Structural biology of CRISPR–Cas immunity and genome editing enzymes

Non-coding RNA structure and function are essential to understanding various biological processes, such as cell signaling, gene expression, and post-transcriptional regulations. These are all among the core problems in the RNA field. With the rapid growth of sequencing technology, we have accumulated a massive amount of unannotated RNA sequences. On the other hand, expensive experimental observatory results in only limited numbers of annotated data and 3D structures. Hence, it is still challenging to design computational methods for predicting their structures and functions. The lack of annotated data and systematic study causes inferior performance. To resolve the issue, we propose a novel RNA foundation model (RNA-FM) to take advantage of all the 23 million non-coding RNA sequences through self-supervised learning. Within this approach, we discover that the pre-trained RNA-FM could infer sequential and evolutionary information of non-coding RNAs without using any labels. Furthermore, we demonstrate RNA-FM’s effectiveness by applying it to the downstream secondary/3D structure prediction, SARS-CoV-2 genome structure and evolution prediction, protein-RNA binding preference modeling, and gene expression regulation modeling. The comprehensive experiments show that the proposed method improves the RNA structural and functional modelling results significantly and consistently. Despite only being trained with unlabelled data, RNA-FM can serve as the foundational model for the field.

/pdf/interpretable-rna-foundation-model-from-unannotated-data-for-3vbrraku.pdf

Interpretable RNA Foundation Model from Unannotated Data for Highly Accurate RNA Structure and Function Predictions

The structure of a protein is of great importance in determining its functionality, and this characteristic can be leveraged to train data-driven prediction models. However, the limited number of available protein structures severely limits the performance of these models. AlphaFold2 and its open-source data set of predicted protein structures have provided a promising solution to this problem, and these predicted structures are expected to benefit the model performance by increasing the number of training samples. In this work, we constructed a new data set that acted as a benchmark and implemented a state-of-the-art structure-based approach for determining whether the performance of the function prediction model can be improved by putting additional AlphaFold-predicted structures into the training set and further compared the performance differences between two models separately trained with real structures only and AlphaFold-predicted structures only. Experimental results indicated that structure-based protein function prediction models could benefit from virtual training data consisting of AlphaFold-predicted structures. First, model performances were improved in all three categories of Gene Ontology terms (GO terms) after adding predicted structures as training samples. Second, the model trained only on AlphaFold-predicted virtual samples achieved comparable performances to the model based on experimentally solved real structures, suggesting that predicted structures were almost equally effective in predicting protein functionality.

Enhancing Protein Function Prediction Performance by Utilizing AlphaFold-Predicted Protein Structures

Gene regulation is a central topic in cell biology. Advances in omics technologies and the accumulation of omics data have provided better opportunities for gene regulation studies than ever before. For this reason deep learning, as a data-driven predictive modeling approach, has been successfully applied to this field during the past decade. In this article, we aim to give a brief yet comprehensive overview of representative deep-learning methods for gene regulation. Specifically, we discuss and compare the design principles and datasets used by each method, creating a reference for researchers who wish to replicate or improve existing methods. We also discuss the common problems of existing approaches and prospectively introduce the emerging deep-learning paradigms that will potentially alleviate them. We hope that this article will provide a rich and up-to-date resource and shed light on future research directions in this area. 

https://doi.org/10.1016/j.crmeth.2022.100384

Applications of deep learning in understanding gene regulation

The reconstruction of microbial genomes from large metagenomic datasets is a critical procedure for finding uncultivated microbial populations and defining their microbial functional roles. To achieve that, we need to perform metagenomic binning, clustering the assembled contigs into draft genomes. Despite the existing computational tools, most of them neglect one important property of the metagenomic data, that is, the noise. To further improve the metagenomic binning step and reconstruct better metagenomes, we propose a deep Contrastive Learning framework for Metagenome Binning (CLMB), which can efficiently eliminate the disturbance of noise and produce more stable and robust results. Essentially, instead of denoising the data explicitly, we add simulated noise to the training data and force the deep learning model to produce similar and stable representations for both the noise-free data and the distorted data. Consequently, the trained model will be robust to noise and handle it implicitly during usage. CLMB outperforms the previous state-of-the-art binning methods significantly, recovering the most near-complete genomes on almost all the benchmarking datasets (up to 17% more reconstructed genomes compared to the second-best method). It also improves the performance of bin refinement, reconstructing 8-22 more high-quality genomes and 15-32 more middle-quality genomes than the second-best result. Impressively, in addition to being compatible with the binning refiner, single CLMB even recovers on average 15 more HQ genomes than the refiner of VAMB and Maxbin on the benchmarking datasets. On a real mother-infant microbiome dataset with 110 samples, CLMB is scalable and practical to recover 365 high-quality and middle-quality genomes (including 21 new ones), providing insights into the microbiome transmission. CLMB is open-source and available at https://github.com/zpf0117b/CLMB/.

https://biorxiv.org/content/10.1101/2021.11.15.468566v1.full.pdf

CLMB: deep contrastive learning for robust metagenomic binning.

Protein-RNA interactions are of vital importance to a variety of cellular activities. Both experimental and computational techniques have been developed to study the interactions. Due to the limitation of the previous database, especially the lack of protein structure data, most of the existing computational methods rely heavily on the sequence data, with only a small portion of the methods utilizing the structural information. Recently, AlphaFold has revolutionized the entire protein and biology field. Foreseeably, the protein-RNA interaction prediction will also be promoted significantly in the upcoming years. In this work, we give a thorough review of this field, surveying both the binding site and binding preference prediction problems and covering the commonly used datasets, features, and models. We also point out the potential challenges and opportunities in this field. This survey summarizes the development of the RBP-RNA interaction field in the past and foresees its future development in the post-AlphaFold era.

Protein-RNA interaction prediction with deep learning: Structure matters

AAV-delivered CRISPR/Cas9 (AAV-CRISPR) has shown promising potentials in preclinical models to efficiently insert therapeutic gene sequences in somatic tissues. However, the AAV input doses required were prohibitively high and posed serious risk of toxicity. Here, we performed AAV-CRISPR mediated homology-independent knock-in at a new target site in mAlb 3'UTR and demonstrated that single dose of AAVs enabled long-term integration and expression of hF9 transgene in both adult and neonatal hemophilia B mice (mF9 -/-), yielding high levels of circulating human Factor IX (hFIX) and stable hemostasis restoration during entire 48-week observation period. Furthermore, we achieved hemostasis correction with a significantly lower AAV dose (2 × 109 vg/neonate and 1 × 1010 vg/adult mouse) through liver-specific gene knock-in using hyperactive hF9R338L variant. The plasma antibodies against Cas9 and AAV in the neonatal mice receiving low-dose AAV-CRISPR were negligible, which lent support to the development of AAV-CRISPR mediated somatic knock-in for treating inherited diseases. 

/pdf/low-dose-aav-crispr-mediated-liver-specific-knock-in-7ra6dhby.pdf

Low-dose AAV-CRISPR-mediated liver-specific knock-in restored hemostasis in neonatal hemophilia B mice with subtle antibody response

The genus Sporothrix belongs to the order Ophiostomatales and contains mainly saprobic soil and plant fungi, although pathogenic species capable of causing human infections are also present. The whole-genomes of disease-causing species have already been sequenced and annotated but no comprehensive genomic resources for environmental Sporothrix species are available, thus limiting our understanding of the evolutionary origin of virulence-related genes and pathogenicity.The genome assembly of four environmental Sporothrix species resulted in genome size of ~ 30.9 Mbp in Sporothrix phasma, ~ 35 Mbp in S. curviconia, ~ 38.7 Mbp in S. protearum, and ~ 39 Mbp in S. variecibatus, with a variable gene content, ranging from 8142 (S. phasma) to 9502 (S. variecibatus). The analysis of mobile genetic elements showed significant differences in the content of transposable elements within the sequenced genomes, with the genome of S. phasma lacking several class I and class II transposons, compared to the other Sporothrix genomes investigated. Moreover, the comparative analysis of orthologous genes shared by clinical and environmental Sporothrix genomes revealed the presence of 3622 orthogroups shared by all species, whereas over 4200 genes were species-specific single-copy gene products. Carbohydrate-active enzyme analysis revealed a total of 2608 protein-coding genes containing single and/or multiple CAZy domains, resulting in no statistically significant differences among pathogenic and environmental species. Nevertheless, some families were not found in clinical species. Furthermore, for each sequenced Sporothrix species, the mitochondrial genomes was assembled in a single circular DNA molecule, ranging from 25,765 bp (S. variecibatus) to 58,395 bp (S. phasma).In this study, we present four annotated genome assemblies generated using PacBio SMRT sequencing data from four environmental species: S. curviconia, S. phasma, S. protearum and S. variecibatus with the aim to provide a starting point for future comparative genome evolution studies addressing species diversification, ecological/host adaptation and origin of pathogenic lineages within the genus Sporothrix. 

/pdf/long-read-pacbio-genome-sequencing-of-four-environmental-dv7p4r5k.pdf

Long-read PacBio genome sequencing of four environmental saprophytic Sporothrix species spanning the pathogenic clade

The rapid growth in the number of experimental and predicted protein structures and more complicated protein structures challenge users in computational biology for utilizing the structural information and protein surface property representation. Recently, AlphaFold2 released the comprehensive proteome of various species, and protein surface property representation plays a crucial role in protein-molecule interaction prediction such as protein-protein interaction, protein-nucleic acid interaction, and protein-compound interaction. Here, we propose the ﬁrst comprehensive database, namely ProNet DB, which incorporates multiple protein surface representations and RNA-binding landscape for more than 33,000 protein structures covering the proteome from AlphaFold Protein Structure Database (AlphaFold DB) and experimentally validated protein structures deposited in Protein Data Bank (PDB). For each protein, we provide the original protein structure, surface property representation including hydrophobicity, charge distribution, hydrogen bond, interacting face, and RNA-binding landscape such as RNA binding sites and RNA binding preference. To interpret protein surface property representation and RNA binding landscape intuitively, we also integrate Mol* and Online 3D Viewer to visualize the representation on the protein surface. The pre-computed features are available for the users instantaneously and their potential applications are including molecular mechanism exploration, drug discovery, and novel therapeutics development. The server is now available on and future releases will expand the species and property coverage.

ProNet DB: A proteome-wise database for protein surface property representations and RNA-binding profiles

Single-strand breaks (SSBs) are the major DNA damage in the genome arising spontaneously as the outcome of genotoxins and intermediates of DNA transactions. SSBs play a crucial role in various biological processes and show a non-random distribution in the genome. Several SSB detection approaches such as S1 END-seq and SSiNGLe-ILM emerged to characterize the genomic landscape of SSB with nucleotide resolution. However, these sequencing-based methods are costly and unfeasible for large-scale analysis of diverse species. Thus, we proposed the ﬁrst computational approach, SSBlazer, which is an explainable and scalable deep learning framework for genome-wide nucleotide-resolution SSB site prediction. We demonstrated that SSBlazer can accurately predict SSB sites and sufﬁciently alleviate false positives by constructing an imbalanced dataset to simulate the realistic SSB distribution. The model interpretation analysis reveals that SSBlazer captures the pattern of individual CpG in genomic context and the motif of TGCC in the center region as critical features. Besides, SSBlazer is a lightweight model with robust cross-species generalization ability in the cross-species evaluation, which enables the large-scale genome-wide application in diverse species. Strikingly, the putative SSB genomic landscapes of 216 vertebrates reveal a negative correlation between SSB frequency and evolutionary hierarchy, suggesting that the genome tends to be integrity during evolution.

Junkang Wei

Papers

Protein-RNA interaction prediction with deep learning: Structure matters

Low-dose AAV-CRISPR-mediated liver-specific knock-in restored hemostasis in neonatal hemophilia B mice with subtle antibody response

Long-read PacBio genome sequencing of four environmental saprophytic Sporothrix species spanning the pathogenic clade

ProNet DB: A proteome-wise database for protein surface property representations and RNA-binding profiles

Genome-wide nucleotide-resolution model of single-strand break site reveals species evolutionary hierarchy