Over the past 20 years, neuroscience has been propelled forward by theory-driven experimentation. We consider the future outlook for the field in the age of big neural data and powerful artificial intelligence models.
The convolution of membranes called cristae is a critical structural and functional feature of mitochondria. Crista structure is highly diverse between different cell types, reflecting their role in metabolic adaptation. However, their precise three-...
In this issue of PLOS Biology, Ibrahim and colleagues demonstrate how AlphaFold-multimer, an artificial intelligence-based structure prediction tool, can be used to identify sequence motifs binding to the ATG8 family of proteins central to autophagy.
Cloud labs, where experiments are executed remotely in robotic facilities, can improve the reproducibility, accessibility, and scalability of experimental biology. Funding and training programs will enable academics to overcome barriers to adopting s...
Does reductionism, in the era of machine learning and now interpretable AI, facilitate or hinder scientific insight? The protein ribbon diagram, as a means of visual reductionism, is a case in point.
The rate of adaptive evolution depends on the rate at which beneficial mutations are introduced into a population and the fitness effects of those mutations. The rate of beneficial mutations and their expected fitness effects is often difficult to em...
The best performing computer vision systems are based on deep neural networks (DNNs). A study in this issue of PLOS Biology shows that DNNs trained on noisy stimuli are better than standard DNNs at mirroring both human behavioral and neural visual re...
Deep neural networks (DNNs) for object classification have been argued to provide the most promising model of the visual system, accompanied by claims that they have attained or even surpassed human-level performance. Here, we evaluated whether DNNs ...
Michaelis constants (Km) are essential to predict the catalytic rate of enzymes, but are not widely available. A new study in PLOS Biology uses artificial intelligence (AI) to accurately predict Km on a proteome-wide scale, paving the way for dynamic...
The Michaelis constant KM describes the affinity of an enzyme for a specific substrate and is a central parameter in studies of enzyme kinetics and cellular physiology. As measurements of KM are often difficult and time-consuming, experimental estima...