RESUMO
Human tissue, which is inherently three-dimensional (3D), is traditionally examined through standard-of-care histopathology as limited two-dimensional (2D) cross-sections that can insufficiently represent the tissue due to sampling bias. To holistically characterize histomorphology, 3D imaging modalities have been developed, but clinical translation is hampered by complex manual evaluation and lack of computational platforms to distill clinical insights from large, high-resolution datasets. We present TriPath, a deep-learning platform for processing tissue volumes and efficiently predicting clinical outcomes based on 3D morphological features. Recurrence risk-stratification models were trained on prostate cancer specimens imaged with open-top light-sheet microscopy or microcomputed tomography. By comprehensively capturing 3D morphologies, 3D volume-based prognostication achieves superior performance to traditional 2D slice-based approaches, including clinical/histopathological baselines from six certified genitourinary pathologists. Incorporating greater tissue volume improves prognostic performance and mitigates risk prediction variability from sampling bias, further emphasizing the value of capturing larger extents of heterogeneous morphology.
Assuntos
Imageamento Tridimensional , Neoplasias da Próstata , Aprendizado de Máquina Supervisionado , Humanos , Masculino , Aprendizado Profundo , Imageamento Tridimensional/métodos , Prognóstico , Neoplasias da Próstata/patologia , Neoplasias da Próstata/diagnóstico por imagem , Microtomografia por Raio-X/métodosRESUMO
Methods from artificial intelligence (AI) trained on large datasets of sequences and structures can now "write" proteins with new shapes and molecular functions de novo, without starting from proteins found in nature. In this Perspective, I will discuss the state of the field of de novo protein design at the juncture of physics-based modeling approaches and AI. New protein folds and higher-order assemblies can be designed with considerable experimental success rates, and difficult problems requiring tunable control over protein conformations and precise shape complementarity for molecular recognition are coming into reach. Emerging approaches incorporate engineering principles-tunability, controllability, and modularity-into the design process from the beginning. Exciting frontiers lie in deconstructing cellular functions with de novo proteins and, conversely, constructing synthetic cellular signaling from the ground up. As methods improve, many more challenges are unsolved.
Assuntos
Inteligência Artificial , Proteínas , Conformação Proteica , Proteínas/química , Proteínas/metabolismo , Engenharia de Proteínas , Aprendizado ProfundoRESUMO
Cell cycle progression relies on coordinated changes in the composition and subcellular localization of the proteome. By applying two distinct convolutional neural networks on images of millions of live yeast cells, we resolved proteome-level dynamics in both concentration and localization during the cell cycle, with resolution of â¼20 subcellular localization classes. We show that a quarter of the proteome displays cell cycle periodicity, with proteins tending to be controlled either at the level of localization or concentration, but not both. Distinct levels of protein regulation are preferentially utilized for different aspects of the cell cycle, with changes in protein concentration being mostly involved in cell cycle control and changes in protein localization in the biophysical implementation of the cell cycle program. We present a resource for exploring global proteome dynamics during the cell cycle, which will aid in understanding a fundamental biological process at a systems level.
Assuntos
Proteínas de Saccharomyces cerevisiae , Saccharomyces cerevisiae , Células Eucarióticas/metabolismo , Redes Neurais de Computação , Proteoma/metabolismo , Saccharomyces cerevisiae/citologia , Saccharomyces cerevisiae/metabolismo , Proteínas de Saccharomyces cerevisiae/metabolismoRESUMO
Applications of prime editing are often limited due to insufficient efficiencies, and it can require substantial time and resources to determine the most efficient pegRNAs and prime editors (PEs) to generate a desired edit under various experimental conditions. Here, we evaluated prime editing efficiencies for a total of 338,996 pairs of pegRNAs including 3,979 epegRNAs and target sequences in an error-free manner. These datasets enabled a systematic determination of factors affecting prime editing efficiencies. Then, we developed computational models, named DeepPrime and DeepPrime-FT, that can predict prime editing efficiencies for eight prime editing systems in seven cell types for all possible types of editing of up to 3 base pairs. We also extensively profiled the prime editing efficiencies at mismatched targets and developed a computational model predicting editing efficiencies at such targets. These computational models, together with our improved knowledge about prime editing efficiency determinants, will greatly facilitate prime editing applications.
Assuntos
Simulação por Computador , Edição de Genes , RNA Guia de Sistemas CRISPR-Cas , Sistemas CRISPR-Cas , Edição de Genes/métodos , Conhecimento , RNA Guia de Sistemas CRISPR-Cas/química , Especificidade de Órgãos , Conjuntos de Dados como AssuntoRESUMO
Spatial molecular profiling of complex tissues is essential to investigate cellular function in physiological and pathological states. However, methods for molecular analysis of large biological specimens imaged in 3D are lacking. Here, we present DISCO-MS, a technology that combines whole-organ/whole-organism clearing and imaging, deep-learning-based image analysis, robotic tissue extraction, and ultra-high-sensitivity mass spectrometry. DISCO-MS yielded proteome data indistinguishable from uncleared samples in both rodent and human tissues. We used DISCO-MS to investigate microglia activation along axonal tracts after brain injury and characterized early- and late-stage individual amyloid-beta plaques in a mouse model of Alzheimer's disease. DISCO-bot robotic sample extraction enabled us to study the regional heterogeneity of immune cells in intact mouse bodies and aortic plaques in a complete human heart. DISCO-MS enables unbiased proteome analysis of preclinical and clinical tissues after unbiased imaging of entire specimens in 3D, identifying diagnostic and therapeutic opportunities for complex diseases. VIDEO ABSTRACT.
Assuntos
Doença de Alzheimer , Proteoma , Camundongos , Humanos , Animais , Proteoma/análise , Proteômica/métodos , Doença de Alzheimer/patologia , Peptídeos beta-Amiloides , Espectrometria de Massas , Placa AmiloideRESUMO
The continual evolution of SARS-CoV-2 and the emergence of variants that show resistance to vaccines and neutralizing antibodies threaten to prolong the COVID-19 pandemic. Selection and emergence of SARS-CoV-2 variants are driven in part by mutations within the viral spike protein and in particular the ACE2 receptor-binding domain (RBD), a primary target site for neutralizing antibodies. Here, we develop deep mutational learning (DML), a machine-learning-guided protein engineering technology, which is used to investigate a massive sequence space of combinatorial mutations, representing billions of RBD variants, by accurately predicting their impact on ACE2 binding and antibody escape. A highly diverse landscape of possible SARS-CoV-2 variants is identified that could emerge from a multitude of evolutionary trajectories. DML may be used for predictive profiling on current and prospective variants, including highly mutated variants such as Omicron, thus guiding the development of therapeutic antibody treatments and vaccines for COVID-19.
Assuntos
Enzima de Conversão de Angiotensina 2/metabolismo , COVID-19 , SARS-CoV-2 , Glicoproteína da Espícula de Coronavírus/metabolismo , Enzima de Conversão de Angiotensina 2/química , Enzima de Conversão de Angiotensina 2/genética , Anticorpos Neutralizantes , Anticorpos Antivirais , Vacinas contra COVID-19 , Humanos , Mutação , Pandemias , Ligação Proteica , SARS-CoV-2/genética , Glicoproteína da Espícula de Coronavírus/química , Glicoproteína da Espícula de Coronavírus/genéticaRESUMO
Muscle spasticity after nervous system injuries and painful low back spasm affect more than 10% of global population. Current medications are of limited efficacy and cause neurological and cardiovascular side effects because they target upstream regulators of muscle contraction. Direct myosin inhibition could provide optimal muscle relaxation; however, targeting skeletal myosin is particularly challenging because of its similarity to the cardiac isoform. We identified a key residue difference between these myosin isoforms, located in the communication center of the functional regions, which allowed us to design a selective inhibitor, MPH-220. Mutagenic analysis and the atomic structure of MPH-220-bound skeletal muscle myosin confirmed the mechanism of specificity. Targeting skeletal muscle myosin by MPH-220 enabled muscle relaxation, in human and model systems, without cardiovascular side effects and improved spastic gait disorders after brain injury in a disease model. MPH-220 provides a potential nervous-system-independent option to treat spasticity and muscle stiffness.
Assuntos
Músculo Esquelético/metabolismo , Miosinas de Músculo Esquelético/efeitos dos fármacos , Miosinas de Músculo Esquelético/genética , Adulto , Animais , Miosinas Cardíacas/genética , Miosinas Cardíacas/metabolismo , Linhagem Celular , Sistemas de Liberação de Medicamentos , Feminino , Humanos , Masculino , Camundongos , Contração Muscular/fisiologia , Fibras Musculares Esqueléticas/fisiologia , Espasticidade Muscular/genética , Espasticidade Muscular/fisiopatologia , Músculo Esquelético/fisiologia , Miosinas/efeitos dos fármacos , Miosinas/genética , Miosinas/metabolismo , Isoformas de Proteínas , Ratos , Ratos Wistar , Miosinas de Músculo Esquelético/metabolismoRESUMO
Many COVID-19 patients infected by SARS-CoV-2 virus develop pneumonia (called novel coronavirus pneumonia, NCP) and rapidly progress to respiratory failure. However, rapid diagnosis and identification of high-risk patients for early intervention are challenging. Using a large computed tomography (CT) database from 3,777 patients, we developed an AI system that can diagnose NCP and differentiate it from other common pneumonia and normal controls. The AI system can assist radiologists and physicians in performing a quick diagnosis especially when the health system is overloaded. Significantly, our AI system identified important clinical markers that correlated with the NCP lesion properties. Together with the clinical data, our AI system was able to provide accurate clinical prognosis that can aid clinicians to consider appropriate early clinical management and allocate resources appropriately. We have made this AI system available globally to assist the clinicians to combat COVID-19.
Assuntos
Inteligência Artificial , Infecções por Coronavirus/diagnóstico , Pneumonia Viral/diagnóstico , Tomografia Computadorizada por Raios X , COVID-19 , China , Estudos de Coortes , Infecções por Coronavirus/patologia , Infecções por Coronavirus/terapia , Conjuntos de Dados como Assunto , Humanos , Pulmão/patologia , Modelos Biológicos , Pandemias , Projetos Piloto , Pneumonia Viral/patologia , Pneumonia Viral/terapia , Prognóstico , Radiologistas , Insuficiência Respiratória/diagnósticoRESUMO
The splicing of pre-mRNAs into mature transcripts is remarkable for its precision, but the mechanisms by which the cellular machinery achieves such specificity are incompletely understood. Here, we describe a deep neural network that accurately predicts splice junctions from an arbitrary pre-mRNA transcript sequence, enabling precise prediction of noncoding genetic variants that cause cryptic splicing. Synonymous and intronic mutations with predicted splice-altering consequence validate at a high rate on RNA-seq and are strongly deleterious in the human population. De novo mutations with predicted splice-altering consequence are significantly enriched in patients with autism and intellectual disability compared to healthy controls and validate against RNA-seq in 21 out of 28 of these patients. We estimate that 9%-11% of pathogenic mutations in patients with rare genetic disorders are caused by this previously underappreciated class of disease variation.
Assuntos
Previsões/métodos , Precursores de RNA/genética , Splicing de RNA/genética , Algoritmos , Processamento Alternativo/genética , Transtorno Autístico/genética , Aprendizado Profundo , Éxons/genética , Humanos , Deficiência Intelectual/genética , Íntrons/genética , Redes Neurais de Computação , Precursores de RNA/metabolismo , Sítios de Splice de RNA/genética , Sítios de Splice de RNA/fisiologiaRESUMO
Reliable detection of disseminated tumor cells and of the biodistribution of tumor-targeting therapeutic antibodies within the entire body has long been needed to better understand and treat cancer metastasis. Here, we developed an integrated pipeline for automated quantification of cancer metastases and therapeutic antibody targeting, named DeepMACT. First, we enhanced the fluorescent signal of cancer cells more than 100-fold by applying the vDISCO method to image metastasis in transparent mice. Second, we developed deep learning algorithms for automated quantification of metastases with an accuracy matching human expert manual annotation. Deep learning-based quantification in 5 different metastatic cancer models including breast, lung, and pancreatic cancer with distinct organotropisms allowed us to systematically analyze features such as size, shape, spatial distribution, and the degree to which metastases are targeted by a therapeutic monoclonal antibody in entire mice. DeepMACT can thus considerably improve the discovery of effective antibody-based therapeutics at the pre-clinical stage. VIDEO ABSTRACT.
Assuntos
Anticorpos/uso terapêutico , Aprendizado Profundo , Diagnóstico por Computador/métodos , Quimioterapia Assistida por Computador/métodos , Neoplasias/patologia , Animais , Humanos , Células MCF-7 , Camundongos , Camundongos Endogâmicos C57BL , Camundongos Nus , Camundongos SCID , Metástase Neoplásica , Neoplasias/diagnóstico por imagem , Neoplasias/tratamento farmacológico , Software , Microambiente TumoralRESUMO
Alternative polyadenylation (APA) is a major driver of transcriptome diversity in human cells. Here, we use deep learning to predict APA from DNA sequence alone. We trained our model (APARENT, APA REgression NeT) on isoform expression data from over 3 million APA reporters. APARENT's predictions are highly accurate when tasked with inferring APA in synthetic and human 3'UTRs. Visualizing features learned across all network layers reveals that APARENT recognizes sequence motifs known to recruit APA regulators, discovers previously unknown sequence determinants of 3' end processing, and integrates these features into a comprehensive, interpretable, cis-regulatory code. We apply APARENT to forward engineer functional polyadenylation signals with precisely defined cleavage position and isoform usage and validate predictions experimentally. Finally, we use APARENT to quantify the impact of genetic variants on APA. Our approach detects pathogenic variants in a wide range of disease contexts, expanding our understanding of the genetic origins of disease.
Assuntos
Aprendizado Profundo , Modelos Genéticos , Poliadenilação/genética , Regiões 3' não Traduzidas/genética , Sequência de Bases/genética , Bases de Dados Genéticas , Expressão Gênica/genética , Células HEK293 , Humanos , Mutagênese/genética , Clivagem do RNA/genética , RNA Mensageiro/genética , RNA-Seq , Biologia Sintética , TranscriptomaRESUMO
Microscopy is a central method in life sciences. Many popular methods, such as antibody labeling, are used to add physical fluorescent labels to specific cellular constituents. However, these approaches have significant drawbacks, including inconsistency; limitations in the number of simultaneous labels because of spectral overlap; and necessary perturbations of the experiment, such as fixing the cells, to generate the measurement. Here, we show that a computational machine-learning approach, which we call "in silico labeling" (ISL), reliably predicts some fluorescent labels from transmitted-light images of unlabeled fixed or live biological samples. ISL predicts a range of labels, such as those for nuclei, cell type (e.g., neural), and cell state (e.g., cell death). Because prediction happens in silico, the method is consistent, is not limited by spectral overlap, and does not disturb the experiment. ISL generates biological measurements that would otherwise be problematic or impossible to acquire.
Assuntos
Corantes Fluorescentes/química , Processamento de Imagem Assistida por Computador/métodos , Microscopia de Fluorescência/métodos , Neurônios Motores/citologia , Algoritmos , Animais , Linhagem Celular Tumoral , Sobrevivência Celular , Córtex Cerebral/citologia , Humanos , Células-Tronco Pluripotentes Induzidas/citologia , Aprendizado de Máquina , Redes Neurais de Computação , Neurociências , Ratos , Software , Células-Tronco/citologiaRESUMO
Machine learning, a collection of data-analytical techniques aimed at building predictive models from multi-dimensional datasets, is becoming integral to modern biological research. By enabling one to generate models that learn from large datasets and make predictions on likely outcomes, machine learning can be used to study complex cellular systems such as biological networks. Here, we provide a primer on machine learning for life scientists, including an introduction to deep learning. We discuss opportunities and challenges at the intersection of machine learning and network biology, which could impact disease biology, drug discovery, microbiome research, and synthetic biology.
Assuntos
Biologia Computacional/métodos , Aprendizado de Máquina , Algoritmos , Bases de Dados Factuais , Descoberta de Drogas , Efeitos Colaterais e Reações Adversas Relacionados a Medicamentos , Humanos , Microbiota , Redes Neurais de ComputaçãoRESUMO
A fundamental challenge of biology is to understand the vast heterogeneity of cells, particularly how cellular composition, structure, and morphology are linked to cellular physiology. Unfortunately, conventional technologies are limited in uncovering these relations. We present a machine-intelligence technology based on a radically different architecture that realizes real-time image-based intelligent cell sorting at an unprecedented rate. This technology, which we refer to as intelligent image-activated cell sorting, integrates high-throughput cell microscopy, focusing, and sorting on a hybrid software-hardware data-management infrastructure, enabling real-time automated operation for data acquisition, data processing, decision-making, and actuation. We use it to demonstrate real-time sorting of microalgal and blood cells based on intracellular protein localization and cell-cell interaction from large heterogeneous populations for studying photosynthesis and atherothrombosis, respectively. The technology is highly versatile and expected to enable machine-based scientific discovery in biological, pharmaceutical, and medical sciences.
Assuntos
Citometria de Fluxo/métodos , Ensaios de Triagem em Larga Escala/métodos , Processamento de Imagem Assistida por Computador/métodos , Animais , Aprendizado Profundo , HumanosRESUMO
The implementation of clinical-decision support algorithms for medical imaging faces challenges with reliability and interpretability. Here, we establish a diagnostic tool based on a deep-learning framework for the screening of patients with common treatable blinding retinal diseases. Our framework utilizes transfer learning, which trains a neural network with a fraction of the data of conventional approaches. Applying this approach to a dataset of optical coherence tomography images, we demonstrate performance comparable to that of human experts in classifying age-related macular degeneration and diabetic macular edema. We also provide a more transparent and interpretable diagnosis by highlighting the regions recognized by the neural network. We further demonstrate the general applicability of our AI system for diagnosis of pediatric pneumonia using chest X-ray images. This tool may ultimately aid in expediting the diagnosis and referral of these treatable conditions, thereby facilitating earlier treatment, resulting in improved clinical outcomes. VIDEO ABSTRACT.
Assuntos
Aprendizado Profundo , Diagnóstico por Imagem , Pneumonia/diagnóstico , Criança , Humanos , Redes Neurais de Computação , Pneumonia/diagnóstico por imagem , Curva ROC , Reprodutibilidade dos Testes , Tomografia de Coerência ÓpticaRESUMO
Global research to combat the COVID-19 pandemic has led to the isolation and characterization of thousands of human antibodies to the SARS-CoV-2 spike protein, providing an unprecedented opportunity to study the antibody response to a single antigen. Using the information derived from 88 research publications and 13 patents, we assembled a dataset of â¼8,000 human antibodies to the SARS-CoV-2 spike protein from >200 donors. By analyzing immunoglobulin V and D gene usages, complementarity-determining region H3 sequences, and somatic hypermutations, we demonstrated that the common (public) responses to different domains of the spike protein were quite different. We further used these sequences to train a deep-learning model to accurately distinguish between the human antibodies to SARS-CoV-2 spike protein and those to influenza hemagglutinin protein. Overall, this study provides an informative resource for antibody research and enhances our molecular understanding of public antibody responses.
Assuntos
COVID-19 , SARS-CoV-2 , Anticorpos Neutralizantes , Anticorpos Antivirais , Formação de Anticorpos , Humanos , Pandemias , Glicoproteína da Espícula de CoronavírusRESUMO
tRNA function is based on unique structures that enable mRNA decoding using anticodon trinucleotides. These structures interact with specific aminoacyl-tRNA synthetases and ribosomes using 3D shape and sequence signatures. Beyond translation, tRNAs serve as versatile signaling molecules interacting with other RNAs and proteins. Through evolutionary processes, tRNA fragmentation emerges as not merely random degradation but an act of recreation, generating specific shorter molecules called tRNA-derived small RNAs (tsRNAs). These tsRNAs exploit their linear sequences and newly arranged 3D structures for unexpected biological functions, epitomizing the tRNA "renovatio" (from Latin, meaning renewal, renovation, and rebirth). Emerging methods to uncover full tRNA/tsRNA sequences and modifications, combined with techniques to study RNA structures and to integrate AI-powered predictions, will enable comprehensive investigations of tRNA fragmentation products and new interaction potentials in relation to their biological functions. We anticipate that these directions will herald a new era for understanding biological complexity and advancing pharmaceutical engineering.
Assuntos
Aminoacil-tRNA Sintetases , RNA de Transferência , RNA de Transferência/metabolismo , Anticódon , Aminoacil-tRNA Sintetases/metabolismo , Ribossomos/metabolismo , RNA Mensageiro/genéticaRESUMO
Acidic transcription activation domains (ADs) are encoded by a wide range of seemingly unrelated amino acid sequences, making it difficult to recognize features that promote their dynamic behavior, "fuzzy" interactions, and target specificity. We screened a large set of random 30-mer peptides for AD function in yeast and trained a deep neural network (ADpred) on the AD-positive and -negative sequences. ADpred identifies known acidic ADs within transcription factors and accurately predicts the consequences of mutations. Our work reveals that strong acidic ADs contain multiple clusters of hydrophobic residues near acidic side chains, explaining why ADs often have a biased amino acid composition. ADs likely use a binding mechanism similar to avidity where a minimum number of weak dynamic interactions are required between activator and target to generate biologically relevant affinity and in vivo function. This mechanism explains the basis for fuzzy binding observed between acidic ADs and targets.
Assuntos
Ensaios de Triagem em Larga Escala/métodos , Fatores de Transcrição/genética , Ativação Transcricional/genética , Sequência de Aminoácidos/genética , Fatores de Transcrição de Zíper de Leucina Básica/genética , Proteínas de Ligação a DNA/metabolismo , Aprendizado Profundo , Ligação Proteica , Domínios Proteicos/genética , Saccharomyces cerevisiae/genética , Proteínas de Saccharomyces cerevisiae/genética , Proteínas de Saccharomyces cerevisiae/metabolismo , Transativadores/genética , Transativadores/metabolismo , Fatores de Transcrição/metabolismo , Ativação Transcricional/fisiologiaRESUMO
Drug discovery is adapting to novel technologies such as data science, informatics, and artificial intelligence (AI) to accelerate effective treatment development while reducing costs and animal experiments. AI is transforming drug discovery, as indicated by increasing interest from investors, industrial and academic scientists, and legislators. Successful drug discovery requires optimizing properties related to pharmacodynamics, pharmacokinetics, and clinical outcomes. This review discusses the use of AI in the three pillars of drug discovery: diseases, targets, and therapeutic modalities, with a focus on small-molecule drugs. AI technologies, such as generative chemistry, machine learning, and multiproperty optimization, have enabled several compounds to enter clinical trials. The scientific community must carefully vet known information to address the reproducibility crisis. The full potential of AI in drug discovery can only be realized with sufficient ground truth and appropriate human intervention at later pipeline stages.
Assuntos
Inteligência Artificial , Médicos , Animais , Humanos , Reprodutibilidade dos Testes , Descoberta de Drogas , TecnologiaRESUMO
Harnessing cutting-edge technologies to enhance crop productivity is a pivotal goal in modern plant breeding. Artificial intelligence (AI) is renowned for its prowess in big data analysis and pattern recognition, and is revolutionizing numerous scientific domains including plant breeding. We explore the wider potential of AI tools in various facets of breeding, including data collection, unlocking genetic diversity within genebanks, and bridging the genotype-phenotype gap to facilitate crop breeding. This will enable the development of crop cultivars tailored to the projected future environments. Moreover, AI tools also hold promise for refining crop traits by improving the precision of gene-editing systems and predicting the potential effects of gene variants on plant phenotypes. Leveraging AI-enabled precision breeding can augment the efficiency of breeding programs and holds promise for optimizing cropping systems at the grassroots level. This entails identifying optimal inter-cropping and crop-rotation models to enhance agricultural sustainability and productivity in the field.