Single-cell RNA sequencing (scRNA-seq) can characterize cell types and states through unsupervised clustering, but the ever increasing number of cells and batch effect impose computational challenges. We present DESC, an unsupervised deep embedding algorithm that clusters scRNA-seq data by iteratively optimizing a clustering objective function. Through iterative self-learning, DESC gradually removes batch effects, as long as technical differences across batches are smaller than true biological variations. As a soft clustering algorithm, cluster assignment probabilities from DESC are biologically interpretable and can reveal both discrete and pseudotemporal structure of cells. Comprehensive evaluations show that DESC offers a proper balance of clustering accuracy and stability, has a small footprint on memory, does not explicitly require batch information for batch effect removal, and can utilize GPU when available. As the scale of single-cell studies continues to grow, we believe DESC will offer a valuable tool for biomedical researchers to disentangle complex cellular heterogeneity.

Identification of intrinsic disorder in proteins relies in large part on computational predictors, which demands that their accuracy should be high. Since intrinsic disorder carries out a broad range of cellular functions, it is desirable to couple the disorder and disorder function predictions. We report a computational tool, flDPnn, that provides accurate, fast and comprehensive disorder and disorder function predictions from protein sequences. The recent Critical Assessment of protein Intrinsic Disorder prediction (CAID) experiment and results on other test datasets demonstrate that flDPnn offers accurate predictions of disorder, fully disordered proteins and four common disorder functions. These predictions are substantially better than the results of the existing disorder predictors and methods that predict functions of disorder. Ablation tests reveal that the high predictive performance stems from innovative ways used in flDPnn to derive sequence profiles and encode inputs. flDPnn’s webserver is available at http://biomine.cs.vcu.edu/servers/flDPnn/

Some of the intrinsically disordered proteins and protein regions are promiscuous interactors that are involved in one-to-many and many-to-one binding. Several studies have analyzed enrichment of intrinsic disorder among the promiscuous hub proteins. We extended these works by providing a detailed functional characterization of the disorder-enriched hub protein-protein interactions (PPIs), including both hubs and their interactors, and by analyzing their enrichment among disease-associated proteins. We focused on the human interactome, given its high degree of completeness and relevance to the analysis of the disease-linked proteins. We quantified and investigated numerous functional and structural characteristics of the disorder-enriched hub PPIs, including protein binding, structural stability, evolutionary conservation, several categories of functional sites, and presence of over twenty types of posttranslational modifications (PTMs). We showed that the disorder-enriched hub PPIs have a significantly enlarged number of disordered protein binding regions and long intrinsically disordered regions. They also include high numbers of targeting, catalytic, and many types of PTM sites. We empirically demonstrated that these hub PPIs are significantly enriched among 11 out of 18 considered classes of human diseases that are associated with at least 100 human proteins. Finally, we also illustrated how over a dozen specific human hubs utilize intrinsic disorder for their promiscuous PPIs.

Deep learning has significantly enhanced protein structure prediction, and AlphaFold2 marked a particular milestone among these methods for predicting protein monomer and complex structures. The AlphaFold3 represents a pivotal further advancement in biomolecular structure prediction, extending beyond proteins to model diverse assemblies. Despite attracting a huge number of users, there is still an absence of third-party benchmarks to fairly demonstrate the performance of the AlphaFold3. In this work, we benchmark AlphaFold3’s performance across nine datasets, protein monomers, orphan proteins, alternative conformations, protein multimers, peptide-protein complexes, antigen-antibody complexes, RNA, RNA multimers, and protein-nucleic acid complexes, compared to AlphaFold2, AlphaFold-Multimer, and RoseTTAFoldNA, RhoFold+, NuFold and trRosettaRNA. For protein monomers, AlphaFold3 demonstrates improved local structural accuracy over AlphaFold2, though global accuracy gains are limited. In modeling general protein complexes, AlphaFold3 surpasses AlphaFold-Multimer in local structural prediction. For peptide-protein complexes, their performances are nearly indistinguishable, whereas on antigen-antibody complexes, AlphaFold3 is significantly superior. AlphaFold3 shows substantial superiority over RoseTTAFoldNA in protein-nucleic acid predictions, with significant gains in TM-score, local distance difference test scores, and interaction network fidelity scores, whereas for RNA multimers its advantage is limited to significant gains in local distance difference test scores. For RNA monomers, trRosettaRNA achieves higher global prediction accuracy. These results highlight AlphaFold3’s ability to predict both structural detail and interactions, positioning it as a versatile tool for diverse biomolecular systems and suggesting promising applications in structural biology and molecular interaction research, while at the same time highlighting areas ripe for continuing improvements in performance.