A Pan-plant Protein Complex Map Reveals Deep Conservation and Novel Assemblies.

Plants are foundational for global ecological and economic systems, but most plant proteins remain uncharacterized. Protein interaction networks often suggest protein functions and open new avenues to characterize genes and proteins. We therefore systematically determined protein complexes from 13 plant species of scientific and agricultural importance, greatly expanding the known repertoire of stable protein complexes in plants. By using co-fractionation mass spectrometry, we recovered known complexes, confirmed complexes predicted to occur in plants, and identified previously unknown interactions conserved over 1.1 billion years of green plant evolution. Several novel complexes are involved in vernalization and pathogen defense, traits critical for agriculture. We also observed plant analogs of animal complexes with distinct molecular assemblies, including a megadalton-scale tRNA multi-synthetase complex. The resulting map offers a cross-species view of conserved, stable protein assemblies shared across plant cells and provides a mechanistic, biochemical framework for interpreting plant genetics and mutant phenotypes.

Scaffold Matcher: A CMA-ES based algorithm for identifying hotspot aligned peptidomimetic scaffolds.

The design of protein interaction inhibitors is a promising approach to address aberrant protein interactions that cause disease. One strategy in designing inhibitors is to use peptidomimetic scaffolds that mimic the natural interaction interface. A central challenge in using peptidomimetics as protein interaction inhibitors, however, is determining how best the molecular scaffold aligns to the residues of the interface it is attempting to mimic. Here we present the Scaffold Matcher algorithm that aligns a given molecular scaffold onto hotspot residues from a protein interaction interface. To optimize the degrees of freedom of the molecular scaffold we implement the covariance matrix adaptation evolution strategy (CMA-ES), a state-of-the-art derivative-free optimization algorithm in Rosetta. To evaluate the performance of the CMA-ES, we used 26 peptides from the FlexPepDock Benchmark and compared with three other algorithms in Rosetta, specifically, Rosetta's default minimizer, a Monte Carlo protocol of small backbone perturbations, and a Genetic algorithm. We test the algorithms' performance on their ability to align a molecular scaffold to a series of hotspot residues (i.e., constraints) along native peptides. Of the 4 methods, CMA-ES was able to find the lowest energy conformation for all 26 benchmark peptides. Additionally, as a proof of concept, we apply the Scaffold Match algorithm with CMA-ES to align a peptidomimetic oligooxopiperazine scaffold to the hotspot residues of the substrate of the main protease of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Our implementation of CMA-ES into Rosetta allows for an alternative optimization method to be used on macromolecular modeling problems with rough energy landscapes. Finally, our Scaffold Matcher algorithm allows for the identification of initial conformations of interaction inhibitors that can be further designed and optimized as high-affinity reagents.

Transferability of the NHS low-calorie diet programme: A qualitative exploration of factors influencing the programme's transfer ahead of wide-scale adoption.

INTRODUCTION: Although behavioural interventions have been found to help control type 2 diabetes (T2D), it is important to understand how the delivery context can influence implementation and outcomes. The NHS committed to testing a low-calorie diet (LCD) programme designed to support people living with excess weight and T2D to lose weight and improve diabetes outcomes. Understanding what influenced implementation during the programme pilot is important in optimising rollout. This study explored the transferability of the NHS LCD Programme prior to wider adoption. METHODS: Twenty-five interviews were undertaken with stakeholders involved in implementing the LCD programme in pilot sites (health service leads, referring health professionals and programme deliverers). Interviews with programme participants (people living with T2D) were undertaken within a larger programme of work, exploring what worked, for whom and why, which is reported separately. The conceptual Population-Intervention-Environment-Transfer Model of Transferability (PIET-T) guided study design and data collection. Constructs of the model were also used as a deductive coding frame during data analysis. Key themes were identified which informed recommendations to optimise programme transfer. RESULTS: Population: Referral strategies in some areas lacked consideration of population characteristics. Many believed that offering a choice of delivery model would promote acceptability and accessibility of the eligible population. INTERVENTION: Overall, stakeholders had confidence in the LCD programme due to the robust evidence base along with anecdotal evidence, but some felt the complex referral process hindered engagement from GP practices. ENVIRONMENT: Stakeholders described barriers to accessing the programme, including language and learning difficulties. Transferability: Multidisciplinary working and effective communication supported successful implementation. CONCLUSION: Referral strategies to reach underrepresented groups should be considered during programme transfer, along with timely data from service providers on access and programme benefits. A choice of delivery models may optimise uptake. Knowledge sharing between sites on good working practices is encouraged, including increasing engagement with key stakeholders.

Can the delivery of behavioural support be improved in the NHS England Low-Calorie Diet Programme? An observational study of behaviour change techniques.

BACKGROUND: Previous research has illustrated a drift in the fidelity of behaviour change techniques (BCTs) during the design of the pilot NHS England Low-Calorie Diet (NHS-LCD) Programme. This study evaluated a subsequent domain of fidelity, intervention delivery. Two research questions were addressed: (1) To what extent were BCTs delivered with fidelity to providers programme plans? (2) What were the observed barriers and facilitators to delivery? METHODS: A mixed-methods sequential explanatory design was employed. Remote delivery of one-to-one and group-based programmes were observed. A BCT checklist was developed using the BCT Taxonomy v1; BCTs were coded as present, partially delivered, or absent during live sessions. Relational content analysis of field notes identified observed barriers and facilitators to fidelity. RESULTS: Observations of 122 sessions across eight samples and two service providers were completed. Delivery of the complete NHS-LCD was observed for five samples. Fidelity ranged from 33% to 70% across samples and was higher for group-based delivery models (64%) compared with one-to-one models (46%). Barriers and facilitators included alignment with the programme's target behaviours and outcomes, session content, time availability and management, group-based remote delivery, and deviation from the session plan. CONCLUSIONS: Overall, BCTs were delivered with low-to-moderate fidelity. Findings indicate a dilution in fidelity during the delivery of the NHS-LCD and variation in the fidelity of programmes delivered across England. Staff training could provide opportunities to practice the delivery of BCTs. Programme-level changes such as structured activities supported by participant materials and with sufficient allocated time, might improve the delivery of BCTs targeting self-regulation.

Normalisation and equity of referral to the NHS Low Calorie Diet programme pilot; a qualitative evaluation of the experiences of health care staff.

BACKGROUND: Health and wellbeing can be profoundly impacted by both obesity and type 2 diabetes, while the normalisation and equity of care for people living with these non-communicable diseases remain as challenges for local health systems. The National Health Service Low Calorie Diet programme in England, aims to support people to achieve type 2 diabetes remission, while also reducing health inequalities. We have explored the experiences of health care staff who have made a referral to the LCD programme, while identifying effective and equitable delivery of programme referrals, and their normalisation into routine care. METHODS: Nineteen individual semi-structured interviews were completed health care staff in the first year of the Low Calorie Diet programme. Interviewees were purposively sampled from the ten localities who undertook the Low Calorie Diet programme pilot. Each interview explored a number of topics of interest including communication and training, referrals, equity, and demands on primary care, before being subjected to a thematic analysis. RESULTS: From the data, five core themes were identified: Covid-19 and the demands on primary care, the expertise and knowledge of referrers, patient identification and the referral process, barriers to referrals and who gets referred to the NHS LCD programme. Our findings demonstrate the variation in the real world settings of a national diabetes programme. It highlights the challenge of COVID-19 for health care staff, whereby the increased workload of referrals occurred at a time when capacity was curtailed. We have also identified several barriers to referral and have shown that referrals had not yet been normalised into routine care at the point of data collection. We also raise issues of equity in the referral process, as not all eligible people are informed about the programme. CONCLUSIONS: Referral generation had not yet been consistently normalised into routine care, yet our findings suggest that the LCD programme runs the risk of normalising an inequitable referral process. Inequalities remain a significant challenge, and the adoption of an equitable referral process, normalised at a service delivery level, has the capacity to contribute to the improvement of health inequalities.

A protein domain-based interactome network for C. elegans early embryogenesis.

Many protein-protein interactions are mediated through independently folding modular domains. Proteome-wide efforts to model protein-protein interaction or "interactome" networks have largely ignored this modular organization of proteins. We developed an experimental strategy to efficiently identify interaction domains and generated a domain-based interactome network for proteins involved in C. elegans early-embryonic cell divisions. Minimal interacting regions were identified for over 200 proteins, providing important information on their domain organization. Furthermore, our approach increased the sensitivity of the two-hybrid system, resulting in a more complete interactome network. This interactome modeling strategy revealed insights into C. elegans centrosome function and is applicable to other biological processes in this and other organisms.

hu.MAP 2.0: integration of over 15,000 proteomic experiments builds a global compendium of human multiprotein assemblies.

A general principle of biology is the self-assembly of proteins into functional complexes. Characterizing their composition is, therefore, required for our understanding of cellular functions. Unfortunately, we lack knowledge of the comprehensive set of identities of protein complexes in human cells. To address this gap, we developed a machine learning framework to identify protein complexes in over 15,000 mass spectrometry experiments which resulted in the identification of nearly 7,000 physical assemblies. We show our resource, hu.MAP 2.0, is more accurate and comprehensive than previous state of the art high-throughput protein complex resources and gives rise to many new hypotheses, including for 274 completely uncharacterized proteins. Further, we identify 253 promiscuous proteins that participate in multiple complexes pointing to possible moonlighting roles. We have made hu.MAP 2.0 easily searchable in a web interface (http://humap2.proteincomplexes.org/), which will be a valuable resource for researchers across a broad range of interests including systems biology, structural biology, and molecular explanations of disease.

Systematic Identification of Protein Phosphorylation-Mediated Interactions.

Protein phosphorylation is a key regulatory mechanism involved in nearly every eukaryotic cellular process. Increasingly sensitive mass spectrometry approaches have identified hundreds of thousands of phosphorylation sites, but the functions of a vast majority of these sites remain unknown, with fewer than 5% of sites currently assigned a function. To increase our understanding of functional protein phosphorylation we developed an approach (phospho-DIFFRAC) for identifying the phosphorylation-dependence of protein assemblies in a systematic manner. A combination of nonspecific protein phosphatase treatment, size-exclusion chromatography, and mass spectrometry allowed us to identify changes in protein interactions after the removal of phosphate modifications. With this approach we were able to identify 316 proteins involved in phosphorylation-sensitive interactions. We recovered known phosphorylation-dependent interactors such as the FACT complex and spliceosome, as well as identified novel interactions such as the tripeptidyl peptidase TPP2 and the supraspliceosome component ZRANB2. More generally, we find phosphorylation-dependent interactors to be strongly enriched for RNA-binding proteins, providing new insight into the role of phosphorylation in RNA binding. By searching directly for phosphorylated amino acid residues in mass spectrometry data, we identified the likely regulatory phosphosites on ZRANB2 and FACT complex subunit SSRP1. This study provides both a method and resource for obtaining a better understanding of the role of phosphorylation in native macromolecular assemblies. All mass spectrometry data are available through PRIDE (accession #PXD021422).

A systematic, label-free method for identifying RNA-associated proteins in vivo provides insights into vertebrate ciliary beating machinery.

Cell-type specific RNA-associated proteins are essential for development and homeostasis in animals. Despite a massive recent effort to systematically identify RNA-associated proteins, we currently have few comprehensive rosters of cell-type specific RNA-associated proteins in vertebrate tissues. Here, we demonstrate the feasibility of determining the RNA-associated proteome of a defined vertebrate embryonic tissue using DIF-FRAC, a systematic and universal (i.e., label-free) method. Application of DIF-FRAC to cultured tissue explants of Xenopus mucociliary epithelium identified dozens of known RNA-associated proteins as expected, but also several novel RNA-associated proteins, including proteins related to assembly of the mitotic spindle and regulation of ciliary beating. In particular, we show that the inner dynein arm tether Cfap44 is an RNA-associated protein that localizes not only to axonemes, but also to liquid-like organelles in the cytoplasm called DynAPs. This result led us to discover that DynAPs are generally enriched for RNA. Together, these data provide a useful resource for a deeper understanding of mucociliary epithelia and demonstrate that DIF-FRAC will be broadly applicable for systematic identification of RNA-associated proteins from embryonic tissues.

Panorama of ancient metazoan macromolecular complexes.

Macromolecular complexes are essential to conserved biological processes, but their prevalence across animals is unclear. By combining extensive biochemical fractionation with quantitative mass spectrometry, here we directly examined the composition of soluble multiprotein complexes among diverse metazoan models. Using an integrative approach, we generated a draft conservation map consisting of more than one million putative high-confidence co-complex interactions for species with fully sequenced genomes that encompasses functional modules present broadly across all extant animals. Clustering reveals a spectrum of conservation, ranging from ancient eukaryotic assemblies that have probably served cellular housekeeping roles for at least one billion years, ancestral complexes that have accrued contemporary components, and rarer metazoan innovations linked to multicellularity. We validated these projections by independent co-fractionation experiments in evolutionarily distant species, affinity purification and functional analyses. The comprehensiveness, centrality and modularity of these reconstructed interactomes reflect their fundamental mechanistic importance and adaptive value to animal cell systems.

The mRNA-bound proteome and its global occupancy profile on protein-coding transcripts.

Protein-RNA interactions are fundamental to core biological processes, such as mRNA splicing, localization, degradation, and translation. We developed a photoreactive nucleotide-enhanced UV crosslinking and oligo(dT) purification approach to identify the mRNA-bound proteome using quantitative proteomics and to display the protein occupancy on mRNA transcripts by next-generation sequencing. Application to a human embryonic kidney cell line identified close to 800 proteins. To our knowledge, nearly one-third were not previously annotated as RNA binding, and about 15% were not predictable by computational methods to interact with RNA. Protein occupancy profiling provides a transcriptome-wide catalog of potential cis-regulatory regions on mammalian mRNAs and showed that large stretches in 3' UTRs can be contacted by the mRNA-bound proteome, with numerous putative binding sites in regions harboring disease-associated nucleotide polymorphisms. Our observations indicate the presence of a large number of mRNA binders with diverse molecular functions participating in combinatorial posttranscriptional gene-expression networks.

Professional football clubs' involvement in health promotion in Spain: an audit of current practices.

The implementation of effective community-based health interventions within Spanish football clubs has the potential to positively influence the public health agenda and enable the healthcare system in Spain to be more successful and sustainable. This paper aims to explore the involvement of Spanish football clubs in health promotion activities, their potential for future involvement and what that would require. A mixed methods explanatory sequential design, with a purposive sample of La Liga clubs. Data collection included online questionnaires and phone interviews. Quantitative methods enabled us to describe the number and types of programmes the clubs are currently involved in. Qualitative data was useful to further unpick the processes followed by the clubs in planning and developing health promotion programmes, while identifying any determinants to change. Seventeen clubs completed questionnaires and 11 participated in interviews. Clubs generally support inclusive programmes that target disadvantaged groups. Health-related programmes focus on healthy eating, physical activity and blood donation. Thematic analysis of interviews with 11 representatives of La Liga clubs resulted in three-key themes. These related to: (i) Diversity of programmes; (ii) (Lack of) evidence-based approaches to intervention design and evaluation and (iii) Contrasting views about a club's role in health promotion interventions. Spanish football clubs have potential to reach into communities that are currently underserved. However, there is limited infrastructure and understanding within the clubs to do this. Nevertheless, there is huge opportunity for organizations with public health responsibility in Spain to implement translational approaches within football-based settings.

Integration of over 9,000 mass spectrometry experiments builds a global map of human protein complexes.

Macromolecular protein complexes carry out many of the essential functions of cells, and many genetic diseases arise from disrupting the functions of such complexes. Currently, there is great interest in defining the complete set of human protein complexes, but recent published maps lack comprehensive coverage. Here, through the synthesis of over 9,000 published mass spectrometry experiments, we present hu.MAP, the most comprehensive and accurate human protein complex map to date, containing > 4,600 total complexes, > 7,700 proteins, and > 56,000 unique interactions, including thousands of confident protein interactions not identified by the original publications. hu.MAP accurately recapitulates known complexes withheld from the learning procedure, which was optimized with the aid of a new quantitative metric (k-cliques) for comparing sets of sets. The vast majority of complexes in our map are significantly enriched with literature annotations, and the map overall shows improved coverage of many disease-associated proteins, as we describe in detail for ciliopathies. Using hu.MAP, we predicted and experimentally validated candidate ciliopathy disease genes in vivo in a model vertebrate, discovering CCDC138, WDR90, and KIAA1328 to be new cilia basal body/centriolar satellite proteins, and identifying ANKRD55 as a novel member of the intraflagellar transport machinery. By offering significant improvements to the accuracy and coverage of human protein complexes, hu.MAP (http://proteincomplexes.org) serves as a valuable resource for better understanding the core cellular functions of human proteins and helping to determine mechanistic foundations of human disease.

Identifying direct contacts between protein complex subunits from their conditional dependence in proteomics datasets.

Determining the three dimensional arrangement of proteins in a complex is highly beneficial for uncovering mechanistic function and interpreting genetic variation in coding genes comprising protein complexes. There are several methods for determining co-complex interactions between proteins, among them co-fractionation / mass spectrometry (CF-MS), but it remains difficult to identify directly contacting subunits within a multi-protein complex. Correlation analysis of CF-MS profiles shows promise in detecting protein complexes as a whole but is limited in its ability to infer direct physical contacts among proteins in sub-complexes. To identify direct protein-protein contacts within human protein complexes we learn a sparse conditional dependency graph from approximately 3,000 CF-MS experiments on human cell lines. We show substantial performance gains in estimating direct interactions compared to correlation analysis on a benchmark of large protein complexes with solved three-dimensional structures. We demonstrate the method's value in determining the three dimensional arrangement of proteins by making predictions for complexes without known structure (the exocyst and tRNA multi-synthetase complex) and by establishing evidence for the structural position of a recently discovered component of the core human EKC/KEOPS complex, GON7/C14ORF142, providing a more complete 3D model of the complex. Direct contact prediction provides easily calculable additional structural information for large-scale protein complex mapping studies and should be broadly applicable across organisms as more CF-MS datasets become available.

Rational design of topographical helix mimics as potent inhibitors of protein-protein interactions.

Protein-protein interactions encompass large surface areas, but often a handful of key residues dominate the binding energy landscape. Rationally designed small molecule scaffolds that reproduce the relative positioning and disposition of important binding residues, termed "hotspot residues", have been shown to successfully inhibit specific protein complexes. Although this strategy has led to development of novel synthetic inhibitors of protein complexes, often direct mimicry of natural amino acid residues does not lead to potent inhibitors. Experimental screening of focused compound libraries is used to further optimize inhibitors but the number of possible designs that can be efficiently synthesized and experimentally tested in academic settings is limited. We have applied the principles of computational protein design to optimization of nonpeptidic helix mimics as ligands for protein complexes. We describe the development of computational tools to design helix mimetics from canonical and noncanonical residue libraries and their application to two therapeutically important protein-protein interactions: p53-MDM2 and p300-HIF1α. The overall study provides a streamlined approach for discovering potent peptidomimetic inhibitors of protein-protein interactions.

The Proteome Folding Project: proteome-scale prediction of structure and function.

The incompleteness of proteome structure and function annotation is a critical problem for biologists and, in particular, severely limits interpretation of high-throughput and next-generation experiments. We have developed a proteome annotation pipeline based on structure prediction, where function and structure annotations are generated using an integration of sequence comparison, fold recognition, and grid-computing-enabled de novo structure prediction. We predict protein domain boundaries and three-dimensional (3D) structures for protein domains from 94 genomes (including human, Arabidopsis, rice, mouse, fly, yeast, Escherichia coli, and worm). De novo structure predictions were distributed on a grid of more than 1.5 million CPUs worldwide (World Community Grid). We generated significant numbers of new confident fold annotations (9% of domains that are otherwise unannotated in these genomes). We demonstrate that predicted structures can be combined with annotations from the Gene Ontology database to predict new and more specific molecular functions.

Parametric Bayesian priors and better choice of negative examples improve protein function prediction.

MOTIVATION: Computational biologists have demonstrated the utility of using machine learning methods to predict protein function from an integration of multiple genome-wide data types. Yet, even the best performing function prediction algorithms rely on heuristics for important components of the algorithm, such as choosing negative examples (proteins without a given function) or determining key parameters. The improper choice of negative examples, in particular, can hamper the accuracy of protein function prediction. RESULTS: We present a novel approach for choosing negative examples, using a parameterizable Bayesian prior computed from all observed annotation data, which also generates priors used during function prediction. We incorporate this new method into the GeneMANIA function prediction algorithm and demonstrate improved accuracy of our algorithm over current top-performing function prediction methods on the yeast and mouse proteomes across all metrics tested. AVAILABILITY: Code and Data are available at: http://bonneaulab.bio.nyu.edu/funcprop.html

Conformational preferences of peptide-peptoid hybrid oligomers.

Peptomers are oligomeric molecules composed of both α-amino acids and N-substituted glycine monomers, thus creating a hybrid of peptide and peptoid units. Peptomers have been used in several applications such as antimicrobials, protease inhibitors, and antibody mimics. Despite the considerable promise of peptomers as chemically diverse molecular scaffolds, we know little about their conformational tendencies. This lack of knowledge limits the ability to implement computational approaches for peptomer design. Here we computationally evaluate the local structural propensities of the peptide-peptoid linkage. We find some general similarities between the peptide residue conformational preferences and the Ramachandran distribution of residues that precede proline in folded protein structures. However, there are notable differences. For example, several ß-turn motifs are disallowed when the i+2 residue is also a peptoid monomer. Significantly, the lowest energy geometry, when dispersion forces are accounted for, corresponds to a "cis-Pro touch-turn" conformation, an unusual turn motif that has been observed at protein catalytic centers and binding sites. The peptomer touch-turn thus represents a useful design element for the construction of folded oligomers capable of molecular recognition and as modules in the assembly of structurally complex peptoid-protein hybrid macromolecules.

A Qualitative evaluation in community settings in England exploring the experiences of coaches delivering the NHS Low Calorie Diet programme pilot to ethnically diverse participants.

BACKGROUND: The management of type 2 diabetes (T2D) within diverse ethnic populations requires a culturally tailored approach. However, little is known about the experiences of coaches delivering interventions for T2D, such as the National Health Service (NHS) Low Calorie Diet (LCD) programme, to people from diverse ethnic backgrounds. OBJECTIVE: To explore the experiences of coaches delivering an NHS programme using total diet replacement approaches to individuals from diverse ethnic backgrounds, to inform the effective tailoring and equitable delivery of future interventions. DESIGN: Qualitative study. SETTING: Individuals delivering the NHS LCD programme. PARTICIPANTS: One-to-one semistructured interviews were conducted with seven health coaches delivering the NHS LCD programme. Inclusion criteria included participants delivering the NHS LCD programme either from a minoritised ethnic background or delivering the programme to those from ethnic minority and white British backgrounds. MAIN OUTCOME MEASURES: Qualitative semistructured interviews conducted through different stages of the programme. Reflexive thematic analysis was used to analyse the transcripts. RESULTS: Key themes highlighted the following experiences of delivering the LCD programme: (1) training and support needs; (2) needing to understand culture and ethnicity; (3) the impact of language; (4) the use of resources in providing dietary advice and (5) experiences of cultural tailoring. The themes highlight the need to prioritise person-centred care, to integrate culturally tailored approaches and for provision of education and training to those delivering health programmes. CONCLUSION: These findings describe the experiences of health coaches in tailoring delivery and emphasise the role of cultural competence in ensuring equitable and effective healthcare interventions for diverse populations. This learning can inform future programmes and policies aimed at promoting inclusive healthcare practices.