De novo protein design-From new structures to programmable functions.

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.

Using artificial intelligence to document the hidden RNA virosphere.

Current metagenomic tools can fail to identify highly divergent RNA viruses. We developed a deep learning algorithm, termed LucaProt, to discover highly divergent RNA-dependent RNA polymerase (RdRP) sequences in 10,487 metatranscriptomes generated from diverse global ecosystems. LucaProt integrates both sequence and predicted structural information, enabling the accurate detection of RdRP sequences. Using this approach, we identified 161,979 potential RNA virus species and 180 RNA virus supergroups, including many previously poorly studied groups, as well as RNA virus genomes of exceptional length (up to 47,250 nucleotides) and genomic complexity. A subset of these novel RNA viruses was confirmed by RT-PCR and RNA/DNA sequencing. Newly discovered RNA viruses were present in diverse environments, including air, hot springs, and hydrothermal vents, with virus diversity and abundance varying substantially among ecosystems. This study advances virus discovery, highlights the scale of the virosphere, and provides computational tools to better document the global RNA virome.

Liquid-biopsy proteomics combined with AI identifies cellular drivers of eye aging and disease in vivo.

Single-cell analysis in living humans is essential for understanding disease mechanisms, but it is impractical in non-regenerative organs, such as the eye and brain, because tissue biopsies would cause serious damage. We resolve this problem by integrating proteomics of liquid biopsies with single-cell transcriptomics from all known ocular cell types to trace the cellular origin of 5,953 proteins detected in the aqueous humor. We identified hundreds of cell-specific protein markers, including for individual retinal cell types. Surprisingly, our results reveal that retinal degeneration occurs in Parkinson's disease, and the cells driving diabetic retinopathy switch with disease stage. Finally, we developed artificial intelligence (AI) models to assess individual cellular aging and found that many eye diseases not associated with chronological age undergo accelerated molecular aging of disease-specific cell types. Our approach, which can be applied to other organ systems, has the potential to transform molecular diagnostics and prognostics while uncovering new cellular disease and aging mechanisms.

Sounds emitted by plants under stress are airborne and informative.

Stressed plants show altered phenotypes, including changes in color, smell, and shape. Yet, airborne sounds emitted by stressed plants have not been investigated before. Here we show that stressed plants emit airborne sounds that can be recorded from a distance and classified. We recorded ultrasonic sounds emitted by tomato and tobacco plants inside an acoustic chamber, and in a greenhouse, while monitoring the plant's physiological parameters. We developed machine learning models that succeeded in identifying the condition of the plants, including dehydration level and injury, based solely on the emitted sounds. These informative sounds may also be detectable by other organisms. This work opens avenues for understanding plants and their interactions with the environment and may have significant impact on agriculture.

Human T cell generation is restored in CD3δ severe combined immunodeficiency through adenine base editing.

CD3δ SCID is a devastating inborn error of immunity caused by mutations in CD3D, encoding the invariant CD3δ chain of the CD3/TCR complex necessary for normal thymopoiesis. We demonstrate an adenine base editing (ABE) strategy to restore CD3δ in autologous hematopoietic stem and progenitor cells (HSPCs). Delivery of mRNA encoding a laboratory-evolved ABE and guide RNA into a CD3δ SCID patient's HSPCs resulted in a 71.2% ± 7.85% (n = 3) correction of the pathogenic mutation. Edited HSPCs differentiated in artificial thymic organoids produced mature T cells exhibiting diverse TCR repertoires and TCR-dependent functions. Edited human HSPCs transplanted into immunodeficient mice showed 88% reversion of the CD3D defect in human CD34+ cells isolated from mouse bone marrow after 16 weeks, indicating correction of long-term repopulating HSCs. These findings demonstrate the preclinical efficacy of ABE in HSPCs for the treatment of CD3δ SCID, providing a foundation for the development of a one-time treatment for CD3δ SCID patients.

Personalized microbiome-driven effects of non-nutritive sweeteners on human glucose tolerance.

Non-nutritive sweeteners (NNS) are commonly integrated into human diet and presumed to be inert; however, animal studies suggest that they may impact the microbiome and downstream glycemic responses. We causally assessed NNS impacts in humans and their microbiomes in a randomized-controlled trial encompassing 120 healthy adults, administered saccharin, sucralose, aspartame, and stevia sachets for 2 weeks in doses lower than the acceptable daily intake, compared with controls receiving sachet-contained vehicle glucose or no supplement. As groups, each administered NNS distinctly altered stool and oral microbiome and plasma metabolome, whereas saccharin and sucralose significantly impaired glycemic responses. Importantly, gnotobiotic mice conventionalized with microbiomes from multiple top and bottom responders of each of the four NNS-supplemented groups featured glycemic responses largely reflecting those noted in respective human donors, which were preempted by distinct microbial signals, as exemplified by sucralose. Collectively, human NNS consumption may induce person-specific, microbiome-dependent glycemic alterations, necessitating future assessment of clinical implications.

Domestic dog lineages reveal genetic drivers of behavioral diversification.

Selective breeding of domestic dogs has generated diverse breeds often optimized for performing specialized tasks. Despite the heritability of breed-typical behavioral traits, identification of causal loci has proven challenging due to the complexity of canine population structure. We overcome longstanding difficulties in identifying genetic drivers of canine behavior by developing a framework for understanding relationships between breeds and the behaviors that define them, utilizing genetic data for over 4,000 domestic, semi-feral, and wild canids and behavioral survey data for over 46,000 dogs. We identify ten major canine genetic lineages and their behavioral correlates and show that breed diversification is predominantly driven by non-coding regulatory variation. We determine that lineage-associated genes converge in neurodevelopmental co-expression networks, identifying a sheepdog-associated enrichment for interrelated axon guidance functions. This work presents a scaffold for canine diversification that positions the domestic dog as an unparalleled system for revealing the genetic origins of behavioral diversity.

Deep mutational learning predicts ACE2 binding and antibody escape to combinatorial mutations in the SARS-CoV-2 receptor-binding domain.

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.

Single Residue Variation in Skeletal Muscle Myosin Enables Direct and Selective Drug Targeting for Spasticity and Muscle Stiffness.

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.

The Geometry of Abstraction in the Hippocampus and Prefrontal Cortex.

The curse of dimensionality plagues models of reinforcement learning and decision making. The process of abstraction solves this by constructing variables describing features shared by different instances, reducing dimensionality and enabling generalization in novel situations. Here, we characterized neural representations in monkeys performing a task described by different hidden and explicit variables. Abstraction was defined operationally using the generalization performance of neural decoders across task conditions not used for training, which requires a particular geometry of neural representations. Neural ensembles in prefrontal cortex, hippocampus, and simulated neural networks simultaneously represented multiple variables in a geometry reflecting abstraction but that still allowed a linear classifier to decode a large number of other variables (high shattering dimensionality). Furthermore, this geometry changed in relation to task events and performance. These findings elucidate how the brain and artificial systems represent variables in an abstract format while preserving the advantages conferred by high shattering dimensionality.

Predicting Splicing from Primary Sequence with Deep Learning.

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.

Human Artificial Chromosomes that Bypass Centromeric DNA.

Recent breakthroughs with synthetic budding yeast chromosomes expedite the creation of synthetic mammalian chromosomes and genomes. Mammals, unlike budding yeast, depend on the histone H3 variant, CENP-A, to epigenetically specify the location of the centromere-the locus essential for chromosome segregation. Prior human artificial chromosomes (HACs) required large arrays of centromeric α-satellite repeats harboring binding sites for the DNA sequence-specific binding protein, CENP-B. We report the development of a type of HAC that functions independently of these constraints. Formed by an initial CENP-A nucleosome seeding strategy, a construct lacking repetitive centromeric DNA formed several self-sufficient HACs that showed no uptake of genomic DNA. In contrast to traditional α-satellite HAC formation, the non-repetitive construct can form functional HACs without CENP-B or initial CENP-A nucleosome seeding, revealing distinct paths to centromere formation for different DNA sequence types. Our developments streamline the construction and characterization of HACs to facilitate mammalian synthetic genome efforts.

VEGAS as a Platform for Facile Directed Evolution in Mammalian Cells.

Directed evolution, artificial selection toward designed objectives, is routinely used to develop new molecular tools and therapeutics. Successful directed molecular evolution campaigns repeatedly test diverse sequences with a designed selective pressure. Unicellular organisms and their viral pathogens are exceptional for this purpose and have been used for decades. However, many desirable targets of directed evolution perform poorly or unnaturally in unicellular backgrounds. Here, we present a system for facile directed evolution in mammalian cells. Using the RNA alphavirus Sindbis as a vector for heredity and diversity, we achieved 24-h selection cycles surpassing 10-3 mutations per base. Selection is achieved through genetically actuated sequences internal to the host cell, thus the system's name: viral evolution of genetically actuating sequences, or "VEGAS." Using VEGAS, we evolve transcription factors, GPCRs, and allosteric nanobodies toward functional signaling endpoints each in less than 1 weeks' time.

Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning.

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.

Language in Brains, Minds, and Machines.

It has long been argued that only humans could produce and understand language. But now, for the first time, artificial language models (LMs) achieve this feat. Here we survey the new purchase LMs are providing on the question of how language is implemented in the brain. We discuss why, a priori, LMs might be expected to share similarities with the human language system. We then summarize evidence that LMs represent linguistic information similarly enough to humans to enable relatively accurate brain encoding and decoding during language processing. Finally, we examine which LM properties-their architecture, task performance, or training-are critical for capturing human neural responses to language and review studies using LMs as in silico model organisms for testing hypotheses about language. These ongoing investigations bring us closer to understanding the representations and processes that underlie our ability to comprehend sentences and express thoughts in language.

Genetic Mapping and Biochemical Basis of Yellow Feather Pigmentation in Budgerigars.

Parrot feathers contain red, orange, and yellow polyene pigments called psittacofulvins. Budgerigars are parrots that have been extensively bred for plumage traits during the last century, but the underlying genes are unknown. Here we use genome-wide association mapping and gene-expression analysis to map the Mendelian blue locus, which abolishes yellow pigmentation in the budgerigar. We find that the blue trait maps to a single amino acid substitution (R644W) in an uncharacterized polyketide synthase (MuPKS). When we expressed MuPKS heterologously in yeast, yellow pigments accumulated. Mass spectrometry confirmed that these yellow pigments match those found in feathers. The R644W substitution abolished MuPKS activity. Furthermore, gene-expression data from feathers of different bird species suggest that parrots acquired their colors through regulatory changes that drive high expression of MuPKS in feather epithelia. Our data also help formulate biochemical models that may explain natural color variation in parrots. VIDEO ABSTRACT.

Leveraging physiology and artificial intelligence to deliver advancements in health care.

Artificial intelligence in health care has experienced remarkable innovation and progress in the last decade. Significant advancements can be attributed to the utilization of artificial intelligence to transform physiology data to advance health care. In this review, we explore how past work has shaped the field and defined future challenges and directions. In particular, we focus on three areas of development. First, we give an overview of artificial intelligence, with special attention to the most relevant artificial intelligence models. We then detail how physiology data have been harnessed by artificial intelligence to advance the main areas of health care: automating existing health care tasks, increasing access to care, and augmenting health care capabilities. Finally, we discuss emerging concerns surrounding the use of individual physiology data and detail an increasingly important consideration for the field, namely the challenges of deploying artificial intelligence models to achieve meaningful clinical impact.

Progress toward synthetic cells.

The complexity of even the simplest known life forms makes efforts to synthesize living cells from inanimate components seem like a daunting task. However, recent progress toward the creation of synthetic cells, ranging from simple protocells to artificial cells approaching the complexity of bacteria, suggests that the synthesis of life is now a realistic goal. Protocell research, fueled by advances in the biophysics of primitive membranes and the chemistry of nucleic acid replication, is providing new insights into the origin of cellular life. Parallel efforts to construct more complex artificial cells, incorporating translational machinery and protein enzymes, are providing information about the requirements for protein-based life. We discuss recent advances and remaining challenges in the synthesis of artificial cells, the possibility of creating new forms of life distinct from existing biology, and the promise of this research for gaining a deeper understanding of the nature of living systems.

The science and engineering behind sensitized brain-controlled bionic hands.

Advances in our understanding of brain function, along with the development of neural interfaces that allow for the monitoring and activation of neurons, have paved the way for brain-machine interfaces (BMIs), which harness neural signals to reanimate the limbs via electrical activation of the muscles or to control extracorporeal devices, thereby bypassing the muscles and senses altogether. BMIs consist of reading out motor intent from the neuronal responses monitored in motor regions of the brain and executing intended movements with bionic limbs, reanimated limbs, or exoskeletons. BMIs also allow for the restoration of the sense of touch by electrically activating neurons in somatosensory regions of the brain, thereby evoking vivid tactile sensations and conveying feedback about object interactions. In this review, we discuss the neural mechanisms of motor control and somatosensation in able-bodied individuals and describe approaches to use neuronal responses as control signals for movement restoration and to activate residual sensory pathways to restore touch. Although the focus of the review is on intracortical approaches, we also describe alternative signal sources for control and noninvasive strategies for sensory restoration.

Principles of odor coding in vertebrates and artificial chemosensory systems.

The biological olfactory system is the sensory system responsible for the detection of the chemical composition of the environment. Several attempts to mimic biological olfactory systems have led to various artificial olfactory systems using different technical approaches. Here we provide a parallel description of biological olfactory systems and their technical counterparts. We start with a presentation of the input to the systems, the stimuli, and treat the interface between the external world and the environment where receptor neurons or artificial chemosensors reside. We then delineate the functions of receptor neurons and chemosensors as well as their overall input-output (I/O) relationships. Up to this point, our accounts of the systems go along similar lines. The next processing steps differ considerably: whereas in biology the processing step following the receptor neurons is the "integration" and "processing" of receptor neuron outputs in the olfactory bulb, this step has various realizations in electronic noses. For a long period of time, the signal processing stages beyond the olfactory bulb, i.e., the higher olfactory centers, were little studied. Only recently has there been a marked growth of studies tackling the information processing in these centers. In electronic noses, a third stage of processing has virtually never been considered. In this review, we provide an up-to-date overview of the current knowledge of both fields and, for the first time, attempt to tie them together. We hope it will be a breeding ground for better information, communication, and data exchange between very related but so far little-connected fields.