Widespread Expansion of Protein Interaction Capabilities by Alternative Splicing.

While alternative splicing is known to diversify the functional characteristics of some genes, the extent to which protein isoforms globally contribute to functional complexity on a proteomic scale remains unknown. To address this systematically, we cloned full-length open reading frames of alternatively spliced transcripts for a large number of human genes and used protein-protein interaction profiling to functionally compare hundreds of protein isoform pairs. The majority of isoform pairs share less than 50% of their interactions. In the global context of interactome network maps, alternative isoforms tend to behave like distinct proteins rather than minor variants of each other. Interaction partners specific to alternative isoforms tend to be expressed in a highly tissue-specific manner and belong to distinct functional modules. Our strategy, applicable to other functional characteristics, reveals a widespread expansion of protein interaction capabilities through alternative splicing and suggests that many alternative "isoforms" are functionally divergent (i.e., "functional alloforms").

A reference map of the human binary protein interactome.

Global insights into cellular organization and genome function require comprehensive understanding of the interactome networks that mediate genotype-phenotype relationships1,2. Here we present a human 'all-by-all' reference interactome map of human binary protein interactions, or 'HuRI'. With approximately 53,000 protein-protein interactions, HuRI has approximately four times as many such interactions as there are high-quality curated interactions from small-scale studies. The integration of HuRI with genome3, transcriptome4 and proteome5 data enables cellular function to be studied within most physiological or pathological cellular contexts. We demonstrate the utility of HuRI in identifying the specific subcellular roles of protein-protein interactions. Inferred tissue-specific networks reveal general principles for the formation of cellular context-specific functions and elucidate potential molecular mechanisms that might underlie tissue-specific phenotypes of Mendelian diseases. HuRI is a systematic proteome-wide reference that links genomic variation to phenotypic outcomes.

Advancing Targeted Protein Degradation via Multiomics Profiling and Artificial Intelligence.

Only around 20% of the human proteome is considered to be druggable with small-molecule antagonists. This leaves some of the most compelling therapeutic targets outside the reach of ligand discovery. The concept of targeted protein degradation (TPD) promises to overcome some of these limitations. In brief, TPD is dependent on small molecules that induce the proximity between a protein of interest (POI) and an E3 ubiquitin ligase, causing ubiquitination and degradation of the POI. In this perspective, we want to reflect on current challenges in the field, and discuss how advances in multiomics profiling, artificial intelligence, and machine learning (AI/ML) will be vital in overcoming them. The presented roadmap is discussed in the context of small-molecule degraders but is equally applicable for other emerging proximity-inducing modalities.

Bioactivity Profile Similarities to Expand the Repertoire of COVID-19 Drugs.

Until a vaccine becomes available, the current repertoire of drugs is our only therapeutic asset to fight the SARS-CoV-2 outbreak. Indeed, emergency clinical trials have been launched to assess the effectiveness of many marketed drugs, tackling the decrease of viral load through several mechanisms. Here, we present an online resource, based on small-molecule bioactivity signatures and natural language processing, to expand the portfolio of compounds with potential to treat COVID-19. By comparing the set of drugs reported to be potentially active against SARS-CoV-2 to a universe of 1 million bioactive molecules, we identify compounds that display analogous chemical and functional features to the current COVID-19 candidates. Searches can be filtered by level of evidence and mechanism of action, and results can be restricted to drug molecules or include the much broader space of bioactive compounds. Moreover, we allow users to contribute COVID-19 drug candidates, which are automatically incorporated to the pipeline once per day. The computational platform, as well as the source code, is available at https://sbnb.irbbarcelona.org/covid19.

A PanorOmic view of personal cancer genomes.

The massive molecular profiling of thousands of cancer patients has led to the identification of many tumor type specific driver genes. However, only a few (or none) of them are present in each individual tumor and, to enable precision oncology, we need to interpret the alterations found in a single patient. Cancer PanorOmics (http://panoromics.irbbarcelona.org) is a web-based resource to contextualize genomic variations detected in a personal cancer genome within the body of clinical and scientific evidence available for 26 tumor types, offering complementary cohort- and patient-centric views. Additionally, it explores the cellular environment of mutations by mapping them on the human interactome and providing quasi-atomic structural details, whenever available. This 'PanorOmic' molecular view of individual tumors, together with the appropriate genetic counselling and medical advice, should contribute to the identification of actionable alterations ultimately guiding the clinical decision-making process.

Detecting similar binding pockets to enable systems polypharmacology.

In the era of systems biology, multi-target pharmacological strategies hold promise for tackling disease-related networks. In this regard, drug promiscuity may be leveraged to interfere with multiple receptors: the so-called polypharmacology of drugs can be anticipated by analyzing the similarity of binding sites across the proteome. Here, we perform a pairwise comparison of 90,000 putative binding pockets detected in 3,700 proteins, and find that 23,000 pairs of proteins have at least one similar cavity that could, in principle, accommodate similar ligands. By inspecting these pairs, we demonstrate how the detection of similar binding sites expands the space of opportunities for the rational design of drug polypharmacology. Finally, we illustrate how to leverage these opportunities in protein-protein interaction networks related to several therapeutic classes and tumor types, and in a genome-scale metabolic model of leukemia.

Systems-Wide Prediction of Enzyme Promiscuity Reveals a New Underground Alternative Route for Pyridoxal 5'-Phosphate Production in E. coli.

Recent insights suggest that non-specific and/or promiscuous enzymes are common and active across life. Understanding the role of such enzymes is an important open question in biology. Here we develop a genome-wide method, PROPER, that uses a permissive PSI-BLAST approach to predict promiscuous activities of metabolic genes. Enzyme promiscuity is typically studied experimentally using multicopy suppression, in which over-expression of a promiscuous 'replacer' gene rescues lethality caused by inactivation of a 'target' gene. We use PROPER to predict multicopy suppression in Escherichia coli, achieving highly significant overlap with published cases (hypergeometric p = 4.4e-13). We then validate three novel predicted target-replacer gene pairs in new multicopy suppression experiments. We next go beyond PROPER and develop a network-based approach, GEM-PROPER, that integrates PROPER with genome-scale metabolic modeling to predict promiscuous replacements via alternative metabolic pathways. GEM-PROPER predicts a new indirect replacer (thiG) for an essential enzyme (pdxB) in production of pyridoxal 5'-phosphate (the active form of Vitamin B6), which we validate experimentally via multicopy suppression. We perform a structural analysis of thiG to determine its potential promiscuous active site, which we validate experimentally by inactivating the pertaining residues and showing a loss of replacer activity. Thus, this study is a successful example where a computational investigation leads to a network-based identification of an indirect promiscuous replacement of a key metabolic enzyme, which would have been extremely difficult to identify directly.

IntSide: a web server for the chemical and biological examination of drug side effects.

SUMMARY: Drug side effects are one of the main health threats worldwide, and an important obstacle in drug development. Understanding how adverse reactions occur requires knowledge on drug mechanisms at the molecular level. Despite recent advances, the need for tools and methods that facilitate side effect anticipation still remains. Here, we present IntSide, a web server that integrates chemical and biological information to elucidate the molecular mechanisms underlying drug side effects. IntSide currently catalogs 1175 side effects caused by 996 drugs, associated with drug features divided into eight categories, belonging to either biology or chemistry. On the biological side, IntSide reports drug targets and off-targets, pathways, molecular functions and biological processes. From a chemical viewpoint, it includes molecular fingerprints, scaffolds and chemical entities. Finally, we also integrate additional biological data, such as protein interactions and disease-related genes, to facilitate mechanistic interpretations. AVAILABILITY AND IMPLEMENTATION: Our data and web resource are available online (http://intside.irbbarcelona.org/). CONTACT: patrick.aloy@irbbarcelona.org SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Drug sensitivity in cancer cell lines is not tissue-specific.

BACKGROUND: Cancer cell lines have a prominent role in the initial stages of drug discovery, facilitating high-throughput screening of potential drugs. However, their clinical relevance remains controversial. FINDINGS: We assess whether drug sensitivity in cancer cell lines is able to discriminate tissue specificity. We find that cancer-specific drugs do not show higher efficacies in cell lines representing the respective tissues. Even when considering distinct cancer subtypes and targeted therapies, most drugs are evenly effective/ineffective throughout all cell lines. CONCLUSIONS: To get the most out of cell line panels, it will be necessary to look into their molecular characteristics, and integrate them into systems biology frameworks.

Charting the molecular links between driver and susceptibility genes in colorectal cancer.

Despite significant advances in the identification of specific genes and pathways important in the onset and progression of colorectal cancer (CRC), mechanistic insight into the relationship between driver and susceptibility genes is needed. In this paper, we systematically explore physical interactions between causative and putative CRC susceptibility genes to reveal the molecular mechanisms involved in tumor biology. In total, we identify 622 high-confidence protein-protein interactions between 42 CRC causative and 65 candidate susceptibility genes. Among the latter, 28 are located in the CRCS9 loci, related to the etiology of CRC, and 17 are co-expressed with well-established CRC drivers, which makes them excellent candidates for further functional studies. Moreover, we find a high degree of functional coherence between connected driver and susceptibility genes, which indicates that our network-based strategy is useful to gain insight into the underlying mechanisms of those proteins with unknown roles in CRC.

Large-scale chemoproteomics expedites ligand discovery and predicts ligand behavior in cells.

Chemical modulation of proteins enables a mechanistic understanding of biology and represents the foundation of most therapeutics. However, despite decades of research, 80% of the human proteome lacks functional ligands. Chemical proteomics has advanced fragment-based ligand discovery toward cellular systems, but throughput limitations have stymied the scalable identification of fragment-protein interactions. We report proteome-wide maps of protein-binding propensity for 407 structurally diverse small-molecule fragments. We verified that identified interactions can be advanced to active chemical probes of E3 ubiquitin ligases, transporters, and kinases. Integrating machine learning binary classifiers further enabled interpretable predictions of fragment behavior in cells. The resulting resource of fragment-protein interactions and predictive models will help to elucidate principles of molecular recognition and expedite ligand discovery efforts for hitherto undrugged proteins.

Modifications of microvascular EC surface modulate phototoxicity of a porphycene anti-ICAM-1 immunoconjugate; therapeutic implications.

Inflammation and shear stress can upregulate expression of cellular adhesion molecules in endothelial cells (EC). The modified EC surface becomes a mediating interface between the circulating blood elements and the endothelium, and grants opportunity for immunotherapy. In photodynamic therapy (PDT), immunotargeting might overcome the lack of selectivity of currently used sensitizers. In this study, we hypothesized that differential ICAM-1 expression modulates the effects of a drug targeted to surface ICAM-1. A novel porphycene-anti-ICAM-1 conjugate was synthesized and applied to treat endothelial cells from macro and microvasculature. Results show that the conjugate induces phototoxicity in inflamed, but not in healthy, microvascular EC. Conversely, macrovascular EC exhibited phototoxicity regardless of their state. These findings have two major implications; the relevance of ICAM-1 as a modulator of drug effects in microvasculature, and the potential of the porphycene bioconjugate as a promising novel PDT agent.

First fully-automated AI/ML virtual screening cascade implemented at a drug discovery centre in Africa.

Streamlined data-driven drug discovery remains challenging, especially in resource-limited settings. We present ZairaChem, an artificial intelligence (AI)- and machine learning (ML)-based tool for quantitative structure-activity/property relationship (QSAR/QSPR) modelling. ZairaChem is fully automated, requires low computational resources and works across a broad spectrum of datasets. We describe an end-to-end implementation at the H3D Centre, the leading integrated drug discovery unit in Africa, at which no prior AI/ML capabilities were available. By leveraging in-house data collected over a decade, we have developed a virtual screening cascade for malaria and tuberculosis drug discovery comprising 15 models for key decision-making assays ranging from whole-cell phenotypic screening and cytotoxicity to aqueous solubility, permeability, microsomal metabolic stability, cytochrome inhibition, and cardiotoxicity. We show how computational profiling of compounds, prior to synthesis and testing, can inform progression of frontrunner compounds at H3D. This project is a first-of-its-kind deployment at scale of AI/ML tools in a research centre operating in a low-resource setting.

Machine Learning Approaches Identify Chemical Features for Stage-Specific Antimalarial Compounds.

Efficacy data from diverse chemical libraries, screened against the various stages of the malaria parasite Plasmodium falciparum, including asexual blood stage (ABS) parasites and transmissible gametocytes, serve as a valuable reservoir of information on the chemical space of compounds that are either active (or not) against the parasite. We postulated that this data can be mined to define chemical features associated with the sole ABS activity and/or those that provide additional life cycle activity profiles like gametocytocidal activity. Additionally, this information could provide chemical features associated with inactive compounds, which could eliminate any future unnecessary screening of similar chemical analogs. Therefore, we aimed to use machine learning to identify the chemical space associated with stage-specific antimalarial activity. We collected data from various chemical libraries that were screened against the asexual (126 374 compounds) and sexual (gametocyte) stages of the parasite (93 941 compounds), calculated the compounds' molecular fingerprints, and trained machine learning models to recognize stage-specific active and inactive compounds. We were able to build several models that predict compound activity against ABS and dual activity against ABS and gametocytes, with Support Vector Machines (SVM) showing superior abilities with high recall (90 and 66%) and low false-positive predictions (15 and 1%). This allowed the identification of chemical features enriched in active and inactive populations, an important outcome that could be mined for essential chemical features to streamline hit-to-lead optimization strategies of antimalarial candidates. The predictive capabilities of the models held true in diverse chemical spaces, indicating that the ML models are therefore robust and can serve as a prioritization tool to drive and guide phenotypic screening and medicinal chemistry programs.

Endothelial transcriptomic analysis identifies biomarkers of severe and cerebral malaria.

Malaria can quickly progress from an uncomplicated infection into a life-threatening severe disease. However, the unspecificity of early symptoms often makes it difficult to identify patients at high risk of developing severe disease. Additionally, one of the most feared malaria complications - cerebral malaria - is challenging to diagnose, often resulting in treatment delays that can lead to adverse outcomes. To identify candidate biomarkers for the prognosis and/or diagnosis of severe and cerebral malaria, we have analyzed the transcriptomic response of human brain microvascular endothelial cells to erythrocytes infected with Plasmodium falciparum. Candidates were validated in plasma samples from a cohort of pediatric patients with malaria from Mozambique, resulting in the identification of several markers with capacity to distinguish uncomplicated from severe malaria, the most potent being the metallopeptidase ADAMTS18. Two other biomarkers, Angiopoietin-like-4 and Inhibin-ßE were able to differentiate children with cerebral malaria within the severe malaria group, showing increased sensitivity after combination in a biomarker signature. The validation of the predicted candidate biomarkers in plasma of children with severe and cerebral malaria underscores the power of this transcriptomic approach and indicates that a specific endothelial response to P. falciparum-infected erythrocytes is linked to the pathophysiology of severe malaria.

Xanthine oxidase levels and immune dysregulation are independently associated with anemia in Plasmodium falciparum malaria.

Severe anemia is an important contributor to mortality in children with severe malaria. Anemia in malaria is a multi-factorial complication, since dyserythropoiesis, hemolysis and phagocytic clearance of uninfected red blood cells (RBCs) can contribute to this syndrome. High levels of oxidative stress and immune dysregulation have been proposed to contribute to severe malarial anemia, facilitating the clearance of uninfected RBCs. In a cohort of 552 Ugandan children with severe malaria, we measured the levels of xanthine oxidase (XO), an oxidative enzyme that is elevated in the plasma of malaria patients. The levels of XO in children with severe anemia were significantly higher compared to children with severe malaria not suffering from severe anemia. Levels of XO were inversely associated with RBC hemoglobin (ρ = - 0.25, p < 0.0001), indicating a relation between this enzyme and severe anemia. When compared with the levels of immune complexes and of autoimmune antibodies to phosphatidylserine, factors previously associated with severe anemia in malaria patients, we observed that XO is not associated with them, suggesting that XO is associated with severe anemia through an independent mechanism. XO was associated with prostration, acidosis, jaundice, respiratory distress, and kidney injury, which may reflect a broader relation of this enzyme with severe malaria pathology. Since inhibitors of XO are inexpensive and well-tolerated drugs already approved for use in humans, the validation of XO as a contributor to severe malarial anemia and other malaria complications may open new possibilities for much needed adjunctive therapy in malaria.

Correction: Evaluation of kidney function among people living with HIV initiating antiretroviral therapy in Zambia.

[This corrects the article DOI: 10.1371/journal.pgph.0000124.].

Causes of Death Among Infants and Children in the Child Health and Mortality Prevention Surveillance (CHAMPS) Network.

Importance: The number of deaths of children younger than 5 years has been steadily decreasing worldwide, from more than 17 million annual deaths in the 1970s to an estimated 5.3 million in 2019 (with 2.8 million deaths occurring in those aged 1-59 months [53% of all deaths in children aged <5 years]). More detailed characterization of childhood deaths could inform interventions to improve child survival. Objective: To describe causes of postneonatal child deaths across 7 mortality surveillance sentinel sites in Africa and Asia. Design, Setting, and Participants: The Child Health and Mortality Prevention Surveillance (CHAMPS) Network conducts childhood mortality surveillance in sub-Saharan Africa and South Asia using innovative postmortem minimally invasive tissue sampling (MITS). In this cross-sectional study, MITS was conducted in deceased children aged 1 to 59 months at 7 sites in sub-Saharan Africa and South Asia from December 3, 2016, to December 3, 2020. Data analysis was conducted between October and November 2021. Main Outcomes and Measures: The expert panel attributed underlying, intermediate, and immediate conditions in the chain of events leading to death, based on histopathologic analysis, microbiological diagnostics, clinical data, and verbal autopsies. Results: In this study, MITS was performed in 632 deceased children (mean [SD] age at death, 1.3 [0.3] years; 342 [54.1%] male). The 6 most common underlying causes of death were malnutrition (104 [16.5%]), HIV (75 [11.9%]), malaria (71 [11.2%]), congenital birth defects (64 [10.1%]), lower respiratory tract infections (LRTIs; 53 [8.4%]), and diarrheal diseases (46 [7.2%]). When considering immediate causes only, sepsis (191 [36.7%]) and LRTI (129 [24.8%]) were the 2 dominant causes. An infection was present in the causal chain in 549 of 632 deaths (86.9%); pathogens most frequently contributing to infectious deaths included Klebsiella pneumoniae (155 of 549 infectious deaths [28.2%]; 127 [81.9%] considered nosocomial), Plasmodium falciparum (122 of 549 [22.2%]), and Streptococcus pneumoniae (109 of 549 [19.9%]). Other organisms, such as cytomegalovirus (57 [10.4%]) and Acinetobacter baumannii (39 [7.1%]; 35 of 39 [89.7%] considered nosocomial), also played important roles. For the top underlying causes of death, the median number of conditions in the chain of events leading to death was 3 for malnutrition, 3 for HIV, 1 for malaria, 3 for congenital birth defects, and 1 for LRTI. Expert panels considered 494 of 632 deaths (78.2%) preventable and 26 of 632 deaths (4.1%) preventable under certain conditions. Conclusions and Relevance: In this cross-sectional study investigating causes of child mortality in the CHAMPS Network, results indicate that, in these high-mortality settings, infectious diseases continue to cause most deaths in infants and children, often in conjunction with malnutrition. These results also highlight opportunities for action to prevent deaths and reveal common interaction of various causes in the path toward death.

Connecting chemistry and biology through molecular descriptors.

Through the representation of small molecule structures as numerical descriptors and the exploitation of the similarity principle, chemoinformatics has made paramount contributions to drug discovery, from unveiling mechanisms of action and repurposing approved drugs to de novo crafting of molecules with desired properties and tailored targets. Yet, the inherent complexity of biological systems has fostered the implementation of large-scale experimental screenings seeking a deeper understanding of the targeted proteins, the disrupted biological processes and the systemic responses of cells to chemical perturbations. After this wealth of data, a new generation of data-driven descriptors has arisen providing a rich portrait of small molecule characteristics that goes beyond chemical properties. Here, we give an overview of biologically relevant descriptors, covering chemical compounds, proteins and other biological entities, such as diseases and cell lines, while aligning them to the major contributions in the field from disciplines, such as natural language processing or computer vision. We now envision a new scenario for chemical and biological entities where they both are translated into a common numerical format. In this computational framework, complex connections between entities can be unveiled by means of simple arithmetic operations, such as distance measures, additions, and subtractions.