Jeffrey Skolnick

Director, Center for the Study of Systems Biology
Mary and Maisie Gibson Chair & GRA Eminent Scholar in Computational Systems Biology
Director, Integrative BioSystems Institute
Professor, School of Biology

Ph.D., Yale University, 1978

Center for the Study of Systems Biology
250 14th Street, NW, Room 138
Atlanta, GA 30318
Tel: (404) 407-8975
Fax: (404) 385-7478
skolnick@gatech.edu

Research Interests

  • Systems Biology, Computational Biology, and Bioinformatics
  • Cancer Metabolomics
  • Prediction of protein tertiary and quaternary structure and folding pathways
  • Prediction of membrane protein tertiary structure
  • Prediction of DNA-binding proteins
  • Protein Evolution
  • Prediction of small molecule ligands for drug discovery
  • Prediction of druggable protein targets
  • Drug Design
  • Automatic assignment of enzymes to metabolic pathways
  • Simulation of Virtual Cells

School of Biology Faculty Page | CV

Publications

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331
Kumar, N, Skolnick J.  Submitted.  EFICAz2.5: Application of a high-precision enzyme function predictor to 373 proteomes. BMC Bioinformatics.
330
Zhou, H, Gao M, Kumar N, Skolnick J.  Submitted.  SUNPRO: Structure and function predictions of proteins from representative organisms. BMC Bioinformatics.
329
Skolnick, J, Zhou H, Brylinski M.  In Press.  Further evidence for the likely completeness of the library of solved single domain protein structures. Journal of Physical Chemistry, B. PDF
328
Zhou, H, Skolnick J.  Submitted.  FINDSITEX: A structure based, small molecule virtual screening approach with application to all identified human GPCRs. Molecular Pharmaceutics .
327
Gao, M, Skolnick J.  2012.  The distribution of ligand-binding pockets around protein-protein interfaces suggests a general mechanism for pocket formation. Proceedings of the National Academy of Sciences. 109(10):3784-3789. PDF
326
Zhou, H, Skolnick J.  2011.  GOAP: A Generalized Orientation-Dependent, All-Atom Statistical Potential for Protein Structure Prediction. Biophysical Journal. 101(8):2043-2052. PDF
325
Ando, T, Skolnick J.  Submitted.  Importance of excluded volume and hydrodynamic interactions on macromolecular diffusion in vivo. Proceedings of the International Conference of the Quantum Bio-Informatics IV.
324
Zhou, H, Skolnick J.  2012.  Template-based protein structure modeling using TASSERVMT. Proteins: Structure, Function, and Bioinformatics. 80(2):352-361. PDF
323
Brylinski, M, Gao M, Skolnick J.  2011.  Why not consider a spherical protein? Implications of backbone hydrogen bonding for protein structure and function Physical Chemistry Chemical Physics. 13 (38):17044-17055. PDF
322
Skolnick, J, Friesner R.  2011.  Editorial overview. Current Opinion in Structural Biology. 21:147-149. PDF
321
Gao, M, Skolnick J.  2011.  New benchmark metrics for protein-protein docking methods. Proteins: Structure, Function, and Bioinformatics. 79(5):1623-1634. PDF
320
Brylinski, M, Skolnick J.  2010.  Cross-Reactivity Virtual Profiling of the Human Kinome by X-ReactKIN: A Chemical Systems Biology Approach. Molecular Pharmaceutics. 7(6):2324–2333. PDF
319
Gao, M, Skolnick J.  2010.  Structural space of protein-protein interfaces is degenerate, close to complete, and highly connected. Proceedings of the National Academy of Sciences of the United States of America. 107(52):22517-22. PDF Supplementary Data Abstract
318
Brylinski, M, Skolnick J.  2011.  FINDSITE-metal: Integrating evolutionary information and machine learning for structure-based metal-binding site prediction at the proteome level. Proteins: Structure, Function, and Bioinformatics. 79(3):735-751. PDF
317
Lee, SY, Skolnick J.  2010.  TASSER_WT: A protein structure prediction algorithm with accurate predicted contact restraints for difficult protein targets. Biophysical Journal. 99(9):3066-75. PDF Supplementary Data Abstract
316
Ando, T, Skolnick J.  2010.  Crowding and hydrodynamic interactions likely dominate in vivo macromolecular motion. Proceedings of the National Academy of Sciences of the United States of America. 107:18457-18462. PDF Supplementary Data
315
Ando, T, Skolnick J.  2011.  Brownian dynamics simulation of macromolecule diffusion in a protocell. Proceedings of the International Conference of the Quantum Bio-Informatics IV. 28:413-426. PDF
314
Brylinski, M, Lee S Y, Zhou H, Skolnick J.  2011.  The utility of geometrical and chemical restraint information extracted from predicted ligand-binding sites in protein structure refinement. Journal of Structural Biology. 173(3):558-569. PDF
313
Brylinski, M, Skolnick J.  2010.  Comprehensive structural and functional characterization of the human kinome by protein structure modeling and ligand virtual screening. Journal of Chemical Information and Modeling. 50:1839-1854. PDF
312
Gao, M, Skolnick J.  2010.  iAlign: a method for the structural comparison of protein-protein interfaces. Bioinformatics (Oxford, England). 26(18):2259-65. PDF Supplementary Data Abstract
311
Pandit, S B, Skolnick J.  2010.  TASSER_low-zsc: An approach to improve structure prediction using low z-score ranked templates. Protiens. 78(13):2769-2080. PDF
310
Zhou, H, Skolnick J.  2010.  Improving threading algorithms for remote homology modeling by combining fragment and template comparisons. Proteins. 78(9):2041-8. PDF Abstract
309
Skolnick, J, Brylinski M, Lee SY.  2009.  Reply to Zimmerman et al: The space of single domain protein structures is continuous and highly connected. Proc Natl Acad Science 2009:106(51): E138. PDF Abstract
308
Pandit, S B, Brylinski M, Zhou H, Gao M, Arakaki AK, Skolnick J.  2010.  PSiFR: an integrated resource for prediction of protein structure and function. Bioinformatics (Oxford, England). 26(5):687-8. PDF Abstract
307
Skolnick, J, Brylinski M.  2009.  Novel computational approaches to drug discovery. Proceedings of the International Conference of the Quantum Bio-Informatics III. PDF
306
Brylinski, M, Skolnick J.  2010.  Q-Dock(LHM): Low-resolution refinement for ligand comparative modeling. Journal of computational chemistry. 31(5):1093-105. PDF Abstract
305
Gao, M, Skolnick J.  2009.  A threading-based method for the prediction of DNA-binding proteins with application to the human genome. PLoS computational biology. 5(11):e1000567. PDF Supplementary Data Abstract
304
Skolnick, J, Friesner RA.  2009.  Theory and Simulation (Editorial overview). Current opinion in structural biology. 19(2):117-9. PDF Abstract
303
Brylinski, M, Skolnick J.  2010.  Comparison of structure-based and threading-based approaches to protein functional annotation. Proteins. 78(1):118-34. PDF Supplementary Data Abstract
302
Skolnick, J, Arakaki AK, Lee SY, Brylinski M.  2009.  The continuity of protein structure space is an intrinsic property of proteins. Proceedings of the National Academy of Sciences of the United States of America. 106(37):15690-5. PDF Abstract
301
Zhou, H, Pandit S B, Skolnick J.  2009.  Performance of the Pro-sp3-TASSER server in CASP8. Proteins. 77 Suppl 9:123-7. PDF Abstract
300
Skolnick, J, Brylinski M.  2009.  FINDSITE: a combined evolution/structure-based approach to protein function prediction. Briefings in bioinformatics. 10(4):378-91. PDF Abstract
299
Pandit, S B, Zhou H, Skolnick J.  2010.  TASSER-based protein structure prediction. Introduction to Protein Structure Prediction: Methods and Algorithms. 14:219-242. PDF
298
Brylinski, M, Skolnick J.  2009.  FINDSITE: a threading-based approach to ligand homology modeling. PLoS computational biology. 5(6):e1000405. PDF Abstract
297
Arakaki, AK, Huang Y, Skolnick J.  2009.  EFICAz2: enzyme function inference by a combined approach enhanced by machine learning. BMC bioinformatics. 10:107. PDF Abstract
296
Arakaki, AK, Skolnick J, McDonald JF.  2008.  Marker metabolites can be therapeutic targets as well. Nature. 456(7221):443. PDF
295
Gao, M, Skolnick J.  2009.  From nonspecific DNA-protein encounter complexes to the prediction of DNA-protein interactions. PLoS computational biology. 5(3):e1000341. PDF Abstract
294
Zhou, H, Skolnick J.  2009.  Protein structure prediction by pro-Sp3-TASSER. Biophysical journal. 96(6):2119-27. PDF Abstract
293
Pandit, S B, Skolnick J.  2008.  Fr-TM-align: a new protein structural alignment method based on fragment alignments and the TM-score. BMC bioinformatics. 9:531. PDF Abstract
292
Gao, M, Skolnick J.  2008.  DBD-Hunter: a knowledge-based method for the prediction of DNA-protein interactions. Nucleic acids research. 36(12):3978-92. PDF Supplementary Data Abstract
291
Jagielska, A, Wroblewska L, Skolnick J.  2008.  Protein model refinement using an optimized physics-based all-atom force field. Proceedings of the National Academy of Sciences of the United States of America. 105(24):8268-73. PDF Abstract
290
Thomas, SH, Wagner RD, Arakaki AK, Skolnick J, Kirby JR, Shimkets LJ, Sanford RA, Löffler FE.  2008.  The mosaic genome of Anaeromyxobacter dehalogenans strain 2CP-C suggests an aerobic common ancestor to the delta-proteobacteria. PloS one. 3(5):e2103. PDF Abstract
289
Somarelli, JA, Lee SY, Skolnick J, Herrera RJ.  2008.  Structure-based classification of 45 FK506-binding proteins. Proteins. 72(1):197-208. PDF Abstract
288
Lee, SY, Skolnick J.  2008.  Benchmarking of TASSER_2.0: an improved protein structure prediction algorithm with more accurate predicted contact restraints. Biophysical journal. 95(4):1956-64. PDF Abstract
287
Arakaki, AK, Mezencev R, Bowen NJ, Huang Y, McDonald JF, Skolnick J.  2008.  Identification of metabolites with anticancer properties by computational metabolomics. Molecular cancer. 7:57. PDF Abstract
286
Rotkiewicz, P, Skolnick J.  2008.  Fast procedure for reconstruction of full-atom protein models from reduced representations. Journal of computational chemistry. 29(9):1460-5. PDF Abstract
285
Wroblewska, L, Jagielska A, Skolnick J.  2008.  Development of a physics-based force field for the scoring and refinement of protein models. Biophysical journal. 94(8):3227-40. PDF Abstract
284
Brylinski, M, Skolnick J.  2008.  Q-Dock: Low-resolution flexible ligand docking with pocket-specific threading restraints. Journal of computational chemistry. 29(10):1574-88. PDF Supplementary Data Abstract
283
Brylinski, M, Skolnick J.  2008.  A threading-based method (FINDSITE) for ligand-binding site prediction and functional annotation. Proceedings of the National Academy of Sciences of the United States of America. 105(1):129-34. PDF Supplementary Data Abstract
282
Chen, H, Skolnick J.  2008.  M-TASSER: an algorithm for protein quaternary structure prediction. Biophysical journal. 94(3):918-28. PDF Abstract
more

Skolnick Group

Services
PSIFR
Protein Structure Prediction
chunk-TASSER
MetaTASSER
pro-sp3-TASSER
TASSER
Protein Structure Refinement
BSR
Protein Structure Alignment
Fr-TM-align
iAlign
DNA-binding Prediction
DBD-Threader
DBD-Hunter
DP-dock
Protein Function Prediction
FINDSITE
FINDSITE-metal
EFICAz2.5
Ligand Docking/Screening
FINDSITELHM
Q-DockLHM
Software
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EFICAz2.5
FINDSITE
FINDSITELHM
FINDSITE-metal
Fr-TM-align
iAlign
PULCHRA
TASSER-Lite
Databases
Structure Prediction Libraries
gpcr_0.jpg
Human GPCRs
E. coli Proteome
PDB-like Structures
Function Prediction Libraries
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KinomeLHM
X-ReactKIN
Human Proteome
Human DNA Binders
Reference Libraries
3jxv_bio_r_500.jpg
Active Site Templates
Apo/Holo Pairs
Simulations
molecular_system.png
E. coli Intracellular Dynamics
PSIFR - a web portal that provides access to TASSER, TASSER-Lite and MetaTASSER and DBD-Hunter, and the enzyme function inference engine EFICaz2.
MetaTASSER - a protein structure prediction server that uses three
state-of-the-art threading methods PROSPECTOR_3, SP3 and SPARKS to identify templates that are selected by 3D-Jury. For targets with no reliable templates, chunk models from chunk-TASSER are also included. SPICKER clustering is used to select the final models from the TASSER trajectory. Detailed atomic models are then built using PULCHRA.
pro-sp3-TASSER - uses a single threading method with multiple scoring to identify templates. Short TASSER runs generate full length models that are selected by TASSER-QA and FTCOM ranking procedures. pro-sp3-TASSER performs better than MetaTASSER for medium/hard targets but is computationally more expensive.
Fr-TM-align - structural alignment program for monomeric proteins that uses the TM-score as the structure comparison metric.
iAlign - structural alignment algorithm for protein-protein interfaces that uses the TM-score as well as a side-chain contact overlap weighted TM-score as the interface comparison metric.
DBD-Threader - a server that predicts DNA-binding protein domains from a protein's sequence.
DBD-Hunter - a server that predicts DNA-binding protein domains given a protein’s structure.
DP-dock - a server that predicts the structure of the DNA/protein complex structure given the tertiary structure of a known DNA-binding protein.
FINDSITE - a threading-based binding site prediction/protein functional inference/ligand screening algorithm that detects common ligand binding sites in a set of evolutionarily related proteins. Crystal structures as well as protein models can be used as the target structures.
FINDSITE-metal - a simple extension of FINDSITE that predicts metal-binding sites, residues and binding metal preferences from evolutionarily related templates detected by threading. Furthermore, it employs machine learning to significantly improve the prediction accuracy particularly against modeled protein structures.
FINDSITELHM - a homology modeling approach to flexible ligand docking. It uses a collection of common molecule substructures derived from evolutionarily related templates as the reference compounds in similarity-based ligand binding pose prediction.
Q-DockLHM - a low-resolution ligand docking/refinement approach applicable to the modeled structures of target receptors. It uses pocket-specific statistical potentials and harmonic restraints imposed on the binding poses of the common molecule substructures extracted from evolutionarily related proteins.
EFICAz2.5 - an accurate sequence based approach to enzyme function inference.
chunk-TASSER - a protein structure prediction method that combines threading templates from SP3 and ab initio folded chunk structures (three consecutive segments of regular secondary structures).
BSR - Binding Site Refinement employs a new template-based method for the local refinement of ligand-binding regions in protein models using closely as well as distantly related templates identified by threading.
FINDSITE - a threading-based binding site prediction/protein functional inference/ligand screening algorithm that detects common ligand binding sites in a set of evolutionarily related proteins.
Fr-TM-align - a program for protein structural alignment that uses the TM-score as the structural comparison metric. This is an improved version of Tm-align.
iAlign - a structural alignment algorithm for the protein-protein interface that uses the TM-score as well as a side-chain contact overlap weighted TM-score as the interface comparison metric.
PULCHRA - a program for all-atom reconstruction from the backbone C-alpha atoms.
TASSER-Lite - a comparative protein structure modeling tool.
Human GPCRs - Predicted structures of 907 G protein-coupled receptors in the Human proteome.
Human DNA Binders - DBD-Threader Predictions on the Human proteome. A list of predicted Human DNA-binding proteins their Sequences and predicted 3D models.
E. coli Poteome - Predicted structures of the E. Coli proteome.
PDB-like Structures - A library of 15,000 compact homopolypeptide structures that cover the PDB.
Active Site Templates - Automated Functional Templates (AFT) for Enzyme Active Site Prediction.
Apo/Holo Pairs - Apo/Holo and Holo/Holo protein pairs. The global structures of apo and holo protein.
KinomeLHM - Comprehensive structural and functional characterization of the human kinome by protein structure predication and ligand virtual screening.
Human Proteome - Structure modeling, function annotation and ligand virtual screening of the human proteome.
TASSER - A meta server for protein structure prediction that combines chunk-TASSER, pro-sp3-TASSER and MetaTASSER.
FINDSITELHM - A homology modeling approach to flexible ligand docking. It uses a collection of common molecule substructures derived from evolutionarily related templates as the reference compounds in similarity-based ligand binding pose prediction.
E. coli Intracellular Dynamics - The inside of cells is highly crowded with bio-macromolecules, such as proteins, DNA, RNA. This environment is quite different from the conditions in a test tube that we usually use for analysis of biomolecular functions, where the macromolecular concentration is ~100 times less than inside cells.
X-ReactKIN - Virtual cross-reactivity profiles of the human kinome.