Conservation of Specificity in Two Low-Specificity Proteins.

Many regulatory proteins bind peptide regions of target proteins and modulate their activity. Such regulatory proteins can often interact with highly diverse target peptides. In many instances, it is not known if the peptide-binding interface discriminates targets in a biological context, or whether biological specificity is achieved exclusively through external factors such as subcellular localization. We used an evolutionary biochemical approach to distinguish these possibilities for two such low-specificity proteins: S100A5 and S100A6. We used isothermal titration calorimetry to study the binding of peptides with diverse sequence and biochemistry to human S100A5 and S100A6. These proteins bound distinct, but overlapping, sets of peptide targets. We then studied the peptide binding properties of orthologs sampled from across five amniote species. Binding specificity was conserved along all lineages, for the last 320 million years, despite the low specificity of each protein. We used ancestral sequence reconstruction to determine the binding specificity of the last common ancestor of the paralogs. The ancestor bound the entire set of peptides bound by modern S100A5 and S100A6 proteins, suggesting that paralog specificity evolved via subfunctionalization. To rule out the possibility that specificity is conserved because it is difficult to modify, we identified a single historical mutation that, when reverted in human S100A5, gave it the ability to bind an S100A6-specific peptide. These results reveal strong evolutionary constraints on peptide binding specificity. Despite being able to bind a large number of targets, the specificity of S100 peptide interfaces is likely important for the biology of these proteins.

Biological sex and BMI influence the longitudinal evolution of adolescent and young adult MRI-visible perivascular spaces.

Background and Purpose: An association recently emerged between magnetic resonance imaging (MRI)-visible perivascular spaces (MV-PVS) with intracerebral solute clearance and neuroinflammation, in adults. However, it is unknown how MV-PVS change throughout adolescence and what factors influence MV-PVS volume and morphology. This study assesses the temporal evolution of MV-PVS volume in adolescents and young adults, and secondarily evaluates the relationship between MV-PVS, age, sex, and body mass index (BMI). Materials and Methods: This analysis included a 783 participant cohort from the longitudinal multicenter National Consortium on Alcohol and Neurodevelopment in Adolescence study that involved up to 6 imaging visits spanning 5 years. Healthy adolescents aged 12-21 years at study entry with at least two MRI scans were included. The primary outcome was mean MV-PVS volume (mm 3 /white matter cm 3 ). Results: On average, males had greater MV-PVS volume at all ages compared to females. A linear mixed-effect model for MV-PVS volume was performed. Mean BMI and increases in a person's BMI were associated with increases in MV-PVS volume over time. In females only, changes in BMI correlated with MV-PVS volume. One unit increase in BMI above a person's average BMI was associated with a 0.021 mm 3 /cm 3 increase in MV-PVS volume (p<0.001). Conclusion: This longitudinal study showed sex differences in MV-PVS features during adolescence and young adulthood. Importantly, we report that increases in BMI from a person's mean BMI are associated with increases in MV-PVS volume in females only. These findings suggest a potential link between MV-PVS, sex, and BMI that warrants future study.

Defining Expressive Language Benchmarks for Children with Down Syndrome.

Establishing expressive language benchmarks (ELBs) for children with Down syndrome (DS), as developed by Tager-Flusberg et al. for children with autism, is critically needed to inform the development of novel treatments, identify individualized treatment targets, and promote accurate monitoring of progress. In the present study, we assessed ELB assignments in three language domains (phonology, vocabulary, and grammar) for 53 young children with DS (CA range: 2.50-7.99 years) using standardized assessments. The participants were classified into one of four ELB levels (preverbal, first words, word combinations, and sentences) in each language domain. Associations with additional measures of language, chronological age, nonverbal cognition, and verbal short-term memory were considered. Analyses of individual ELB profiles indicated substantial variability across the three language domains, with six different patterns of variation across domains emerging. At the same time, the ELB categories were significantly associated with independent language measures and broader developmental domains. Moreover, ELB changes were observed in a small sample of children with DS reassessed 18-24 months after the initial visit. Results from the present study suggest the procedures outlined by Tager-Flusberg et al. for defining ELBs are a potentially useful tool for describing the language abilities of children with DS.

Learning peptide recognition rules for a low-specificity protein.

Many proteins interact with short linear regions of target proteins. For some proteins, however, it is difficult to identify a well-defined sequence motif that defines its target peptides. To overcome this difficulty, we used supervised machine learning to train a model that treats each peptide as a collection of easily-calculated biochemical features rather than as an amino acid sequence. As a test case, we dissected the peptide-recognition rules for human S100A5 (hA5), a low-specificity calcium binding protein. We trained a Random Forest model against a recently released, high-throughput phage display dataset collected for hA5. The model identifies hydrophobicity and shape complementarity, rather than polar contacts, as the primary determinants of peptide binding specificity in hA5. We tested this hypothesis by solving a crystal structure of hA5 and through computational docking studies of diverse peptides onto hA5. These structural studies revealed that peptides exhibit multiple binding modes at the hA5 peptide interface-all of which have few polar contacts with hA5. Finally, we used our trained model to predict new, plausible binding targets in the human proteome. This revealed a fragment of the protein α-1-syntrophin that binds to hA5. Our work helps better understand the biochemistry and biology of hA5, as well as demonstrating how high-throughput experiments coupled with machine learning of biochemical features can reveal the determinants of binding specificity in low-specificity proteins.