Biology subjects

Barzilay, R.

Publications and source records attributed to Barzilay, R..

5 recordsLinked to original sources

bioRxiv2023

Improving influenza A vaccine strain selection through deep evolutionary models

Current vaccines provide limited protection against rapidly evolving viruses. For example, the flu vaccines effectiveness has averaged below 40% for the past five years. Today, clinical outcomes of vaccine effectiveness can only be assessed retrospectively. Since vaccine strains are selected at least six months ahead of flu season, prospective estimation of their effectiveness is crucial but remains under-explored. In this paper, we propose an in-silico method named VaxSeer that selects vaccine strains based on their coverage scores, which quantifies expected vaccine effectiveness in future seasons. This score considers both the future dominance of circulating viruses and antigenic profiles of vaccine candidates. Based on historical WHO data, our approach consistently selects superior strains than the annual recommendations. Finally, the prospective coverage score exhibits a strong correlation with retrospective vaccine effectiveness and reduced disease burden, highlighting the promise of this framework in driving the vaccine selection process.

immunology↗

bioRxiv2023

A general exposome factor explains individual differences in functional brain network topography and cognition in youth

Our minds and brains are highly unique. Despite the long-recognized importance of the environment in shaping individual differences in cognitive neurodevelopment, only with the combination of deep phenotyping approaches and the availability of large-scale datasets have we been able to more comprehensively characterize the many inter-connected features of an individuals environment and experience ("exposome"). Moreover, despite clear evidence that brain organization is highly individualized, most neuroimaging studies still rely on group atlases to define functional networks, smearing away inter-individual variation in the spatial layout of functional networks across the cortex ("functional topography"). Here, we leverage the largest longitudinal study of brain and behavior development in the United States to investigate how an individuals exposome may contribute to functional brain network organization leading to differences in cognitive functioning. To do so, we apply three previously-validated data driven computational models to characterize an individuals multidimensional exposome, define individual-specific maps of functional brain networks, and measure cognitive functioning across broad domains. In pre-registered analyses replicated across matched discovery (n=5,139, 48.5% female) and replication (n=5,137, 47.1% female) samples, we find that a childs exposome is associated with multiple domains of cognitive functioning both at baseline assessment and two years later - over and above associations with baseline cognition. Cross-validated ridge regression models reveal that the exposome is reflected in childrens unique patterns of functional topography. Finally, we uncover both shared and unique contributions of the exposome and functional topography to cognitive abilities, finding that models trained on a single variable capturing a childs exposome can more accurately and parsimoniously predict future cognitive performance than models trained on a wealth of personalized neuroimaging data. This study advances our understanding of how childhood environments contribute to unique patterns of functional brain organization and variability in cognitive abilities.

neuroscience↗

bioRxiv2022

Broadly applicable and accurate protein design by integrating structure prediction networks and diffusion generative models

There has been considerable recent progress in designing new proteins using deep learning methods1-9. Despite this progress, a general deep learning framework for protein design that enables solution of a wide range of design challenges, including de novo binder design and design of higher order symmetric architectures, has yet to be described. Diffusion models10,11 have had considerable success in image and language generative modeling but limited success when applied to protein modeling, likely due to the complexity of protein backbone geometry and sequence-structure relationships. Here we show that by fine tuning the RoseTTAFold structure prediction network on protein structure denoising tasks, we obtain a generative model of protein backbones that achieves outstanding performance on unconditional and topology-constrained protein monomer design, protein binder design, symmetric oligomer design, enzyme active site scaffolding, and symmetric motif scaffolding for therapeutic and metal-binding protein design. We demonstrate the power and generality of the method, called RoseTTAFold Diffusion (RFdiffusion), by experimentally characterizing the structures and functions of hundreds of new designs. In a manner analogous to networks which produce images from user-specified inputs, RFdiffusion enables the design of diverse, complex, functional proteins from simple molecular specifications.

biochemistry↗

bioRxiv2022

Personalized Functional Brain Network Topography Predicts Individual Differences in Youth Cognition

Individual differences in cognition during childhood are associated with important social, physical, and mental health outcomes in adolescence and adulthood. Given that cortical surface arealization during development reflects the brains functional prioritization, quantifying variation in the topography of functional brain networks across the developing cortex may provide insight regarding individual differences in cognition. We test this idea by defining personalized functional networks (PFNs) that account for interindividual heterogeneity in functional brain network topography in 9-10 year olds from the Adolescent Brain Cognitive DevelopmentSM Study. Across matched discovery (n=3,525) and replication (n=3,447) samples, the total cortical representation of fronto-parietal PFNs positively correlated with general cognition. Cross-validated ridge regressions trained on PFN topography predicted cognition across domains, with prediction accuracy increasing along the cortexs sensorimotor-association organizational axis. These results establish that functional network topography heterogeneity is associated with individual differences in cognition before the critical transition into adolescence.

neuroscience↗

bioRxiv2021

Stability of Polygenic Scores Across Discovery Genome-Wide Association Studies

Polygenic scores (PGS) are commonly evaluated in terms of their predictive accuracy at the population level by the proportion of phenotypic variance they explain. To be useful for precision medicine applications, they also need to be evaluated at the individual patient level when phenotypes are not necessarily already known. Hence, we investigated the stability of PGS in European-American (EUR)- and African-American (AFR)-ancestry individuals from the Philadelphia Neurodevelopmental Cohort (PNC) and the Adolescent Brain Cognitive Development (ABCD) cohort using different discovery GWAS for post-traumatic stress disorder (PTSD), type-2 diabetes (T2D), and height. We found that pairs of EUR-ancestry GWAS for the same trait had genetic correlations > 0.92. However, PGS calculated from pairs of sameancestry and different-ancestry GWAS had correlations that ranged from <0.01 to 0.74. PGS stability was higher for GWAS that explained more of the trait variance, with height PGS being more stable than PTSD or T2D PGS. Focusing on the upper end of the PGS distribution, different discovery GWAS do not consistently identify the same individuals in the upper quantiles, with the best case being 60% of individuals above the 80th percentile of PGS overlapping from one height GWAS to another. The degree of overlap decreases sharply as higher quantiles, less heritable traits, and different-ancestry GWAS are considered. PGS computed from different discovery GWAS have only modest correlation at the level of the individual patient, underscoring the need to proceed cautiously with integrating PGS into precision medicine applications.

genetics↗