Reduced bacterial resistance antibiotics with improved microbiota tolerance in human intestinal: Molecular design and mechanism analysis

Antibiotic selectivity and bacterial resistance are critical global public health issues. We constructed a multi-class machine learning model to study antibiotic effects on human intestinal microbiota abundance and identified key features. Binding energies of β-lactam antibiotics with Escherichia coli PBP3 mutant protein were calculated, and a 2D-QSAR model for bacterial resistance was established. Sensitivity analysis identified key features affecting bacterial resistance. By coupling key features from the machine learning model and 2D-QSAR model, we designed ten flucloxacillin (FLU) substitutes that improved intestinal microbiota tolerance and reduced antibiotic bacterial resistance. Concurrently, the substitutes exhibited superior degradability in soil, aquatic environments, and under photolytic conditions, coupled with a reduced environmental toxicity compared to the FLU. Evaluations under combined medication revealed significant improvements in functionality and bacterial resistance for 80% of FLU substitutes, with 50% showing more than a twofold increase. Mechanistic analysis demonstrated enhanced binding to target proteins and increased biodegradability for FLU substitutes due to more concentrated surface charges. Reduced solvent hindrance and increased cell membrane permeability of FLU substitutes, mainly due to enhanced interactions with phospholipid bilayers, contributed to their functional selectivity. This study aims to address poor antibiotic selectivity and strong bacterial resistance, providing guidance for designing antibiotic substitutes.

A building-block database of distributed polarizabilities and dipole moments to estimate optical properties of biomacromolecules in isolation or in an explicitly solvated medium

Since atomic or functional-group properties in the bulk are generally not available from experimental methods, computational approaches based on partitioning schemes have emerged as a rapid yet accurate pathway to estimate the materials behavior from chemically meaningful building blocks. Among several applications, a comprehensive and systematically built database of atomic or group polarizabilities and related opto-electronic quantities would be very useful not only to envisage linear or non-linear optical properties of biomacromolecules but also to improve the accuracy of classical force fields devoted to simulate biochemical processes. In this work, we propose the first entries of such database that contains distributed polarizabilities and dipole moments extracted from fragments of peptides. Twenty three prototypical conformers of the dipeptides alanine-alanine and glycine-glycine were used to extract functional groups such as CH₂ , CHCH₃ , NH₂ , COOH, CONH, thus allowing construction of a diversity of chemically relevant environments. To evaluate the accuracy of our database, reconstructed properties of larger peptides containing up to six residues of alanine and glycine were tested against density functional theory calculations at the M06-HF/aug-cc-pVDZ level of theory. The procedure is particularly accurate for the diagonal components of the polarizability tensor with errors up to 15%. In order to include solvent effects explicitly, the peptides were also surrounded by a box of water molecules whose distribution was optimized using the CHARMM force field. Solvent effects introduced by a classical dipole-dipole interaction model were compared to those obtained from polarizable-continuum model calculations.

Distributed functional-group polarizabilities in polypeptides and peptide clusters toward accurate prediction of electro-optical properties of biomacromolecules

CONTEXT

Aiming at accurately predicting electro-optical properties of biomolecules, this work presents distributed atomic and functional-group polarizability tensors for a series of polypeptides and peptide clusters constructed from glycine and its residuals. By partitioning the electron density using the quantum theory of atoms in molecules, we demonstrated a very good transferability of the group polarizabilities. We were able to identify and extract the most efficient functional groups capable of generating the largest electrical susceptibility in condensed phases. Both the isotropic polarizability and its anisotropy were used to understand the way functional groups act as sources of linear optical responses, how they interact with each other reinforcing the macroscopic optical behavior within the material, and how covalent bonds and non-covalent interactions, such as hydrogen bonds, determine refractive indices and birefringence. Particular attention is devoted to the peptide bonds as they provide links to build biomacromolecules or polymers. An adequate quantum-mechanical treatment of at least the first interaction sphere of a given functional group is required to properly describe the effects of mutual polarization, but we identified optimum cluster size and shape to better estimate polarizabilities and dipole moments of larger molecules or molecular aggregates from the knowledge of the electron density of a central molecule or amino acid residual that is representative of the bulk. The strategy outlined here is a fast yet effective tool for estimating the optical properties of proteins but could eventually find application in the rational design of optical organic materials as well.

METHODS

Electronic-structure calculations were performed on the Gaussin16 program at the DFT level using the CAMB3LYP functional and the double-ζ quality Dunning basis set aug-cc-pVDZ. Electron density partitioning followed the concepts of the Quantum Theory of Atoms and Molecules (QTAIM) and was performed using the AIMAll program. The locally developed Polaber routine was applied to calculate dipole moment vectors and polarizability tensors. It was amended to include the effects of the local field on a given central molecule by means of a modified Atom-Dipole Interaction Model (ADIM).

Benchmark of a functional-group database for distributed polarizability and dipole moment in biomolecules

The extraction of functional-group properties in condensed phases is very useful for predicting material behaviors, including those of biomaterials. For this reason, computational approaches based on partitioning schemes have been developed aiming at rapidly and accurately estimating properties from chemically meaningful building blocks. A comprehensive database of group polarizabilities and dipole moments is useful not only to predict the optical properties of biomacromolecules but also to improve molecular force fields focused on simulating biochemical processes. In this work we benchmark a database of distributed polarizabilities and dipole moments for functional groups extracted from a series of polypeptides. This allows reconstruction of a variety of relevant chemical environments. The accuracy of our database was tested to predict the electro-optical properties of larger peptides and also simpler amino acids for which density functional theory calculations at the M06-HF/aug-cc-pVDZ level of theory was chosen as the reference. This approach is reasonably accurate for the diagonal components of the polarizability tensor, with errors not larger than 15-20%. The anisotropy of the polarizability is predicted with smaller efficacy though. Solvent effects were included explicitly by surrounding the database entries by a box of water molecules whose distribution was optimized using the CHARMM force field.

Accurate Atom-Dipole Interaction Model for Prediction of Electro-optical Properties: From van der Waals Aggregates to Covalently Bonded Clusters

This work aims at the accurate estimation of the electro-optical properties of atoms and functional groups in organic crystals. A better understanding of the nature of building blocks and the way they interact with each other enables more efficient prediction of self-assembly, and thus physical properties in condensed matter. We propose a modified version of an atom-dipole interaction model that is based on atomic dipole moments calculated from the quantum theory of atoms in molecules. The method is very reliable for the prediction of various optical and electric properties in diverse chemical environments, ranging from hydrocarbon molecules bonded by dispersive interactions to polar rings connected by hydrogen bonds, or even polymeric structures whose monomers are covalently linked. Distributed polarizabilities and electrostatic potentials are compared to those obtained using a complete quantum-mechanical approach on finite-size aggregates. Our electrostatic approximation recovers isotropic polarizabilities with an accuracy of ca. 5 au and electrostatic potentials of ca. 0.05 au, even in the worst-case scenario in which polarization and charge-transfer effects are large. Functional groups are highly exportable, estimating the properties of small peptides and polyaromatics with a maximum deviation as low as ca. 15%.

Origin-Independent Decomposition of the Static Polarizability

Real-space analysis tools afford additive and transferable contributions of atoms to molecular properties. In the case of the molecular (hyper)polarizabilities, the atomic contributions that have been derived so far include a charge-transfer term that is origin-dependent. In this letter, we present the first genuinely origin-independent energy-based (OIEB) methodology for the decomposition of the static (hyper)polarizabilities that benefits from real-space molecular energy decomposition schemes, focusing on the static polarizability and showing that extension to static hyperpolarizabilities is straightforward. The numerical realization of the OIEB method shows the expected origin independence, atomic additivity, and transferability of atomic and functional group polarizability tensors. Furthermore, the OIEB atomic (fragment) polarizability tensors are symmetric by definition.