Check out our Editors-in-Chief’s selection of papers from the November issue of PLOS Computational Biology with a special look our new Benchmarking collection.
Motif-Aware PRALINE: Improving the alignment of motif regions
The most important functional parts of proteins are often small—but very specific—sequence motifs. Moreover, these motifs tend to be strongly conserved during evolution due to their functional role. Nevertheless, when trying to align protein sequences of the same family, it is often very difficult to align such motifs using standard multiple sequence alignment methods. Aligning functional residues correctly is essential to detect motif conservation, which can be used to filter out spuriously occurring motifs. Additionally, many downstream analyses, such as phylogenetics, are strongly reliant on alignment quality. Maurits Dijkstra and colleagues have developed a sequence alignment program named Motif-Aware PRALINE (MA-PRALINE) that incorporates information about motifs explicitly. Motifs are provided to MA-PRALINE in the PROSITE pattern syntax; it then scans the input sequences for instances of the pattern and provides a score bonus to matching sequence positions. Their method provides a reproducible alternative to editing alignments by hand in order to account for motif conservation, which is a tedious and error-prone process. They show that MA-PRALINE allows the alignment of motif-rich regions to be fine-tuned while not degrading the rest of the alignment. MA-PRALINE is available on GitHub as open source software; this allows it to be easily tailored to similar problems. They apply MA-PRALINE on the HIV-1 envelope glycoprotein (gp120) to get an improved alignment of the N-terminal glycosylation motifs. The presence of these motifs is essential for the virus in evading the immune response of the host.
The Cultural Brain Hypothesis: How culture drives brain expansion, sociality, and life history
Humans have extraordinarily large brains, which tripled in size in the last few million years. Other animals also experienced a significant, though smaller, increase in brain size. These increases are puzzling, because brain tissue is energetically expensive—a smaller brain is easier to maintain in terms of calories. Here Michael Muthukrishna and colleagues present a theory, captured in an analytic and computational model, that explains these increases in brain size: The Cultural Brain Hypothesis. The theory relies on the idea that brains expand to store and manage more information. Brains expand in response to the availability of information and calories. Information availability is affected by learning strategies (e.g. learning from others or learning by yourself), group size, mating structure, and the length of the juvenile period, which co-evolve with brain size. The model captures this co-evolution under different conditions and describes the specific and narrow conditions that can lead to a take-off in brain size—a possible pathway that led to the extraordinary expansion in their own species. They call these conditions the Cumulative Cultural Brain Hypothesis. These theories are supported by the tests using existing empirical data.
Atlases of cognition with large-scale human brain mapping
Cognitive neuroscience uses neuroimaging to identify brain systems engaged in specific cognitive tasks. However, unequivocally linking brain systems with cognitive functions is difficult: each task probes only a small number of facets of cognition, while brain systems are often engaged in many tasks. Gaël Varoquaux and colleagues develop a new approach to generate a functional atlas of cognition, demonstrating that brain systems selectively associated with specific cognitive functions. This approach relies upon an ontology that defines specific cognitive functions and the relations between them, along with an analysis scheme tailored to this ontology. Using a database of thirty neuroimaging studies, they show that this approach provides a highly-specific atlas of mental functions, and that it can decode the mental processes engaged in new tasks.
How pupil responses track value-based decision-making during and after reinforcement learning
It has long been known that the pupil dilates when we decide. These pupil dilations have predominantly been linked to arousal. However, reward-related processes may trigger pupil dilations as well, as dilations have been linked to activity in the dopaminergic midbrain, a region important for reward processing and reinforcement learning. Using a learning task and a computational model to quantitatively describe the cognitive processes that drive reinforcement learning behavior, Joanne C. Van Slooten and colleagues show that the pupil closely tracks different aspects of the reinforcement learning process. Prior to making a value-based choice, pupil dilation reflected the value of the soon-to-be-chosen option. After receiving choice feedback, early dilation reflected uncertainty about the value of recent choice options, while late constriction reflected how strongly an outcome violated current value beliefs. These findings provide the novel insight that the pupil can be used to track value-based decision-making, opening up a new method for online tracking of reinforcement learning processes.
PLOS Computational Biology Benchmarking Collection
Research in computational biology has given rise to a vast number of methods developed to solve scientific problems. For areas in which many approaches exist, researchers have a hard time deciding which tool to select to address a scientific challenge, as essentially all publications introducing a new method will claim better performance than all others. In this collection we gather the research articles published in the PLOS Computational Biology Benchmarking section that will help in this decision process. Our current research articles focus on a guide to binding site comparison and systematically Benchmarking peptide-MHC binding predictors.