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With an ever-increasing amount of (meta)genomic data being deposited in sequence databases, (meta)genome mining for natural product biosynthetic pathways occupies a critical role in the discovery of novel pharmaceutical drugs, crop protection agents and biomaterials. The genes that encode these pathways are often organised into biosynthetic gene clusters (BGCs). In 2015, we defined the Minimum Information about a Biosynthetic Gene cluster (MIBiG): a standardised data format that describes the minimally required information to uniquely characterise a BGC. We simultaneously constructed an accompanying online database of BGCs, which has since been widely used by the community as a reference dataset for BGCs and was expanded to 2021 entries in 2019 (MIBiG 2.0). Here, we describe MIBiG 3.0, a database update comprising large-scale validation and re-annotation of existing entries and 661 new entries. Particular attention was paid to the annotation of compound structures and biological activities, as well as protein domain selectivities. Together, these new features keep the database up-to-date, and will provide new opportunities for the scientific community to use its freely available data, e.g. for the training of new machine learning models to predict sequence-structure-function relationships for diverse natural products. MIBiG 3.0 is accessible online at https://mibig.secondarymetabolites.org/.
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Genoma , Genômica , Família Multigênica , Vias Biossintéticas/genéticaRESUMO
Artificial intelligence (AI) is accelerating how we conduct science, from folding proteins with AlphaFold and summarizing literature findings with large language models, to annotating genomes and prioritizing newly generated molecules for screening using specialized software. However, the application of AI to emulate human cognition in natural product research and its subsequent impact has so far been limited. One reason for this limited impact is that available natural product data is multimodal, unbalanced, unstandardized, and scattered across many data repositories. This makes natural product data challenging to use with existing deep learning architectures that consume fairly standardized, often non-relational, data. It also prevents models from learning overarching patterns in natural product science. In this Viewpoint, we address this challenge and support ongoing initiatives aimed at democratizing natural product data by collating our collective knowledge into a knowledge graph. By doing so, we believe there will be an opportunity to use such a knowledge graph to develop AI models that can truly mimic natural product scientists' decision-making.
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BACKGROUND: Untargeted metabolomics approaches based on mass spectrometry obtain comprehensive profiles of complex biological samples. However, on average only 10% of the molecules can be annotated. This low annotation rate hampers biochemical interpretation and effective comparison of metabolomics studies. Furthermore, de novo structural characterization of mass spectral data remains a complicated and time-intensive process. Recently, the field of computational metabolomics has gained traction and novel methods have started to enable large-scale and reliable metabolite annotation. Molecular networking and machine learning-based in-silico annotation tools have been shown to greatly assist metabolite characterization in diverse fields such as clinical metabolomics and natural product discovery. AIM OF REVIEW: We highlight recent advances in computational metabolite annotation workflows with a special focus on their evaluation and comparison with other tools. Whilst the progress is substantial and promising, we also argue that inconsistencies in benchmarking different tools hamper users from selecting the most appropriate and promising method for their research. We summarize benchmarking strategies of the different tools and outline several recommendations for benchmarking and comparing novel tools. KEY SCIENTIFIC CONCEPTS OF REVIEW: This review focuses on recent advances in mass spectral library-based and machine learning-supported metabolite annotation workflows. We discuss large-scale library matching and analogue search, the current bloom of mass spectral similarity scores, and how molecular networking has changed the field. In addition, the potentials and challenges of machine learning-supported metabolite annotation workflows are highlighted. Overall, recent developments in computational metabolomics have started to fundamentally change metabolomics workflows, and we expect that as a community we will be able to overcome current method performance ambiguities and annotation bottlenecks.
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Benchmarking , Metabolômica , Metabolômica/métodos , Espectrometria de Massas , Aprendizado de MáquinaRESUMO
Modulations of beta oscillatory power serve a predictive role, in preparation of future actions. It is well known that beta amplitude decreases prior to voluntary movements and expected tactile stimuli. Paradoxically, recent studies have reported a beta amplitude increase prior to expected visual and auditory stimuli. Moreover, it has been suggested that, in isochronic stimulus series, the rising beta slope is adjusted to the duration of the interstimulus interval. We investigated the characteristics of such timing related pre-stimulus beta power increases using visual stimulus sequences that were presented at three regular rates (0.61, 0.74 and 0.95Hz). EEG was recorded from twenty participants while they attended the sequences by performing a clock reading task. Time-frequency analyses showed a consistent pattern of beta modulation: the post-stimulus beta power decrease was followed by a steep increase. Contrary to recent views, we found that the peaks of beta power, for the three presentation rates, were reached at a similar latency post-stimulus, instead of a fixed interval preceding the next stimulus. This demonstrates that, at interstimulus intervals between 1-2s, beta synchronization slopes are not modulated by timing mechanisms related to prediction of upcoming stimuli. We reconcile the discrepant results by proposing that when shorter interval durations are used, as in previous studies, beta resynchronization is interrupted by the presentation of a new stimulus, making it seem as if beta power peaks prior to upcoming stimuli. We emphasize caution with respect to the notion that the timing of beta synchronization is an expression of predictive timing.
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Estimulação Acústica/métodos , Antecipação Psicológica/fisiologia , Atenção/fisiologia , Ritmo beta/fisiologia , Encéfalo/fisiologia , Estimulação Luminosa/métodos , Percepção do Tempo/fisiologia , Adulto , Idoso , Mapeamento Encefálico/métodos , Eletroencefalografia/métodos , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Reprodutibilidade dos Testes , Sensibilidade e EspecificidadeRESUMO
Effective visualization of small molecules is paramount in conveying concepts and results in cheminformatics. Scalable vector graphics (SVG) are preferred for creating such visualizations, as SVGs can be easily altered in post-production and exported to other formats. A wide spectrum of software applications already exist that can visualize molecules, and customize these visualizations, in many ways. However, software packages that can output projected 3D models onto a 2D canvas directly as SVG, while being programmatically accessible from Python, are lacking. Here, we introduce CineMol, which can draw vectorized approximations of three-dimensional small molecule models in seconds, without triangulation or ray tracing, resulting in files of around 50-300 kilobytes per molecule model for compounds with up to 45 heavy atoms. The SVGs outputted by CineMol can be readily modified in popular vector graphics editing software applications. CineMol is written in Python and can be incorporated into any existing Python cheminformatics workflow, as it only depends on native Python libraries. CineMol also provides programmatic access to all its internal states, allowing for per-atom and per-bond-based customization. CineMol's capacity to programmatically create molecular visualizations suitable for post-production offers researchers and scientists a powerful tool for enhancing the clarity and visual impact of their scientific presentations and publications in cheminformatics, metabolomics, and related scientific disciplines.Scientific contributionWe introduce CineMol, a Python-based tool that provides a valuable solution for cheminformatics researchers by enabling the direct generation of high-quality approximations of two-dimensional SVG visualizations from three-dimensional small molecule models, all within a programmable Python framework. CineMol offers a unique combination of speed, efficiency, and accessibility, making it an indispensable tool for researchers in cheminformatics, especially when working with SVG visualizations.
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The cell painting (CP) assay has emerged as a potent imaging-based high-throughput phenotypic profiling (HTPP) tool that provides comprehensive input data for in silico prediction of compound activities and potential hazards in drug discovery and toxicology. CP enables the rapid, multiplexed investigation of various molecular mechanisms for thousands of compounds at the single-cell level. The resulting large volumes of image data provide great opportunities but also pose challenges to image and data analysis routines as well as property prediction models. This review addresses the integration of CP-based phenotypic data together with or in substitute of structural information from compounds into machine (ML) and deep learning (DL) models to predict compound activities for various human-relevant disease endpoints and to identify the underlying modes-of-action (MoA) while avoiding unnecessary animal testing. The successful application of CP in combination with powerful ML/DL models promises further advances in understanding compound responses of cells guiding therapeutic development and risk assessment. Therefore, this review highlights the importance of unlocking the potential of CP assays when combined with molecular fingerprints for compound evaluation and discusses the current challenges that are associated with this approach.
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Almost all decisions in everyday life rely on multiple sensory inputs that can come from common or independent causes. These situations invoke perceptual uncertainty about environmental properties and the signals' causal structure. Using the audiovisual McGurk illusion, this study investigated how observers formed perceptual and causal confidence judgements in information integration tasks under causal uncertainty. Observers were presented with spoken syllables, their corresponding articulatory lip movements or their congruent and McGurk combinations (e.g. auditory B/P with visual G/K). Observers reported their perceived auditory syllable, the causal structure and confidence for each judgement. Observers were more accurate and confident on congruent than unisensory trials. Their perceptual and causal confidence were tightly related over trials as predicted by the interactive nature of perceptual and causal inference. Further, observers assigned comparable perceptual and causal confidence to veridical 'G/K' percepts on audiovisual congruent trials and their causal and perceptual metamers on McGurk trials (i.e. illusory 'G/K' percepts). Thus, observers metacognitively evaluate the integrated audiovisual percept with limited access to the conflicting unisensory stimulus components on McGurk trials. Collectively, our results suggest that observers form meaningful perceptual and causal confidence judgements about multisensory scenes that are qualitatively consistent with principles of Bayesian causal inference. This article is part of the theme issue 'Decision and control processes in multisensory perception'.
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Ilusões , Metacognição , Percepção da Fala , Humanos , Percepção Auditiva , Percepção Visual , Teorema de Bayes , Estimulação Luminosa , Estimulação AcústicaRESUMO
Developments in computational omics technologies have provided new means to access the hidden diversity of natural products, unearthing new potential for drug discovery. In parallel, artificial intelligence approaches such as machine learning have led to exciting developments in the computational drug design field, facilitating biological activity prediction and de novo drug design for molecular targets of interest. Here, we describe current and future synergies between these developments to effectively identify drug candidates from the plethora of molecules produced by nature. We also discuss how to address key challenges in realizing the potential of these synergies, such as the need for high-quality datasets to train deep learning algorithms and appropriate strategies for algorithm validation.
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Inteligência Artificial , Produtos Biológicos , Humanos , Algoritmos , Aprendizado de Máquina , Descoberta de Drogas , Desenho de Fármacos , Produtos Biológicos/farmacologiaRESUMO
Aging has been shown to impact multisensory perception, but the underlying computational mechanisms are unclear. For effective interactions with the environment, observers should integrate signals that share a common source, weighted by their reliabilities, and segregate those from separate sources. Observers are thought to accumulate evidence about the world's causal structure over time until a decisional threshold is reached. Combining psychophysics and Bayesian modeling, we investigated how aging affects audiovisual perception of spatial signals. Older and younger adults were comparable in their final localization and common-source judgment responses under both speeded and unspeeded conditions, but were disproportionately slower for audiovisually incongruent trials. Bayesian modeling showed that aging did not affect the ability to arbitrate between integration and segregation under either unspeeded or speeded conditions. However, modeling the within-trial dynamics of evidence accumulation under speeded conditions revealed that older observers accumulate noisier auditory representations for longer, set higher decisional thresholds, and have impaired motor speed. Older observers preserve audiovisual localization performance, despite noisier sensory representations, by sacrificing response speed.
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Envelhecimento/fisiologia , Percepção , Desempenho Psicomotor , Tempo de Reação , Idoso , Idoso de 80 Anos ou mais , HumanosRESUMO
Multisensory perception is regarded as one of the most prominent examples where human behaviour conforms to the computational principles of maximum likelihood estimation (MLE). In particular, observers are thought to integrate auditory and visual spatial cues weighted in proportion to their relative sensory reliabilities into the most reliable and unbiased percept consistent with MLE. Yet, evidence to date has been inconsistent. The current pre-registered, large-scale (N = 36) replication study investigated the extent to which human behaviour for audiovisual localization is in line with maximum likelihood estimation. The acquired psychophysics data show that while observers were able to reduce their multisensory variance relative to the unisensory variances in accordance with MLE, they weighed the visual signals significantly stronger than predicted by MLE. Simulations show that this dissociation can be explained by a greater sensitivity of standard estimation procedures to detect deviations from MLE predictions for sensory weights than for audiovisual variances. Our results therefore suggest that observers did not integrate audiovisual spatial signals weighted exactly in proportion to their relative reliabilities for localization. These small deviations from the predictions of maximum likelihood estimation may be explained by observers' uncertainty about the world's causal structure as accounted for by Bayesian causal inference.