SaLT&PepPr is an interface-predicting language model for designing peptide-guided protein degraders.

Protein-protein interactions (PPIs) are critical for biological processes and predicting the sites of these interactions is useful for both computational and experimental applications. We present a Structure-agnostic Language Transformer and Peptide Prioritization (SaLT&PepPr) pipeline to predict interaction interfaces from a protein sequence alone for the subsequent generation of peptidic binding motifs. Our model fine-tunes the ESM-2 protein language model (pLM) with a per-position prediction task to identify PPI sites using data from the PDB, and prioritizes motifs which are most likely to be involved within inter-chain binding. By only using amino acid sequence as input, our model is competitive with structural homology-based methods, but exhibits reduced performance compared with deep learning models that input both structural and sequence features. Inspired by our previous results using co-crystals to engineer target-binding "guide" peptides, we curate PPI databases to identify partners for subsequent peptide derivation. Fusing guide peptides to an E3 ubiquitin ligase domain, we demonstrate degradation of endogenous ß-catenin, 4E-BP2, and TRIM8, and highlight the nanomolar binding affinity, low off-targeting propensity, and function-altering capability of our best-performing degraders in cancer cells. In total, our study suggests that prioritizing binders from natural interactions via pLMs can enable programmable protein targeting and modulation.

Directed differentiation of human iPSCs to functional ovarian granulosa-like cells via transcription factor overexpression.

An in vitro model of human ovarian follicles would greatly benefit the study of female reproduction. Ovarian development requires the combination of germ cells and several types of somatic cells. Among these, granulosa cells play a key role in follicle formation and support for oogenesis. Whereas efficient protocols exist for generating human primordial germ cell-like cells (hPGCLCs) from human induced pluripotent stem cells (hiPSCs), a method of generating granulosa cells has been elusive. Here, we report that simultaneous overexpression of two transcription factors (TFs) can direct the differentiation of hiPSCs to granulosa-like cells. We elucidate the regulatory effects of several granulosa-related TFs and establish that overexpression of NR5A1 and either RUNX1 or RUNX2 is sufficient to generate granulosa-like cells. Our granulosa-like cells have transcriptomes similar to human fetal ovarian cells and recapitulate key ovarian phenotypes including follicle formation and steroidogenesis. When aggregated with hPGCLCs, our cells form ovary-like organoids (ovaroids) and support hPGCLC development from the premigratory to the gonadal stage as measured by induction of DAZL expression. This model system will provide unique opportunities for studying human ovarian biology and may enable the development of therapies for female reproductive health.

Ovaries are responsible for forming the eggs humans and other mammals need to reproduce. Once mature, the egg cell is released into the fallopian tube where it can be potentially fertilized by a sperm. Despite their crucial role, how eggs are made in the ovary is poorly understood. This is because ovaries are hard to access, making it difficult to conduct experiments on them. To overcome this, researchers have built artificial ovaries in the laboratory using stem cells from the embryos of mice which can develop into all cell types in the adult body. By culturing these embryonic stem cells under special conditions, researchers can convert them in to the two main cell types of the developing ovary: germ cells which go on to form eggs, and granulosa cells which help eggs grow and mature. The resulting lab-grown ovary can make eggs that produce live mice when fertilized. This approach has also been applied to human induced pluripotent stem cells (iPSCs), adult human cells which have been reprogrammed to a stem-like state. While this has produced human germ cells, generating human granulosa cells has been more challenging. Here, Pierson Smela, Kramme et al. show that activating a specific set of transcription factors (proteins that switch genes on or off) in iPSCs can make them transition to granulosa cells. First, the team tested random combinations of 35 transcription factors which, based on previous literature and genetic data, were likely to play a role in the formation of granulosa cells. This led to the identification of a small number of factors that caused the human iPSCs to develop features and carry out roles seen in mature granulosa cells; this includes producing an important reproductive hormone and supporting the maturation of germ cells. Pierson Smela, Kramme et al. found that growing these granulosa-like cells together with germ cells (also generated via iPSCs) resulted in structures similar to ovarian follicles which help eggs develop. These findings could help researchers build stable systems for studying how granulosa cells behave in human ovaries. This could lead to new insights about reproductive health.

Ready-to-Use Therapeutic Food (RUTF) Containing Low or No Dairy Compared to Standard RUTF for Children with Severe Acute Malnutrition: A Systematic Review and Meta-Analysis.

Ready-to-use therapeutic food (RUTF) containing less dairy may be a lower-cost treatment option for severe acute malnutrition (SAM). The objective was to understand the effectiveness of RUTF containing alternative sources of protein (nondairy), or <50% of protein from dairy products, compared with standard RUTF in children with SAM. The Cochrane Library, MEDLINE, Embase, CINAHL, and Web of Science were searched using terms relating to RUTF. Studies were eligible if they included children with SAM and evaluated RUTF with <50% of protein from dairy products compared with standard RUTF. Meta-analysis and meta-regression were completed to assess the effectiveness of intervention RUTF on a range of child outcomes. The quality of the evidence across outcomes was assessed using the GRADE (Grading of Recommendations Assessment, Development and Evaluation) approach. A total of 5868 studies were identified, of which 8 articles of 6 studies met the inclusion criteria evaluating 7 different intervention RUTF recipes. Nondairy or lower-dairy RUTF showed less weight gain (standardized mean difference: -0.20; 95% CI: -0.26, -0.15; P < 0.001), lower recovery (relative risk ratio: 0.93; 95% CI: 0.87, 1.00; P = 0.046), and lower weight-for-age z scores (WAZ) near program discharge (mean difference: -0.10; 95% CI: -0.20, 0.0; P = 0.047). Mortality, time to recovery, default (consecutive absences from outpatient therapeutic feeding program visits), nonresponse, and other anthropometric measures did not differ between groups. The certainty of evidence was high for weight gain and ranged from very low to moderate for other outcomes. RUTF with lower protein from dairy or dairy-free RUTF may not be as effective as standard RUTF for treatment of children with SAM based on weight gain, recovery, and WAZ evaluated using meta-analysis, although further research is required to explore the potential of alternative formulations. This review was registered at https://www.crd.york.ac.uk/prospero/ as CRD42020160762.

Innovative optimization of ready to use food for treatment of acute malnutrition.

Treatment of acute malnutrition typically requires the provision of ready-to-use food (RUF). Common RUF is effective but expensive, being manufactured from costly ingredients, and shipped worldwide from few global suppliers. I developed a linear programming tool to create RUF optimized for low cost using locally grown crops while maintaining necessary nutritional goals and other constraints. My tool utilizes a database of the nutritional value, price, and water efficiency of suitable ingredients and allows adjustment of constraints, including nutrients, flavour, and crop water efficiency. It is designed to (a) address nutrient requirements conforming to current standards and practice; (b) optimize RUF formulae for low cost using a wide range of ingredients for nutritional value and acceptability improvement; (c) ensure protein quality through protein digestibility corrected amino acid score; and (d) adjust RUF formulae according to locally grown crop selection, local prices, and crop water footprint. The tool creates formulae free of expensive dairy ingredients, ensuring desired protein digestibility corrected amino acid score by automatically balancing proteins with complementary quantities of essential amino acids. Using publicly available data with an application to Nigeria, my tool created RUF formulae suitable for local production using local crops to meet all nutrient requirements at a fraction of the ingredient cost and water footprint of current formulae, demonstrating the tool's effectiveness. Optimization of RUF for low cost using locally grown crops will facilitate local production and reduce ingredient as well as transport costs, so more patients can receive lifesaving treatment.