Publications

In preparation / Submitted / In press

A computational framework for determining the breadth of antibodies against highly mutable pathogens. S. Conti and M. Karplus. .

ppdx: automated modeling of protein-protein interaction descriptors for use with machine learning. S. Conti, V. Ovchinnikov, and M. Karplus. Journal of Computational Chemistry, 2022, 43(25), pp. 1747–1757. See details.
On the rapid calculation of binding affinities for antigen and antibody design and affinity maturation simulations. S. Conti, E. Y. Lau, and V. Ovchinnikov. Antibodies, 2022, 11(3), pp. 1–19. See details.
Multiscale affinity maturation simulations to elicit broadly neutralizing antibodies against HIV. S. Conti, V. Ovchinnikov, J. G. Faris, A. K. Chakraborty, M. Karplus, and K. G. Sprenger. PLOS Computational Biology, 2022, 18(4), p. e1009391. See details.

Design of immunogens to elicit broadly neutralizing antibodies against HIV targeting the CD4 binding site. S. Conti, K. J. Kaczorowski, G. Song, K. Porter, R. Andrabi, D. R. Burton, A. K. Chakraborty, and M. Karplus. Proceedings of the National Academy of Sciences (PNAS), 2021, 118(9), p. e2018338118. See details.

A restrained locally enhanced sampling method (RLES) for finding free energy minima in complex systems. V. Ovchinnikov, S. Conti, and M. Karplus. The Journal of Chemical Physics, 2020, 153, p. 121103. See details.
Microsecond Molecular Dynamics Simulations of Proteins Using a Quasi-Equilibrium Solvation Shell Model. V. Ovchinnikov, S. Conti, E. Y. Lau, F. C. Lightstone, and M. Karplus. Journal of Chemical Theory and Computation, 2020, 16(3), pp. 1866–1881. See details.

Estimation of the breadth of CD4bs targeting HIV antibodies by molecular modeling and machine learning. S. Conti and M. Karplus. PLOS Computational Biology, 2019, 15(4). See details.

Modeling the adsorption equilibrium of small-molecules gases on graphene: effect of the volume to surface ratio. S. Conti and M. Cecchini. Physical Chemistry Chemical Physics, 2018, 20, pp. 9770–9779. See details.

Predicting molecular self-assembly at surfaces: a statistical thermodynamics and modeling approach. S. Conti and M. Cecchini. Physical Chemistry Chemical Physics, 2016, 18, pp. 1480–31493. See details.
Computational studies of molecular self-assembly at surfaces: from rational design to function. S. Conti. PhD Thesis, 2016, Université de Strasbourg. See details.
Perchlorination of Coronene Enhances its Propensity to Self-Assembly on Graphene. S. Conti, M. G. del Rosso, A. Ciesielski, J. Weippert, A. Böttcher, Y. Shin, G. Melinte, O. Ersen, C. Casiraghi, X. Feng, K. Müllen, M. M. Kappes, P. Samorì, and M. Cecchini. ChemPhysChem, 2016, 17(3), pp. 352–357. See details.

Surface-Induced Selection During In Situ Photoswitching at the Solid/Liquid Interface. S. Bonacchi, M. El Garah, A. Ciesielski, M. Herder, S. Conti, M. Cecchini, S. Hecht, and P. Samorì. Angewandte Chemie International Edition, 2015, 54(16), pp. 4865–4869. See details.
Accurate and Efficient Calculation of the Desorption Energy of Small Molecules from Graphene. S. Conti and M. Cecchini. The Journal of Physical Chemistry C, 2015, 119(4), pp. 1867–1879. See details.
A supramolecular strategy to leverage the liquid-phase exfoliation of graphene in presence of surfactants: unraveling the role of the length of fatty acids. S. Haar, A. Ciesielski, J. Clough, H. Yang, R. Mazzaro, F. Richard, S. Conti, N. Merstorf, M. Cecchini, V. Morandi, C. Casiraghi, and P. Samorì. Small, 2015, 11(14), pp. 1736–1736. See details.

Molecular insights into the stabilization of protein–protein interactions with small molecule: The FKBP12–rapamycin–FRB case study. S. Chaurasia, S. Pieraccini, R. De Gonda, S. Conti, and M. Sironi. Chemical Physics Letters, 2013, 587, pp. 68–74. See details.
Modelling the effect of osmolytes on peptide mechanical unfolding. S. Pieraccini, S. Conti, S. Chaurasia, and M. Sironi. Chemical Physics Letters, 2013, 578, pp. 138–143. See details.

Last update: 28 July 2022