Estimation of protein function using template-based alignment of enzyme active sites

BACKGROUND: The accumulation of protein structural data occurs more rapidly than it can be characterized by traditional laboratory means. This has motivated widespread efforts to predict enzyme function computationally. The most useful/accurate strategies employed to date are based on the detection of motifs in novel structures that correspond to a specific function. Functional residues are critical components of predictively useful motifs. We have implemented a novel method, to complement current approaches, which detects motifs solely on the basis of distance restraints between catalytic residues. RESULTS: ProMOL is a plugin for the PyMOL molecular graphics environment that can be used to create active site motifs for enzymes. A library of 181 active site motifs has been created with ProMOL, based on definitions published in the Catalytic Site Atlas (CSA). Searches with ProMOL produce better than 50% useful Enzyme Commission (EC) class suggestions for level 1 searches in EC classes 1, 4 and 5, and produce some useful results for other classes. 261 additional motifs automatically translated from Jonathan Barker’s JESS motif set [Bioinformatics 19:1644–1649, 2003] and a set of NMR motifs is under development. Alignments are evaluated by visual superposition, Levenshtein distance and root-mean-square deviation (RMSD) and are reasonably consistent with related search methods. CONCLUSION: The ProMOL plugin for PyMOL provides ready access to template-based local alignments. Recent improvements to ProMOL, including the expanded motif library, RMSD calculations and output selection formatting, have greatly increased the program’s usability and speed, and have improved the way that the results are presented

Temporal Dysbiosis of Infant Nasal Microbiota Relative to Respiratory Syncytial Virus Infection

Background: Respiratory syncytial virus (RSV) is a leading cause of infant respiratory disease. Infant airway microbiota has been associated with respiratory disease risk and severity. The extent to which interactions between RSV and microbiota occur in the airway, and their impact on respiratory disease susceptibility and severity, are unknown. Methods: We carried out 16S rRNA microbiota profiling of infants in the first year of life from (1) a cross-sectional cohort of 89 RSV-infected infants sampled during illness and 102 matched healthy controls, and (2) a matched longitudinal cohort of 12 infants who developed RSV infection and 12 who did not, sampled before, during, and after infection. Results: We identified 12 taxa significantly associated with RSV infection. All 12 taxa were differentially abundant during infection, with 8 associated with disease severity. Nasal microbiota composition was more discriminative of healthy vs infected than of disease severity. Conclusions: Our findings elucidate the chronology of nasal microbiota dysbiosis and suggest an altered developmental trajectory associated with RSV infection. Microbial temporal dynamics reveal indicators of disease risk, correlates of illness and severity, and impact of RSV infection on microbiota composition

Neonatal gut and respiratory microbiota: coordinated development through time and space

Abstract Background Postnatal development of early life microbiota influences immunity, metabolism, neurodevelopment, and infant health. Microbiome development occurs at multiple body sites, with distinct community compositions and functions. Associations between microbiota at multiple sites represent an unexplored influence on the infant microbiome. Here, we examined co-occurrence patterns of gut and respiratory microbiota in pre- and full-term infants over the first year of life, a period critical to neonatal development. Results Gut and respiratory microbiota collected as longitudinal rectal, throat, and nasal samples from 38 pre-term and 44 full-term infants were first clustered into community state types (CSTs) on the basis of their compositional profiles. Multiple methods were used to relate the occurrence of CSTs to temporal microbiota development and measures of infant maturity, including gestational age (GA) at birth, week of life (WOL), and post-menstrual age (PMA). Manifestation of CSTs followed one of three patterns with respect to infant maturity: (1) chronological, with CST occurrence frequency solely a function of post-natal age (WOL), (2) idiosyncratic to maturity at birth, with the interval of CST occurrence dependent on infant post-natal age but the frequency of occurrence dependent on GA at birth, and (3) convergent, in which CSTs appear first in infants of greater maturity at birth, with occurrence frequency in pre-terms converging after a post-natal interval proportional to pre-maturity. The composition of CSTs was highly dissimilar between different body sites, but the CST of any one body site was highly predictive of the CSTs at other body sites. There were significant associations between the abundance of individual taxa at each body site and the CSTs of the other body sites, which persisted after stringent control for the non-linear effects of infant maturity. Canonical correlations exist between the microbiota composition at each pair of body sites, with the strongest correlations between proximal locations. Conclusion These findings suggest that early microbiota is shaped by neonatal innate and adaptive developmental responses. Temporal progression of CST occurrence is influenced by infant maturity at birth and post-natal age. Significant associations of microbiota across body sites reveal distal connections and coordinated development of the infant microbial ecosystem

Impact of prematurity and nutrition on the developing gut microbiome and preterm infant growth

Abstract Background Identification of factors that influence the neonatal gut microbiome is urgently needed to guide clinical practices that support growth of healthy preterm infants. Here, we examined the influence of nutrition and common practices on the gut microbiota and growth in a cohort of preterm infants. Results With weekly gut microbiota samples spanning postmenstrual age (PMA) 24 to 46 weeks, we developed two models to test associations between the microbiota, nutrition and growth: a categorical model with three successive microbiota phases (P1, P2, and P3) and a model with two periods (early and late PMA) defined by microbiota composition and PMA, respectively. The more significant associations with phase led us to use a phase-based framework for the majority of our analyses. Phase transitions were characterized by rapid shifts in the microbiota, with transition out of P1 occurring nearly simultaneously with the change from meconium to normal stool. The rate of phase progression was positively associated with gestational age at birth, and delayed transition to a P3 microbiota was associated with growth failure. We found distinct bacterial metabolic functions in P1–3 and significant associations between nutrition, microbiota phase, and infant growth. Conclusion The phase-dependent impact of nutrition on infant growth along with phase-specific metabolic functions suggests a pioneering potential for improving growth outcomes by tailoring nutrient intake to microbiota phase