Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort1–4, the structures of around 100,000 unique proteins have been determined5, but this represents a small fraction of the billions of known protein sequences6,7. Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem’8—has been an important open research problem for more than 50 years9. Despite recent progress10–14, existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14)15, demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm. AlphaFold predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture.

/pdf/highly-accurate-protein-structure-prediction-with-alphafold-2xjbzfwu3n.pdf

Highly accurate protein structure prediction with AlphaFold

Prediction tasks over nodes and edges in networks require careful effort in engineering features used by learning algorithms. Recent research in the broader field of representation learning has led to significant progress in automating prediction by learning the features themselves. However, present feature learning approaches are not expressive enough to capture the diversity of connectivity patterns observed in networks. Here we propose node2vec, an algorithmic framework for learning continuous feature representations for nodes in networks. In node2vec, we learn a mapping of nodes to a low-dimensional space of features that maximizes the likelihood of preserving network neighborhoods of nodes. We define a flexible notion of a node's network neighborhood and design a biased random walk procedure, which efficiently explores diverse neighborhoods. Our algorithm generalizes prior work which is based on rigid notions of network neighborhoods, and we argue that the added flexibility in exploring neighborhoods is the key to learning richer representations. We demonstrate the efficacy of node2vec over existing state-of-the-art techniques on multi-label classification and link prediction in several real-world networks from diverse domains. Taken together, our work represents a new way for efficiently learning state-of-the-art task-independent representations in complex networks.

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node2vec: Scalable Feature Learning for Networks

The iterative threading assembly refinement (I-TASSER) server is an integrated platform for automated protein structure and function prediction based on the sequence-to-structure-to-function paradigm. Starting from an amino acid sequence, I-TASSER first generates three-dimensional (3D) atomic models from multiple threading alignments and iterative structural assembly simulations. The function of the protein is then inferred by structurally matching the 3D models with other known proteins. The output from a typical server run contains full-length secondary and tertiary structure predictions, and functional annotations on ligand-binding sites, Enzyme Commission numbers and Gene Ontology terms. An estimate of accuracy of the predictions is provided based on the confidence score of the modeling. This protocol provides new insights and guidelines for designing of online server systems for the state-of-the-art protein structure and function predictions. The server is available at http://zhanglab.ccmb.med.umich.edu/I-TASSER.

/pdf/i-tasser-a-unified-platform-for-automated-protein-structure-1ek4kamd4l.pdf

I-TASSER: a unified platform for automated protein structure and function prediction

Prediction of 3-dimensional protein structures from amino acid sequences represents one of the most important problems in computational structural biology. The community-wide Critical Assessment of Structure Prediction (CASP) experiments have been designed to obtain an objective assessment of the state-of-the-art of the field, where I-TASSER was ranked as the best method in the server section of the recent 7th CASP experiment. Our laboratory has since then received numerous requests about the public availability of the I-TASSER algorithm and the usage of the I-TASSER predictions. An on-line version of I-TASSER is developed at the KU Center for Bioinformatics which has generated protein structure predictions for thousands of modeling requests from more than 35 countries. A scoring function (C-score) based on the relative clustering structural density and the consensus significance score of multiple threading templates is introduced to estimate the accuracy of the I-TASSER predictions. A large-scale benchmark test demonstrates a strong correlation between the C-score and the TM-score (a structural similarity measurement with values in [0, 1]) of the first models with a correlation coefficient of 0.91. Using a C-score cutoff > -1.5 for the models of correct topology, both false positive and false negative rates are below 0.1. Combining C-score and protein length, the accuracy of the I-TASSER models can be predicted with an average error of 0.08 for TM-score and 2 A for RMSD. The I-TASSER server has been developed to generate automated full-length 3D protein structural predictions where the benchmarked scoring system helps users to obtain quantitative assessments of the I-TASSER models. The output of the I-TASSER server for each query includes up to five full-length models, the confidence score, the estimated TM-score and RMSD, and the standard deviation of the estimations. The I-TASSER server is freely available to the academic community at http://zhang.bioinformatics.ku.edu/I-TASSER
 .

/pdf/i-tasser-server-for-protein-3d-structure-prediction-2d4cisf9fo.pdf

I-TASSER server for protein 3D structure prediction.

Recent technological advances have opened unprecedented opportunities for large-scale sequencing and analysis of populations of pathogenic species in disease outbreaks, as well as for large-scale diversity studies aimed at expanding our knowledge across the whole domain of prokaryotes. To meet the challenge of timely interpretation of structure, function and meaning of this vast genetic information, a comprehensive approach to automatic genome annotation is critically needed. In collaboration with Georgia Tech, NCBI has developed a new approach to genome annotation that combines alignment based methods with methods of predicting protein-coding and RNA genes and other functional elements directly from sequence. A new gene finding tool, GeneMarkS+, uses the combined evidence of protein and RNA placement by homology as an initial map of annotation to generate and modify ab initio gene predictions across the whole genome. Thus, the new NCBI's Prokaryotic Genome Annotation Pipeline (PGAP) relies more on sequence similarity when confident comparative data are available, while it relies more on statistical predictions in the absence of external evidence. The pipeline provides a framework for generation and analysis of annotation on the full breadth of prokaryotic taxonomy. For additional information on PGAP see https://www.ncbi.nlm.nih.gov/genome/annotation_prok/ and the NCBI Handbook, https://www.ncbi.nlm.nih.gov/books/NBK174280/.

/pdf/ncbi-prokaryotic-genome-annotation-pipeline-2xblaudom9.pdf

NCBI prokaryotic genome annotation pipeline

<h3>Introduction</h3> Interleukin (IL) 22 is a key cytokine involved in regulation of epithelial function and its role in IBD remains highly controversial. Some studies indicate a protective role in epithelial regeneration, whereas other studies imply a pro-inflammatory role. Insights are now needed to improve of understanding of the functional role of this important cytokine and how its expression might impact patients with ulcerative colitis (UC). In this study, we have probed the clinical and functional significance of IL-22 responsive transcriptional modules and causal networks in diseased tissue of over 500 UC patients and in preclinical models of colitis. <h3>Methods</h3> We mapped the transcriptional landscape of human colonic epithelial 3D mini-gut organoids in response to treatment with IL22, and other pro-inflammatory cytokines. We tested the clinical significance of the IL22-regulated transcriptome by probing whole colonic biopsies from 550 ulcerative colitis (UC) patients from UNIFI clinical trial programme (patients with UC treated with ustekinumab, an anti-IL12p40 antibody). The functional role of IL22 regulated biological pathways were evaluated in pre-clinical models of UC. <h3>Results</h3> IL-22 regulated pro-inflammatory biological pathways involved in microbial recognition, cancer and immune cell chemotaxis. IL-22 was an especially potent regulator of the CXC family chemokines CXCL1, CXCL2, CXCL5, CXCL6 and CXCL8, which are all powerful neutrophil-selective chemokines. There was a positive correlation between the IL-22 transcriptional programme and the histological severity of inflammation (r=0.49, p&lt;0.0001) and, in particular, neutrophil infiltration in lamina propria and the colonic epithelium of patients with UC. Patients with the greatest magnitude of enrichment for IL22-responisve transcripts in whole colonic biopsies sampled immediately prior to ustekinumab initiation was associated with failure to achieve mucosal healing (8%) in comparison with patients with low enrichment scores (25%, P=0.002) at week 8 following induction. IL-22-mediated transcriptional regulation of CXC-family neutrophil-active chemokine expression was highly conserved across species and was dependent on JAK1/STAT3 signalling. In preclinical models of colitis, IL-22 induction of neutrophil-active chemokines was functionally and pathologically important in the recruitment of CXCR2<sup>+</sup> neutrophils to the colon. <h3>Conclusions</h3> The interleukin 22/neutrophil axis is functionally important in colitis and is associated with treatment resistance in ulcerative colitis 

Domenico Cozzetto

Papers

O32 The interleukin 22//neutrophil axis is associated with treatment resistance in ulcerative colitis