Biology subjects

Ranganathan, R.

Publications and source records attributed to Ranganathan, R..

5 recordsLinked to original sources

bioRxiv2018

Stroke survivors show task-dependent modulation of motor variability during bimanual coordination

Stroke often results in hemiparesis, leaving one side of the body affected relative to the other side. Prior research has shown that the affected arm has higher variability - however, the extent to which this variability can be modulated is unclear. Here we used a shared bimanual task to examine the degree to which participants could modulate the variability in the affected arm after stroke. Participants with chronic stroke (n = 11), and age-matched controls (n = 11) performed unimanual and bimanual reaching movements to move a cursor on a screen to different targets. In the unimanual condition, the cursor was controlled only by the movement of a single arm whereas in the bimanual condition, the cursor position was \"shared\" between the two arms by using a weighted average of the two hand positions. Unknown to the participants, we altered the weightings of the affected and unaffected arms to cursor motion and examined how the movement variability on each arm changed depending on its contribution to the task. Results showed that stroke survivors had higher movement variability on the affected arm - however, like age-matched controls, they were able to modulate the variability in both the affected and unaffected arms according to the weighting condition. Specifically, as the weighting on a particular arm increased (i.e. it became more important to the task), the movement variability decreased. These results show that stroke survivors are capable of modulating variability depending on the task context, and this feature may potentially be exploited for rehabilitation paradigms

neuroscience↗

bioRxiv2017

Inferring amino acid interactions underlying protein function

Protein function arises from a poorly defined pattern of cooperative energetic interactions between amino acid residues. Strategies for deducing this pattern have been proposed, but lack of benchmark data has limited experimental verification. Here, we extend deep-mutation technologies to enable measurement of many thousands of pairwise amino acid couplings in members of a protein family. The data show that despite great evolutionary divergence, homologous proteins conserve a sparse, spatially distributed network of cooperative interactions between amino acids that underlies function. This pattern is quantitatively captured in the coevolution of amino acid positions, especially as indicated by the statistical coupling analysis (SCA), providing experimental confirmation of the key tenets of this method. This work establishes a clear link between physical constraints on protein function and sequence analysis, enabling a general practical approach for understanding the structural basis for protein function.

biochemistry↗

bioRxiv2017

Learning the pattern of epistasis linking genotype and phenotype in a protein

Understanding the pattern of epistasis - the non-independence of mutations - is critical for relating genotype and phenotype in biological systems. However, the complexity of potential epistatic interactions has limited approaches to this problem at any level. To develop practical strategies, we carried out a comprehensive experimental study of epistasis between all mutations that link two phenotypically distinct variants of the Entacmaea quadricolor fluorescent protein. The data demonstrate significant high-order epistatic interactions between mutations, but also reveals extraordinary sparsity, enabling novel experimental strategies and sequence-based statistical methods for learning the relevant epistasis. The sequence space linking the parental fluorescent proteins is functionally connected through paths of single mutations; thus, high-order epistasis in proteins is consistent with evolution through stepwise variation and selection. This work initiates a path towards characterizing epistasis in proteins in general.

systems biology↗

bioRxiv2017

Hierarchical organization endows the kinase domain with regulatory plasticity

The functional diversity of kinases enables specificity in cellular signal transduction. Yet general rules for how the kinase domain allows the more than 500 members of the human kinome to receive specific regulatory inputs and convey information to appropriate substrates - all while using the common signaling currency of phosphorylation - remain enigmatic. Here, using co-evolution analysis and quantitative live-cell assays, we reveal a deep hierarchical organization of the kinase domain that facilitates the orthogonal evolution of regulatory inputs and substrate outputs while maintaining catalytic function. Three quasi-independent functional units in the kinase domain (known as protein sectors) encode for catalysis, substrate specificity and regulation, and these distinct subdomains are differentially exploited by somatic cancer mutations and harnessed by allosteric inhibitors. We propose that this functional architecture endows the kinase domain with inherent regulatory plasticity.

systems biology↗

bioRxiv2017

High variability impairs motor learning regardless of whether it affects task performance

Motor variability plays an important role in motor learning, although the exact mechanisms of how variability affects learning is not well understood. Recent evidence suggests that motor variability may have different effects on learning in redundant tasks, depending on whether it is present in the task space (where it affects task performance), or in the null space (where it has no effect on task performance). Here we examined the effect of directly introducing null and task space variability using a manipulandum during the learning of a motor task. Participants learned a bimanual shuffleboard task for 2 days, where their goal was to slide a virtual puck as close as possible towards a target. Critically, the distance traveled by the puck was determined by the sum of the left and right hand velocities, which meant that there was redundancy in the task. Participants were divided into five groups - based on both the dimension in which the variability was introduced and the amount of variability that was introduced during training. Results showed that although all groups were able to reduce error with practice, learning was affected more by the amount of variability introduced rather than the dimension in which variability was introduced. Specifically, groups with higher movement variability during practice showed larger errors at the end of practice compared to groups that had low variability during learning. These results suggest that although introducing variability can increase exploration of new solutions, this may come at a cost of decreased stability of the learned solution.

neuroscience↗