@article{85,

title = {Protein-protein docking with simultaneous optimization of rigid-body displacement and side-chain conformations},

author = { Jeffrey J Gray and Stewart Moughon and Chu Wang and Ora Schueler-Furman and Brian Kuhlman and Carol A Rohl and David Baker},

issn = {0022-2836},

year  = {2003},

date = {2003-08-01},

journal = {Journal of molecular biology},

volume = {331},

pages = {281-99},

abstract = {Protein-protein docking algorithms provide a means to elucidate structural details for presently unknown complexes. Here, we present and evaluate a new method to predict protein-protein complexes from the coordinates of the unbound monomer components. The method employs a low-resolution, rigid-body, Monte Carlo search followed by simultaneous optimization of backbone displacement and side-chain conformations using Monte Carlo minimization. Up to 10(5) independent simulations are carried out, and the resulting "decoys" are ranked using an energy function dominated by van der Waals interactions, an implicit solvation model, and an orientation-dependent hydrogen bonding potential. Top-ranking decoys are clustered to select the final predictions. Small-perturbation studies reveal the formation of binding funnels in 42 of 54 cases using coordinates derived from the bound complexes and in 32 of 54 cases using independently determined coordinates of one or both monomers. Experimental binding affinities correlate with the calculated score function and explain the predictive success or failure of many targets. Global searches using one or both unbound components predict at least 25% of the native residue-residue contacts in 28 of the 32 cases where binding funnels exist. The results suggest that the method may soon be useful for generating models of biologically important complexes from the structures of the isolated components, but they also highlight the challenges that must be met to achieve consistent and accurate prediction of protein-protein interactions.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{86,

title = {Protein-protein docking predictions for the CAPRI experiment},

author = { Jeffrey J Gray and Stewart E Moughon and Tanja Kortemme and Ora Schueler-Furman and Kira M S Misura and Alexandre V Morozov and David Baker},

url = {https://www.bakerlab.org/wp-content/uploads/2016/06/gray03B.pdf},

issn = {1097-0134},

year  = {2003},

date = {2003-07-01},

journal = {Proteins},

volume = {52},

pages = {118-22},

abstract = {We predicted structures for all seven targets in the CAPRI experiment using a new method in development at the time of the challenge. The technique includes a low-resolution rigid body Monte Carlo search followed by high-resolution refinement with side-chain conformational changes and rigid body minimization. Decoys (approximately 10(6) per target) were discriminated using a scoring function including van der Waals and solvation interactions, hydrogen bonding, residue-residue pair statistics, and rotamer probabilities. Decoys were ranked, clustered, manually inspected, and selected. The top ranked model for target 6 predicted the experimental structure to 1.5 A RMSD and included 48 of 65 correct residue-residue contacts. Target 7 was predicted at 5.3 A RMSD with 22 of 37 correct residue-residue contacts using a homology model from a known complex structure. Using a preliminary version of the protocol in round 1, target 1 was predicted within 8.8 A although few contacts were correct. For targets 2 and 3, the interface locations and a small fraction of the contacts were correctly identified.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{83,

title = {An orientation-dependent hydrogen bonding potential improves prediction of specificity and structure for proteins and protein-protein complexes},

author = { Tanja Kortemme and Alexandre V Morozov and David Baker},

issn = {0022-2836},

year  = {2003},

date = {2003-02-01},

journal = {Journal of molecular biology},

volume = {326},

pages = {1239-59},

abstract = {Hydrogen bonding is a key contributor to the specificity of intramolecular and intermolecular interactions in biological systems. Here, we develop an orientation-dependent hydrogen bonding potential based on the geometric characteristics of hydrogen bonds in high-resolution protein crystal structures, and evaluate it using four tests related to the prediction and design of protein structures and protein-protein complexes. The new potential is superior to the widely used Coulomb model of hydrogen bonding in prediction of the sequences of proteins and protein-protein interfaces from their structures, and improves discrimination of correctly docked protein-protein complexes from large sets of alternative structures.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{88,

title = {Automated prediction of CASP-5 structures using the Robetta server},

author = { Dylan Chivian and David E Kim and Lars Malmstr"om and Philip Bradley and Timothy Robertson and Paul Murphy and Charles E M Strauss and Richard Bonneau and Carol A Rohl and David Baker},

url = {https://www.bakerlab.org/wp-content/uploads/2016/06/chivian03A.pdf},

issn = {1097-0134},

year  = {2003},

date = {2003-00-01},

journal = {Proteins},

volume = {53 Suppl 6},

pages = {524-33},

abstract = {Robetta is a fully automated protein structure prediction server that uses the Rosetta fragment-insertion method. It combines template-based and de novo structure prediction methods in an attempt to produce high quality models that cover every residue of a submitted sequence. The first step in the procedure is the automatic detection of the locations of domains and selection of the appropriate modeling protocol for each domain. For domains matched to a homolog with an experimentally characterized structure by PSI-BLAST or Pcons2, Robetta uses a new alignment method, called K*Sync, to align the query sequence onto the parent structure. It then models the variable regions by allowing them to explore conformational space with fragments in fashion similar to the de novo protocol, but in the context of the template. When no structural homolog is available, domains are modeled with the Rosetta de novo protocol, which allows the full length of the domain to explore conformational space via fragment-insertion, producing a large decoy ensemble from which the final models are selected. The Robetta server produced quite reasonable predictions for targets in the recent CASP-5 and CAFASP-3 experiments, some of which were at the level of the best human predictions.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{90,

title = {Rosetta predictions in CASP5: successes, failures, and prospects for complete automation},

author = { Philip Bradley and Dylan Chivian and Jens Meiler and Kira M S Misura and Carol A Rohl and William R Schief and William J Wedemeyer and Ora Schueler-Furman and Paul Murphy and Jack Schonbrun and Charles E M Strauss and David Baker},

url = {https://www.bakerlab.org/wp-content/uploads/2016/06/bradley03A.pdf},

issn = {1097-0134},

year  = {2003},

date = {2003-00-01},

journal = {Proteins},

volume = {53 Suppl 6},

pages = {457-68},

abstract = {We describe predictions of the structures of CASP5 targets using Rosetta. The Rosetta fragment insertion protocol was used to generate models for entire target domains without detectable sequence similarity to a protein of known structure and to build long loop insertions (and N-and C-terminal extensions) in cases where a structural template was available. Encouraging results were obtained both for the de novo predictions and for the long loop insertions; we describe here the successes as well as the failures in the context of current efforts to improve the Rosetta method. In particular, de novo predictions failed for large proteins that were incorrectly parsed into domains and for topologically complex (high contact order) proteins with swapping of segments between domains. However, for the remaining targets, at least one of the five submitted models had a long fragment with significant similarity to the native structure. A fully automated version of the CASP5 protocol produced results that were comparable to the human-assisted predictions for most of the targets, suggesting that automated genomic-scale, de novo protein structure prediction may soon be worthwhile. For the three targets where the human-assisted predictions were significantly closer to the native structure, we identify the steps that remain to be automated.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{184,

title = {De novo prediction of three-dimensional structures for major protein families},

author = { Richard Bonneau and Charlie E M Strauss and Carol A Rohl and Dylan Chivian and Phillip Bradley and Lars Malmstr"om and Tim Robertson and David Baker},

url = {https://www.bakerlab.org/wp-content/uploads/2016/06/bonneau02B.pdf},

issn = {0022-2836},

year  = {2002},

date = {2002-09-01},

journal = {Journal of molecular biology},

volume = {322},

pages = {65-78},

abstract = {We use the Rosetta de novo structure prediction method to produce three-dimensional structure models for all Pfam-A sequence families with average length under 150 residues and no link to any protein of known structure. To estimate the reliability of the predictions, the method was calibrated on 131 proteins of known structure. For approximately 60% of the proteins one of the top five models was correctly predicted for 50 or more residues, and for approximately 35%, the correct SCOP superfamily was identified in a structure-based search of the Protein Data Bank using one of the models. This performance is consistent with results from the fourth critical assessment of structure prediction (CASP4). Correct and incorrect predictions could be partially distinguished using a confidence function based on a combination of simulation convergence, protein length and the similarity of a given structure prediction to known protein structures. While the limited accuracy and reliability of the method precludes definitive conclusions, the Pfam models provide the only tertiary structure information available for the 12% of publicly available sequences represented by these large protein families.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{233,

title = {Evaluation of structural and evolutionary contributions to deleterious mutation prediction},

author = { Christopher T Saunders and David Baker},

url = {https://www.bakerlab.org/wp-content/uploads/2016/06/saunders02A.pdf},

issn = {0022-2836},

year  = {2002},

date = {2002-09-01},

journal = {Journal of molecular biology},

volume = {322},

pages = {891-901},

abstract = {Methods for automated prediction of deleterious protein mutations have utilized both structural and evolutionary information but the relative contribution of these two factors remains unclear. To address this, we have used a variety of structural and evolutionary features to create simple deleterious mutation models that have been tested on both experimental mutagenesis and human allele data. We find that the most accurate predictions are obtained using a solvent-accessibility term, the C(beta) density, and a score derived from homologous sequences, SIFT. A classification tree using these two features has a cross-validated prediction error of 20.5% on an experimental mutagenesis test set when the prior probability for deleterious and neutral cases is equal, whereas this prediction error is 28.8% and 22.2% using either the C(beta) density or SIFT alone. The improvement imparted by structure increases when fewer homologs are available: when restricted to three homologs the prediction error improves from 26.9% using SIFT alone to 22.4% using SIFT and the C(beta) density, or 24.8% using SIFT and a noisy C(beta) density term approximating the inaccuracy of ab initio structures modeled by the Rosetta method. We conclude that methods for deleterious mutation prediction should include structural information when fewer than five to ten homologs are available, and that ab initio predicted structures may soon be useful in such cases when high-resolution structures are unavailable.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{182,

title = {Simple physical models connect theory and experiment in protein folding kinetics},

author = { Eric Alm and Alexandre V Morozov and Tanja Kortemme and David Baker},

url = {https://www.bakerlab.org/wp-content/uploads/2016/06/alm02A.pdf},

issn = {0022-2836},

year  = {2002},

date = {2002-09-01},

journal = {Journal of molecular biology},

volume = {322},

pages = {463-76},

abstract = {Our understanding of the principles underlying the protein-folding problem can be tested by developing and characterizing simple models that make predictions which can be compared to experimental data. Here we extend our earlier model of folding free energy landscapes, in which each residue is considered to be either folded as in the native state or completely disordered, by investigating the role of additional factors representing hydrogen bonding and backbone torsion strain, and by using a hybrid between the master equation approach and the simple transition state theory to evaluate kinetics near the free energy barrier in greater detail. Model calculations of folding phi-values are compared to experimental data for 19 proteins, and for more than half of these, experimental data are reproduced with correlation coefficients between r=0.41 and 0.88; calculations of transition state free energy barriers correlate with rates measured for 37 single domain proteins (r=0.69). The model provides insight into the contribution of alternative-folding pathways, the validity of quasi-equilibrium treatments of the folding landscape, and the magnitude of the Arrhenius prefactor for protein folding. Finally, we discuss the limitations of simple native-state-based models, and as a more general test of such models, provide predictions of folding rates and mechanisms for a comprehensive set of over 400 small protein domains of known structure.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{183,

title = {Contact order and ab initio protein structure prediction},

author = { Richard Bonneau and Ingo Ruczinski and Jerry Tsai and David Baker},

url = {https://www.bakerlab.org/wp-content/uploads/2016/06/bonneau02A.pdf},

issn = {0961-8368},

year  = {2002},

date = {2002-08-01},

journal = {Protein science},

volume = {11},

pages = {1937-44},

abstract = {Although much of the motivation for experimental studies of protein folding is to obtain insights for improving protein structure prediction, there has been relatively little connection between experimental protein folding studies and computational structural prediction work in recent years. In the present study, we show that the relationship between protein folding rates and the contact order (CO) of the native structure has implications for ab initio protein structure prediction. Rosetta ab initio folding simulations produce a dearth of high CO structures and an excess of low CO structures, as expected if the computer simulations mimic to some extent the actual folding process. Consistent with this, the majority of failures in ab initio prediction in the CASP4 (critical assessment of structure prediction) experiment involved high CO structures likely to fold much more slowly than the lower CO structures for which reasonable predictions were made. This bias against high CO structures can be partially alleviated by performing large numbers of additional simulations, selecting out the higher CO structures, and eliminating the very low CO structures; this leads to a modest improvement in prediction quality. More significant improvements in predictions for proteins with complex topologies may be possible following significant increases in high-performance computing power, which will be required for thoroughly sampling high CO conformations (high CO proteins can take six orders of magnitude longer to fold than low CO proteins). Importantly for such a strategy, simulations performed for high CO structures converge much less strongly than those for low CO structures, and hence, lack of simulation convergence can indicate the need for improved sampling of high CO conformations. The parallels between Rosetta simulations and folding in vivo may extend to misfolding: The very low CO structures that accumulate in Rosetta simulations consist primarily of local up-down beta-sheets that may resemble precursors to amyloid formation.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{191,

title = {Distributions of beta sheets in proteins with application to structure prediction},

author = { Ingo Ruczinski and Charles Kooperberg and Richard Bonneau and David Baker},

url = {https://www.bakerlab.org/wp-content/uploads/2016/06/ruczinski02A.pdf},

issn = {1097-0134},

year  = {2002},

date = {2002-07-01},

journal = {Proteins},

volume = {48},

pages = {85-97},

abstract = {We recently developed the Rosetta algorithm for ab initio protein structure prediction, which generates protein structures from fragment libraries using simulated annealing. The scoring function in this algorithm favors the assembly of strands into sheets. However, it does not discriminate between different sheet motifs. After generating many structures using Rosetta, we found that the folding algorithm predominantly generates very local structures. We surveyed the distribution of beta-sheet motifs with two edge strands (open sheets) in a large set of non-homologous proteins. We investigated how much of that distribution can be accounted for by rules previously published in the literature, and developed a filter and a scoring method that enables us to improve protein structure prediction for beta-sheet proteins. Proteins 2002;48:85-97.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{234,

title = {Protein structure prediction in 2002.},

author = { Jack Schonbrun and William J Wedemeyer and David Baker},

issn = {0959-440X},

year  = {2002},

date = {2002-06-01},

journal = {Current opinion in structural biology},

volume = {12},

pages = {348-54},

abstract = {Central issues concerning protein structure prediction have been highlighted by the recently published summary of the fourth community-wide protein structure prediction experiment (CASP4). Although sequence/structure alignment remains the bottleneck in comparative modeling, there has been substantial progress in fully automated remote homolog detection and in de novo structure prediction. Significant further progress will probably require improvements in high-resolution modeling.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{190,

title = {De novo determination of protein backbone structure from residual dipolar couplings using Rosetta},

author = { Carol A Rohl and David Baker},

url = {https://www.bakerlab.org/wp-content/uploads/2016/06/rohl02A.pdf},

issn = {0002-7863},

year  = {2002},

date = {2002-03-01},

journal = {Journal of the American Chemical Society},

volume = {124},

pages = {2723-9},

abstract = {As genome-sequencing projects rapidly increase the database of protein sequences, the gap between known sequences and known structures continues to grow exponentially, increasing the demand to accelerate structure determination methods. Residual dipolar couplings (RDCs) are an attractive source of experimental restraints for NMR structure determination, particularly rapid, high-throughput methods, because they yield both local and long-range orientational information and can be easily measured and assigned once the backbone resonances of a protein have been assigned. While very extensive RDC data sets have been used to determine the structure of ubiquitin, it is unclear to what extent such methods will generalize to larger proteins with less complete data sets. Here we incorporate experimental RDC restraints into Rosetta, an ab initio structure prediction method, and demonstrate that the combined algorithm provides a general method for de novo determination of a variety of protein folds from RDC data. Backbone structures for multiple proteins up to approximately 125 residues in length and spanning a range of topological complexities are rapidly and reproducibly generated using data sets that are insufficient in isolation to uniquely determine the protein fold de novo, although ambiguities and errors are observed for proteins with symmetry about an axis of the alignment tensor. The models generated are not high-resolution structures completely defined by experimental data but are sufficiently accurate to accelerate traditional high-resolution NMR structure determination and provide structure-based functional insights.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{71,

title = {Protein structure prediction and structural genomics.},

author = { David Baker and A Sali},

issn = {0036-8075},

year  = {2001},

date = {2001-10-01},

journal = {Science (New York, N.Y.)},

volume = {294},

pages = {93-6},

abstract = {Genome sequencing projects are producing linear amino acid sequences, but full understanding of the biological role of these proteins will require knowledge of their structure and function. Although experimental structure determination methods are providing high-resolution structure information about a subset of the proteins, computational structure prediction methods will provide valuable information for the large fraction of sequences whose structures will not be determined experimentally. The first class of protein structure prediction methods, including threading and comparative modeling, rely on detectable similarity spanning most of the modeled sequence and at least one known structure. The second class of methods, de novo or ab initio methods, predict the structure from sequence alone, without relying on similarity at the fold level between the modeled sequence and any of the known structures. In this Viewpoint, we begin by describing the essential features of the methods, the accuracy of the models, and their application to the prediction and understanding of protein function, both for single proteins and on the scale of whole genomes. We then discuss the important role that protein structure prediction methods play in the growing worldwide effort in structural genomics.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{62,

title = {Molecular dynamics in the endgame of protein structure prediction},

author = { M R Lee and J Tsai and David Baker and P A Kollman},

url = {https://www.bakerlab.org/wp-content/uploads/2016/06/lee01B.pdf},

issn = {0022-2836},

year  = {2001},

date = {2001-10-01},

journal = {Journal of molecular biology},

volume = {313},

pages = {417-30},

abstract = {In order adequately to sample conformational space, methods for protein structure prediction make necessary simplifications that also prevent them from being as accurate as desired. Thus, the idea of feeding them, hierarchically, into a more accurate method that samples less effectively was introduced a decade ago but has not met with more than limited success in a few isolated instances. Ideally, the final stages should be able to identify the native state, show a good correlation with native similarity in order to add value to the selection process, and refine the structures even further. In this work, we explore the possibility of using state-of-the-art explicit solvent molecular dynamics and implicit solvent free energy calculations to accomplish all three of those objectives on 12 small, single-domain proteins, four each of alpha, beta and mixed topologies. We find that this approach is very successful in ranking the native and also enhances the structure selection of predictions generated from the Rosetta method.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{67,

title = {Functional inferences from blind ab initio protein structure predictions},

author = { R Bonneau and J Tsai and I Ruczinski and David Baker},

url = {https://www.bakerlab.org/wp-content/uploads/2016/06/bonneau01B.pdf},

issn = {1047-8477},

year  = {2001},

date = {2001-05-01},

journal = {Journal of structural biology},

volume = {134},

pages = {186-90},

abstract = {Ab initio protein structure prediction methods have improved dramatically in the past several years. Because these methods require only the sequence of the protein of interest, they are potentially applicable to the open reading frames in the many organisms whose sequences have been and will be determined. Ab initio methods cannot currently produce models of high enough resolution for use in rational drug design, but there is an exciting potential for using the methods for functional annotation of protein sequences on a genomic scale. Here we illustrate how functional insights can be obtained from low-resolution predicted structures using examples from blind ab initio structure predictions from the third and fourth critical assessment of structure prediction (CASP3, CASP4) experiments.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{70,

title = {Improving the performance of Rosetta using multiple sequence alignment information and global measures of hydrophobic core formation},

author = { R Bonneau and C E Strauss and David Baker},

url = {https://www.bakerlab.org/wp-content/uploads/2016/06/bonneau01A.pdf},

issn = {0887-3585},

year  = {2001},

date = {2001-04-01},

journal = {Proteins},

volume = {43},

pages = {1-11},

abstract = {This study explores the use of multiple sequence alignment (MSA) information and global measures of hydrophobic core formation for improving the Rosetta ab initio protein structure prediction method. The most effective use of the MSA information is achieved by carrying out independent folding simulations for a subset of the homologous sequences in the MSA and then identifying the free energy minima common to all folded sequences via simultaneous clustering of the independent folding runs. Global measures of hydrophobic core formation, using ellipsoidal rather than spherical representations of the hydrophobic core, are found to be useful in removing non-native conformations before cluster analysis. Through this combination of MSA information and global measures of protein core formation, we significantly increase the performance of Rosetta on a challenging test set. Proteins 2001;43:1-11.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{56,

title = {Prospects for ab initio protein structural genomics},

author = { K T Simons and C Strauss and David Baker},

url = {https://www.bakerlab.org/wp-content/uploads/2016/06/simons01A.pdf},

issn = {0022-2836},

year  = {2001},

date = {2001-03-01},

journal = {Journal of molecular biology},

volume = {306},

pages = {1191-9},

abstract = {We present the results of a large-scale testing of the ROSETTA method for ab initio protein structure prediction. Models were generated for two independently generated lists of small proteins (up to 150 amino acid residues), and the results were evaluated using traditional rmsd based measures and a novel measure based on the structure-based comparison of the models to the structures in the PDB using DALI. For 111 of 136 all alpha and alpha/beta proteins 50 to 150 residues in length, the method produced at least one model within 7 A rmsd of the native structure in 1000 attempts. For 60 of these proteins, the closest structure match in the PDB to at least one of the ten most frequently generated conformations was found to be structurally related (four standard deviations above background) to the native protein. These results suggest that ab initio structure prediction approaches may soon be useful for generating low resolution models and identifying distantly related proteins with similar structures and perhaps functions for these classes of proteins on the genome scale.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{61,

title = {2.1 and 1.8 A average C(alpha) RMSD structure predictions on two small proteins, HP-36 and s15},

author = { M R Lee and David Baker and P A Kollman},

url = {https://www.bakerlab.org/wp-content/uploads/2016/06/lee01A.pdf},

issn = {0002-7863},

year  = {2001},

date = {2001-02-01},

journal = {Journal of the American Chemical Society},

volume = {123},

pages = {1040-6},

abstract = {On two different small proteins, the 36-mer villin headpiece domain (HP-36) and the 65-mer structured region of ribosomal protein (S15), several model predictions from the ab initio approach Rosetta were subjected to molecular dynamics simulations for refinement. After clustering the resulting trajectories into conformational families, the average molecular mechanics--Poisson Boltzmann/surface area (MM-PBSA) free energies and alpha carbon (C(alpha)) RMSDs were then calculated for each family. Those conformational families with the lowest average free energies also contained the best C(alpha) RMSD structures (1.4 A for S15 and HP-36 core) and the lowest average C(alpha) RMSDs (1.8 A for S15, 2.1 A for HP-36 core). For comparison, control simulations starting with the two experimental structures were very stable, each consisting of a single conformational family, with an average C(alpha) RMSD of 1.3 A for S15 and 1.2 A for HP-36 core (1.9 A over all residues). In addition, the average free energiestextquoteright ranks (Spearman rank, r(s)) correlate well with the average C(alpha) RMSDs (r(s) = 0.77 for HP-36, r(s) = 0.83 for S15). Molecular dynamics simulations combined with the MM--PBSA free energy function provide a potentially powerful tool for the protein structure prediction community in allowing for both high-resolution structural refinement and accurate ranking of model predictions. With all of the information that genomics is now providing, this methodology may allow for advances in going from sequence to structure.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{69,

title = {Ab initio protein structure prediction: progress and prospects.},

author = { R Bonneau and David Baker},

issn = {1056-8700},

year  = {2001},

date = {2001-00-01},

journal = {Annual review of biophysics and biomolecular structure},

volume = {30},

pages = {173-89},

abstract = {Considerable recent progress has been made in the field of ab initio protein structure prediction, as witnessed by the third Critical Assessment of Structure Prediction (CASP3). In spite of this progress, much work remains, for the field has yet to produce consistently reliable ab initio structure prediction protocols. In this work, we review the features of current ab initio protocols in an attempt to highlight the foundations of recent progress in the field and suggest promising directions for future work.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{68,

title = {Rosetta in CASP4: progress in ab initio protein structure prediction},

author = { R Bonneau and J Tsai and I Ruczinski and D Chivian and C Rohl and C E Strauss and David Baker},

url = {https://www.bakerlab.org/wp-content/uploads/2016/06/bonneau01C.pdf},

issn = {0887-3585},

year  = {2001},

date = {2001-00-01},

journal = {Proteins},

volume = {Suppl 5},

pages = {119-26},

abstract = {Rosetta ab initio protein structure predictions in CASP4 were considerably more consistent and more accurate than previous ab initio structure predictions. Large segments were correctly predicted (>50 residues superimposed within an RMSD of 6.5 A) for 16 of the 21 domains under 300 residues for which models were submitted. Models with the global fold largely correct were produced for several targets with new folds, and for several difficult fold recognition targets, the Rosetta models were more accurate than those produced with traditional fold recognition models. These promising results suggest that Rosetta may soon be able to contribute to the interpretation of genome sequence information.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}