Michal Michalski

Chemical bonding can be characterized within quantum chemical topology (QCT), which provides a real-space description via the topological analysis of the electron density and the electron localization function (ELF). While QCT has traditionally been applied on a molecule-by-molecule basis, recent advances in machine learning (ML) and the availability of large quantum chemical datasets now enable bonding analysis at scale. Here, we integrate ELF-based topological descriptors with ML to establish a data-driven framework for mapping chemical bonding across the QM9 dataset. Wavefunctions computed at the B3LYP/6-31G(2df,p) level were used to extract ELF basin populations, which were paired with geometric and bonding descriptors to construct a bond-level dataset. Statistical analysis revealed relationships between ELF populations, bond lengths, and local chemical environments. Regression models were trained to predict ELF electron populations directly from molecular geometry. The best performance was obtained when local environmental descriptors were included, reducing the prediction error by a factor of two relative to models using only the bond type and bond length. These results demonstrate that real-space bonding parameters, such as bond electron populations, can be predicted from simple structural features, enabling scalable and interpretable exploration of chemical bonding across large chemical spaces.

The chemical bonding can be characterized within quantum chemical topology (QCT), which provides a real-space description via the topological analysis of the electron density and the electron localization function (ELF). While QCT has traditionally been applied on a molecule-by-molecule basis, recent advances in machine learning (ML) and the availability of large quantum chemical datasets now enable bonding analysis at scale. Here, we integrate ELF-based topological descriptors with ML to establish a data-driven framework for mapping chemical bonding across the QM9 dataset. Wavefunctions computed at the B3LYP/6-31G(2df,p) level were used to extract ELF basin populations, which were paired with geometric and bonding descriptors to construct a bond-level dataset. Statistical analysis revealed relationships between ELF populations, bond lengths, and local chemical environments. Regression models were trained to predict ELF electron populations directly from molecular geometry. The best performance was obtained when local environmental descriptors were included, reducing the prediction error by a factor of two relative to models using only bond type and bond length. These results demonstrate real-space bonding parameters, such as bond electron populations, can be predicted from simple structural features, enabling scalable and interpretable exploration of chemical bonding across large chemical spaces.

The chemical bonding can be characterized within quantum chemical topology (QCT), which provides a real-space description via the topological analysis of the electron density and the electron localization function (ELF). While QCT has traditionally been applied on a molecule-by-molecule basis, recent advances in machine learning (ML) and the availability of large quantum chemical datasets now enable bonding analysis at scale. Here, we integrate ELF-based topological descriptors with ML to establish a data-driven framework for mapping chemical bonding across the QM9 dataset. Wavefunctions computed at the B3LYP/6-31G(2df,p) level were used to extract ELF basin populations, which were paired with geometric and bonding descriptors to construct a bond-level dataset. Statistical analysis revealed relationships between ELF populations, bond lengths, and local chemical environments. Regression models were trained to predict ELF electron populations directly from molecular geometry. The best performance was obtained when local environmental descriptors were included, reducing the prediction error by a factor of two relative to models using only bond type and bond length. These results demonstrate real-space bonding parameters, such as bond electron populations, can be predicted from simple structural features, enabling scalable and interpretable exploration of chemical bonding across large chemical spaces.

This research investigates the mechanism of the cyanide-type umpolung reaction in benzoin condensation using topological analysis of ELF and catastrophe theory. The study achieves a comprehensive understanding of the evolution of chemical bonds and non-bonding electron density in the reaction of benzaldehyde and cyanide ions. The results reveal that the reaction proceeds through five transition state structures, with the formation of Lapworth’s cyanohydrin being the rate-determining step. The study characterises topological catastrophes in the evolution of the ELF field and provides a detailed description of the evolution of electron density in the mechanism of the reaction. An in-depth analysis of ELF catastrophes confirms the well-established Lapworth mechanism.

Membrane fusion is a fundamental process that is exploited by enveloped viruses to enter host cells. In the case of the influenza virus, fusion is facilitated by the trimeric viral hemagglutinin protein (HA). So far, major focus has been put on its N-terminal fusion peptides, which are directly responsible for fusion initiation. A growing body of evidence points also to a significant functional role of the HA C-terminal domain, which however remains incompletely understood. Our computational study aimed to elucidate the structural and functional interdependencies within the HA C-terminal region encompassing the transmembrane domain (TMD) and the cytoplasmic tail (CT). In particular, we were interested in the conformational shift of the TMD in response to varying cholesterol concentration in the viral membrane and in its modulation by the presence of CT. Using free-energy calculations based on atomistic molecular dynamics simulations, we characterized transitions between straight and tilted metastable TMD configurations under varying conditions. We found that the presence of CT is essential for achieving a stable, highly tilted TMD configuration. As we demonstrate, such a configuration of HA membrane anchor likely supports the tilting motion of its ectodomain, which needs to be executed during membrane fusion. This finding highlights the functional role of, so far, the relatively overlooked CT region.

Membrane fusion is a fundamental process exploited by enveloped viruses to enter host cells. In the case of influenza virus, fusion is facilitated by the trimeric viral hemagglutinin protein (HA). So far, major focus has been put on its N-terminal fusion peptides, which are directly responsible for fusion initiation. A growing body of evidence points also to a significant functional role of HA C-terminal domain, which however remains incompletely understood. Our computational study aimed to elucidate the structural and functional interdependencies within the HA C-terminal region, encompassing the transmembrane domain (TMD) and the cytoplasmic tail (CT). In particular, we were interested in conformational shift of the TMD in response to varying cholesterol concentration in the viral membrane and in its modulation by the presence of CT. Using free energy calculations based on atomistic molecular dynamics simulations, we characterized transitions between straight and tilted metastable TMD configurations under varying conditions. We found that the presence of CT is essential for achieving a stable, highly tilted TMD configuration. As we demonstrate, such configuration of HA membrane anchor likely supports the tilting motion of its ectodomain which needs to be executed during membrane fusion. This finding highlights the functional role of, so far relatively overlooked, CT region.

The entry of influenza virus into the host cell requires fusion of its lipid envelope with the host membrane. It is catalysed by viral hemagglutinin protein, whose fragments called fusion peptides become inserted into the target bilayer and initiate its merging with the viral membrane. Isolated fusion peptides are already capable of inducing lipid mixing between liposomes. Years of studies indicate that upon membrane binding they form bend helical structure whose degree of opening fluctuates between tightly closed hairpin and an extended boomerang. The actual way in which they initiate fusion remains elusive. In this work we employ atomistic simulations of wild type and fusion inactive W14A mutant of influenza fusion peptides confined between two closely apposed lipid bilayers. We characterise peptide induced membrane perturbation and determine the potential of mean force for the formation of the first fusion intermediate, an interbilayer lipid bridge called stalk. Our results demonstrate two routes through which the peptides can lower free energy barrier towards fusion. The first one assumes peptides capability to adopt transmembrane configuration which subsequently promotes the creation of a stalk-hole complex. The second involves surface bound peptide configuration and proceeds owing to its ability to stabilise stalk by fitting into the region of extreme negative membrane curvature resulting from its formation. In both cases, the active peptide conformation corresponds to tight helical hairpin, whereas extended boomerang geometry appears to be unable to provide favourable thermodynamic effect. The latter observation offers plausible explanation for long known inactivity of boomerang-stabilising W14A mutation.

The entry of influenza virus into the host cell requires fusion of its lipid envelope with the host membrane. It is catalysed by viral hemagglutinin protein, whose fragments called fusion peptides become inserted into the target bilayer and initiate its merging with the viral membrane. Isolated fusion peptides are already capable of inducing lipid mixing between liposomes. Years of studies indicate that upon membrane binding they form bend helical structure whose degree of opening fluctuates between tightly closed hairpin and an extended boomerang. The actual way in which they initiate fusion remains elusive. In this work we employ atomistic simulations of wild type and fusion inactive W14A mutant of influenza fusion peptides confined between two closely apposed lipid bilayers. We characterise peptide induced membrane perturbation and determine the potential of mean force for the formation of the first fusion intermediate, an interbilayer lipid bridge called stalk. Our results demonstrate two routes through which the peptides can lower free energy barrier towards fusion. The first one assumes peptides capability to adopt transmembrane configuration which subsequently promotes the creation of a stalk-hole complex. The second involves surface bound peptide configuration and proceeds owing to its ability to stabilise stalk by fitting into the region of extreme negative membrane curvature resulting from its formation. In both cases, the active peptide conformation corresponds to tight helical hairpin, whereas extended boomerang geometry appears to be unable to provide favourable thermodynamic effect. The latter observation offers plausible explanation for long known inactivity of boomerang-stabilising W14A mutation.

Antimicrobial peptides emerge as compounds that can alleviate the global health hazard of antimicrobial resistance, prompting a need for novel computational approaches to peptide generation. Here, we propose HydrAMP, a conditional variational autoencoder that learns lower-dimensional, continuous representation of peptides and captures their antimicrobial properties. The model disentangles the learnt representation of a peptide from its antimicrobial conditions and leverages parameter-controlled creativity. HydrAMP is the first model that is directly optimized for diverse tasks, including unconstrained and analogue generation and outperforms other approaches in these tasks. An additional preselection procedure based on ranking of generated peptides and molecular dynamics simulations increases experimental validation rate. Wet-lab experiments on five bacterial strains confirm high activity of nine peptides generated as analogues of clinically relevant prototypes, as well as six analogues of an inactive peptide. HydrAMP enables generation of diverse and potent peptides, making a step towards resolving the antimicrobial resistance crisis.