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Towards the online computer-aided design of catalytic pockets


The engineering of catalysts with desirable properties can be accelerated by computer-aided design. To achieve this aim, features of molecular catalysts can be condensed into numerical descriptors that can then be used to correlate reactivity and structure. Based on such descriptors, we have introduced topographic steric maps that provide a three-dimensional image of the catalytic pocket—the region of the catalyst where the substrate binds and reacts—enabling it to be visualized and also reshaped by changing various parameters. These topographic steric maps, especially when used in conjunction with density functional theory calculations, enable catalyst structural modifications to be explored quickly, making the online design of new catalysts accessible to the wide chemical community. In this Perspective, we discuss the application of topographic steric maps either to rationalize the behaviour of known catalysts—from synthetic molecular species to metalloenzymes—or to design improved catalysts.

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Fig. 1: Catalytic pocket in enzymes and synthetic catalysts.
Fig. 2: Schematic representation of descriptors used in catalysis.
Fig. 3: Topographic steric maps of transition metal complexes.
Fig. 4: Application of topographic steric maps in catalyst design.
Fig. 5: Rh-catalysed asymmetric addition of phenylboronic acid to 2-cyclohexenone.
Fig. 6: Steric maps of the catalytic pocket of metalloproteins.

Code availability

The source code calculating buried volumes and steric maps is downloadable from the SambVca 2.1 web server,


  1. 1.

    Chemistry for Tomorrow’s World (Royal Society of Chemistry, 2009);

  2. 2.

    Basic Research Needs for Catalysis Science (US Department of Energy, 2017);

  3. 3.

    Placzek, S. et al. BRENDA in 2017: new perspectives and new tools in BRENDA. Nucleic Acids Res. 45, D380–D388 (2017).

    CAS  Article  Google Scholar 

  4. 4.

    Porter, C. T., Bartlett, G. J. & Thornton, J. M. The catalytic site atlas: a resource of catalytic sites and residues identified in enzymes using structural data. Nucleic Acids Res. 32, D129–D133 (2004).

    CAS  Article  Google Scholar 

  5. 5.

    Neurath, H. Evolution of proteolytic enzymes. Science 224, 350–357 (1984).

    CAS  Article  Google Scholar 

  6. 6.

    Holzwarth, A., Schmidt, H.-W. & Maier, W. F. Detection of catalytic activity in combinatorial libraries of heterogeneous catalysts by IR thermography. Angew. Chem. Int. Ed. 37, 2644–2647 (1998).

    CAS  Article  Google Scholar 

  7. 7.

    Boussie, T. R. et al. A fully integrated high-throughput screening methodology for the discovery of new polyolefin catalysts: Discovery of a new class of high temperature single-site group (IV) copolymerization catalysts. J. Am. Chem. Soc. 125, 4306–4317 (2003).

    CAS  Article  Google Scholar 

  8. 8.

    Taylor, S. J. & Morken, J. P. Thermographic selection of effective catalysts from an encoded polymer-bound library. Science 280, 267–270 (1998).

    CAS  Article  Google Scholar 

  9. 9.

    Babin, V., Leforestier, C. & Paesani, F. Development of a “first principles” water potential with flexible monomers: Dimer potential energy surface, VRT spectrum, and second virial coefficient. J. Chem. Theory Comput. 9, 5395–5403 (2013).

    CAS  Article  Google Scholar 

  10. 10.

    Todeschini, R. & Viviana Consonni, V. Handbook of Molecular Descriptors (Wiley, 2000).

  11. 11.

    Foscato, M., Occhipinti, G., Venkatraman, V., Alsberg, B. K. & Jensen, V. R. Automated design of realistic organometallic molecules from fragments. J. Chem. Inf. Model. 54, 767–780 (2014).

    CAS  Article  Google Scholar 

  12. 12.

    Fey, N., Orpen, A. G. & Harvey, J. N. Building ligand knowledge bases for organometallic chemistry: Computational description of phosphorus(III)-donor ligands and the metal–phosphorus bond. Coord. Chem. Rev. 253, 704–722 (2009).

    CAS  Article  Google Scholar 

  13. 13.

    Fey, N. The contribution of computational studies to organometallic catalysis: Descriptors, mechanisms and models. Dalton Trans. 39, 296–310 (2010).

    CAS  Article  Google Scholar 

  14. 14.

    Hammett, L. P. The effect of structure upon the reactions of organic compounds. Benzene derivatives. J. Am. Chem. Soc. 59, 96–103 (1937).

    CAS  Article  Google Scholar 

  15. 15.

    Tolman, C. A. Steric effects of phosphorus ligands in organometallic chemistry and homogeneous catalysis. Chem. Rev. 77, 313–348 (1977).

    CAS  Article  Google Scholar 

  16. 16.

    Crabtree, R. H. The Organometallic Chemistry of the Transition Metals (Wiley, 2014).

  17. 17.

    Hansch, C., Leo, A. & Taft, R. W. A survey of Hammett substituent constants and resonance and field parameters. Chem. Rev. 91, 165–195 (1991).

    CAS  Article  Google Scholar 

  18. 18.

    Charton, M. & Charton, B. Steric effects. v. barriers to internal-rotation. J. Am. Chem. Soc. 97, 6472–6473 (1975).

    CAS  Article  Google Scholar 

  19. 19.

    Charton, M. Linear free-energy relationships. II. Proximity effects. Chem. Tech. 5, 245–255 (1975).

    CAS  Google Scholar 

  20. 20.

    Verloop, A., Hoogenstraaten, W. & Tipker, J. Development and Application of New Steric Substituent Parameters in Drug Design, Vol. 7, 165–207 (Academic Press, 1976).

  21. 21.

    Verloop, A. The STERIMOL approach to drug design (Marcel Dekker, 1987).

  22. 22.

    Poater, A. et al. SambVca: A web application for the calculation of the buried volume of N-heterocyclic carbene ligands. Eur. J. Inorg. Chem. (2009).

    Article  Google Scholar 

  23. 23.

    Cavallo, L., Correa, A., Costabile, C. & Jacobsen, H. Steric and electronic effects in the bonding of N-heterocyclic ligands to transition metals. J. Organomet. Chem. 690, 5407–5413 (2005).

    CAS  Article  Google Scholar 

  24. 24.

    Poater, A. et al. Thermodynamics of N-heterocyclic carbene dimerization: The balance of sterics and electronics. Organometallics 27, 2679–2681 (2008).

    CAS  Article  Google Scholar 

  25. 25.

    Hillier, A. C. et al. A combined experimental and theoretical study examining the binding of N-heterocyclic carbenes (NHC) to the Cp*RuCl (Cp* = η5-C5Me5) moiety: insight into stereoelectronic differences between unsaturated and saturated NHC ligands. Organometallics 22, 4322–4326 (2003).

    CAS  Article  Google Scholar 

  26. 26.

    Billow, B. S., McDaniel, T. J. & Odom, A. L. Quantifying ligand effects in high-oxidation-state metal catalysis. Nat. Chem. 9, 837–842 (2017).

    CAS  Article  Google Scholar 

  27. 27.

    Wu, K. & Doyle, A. G. Parameterization of phosphine ligands demonstrates enhancement of nickel catalysis via remote steric effects. Nat. Chem. 9, 779–784 (2017).

    CAS  Article  Google Scholar 

  28. 28.

    Lipkowitz, K. B., D’Hue, C. A., Sakamoto, T. & Stack, J. N. Stereocartography: a computational mapping technique that can locate regions of maximum stereoinduction around chiral catalysts. J. Am. Chem. Soc. 124, 14255–14267 (2002).

    CAS  Article  Google Scholar 

  29. 29.

    Angermund, K. et al. Complexes (P2)Rh(hfacac) as model compounds for the fragment (P2)Rh and as highly active catalysts for CO2 hydrogenation: The accessible molecular surface (AMS) model as an approach to quantifying the intrinsic steric properties of chelating ligands in homogeneous catalysis. Chem. Eur. J. 3, 755–764 (1997).

    CAS  Article  Google Scholar 

  30. 30.

    Harper, K. C., Vilardi, S. C. & Sigman, M. S. Prediction of catalyst and substrate performance in the enantioselective propargylation of aliphatic ketones by a multidimensional model of steric effects. J. Am. Chem. Soc. 135, 2482–2485 (2013).

    CAS  Article  Google Scholar 

  31. 31.

    Harper, K. C. & Sigman, M. S. Using physical organic parameters to correlate asymmetric catalyst performance. J. Org. Chem. 78, 2813–2818 (2013).

    CAS  Article  Google Scholar 

  32. 32.

    Fischer, E. Einfluss der configuration auf die wirkung der enzyme. Berichte Deutsch. Chem. Gesell. 27, 2985–2993 (1894).

    CAS  Article  Google Scholar 

  33. 33.

    Pauling, L. & Corey, R. B. The structure of fibrous proteins of the collagen-gelatin group. Proc. Natl Acad. Sci. USA 37, 272–281 (1951).

    CAS  Article  Google Scholar 

  34. 34.

    Pauling, L., Corey, R. B. & Branson, H. R. The structure of proteins: Two hydrogen-bonded helical configurations of the polypeptide chain. Proc. Natl Acad. Sci. USA 37, 205–211 (1951).

    CAS  Article  Google Scholar 

  35. 35.

    Poater, A., Ragone, F., Mariz, R., Dorta, R. & Cavallo, L. Comparing the enantioselective power of steric and electrostatic effects in transition-metal-catalyzed asymmetric synthesis. Chem. Eur. J. 16, 14348–14353 (2010).

    CAS  Article  Google Scholar 

  36. 36.

    Falivene, L. et al. SambVca 2. A web tool for analyzing catalytic pockets with topographic steric maps. Organometallics 35, 2286–2293 (2016).

    CAS  Article  Google Scholar 

  37. 37.

    Liu, P., Montgomery, J. & Houk, K. N. Ligand steric contours to understand the effects of N-heterocyclic carbene ligands on the reversal of regioselectivity in Ni-catalyzed reductive couplings of alkynes and aldehydes. J. Am. Chem. Soc. 133, 6956–6959 (2011).

    CAS  Article  Google Scholar 

  38. 38.

    Wang, H. et al. NHC ligands tailored for simultaneous regio- and enantiocontrol in Nickel-catalyzed reductive couplings. J. Am. Chem. Soc. 139, 9317–9324 (2017).

    CAS  Article  Google Scholar 

  39. 39.

    Yoon, T. P. & Jacobsen, E. N. Privileged chiral catalysts. Science 299, 1691–1693 (2003).

    CAS  Article  Google Scholar 

  40. 40.

    Weinstein, C. M. et al. Highly ambiphilic room temperature stable six-membered cyclic (alkyl)(amino)carbenes. J. Am. Chem. Soc. 140, 9255–9260 (2018).

    CAS  Article  Google Scholar 

  41. 41.

    Zhang, W. et al. Electron-rich metal cations enable synthesis of high molecular weight, linear functional polyethylenes. J. Am. Chem. Soc. 140, 8841–8850 (2018).

    CAS  Article  Google Scholar 

  42. 42.

    Deng, L., Woo, T. K., Cavallo, L., Margl, P. M. & Ziegler, T. The role of bulky substituents in Brookhart-type Ni(II) diimine catalyzed olefin polymerization: a combined density functional theory and molecular mechanics study. J. Am. Chem. Soc. 119, 6177–6186 (1997).

    CAS  Article  Google Scholar 

  43. 43.

    Talarico, G., Busico, V. & Cavallo, L. “Living” propene polymerization with bis(phenoxyimine) group 4 metal catalysts: New strategies and old concepts. Organometallics 23, 5989–5993 (2004).

    CAS  Article  Google Scholar 

  44. 44.

    Schwizer, F. et al. Artificial metalloenzymes: reaction scope and optimization strategies. Chem. Rev. 118, 142–231 (2018).

    CAS  Article  Google Scholar 

  45. 45.

    Rothlisberger, D. et al. Kemp elimination catalysts by computational enzyme design. Nature 453, 190–195 (2008).

    Article  Google Scholar 

  46. 46.

    Choroba, O. W., Williams, D. H. & Spencer, J. B. Biosynthesis of the vancomycin group of antibiotics: involvement of an unusual dioxygenase in the pathway to (S)-4-hydroxyphenylglycine. J. Am. Chem. Soc. 122, 5389–5390 (2000).

    CAS  Article  Google Scholar 

  47. 47.

    Hubbard, B. K., Thomas, M. G. & Walsh, C. T. Biosynthesis of L-p-hydroxyphenylglycine, a non-proteinogenic amino acid constituent of peptide antibiotics. Chem. Biol. 7, 931–942 (2000).

    CAS  Article  Google Scholar 

  48. 48.

    Pratter, S. M. et al. Inversion of enantioselectivity of a mononuclear non-heme iron(II)-dependent hydroxylase by tuning the interplay of metal-center geometry and protein structure. Angew. Chem. Int. Ed. 52, 9677–9681 (2013).

    CAS  Article  Google Scholar 

  49. 49.

    Brownlee, J., He, P., Moran, G. R. & Harrison, D. H. Two roads diverged: the structure of hydroxymandelate synthase from amycolatopsis orientalis in complex with 4-hydroxymandelate. Biochem. 47, 2002–2013 (2008).

    CAS  Article  Google Scholar 

  50. 50.

    Zahrt, A. F. et al. Prediction of higher-selectivity catalysts by computer-driven workflow and machine learning. Science 363, eaau5631 (2019).

    Article  Google Scholar 

  51. 51.

    Falivene, L. et al. SambVca 2. A web tool for analyzing catalytic pockets with topographic steric maps. Organometallics 35, 2286–2293 (2016).

    CAS  Article  Google Scholar 

  52. 52.

    GNU v.3 (Free Software Foundation, 2007).

  53. 53.

    Hubbard, S. J., Campbell, S. F. & Thornton, J. M. Molecular recognition: conformational analysis of limited proteolytic sites and serine proteinase protein inhibitors. J. Mol. Biol. 220, 507–530 (1991).

    CAS  Article  Google Scholar 

  54. 54.

    Hostaš, J. & Řezáč, J. Accurate DFT-D3 calculations in a small basis set. J. Chem. Theory Comput. 13, 3575–3585 (2017).

    Article  Google Scholar 

  55. 55.

    Brandenburg, J. G., Bannwarth, C., Hansen, A. & Grimme, S. B97-3c: A revised low-cost variant of the B97-D density functional method. J. Chem. Phys. 148, 064104 (2018).

    Article  Google Scholar 

  56. 56.

    Bannwarth, C., Ehlert, S. & Grimme, S. GFN2-xTB—an accurate and broadly parametrized self-consistent tight-binding quantum chemical method with multipole electrostatics and density-dependent dispersion contributions. J. Chem. Theory Comput. 15, 1652–1671 (2019).

    CAS  Article  Google Scholar 

  57. 57.

    Mühlbach, A. H., Vaucher, A. C. & Reiher, M. Accelerating wave function convergence in interactive quantum chemical reactivity studies. J. Chem. Theory Comput. 12, 1228–1235 (2016).

    Article  Google Scholar 

  58. 58.

    Wu, D., Rosen, D. W., Wang, L. & Schaefer, D. Cloud-based design and manufacturing: a new paradigm in digital manufacturing and design innovation. Comput. Aided Des. 59, 1–14 (2015).

    Article  Google Scholar 

  59. 59.

    Santiago, C. B., Guo, J.-Y. & Sigman, M. S. Predictive and mechanistic multivariate linear regression models for reaction development. Chem. Sci. 9, 2398–2412 (2018).

    CAS  Article  Google Scholar 

  60. 60.

    Sigman, M. S., Harper, K. C., Bess, E. N. & Milo, A. The development of multidimensional analysis tools for asymmetric catalysis and beyond. Acc. Chem. Res. 49, 1292–1301 (2016).

    CAS  Article  Google Scholar 

  61. 61.

    Harper, K. C. & Sigman, M. S. Three-dimensional correlation of steric and electronic free energy relationships guides asymmetric propargylation. Science 333, 1875–1878 (2011).

    CAS  Article  Google Scholar 

  62. 62.

    Ahneman, D. T., Estrada, J. G., Lin, S., Dreher, S. D. & Doyle, A. G. Predicting reaction performance in C–N cross-coupling using machine learning. Science 360, 186–190 (2018).

    CAS  Article  Google Scholar 

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L.C. thanks the King Abdullah University of Science and Technology (KAUST). This research used resources of the Core Labs and of the KAUST Supercomputing Laboratory. A.P. is a Serra Húnter fellow and thanks the Spanish MICINN for the project PGC2018-097722-B-I00. R.O. thanks University Parthenope ‘Finanziamento per il Sostegno alla Ricerca Individuale di Ateneo – Annualità 2017’ for funding.

Author information




L.C. conceived and designed the project. L.F. and A.P. provided the DFT calculations and the buried volume and steric maps analyses. Z.C. wrote the SambVca source code. R.O. provided the analysis of the biomolecules. A.P., L.S. and V.S designed and implemented the SambVca web application. All authors contributed to the discussion, L.C. and R.O. wrote the manuscript and all authors commented on the manuscript.

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Correspondence to Luigi Cavallo.

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Falivene, L., Cao, Z., Petta, A. et al. Towards the online computer-aided design of catalytic pockets. Nat. Chem. 11, 872–879 (2019).

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