HessFit: A Toolkit to Derive Automated Force Fields from Quantum Mechanical Information.

In this study, we introduce a novel approach to enhance the accuracy of molecular dynamics simulations by refining the force fields (FFs) through a combination of transferable parameters and molecule-specific characteristics derived from quantum mechanical (QM) calculations. Traditional FFs often prioritize generality over precision, leading to limitations in the accuracy of accurately capturing intra- and intermolecular interactions. To address this, we present an open-source toolkit, called HessFit, designed to integrate QM-derived bonded parameters and atomic charges into existing FFs. In combination with bond, angle, torsional, and nonbonded parameters derivation, HessFit can easily extract multiple barrier terms of dihedrals from QM Hessian and gradient or return all terms through a fitting procedure scheme of QM potential energy surface. We showcase the effectiveness of HessFit through comprehensive evaluations of vibrational properties across a diverse set of small molecules and demonstrate that experimental results support its ability in predicting thermodynamic properties of organic molecules compared to previous state-of-the-art approaches. We further explore its application to Zn2+ metalloprotein models, which are hard systems to treat with automatic approaches. Our results demonstrate that HessFit parameters compete with GAFF2 and OPLS parameters to describing small organic molecules, and its feasibility is also comparable to current FFs used to modeling nonstandard residues in Zn proteins for molecular dynamics simulations. The effectiveness of the HessFit protocol makes it a valuable tool for deriving or extending force field parameters for novel compounds in several molecular modeling applications.

A chemical modification of a peroxisome proliferator-activated receptor pan agonist produced a shift to a new dual alpha/gamma partial agonist endowed with mitochondrial pyruvate carrier inhibition and antidiabetic properties.

New analogs of the PPAR pan agonist AL29-26 encompassed ligand (S)-7 showing potent activation of PPARα and -γ subtypes as a partial agonist. In vitro experiments and docking studies in the presence of PPAR antagonists were performed to help interpretation of biological data and investigate the main interactions at the binding sites. Further in vitro experiments showed that (S)-7 induced anti-steatotic effects and enhancement of the glucose uptake. This latter effect could be partially ascribed to a significant inhibition of the mitochondrial pyruvate carrier demonstrating that (S)-7 also acted through insulin-independent mechanisms. In vivo experiments showed that this compound reduced blood glucose and lipid levels in a diabetic mice model displaying no toxicity on bone, kidney, and liver. To our knowledge, this is the first example of dual PPARα/γ partial agonist showing these combined effects representing, therefore, the potential lead of new drugs for treatment of dyslipidemic type 2 diabetes.

Untangling the Context-Specificity of Essential Genes by Means of Machine Learning: A Constructive Experience.

Gene essentiality is a genetic concept crucial for a comprehensive understanding of life and evolution. In the last decade, many essential genes (EGs) have been determined using different experimental and computational approaches, and this information has been used to reduce the genomes of model organisms. A growing amount of evidence highlights that essentiality is a property that depends on the context. Because of their importance in vital biological processes, recognising context-specific EGs (csEGs) could help for identifying new potential pharmacological targets and to improve precision therapeutics. Since most of the computational procedures proposed to identify and predict EGs neglect their context-specificity, we focused on this aspect, providing a theoretical and experimental overview of the literature, data and computational methods dedicated to recognising csEGs. To this end, we adapted existing computational methods to exploit a specific context (the kidney tissue) and experimented with four different prediction methods using the labels provided by four different identification approaches. The considerations derived from the analysis of the obtained results, confirmed and validated also by further experiments for a different tissue context, provide the reader with guidance on exploiting existing tools for achieving csEGs identification and prediction.

Integration of Quantum Chemistry, Statistical Mechanics, and Artificial Intelligence for Computational Spectroscopy: The UV-Vis Spectrum of TEMPO Radical in Different Solvents.

The ongoing integration of quantum chemistry, statistical mechanics, and artificial intelligence is paving the route toward more effective and accurate strategies for the investigation of the spectroscopic properties of medium-to-large size chromophores in condensed phases. In this context we are developing a novel workflow aimed at improving the generality, reliability, and ease of use of the available computational tools. In this paper we report our latest developments with specific reference to unsupervised atomistic simulations employing non periodic boundary conditions (NPBC) followed by clustering of the trajectories employing optimized feature spaces. Next accurate variational computations are performed for a representative point of each cluster, whereas intracluster fluctuations are taken into account by a cheap yet reliable perturbative approach. A number of methodological improvements have been introduced including, e.g., more realistic reaction field effects at the outer boundary of the simulation sphere, automatic definition of the feature space by continuous perception of solute-solvent interactions, full account of polarization and charge transfer in the first solvation shell, and inclusion of vibronic contributions. After its validation, this new approach has been applied to the challenging case of solvatochromic effects on the UV-vis spectra of a prototypical nitroxide radical (TEMPO) in different solvents. The reliability, effectiveness, and robustness of the new platform is demonstrated by the remarkable agreement with experiment of the results obtained through an unsupervised approach characterized by a strongly reduced computational cost as compared to that of conventional quantum mechanics and molecular mechanics models without any accuracy reduction.

A monomeric methyllithium complex: synthesis and structure.

Methyllithium (MeLi) is the parent archetypal organolithium complex. MeLi exists as aggregates in solutions and solid states. Monomeric MeLi is postulated as a highly reactive intermediate and plays a vital role in understanding MeLi-mediated reactions but has not been isolated. Herein, we report the synthesis and structure of the first monomeric MeLi complex enabled by a new hexadentate neutral amine ligand.