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1. Open science discovery of potent noncovalent SARS-CoV-2 main protease inhibitors.

Author

Boby, M.L., Fearon, D., Ferla, M., Filep, M., Koekemoer, L., Robinson, M.C., Chodera, J.D., Lee, A.A., London, N., Delft, A. von, Delft, F. von, Achdout, H., Aimon, A., Alonzi, D.S., Arbon, R., Aschenbrenner, J.C., Balcomb, B.H., Bar-David, E., Barr, H., Ben-Shmuel, A., Bennett, J., Bilenko, V.A., Borden, B., Boulet, P., Bowman, G.R., Brewitz, L., Brun, J., Bvnbs, S., Calmiano, M., Carbery, A., Carney, D.W., Cattermole, E., Chang, E., Chernyshenko, E., Clyde, A., Coffland, J.E., Cohen, G., Cole, J.C., Contini, A., Cox, L., Croll, T.I., Cvitkovic, M., Jonghe, S. De, Dias, A., Donckers, K., Dotson, D.L., Douangamath, A., Duberstein, S., Dudgeon, T., Dunnett, L.E., Eastman, P., Erez, N., Eyermann, C.J., Fairhead, M., Fate, G., Fedorov, O., Fernandes, R.S., Ferrins, L., Foster, R., Foster, H., Fraisse, L., Gabizon, R., García-Sastre, A., Gawriljuk, V.O., Gehrtz, P., Gileadi, C., Giroud, C., Glass, W.G., Glen, R.C., Glinert, I., Godoy, A.S., Gorichko, M., Gorrie-Stone, T., Griffen, E.J., Haneef, A., Hassell Hart, S, Heer, J., Henry, M., Hill, M., Horrell, S., Huang, Q.Y.J., Huliak, V.D., Hurley, M.F.D., Israely, T., Jajack, A., Jansen, J, Jnoff, E., Jochmans, D., John, T., Kaminow, B., Kang, L., Kantsadi, A.L., Kenny, P.W., Kiappes, J.L., Kinakh, S.O., Kovar, B., Krojer, T., La, V.N.T., Laghnimi-Hahn, S., Lefker, B.A., Levy, H., Lithgo, R.M., Logvinenko, I.G., Lukacik, P., Macdonald, H.B., MacLean, E.M., Makower, L.L., Malla, T.R., Marples, P.G., Matviiuk, T., McCorkindale, W., McGovern, B.L., Melamed, S., Melnykov, K.P., Michurin, O., Miesen, P., Mikolajek, H., Milne, B.F., Minh, D., Morris, A., Morris, G.M., Morwitzer, M.J., Moustakas, D., Mowbray, C.E., Nakamura, A.M., Neto, J.B., Neyts, J., Nguyen, L, Noske, G.D., Oleinikovas, V., Oliva, G., Overheul, G.J., Owen, C.D., Pai, R., Pan, J., Paran, N., Payne, A.M., Perry, B., Pingle, M., Pinjari, J., Politi, B., Powell, A., Pšenák, V., Pulido, I., Puni, R., Rangel, V.L., Reddi, R.N., Rees, P., Reid, S.P., Reid, L., Resnick, E., Ripka, E.G., Robinson, R.P., Rodriguez-Guerra, J., Rosales, R., Rufa, D.A., Saar, K., Saikatendu, K.S., Salah, E., Schaller, D., Scheen, J., Schiffer, C.A., Schofield, C.J., Shafeev, M., Shaikh, A., Shaqra, A.M., Shi, J., Shurrush, K., Singh, S., Sittner, A., Sjö, P., Skyner, R., Smalley, A., Smeets, B., Smilova, M.D., Solmesky, L.J., Spencer, J., Strain-Damerell, C., Swamy, V., Tamir, H., Taylor, J.C., Tennant, R.E., Thompson, W., Thompson, A., Tomásio, S., Tomlinson, C.W.E., Tsurupa, I.S., Tumber, A., Vakonakis, I., Rij, R.P. van, Vangeel, L., Varghese, F.S., Vaschetto, M., Vitner, E.B., Voelz, V., Volkamer, A., Walsh, M.A., Ward, W., Weatherall, C., Weiss, S., White, K.M., Wild, C.F., Witt, K.D., Wittmann, M., Wright, N., Yahalom-Ronen, Y., Yilmaz, N.K., Zaidmann, D., Zhang, I., Zidane, H., Zitzmann, N., Zvornicanin, S.N., Boby, M.L., Fearon, D., Ferla, M., Filep, M., Koekemoer, L., Robinson, M.C., Chodera, J.D., Lee, A.A., London, N., Delft, A. von, Delft, F. von, Achdout, H., Aimon, A., Alonzi, D.S., Arbon, R., Aschenbrenner, J.C., Balcomb, B.H., Bar-David, E., Barr, H., Ben-Shmuel, A., Bennett, J., Bilenko, V.A., Borden, B., Boulet, P., Bowman, G.R., Brewitz, L., Brun, J., Bvnbs, S., Calmiano, M., Carbery, A., Carney, D.W., Cattermole, E., Chang, E., Chernyshenko, E., Clyde, A., Coffland, J.E., Cohen, G., Cole, J.C., Contini, A., Cox, L., Croll, T.I., Cvitkovic, M., Jonghe, S. De, Dias, A., Donckers, K., Dotson, D.L., Douangamath, A., Duberstein, S., Dudgeon, T., Dunnett, L.E., Eastman, P., Erez, N., Eyermann, C.J., Fairhead, M., Fate, G., Fedorov, O., Fernandes, R.S., Ferrins, L., Foster, R., Foster, H., Fraisse, L., Gabizon, R., García-Sastre, A., Gawriljuk, V.O., Gehrtz, P., Gileadi, C., Giroud, C., Glass, W.G., Glen, R.C., Glinert, I., Godoy, A.S., Gorichko, M., Gorrie-Stone, T., Griffen, E.J., Haneef, A., Hassell Hart, S, Heer, J., Henry, M., Hill, M., Horrell, S., Huang, Q.Y.J., Huliak, V.D., Hurley, M.F.D., Israely, T., Jajack, A., Jansen, J, Jnoff, E., Jochmans, D., John, T., Kaminow, B., Kang, L., Kantsadi, A.L., Kenny, P.W., Kiappes, J.L., Kinakh, S.O., Kovar, B., Krojer, T., La, V.N.T., Laghnimi-Hahn, S., Lefker, B.A., Levy, H., Lithgo, R.M., Logvinenko, I.G., Lukacik, P., Macdonald, H.B., MacLean, E.M., Makower, L.L., Malla, T.R., Marples, P.G., Matviiuk, T., McCorkindale, W., McGovern, B.L., Melamed, S., Melnykov, K.P., Michurin, O., Miesen, P., Mikolajek, H., Milne, B.F., Minh, D., Morris, A., Morris, G.M., Morwitzer, M.J., Moustakas, D., Mowbray, C.E., Nakamura, A.M., Neto, J.B., Neyts, J., Nguyen, L, Noske, G.D., Oleinikovas, V., Oliva, G., Overheul, G.J., Owen, C.D., Pai, R., Pan, J., Paran, N., Payne, A.M., Perry, B., Pingle, M., Pinjari, J., Politi, B., Powell, A., Pšenák, V., Pulido, I., Puni, R., Rangel, V.L., Reddi, R.N., Rees, P., Reid, S.P., Reid, L., Resnick, E., Ripka, E.G., Robinson, R.P., Rodriguez-Guerra, J., Rosales, R., Rufa, D.A., Saar, K., Saikatendu, K.S., Salah, E., Schaller, D., Scheen, J., Schiffer, C.A., Schofield, C.J., Shafeev, M., Shaikh, A., Shaqra, A.M., Shi, J., Shurrush, K., Singh, S., Sittner, A., Sjö, P., Skyner, R., Smalley, A., Smeets, B., Smilova, M.D., Solmesky, L.J., Spencer, J., Strain-Damerell, C., Swamy, V., Tamir, H., Taylor, J.C., Tennant, R.E., Thompson, W., Thompson, A., Tomásio, S., Tomlinson, C.W.E., Tsurupa, I.S., Tumber, A., Vakonakis, I., Rij, R.P. van, Vangeel, L., Varghese, F.S., Vaschetto, M., Vitner, E.B., Voelz, V., Volkamer, A., Walsh, M.A., Ward, W., Weatherall, C., Weiss, S., White, K.M., Wild, C.F., Witt, K.D., Wittmann, M., Wright, N., Yahalom-Ronen, Y., Yilmaz, N.K., Zaidmann, D., Zhang, I., Zidane, H., Zitzmann, N., and Zvornicanin, S.N.

Abstract

Item does not contain fulltext, We report the results of the COVID Moonshot, a fully open-science, crowdsourced, and structure-enabled drug discovery campaign targeting the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease. We discovered a noncovalent, nonpeptidic inhibitor scaffold with lead-like properties that is differentiated from current main protease inhibitors. Our approach leveraged crowdsourcing, machine learning, exascale molecular simulations, and high-throughput structural biology and chemistry. We generated a detailed map of the structural plasticity of the SARS-CoV-2 main protease, extensive structure-activity relationships for multiple chemotypes, and a wealth of biochemical activity data. All compound designs (>18,000 designs), crystallographic data (>490 ligand-bound x-ray structures), assay data (>10,000 measurements), and synthesized molecules (>2400 compounds) for this campaign were shared rapidly and openly, creating a rich, open, and intellectual property-free knowledge base for future anticoronavirus drug discovery.

Published
2023

3. A cooperative molecular modeling exercise - The hypersurface as classroom

Author

Cramer, C. J., Kormos, B. L., Winget, P., Audette, V. M., Beebe, J. M., Brauer, C. S., Burdick, W. R., Cochran, E. W., Eklov, B. M., Giese, T. J., Jun, Y., Kesavan, L. S. D., Kinsinger, C. R., Minyaev, M. E., Rajamani, R., Salsbury, J. S., John Stubbs, Surek, J. T., Thompson, J. D., Voelz, V. A., Wick, C. D., and Zhang, L.

4. Open science discovery of potent noncovalent SARS-CoV-2 main protease inhibitors.

Author

Boby ML, Fearon D, Ferla M, Filep M, Koekemoer L, Robinson MC, Chodera JD, Lee AA, London N, von Delft A, von Delft F, Achdout H, Aimon A, Alonzi DS, Arbon R, Aschenbrenner JC, Balcomb BH, Bar-David E, Barr H, Ben-Shmuel A, Bennett J, Bilenko VA, Borden B, Boulet P, Bowman GR, Brewitz L, Brun J, Bvnbs S, Calmiano M, Carbery A, Carney DW, Cattermole E, Chang E, Chernyshenko E, Clyde A, Coffland JE, Cohen G, Cole JC, Contini A, Cox L, Croll TI, Cvitkovic M, De Jonghe S, Dias A, Donckers K, Dotson DL, Douangamath A, Duberstein S, Dudgeon T, Dunnett LE, Eastman P, Erez N, Eyermann CJ, Fairhead M, Fate G, Fedorov O, Fernandes RS, Ferrins L, Foster R, Foster H, Fraisse L, Gabizon R, García-Sastre A, Gawriljuk VO, Gehrtz P, Gileadi C, Giroud C, Glass WG, Glen RC, Glinert I, Godoy AS, Gorichko M, Gorrie-Stone T, Griffen EJ, Haneef A, Hassell Hart S, Heer J, Henry M, Hill M, Horrell S, Huang QYJ, Huliak VD, Hurley MFD, Israely T, Jajack A, Jansen J, Jnoff E, Jochmans D, John T, Kaminow B, Kang L, Kantsadi AL, Kenny PW, Kiappes JL, Kinakh SO, Kovar B, Krojer T, La VNT, Laghnimi-Hahn S, Lefker BA, Levy H, Lithgo RM, Logvinenko IG, Lukacik P, Macdonald HB, MacLean EM, Makower LL, Malla TR, Marples PG, Matviiuk T, McCorkindale W, McGovern BL, Melamed S, Melnykov KP, Michurin O, Miesen P, Mikolajek H, Milne BF, Minh D, Morris A, Morris GM, Morwitzer MJ, Moustakas D, Mowbray CE, Nakamura AM, Neto JB, Neyts J, Nguyen L, Noske GD, Oleinikovas V, Oliva G, Overheul GJ, Owen CD, Pai R, Pan J, Paran N, Payne AM, Perry B, Pingle M, Pinjari J, Politi B, Powell A, Pšenák V, Pulido I, Puni R, Rangel VL, Reddi RN, Rees P, Reid SP, Reid L, Resnick E, Ripka EG, Robinson RP, Rodriguez-Guerra J, Rosales R, Rufa DA, Saar K, Saikatendu KS, Salah E, Schaller D, Scheen J, Schiffer CA, Schofield CJ, Shafeev M, Shaikh A, Shaqra AM, Shi J, Shurrush K, Singh S, Sittner A, Sjö P, Skyner R, Smalley A, Smeets B, Smilova MD, Solmesky LJ, Spencer J, Strain-Damerell C, Swamy V, Tamir H, Taylor JC, Tennant RE, Thompson W, Thompson A, Tomásio S, Tomlinson CWE, Tsurupa IS, Tumber A, Vakonakis I, van Rij RP, Vangeel L, Varghese FS, Vaschetto M, Vitner EB, Voelz V, Volkamer A, Walsh MA, Ward W, Weatherall C, Weiss S, White KM, Wild CF, Witt KD, Wittmann M, Wright N, Yahalom-Ronen Y, Yilmaz NK, Zaidmann D, Zhang I, Zidane H, Zitzmann N, and Zvornicanin SN

Subjects
Humans, Molecular Docking Simulation, Structure-Activity Relationship, Crystallography, X-Ray, Coronavirus 3C Proteases antagonists & inhibitors, Coronavirus 3C Proteases chemistry, SARS-CoV-2, Drug Discovery, Coronavirus Protease Inhibitors chemical synthesis, Coronavirus Protease Inhibitors chemistry, Coronavirus Protease Inhibitors pharmacology, COVID-19 Drug Treatment
Abstract

We report the results of the COVID Moonshot, a fully open-science, crowdsourced, and structure-enabled drug discovery campaign targeting the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease. We discovered a noncovalent, nonpeptidic inhibitor scaffold with lead-like properties that is differentiated from current main protease inhibitors. Our approach leveraged crowdsourcing, machine learning, exascale molecular simulations, and high-throughput structural biology and chemistry. We generated a detailed map of the structural plasticity of the SARS-CoV-2 main protease, extensive structure-activity relationships for multiple chemotypes, and a wealth of biochemical activity data. All compound designs (>18,000 designs), crystallographic data (>490 ligand-bound x-ray structures), assay data (>10,000 measurements), and synthesized molecules (>2400 compounds) for this campaign were shared rapidly and openly, creating a rich, open, and intellectual property-free knowledge base for future anticoronavirus drug discovery.

Published
2023
Full Text
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5. Blind test of physics-based prediction of protein structures.

Author

Shell MS, Ozkan SB, Voelz V, Wu GA, and Dill KA

Subjects
Amino Acid Sequence, Computer Simulation, Databases, Protein, Molecular Sequence Data, Peptide Fragments chemistry, Protein Conformation, Protein Folding, Time Factors, Models, Molecular, Physics methods, Proteins chemistry
Abstract

We report here a multiprotein blind test of a computer method to predict native protein structures based solely on an all-atom physics-based force field. We use the AMBER 96 potential function with an implicit (GB/SA) model of solvation, combined with replica-exchange molecular-dynamics simulations. Coarse conformational sampling is performed using the zipping and assembly method (ZAM), an approach that is designed to mimic the putative physical routes of protein folding. ZAM was applied to the folding of six proteins, from 76 to 112 monomers in length, in CASP7, a community-wide blind test of protein structure prediction. Because these predictions have about the same level of accuracy as typical bioinformatics methods, and do not utilize information from databases of known native structures, this work opens up the possibility of predicting the structures of membrane proteins, synthetic peptides, or other foldable polymers, for which there is little prior knowledge of native structures. This approach may also be useful for predicting physical protein folding routes, non-native conformations, and other physical properties from amino acid sequences.

Published
2009
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6. A multiscale approach to sampling nascent peptide chains in the ribosomal exit tunnel.

Author

Voelz VA, Petrone P, and Pande VS

Subjects
Algorithms, Biometry, Ligands, Models, Molecular, Monte Carlo Method, Multiprotein Complexes chemistry, Multiprotein Complexes metabolism, Peptides chemistry, Ribosomal Proteins chemistry, Ribosomal Proteins metabolism, Thermodynamics, Models, Biological, Peptides metabolism, Ribosomes metabolism
Abstract

We present a new multiscale method that combines all-atom molecular dynamics with coarse-grained sampling, towards the aim of bridging two levels of physiology: the atomic scale of protein side chains and small molecules, and the huge scale of macromolecular complexes like the ribosome. Our approach uses all-atom simulations of peptide (or other ligand) fragments to calculate local 3D spatial potentials of mean force (PMF). The individual fragment PMFs are then used as a potential for a coarse-grained chain representation of the entire molecule. Conformational space and sequence space are sampled efficiently using generalized ensemble Monte Carlo. Here, we apply this method to the study of nascent polypeptides inside the cavity of the ribosome exit tunnel. We show how the method can be used to explore the accessible conformational and sequence space of nascent polypeptide chains near the ribosome peptidyl transfer center (PTC), with the eventual aim of understanding the basis of specificity for co-translational regulation. The method has many potential applications to predicting binding specificity and design, and is sufficiently general to allow even greater separation of scales in future work.

Published
2009
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7. Molecular dynamics simulation of site-directed spin labeling: experimental validation in muscle fibers.

Author

LaConte LE, Voelz V, Nelson W, Enz M, and Thomas DD

Subjects
Animals, Chickens, Computer Simulation, Electron Spin Resonance Spectroscopy, Models, Molecular, Models, Theoretical, Mollusca, Mutation, Myosins chemistry, Protein Conformation, Protein Structure, Secondary, Protein Structure, Tertiary, Time Factors, Spin Labels
Abstract

We have developed a computational molecular dynamics technique to simulate the motions of spin labels bound to the regulatory domain of scallop myosin. These calculations were then directly compared with site-directed spin labeling experimental results obtained by preparing seven single-cysteine mutants of the smooth muscle (chicken gizzard) myosin regulatory light chain and performing electron paramagnetic resonance experiments on these spin-labeled regulatory light chains in functional scallop muscle fibers. We determined molecular dynamics simulation conditions necessary for obtaining a convergent orientational trajectory of the spin label, and from these trajectories we then calculated correlation times, orientational distributions, and order parameters. Simulated order parameters closely match those determined experimentally, validating our molecular dynamics modeling technique, and demonstrating our ability to predict preferred sites for labeling by computer simulation. In several cases, more than one rotational mode was observed within the 14-ns trajectory, suggesting that the spin label samples several local energy minima. This study uses molecular dynamics simulations of an experimental system to explore and enhance the site-directed spin labeling technique.

Published
2002
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