No abstract

The Rosetta software suite for macromolecular modeling, docking, and design is widely used in pharmaceutical, industrial, academic, non-profit, and government laboratories. Despite its broad modeling capabilities, Rosetta remains consistently among leading software suites when compared to other methods created for highly specialized protein modeling and design tasks. Developed for over two decades by a global community of over 60 laboratories, Rosetta has undergone multiple refactorings, and now comprises over three million lines of code. Here we discuss methods developed in the last five years in Rosetta, involving the latest protocols for structure prediction; protein-protein and protein-small molecule docking; protein structure and interface design; loop modeling; the incorporation of various types of experimental data; modeling of peptides, antibodies and proteins in the immune system, nucleic acids, non-standard chemistries, carbohydrates, and membrane proteins. We briefly discuss improvements to the energy function, user interfaces, and usability of the software. Rosetta is available at www.rosettacommons.org.

The Rosetta molecular modeling software package provides experimentally tested and rapidly evolving tools for the 3D structure prediction and high-resolution design of proteins, nucleic acids, and a growing number of non-natural polymers. Despite its free availability to academic users and improving documentation, use of Rosetta has largely remained confined to developers and their immediate collaborators due to the code’s difficulty of use, the requirement for large computational resources, and the unavailability of servers for most of the Rosetta applications. Here, we present a unified web framework for Rosetta applications called ROSIE (Rosetta Online Server that Includes Everyone). ROSIE provides (a) a common user interface for Rosetta protocols, (b) a stable application programming interface for developers to add additional protocols, (c) a flexible back-end to allow leveraging of computer cluster resources shared by RosettaCommons member institutions, and (d) centralized administration by the RosettaCommons to ensure continuous maintenance. This paper describes the ROSIE server infrastructure, a step-by-step ‘serverification’ protocol for use by Rosetta developers, and the deployment of the first nine ROSIE applications by six separate developer teams: Docking, RNA de novo, ERRASER, Antibody, Sequence Tolerance, Supercharge, Beta peptide design, NCBB design, and VIP redesign. As illustrated by the number and diversity of these applications, ROSIE offers a general and speedy paradigm for serverification of Rosetta applications that incurs negligible cost to developers and lowers barriers to Rosetta use for the broader biological community. ROSIE is available at http://rosie.rosettacommons.org.

CONUT is useful for not only estimating nutritional status but also for predicting long-term OS in gastric cancer patients after curative resection.

Elucidating how antigen exposure and selection shape the human antibody repertoire is fundamental to our understanding of B-cell immunity. We sequenced the paired heavy-and light-chain variable regions (VH and VL, respectively) from large populations of single B cells combined with computational modeling of antibody structures to evaluate sequence and structural features of human antibody repertoires at unprecedented depth. Analysis of a dataset comprising 55,000 antibody clusters from CD19− IgM-naive B cells, >120,000 antibody clusters from CD19+ antigenexperienced B cells, and >2,000 RosettaAntibody-predicted structural models across three healthy donors led to a number of key findings: (i) VH and VL gene sequences pair in a combinatorial fashion without detectable pairing restrictions at the population level; (ii) certain VH:VL gene pairs were significantly enriched or depleted in the antigen-experienced repertoire relative to the naive repertoire; (iii) antigen selection increased antibody paratope net charge and solvent-accessible surface area; and (iv) public heavy-chain third complementarity-determining region (CDR-H3) antibodies in the antigen-experienced repertoire showed signs of convergent paired light-chain genetic signatures, including shared light-chain third complementarity-determining region (CDR-L3) amino acid sequences and/or Vκ,λ-Jκ,λ genes. The data reported here address several longstanding questions regarding antibody repertoire selection and development and provide a benchmark for future repertoire-scale analyses of antibody responses to vaccination and disease.antibody | B cell | immunology | high-throughput sequencing | computational modeling E ffective antigen recognition by the humoral immune system is predicated on the somatic generation of a large antibody repertoire that encompasses the sequence and conformational diversity to respond to a highly diversified set of antigens (1-3). Upon antigen challenge, naive B cells (NBCs) expressing unmutated antibodies capable of binding antigen with an affinity sufficient to initiate B-cell receptor (BCR) signaling may be stimulated to undergo somatic hypermutation (SHM) of the antibody genes. B cells expressing higher-affinity BCRs are better equipped to compete for antigen and thus receive signals that enable their preferential proliferation and further antibody sequence diversification in additional rounds of SHM. This process generates a repertoire of somatically mutated antibodies that, at the structural level, generally display decreased conformational flexibility (4, 5), slower antigen dissociation rates, and increased binding selectivity relative to the germline repertoire.Understanding the salient features of the human antibody repertoire is critical for immunology research (6, 7). Specifically, additional information is needed regarding how a history of pathogen and environmental exposure modulates the sequence and conformational properties of naive antibodies to yield a mature antibody repertoire that confers effective protection. Hi...

We present the results for CAPRI Round 30, the first joint CASP-CAPRI experiment, which brought together experts from the protein structure prediction and protein-protein docking communities. The Round comprised 25 targets from amongst those submitted for the CASP11 prediction experiment of 2014. The targets included mostly homodimers, a few homotetramers, and two heterodimers, and comprised protein chains that could readily be modeled using templates from the Protein Data Bank. On average 24 CAPRI groups and 7 CASP groups submitted docking predictions for each target, and 12 CAPRI groups per target participated in the CAPRI scoring experiment. In total more than 9500 models were assessed against the 3D structures of the corresponding target complexes. Results show that the prediction of homodimer assemblies by homology modeling techniques and docking calculations is quite successful for targets featuring large enough subunit interfaces to represent stable associations. Targets with ambiguous or inaccurate oligomeric state assignments, often featuring crystal contact-sized interfaces, represented a confounding factor. For those, a much poorer prediction performance was achieved, while nonetheless often providing helpful clues on the correct oligomeric state of the protein. The prediction performance was very poor for genuine tetrameric targets, where the inaccuracy of the homology-built subunit models and the smaller pair-wise interfaces severely limited the ability to derive the correct assembly mode. Our analysis also shows that docking procedures tend to perform better than standard homology modeling techniques and that highly accurate models of the protein components are not always required to identify their association modes with acceptable accuracy.

Tumor associated macrophages (TAMs) are the most abundant cancer stromal cells educated by tumor microenvironment to acquire trophic functions facilitating angiogenesis, matrix breakdown and cancer cell motility. Tumor associated macrophages have anti-inflammatory properties or "alternatively" activated (M2) phenotype expressing CD204 and ⁄ or CD163. To know the role of TAMs in the growth and progression of esophageal squamous cell carcinomas (ESCCs), we calculated intratumoral CD204, CD163 or CD68 expressing macrophage count (M/C) and CD34-positive microvessel density (MVD) by immunohistochemistry in 70 cases of surgically resected ESCCs and compared them with the clinicopathological factors and prognosis of patients. M/C had positive linear association with MVD. High CD204 + M/C were significantly correlated with more malignant phenotypes including depth of tumor invasion, lymph and blood vessel invasion, lymph node metastasis as well as clinical stages. On the other hand, CD163+ M/C did not associate with these clinicopathological factors with the exception of depth of tumor invasion and blood vessel invasion. Patients with high CD204 + M/C ESCCs showed poor disease-free survival (P = 0.021). Conditioned media of five ESCC cell lines (TE-8, -9, -10, -11 and -15) induced mRNA as well as protein expression of CD204 but not of CD163 with upregulation of vascular endothelial growth factor-A mRNA in TPA treated human acute monocytic leukemia cell line THP-1. These results overall indicate that CD204 is a useful marker for TAMs contributing to the angiogenesis, progression and prognosis of ESCCs whose specific tumor microenvironment may educate macrophages to be CD204

We describe Rosetta-based computational protocols for predicting the three-dimensional structure of an antibody from sequence (RosettaAntibody) and then docking the antibody to protein antigens (SnugDock). Antibody modeling leverages canonical loop conformations to graft large segments from experimentally-determined structures as well as (1) energetic calculations to minimize loops, (2) docking methodology to refine the VL–VH relative orientation, and (3) de novo prediction of the elusive complementarity determining region (CDR) H3 loop. To alleviate model uncertainty, antibody–antigen docking resamples CDR loop conformations and can use multiple models to represent an ensemble of conformations for the antibody, the antigen or both. These protocols can be run fully-automated via the ROSIE web server (http://rosie.rosettacommons.org/) or manually on a computer with user control of individual steps. For best results, the protocol requires roughly 1,000 CPU-hours for antibody modeling and 250 CPU-hours for antibody–antigen docking. Tasks can be completed in under a day by using public supercomputers.