrasting some human and artificial intelligence achievements in de novo drug design.

Redesigning the Eterna100 for the Vienna 2 folding engine

The rational design of RNA is becoming important for rapidly developing technologies in medicine and biochemistry. Recent work has led to the development of several RNA secondary structure design algorithms and corresponding benchmarks to evaluate their performance. However, the performance of these algorithms is linked to the nature of the underlying algorithms for predicting secondary structure from sequences. Here, we show that an online community of RNA design experts is capable of modifying an existing RNA secondary structure design benchmark (Eterna100) with minimal alterations to address changes in the folding engine used (Vienna 1.8 updated to Vienna 2.4). We tested this new Eterna100-V2 benchmark with five RNA design algorithms, and found that neural network-based methods exhibited reduced performance in the folding engine they were evaluated on in their respective papers. We investigated this discrepancy, and determined that structural features, previously classified as difficult, may be dependent on parameters inherent to the RNA energy function itself. These findings suggest that for optimal performance, future algorithms should focus on finding strategies capable of solving RNA secondary structure design benchmarks independently of the free energy benchmark used. Eterna100-V1 and Eterna100-V2 benchmarks and example solutions are freely available at https://github.com/eternagame/eterna100-benchmarking.

Design of the recombinant influenza neuraminidase antigen is crucial for protective efficacy

Supplementing influenza vaccines with recombinant neuraminidase (rNA) remains a promising approach for improving the suboptimal efficacy. However, correlations among rNA designs, properties, and protection have not been systematically investigated. Here, we performed a comparative analysis of several rNAs produced from different construct designs using the baculovirus/insect cell system. The rNAs were designed with different tetramerization motifs and NA domains from a recent H1N1 vaccine strain (A/Brisbane/02/2018) and were analyzed for enzymatic properties, antigenicity, thermal and size stability, and protection in mice. We found that rNAs containing the NA head-domain versus the full-ectodomain possess distinct enzymatic properties and that the molecular size stability is tetramerization domain-dependent, whereas protection is more contingent on the combination of the tetramerization and NA domains. Following single-dose immunizations, a rNA possessing the full-ectodomain, non-native enzymatic activity, and the tetramerization motif from the human vasodilator-stimulated phosphoprotein provided substantially higher protection than a rNA possessing the head-domain, native activity and the same tetramerization motif. In contrast, these two rNAs provided comparable protection when the tetramerization motif was exchanged with the one from the tetrabrachion protein. These findings demonstrate that the rNA design is crucial for the protective efficacy and should be thoroughly evaluated for vaccine development, as the unpredictable nature of the heterologous domain combination can result in rNAs with similar key attributes but vastly differ in protection.

RNA Engineering for Public Health: Innovations in RNA-Based Diagnostics and Therapeutics

RNA is essential for cellular function: From sensing intra- and extracellular signals to controlling gene expression, RNA mediates a diverse and expansive list of molecular processes. A long-standing goal of synthetic biology has been to develop RNA engineering principles that can be used to harness and reprogram these RNA-mediated processes to engineer biological systems solving pressing global challenges. Recent advances in the field of RNA engineering are bringing this to fruition, enabling the creation of RNA-based tools to combat some of the most urgent public health crises. Specifically, new diagnostics using engineered RNAs are able to detect both pathogens and chemicals while generating an easily detectable fluorescent signal as an indicator. New classes of vaccines and therapeutics are also using engineered RNAs to target a wide range of genetic and pathogenic diseases. Here, we discuss the recent breakthroughs in RNA engineering enabling these innovations and examine how advances in RNA design promise to accelerate the impact of engineered RNA systems. Expected final online publication date for the Annual Review of Chemical and Biomolecular Engineering, Volume 12 is June 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.