Cluster Analysis

AlphaFold2 (ref. 1) has revolutionized structural biology by accurately predicting single structures of proteins. However, a protein's biological function often depends on multiple conformational substates2, and disease-causing point mutations often cause population changes within these substates3,4. We demonstrate that clustering a multiple-sequence alignment by sequence similarity enables AlphaFold2 to sample alternative states of known metamorphic proteins with high confidence. Using this method, named AF-Cluster, we investigated the evolutionary distribution of predicted structures for the metamorphic protein KaiB5 and found that predictions of both conformations were distributed in clusters across the KaiB family. We used nuclear magnetic resonance spectroscopy to confirm an AF-Cluster prediction: a cyanobacteria KaiB variant is stabilized in the opposite state compared with the more widely studied variant. To test AF-Cluster's sensitivity to point mutations, we designed and experimentally verified a set of three mutations predicted to flip KaiB from Rhodobacter sphaeroides from the ground to the fold-switched state. Finally, screening for alternative states in protein families without known fold switching identified a putative alternative state for the oxidoreductase Mpt53 in Mycobacterium tuberculosis. Further development of such bioinformatic methods in tandem with experiments will probably have a considerable impact on predicting protein energy landscapes, essential for illuminating biological function.

BackgroundTaxonomic profiles of vaginal microbial communities can be sorted into a discrete number of categories termed community state types (CSTs). This approach is advantageous because collapsing a hyper-dimensional taxonomic profile into a single categorical variable enables efforts such as data exploration, epidemiological studies, and statistical modeling. Vaginal communities are typically assigned to CSTs based on the results of hierarchical clustering of the pairwise distances between samples. However, this approach is problematic because it complicates between-study comparisons and because the results are entirely dependent on the particular set of samples that were analyzed. We sought to standardize and advance the assignment of samples to CSTs.ResultsWe developed VALENCIA (VAginaL community state typE Nearest CentroId clAssifier), a nearest centroid-based tool which classifies samples based on their similarity to a set of reference centroids. The references were defined using a comprehensive set of 13,160 taxonomic profiles from 1975 women in the USA. This large dataset allowed us to comprehensively identify, define, and characterize vaginal CSTs common to reproductive age women and expand upon the CSTs that had been defined in previous studies. We validated the broad applicability of VALENCIA for the classification of vaginal microbial communities by using it to classify three test datasets which included reproductive age eastern and southern African women, adolescent girls, and a racially/ethnically and geographically diverse sample of postmenopausal women. VALENCIA performed well on all three datasets despite the substantial variations in sequencing strategies and bioinformatics pipelines, indicating its broad application to vaginal microbiota. We further describe the relationships between community characteristics (vaginal pH, Nugent score) and participant demographics (race, age) and the CSTs defined by VALENCIA.ConclusionVALENCIA provides a much-needed solution for the robust and reproducible assignment of vaginal community state types. This will allow unbiased analysis of both small and large vaginal microbiota datasets, comparisons between datasets and meta-analyses that combine multiple datasets. Video abstract.

Proteins are key to all cellular processes and their structure is important in understanding their function and evolution. Sequence-based predictions of protein structures have increased in accuracy1, and over 214 million predicted structures are available in the AlphaFold database2. However, studying protein structures at this scale requires highly efficient methods. Here, we developed a structural-alignment-based clustering algorithm-Foldseek cluster-that can cluster hundreds of millions of structures. Using this method, we have clustered all of the structures in the AlphaFold database, identifying 2.30 million non-singleton structural clusters, of which 31% lack annotations representing probable previously undescribed structures. Clusters without annotation tend to have few representatives covering only 4% of all proteins in the AlphaFold database. Evolutionary analysis suggests that most clusters are ancient in origin but 4% seem to be species specific, representing lower-quality predictions or examples of de novo gene birth. We also show how structural comparisons can be used to predict domain families and their relationships, identifying examples of remote structural similarity. On the basis of these analyses, we identify several examples of human immune-related proteins with putative remote homology in prokaryotic species, illustrating the value of this resource for studying protein function and evolution across the tree of life.

The envelope model has gained significant attention since its proposal, offering a fresh perspective on dimension reduction in multivariate regression models and improving estimation efficiency. One of its appealing features is its adaptability to diverse regression contexts. This article introduces the integration of envelope methods into the factor analysis model. In contrast to previous research primarily focused on the frequentist approach, the study proposes a Bayesian approach for estimation and envelope dimension selection. A Metropolis-within-Gibbs sampling algorithm is developed to draw posterior samples for Bayesian inference. A simulation study is conducted to illustrate the effectiveness of the proposed method. Additionally, the proposed methodology is applied to the ADNI dataset to explore the relationship between cognitive decline and the changes occurring in various brain regions. This empirical application further highlights the practical utility of the proposed model in real-world scenarios.