@article{136,

title = {RosettaHoles: rapid assessment of protein core packing for structure prediction, refinement, design, and validation},

author = { Will Sheffler and David Baker},

url = {https://onlinelibrary.wiley.com/doi/full/10.1002/pro.8

https://www.bakerlab.org/wp-content/uploads/2020/08/pro.8.pdf},

doi = {10.1002/pro.8},

issn = {1469-896X},

year  = {2009},

date = {2009-01-01},

journal = {Protein science},

volume = {18},

pages = {229-39},

abstract = {We present a novel method called RosettaHoles for visual and quantitative assessment of underpacking in the protein core. RosettaHoles generates a set of spherical cavity balls that fill the empty volume between atoms in the protein interior. For visualization, the cavity balls are aggregated into contiguous overlapping clusters and small cavities are discarded, leaving an uncluttered representation of the unfilled regions of space in a structure. For quantitative analysis, the cavity ball data are used to estimate the probability of observing a given cavity in a high-resolution crystal structure. RosettaHoles provides excellent discrimination between real and computationally generated structures, is predictive of incorrect regions in models, identifies problematic structures in the Protein Data Bank, and promises to be a useful validation tool for newly solved experimental structures.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{273,

title = {Structure prediction for CASP8 with all-atom refinement using Rosetta},

author = { Srivatsan Raman and Robert Vernon and James Thompson and Michael Tyka and Ruslan Sadreyev and Jimin Pei and David Kim and Elizabeth Kellogg and Frank DiMaio and Oliver Lange and Lisa Kinch and Will Sheffler and Bong-Hyun Kim and Rhiju Das and Nick V Grishin and David Baker},

issn = {1097-0134},

year  = {2009},

date = {2009-00-01},

journal = {Proteins},

volume = {77 Suppl 9},

pages = {89-99},

abstract = {We describe predictions made using the Rosetta structure prediction methodology for the Eighth Critical Assessment of Techniques for Protein Structure Prediction. Aggressive sampling and all-atom refinement were carried out for nearly all targets. A combination of alignment methodologies was used to generate starting models from a range of templates, and the models were then subjected to Rosetta all atom refinement. For the 64 domains with readily identified templates, the best submitted model was better than the best alignment to the best template in the Protein Data Bank for 24 cases, and improved over the best starting model for 43 cases. For 13 targets where only very distant sequence relationships to proteins of known structure were detected, models were generated using the Rosetta de novo structure prediction methodology followed by all-atom refinement; in several cases the submitted models were better than those based on the available templates. Of the 12 refinement challenges, the best submitted model improved on the starting model in seven cases. These improvements over the starting template-based models and refinement tests demonstrate the power of Rosetta structure refinement in improving model accuracy.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{221,

title = {Experimental and computational analyses of the energetic basis for dual recognition of immunity proteins by colicin endonucleases},

author = { Anthony H Keeble and Lukasz A Joachimiak and Mar'ia Jesus Mat'e and Nicola Meenan and Nadine Kirkpatrick and David Baker and Colin Kleanthous},

issn = {1089-8638},

year  = {2008},

date = {2008-06-01},

journal = {Journal of molecular biology},

volume = {379},

pages = {745-59},

abstract = {Colicin endonucleases (DNases) are bound and inactivated by immunity (Im) proteins. Im proteins are broadly cross-reactive yet specific inhibitors binding cognate and non-cognate DNases with K(d) values that vary between 10(-4) and 10(-14) M, characteristics that are explained by a textquoterightdual-recognitiontextquoteright mechanism. In this work, we addressed for the first time the energetics of Im protein recognition by colicin DNases through a combination of E9 DNase alanine scanning and double-mutant cycles (DMCs) coupled with kinetic and calorimetric analyses of cognate Im9 and non-cognate Im2 binding, as well as computational analysis of alanine scanning and DMC data. We show that differential DeltaDeltaGs observed for four E9 DNase residues cumulatively distinguish cognate Im9 association from non-cognate Im2 association. E9 DNase Phe86 is the primary specificity hotspot residue in the centre of the interface, which is coordinated by conserved and variable hotspot residues of the cognate Im protein. Experimental DMC analysis reveals that only modest coupling energies to Im9 residues are observed, in agreement with calculated DMCs using the program ROSETTA and consistent with the largely hydrophobic nature of E9 DNase-Im9 specificity contacts. Computed values for the 12 E9 DNase alanine mutants showed reasonable agreement with experimental DeltaDeltaG data, particularly for interactions not mediated by interfacial water molecules. DeltaDeltaG predictions for residues that contact buried water molecules calculated using solvated rotamer models met with mixed success; however, we were able to predict with a high degree of accuracy the location and energetic contribution of one such contact. Our study highlights how colicin DNases are able to utilise both conserved and variable amino acids to distinguish cognate from non-cognate Im proteins, with the energetic contributions of the conserved residues modulated by neighbouring specificity sites.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{220,

title = {Ranking predicted protein structures with support vector regression},

author = { Jian Qiu and Will Sheffler and David Baker and William Stafford Noble},

issn = {1097-0134},

year  = {2008},

date = {2008-05-01},

journal = {Proteins},

volume = {71},

pages = {1175-82},

abstract = {Protein structure prediction is an important problem of both intellectual and practical interest. Most protein structure prediction approaches generate multiple candidate models first, and then use a scoring function to select the best model among these candidates. In this work, we develop a scoring function using support vector regression (SVR). Both consensus-based features and features from individual structures are extracted from a training data set containing native protein structures and predicted structural models submitted to CASP5 and CASP6. The SVR learns a scoring function that is a linear combination of these features. We test this scoring function on two data sets. First, when used to rank server models submitted to CASP7, the SVR score selects predictions that are comparable to the best performing server in CASP7, Zhang-Server, and significantly better than all the other servers. Even if the SVR score is not allowed to select Zhang-Server models, the SVR score still selects predictions that are significantly better than all the other servers. In addition, the SVR is able to select significantly better models and yield significantly better Pearson correlation coefficients than the two best Quality Assessment groups in CASP7, QA556 (LEE), and QA634 (Pcons). Second, this work aims to improve the ability of the Robetta server to select best models, and hence we evaluate the performance of the SVR score on ranking the Robetta server template-based models for the CASP7 targets. The SVR selects significantly better models than the Robetta K*Sync consensus alignment score.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{280,

title = {Advances in Rosetta Protein Structure Prediction on Massively Parallel Systems},

author = { Raman S and Qian B and Baker D and Walker RC},

year  = {2008},

date = {2008-01-01},

journal = {Journal of Research and Development},

volume = {52(1-2):7-17},

abstract = {One of the key challenges in computational biology is prediction of three-dimensional protein structures from amino-acid sequences. For most proteins, the "native state" lies at the bottom of a free-energy landscape. Protein structure prediction involves varying the degrees of freedom of the protein in a constrained manner until it approaches its native state. In the Rosetta protein structure prediction protocols, a large number of independent folding trajectories are simulated, and several lowest-energy results are likely to be close to the native state. The availability of hundred-teraflop, and shortly, petaflop, computing resources is revolutionizing the approaches available for protein structure prediction. Here, we discuss issues involved in utilizing such machines efficiently with the Rosetta code, including an overview of recent results of the Critical Assessment of Techniques for Protein Structure Prediction 7 (CASP7) in which the computationally demanding structure-refinement process was run on 16 racks of the IBM Blue Gene/L (TM) system at the IBM T. J. Watson Research Center. We highlight recent advances in high-performance computing and discuss,future development paths that make use of the next-generation petascale (> 10(12) floating-point operations per second) machines.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{227,

title = {Macromolecular modeling with rosetta},

author = { Rhiju Das and David Baker},

issn = {0066-4154},

year  = {2008},

date = {2008-00-01},

journal = {Annual review of biochemistry},

volume = {77},

pages = {363-82},

abstract = {Advances over the past few years have begun to enable prediction and design of macromolecular structures at near-atomic accuracy. Progress has stemmed from the development of reasonably accurate and efficiently computed all-atom potential functions as well as effective conformational sampling strategies appropriate for searching a highly rugged energy landscape, both driven by feedback from structure prediction and design tests. A unified energetic and kinematic framework in the Rosetta program allows a wide range of molecular modeling problems, from fibril structure prediction to RNA folding to the design of new protein interfaces, to be readily investigated and highlights areas for improvement. The methodology enables the creation of novel molecules with useful functions and holds promise for accelerating experimental structural inference. Emerging connections to crystallographic phasing, NMR modeling, and lower-resolution approaches are described and critically assessed.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{225,

title = {Structural genomics of pathogenic protozoa: an overview},

author = { Erkang Fan and David Baker and Stanley Fields and Michael H Gelb and Frederick S Buckner and Wesley C Van Voorhis and Eric Phizicky and Mark Dumont and Christopher Mehlin and Elizabeth Grayhack and Mark Sullivan and Christophe Verlinde and George Detitta and Deirdre R Meldrum and Ethan A Merritt and Thomas Earnest and Michael Soltis and Frank Zucker and Peter J Myler and Lori Schoenfeld and David E Kim and Liz Worthey and Doug Lacount and Marissa Vignali and Jizhen Li and Somnath Mondal and Archna Massey and Brian Carroll and Stacey Gulde and Joseph Luft and Larry Desoto and Mark Holl and Jonathan Caruthers and J"urgen Bosch and Mark Robien and Tracy Arakaki and Margaret Holmes and Isolde Le Trong and Wim G J Hol},

issn = {1064-3745},

year  = {2008},

date = {2008-00-01},

journal = {Methods in molecular biology},

volume = {426},

pages = {497-513},

abstract = {The Structural Genomics of Pathogenic Protozoa (SGPP) Consortium aimed to determine crystal structures of proteins from trypanosomatid and malaria parasites in a high throughput manner. The pipeline of target selection, protein production, crystallization, and structure determination, is sketched. Special emphasis is given to a number of technology developments including domain prediction, the use of "co-crystallants," and capillary crystallization. "Fragment cocktail crystallography" for medical structural genomics is also described.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{112,

title = {RosettaDock in CAPRI rounds 6-12},

author = { Chu Wang and Ora Schueler-Furman and Ingemar Andre and Nir London and Sarel J Fleishman and Philip Bradley and Bin Qian and David Baker},

url = {https://www.bakerlab.org/wp-content/uploads/2016/08/wang07B.pdf},

issn = {1097-0134},

year  = {2007},

date = {2007-12-01},

journal = {Proteins},

volume = {69},

pages = {758-63},

abstract = {A challenge in protein-protein docking is to account for the conformational changes in the monomers that occur upon binding. The RosettaDock method, which incorporates sidechain flexibility but keeps the backbone fixed, was found in previous CAPRI rounds (4 and 5) to generate docking models with atomic accuracy, provided that conformational changes were mainly restricted to protein sidechains. In the recent rounds of CAPRI (6-12), large backbone conformational changes occur upon binding for several target complexes. To address these challenges, we explicitly introduced backbone flexibility in our modeling procedures by combining rigid-body docking with protein structure prediction techniques such as modeling variable loops and building homology models. Encouragingly, using this approach we were able to correctly predict a significant backbone conformational change of an interface loop for Target 20 (12 A rmsd between those in the unbound monomer and complex structures), but accounting for backbone flexibility in protein-protein docking is still very challenging because of the significantly larger conformational space, which must be surveyed. Motivated by these CAPRI challenges, we have made progress in reformulating RosettaDock using a "fold-tree" representation, which provides a general framework for treating a wide variety of flexible-backbone docking problems.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{115,

title = {High-resolution structure prediction and the crystallographic phase problem},

author = { Bin Qian and Srivatsan Raman and Rhiju Das and Philip Bradley and Airlie J McCoy and Randy J Read and David Baker},

url = {https://www.bakerlab.org/wp-content/uploads/2016/08/qian07A.pdf},

issn = {1476-4687},

year  = {2007},

date = {2007-11-01},

journal = {Nature},

volume = {450},

pages = {259-64},

abstract = {The energy-based refinement of low-resolution protein structure models to atomic-level accuracy is a major challenge for computational structural biology. Here we describe a new approach to refining protein structure models that focuses sampling in regions most likely to contain errors while allowing the whole structure to relax in a physically realistic all-atom force field. In applications to models produced using nuclear magnetic resonance data and to comparative models based on distant structural homologues, the method can significantly improve the accuracy of the structures in terms of both the backbone conformations and the placement of core side chains. Furthermore, the resulting models satisfy a particularly stringent test: they provide significantly better solutions to the X-ray crystallographic phase problem in molecular replacement trials. Finally, we show that all-atom refinement can produce de novo protein structure predictions that reach the high accuracy required for molecular replacement without any experimental phase information and in the absence of templates suitable for molecular replacement from the Protein Data Bank. These results suggest that the combination of high-resolution structure prediction with state-of-the-art phasing tools may be unexpectedly powerful in phasing crystallographic data for which molecular replacement is hindered by the absence of sufficiently accurate previous models.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{121,

title = {Prediction of the structure of symmetrical protein assemblies},

author = { Ingemar Andr'e and Philip Bradley and Chu Wang and David Baker},

url = {https://www.bakerlab.org/wp-content/uploads/2016/08/André07A.pdf},

issn = {0027-8424},

year  = {2007},

date = {2007-11-01},

journal = {Proceedings of the National Academy of Sciences of the United States of America},

volume = {104},

pages = {17656-61},

abstract = {Biological supramolecular systems are commonly built up by the self-assembly of identical protein subunits to produce symmetrical oligomers with cyclical, icosahedral, or helical symmetry that play roles in processes ranging from allosteric control and molecular transport to motor action. The large size of these systems often makes them difficult to structurally characterize using experimental techniques. We have developed a computational protocol to predict the structure of symmetrical protein assemblies based on the structure of a single subunit. The method carries out simultaneous optimization of backbone, side chain, and rigid-body degrees of freedom, while restricting the search space to symmetrical conformations. Using this protocol, we can reconstruct, starting from the structure of a single subunit, the structure of cyclic oligomers and the icosahedral virus capsid of satellite panicum virus using a rigid backbone approximation. We predict the oligomeric state of EscJ from the type III secretion system both in its proposed cyclical and crystallized helical form. Finally, we show that the method can recapitulate the structure of an amyloid-like fibril formed by the peptide NNQQNY from the yeast prion protein Sup35 starting from the amino acid sequence alone and searching the complete space of backbone, side chain, and rigid-body degrees of freedom.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{120,

title = {Toward high-resolution prediction and design of transmembrane helical protein structures},

author = { P Barth and J Schonbrun and David Baker},

url = {https://www.bakerlab.org/wp-content/uploads/2016/08/barth07A.pdf},

issn = {0027-8424},

year  = {2007},

date = {2007-10-01},

journal = {Proceedings of the National Academy of Sciences of the United States of America},

volume = {104},

pages = {15682-7},

abstract = {The prediction and design at the atomic level of membrane protein structures and interactions is a critical but unsolved challenge. To address this problem, we have developed an all-atom physical model that describes intraprotein and protein-solvent interactions in the membrane environment. We evaluated the ability of the model to recapitulate the energetics and structural specificities of polytopic membrane proteins by using a battery of in silico prediction and design tests. First, in side-chain packing and design tests, the model successfully predicts the side-chain conformations at 73% of nonexposed positions and the native amino acid identities at 34% of positions in naturally occurring membrane proteins. Second, the model predicts significant energy gaps between native and nonnative structures of transmembrane helical interfaces and polytopic membrane proteins. Third, distortions in transmembrane helices are successfully recapitulated in docking experiments by using fragments of ideal helices judiciously defined around helical kinks. Finally, de novo structure prediction reaches near-atomic accuracy (<2.5 A) for several small membrane protein domains (<150 residues). The success of the model highlights the critical role of van der Waals and hydrogen-bonding interactions in the stability and structural specificity of membrane protein structures and sets the stage for the high-resolution prediction and design of complex membrane protein architectures.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{117,

title = {Automated de novo prediction of native-like RNA tertiary structures},

author = { Rhiju Das and David Baker},

url = {https://www.bakerlab.org/wp-content/uploads/2016/08/das07A.pdf},

issn = {0027-8424},

year  = {2007},

date = {2007-09-01},

journal = {Proceedings of the National Academy of Sciences of the United States of America},

volume = {104},

pages = {14664-9},

abstract = {RNA tertiary structure prediction has been based almost entirely on base-pairing constraints derived from phylogenetic covariation analysis. We describe here a complementary approach, inspired by the Rosetta low-resolution protein structure prediction method, that seeks the lowest energy tertiary structure for a given RNA sequence without using evolutionary information. In a benchmark test of 20 RNA sequences with known structure and lengths of approximately 30 nt, the new method reproduces better than 90% of Watson-Crick base pairs, comparable with the accuracy of secondary structure prediction methods. In more than half the cases, at least one of the top five models agrees with the native structure to better than 4 A rmsd over the backbone. Most importantly, the method recapitulates more than one-third of non-Watson-Crick base pairs seen in the native structures. Tandem stacks of "sheared" base pairs, base triplets, and pseudoknots are among the noncanonical features reproduced in the models. In the cases in which none of the top five models were native-like, higher energy conformations similar to the native structures are still sampled frequently but not assigned low energies. These results suggest that modest improvements in the energy function, together with the incorporation of information from phylogenetic covariance, may allow confident and accurate structure prediction for larger and more complex RNA chains.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{116,

title = {Superfamily assignments for the yeast proteome through integration of structure prediction with the gene ontology},

author = { Lars Malmstrom and Michael Riffle and Charlie E M Strauss and Dylan Chivian and Trisha N Davis and Richard Bonneau and David Baker},

url = {https://www.bakerlab.org/wp-content/uploads/2016/08/malmström07A.pdf},

issn = {1545-7885},

year  = {2007},

date = {2007-04-01},

journal = {PLoS biology},

volume = {5},

pages = {e76},

abstract = {Saccharomyces cerevisiae is one of the best-studied model organisms, yet the three-dimensional structure and molecular function of many yeast proteins remain unknown. Yeast proteins were parsed into 14,934 domains, and those lacking sequence similarity to proteins of known structure were folded using the Rosetta de novo structure prediction method on the World Community Grid. This structural data was integrated with process, component, and function annotations from the Saccharomyces Genome Database to assign yeast protein domains to SCOP superfamilies using a simple Bayesian approach. We have predicted the structure of 3,338 putative domains and assigned SCOP superfamily annotations to 581 of them. We have also assigned structural annotations to 7,094 predicted domains based on fold recognition and homology modeling methods. The domain predictions and structural information are available in an online database at http://rd.plos.org/10.1371_journal.pbio.0050076_01.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{109,

title = {Prediction of structures of multidomain proteins from structures of the individual domains},

author = { Andrew M Wollacott and Alexandre Zanghellini and Paul Murphy and David Baker},

url = {https://www.bakerlab.org/wp-content/uploads/2016/08/wollacott07A.pdf},

issn = {0961-8368},

year  = {2007},

date = {2007-02-01},

journal = {Protein science},

volume = {16},

pages = {165-75},

abstract = {We describe the development of a method for assembling structures of multidomain proteins from structures of isolated domains. The method consists of an initial low-resolution search in which the conformational space of the domain linker is explored using the Rosetta de novo structure prediction method, followed by a high-resolution search in which all atoms are treated explicitly and backbone and side chain degrees of freedom are simultaneously optimized. The method recapitulates, often with very high accuracy, the structures of existing multidomain proteins.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{286,

title = {Assessment of predictions submitted for the CASP7 domain prediction category},

author = { Michael Tress and Jianlin Cheng and Pierre Baldi and Keehyoung Joo and Jinwoo Lee and Joo-Hyun Seo and Jooyoung Lee and David Baker and Dylan Chivian and David Kim and Iakes Ezkurdia},

issn = {1097-0134},

year  = {2007},

date = {2007-00-01},

journal = {Proteins},

volume = {69 Suppl 8},

pages = {137-51},

abstract = {This paper details the assessment process and evaluation results for the Critical Assessment of Protein Structure Prediction (CASP7) domain prediction category. Domain predictions were assessed using the Normalized Domain Overlap score introduced in CASP6 and the accuracy of prediction of domain break points. The results of the analysis clearly demonstrate that the best methods are able to make consistently reliable predictions when the target has a structural template, although they are less good when the domain break occurs in a region not covered by a template. The conditions of the experiment meant that it was impossible to draw any conclusions about domain prediction for free modeling targets and it was also difficult to draw many distinctions between the best groups. Two thirds of the targets submitted were single domains and hence regarded as easy to predict. Even those targets defined as having multiple domains always had at least one domain with a similar template structure.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{281,

title = {Conservation, variability and the modeling of active protein kinases},

author = { James D R Knight and Bin Qian and David Baker and Rashmi Kothary},

issn = {1932-6203},

year  = {2007},

date = {2007-00-01},

journal = {PloS one},

volume = {2},

pages = {e982},

abstract = {The human proteome is rich with protein kinases, and this richness has made the kinase of crucial importance in initiating and maintaining cell behavior. Elucidating cell signaling networks and manipulating their components to understand and alter behavior require well designed inhibitors. These inhibitors are needed in culture to cause and study network perturbations, and the same compounds can be used as drugs to treat disease. Understanding the structural biology of protein kinases in detail, including their commonalities, differences and modes of substrate interaction, is necessary for designing high quality inhibitors that will be of true use for cell biology and disease therapy. To this end, we here report on a structural analysis of all available active-conformation protein kinases, discussing residue conservation, the novel features of such conservation, unique properties of atypical kinases and variability in the context of substrate binding. We also demonstrate how this information can be used for structure prediction. Our findings will be of use not only in understanding protein kinase function and evolution, but they highlight the flaws inherent in kinase drug design as commonly practiced and dictate an appropriate strategy for the sophisticated design of specific inhibitors for use in the laboratory and disease therapy.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{118,

title = {Structure prediction for CASP7 targets using extensive all-atom refinement with Rosetta@home},

author = { Rhiju Das and Bin Qian and Srivatsan Raman and Robert Vernon and James Thompson and Philip Bradley and Sagar Khare and Michael D Tyka and Divya Bhat and Dylan Chivian and David E Kim and William H Sheffler and Lars Malmstr"om and Andrew M Wollacott and Chu Wang and Ingemar Andre and David Baker},

issn = {1097-0134},

year  = {2007},

date = {2007-00-01},

journal = {Proteins},

volume = {69 Suppl 8},

pages = {118-28},

abstract = {We describe predictions made using the Rosetta structure prediction methodology for both template-based modeling and free modeling categories in the Seventh Critical Assessment of Techniques for Protein Structure Prediction. For the first time, aggressive sampling and all-atom refinement could be carried out for the majority of targets, an advance enabled by the Rosetta@home distributed computing network. Template-based modeling predictions using an iterative refinement algorithm improved over the best existing templates for the majority of proteins with less than 200 residues. Free modeling methods gave near-atomic accuracy predictions for several targets under 100 residues from all secondary structure classes. These results indicate that refinement with an all-atom energy function, although computationally expensive, is a powerful method for obtaining accurate structure predictions.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{154,

title = {Improved beta-protein structure prediction by multilevel optimization of nonlocal strand pairings and local backbone conformation},

author = { Philip Bradley and David Baker},

url = {https://www.bakerlab.org/wp-content/uploads/2016/07/bradley06A.pdf},

issn = {1097-0134},

year  = {2006},

date = {2006-12-01},

journal = {Proteins},

volume = {65},

pages = {922-9},

abstract = {Proteins with complex, nonlocal beta-sheets are challenging for de novo structure prediction, due in part to the difficulty of efficiently sampling long-range strand pairings. We present a new, multilevel approach to beta-sheet structure prediction that circumvents this difficulty by reformulating structure generation in terms of a folding tree. Nonlocal connections in this tree allow us to explicitly sample alternative beta-strand pairings while simultaneously exploring local conformational space using backbone torsion-space moves. An iterative, energy-biased resampling strategy is used to explore the space of beta-strand pairings; we expect that such a strategy will be generally useful for searching large conformational spaces with a high degree of combinatorial complexity.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{159,

title = {ROSETTALIGAND: protein-small molecule docking with full side-chain flexibility},

author = { Jens Meiler and David Baker},

url = {https://www.bakerlab.org/wp-content/uploads/2016/07/meiler06A.pdf},

issn = {1097-0134},

year  = {2006},

date = {2006-11-01},

journal = {Proteins},

volume = {65},

pages = {538-48},

abstract = {Protein-small molecule docking algorithms provide a means to model the structure of protein-small molecule complexes in structural detail and play an important role in drug development. In recent years the necessity of simulating protein side-chain flexibility for an accurate prediction of the protein-small molecule interfaces has become apparent, and an increasing number of docking algorithms probe different approaches to include protein flexibility. Here we describe a new method for docking small molecules into protein binding sites employing a Monte Carlo minimization procedure in which the rigid body position and orientation of the small molecule and the protein side-chain conformations are optimized simultaneously. The energy function comprises van der Waals (VDW) interactions, an implicit solvation model, an explicit orientation hydrogen bonding potential, and an electrostatics model. In an evaluation of the scoring function the computed energy correlated with experimental small molecule binding energy with a correlation coefficient of 0.63 across a diverse set of 229 protein- small molecule complexes. The docking method produced lowest energy models with a root mean square deviation (RMSD) smaller than 2 A in 71 out of 100 protein-small molecule crystal structure complexes (self-docking). In cross-docking calculations in which both protein side-chain and small molecule internal degrees of freedom were varied the lowest energy predictions had RMSDs less than 2 A in 14 of 20 test cases.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{295,

title = {Crystal structure of the HSV-1 Fc receptor bound to Fc reveals a mechanism for antibody bipolar bridging},

author = { Elizabeth R Sprague and Chu Wang and David Baker and Pamela J Bjorkman},

url = {https://www.bakerlab.org/wp-content/uploads/2016/08/sprague06A.pdf},

issn = {1545-7885},

year  = {2006},

date = {2006-06-01},

journal = {PLoS biology},

volume = {4},

pages = {e148},

abstract = {Herpes simplex virus type-1 expresses a heterodimeric Fc receptor, gE-gI, on the surfaces of virions and infected cells that binds the Fc region of host immunoglobulin G and is implicated in the cell-to-cell spread of virus. gE-gI binds immunoglobulin G at the basic pH of the cell surface and releases it at the acidic pH of lysosomes, consistent with a role in facilitating the degradation of antiviral antibodies. Here we identify the C-terminal domain of the gE ectodomain (CgE) as the minimal Fc-binding domain and present a 1.78-angstroms CgE structure. A 5-angstroms gE-gI/Fc crystal structure, which was independently verified by a theoretical prediction method, reveals that CgE binds Fc at the C(H)2-C(H)3 interface, the binding site for several mammalian and bacterial Fc-binding proteins. The structure identifies interface histidines that may confer pH-dependent binding and regions of CgE implicated in cell-to-cell spread of virus. The ternary organization of the gE-gI/Fc complex is compatible with antibody bipolar bridging, which can interfere with the antiviral immune response.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}