The mosaic genome of Anaeromyxobacter dehalogenans strain 2CP-C suggests an aerobic common ancestor to the delta-proteobacteriaThe protein structure prediction problem could be solved using the current PDB libraryTM-align: a protein structure alignment algorithm based on the TM-scoreFurther evidence for the likely completeness of the library of solved single domain protein structuresStructure modeling of all identified G protein-coupled receptors in the human genomeHow special is the biochemical function of native proteins?Implications of the small number of distinct ligand binding pockets in proteins for drug discovery, evolution and biochemical functionSliding of proteins non-specifically bound to DNA: Brownian dynamics studies with coarse-grained protein and DNA modelsFINDSITE: a threading-based approach to ligand homology modelingA comprehensive survey of small-molecule binding pockets in proteinsA minimal physically realistic protein-like lattice model: designing an energy landscape that ensures all-or-none folding to a unique native stateAb initio protein structure prediction on a genomic scale: application to the Mycoplasma genitalium genomeLocal energy landscape flattening: parallel hyperbolic Monte Carlo sampling of protein folding.Development of unified statistical potentials describing protein-protein interactions.TOUCHSTONE II: a new approach to ab initio protein structure predictionTOUCHSTONEX: protein structure prediction with sparse NMR data.TOUCHSTONE: a unified approach to protein structure prediction.Use of residual dipolar couplings as restraints in ab initio protein structure prediction.Large-scale assessment of the utility of low-resolution protein structures for biochemical function assignment.Automated structure prediction of weakly homologous proteins on a genomic scaleApplication of sparse NMR restraints to large-scale protein structure prediction.On the origin and highly likely completeness of single-domain protein structures.In quest of an empirical potential for protein structure prediction.Efficient prediction of nucleic acid binding function from low-resolution protein structures.WeFold: a coopetition for protein structure predictionBenchmarking of TASSER in the ab initio limit.Development and benchmarking of TASSER(iter) for the iterative improvement of protein structure predictions.Ab initio protein structure prediction using chunk-TASSERWhat is the relationship between the global structures of apo and holo proteins?Analysis of TASSER-based CASP7 protein structure prediction results.Fast procedure for reconstruction of full-atom protein models from reduced representations.On the role of physics and evolution in dictating protein structure and functionBenchmarking of TASSER_2.0: an improved protein structure prediction algorithm with more accurate predicted contact restraintsProtein model refinement using an optimized physics-based all-atom force fieldGS-align for glycan structure alignment and similarity measurement.Protein structure prediction by pro-Sp3-TASSER.PoLi: A Virtual Screening Pipeline Based on Template Pocket and Ligand Similarity.Performance of the Pro-sp3-TASSER server in CASP8The continuity of protein structure space is an intrinsic property of proteinsQ-Dock(LHM): Low-resolution refinement for ligand comparative modeling.
P50
Q21144320-46D50616-FC9E-4309-8E2F-5FAD0371CF95Q22248093-69E3DC24-F6C1-4F05-88D7-CFE16CB8E0D1Q24522708-83BAEA5A-34E3-4B81-AE3C-64DEC1BC375DQ24621878-013CFB01-4447-4D60-9F5D-5001B94DC7A5Q25256886-6EC4F597-FFF7-40C7-971D-470683F3BEB2Q26766629-3DC939DD-819E-4016-991B-E7C464F07C43Q27026364-19D0C317-96FA-4262-8EF6-73B1C364781BQ27320118-5F61E3F6-C7A7-4284-8E18-4DF582B3E298Q28475563-9A8748CF-5F42-474A-B254-7FEDF7D18E21Q28534683-8D94C4D6-3AE7-4384-A3E3-E8050FBCD2B1Q30164862-2BD182C4-EC95-455B-996F-B9AC03015B65Q30330160-8DC6B567-78CE-4A07-8B48-B1371ED8339AQ30331169-179D0C33-FABD-4C36-A5DA-A718CBDD0BF3Q30332549-22E12EF6-B770-4CE9-BD65-FFFDAA118D32Q30333234-E57EB0B4-C626-46F7-B50E-DCC0ACB7D849Q30336124-B34C14D7-3FC5-4E74-AFFA-ACC5404DD7C5Q30336234-EDDEA922-3FED-4CAB-997E-2E86EDCCC258Q30336296-7CD82D79-257A-4CB0-B102-E4384B1BFDA7Q30336691-AA59BD3A-BE1B-4782-B47A-759B087C9B87Q30341442-58E2B07A-723A-4595-B7C7-CBDD5CEA0FBCQ30342541-6CC3E942-1E0C-4528-9F12-6781CD33057FQ30352967-2DE171CA-3CC5-48DF-8F82-2E2609C97B81Q30353288-060D8AA9-9FB4-40B6-9350-BA31F0C73632Q30353462-0F805206-645C-4408-937E-9F42DE1466C9Q30360721-103D36B2-088D-4CEC-AEF9-D7307C4D6BE5Q30361172-C4457174-647D-4679-AABA-2B7314C44FD2Q30361367-B3679BFB-216C-4CC9-A7FA-7B20D6F413ADQ30361590-31498EA7-8831-42CB-9600-0EE0F7DDDCFDQ30363271-1D2F4F41-9854-46B1-874F-2AE6FFADDC0FQ30363498-9ACB762E-82E3-47EA-86F9-AA09CB950E14Q30367164-D429E274-27DA-4557-8368-B89E77F48799Q30369480-C802ACBE-BF01-41C5-8333-0C3C797AE8BEQ30369481-F797FF59-ED64-4283-B9B2-3D8C8217FF69Q30369943-E60AEC52-4DE6-4AF7-BA74-726BFF662B49Q30373530-07CD69E5-01EA-40F7-A58A-91E2A55D6CCFQ30375575-04BE7579-0093-44E7-9E1C-F49F5C11BA94Q30377334-43B5746B-FE67-4C74-A999-AE4AE6CB56FFQ30379331-004206E7-9124-4E72-BEBC-9B473674BCBDQ30381356-D2DB0B39-E351-4EEA-975A-A753417D1019Q30381629-F5D2199E-F549-4583-8DB0-74919E938CC6
P50
description
computational biologist
@en
name
Jeffrey Skolnick
@ast
Jeffrey Skolnick
@de
Jeffrey Skolnick
@en
Jeffrey Skolnick
@es
Jeffrey Skolnick
@ga
جفری اسکولنیک
@azb
type
label
Jeffrey Skolnick
@ast
Jeffrey Skolnick
@de
Jeffrey Skolnick
@en
Jeffrey Skolnick
@es
Jeffrey Skolnick
@ga
جفری اسکولنیک
@azb
prefLabel
Jeffrey Skolnick
@ast
Jeffrey Skolnick
@de
Jeffrey Skolnick
@en
Jeffrey Skolnick
@es
Jeffrey Skolnick
@ga
جفری اسکولنیک
@azb
P214
P244
P1207
n2006096831
P166
P19
P21
P213
0000 0001 1007 1652
P214
P2381
P244
P31
P496
0000-0002-1877-4958
P569
1953-01-01T00:00:00Z
P734
P735
P7859
lccn-n96048862