Biology subjects

Ranganathan, R.

Publications and source records attributed to Ranganathan, R..

At least 19 recordsLinked to original sources

bioRxiv2026

The Phenotypic Landscape of a Circadian Clock

Circadian clocks produce near-24-hour oscillations through biochemical feedback loops. To study their architecture, we developed a deep sequencing assay that measures the phenotypes of thousands of mutant clocks in parallel. We reveal a landscape where oscillator properties are factorized: mutations change period without decreasing amplitude and while maintaining a balanced waveform. Mutations that either shorten or lengthen period localize to specific protein-protein interaction surfaces, while a particularly sensitive region near the KaiC interdomain linker can cause extreme effects. After entrainment, high amplitude mutant oscillators form a tunable low-dimensional manifold in the period-phase plane, suggesting that most period mutations leave the coupling to the environment unchanged. In contrast, mutations that reduce amplitude are concentrated in a specific long period phenotype. This correlation structure may support the evolvability of this dynamical molecular system and is a powerful constraint on underlying mechanism.

biophysics↗

bioRxiv2026

Improved inference of multiscale sequence statistics in generative protein models

High dimensionality and multiscale statistical structure are pervasive features of biological data, posing fundamental challenges for modeling. Because model inference generally proceeds with far fewer data than parameters, statistical patterns across scales are often unevenly represented. Protein sequences provide a paradigmatic example: statistics across homologs are inherently multiscale, displaying collective correlations among conserved residue sectors that encode function, alongside localized correlations corresponding to physical contacts outside these sectors. Standard regularization strategies used to mitigate undersampling during model inference have been shown to capture these patterns unevenly, a bias that compromises generative models of protein sequences by limiting their ability to produce both functional and diverse proteins. This limitation is exemplified by Boltzmann Machine-based generative models, which so far have required post hoc corrections to recover functionality, at the cost of reduced sequence diversity and novelty. Here, we introduce the stochastic Boltzmann Machine (sBM), a new regularization strategy that more accurately captures different correlation scales. Through analyses of theoretical models with known ground-truth parameters and experiments on the chorismate mutase family, we show that sBM effectively mitigates distortions in the estimation of model parameters, enabling the generation of functional sequences with greater diversity and without the need for post hoc corrections. These results advance the inference of generative models that more faithfully reflect the evolutionary constraints shaping protein sequences.

systems biology↗

bioRxiv2026

BCAR: A fast and general barcode-sequence mapper for correcting sequencing errors

MotivationDNA barcodes are commonly used as a tool to distinguish genuine mutations from sequencing errors in sequencing-based assays. In the presence of indel errors, utilizing barcodes requires accurate alignment of the raw reads to distinguish genuine indels from indel errors. Existing strategies to do this generally rely on aligners built for homology comparison and do not fully utilize quality scores. We reasoned that developing an aligner purpose-built for error correction could yield higher quality barcode-sequence maps. ResultsHere, we present BCAR, a fast barcode-sequence mapper for correcting sequencing errors. BCAR considers all of the evidence for each base call at each position both during alignment and during final consensus generation. BCAR creates high-accuracy barcode-sequence maps from simulated reads across a broad range of error rates and read lengths, outperforming existing methods. We apply BCAR to two experimental datasets, where it generates high-quality barcode-sequence maps. Availability and implementationBCAR source code, documentation and test data are available from: https://github.com/dry-brews/BCAR

bioinformatics↗

bioRxiv2024

Natural Language Prompts Guide the Design of Novel Functional Protein Sequences

The advent of natural language interaction with machines has ushered in new innovations in text-guided generation of images, audio, video, and more. In this arena, we introduce Biological Multi-Modal Model (BioM3), as a novel framework for designing functional proteins via natural language prompts. This framework integrates natural language with protein design through a three-stage process: aligning protein and text representations in a joint embedding space learned using contrastive learning, refinement of the text embeddings, and conditional generation of protein sequences via a discrete autoregressive diffusion model. BioM3 synthe-sizes protein sequences with detailed descriptions of the protein structure, lineage, and function from text annotations to enable the conditional generation of novel sequences with desired attributes through natural language prompts. We present in silico validation of the model predictions for subcellular localization prediction, reaction classification, remote homology detection, scaffold in-painting, and structural plausibility, and in vivo and in vitro experimental tests of natural language prompt-designed synthetic analogs of Src-homology 3 (SH3) domain proteins that mediate signaling in the Sho1 osmotic stress response pathway in bakers yeast. BioM3 possesses state-of-the-art performance in zero-shot prediction and homology detection tasks, and generates proteins with native-like tertiary folds and wild-type levels of experimentally assayed function.

synthetic biology↗

bioRxiv2024

Craving Connections: Exploring the Relationship Between Pregnancy Cravings, Maternal Health, and Child Preferences in Indian Families

Although Pregnancy cravings are a common phenomenon, their implications on maternal health, family dietary habits, and child food preferences remain underexplored, particularly in the Indian context. This study aimed to investigate the prevalence and nature of pregnancy cravings among Indian women and their potential impact on maternal health outcomes and family dietary patterns. A cross-sectional survey was conducted with 119 women who had experienced pregnancy, collecting data on craving patterns, maternal health indicators, and perceived influences on family diet. Results revealed that most women craved sugary snacks, predominantly in the second trimester. Notably, 61.9% of participants cravings did not align with their spouses food preferences, suggesting potential shifts in family dietary habits. The study also found associations between cravings, mood swings, and morning sickness, highlighting the complex interplay of physiological and psychological factors during pregnancy. These findings underscore the need for culturally sensitive, personalised prenatal care that address both the nutritional and emotional aspects of pregnancy cravings, influencing long-term family health outcomes.

scientific communication and education↗

bioRxiv2023

Functional Protein Dynamics in a Crystal

Proteins are molecular machines and to understand how they work, we need to understand how they move. New pump-probe time-resolved X-ray diffraction methods open up ways to initiate and observe protein motions with atomistic detail in crystals on biologically relevant timescales. However, practical limitations of these experiments demands parallel development of effective molecular dynamics approaches to accelerate progress and extract meaning. Here, we establish robust and accurate methods for simulating dynamics in protein crystals, a nontrivial process requiring careful attention to equilibration, environmental composition, and choice of force fields. With more than seven milliseconds of sampling of a single chain, we identify critical factors controlling agreement between simulation and experiments and show that simulated motions recapitulate ligand-induced conformational changes. This work enables a virtuous cycle between simulation and experiments for visualizing and understanding the basic functional motions of proteins.

biophysics↗

bioRxiv2023

ProtWave-VAE: Integrating autoregressive sampling with latent-based inference for data-driven protein design

Deep generative models (DGMs) have shown great success in the understanding of data-driven design of proteins. Variational autoencoders (VAEs) are a popular DGM approach that can learn the correlated patterns of amino acid mutations within a multiple sequence alignment (MSA) of protein sequences and distill this information into a low-dimensional latent space to expose phylogenetic and functional relationships and guide generative protein design. Autoregressive (AR) models are another popular DGM approach that typically lack a low-dimensional latent embedding but do not require training sequences to be aligned into an MSA and enable the design of variable length proteins. In this work, we propose ProtWave-VAE as a novel and lightweight DGM employing an information maximizing VAE with a dilated convolution encoder and autoregressive WaveNet decoder. This architecture blends the strengths of the VAE and AR paradigms in enabling training over unaligned sequence data and the conditional generative design of variable length sequences from an interpretable low-dimensional learned latent space. We evaluate the models ability to infer patterns and design rules within alignment-free homologous protein family sequences and to design novel synthetic proteins in four diverse protein families. We show that our model can infer meaningful functional and phylogenetic embeddings within latent spaces and make highly accurate predictions within semi-supervised downstream fitness prediction tasks. In an application to the C-terminal SH3 domain in the Sho1 transmembrane osmosensing receptor in bakers yeast, we subject ProtWave-VAE designed sequences to experimental gene synthesis and select-seq assays for osmosensing function to show that the model enables de novo generative design, conditional C-terminus diversification, and engineering of osmosensing function into SH3 paralogs.

synthetic biology↗

bioRxiv2023

ProT-VAE: Protein Transformer Variational AutoEncoder for Functional Protein Design

The data-driven design of protein sequences with desired function is challenged by the absence of good theoretical models for the sequence-function mapping and the vast size of protein sequence space. Deep generative models have demonstrated success in learning the sequence to function relationship over natural training data and sampling from this distribution to design synthetic sequences with engineered functionality. We introduce a deep generative model termed the Protein Transformer Variational AutoEncoder (ProT-VAE) that furnishes an accurate, generative, fast, and transferable model of the sequence-function relationship for data-driven protein engineering by blending the merits of variational autoencoders to learn interpretable, low-dimensional latent embeddings and fully generative decoding for conditional sequence design with the expressive, alignment-free featurization offered by transformers. The model sandwiches a lightweight, task-specific variational autoencoder between generic, pre-trained transformer encoder and decoder stacks to admit alignment-free training in an unsupervised or semi-supervised fashion, and interpretable low-dimensional latent spaces that facilitate understanding, optimization, and generative design of functional synthetic sequences. We implement the model using NVIDIAs BioNeMo framework and validate its performance in retrospective functional prediction and prospective design of novel protein sequences subjected to experimental synthesis and testing. The ProT-VAE latent space exposes ancestral and functional relationships that enable conditional generation of novel sequences with high functionality and substantial sequence diversity. We anticipate that the model can offer an extensible and generic platform for machine learning-guided directed evolution campaigns for the data-driven design of novel synthetic proteins with "super-natural" function.

synthetic biology↗

bioRxiv2022

Deep learning-enabled design of synthetic orthologs of a signaling protein

Evolution-based deep generative models represent an exciting direction in understanding and designing proteins. An open question is whether such models can represent the constraints underlying specialized functions that are necessary for organismal fitness in specific biological contexts. Here, we examine the ability of three different models to produce synthetic versions of SH3 domains that can support function in a yeast stress signaling pathway. Using a select-seq assay, we show that one form of a variational autoencoder (VAE) recapitulates the functional characteristics of natural SH3 domains and classifies fungal SH3 homologs hierarchically by function and phylogeny. Locality in the latent space of the model predicts and extends the function of natural orthologs and exposes amino acid constraints distributed near and far from the SH3 ligand-binding site. The ability of deep generative models to specify orthologous function in vivo opens new avenues for probing and engineering protein function in specific cellular environments.

molecular biology↗

bioRxiv2022

GLT8D1 mutations cause amyotrophic lateral sclerosis via disruption of neurotrophin signalling within membrane lipid rafts

Mutations within GLT8D1 contribute to familial amyotrophic lateral sclerosis. Pathogenic mutations impair GLT8D1 glycosyltransferase enzymatic function via a dominant negative mechanism, yet the downstream mechanism leading to neurotoxicity is unclear. Here we show that a p.R92C mutation causes fragmentation of the Golgi network and reduces ganglioside expression within membrane lipid rafts (MLRs), leading to impaired neurotrophin signalling. Expression of p.R92C-GLT8D1 in HEK293 cells and mouse primary neurons reduces expression of GM1 gangliosides within the cell plasma membrane leading to disruption of MLRs. Furthermore, p.R92C-GLT8D1 reduces TrkB-mediated pro-survival signalling in MLRs isolated from primary neurons. Interestingly, up-regulation of wild-type GLT8D1 enhances MLRs and promotes pro-survival signalling through TrkB. This closely mirrors findings for another ALS gene, CAV1, suggesting convergence on a common pathogenic pathway. Other ALS genes have been associated with Golgi dysfunction and may disrupt the same pathway, suggesting a potential new therapeutic approach via upregulation of GLT8D1. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=193 SRC="FIGDIR/small/497990v1_ufig1.gif" ALT="Figure 1"> View larger version (69K): org.highwire.dtl.DTLVardef@1992dc4org.highwire.dtl.DTLVardef@18be04eorg.highwire.dtl.DTLVardef@333216org.highwire.dtl.DTLVardef@11d76c5_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2021

Augmentation of task-relevant variability enhances consolidation of motor learning

Motor memories undergo a period of consolidation before they become resistant to the practice of another task. Although movement variability is important in motor memory consolidation, its role is not fully understood in redundant tasks where variability can exist along two orthogonal subspaces (the task space and the null space) that have different effects on task performance. Here, we used haptic perturbations to augment variability in these different spaces and examined their effect on motor memory consolidation. Participants learned a shuffleboard task, where they held a bimanual manipulandum and made a discrete throwing motion to slide a virtual puck towards a target. The task was redundant because the distance travelled by the puck was determined by the sum of the left and right hand speeds at the time of release. After participants initially practiced the task, we used haptic perturbations to introduce motor variability in the task space or null space, and subsequently examined consolidation of the original task on the next day. We found that regardless of the amplitude, augmenting variability in the task space resulted in significantly better consolidation. This benefit of increasing task space variability was likely due to the fact that it did not disrupt the pre-existing coordination strategy. These results suggest that the effects of variability on motor memory consolidation depend on the interplay between the induced variability and the pre-existing coordination strategy.

neuroscience↗

bioRxiv2021

Undersampling and the inference of coevolution in proteins

Protein structure, function, and evolution depend on local and collective epistatic interactions between amino acids. A powerful approach to defining these interactions is to construct models of couplings between amino acids that reproduce the empirical statistics (frequencies and correlations) observed in sequences comprising a protein family. The top couplings are then interpreted. Here, we show that as currently implemented, this inference is always biased, a problem that fundamentally arises from the distinct scales at which epistasis occurs in proteins in the context of limited sampling. We show that these issues explain the ability of current approaches to predict tertiary contacts between amino acids and the inability to obviously expose larger networks of functionally-relevant, collectively evolving residues called sectors. This work provides a necessary foundation for more deeply understanding and improving evolution-based models of proteins.

biophysics↗

bioRxiv2020

Motor variability prior to learning is a poor predictor of the ability to adopt new movement solutions

Many contexts in motor learning require a learner to change from an existing movement solution to a novel movement solution to perform the same task. Recent evidence has pointed to motor variability prior to learning as a potential marker for predicting individual differences in motor learning. However, it is not known if this variability is predictive of the ability to adopt a new movement solution for the same task. Here, we examined this question in the context of a redundant precision task requiring control of motor variability. Fifty young adults learned a precision task that involved throwing a virtual puck toward a target using both hands. Because the speed of the puck depended on the sum of speeds of both hands, this task could be achieved using multiple solutions. Participants initially performed a baseline task where there was no constraint on the movement solution, and then performed a novel task where they were constrained to adopt a specific movement solution requiring asymmetric left and right hand speeds. Results showed that participants were able to learn the new solution, and this change was associated with changes in both the amount and structure of variability. However, individual differences in baseline motor variability were only weakly correlated with initial and final task performance when using the new solution, with greater variability being associated with higher errors. We also found a strong specificity component - initial variability when using the new solution was highly correlated with final task performance with the new solution, but once again, higher variability was associated with greater errors. These results suggest that motor variability is not necessarily indicative of flexibility and highlight the need to consider the task context in determining the relation between motor variability and learning.

neuroscience↗

bioRxiv2020

Practice modifies the response to errors during a novel motor sequence learning task

The occurrence of an error when performing a motor sequence causes an immediate reduction in speed on subsequent trials, which is referred to as post-error slowing. However, understanding how post-error slowing changes with practice has been difficult because it requires extended practice on a novel sequence task. To address this issue, we examined post-error slowing in a novel glove-based typing task that participants performed for 15 consecutive days. Speed and accuracy improved from the early to middle stages of practice, but did not show any further improvements between middle and late stage of practice. However, when we analyzed the response to errors, we found that participants decreased both the magnitude and duration of post-error slowing with practice, even after there were no detectable improvements in overall task performance. These results indicate that learning not only improves overall task performance but also modifies the ability to respond to errors.

neuroscience↗

bioRxiv2020

Evolution-based design of chorismate mutase enzymes

The rational design of enzymes is an important goal for both fundamental and practical reasons. Here, we describe a design process in which we learn the constraints for specifying proteins purely from evolutionary sequence data, build libraries of synthetic genes, and test them for activity in vivo using a quantitative complementation assay. For chorismate mutase, a key enzyme in the biosynthesis of aromatic amino acids, we demonstrate the design of natural-like catalytic function with substantial sequence diversity. Further optimization focuses the generative model towards function in a specific genomic context. The data show that sequence-based statistical models suffice to specify proteins and provide access to an enormous space of synthetic functional sequences. This result provides a foundation for a general process for evolution-based design of artificial proteins. One-sentence summaryAn evolution-based, data-driven engineering process can build synthetic functional enzymes.

biochemistry↗

bioRxiv2019

Controlling a robotic arm for functional tasks using a wireless head-joystick: A case study of a child with congenital absence of upper and lower limbs

Children with movement impairments needing assistive devices for activities of daily living often require novel methods for controlling these devices. Body-machine interfaces, which rely on body movements, are particularly well-suited for children as they are non-invasive and have high signal-to-noise ratios. Here, we examined the use of a head-joystick to enable a child with congenital absence of all four limbs to control a seven degree-of-freedom robotic arm. Head movements were measured with a wireless inertial measurement unit and used to control a robotic arm to perform two functional tasks - a drinking task and a block stacking task. The child practiced these tasks over multiple sessions; a control participant performed the same tasks with a manual joystick. Our results showed that the child was able to successfully perform both tasks, with movement times decreasing by ~40-50% over 6-8 sessions of training. The childs performance with the head-joystick was also comparable to the control participant using a manual joystick. These results demonstrate the potential of using head movements for the control of high degree-of-freedom tasks in children with limited movement repertoire.

neuroscience↗

bioRxiv2019

Haptic assistance that restricts use of redundant solutions is detrimental to motor learning

Understanding the use of haptic assistance to facilitate motor learning is a critical issue, especially in the context of tasks requiring control of motor variability. However, the question of how haptic assistance should be designed in tasks with redundancy, where multiple solutions are available, is currently unknown. Here we examined the effect of haptic assistance that either allowed or restricted the use of redundant solutions on the learning of a bimanual steering task. 60 college-aged participants practiced steered a single cursor placed in between their hands along a smooth W-shaped track of a certain width as quickly as possible. Haptic assistance was either applied at the task level using a force channel that only constrained the cursor to the track, allowing for the use of different hand trajectories, or (ii) the individual effector level using a force channel that constrained each hand to a specific trajectory. In addition, we also examined the effect of fading - i.e., decreasing assistance with practice to reduce dependence on haptic assistance. Results showed all groups improved with practice - however, groups with haptic assistance at the individual effector level performed worse than those at the task level. Moreover, fading of assistance did not offer learning benefits over constant assistance. Overall, the results suggest that haptic assistance is not effective for motor learning when it restricts the use of redundant solutions.

neuroscience↗

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↗