RESUMO
Throughout the body, T cells monitor MHC-bound ligands expressed on the surface of essentially all cell types. MHC ligands that trigger a T cell immune response are referred to as T cell epitopes. Identifying such epitopes enables tracking, phenotyping, and stimulating T cells involved in immune responses in infectious disease, allergy, autoimmunity, transplantation, and cancer. The specific T cell epitopes recognized in an individual are determined by genetic factors such as the MHC molecules the individual expresses, in parallel to the individual's environmental exposure history. The complexity and importance of T cell epitope mapping have motivated the development of computational approaches that predict what T cell epitopes are likely to be recognized in a given individual or in a broader population. Such predictions guide experimental epitope mapping studies and enable computational analysis of the immunogenic potential of a given protein sequence region.
Assuntos
Epitopos de Linfócito T/imunologia , Linfócitos T/imunologia , Linfócitos T/metabolismo , Animais , Biomarcadores , Biologia Computacional/métodos , Suscetibilidade a Doenças , Antígenos de Histocompatibilidade/imunologia , Humanos , Ligantes , Aprendizado de Máquina , Ligação ProteicaRESUMO
Molecular docking has become an essential part of a structural biologist's and medicinal chemist's toolkits. Given a chemical compound and the three-dimensional structure of a molecular target-for example, a protein-docking methods fit the compound into the target, predicting the compound's bound structure and binding energy. Docking can be used to discover novel ligands for a target by screening large virtual compound libraries. Docking can also provide a useful starting point for structure-based ligand optimization or for investigating a ligand's mechanism of action. Advances in computational methods, including both physics-based and machine learning approaches, as well as in complementary experimental techniques, are making docking an even more powerful tool. We review how docking works and how it can drive drug discovery and biological research. We also describe its current limitations and ongoing efforts to overcome them.
Assuntos
Descoberta de Drogas , Simulação de Acoplamento Molecular , Ligação Proteica , Proteínas , Ligantes , Descoberta de Drogas/métodos , Humanos , Proteínas/química , Proteínas/metabolismo , Aprendizado de Máquina , Sítios de Ligação , Desenho de FármacosRESUMO
Cellular form and function emerge from complex mechanochemical systems within the cytoplasm. Currently, no systematic strategy exists to infer large-scale physical properties of a cell from its molecular components. This is an obstacle to understanding processes such as cell adhesion and migration. Here, we develop a data-driven modeling pipeline to learn the mechanical behavior of adherent cells. We first train neural networks to predict cellular forces from images of cytoskeletal proteins. Strikingly, experimental images of a single focal adhesion (FA) protein, such as zyxin, are sufficient to predict forces and can generalize to unseen biological regimes. Using this observation, we develop two approaches-one constrained by physics and the other agnostic-to construct data-driven continuum models of cellular forces. Both reveal how cellular forces are encoded by two distinct length scales. Beyond adherent cell mechanics, our work serves as a case study for integrating neural networks into predictive models for cell biology.
Assuntos
Proteínas do Citoesqueleto , Aprendizado de Máquina , Adesão Celular , Citoplasma/metabolismo , Proteínas do Citoesqueleto/metabolismo , Adesões Focais/metabolismo , Modelos BiológicosRESUMO
Novel antibiotics are urgently needed to combat the antibiotic-resistance crisis. We present a machine-learning-based approach to predict antimicrobial peptides (AMPs) within the global microbiome and leverage a vast dataset of 63,410 metagenomes and 87,920 prokaryotic genomes from environmental and host-associated habitats to create the AMPSphere, a comprehensive catalog comprising 863,498 non-redundant peptides, few of which match existing databases. AMPSphere provides insights into the evolutionary origins of peptides, including by duplication or gene truncation of longer sequences, and we observed that AMP production varies by habitat. To validate our predictions, we synthesized and tested 100 AMPs against clinically relevant drug-resistant pathogens and human gut commensals both in vitro and in vivo. A total of 79 peptides were active, with 63 targeting pathogens. These active AMPs exhibited antibacterial activity by disrupting bacterial membranes. In conclusion, our approach identified nearly one million prokaryotic AMP sequences, an open-access resource for antibiotic discovery.
Assuntos
Peptídeos Antimicrobianos , Aprendizado de Máquina , Microbiota , Peptídeos Antimicrobianos/farmacologia , Peptídeos Antimicrobianos/química , Peptídeos Antimicrobianos/genética , Humanos , Animais , Antibacterianos/farmacologia , Camundongos , Metagenoma , Bactérias/efeitos dos fármacos , Bactérias/genética , Microbioma Gastrointestinal/efeitos dos fármacosRESUMO
Machine learning (ML) is increasingly used in clinical oncology to diagnose cancers, predict patient outcomes, and inform treatment planning. Here, we review recent applications of ML across the clinical oncology workflow. We review how these techniques are applied to medical imaging and to molecular data obtained from liquid and solid tumor biopsies for cancer diagnosis, prognosis, and treatment design. We discuss key considerations in developing ML for the distinct challenges posed by imaging and molecular data. Finally, we examine ML models approved for cancer-related patient usage by regulatory agencies and discuss approaches to improve the clinical usefulness of ML.
Assuntos
Aprendizado de Máquina , Neoplasias , Humanos , Neoplasias/diagnóstico , Neoplasias/genética , Neoplasias/terapia , Diagnóstico por Imagem , OncologiaRESUMO
Antibody therapeutics are a large and rapidly expanding drug class providing major health benefits. We provide a snapshot of current antibody therapeutics including their formats, common targets, therapeutic areas, and routes of administration. Our focus is on selected emerging directions in antibody design where progress may provide a broad benefit. These topics include enhancing antibodies for cancer, antibody delivery to organs such as the brain, gastrointestinal tract, and lungs, plus antibody developability challenges including immunogenicity risk assessment and mitigation and subcutaneous delivery. Machine learning has the potential, albeit as yet largely unrealized, for a transformative future impact on antibody discovery and engineering.
Assuntos
Anticorpos , Neoplasias , Anticorpos/química , Anticorpos/uso terapêutico , Sistemas de Liberação de Medicamentos , Humanos , Aprendizado de Máquina , Neoplasias/tratamento farmacológico , Engenharia de ProteínasRESUMO
Treatment of severe COVID-19 is currently limited by clinical heterogeneity and incomplete description of specific immune biomarkers. We present here a comprehensive multi-omic blood atlas for patients with varying COVID-19 severity in an integrated comparison with influenza and sepsis patients versus healthy volunteers. We identify immune signatures and correlates of host response. Hallmarks of disease severity involved cells, their inflammatory mediators and networks, including progenitor cells and specific myeloid and lymphocyte subsets, features of the immune repertoire, acute phase response, metabolism, and coagulation. Persisting immune activation involving AP-1/p38MAPK was a specific feature of COVID-19. The plasma proteome enabled sub-phenotyping into patient clusters, predictive of severity and outcome. Systems-based integrative analyses including tensor and matrix decomposition of all modalities revealed feature groupings linked with severity and specificity compared to influenza and sepsis. Our approach and blood atlas will support future drug development, clinical trial design, and personalized medicine approaches for COVID-19.
Assuntos
Biomarcadores/sangue , COVID-19/patologia , Proteoma/análise , Adulto , Proteínas Sanguíneas/metabolismo , COVID-19/sangue , COVID-19/virologia , Proteínas de Ciclo Celular/genética , Proteínas de Ciclo Celular/metabolismo , Feminino , Humanos , Influenza Humana/sangue , Influenza Humana/patologia , Linfócitos/imunologia , Linfócitos/metabolismo , Aprendizado de Máquina , Masculino , Pessoa de Meia-Idade , Proteína Quinase 14 Ativada por Mitógeno/genética , Proteína Quinase 14 Ativada por Mitógeno/metabolismo , Monócitos/imunologia , Monócitos/metabolismo , Análise de Componente Principal , SARS-CoV-2/isolamento & purificação , Sepse/sangue , Sepse/patologia , Índice de Gravidade de Doença , Fator de Transcrição AP-1/genética , Fator de Transcrição AP-1/metabolismoRESUMO
This Perspective explores the application of machine learning toward improved diagnosis and treatment. We outline a vision for how machine learning can transform three broad areas of biomedicine: clinical diagnostics, precision treatments, and health monitoring, where the goal is to maintain health through a range of diseases and the normal aging process. For each area, early instances of successful machine learning applications are discussed, as well as opportunities and challenges for machine learning. When these challenges are met, machine learning promises a future of rigorous, outcomes-based medicine with detection, diagnosis, and treatment strategies that are continuously adapted to individual and environmental differences.
Assuntos
Aprendizado de Máquina , Medicina de Precisão , HumanosRESUMO
Although base editors are widely used to install targeted point mutations, the factors that determine base editing outcomes are not well understood. We characterized sequence-activity relationships of 11 cytosine and adenine base editors (CBEs and ABEs) on 38,538 genomically integrated targets in mammalian cells and used the resulting outcomes to train BE-Hive, a machine learning model that accurately predicts base editing genotypic outcomes (R ≈ 0.9) and efficiency (R ≈ 0.7). We corrected 3,388 disease-associated SNVs with ≥90% precision, including 675 alleles with bystander nucleotides that BE-Hive correctly predicted would not be edited. We discovered determinants of previously unpredictable C-to-G, or C-to-A editing and used these discoveries to correct coding sequences of 174 pathogenic transversion SNVs with ≥90% precision. Finally, we used insights from BE-Hive to engineer novel CBE variants that modulate editing outcomes. These discoveries illuminate base editing, enable editing at previously intractable targets, and provide new base editors with improved editing capabilities.
Assuntos
Edição de Genes/métodos , Aprendizado de Máquina , Animais , Biblioteca Gênica , Humanos , Camundongos , Células-Tronco Embrionárias Murinas/citologia , Células-Tronco Embrionárias Murinas/metabolismo , Mutação Puntual , RNA Guia de Cinetoplastídeos/metabolismoRESUMO
Serotonin plays a central role in cognition and is the target of most pharmaceuticals for psychiatric disorders. Existing drugs have limited efficacy; creation of improved versions will require better understanding of serotonergic circuitry, which has been hampered by our inability to monitor serotonin release and transport with high spatial and temporal resolution. We developed and applied a binding-pocket redesign strategy, guided by machine learning, to create a high-performance, soluble, fluorescent serotonin sensor (iSeroSnFR), enabling optical detection of millisecond-scale serotonin transients. We demonstrate that iSeroSnFR can be used to detect serotonin release in freely behaving mice during fear conditioning, social interaction, and sleep/wake transitions. We also developed a robust assay of serotonin transporter function and modulation by drugs. We expect that both machine-learning-guided binding-pocket redesign and iSeroSnFR will have broad utility for the development of other sensors and in vitro and in vivo serotonin detection, respectively.
Assuntos
Evolução Molecular Direcionada , Aprendizado de Máquina , Serotonina/metabolismo , Algoritmos , Sequência de Aminoácidos , Tonsila do Cerebelo/fisiologia , Animais , Comportamento Animal , Sítios de Ligação , Encéfalo/metabolismo , Células HEK293 , Humanos , Cinética , Modelos Lineares , Camundongos , Camundongos Endogâmicos C57BL , Fótons , Ligação Proteica , Proteínas da Membrana Plasmática de Transporte de Serotonina/metabolismo , Sono/fisiologia , Vigília/fisiologiaRESUMO
Exploration of novel environments ensures survival and evolutionary fitness. It is expressed through exploratory bouts and arrests that change dynamically based on experience. Neural circuits mediating exploratory behavior should therefore integrate experience and use it to select the proper behavioral output. Using a spatial exploration assay, we uncovered an experience-dependent increase in momentary arrests in locations where animals arrested previously. Calcium imaging in freely exploring mice revealed a genetically and projection-defined neuronal ensemble in the basolateral amygdala that is active during self-paced behavioral arrests. This ensemble was recruited in an experience-dependent manner, and closed-loop optogenetic manipulation of these neurons revealed that they are sufficient and necessary to drive experience-dependent arrests during exploration. Projection-specific imaging and optogenetic experiments revealed that these arrests are effected by basolateral amygdala neurons projecting to the central amygdala, uncovering an amygdala circuit that mediates momentary arrests in familiar places but not avoidance or anxiety/fear-like behaviors.
Assuntos
Complexo Nuclear Basolateral da Amígdala/fisiologia , Núcleo Central da Amígdala/fisiologia , Comportamento Exploratório/fisiologia , Rede Nervosa/fisiologia , Animais , Complexo Nuclear Basolateral da Amígdala/diagnóstico por imagem , Comportamento Animal/fisiologia , Núcleo Central da Amígdala/diagnóstico por imagem , Feminino , Locomoção , Aprendizado de Máquina , Masculino , Camundongos Endogâmicos C57BL , Neurônios/fisiologia , Imagem ÓpticaRESUMO
Early detection and effective treatment of severe COVID-19 patients remain major challenges. Here, we performed proteomic and metabolomic profiling of sera from 46 COVID-19 and 53 control individuals. We then trained a machine learning model using proteomic and metabolomic measurements from a training cohort of 18 non-severe and 13 severe patients. The model was validated using 10 independent patients, 7 of which were correctly classified. Targeted proteomics and metabolomics assays were employed to further validate this molecular classifier in a second test cohort of 19 COVID-19 patients, leading to 16 correct assignments. We identified molecular changes in the sera of COVID-19 patients compared to other groups implicating dysregulation of macrophage, platelet degranulation, complement system pathways, and massive metabolic suppression. This study revealed characteristic protein and metabolite changes in the sera of severe COVID-19 patients, which might be used in selection of potential blood biomarkers for severity evaluation.
Assuntos
Infecções por Coronavirus/sangue , Metabolômica , Pneumonia Viral/sangue , Proteômica , Adulto , Aminoácidos/metabolismo , Biomarcadores/sangue , COVID-19 , Análise por Conglomerados , Infecções por Coronavirus/fisiopatologia , Feminino , Humanos , Metabolismo dos Lipídeos , Aprendizado de Máquina , Macrófagos/patologia , Masculino , Pessoa de Meia-Idade , Pandemias , Pneumonia Viral/fisiopatologia , Índice de Gravidade de DoençaRESUMO
Due to the rapid emergence of antibiotic-resistant bacteria, there is a growing need to discover new antibiotics. To address this challenge, we trained a deep neural network capable of predicting molecules with antibacterial activity. We performed predictions on multiple chemical libraries and discovered a molecule from the Drug Repurposing Hub-halicin-that is structurally divergent from conventional antibiotics and displays bactericidal activity against a wide phylogenetic spectrum of pathogens including Mycobacterium tuberculosis and carbapenem-resistant Enterobacteriaceae. Halicin also effectively treated Clostridioides difficile and pan-resistant Acinetobacter baumannii infections in murine models. Additionally, from a discrete set of 23 empirically tested predictions from >107 million molecules curated from the ZINC15 database, our model identified eight antibacterial compounds that are structurally distant from known antibiotics. This work highlights the utility of deep learning approaches to expand our antibiotic arsenal through the discovery of structurally distinct antibacterial molecules.
Assuntos
Antibacterianos/farmacologia , Descoberta de Drogas/métodos , Aprendizado de Máquina , Tiadiazóis/farmacologia , Acinetobacter baumannii/efeitos dos fármacos , Animais , Antibacterianos/química , Quimioinformática/métodos , Clostridioides difficile/efeitos dos fármacos , Bases de Dados de Compostos Químicos , Camundongos , Camundongos Endogâmicos BALB C , Camundongos Endogâmicos C57BL , Mycobacterium tuberculosis/efeitos dos fármacos , Bibliotecas de Moléculas Pequenas/química , Bibliotecas de Moléculas Pequenas/farmacologia , Tiadiazóis/químicaRESUMO
There is an unmet clinical need for improved tissue and liquid biopsy tools for cancer detection. We investigated the proteomic profile of extracellular vesicles and particles (EVPs) in 426 human samples from tissue explants (TEs), plasma, and other bodily fluids. Among traditional exosome markers, CD9, HSPA8, ALIX, and HSP90AB1 represent pan-EVP markers, while ACTB, MSN, and RAP1B are novel pan-EVP markers. To confirm that EVPs are ideal diagnostic tools, we analyzed proteomes of TE- (n = 151) and plasma-derived (n = 120) EVPs. Comparison of TE EVPs identified proteins (e.g., VCAN, TNC, and THBS2) that distinguish tumors from normal tissues with 90% sensitivity/94% specificity. Machine-learning classification of plasma-derived EVP cargo, including immunoglobulins, revealed 95% sensitivity/90% specificity in detecting cancer. Finally, we defined a panel of tumor-type-specific EVP proteins in TEs and plasma, which can classify tumors of unknown primary origin. Thus, EVP proteins can serve as reliable biomarkers for cancer detection and determining cancer type.
Assuntos
Biomarcadores Tumorais/metabolismo , Vesículas Extracelulares/metabolismo , Neoplasias/diagnóstico , Animais , Biomarcadores Tumorais/sangue , Linhagem Celular , Proteínas de Choque Térmico HSC70/metabolismo , Humanos , Aprendizado de Máquina , Camundongos , Camundongos Endogâmicos C57BL , Proteínas dos Microfilamentos/metabolismo , Neoplasias/metabolismo , Proteoma/análise , Proteoma/metabolismo , Proteômica/métodos , Sensibilidade e Especificidade , Tetraspanina 29/metabolismo , Proteínas rap de Ligação ao GTP/metabolismoRESUMO
The immune system is highly complex and distributed throughout an organism, with hundreds to thousands of cell states existing in parallel with diverse molecular pathways interacting in a highly dynamic and coordinated fashion. Although the characterization of individual genes and molecules is of the utmost importance for understanding immune-system function, high-throughput, high-resolution omics technologies combined with sophisticated computational modeling and machine-learning approaches are creating opportunities to complement standard immunological methods with new insights into immune-system dynamics. Like systems immunology itself, immunology researchers must take advantage of these technologies and form their own diverse networks, connecting with researchers from other disciplines. This Review is an introduction and 'how-to guide' for immunologists with no particular experience in the field of omics but with the intention to learn about and apply these systems-level approaches, and for immunologists who want to make the most of interdisciplinary networks.
Assuntos
Sistema Imunitário , Aprendizado de Máquina , Simulação por ComputadorRESUMO
The expanding scale and inherent complexity of biological data have encouraged a growing use of machine learning in biology to build informative and predictive models of the underlying biological processes. All machine learning techniques fit models to data; however, the specific methods are quite varied and can at first glance seem bewildering. In this Review, we aim to provide readers with a gentle introduction to a few key machine learning techniques, including the most recently developed and widely used techniques involving deep neural networks. We describe how different techniques may be suited to specific types of biological data, and also discuss some best practices and points to consider when one is embarking on experiments involving machine learning. Some emerging directions in machine learning methodology are also discussed.
Assuntos
Biologia , Aprendizado de Máquina , Animais , Aprendizado Profundo , Humanos , Redes Neurais de ComputaçãoRESUMO
In this issue of Cell, Yang, Wright et al. describe a machine learning approach that that can provide mechanistic insight from chemical screens. They use this approach to uncover how the nutritional availability for Escherichia coli impacts lethality toward three widely used antibiotics.
Assuntos
Antibacterianos , Escherichia coli , Aprendizado de Máquina , NutrientesRESUMO
We performed a comprehensive assessment of rare inherited variation in autism spectrum disorder (ASD) by analyzing whole-genome sequences of 2,308 individuals from families with multiple affected children. We implicate 69 genes in ASD risk, including 24 passing genome-wide Bonferroni correction and 16 new ASD risk genes, most supported by rare inherited variants, a substantial extension of previous findings. Biological pathways enriched for genes harboring inherited variants represent cytoskeletal organization and ion transport, which are distinct from pathways implicated in previous studies. Nevertheless, the de novo and inherited genes contribute to a common protein-protein interaction network. We also identified structural variants (SVs) affecting non-coding regions, implicating recurrent deletions in the promoters of DLG2 and NR3C2. Loss of nr3c2 function in zebrafish disrupts sleep and social function, overlapping with human ASD-related phenotypes. These data support the utility of studying multiplex families in ASD and are available through the Hartwell Autism Research and Technology portal.
Assuntos
Transtorno do Espectro Autista/genética , Predisposição Genética para Doença/genética , Linhagem , Mapas de Interação de Proteínas/genética , Animais , Criança , Bases de Dados Genéticas , Modelos Animais de Doenças , Feminino , Deleção de Genes , Guanilato Quinases/genética , Humanos , Padrões de Herança/genética , Aprendizado de Máquina , Masculino , Núcleo Familiar , Regiões Promotoras Genéticas/genética , Receptores de Mineralocorticoides/genética , Fatores de Risco , Proteínas Supressoras de Tumor/genética , Sequenciamento Completo do Genoma , Peixe-Zebra/genéticaRESUMO
Acute myeloid leukemia (AML) is a heterogeneous disease that resides within a complex microenvironment, complicating efforts to understand how different cell types contribute to disease progression. We combined single-cell RNA sequencing and genotyping to profile 38,410 cells from 40 bone marrow aspirates, including 16 AML patients and five healthy donors. We then applied a machine learning classifier to distinguish a spectrum of malignant cell types whose abundances varied between patients and between subclones in the same tumor. Cell type compositions correlated with prototypic genetic lesions, including an association of FLT3-ITD with abundant progenitor-like cells. Primitive AML cells exhibited dysregulated transcriptional programs with co-expression of stemness and myeloid priming genes and had prognostic significance. Differentiated monocyte-like AML cells expressed diverse immunomodulatory genes and suppressed T cell activity in vitro. In conclusion, we provide single-cell technologies and an atlas of AML cell states, regulators, and markers with implications for precision medicine and immune therapies. VIDEO ABSTRACT.
Assuntos
Leucemia Mieloide Aguda/genética , Transcriptoma/genética , Adulto , Sequência de Bases/genética , Medula Óssea , Células da Medula Óssea/citologia , Linhagem Celular Tumoral , Progressão da Doença , Feminino , Genótipo , Humanos , Leucemia Mieloide Aguda/imunologia , Leucemia Mieloide Aguda/fisiopatologia , Aprendizado de Máquina , Masculino , Pessoa de Meia-Idade , Mutação , Prognóstico , RNA , Transdução de Sinais , Análise de Célula Única/métodos , Microambiente Tumoral , Sequenciamento do Exoma/métodosRESUMO
Current machine learning techniques enable robust association of biological signals with measured phenotypes, but these approaches are incapable of identifying causal relationships. Here, we develop an integrated "white-box" biochemical screening, network modeling, and machine learning approach for revealing causal mechanisms and apply this approach to understanding antibiotic efficacy. We counter-screen diverse metabolites against bactericidal antibiotics in Escherichia coli and simulate their corresponding metabolic states using a genome-scale metabolic network model. Regression of the measured screening data on model simulations reveals that purine biosynthesis participates in antibiotic lethality, which we validate experimentally. We show that antibiotic-induced adenine limitation increases ATP demand, which elevates central carbon metabolism activity and oxygen consumption, enhancing the killing effects of antibiotics. This work demonstrates how prospective network modeling can couple with machine learning to identify complex causal mechanisms underlying drug efficacy.