My most recent, major scientific contributions include the following:

  1. titration curves image
    Theoretical titration curves for glutamate residues in arginine kinase – Note curve shape for the catalytic Glu225 *

    I have developed theory and computational methods to predict the amino acid residues in a protein structure that participate in catalysis and/or small molecule recognition. These methods are based on computed electrostatic and chemical properties and only require the structure of the query protein as input. To maximize the sensitivity and selectivity of our predictions, my group developed a novel machine learning methodology called Partial Order Optimum Likelihood (POOL) [1, 2], used to predict catalytic and binding residues from electrostatic and chemical input features. While other approaches are mostly informatics-based, we incorporate computed chemical properties and this increases predictive power. Our methods have been verified on annotated databases.*

  2. nitrile hydratase active site image
    The multilayer active site, computationally predicted and experimentally verified, for Co-type nitrile hydratase [3] *
    My collaborators and I have promoted the concept of the important role of distal residues in enzyme catalysis. My theories predict whether an enzyme has a compact or spatially extended active site and my experimental collaborators test these predictions. These residues have been shown to make significant contributions to catalysis [3-6]; most importantly, the specific residues that do contribute to the biochemical function are predictable with a simple calculation. Although distal residues have been largely neglected in biochemical studies, understanding their roles is critically important in enzyme engineering. *
  3. active site image
    RSc1362 (PDB 3UMB), a Structural Genomics protein from Ralstonia solanacearum, has a predicted active site that matches the known L-2 haloacid dehalogenases **

    My research group members and I have developed theory to predict the function of structural genomics (SG) proteins of unknown function [7-10]. There are now over 13,000 SG protein structures in the Protein Data Bank and most of them are of unknown or uncertain function. The ability to annotate function reliably adds tremendous value to SG information. Most other approaches to functional genomics are informatics based; our unique approach draws on the power of chemical properties. First, biochemically active residues are predicted computationally for proteins of known function, to generate Chemical Signatures that are characteristic of specific functional types. These are compared with the predicted sets of residues for Structural Genomics proteins of unknown function.**

  4. I have been active in the computational aspects of structure-based drug discovery for a variety of different targets of interest [11-14]. These are collaborative projects with medicinal chemists to synthesize the compounds that we design and with biologists who perform the activity assays.
  5. Our theories and computational methods have been used to evaluate the function, activity, and dynamics of a variety of proteins of importance in medicine [15-17].

* Work supported by NSF-MCB-0843603, MCB-1158176, and MCB-1517290

** Work supported by NSF-CHE-1305655

For more information, please consult my complete list of publications.


  1. Tong, W., Y. Wei, L.F. Murga, M.J. Ondrechen, and R.J. Williams, Partial Order Optimum Likelihood (POOL): Maximum likelihood prediction of protein active site residues using 3D structure and sequence properties. PLoS Comp Biol, 2009. 5(1): p. e1000266.
  2. Somarowthu, S., H. Yang, D.G.C. Hildebrand, and M.J. Ondrechen, High-performance prediction of functional residues in proteins with machine learning and computed input features. Biopolymers, 2011. 95(6): p. 390-400.
  3. Brodkin, H.R., W.R. Novak, A.C. Milne, J.A. D’Aquino, N.M. Karabacak, I.G. Goldberg, J.N. Agar, M.S. Payne, G.A. Petsko, M.J. Ondrechen, and D. Ringe, Evidence of the participation of remote residues in the catalytic activity of Co-type nitrile hydratase from Pseudomonas putida. Biochemistry, 2011. 50(22): p. 4923-35.
  4. Somarowthu, S., H.R. Brodkin, J.A. D’Aquino, D. Ringe, M.J. Ondrechen, and P.J. Beuning, A tale of two isomerases: Compact versus extended active sites in ketosteroid isomerase and phosphoglucose isomerase. Biochemistry, 2011. 50: p. 9283-9295.
  5. Walsh, J.M., R. Parasuram, P.R. Rajput, E. Rozners, M.J. Ondrechen, and P.J. Beuning, Effects of non-catalytic, distal amino acid residues on activity of E. coli DinB (DNA polymerase IV). Environmental and Molecular Mutagenesis, 2012. 53(9): p. 766-776
  6. Brodkin, H.R., N.A. DeLateur, S. Somarowthu, C.L. Mills, W.R. Novak, P.J. Beuning, D. Ringe, and M.J. Ondrechen, Prediction of Distal Residue Participation in Enzyme Catalysis. Protein Sci, 2015. 24: p. 762-778.
  7. Han, G.W., J. Ko, C.L. Farr, M.C. Deller, Q. Xu, H.-J. Chiu, M.D. Miller, J. Sefcikova, S. Somarowthu, P.J. Beuning, M.-A. Elsliger, A.M. Deacon, A. Godzik, S.A. Lesley, I.A. Wilson, and M.J. Ondrechen, Crystal structure of a metal-dependent phosphoesterase (YP_910028.1) from Bifidobacterium adolescentis: Computational prediction and experimental validation of phosphoesterase activity. Proteins, 2011. 79: p. 2146-2160.
  8. Parasuram, R., J.S. Lee, P. Yin, S. Somarowthu, and M.J. Ondrechen, Functional classification of protein 3D structures from predicted local interaction sites. J Bioinform Comput Biol., 2010. 8 Suppl 1: p. 1-15.
  9. Wang, Z., P. Yin, J.S. Lee, R. Parasuram, S. Somarowthu, and M.J. Ondrechen, Protein Function Annotation with Structurally Aligned Local Sites of Activity (SALSAs). BMC Bioinformatics, 2013. 14(Suppl3)(S3).
  10. Mills, C.L., P.J. Beuning, and M.J. Ondrechen, Biochemical Functional Predictions for Protein Structures of Unknown or Uncertain Function. Computational and Structural Biotechnology Journal, 2015. 13: p. 182-191.
  11. Bland, N.D., C. Wang, C. Tallman, A.E. Gustafson, Z. Wang, T.D. Ashton, S. Ochiana, G. McAllister, K. Cotter, A.P. Fang, L. Gechijian, N. Garceau, R. Gangurde, R. Ortenberg, M.J. Ondrechen, R.K. Campbell, and M.P. Pollastri, Pharmacological Validation of Trypanosoma brucei B1 and B2 as Druggable Targets for African Sleeping Sickness. J Med Chem, 2011. 54(23): p. 8188-8194.
  12. Ochiana, S.O., V. Pandarinath, Z. Wang, R. Kapoor, M.J. Ondrechen, L. Ruben, and M.P. Pollastri, The human Aurora kinase inhibitor danusertib is a lead compound for anti-trypanosomal drug discovery via target repurposing. Eur J Med Chem, 2013. 62: p. 777-784
  13. Thomas, R., J.S. Lee, V. Chevalier, S. Sadler, S. Selesniemi, M. Hatfield, M. Sitkovsky, M.J. Ondrechen, and G.B. Jones, Design and evaluation of xanthine based adenosine receptor antagonists: Potential hypoxia targeted immunotherapies. Bioorganic and Medicinal Chemistry, 2013. 21: p. 7453-7464.
  14. Hanson, R.N., P. Tongcharoensirikul, K. Barnsley, M.J. Ondrechen, A. Hughes, and E.R. DeSombre, Synthesis and evaluation of 2-Halogenated-1,1-Bis(4-hydroxyphenyl)-2-(3-hydroxyphenyl)-ethylenes as potential estrogen receptor-targeted radiodiagnostic and radiotherapeutic agents. submitted, 2014.
  15. Wei, Y., D. Ringe, M.A. Wilson, and M.J. Ondrechen, Identification of Functional Subclasses in the DJ-1 Superfamily Proteins. PLoS Comp Biol, 2007. 3(e10): p. 120-126.
  16. Chan, C.S., T.M.L. Winstone, L. Chang, H. Li, C. Stevens, M.L. Workentine, Y. Wei, M.J. Ondrechen, M. Paetzel, and R.J. Turner, Identification of a twin-arginine leader peptide binding site in DmsD; Defined through random and bioinformatics-directed mutagenesis. Biochemistry, 2008. 47: p. 2749-2759.
  17. Relloso, M., T.-Y. Cheng, J.S. Im, E. Parisini, C. Roura-Mir, C. DeBono, D.M. Zajonc, L.F. Murga, M.J. Ondrechen, I.A. Wilson, S.A. Porcelli, and D.B. Moody, pH-dependent Interdomain Tethers of CD1b Regulate Its Antigen Capture. Immunity, 2008. 28: p. 774-786.