Sereina Riniker

Last Name

Riniker

First Name

Sereina

ORCID

0000-0003-1893-4031

Organisational unit

09458 - Riniker, Sereina Z. / Riniker, Sereina Z.

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Publications 1 - 10 of 114

Replica-Exchange Enveloping Distribution Sampling Using Generalized AMBER Force-Field Topologies: Application to Relative Hydration Free-Energy Calculations for Large Sets of Molecules
Rieder, Salomé R.; Ries, Benjamin; Schaller, Kay; et al. (2022)
Journal of Chemical Information and Modeling
Free-energy differences between pairs of end-states can be estimated based on molecular dynamics (MD) simulations using standard pathway-dependent methods such as thermodynamic integration (TI), free-energy perturbation, or Bennett's acceptance ratio. Replica-exchange enveloping distribution sampling (RE-EDS), on the other hand, allows for the sampling of multiple end-states in a single simulation without the specification of any pathways. In this work, we use the RE-EDS method as implemented in GROMOS together with generalized AMBER force-field (GAFF) topologies, converted to a GROMOS-compatible format with a newly developed GROMOS++ program amber2gromos, to compute relative hydration free energies for a series of benzene derivatives. The results obtained with RE-EDS are compared to the experimental data as well as calculated values from the literature. In addition, the estimated free-energy differences in water and in vacuum are compared to values from TI calculations carried out with GROMACS. The hydration free energies obtained using RE-EDS for multiple molecules are found to be in good agreement with both the experimental data and the results calculated using other free-energy methods. While all considered free-energy methods delivered accurate results, the RE-EDS calculations required the least amount of total simulation time. This work serves as a validation for the use of GAFF topologies with the GROMOS simulation package and the RE-EDS approach. Furthermore, the performance of RE-EDS for a large set of 28 end-states is assessed with promising results.
Incorporating NOE-Derived Distances in Conformer Generation of Cyclic Peptides with Distance Geometry
Wang, Shuzhe; Krummenacher, Kajo; Landrum, Gregory; et al. (2022)
Journal of Chemical Information and Modeling
Nuclear magnetic resonance (NMR) data from NOESY (nuclear Overhauser enhancement spectroscopy) and ROESY (rotating frame Overhauser enhancement spectroscopy) experiments can easily be combined with distance geometry (DG) based conformer generators by modifying the molecular distance bounds matrix. In this work, we extend the modern DG based conformer generator ETKDG, which has been shown to reproduce experimental crystal structures from small molecules to large macrocycles well, to include NOE-derived interproton distances. In noeETKDG, the experimentally derived interproton distances are incorporated into the distance bounds matrix as loose upper (or lower) bounds to generate large conformer sets. Various subselection techniques can subsequently be applied to yield a conformer bundle that best reproduces the NOE data. The approach is benchmarked using a set of 24 (mostly) cyclic peptides for which NOE-derived distances as well as reference solution structures obtained by other software are available. With respect to other packages currently available, the advantages of noeETKDG are its speed and that no prior force-field parametrization is required, which is especially useful for peptides with unnatural amino acids. The resulting conformer bundles can be further processed with the use of structural refinement techniques to improve the modeling of the intramolecular nonbonded interactions. The noeETKDG code is released as a fully open-source software package available at www.github.com/rinikerlab/customETKDG.
Relative free-energy calculations for scaffold hopping-type transformations with an automated RE-EDS sampling procedure
Ries, Benjamin; Normak, Karl; Weiß, R. Gregor; et al. (2022)
Journal of Computer-Aided Molecular Design
The calculation of relative free-energy differences between different compounds plays an important role in drug design to identify potent binders for a given protein target. Most rigorous methods based on molecular dynamics simulations estimate the free-energy difference between pairs of ligands. Thus, the comparison of multiple ligands requires the construction of a "state graph", in which the compounds are connected by alchemical transformations. The computational cost can be optimized by reducing the state graph to a minimal set of transformations. However, this may require individual adaptation of the sampling strategy if a transformation process does not converge in a given simulation time. In contrast, path-free methods like replica-exchange enveloping distribution sampling (RE-EDS) allow the sampling of multiple states within a single simulation without the pre-definition of alchemical transition paths. To optimize sampling and convergence, a set of RE-EDS parameters needs to be estimated in a pre-processing step. Here, we present an automated procedure for this step that determines all required parameters, improving the robustness and ease of use of the methodology. To illustrate the performance, the relative binding free energies are calculated for a series of checkpoint kinase 1 inhibitors containing challenging transformations in ring size, opening/closing, and extension, which reflect changes observed in scaffold hopping. The simulation of such transformations with RE-EDS can be conducted with conventional force fields and, in particular, without soft bond-stretching terms.
Transferring Knowledge from MM to QM: A Graph Neural Network-Based Implicit Solvent Model for Small Organic Molecules
Katzberger, Paul; Pultar, Felix; Riniker, Sereina; et al. (2025)
ChemRxiv
The conformational ensemble of a molecule is strongly influenced by the surrounding environment. Correctly modeling the effect of any given environment is, hence, of pivotal importance in computational studies. Machine learning (ML) has been shown to be able to model these interactions probabilistically, with successful applications demonstrated for classical molecular dynamics. While first instances of ML implicit solvents for quantum-mechanical (QM) calculations exist, the high computational cost of QM reference calculations hinders the development of a generally applicable ML implicit solvent model for QM calculations. Here, we present a novel way of developing such a general machine-learned QM implicit solvent model, which relies neither on QM reference calculations for training nor experimental data, by transferring knowledge obtained from classical interactions to QM. This strategy makes the obtained graph neural network (GNN) based implicit solvent model (termed QM-GNNIS) independent of the chosen functional and basis set. The performance of QM-GNNIS is validated on NMR and IR experiments, demonstrating that the approach can reproduce experimentally observed trends unattainable by state-of-the-art implicit-solvent models.
Efficient Multistate Free-Energy Calculations with QM/MM Accuracy Using Replica-Exchange Enveloping Distribution Sampling
Pregeljc, Domen; Hugli, Ramon J.R.; Riniker, Sereina (2025)
The Journal of Physical Chemistry B
Calculating free-energy differences using molecular dynamics (MD) simulations is an important task in computational chemistry. In practice, the accuracy of the results is limited by model approximations and insufficient phase-space sampling due to limited computational resources. In the present work, we address these challenges by integrating the quantum-mechanical/molecular-mechanical (QM/MM) scheme with replica-exchange enveloping distribution sampling (RE-EDS) to obtain a multistate and multiscale free-energy method with high computational efficiency. The performance of QM/MM RE-EDS is showcased by calculating hydration free energies for three data sets using semiempirical methods for the QM zone. We highlight the importance of the choice of QM Hamiltonian and the effect of the compatibility between the QM and MM models. Especially the choice of semiempirical method has a substantial effect on the accuracy compared to experiment, but also the choice of MM water model is non-negligible. Our findings indicate that RE-EDS is an efficient approach for calculating free-energy differences with a QM/MM scheme, and lays the foundation for future developments and applications.
The Playbooks of Medicinal Chemistry Design Moves
Awale, Mahendra; Hert, Jérôme; Guasch, Laura; et al. (2021)
Journal of Chemical Information and Modeling
Large databases of biologically relevant molecules, such as ChEMBL, SureChEMBL, or compound collections of pharmaceutical or agrochemical companies, are invaluable sources of medicinal chemistry information, albeit implicit. We developed a modified matched molecular pair approach to systematically and exhaustively extract the transformations in these databases and distill them into snippets of explicit design knowledge that are easily interpretable and directly applicable. The resulting “playbooks of medicinal chemistry design moves” capture the collective pharmaceutical and agrochemical research expertise across multiple chemists, companies, targets, and projects. They can be queried in an automated fashion for systematic prospective design and compound generation. The ChEMBL playbook and an application to exploit it are available at https://github.com/mahendra-awale/medchem_moves.
Influence of Lipidation on the Folding and Stability of Collagen Triple Helices—An Experimental and Theoretical Study
Egli, Jasmine; Esposito, Carmen; Müri, Mike; et al. (2021)
Journal of the American Chemical Society
The folding of triple-helical collagen, the most abundant protein in nature, relies on the nucleation and propagation along the strands. Hydrophobic moieties are crucial for the folding and stability of numerous proteins. Instead, nature uses for collagen a trimerization domain and cis-trans prolyl isomerases to facilitate and accelerate triple helix formation. Yet, pendant hydrophobic moieties endow triple-helical collagen with hyperstability and accelerate the cis-trans isomerization to an extent that thermally induced unfolding and folding of collagen triple helices take place at the same speed. Here, we systematically explored the effect of pendant fatty acids on the folding and stability of collagen triple helices. Thermal denaturation and kinetic studies with a series of collagen mimetic peptides (CMPs) bearing saturated and unsaturated fatty acids with different lengths revealed that longer and more flexible fatty acid appendages increase the stability and the folding rate of collagen triple helices. Molecular dynamics simulations combined with experimental data indicate that the hydrophobic appendages stabilize the triple helix by interaction with the grooves of the collagen triple helix and accelerate the folding and unfolding process by creating a molten globule-like intermediate.
Virtual-screening workflow tutorials and prospective results from the Teach-Discover-Treat competition 2014 against malaria
Riniker, Sereina; Landrum, Gregory A.; Montanari, Floriane; et al. (2017)
F1000Research
Accounting for Solvation Correlation Effects on the Thermodynamics of Water Networks in Protein Cavities
Barros, Emilia P.; Ries, Benjamin; Champion, Candide; et al. (2023)
Journal of Chemical Information and Modeling
Macromolecular recognition and ligand binding are at the core of biological function and drug discovery efforts. Water molecules play a significant role in mediating the protein-ligand interaction, acting as more than just the surrounding medium by affecting the thermodynamics and thus the outcome of the binding process. As individual water contributions are impossible to measure experimentally, a range of computational methods have emerged to identify hydration sites in protein pockets and characterize their energetic contributions for drug discovery applications. Even though several methods model solvation effects explicitly, they focus on determining the stability of specific water sites independently and neglect solvation correlation effects upon replacement of clusters of water molecules, which typically happens in hit-to-lead optimization. In this work, we rigorously determine the conjoint effects of replacing all combinations of water molecules in protein binding pockets through the use of the RE-EDS multistate free-energy method, which combines Hamiltonian replica exchange (RE) and enveloping distribution sampling (EDS). Applications on the small bovine pancreatic trypsin inhibitor and four proteins of the bromodomain family illustrate the extent of solvation correlation effects on water thermodynamics, with the favorability of replacement of the water sites by pharmacophore probes highly dependent on the composition of the water network and the pocket environment. Given the ubiquity of water networks in biologically relevant protein targets, we believe our approach can be helpful for computer-aided drug discovery by providing a pocket-specific and a priori systematic consideration of solvation effects on ligand binding and selectivity.
SYNLETT Best Paper Award 2020: New Musks and the Molecular Modeling of Olfactory Receptors
Kraft, Philip; Riniker, Sereina; Wang, Quanrui (2021)
Synform

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Sereina Riniker

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