Publications

2020

• Desta IT, Porter KA, Xia B, Kozakov D, Vajda S. (2020). Performance and Its Limits in Rigid Body Protein-Protein Docking. Structure.
• 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. Proteins: Structure, Function, and Bioinformatics.
• 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.
• Sun Z, Wakefield AE, Kolossvary I, Beglov D, Vajda S. (2020). Structure-Based Analysis of Cryptic-Site Opening. 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. 
• 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.

2019

• 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, Keseru 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, Keseru 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.

2018

• 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.

2017

• 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.

2016

• 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.

2015

• 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.

2014

• Villar EA, Beglov D, Chennamadhavuni S, Porco JA, Kozakov D et al.. (2014).  How proteins bind macrocycles. Nature Chemical Biology. 
• Shin U, Williams DE, Kozakov D, Hall DR, Beglov D et al.. (2014). Stimulators of translation identified during a small molecule screening campaign. Analytical Biochemistry. 
• Bohnuud T, Kozakov D, Vajda S. (2014). Evidence of conformational selection driving the formation of ligand binding sites in protein-protein interfaces. PLoS Computational Biology
• Chowdhury R, Beglov D, Moghadasi M, Paschalidis IC, Vakili P et al.. (2014). Efficient maintenance and update of nonbonded lists in macromolecular simulations. Journal of Chemical Theory and Computation.
• Mottarella SE, Beglov D, Beglova N, Nugent MA, Kozakov D, Vajda S. (2014). Docking server for the identification of heparin binding sites on proteins. Journal of Chemical Information and Modeling.
• Bogorad AM, Xia B, Sandor DG, Mamonov AB, Cafarella TR et al.. (2014). Insights into the architecture of the eIF2Bα/β/γ regulatory subcomplex. Biochemistry.
• Kozakov D, Li K, Hall DR, Beglov D, Zheng J et al.. (2014). Encounter complexes and dimensionality reduction in protein protein association. eLife.
• Vakili P, Mirzaei H, Zarbafian S, Paschalidis IC, Kozakov D, Vajda S. (2014). Optimization on the space of rigid and flexible motions: an alternative manifold optimization approach. 53rd IEEE Conference on Decision and Control.

2013

• Moretti R, Fleishman SJ, Agius R, Torchala M, Bates PA et al.. (2013). Community-wide evaluation of methods for predicting the effect of mutations on protein-protein interactions. Proteins: Structure, Function, and Bioinformatics.
• Lavi A, Ngan CH, Movshovitz-Attias D, Bohnuud T, Yueh C et al.. (2013). Detection of peptide-binding sites on protein surfaces: The first step toward the modeling and targeting of peptide-mediated interactions. Proteins: Structure, Function, and Bioinformatics. 
• Kozakov D, Beglov D, Bohnuud T, Mottarella SE, Xia B et al.. (2013). How good is automated protein docking? Proteins: Structure, Function, and Bioinformatics.
• Vajda S, Hall DR, Kozakov D. (2013). Sampling and scoring: A marriage made in heaven. Proteins: Structure, Function, and Bioinformatics.
• Grove LE, Hall DR, Beglov D, Vajda S, Kozakov D. (2013). FTFlex: Accounting for binding site flexibility to improve fragment-based identification of druggable hot spots. Bioinformatics.
• Golden MS, Cote SM, Sayeg M, Zerbe BS, Villar EA et al.. (2013). Comprehensive Experimental and Computational Analysis of Binding Energy Hot Spots at the NF-κB Essential Modulator (NEMO)/IKKβ Protein-Protein Interface. Journal of the American Chemical Society, 130318121200001.

2012

• Zerbe BS, Hall DR, Vajda S, Whitty A, Kozakov D. (2012). Relationship between Hot Spot Residues and Ligand Binding Hot Spots in Protein-Protein Interfaces. Journal of chemical information and modeling. 
• Ngan CH, Hall DR, Zerbe BS, Grove LE, Kozakov D, Vajda S. (2012). FTSite: high accuracy detection of ligand binding sites on unbound protein structures. Bioinformatics, 28(2), 286–287.
• Ngan CH, Bohnuud T, Mottarella SE, Beglov D, Villar EA et al.. (2012). FTMAP: extended protein mapping with user-selected probe molecules. Nucleic Acids Research, 40(W1), W271 – W275. 
• Mirzaei H, Beglov D, Paschalidis IC, Vajda S, Vakili P, Kozakov D. (2012). Rigid Body Energy Minimization on Manifolds for Molecular Docking. Journal of Chemical Theory and Computation, 120821123549006. 
• Hingtgen S, Kasmieh R, Elbayly E, Nesterenko I, Figueiredo J-L et al.. (2012). A First-Generation Multi-Functional Cytokine for Simultaneous Optical Tracking and Tumor Therapy. PLoS ONE, 7(7), e40234. 
• Hall DR, Ngan CH, Zerbe BS, Kozakov D, Vajda S. (2012b). Hot Spot Analysis for Driving the Development of Hits into Leads in Fragment-Based Drug Discovery. Journal of Chemical Information and Modeling, 52(1), 199–209.
• Hall DR, Kozakov D, Vajda S. (2012a). Analysis of Protein Binding Sites by Computational Solvent Mapping. Methods in Molecular Biology (Vol. 819). New York, NY: Springer New York.
• Brenke R, Hall DR, Chuang G-Y, Comeau SR, Bohnuud T et al.. (2012). Application of asymmetric statistical potentials to antibody-protein docking. Bioinformatics, 28(20), 2608–2614. 
• Bohnuud T, Beglov D, Ngan CH, Zerbe BS, Hall DR et al.. (2012). Computational mapping reveals dramatic effect of Hoogsteen breathing on duplex DNA reactivity with formaldehyde. Nucleic Acids Research, 40(16), 7644–7652. 
• Beglov D, Hall DR, Brenke R, Shapovalov MV, Dunbrack RL et al.. (2012). Minimal ensembles of side chain conformers for modeling protein-protein interactions. Proteins: Structure, Function, and Bioinformatics, 80(2), 591–601. 

2011

• Kozakov D, Hall DR, Chuang G-Y, Cencic R, Brenke R et al.. (2011). Structural conservation of druggable hot spots in protein-protein interfaces. Proceedings of the National Academy of Sciences, 108(33), 13528–13533. 
• Hall DR, Grove LE, Yueh C, Ngan CH, Kozakov D, Vajda S. (2011). Robust Identification of Binding Hot Spots Using Continuum Electrostatics: Application to Hen Egg-White Lysozyme. Journal of the American Chemical Society, 133(51), 20668–20671.
• Cencic R, Hall DR, Robert F, Du Y, Min J et al.. (2011b). Reversing chemoresistance by small molecule inhibition of the translation initiation complex eIF4F. Proceedings of the National Academy of Sciences of the United States of America, 108(3). 
• Cencic R, Desforges M, Hall DR, Kozakov D, Du Y et al.. (2011a). Blocking eIF4E-eIF4G Interaction as a Strategy To Impair Coronavirus Replication. Journal of Virology, 85(13), 6381–6389.
• Buhrman G, O′Connor C, Zerbe BS, Kearney BM, Napoleon R et al.. (2011). Analysis of Binding Site Hot Spots on the Surface of Ras GTPase. Journal of Molecular Biology, 413(4), 773–789.

2010

• Kozakov D, Hall DR, Beglov D, Brenke R, Comeau SR et al.. (2010b). Achieving reliability and high accuracy in automated protein docking: Cluspro, PIPER, SDU, and stability analysis in CAPRI rounds 13-19. Proteins: Structure, Function, and Bioinformatics, 78(15), 3124–3130.
• Kozakov D, Chuang G-Y, Beglov D, Vajda S. (2010a). Where does amantadine bind to the influenza virus M2 proton channel? Trends in Biochemical Sciences. 
• Chuang G-Y, Mehra-Chaudhary R, Ngan CH, Zerbe BS, Kozakov D et al.. (2010). Domain motion and inter-domain hot spots in a multi-domain enzyme. Protein Science. 

2009

• Vajda S, Kozakov D. (2009). Convergence and combination of methods in protein–protein docking. Current Opinion in Structural Biology, 19(2), 164–170. 
• Schwede T, Sali A, Honig B, Levitt M, Berman HM et al.. (2009). Outcome of a Workshop on Applications of Protein Models in Biomedical Research. Structure, 17(2), 151–159. 
• Ngan CH, Beglov D, Rudnitskaya AN, Kozakov D, Waxman DJ, Vajda S. (2009). The Structural Basis of Pregnane X Receptor Binding Promiscuity. Biochemistry, 48(48), 11572–11581. 
• Landon MR, Lieberman RL, Hoang QQ, Ju S, Caaveiro JMM et al.. (2009). Detection of ligand binding hot spots on protein surfaces via fragment-based methods: application to DJ-1 and glucocerebrosidase. Journal of Computer-Aided Molecular Design, 23(8), 491–500.
• Beglov D, Brenke R, Chuang G-Y, Hall DR, Landon MR et al.. (2009). Identification of Druggable Hot Spots on Proteins and in Protein Protein Interfaces. In Computational Protein-Protein Interactions.
• Chuang G-Y, Kozakov D, Brenke R, Beglov D, Guarnieri F, Vajda S. (2009). Binding Hot Spots and Amantadine Orientation in the Influenza A Virus M2 Proton Channel. Biophysical Journal, 97(10), 2846–2853. 
• Brenke R, Kozakov D, Chuang G-Y, Beglov D, Hall DR et al.. (2009). Fragment-based identification of druggable “hot spots” of proteins using Fourier domain correlation techniques. Bioinformatics, 25(5), 621–627.
• Beglov D, Lee C-J, De Biasio A, Kozakov D, Brenke R et al.. (2009). Structural insights into recognition of β2-glycoprotein I by the lipoprotein receptors. Proteins: Structure, Function, and Bioinformatics, 77(4), 940–949.

2008

• Ritchie DW, Kozakov D, Vajda S. (2008). Accelerating and focusing protein-protein docking correlations using multi-dimensional rotational FFT generating functions. Bioinformatics, 24(17), 1865–1873. 
• Landon MR, Amaro RE, Baron R, Ngan CH, Ozonoff D et al.. (2008). Novel Druggable Hot Spots in Avian Influenza Neuraminidase H5N1 Revealed by Computational Solvent Mapping of a Reduced and Representative Receptor Ensemble. Chemical Biology & Drug Design, 71(2), 106–116.
• Kozakov D, Schueler‐Furman O, Vajda S. (2008). Discrimination of near‐native structures in protein–protein docking by testing the stability of local minima. Proteins: Structure, Function, and Bioinformatics, 72(3), 993–1004.
• Shen Y, Paschalidis IC, Vakili P, Vajda S. (2008). Protein Docking by the Underestimation of Free Energy Funnels in the Space of Encounter Complexes. PLoS Computational Biology, 4(10), e1000191.
• Chuang G-Y, Kozakov D, Brenke R, Comeau SR, Vajda S. (2008). DARS (Decoys As the Reference State) Potentials for Protein-Protein Docking. Biophysical Journal, 95(9), 4217–4227. 

2007

• Silberstein M, Damborsky J, Vajda S. (2007). Exploring the Binding Sites of the Haloalkane Dehalogenase DhlA from Xanthobacter autotrophicus GJ10. Biochemistry, 46(32), 9239–9249.
• Shen Y, Brenke R, Kozakov D, Comeau SR, Beglov D, Vajda S. (2007). Docking with PIPER and refinement with SDU in rounds 6–11 of CAPRI. Proteins: Structure, Function, and Bioinformatics, 69(4), 734–742.
• Prasad JC, Goldstone JV, Camacho CJ, Vajda S, Stegeman JJ. (2007). Ensemble Modeling of Substrate Binding to Cytochromes P450:  Analysis of Catalytic Differences between CYP1A Orthologs. Biochemistry, 46(10), 2640–2654.
• Paschalidis IC, Shen Y, Vakili P, Vajda S. (2007). SDU: A Semidefinite Programming-Based Underestimation Method for Stochastic Global Optimization in Protein Docking. IEEE Transactions on Automatic Control, 52(4), 664–676. 
• Landon MR, Lancia, Jr. DR, Yu J, Thiel SC, Vajda S. (2007). Identification of Hot Spots within Druggable Binding Regions by Computational Solvent Mapping of Proteins. Journal of Medicinal Chemistry, 50(6), 1231–1240. 
• Comeau SR, Kozakov D, Brenke R, Shen Y, Beglov D, Vajda S.. (2007). ClusPro: Performance in CAPRI rounds 6–11 and the new server. Proteins: Structure, Function, and Bioinformatics, 69(4), 781–785.
• Aslan FM, Yu Y, Vajda S, Mohr SC, Cantor CR. (2007). Engineering a novel, stable dimeric streptavidin with lower isoelectric point. Journal of Biotechnology, 128(2), 213–225. 

2006

• Vajda S, Guarnieri F. (2006). Characterization of protein-ligand interaction sites using experimental and computational methods. Current Opinion in Drug Discovery and Development, 9(3), 363–369.
• Vajda S. (2006b). Classification of protein complexes based on the biophysics of association. ASBMB Annual Meeting.
• Vajda S. (2006a). Computational Mapping of Proteins for Fragment Based Drug Design. Keystone Symposium on Structure Based Drug Discovery.
• Silberstein M, Landon MR, Wang YE, Perl A, Vajda S. (2006). Computational methods for functional site identification suggest a substrate access channel in transaldolase. Genome Informatics, 17(1), 13–22.
• Paschalidis IC, Shen Y, Vakili P, Vajda S. (2006). Protein Docking by Exploiting Multi-dimensional Energy Funnels. IEEE 2006 International Conference of EMBS.
• Paschalidis IC, Shen Y, Vakili P, Vajda S. (2006). Protein-protein docking with reduced potentials by exploiting multi-dimensional energy funnels. Conference Proceedings. Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 5330–5333. 
• Landon MR, Yu J, Thiel SC, Lancia, Jr. DR, Vajda S. (2006). Identification of “Hot Spots” in Druggable Binding Pockets by Computational Solvent Mapping of Proteins. Journal of Medicinal Chemistry
• Landon MR, Lancia, Jr. DR, Clodfelter KH, Vajda S. (2006). Clustering of domains of functionally related enzymes in the interaction database PRECISE by the generation of primary sequence patterns. Journal of Molecular Graphics and Modelling, 24(6), 426–433. 
• Kozakov D, Brenke R, Comeau SR, Vajda S. (2006). PIPER: An FFT-based protein docking program with pairwise potentials. Proteins: Structure, Function, and Bioinformatics, 65(2), 392–406.
• Kaya T, Mohr SC, Waxman DJ, Vajda S. (2006). Computational Screening of Phthalate Monoesters for Binding to PPARγ. Chemical Research in Toxicology, 19(8), 999–1009. 
• Clodfelter KH, Waxman DJ, Vajda S. (2006). Computational Solvent Mapping Reveals the Importance of Local Conformational Changes for Broad Substrate Specificity in Mammalian Cytochromes P450. Biochemistry, 45(31), 9393–9407. 

2005

• Vajda S. (2005). Classification of protein complexes based on docking difficulty. Proteins: Structure, Function, and Bioinformatics, 60(2), 176–180. 
• Sheu S-H, Kaya T, Waxman DJ, Vajda S. (2005). Exploring the Binding Site Structure of the PPARγ Ligand-Binding Domain by Computational Solvent Mapping. Biochemistry, 44(4), 1193–1209. 
• Kozakov D, Clodfelter KH, Vajda S, Camacho CJ. (2005). Optimal Clustering for Detecting Near-Native Conformations in Protein Docking. Biophysical Journal, 89(2), 867–875. 
• Comeau SR, Vajda S, Camacho CJ. (2005). Performance of the first protein docking server ClusPro in CAPRI rounds 3-5. Proteins: Structure, Function, and Bioinformatics, 60(2), 239–244. 
• Comeau SR, Camacho CJ. (2005). Predicting oligomeric assemblies: N-mers a primer. Journal of Structural Biology, 150(3), 233–244.

2004

• Vajda S, Camacho CJ. (2004). Protein–protein docking: is the glass half-full or half-empty? Trends in Biotechnology, 22(3), 110–116. 
• Sheu S-H, Lancia, Jr. DR, Clodfelter KH, Landon MR, Vajda S. (2004). PRECISE: a Database of Predicted and Consensus Interaction Sites in Enzymes. Nucleic Acids Research, 33(Database), D206 – D211. 
• Rajamani D, Thiel SC, Vajda S, Camacho CJ. (2004). Anchor residues in protein-protein interactions. Proceedings of the National Academy of Sciences, 101(31), 11287–11292. 
• Prasad JC, Vajda S, Camacho CJ. (2004). Consensus alignment server for reliable comparative modeling with distant templates. Nucleic Acids Research, 32(Web Server), W50 – W54. 
• Comeau SR, Gatchell DW, Vajda S, Camacho CJ (2004b). ClusPro: a fully automated algorithm for protein-protein docking. Nucleic Acids Research, 32(Web Server), W96 – W99.
• Comeau SR, Gatchell DW, Vajda S, Camacho CJ. (2004a). ClusPro: an automated docking and discrimination method for the prediction of protein complexes. Bioinformatics, 20(1), 45–50. 

2003

• Silberstein M, Dennis S, Brown L, Kortvelyesi T, Clodfelter KH, Vajda S. (2003). Identification of Substrate Binding Sites in Enzymes by Computational Solvent Mapping. Journal of Molecular Biology, 332(5), 1095–1113. 
• Prasad JC, Comeau SR, Vajda S, Camacho CJ. (2003). Consensus alignment for reliable framework prediction in homology modeling. Bioinformatics, 19(13), 1682–1691.
• Murphy J, Gatchell DW, Prasad JC, Vajda S. (2003). Combination of scoring functions improves discrimination in protein-protein docking. Proteins: Structure, Function, and Genetics, 53(4), 840–854. 
• Kortvelyesi T, Silberstein M, Dennis S, Vajda S. (2003). Improved mapping of protein binding sites. Journal of Computer-Aided Molecular Design, 17(2/4), 173–186.
• Kortvelyesi T, Dennis S, Silberstein M, Brown L, Vajda S. (2003). Algorithms for computational solvent mapping of proteins. Proteins: Structure, Function, and Genetics, 51(3), 340–351. 
• Janin J, Henrick K, Moult J, Eyck LT, Sternberg MJE et al.. (2003). CAPRI: A Critical Assessment of PRedicted Interactions. Proteins: Structure, Function, and Genetics, 52(1), 2–9.
• Camacho CJ, Gatchell DW. (2003). Successful discrimination of protein interactions. Proteins: Structure, Function, and Genetics, 52(1), 92–97. 

2002

• Valkó PP, Vajda S.. (2002). Inversion of Noise-Free Laplace Transforms: Towards a Standardized Set of Test Problems. Inverse Problems in Engineering, 10(5), 467–483. 
• Vajda S, Vakser IA, Sternberg MJE, Janin J. (2002). Modeling of protein interactions in genomes. Proteins: Structure, Function, and Genetics, 47(4), 444–446. 
• Dennis S, Vajda S. (2002). Semiglobal simplex optimization and its application to determining the preferred solvation sites of proteins. Journal of Computational Chemistry, 23(3), 319–334. 
• Dennis S, Kortvelyesi T, Vajda S. (2002). Computational mapping identifies the binding sites of organic solvents on proteins. Proceedings of the National Academy of Sciences, 99(7), 4290–4295.
• Camacho CJ, Vajda S. (2002). Protein–protein association kinetics and protein docking. Current Opinion in Structural Biology, 12(1), 36–40.

2001

• Kimura SR, Brower RC, Vajda S, Camacho CJ. (2001). Dynamical View of the Positions of Key Side Chains in Protein-Protein Recognition. Biophysical Journal, 80(2), 635–642. 
• Camacho CJ, Vajda S. (2001b). Protein docking along smooth association pathways. Proceedings of the National Academy of Sciences, 98(19), 10636–10641. 
• Camacho CJ, Vajda S. (2001a). Thermodynamic maps of receptor-ligand pairs reveal how some proteins bind. Drug-Receptor Thermodynamics (pp. 581–592). NY: Wiley.

2000-

• Gatchell DW, Dennis S, Vajda S. (2000). Discrimination of near-native protein structures from misfolded models by empirical free energy functions. Proteins: Structure, Function, and Genetics, 41(4), 518–534. 
• Dennis S, Camacho CJ, Vajda S. (2000b). Continuum electrostatic analysis of preferred solvation sites around proteins in solution. Proteins: Structure, Function, and Genetics, 38(2), 176–188. 
• Dennis S, Camacho CJ, Vajda S. (2000a). Exploring potential solvation sites of proteins by multistart local minimization. In Optimization in Computational Chemistry and Molecular Biology (pp. 243–261). Kluwer Academic.
• Camacho CJ, Kimura SR, DeLisi C, Vajda S. (2000). Kinetics of Desolvation-Mediated Protein–Protein Binding. Biophysical Journal, 78(3), 1094–1105.
• Camacho CJ, Gatchell DW, Kimura SR, Vajda S. (2000). Scoring docked conformations generated by rigid-body protein-protein docking. Proteins: Structure, Function, and Genetics, 40(3), 525–537.
• Esposito M, Venkatesh V, Otvos L, Weng Z, Vajda S et al.. (1999). Human Transaldolase and Cross-Reactive Viral Epitopes Identified by Autoantibodies of Multiple Sclerosis Patients. The Journal of Immunology, 163, 4027–4032.
• DeLisi C, Vajda S. (1999). Computational problems in cell biology. Computing in Science & Engineering, 1(3), 26–32. 
• Camacho CJ, Weng Z, Vajda S, DeLisi C. (1999). Free Energy Landscapes of Encounter Complexes in Protein-Protein Association. Biophysical Journal, 76(3), 1166–1178. 
• Zhang C, Brower RC, Kimura SR, Weng Z, Vajda S, DeLisi C. (1998). The waters of life. Journal of the Franklin Institute, 335(2), 213–240. 
• Vajda S. (1998). Conformational Analysis. Encyclopedia of Computational Chemistry. Chichester: John Wiley & Sons Ltd.
• Sano T, Vajda S, Cantor CR. (1998). Genetic engineering of streptavidin, a versatile affinity tag. Journal of Chromatography B: Biomedical Sciences and Applications, 715(1), 85–91. 
• Reznik GO, Vajda S, Sano T, Cantor CR. (1998). A streptavidin mutant with altered ligand-binding specificity. Proceedings of the National Academy of Sciences, 95(23), 13525–13530.
• Janardhan A, Vajda S. (1998). Selecting near-native conformations in homology modeling: The role of molecular mechanics and solvation terms. Protein Science, 7(8), 1772–1780. 
• Weng Z, DeLisi C, Vajda S. (1997). Empirical free energy calculation: Comparison to calorimetric data. Protein Science, 6(9), 1976–1984. 
• Vajda S, Sippl M, Novotny J. (1997). Empirical potentials and functions for protein folding and binding. Current Opinion in Structural Biology, 7(2), 222–228. 
• Sano T, Vajda S, Smith CL, Cantor CR. (1997). Engineering subunit association of multisubunit proteins: A dimeric streptavidin. Proceedings of the National Academy of Sciences, 94(12), 6153–6158.
• Weng Z, Vajda S, DeLisi C. (1996). Prediction of protein complexes using empirical free energy functions. Protein Science, 5, 614–626.
• Sezerman U, Vajda S, DeLisi C. (1996). Free energy mapping of class I MHC molecules and structural determination of bound peptides. Protein Science, 5(7), 1272–1281. 
• Sano T, Vajda S, Reznik GO, Smith CL, Cantor CR. (1996). Molecular Engineering of Streptavidin. Annals of the New York Academy of Sciences, 799(Enzyme Eng), 383–390.
• Reznik GO, Vajda S, Smith CL, Cantor CR, Sano T. (1996). Streptavidins with intersubunit crosslinks have enhanced stability. Nature Biotechnology, 14(8), 1007–1011. 
• Luidens MK, Figge J, Breese K, Vajda S. (1996). Predicted and trifluoroethanol-induced α-helicity of polypeptides. Biopolymers, 39(3), 367–376.
• King B, Vajda S, DeLisi C. (1996). Empirical free energy as a target function in docking and design: application to HIV-1 protease inhibitors. FEBS Letters, 384(1), 87–91.
• Gulukota K, Vajda S, DeLisi C. (1996). Peptide docking using dynamic programming. Journal of Computational Chemistry, 17(4), 418–428. 
• Vajda S, Weng Z, DeLisi C. (1995). Extracting hydrophobicity parameters from solute partition and protein mutation/unfolding experiments. “Protein Engineering, Design and Selection”, 8(11), 1081–1092.
• Rosenfeld R, Vajda S, DeLisi C. (1995). Flexible Docking and Design. Annual Review of Biophysics and Biomolecular Structure, 24(1), 677–700. 
• Rosenfeld R, Zheng Q, Vajda S, DeLisi C. (1995). Flexible docking of peptides to class I major-histocompatibility-complex receptors. Genetic Analysis: Biomolecular Engineering, 12(1), 1–21. 
• Vajda S, Weng Z, Rosenfeld R, DeLisi C. (1994). Effect of Conformational Flexibility and Solvation on Receptor-Ligand Binding Free Energies. Biochemistry, 33(47), 13977–13988. 
• Vajda S, Rabitz H. (1994). Identifiability and Distinguishability of General Reaction Systems. The Journal of Physical Chemistry, 98(20), 5265–5271.
• Godfrey KR, Chapman MJ, Vajda S. (1994). Identifiability and indistinguishability of nonlinear pharmacokinetic models. Journal of Pharmacokinetics and Biopharmaceutics, 22(3), 229–251. 
• Buturović LJ, Smith TF, Vajda S. (1994). Finite-state and reduced-parameter representations of protein backbone conformations. Journal of Computational Chemistry, 15(3), 300–312.
• Zheng Q, Rosenfeld R, Vajda S, DeLisi C. (1993b). Determining protein loop conformation using scaling-relaxation techniques. Protein Science, 2(8), 1242–1248. 
• Zheng Q, Rosenfeld R, Vajda S, DeLisi C. (1993a). Loop closure via bond scaling and relaxation. Journal of Computational Chemistry, 14(5), 556–565.
• Vajda S, Rabitz H. (1993). Generalized parametric sensitivity: Application to a CSTR. Chemical Engineering Science, 48(13), 2453–2461.
• Sezerman U, Vajda S, Cornette J, DeLisi C. (1993). Toward computational determination of peptide-receptor structure. Protein Science, 2(11), 1827–1843. 
• Rosenfeld R, Zheng Q, Vajda S, DeLisi C. (1993). Computing the Structure of Bound Peptides. Journal of Molecular Biology, 234(3), 515–521. 
• Rao S, Zhu Q-L, Vajda S, Smith T. (1993). The local information content of the protein structural database. FEBS Letters, 322(2), 143–146.
• Figge J, Breese K, Vajda S, Zhu Q-L, Eisele L et al.. (1993). The binding domain structure of retinoblastoma-binding proteins. Protein Science, 2(2), 155–164.
• Vajda S, Rabitz H. (1992). Parametric sensitivity and self-similarity in thermal explosion theory. Chemical Engineering Science, 47(5), 1063–1078. 
• Jafri MS, Vajda S, Pasik P, Gillo B. (1992). A membrane model for cytosolic calcium oscillations. A study using Xenopus oocytes. Biophysical Journal, 63(1), 235–246. 
• Eisenfeld J, Vajda S, Sugar I, DeLisi C. (1991). Constrained optimization and protein structure determination. The American journal of physiology, 261(2 Pt 1):C376-86.
• Vajda S, Kataoka R, DeLisi C, Margalit H, Berzofsky JA, Cornette JL. (1990). Molecular Structure and Vaccine Design. Annual Review of Biophysics and Biophysical Chemistry, 19(1), 69–82. 
• Vajda S, DeLisi C. (1990). Determining minimum energy conformations of polypeptides by dynamic programming. Biopolymers, 29(14), 1755–1772. 
• Chappell MJ, Godfrey KR, Vajda S. (1990). Global identifiability of the parameters of nonlinear systems with specified inputs: A comparison of methods. Mathematical Biosciences, 102(1), 41–73. 
• Vajda S, Distefano JJ, Godfrey KR, Fagarasan J. (1989). Parameter space boundaries for unidentifiable compartmental models. Mathematical Biosciences, 97(1), 27–60.
• Vajda S, Godfrey KR, Rabitz H. (1989). Similarity transformation approach to identifiability analysis of nonlinear compartmental models. Mathematical Biosciences, 93(2), 217–248. 
• Vajda S, Rabitz H. (1989). State isomorphism approach to global identifiability of nonlinear systems. IEEE Transactions on Automatic Control, 34(2), 220–223. 
• Vajda S, Rabitz H, Walter E, Lecourtier Y. (1989). QUALITATIVE AND QUANTITATIVE IDENTIFIABILITY ANALYSIS OF NONLINEAR CHEMICAL KINETIC MODELS. Chemical Engineering Communications, 83(1), 191–219.
• Turányi T, Bérces T, Vajda S. (1989). Reaction rate analysis of complex kinetic systems. International Journal of Chemical Kinetics, 21(2), 83–99. 
• Vajda S, Godfrey KR, Valko P. (1988). Numerical deconvolution using system identification methods. Journal of Pharmacokinetics and Biopharmaceutics, 16(1), 85–107. 
• Vajda S, Rabitz H. (1988). Identifiability and distinguishability of first-order reaction systems. The Journal of Physical Chemistry, 92(3), 701–707.
• Vajda S, Valko P, Turányi T. (1985). Principal component analysis of kinetic models. International Journal of Chemical Kinetics, 17(1), 55–81. 
• Vajda S. (1984). Structural equivalence and exhaustive compartmental modeling. Mathematical Biosciences, 69(1), 57–75. 
• Vajda S. (1984). Analysis of unique structural identifiability via submodels. Mathematical Biosciences, 71(2), 125–146. 
• Vajda S. (1982). Further comments on “On parameter and structural identifiability: Nonunique observability/reconstructibility for identifiable systems, other ambiguities, and new definitions”. IEEE Transactions on Automatic Control, 27(5), 1136–1137. 
• Vajda S. (1981). Structural equivalence of linear systems and compartmental models. Mathematical Biosciences, 55(1-2), 39–64.
• Vajda S. (1979). Comments on “Structural identifiability in linear time-invariant systems. IEEE Transactions on Automatic Control, 24(3), 495–496.