• Wakefield AE, Yueh C, Beglov D, Castilho MS, Kozakov D, et al.. (2020). Benchmark Sets for Binding Hot Spot Identification in Fragment-Based Ligand Discovery. Journal of Chemical Information and Modeling.

  • Desta IT, Porter KA, Xia B, Kozakov D, Vajda S. (2020). Performance and Its Limits in Rigid Body Protein-Protein Docking. Structure.

  • Alekseenko A, Kotelnikov S, Ignatov M, Egbert M, Kholodov Y, et al.. (2020). ClusPro LigTBM: Automated Template-Based Small Molecule Docking. Journal of Molecular Biology. 

  • Padhorny D, Porter KA, Ignatov M, Alekseenko A, Beglov D, et al.. (2020). ClusPro in rounds 38 to 45 of CAPRI: Toward combining template‐based methods with free docking. Structure, Function, and

  • Sun Z, Wakefield AE, Kolossvary I, Beglov D, Vajda S. (2020). Structure-Based Analysis of Cryptic-Site Opening. Structure.

  • Khramushin A, Marcu O, Alam N, Shimony O, Padhorny D, et al.. (2020) Modeling beta‐sheet peptide‐protein interactions: Rosetta FlexPepDock in CAPRI rounds 38‐45. Proteins: Structure, Function, and Bioinformatics.

  • Zhong M, Lynch A, Muellers SN, Jehle S, Lingqi L et al.. (2020). Interaction Energetics and Druggability of the Protein–Protein Interaction between Kelch-like ECH-Associated Protein 1 (KEAP1) and Nuclear Factor Erythroid 2 Like 2 (Nrf2). Biochemistry.

  • Porter KA, Padhorny D, Desta I, Ignatov M, Beglov D et al.. (2019). Template‐Based Modeling by ClusPro in CASP13 and the Potential for Using Co‐evolutionary Information in Docking. Proteins: Structure, Function, and Bioinformatics.

  • Vajda S, Emili A. (2019). Mapping global protein contacts. Science.

  • Egbert M, Whitty A, Keserű GM, Vajda S. (2019). Why some targets benefit from beyond rule of five drugs. Journal of Medicinal Chemistry.

  • Yueh C, Rettenmaier J, Xia B, Hall DR, Alekseenko A et al.. (2019). Kinase Atlas: Druggability Analysis of Potential Allosteric Sites in Kinases. Journal of Medicinal Chemistry.

  • Wakefield AE, Mason JS, Vajda S, Keserű GM. (2019). Analysis of tractable allosteric sites in G protein-coupled receptors. Nature: Scientific Reports.

  • Froes TQ, Baldini RL, Vajda S, Castilho MS (2019). Structure-based druggability assessment of anti-virulence targets from Pseudomonas aeruginosa. Current Protein & Peptide Science.

  • Porter KA, Desta I, Kozakov D, Vajda S. (2019). What method to use for protein-protein docking? Current Opinion in Structural Biology.

  • Wodak SJ, Paci E, Dokholyan NV, Berezovsky IN, Horovitz A et al.. (2019). Allostery in Its Many Disguises: From Theory to Applications. Structure.

  • Trilles R, Beglov D, Chen Q, He H, Wireman R et al.. (2019). Discovery of Macrocyclic Inhibitors of Apurinic/Apyrimidinic Endonuclease 1. Journal of Medicinal Chemistry.

  • Ignatov M, Liu C, Alekseenko A, Sun Z, Padhorny D et al.. (2019). Monte Carlo on the manifold and MD refinement for binding pose prediction of protein-ligand complexes: 2017 D3R Grand Challenge. Journal of Computer-Aided Molecular Design.

  • Beglov D, Hall DR, Wakefield AE, Luo L, Allen KN et al.. (2018). Exploring the structural origins of cryptic sites on proteins. PNAS.           

  • Ignatov M, Kazennov A, Kozakov D. (2018). ClusPro FMFT-SAXS: Ultra-fast Filtering Using Small-Angle X-ray Scattering Data in Protein Docking. Journal of Molecular Biology.

  • Zarbafian S, Moghadasi M, Roshandelpoor A, Nan F, Li K et al.. (2018). Protein docking refinement by convex underestimation in the low-dimensional subspace of encounter complexes. Scientific Reports.              

  • Stasyk OG, Denega IO, Padhorny D, Dmytruk KV, Kozakov D et al.. (2018). Glucose regulation in the methylotrophic yeast Hansenula (Ogataea) polymorpha is mediated by a putative transceptor Gcr1. The International Journal of Biochemistry & Cell Biology.      

  • Vajda S, Beglov D, Wakefield AE, Egbert M, Whitty A. (2018). Cryptic binding sites on proteins: definition, detection, and druggability. Current Opinion in Chemical Biology.

  • Padhorny D, Hall DR, Mirzaei H, Mamonov AB, Moghadasi M et al.. (2018). Protein-ligand docking using FFT based sampling: D3R case study. Journal of Computer-Aided Molecular Design.

  • Vajda S, Yueh C, Beglov D, Bohnuud T, Mottarella SE et al.. (2017). New additions to the ClusPro server motivated by CAPRI. Proteins: Structure, Function, and Bioinformatics.

  • Kozakov D, Hall DR, Xia B, Porter KA, Padhorny D et al.. (2017). The ClusPro web server for protein–protein docking.  Nature Protocols.

  • Yueh C, Hall DR, Xia B, Padhorny D, Kozakov D, Vajda S. (2017). ClusPro-DC: Dimer Classification by the Cluspro Server for Protein–Protein Docking. Journal of Molecular Biology.

  • Porter KA, Xia B, Beglov D, Bohnuud T, Alam N et al.. (2017). ClusPro PeptiDock: efficient global docking of peptide recognition motifs using FFT.  Bioinformatics.

  • Alam N, Goldstein O, Xia B, Porter KA, Kozakov D, Schueler-Furman O et al.. (2017). High-resolution global peptide-protein docking using fragments-based PIPER-FlexPepDock. PLoS Computational Biology.

  • Bohnuud T, Jones G, Schueler-Furman O, Kozakov D. (2017). Detection of Peptide-Binding Sites on Protein Surfaces Using the Peptimap Server. Methods in Molecular Biology.

  • Padhorny D, Kazennov A, Zerbe BS, Porter KA, Xia B et al.. (2016). Protein–protein docking by fast generalized Fourier transforms on 5D rotational manifolds. Proceedings of the National Academy of Sciences.

  • Bohnuud T, Luo L, Wodak SJ, Bonvin AMJJ, Weng Z et al.. (2016). A benchmark testing ground for integrating homology modeling and protein docking. Proteins: Structure, Function, and Bioinformatics.

  • Whitty A, Zhong M, Viarengo L, Beglov D, Hall DR, Vajda S. (2016). Quantifying the chameleonic properties of macrocycles and other high-molecular-weight drugs. Drug Discovery Today.

  • Mamonov AB, Moghadasi M, Mirzaei H, Zarbafian S, Grove LE et al.. (2016). Focused grid-based resampling for protein docking and mapping. Journal of Computational Chemistry.

  • Xia B, Vajda S, Kozakov D. (2016). Accounting for pairwise distance restraints in FFT-based protein–protein docking. Bioinformatics.               

  • Lensink MF, Velankar S, Kryshtafovych A, Huang S-Y, Schneidman-Duhovny D et al.. (2016). Prediction of homoprotein and heteroprotein complexes by protein docking and template-based modeling: A CASP-CAPRI experiment. Proteins: Structure, Function, and Bioinformatics.

  • Im W, Liang J, Olson A, Zhou H-X, Vajda S, Vakser IA. (2016). Challenges in structural approaches to cell modeling. Journal of Molecular Biology.

  • Vajda S, Whitty A, Kozakov D. (2015). Fragments and hot spots in drug discovery. Oncotarget.

  • Kozakov D, Grove LE, Hall DR, Bohnuud T, Mottarella SE et al.. (2015). The FTMap family of web servers for determining and characterizing ligand-binding hot spots of proteins. Nature Protocols.

  • Lukose V, Luo L, Kozakov D, Vajda S, Allen KN, Imperiali B. (2015). Conservation and covariance in small bacterial phosphoglycosyltransferases identify the functional catalytic core. Biochemistry. 

  • Hall DR, Kozakov D, Whitty A, Vajda S. (2015). Lessons from hot spot analysis for fragment-based drug discovery. Trends in Pharmacological Sciences. 

  • Kozakov D, Hall DR, Napoleon RL, Yueh C, Whitty A, Vajda S. (2015). New frontiers in druggability. Journal of Medicinal Chemistry.

  • Xia B, Mamonov A, Leysen S, Allen KN, Strelkov SV et al.. (2015). Accounting for observed small angle X-ray scattering profile in the protein-protein docking server cluspro. Journal of Computational Chemistry. 

  • Mirzaei H, Zarbafian S, Villar E, Mottarella S, Beglov D et al.. (2015). Energy Minimization on Manifolds for Docking Flexible Molecules. Journal of Chemical Theory and Computation.

  • Moghadasi M, Mirzaei H, Mamonov A, Vakili P, Vajda S et al.. (2015). The Impact of Side-Chain Packing on Protein Docking Refinement. Journal of Chemical Information and Modeling.

  • Kozakov D, Hall DR, Jehle S, Luo L, Ochiana SO et al.. (2015). Ligand deconstruction: Why some fragment binding positions are conserved and others are not. Proceedings of the National Academy of Sciences.


©2019 Vajda Lab