9,475 research outputs found
Serverification of Molecular Modeling Applications: the Rosetta Online Server that Includes Everyone (ROSIE)
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
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A combined computational-experimental approach to define the structural origin of antibody recognition of sialyl-Tn, a tumor-associated carbohydrate antigen.
Anti-carbohydrate monoclonal antibodies (mAbs) hold great promise as cancer therapeutics and diagnostics. However, their specificity can be mixed, and detailed characterization is problematic, because antibody-glycan complexes are challenging to crystallize. Here, we developed a generalizable approach employing high-throughput techniques for characterizing the structure and specificity of such mAbs, and applied it to the mAb TKH2 developed against the tumor-associated carbohydrate antigen sialyl-Tn (STn). The mAb specificity was defined by apparent KD values determined by quantitative glycan microarray screening. Key residues in the antibody combining site were identified by site-directed mutagenesis, and the glycan-antigen contact surface was defined using saturation transfer difference NMR (STD-NMR). These features were then employed as metrics for selecting the optimal 3D-model of the antibody-glycan complex, out of thousands plausible options generated by automated docking and molecular dynamics simulation. STn-specificity was further validated by computationally screening of the selected antibody 3D-model against the human sialyl-Tn-glycome. This computational-experimental approach would allow rational design of potent antibodies targeting carbohydrates
Computational structureâbased drug design: Predicting target flexibility
The role of molecular modeling in drug design has experienced a significant revamp in the last decade. The increase in computational resources and molecular models, along with software developments, is finally introducing a competitive advantage in early phases of drug discovery. Medium and small companies with strong focus on computational chemistry are being created, some of them having introduced important leads in drug design pipelines. An important source for this success is the extraordinary development of faster and more efficient techniques for describing flexibility in threeâdimensional structural molecular modeling. At different levels, from docking techniques to atomistic molecular dynamics, conformational sampling between receptor and drug results in improved predictions, such as screening enrichment, discovery of transient cavities, etc. In this review article we perform an extensive analysis of these modeling techniques, dividing them into high and low throughput, and emphasizing in their application to drug design studies. We finalize the review with a section describing our Monte Carlo method, PELE, recently highlighted as an outstanding advance in an international blind competition and industrial benchmarks.We acknowledge the BSC-CRG-IRB Joint Research Program in Computational Biology. This work was supported by a grant
from the Spanish Government CTQ2016-79138-R.J.I. acknowledges support from SVP-2014-068797, awarded by the Spanish Government.Peer ReviewedPostprint (author's final draft
Of bits and bugs
Pur-α is a nucleic acid-binding protein involved in cell cycle control, transcription, and neuronal function. Initially no prediction of the three-dimensional structure of Pur-α was possible. However, recently we solved the X-ray structure of Pur-α from the fruitfly Drosophila melanogaster and showed that it contains a so-called PUR domain. Here we explain how we exploited bioinformatics tools in combination with X-ray structure determination of a bacterial homolog to obtain diffracting crystals and the high-resolution structure of Drosophila Pur-α. First, we used sensitive methods for remote-homology detection to find three repetitive regions in Pur-α. We realized that our lack of understanding how these repeats interact to form a globular domain was a major problem for crystallization and structure determination. With our information on the repeat motifs we then identified a distant bacterial homolog that contains only one repeat. We determined the bacterial crystal structure and found that two of the repeats interact to form a globular domain. Based on this bacterial structure, we calculated a computational model of the eukaryotic protein. The model allowed us to design a crystallizable fragment and to determine the structure of Drosophila Pur-α. Key for success was the fact that single repeats of the bacterial protein self-assembled into a globular domain, instructing us on the number and boundaries of repeats to be included for crystallization trials with the eukaryotic protein. This study demonstrates that the simpler structural domain arrangement of a distant prokaryotic protein can guide the design of eukaryotic crystallization constructs. Since many eukaryotic proteins contain multiple repeats or repeating domains, this approach might be instructive for structural studies of a range of proteins
An Exact Algorithm for Side-Chain Placement in Protein Design
Computational protein design aims at constructing novel or improved functions
on the structure of a given protein backbone and has important applications in
the pharmaceutical and biotechnical industry. The underlying combinatorial
side-chain placement problem consists of choosing a side-chain placement for
each residue position such that the resulting overall energy is minimum. The
choice of the side-chain then also determines the amino acid for this position.
Many algorithms for this NP-hard problem have been proposed in the context of
homology modeling, which, however, reach their limits when faced with large
protein design instances.
In this paper, we propose a new exact method for the side-chain placement
problem that works well even for large instance sizes as they appear in protein
design. Our main contribution is a dedicated branch-and-bound algorithm that
combines tight upper and lower bounds resulting from a novel Lagrangian
relaxation approach for side-chain placement. Our experimental results show
that our method outperforms alternative state-of-the art exact approaches and
makes it possible to optimally solve large protein design instances routinely
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