The developments of the open-source OpenMolcas chemistry software environment since spring 2020 are described, with a focus on novel functionalities accessible in the stable branch of the package or via interfaces with other packages. These developments span a wide range of topics in computational chemistry and are presented in thematic sections: electronic structure theory, electronic spectroscopy simulations, analytic gradients and molecular structure optimizations, ab initio molecular dynamics, and other new features. This report offers an overview of the chemical phenomena and processes OpenMolcas can address, while showing that OpenMolcas is an attractive platform for state-of-the-art atomistic computer simulations.

Determining the principal energy-transfer pathways responsible for allosteric communication in biomolecules remains challenging, partially due to the intrinsic complexity of the systems and the lack of effective characterization methods. In this work, we introduce the eigenvector centrality metric based on mutual information to elucidate allosteric mechanisms that regulate enzymatic activity. Moreover, we propose a strategy to characterize the range of correlations that underlie the allosteric processes. We use the V-type allosteric enzyme imidazole glycerol phosphate synthase (IGPS) to test the proposed methodology. The eigenvector centrality method identifies key amino acid residues of IGPS with high susceptibility to effector binding. The findings are validated by solution NMR measurements yielding important biological insights, including direct experimental evidence for interdomain motion, the central role played by helix h$\alpha 1$, and the short-range nature of correlations responsible for the allosteric mechanism. Beyond insights on IGPS allosteric pathways and the nature of residues that could be targeted by therapeutic drugs or site-directed mutagenesis, the reported findings demonstrate the eigenvector centrality analysis as a general cost-effective methodology to gain fundamental understanding of allosteric mechanisms at the molecular level. Significance Allosteric processes are ubiquitous in macromolecules and regulate biochemical information transfer between spatially distant sites. Despite decades of study, allosteric processes remain generally poorly understood at the molecular level. Here, we introduce the eigenvector centrality measure of mutual information to disentangle the complex interplay of amino acid interactions giving rise to allosteric signaling. The analysis of eigenvector centrality is tested in imidazole glycerol phosphate synthase (IGPS), a prototypical V-type allosteric enzyme. The resulting insights allow us to pinpoint key amino acids in terms of their relevance in the allosteric process, suggesting protein-engineering strategies for control of enzymatic activity.

Protein allosteric pathways are investigated in the imidazole glycerol phosphate synthase heterodimer in an effort to elucidate how the effector (PRFAR, N′-[(5′-phosphoribulosyl)formimino]-5-aminoimidazole-4-carboxamide ribonucleotide) activates glutaminase catalysis at a distance of 25 Å from the glutamine-binding site. We apply solution NMR techniques and community analysis of dynamical networks, based on mutual information of correlated protein motions in the active and inactive enzymes. We find evidence that the allosteric pathways in the PRFAR bound enzyme involve conserved residues that correlate motion of the PRFAR binding loop to motion at the protein-protein interface, and ultimately at the glutaminase active site. The imidazole glycerol phosphate synthase bienzyme is an important branch point for the histidine and nucleotide biosynthetic pathways and represents a potential therapeutic target against microbes. The proposed allosteric mechanism and the underlying allosteric pathways provide fundamental insights for the design of new allosteric drugs and/or alternative herbicides.