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Neutral (G.GC, A.AT, G.AT, T.AT, and C(imino).GC) and protonated (CH+.GC and AH+.GC) hydrogen-bonded trimers of nucleic acid bases were characterized by ab initio methods with the inclusion of electron correlation. In addition, the influence of metal cations on the third-strand binding in Purine-Purine-Pyrimidine (Pu.PuPy) reverse-Hoogsteen triplets has been studied. The ab initio calculations were compared with those from recently introduced force fields (AMBER4.1, CHARMM23, and CFF95). The three-body term in neutral trimers is mostly negligible, and the use of empirical potentials is justified. The only exception is the neutral G.GC Hoogsteen trimer with a three-body term of -4 kcal/mol. Protonated trimers are stabilized by molecular ion-molecular dipole attraction and the interaction within the complex is nonadditive, with the three-body term on the order of -3 kcal/mol. There is a significant induction interaction between the third-strand protonated base and guanine. The calculations indicate an enhancement of the third-strand binding in the G.GC reverse-Hoogsteen trimer due to-metal cation coordination to the N7/O6 position of the third-strand guanine. Interactions between metal cations and complexes of DNA bases are in general highly non-additive; the three-body term is above-10 kcal/mol in a complex of a divalent cation (Ca2+) with the GG reverse-Hoogsteen pair. The pairwise additive empirical potentials qualitatively underestimate the binding energy between cation and base.

Therapeutic peptides offer potential advantages over small molecules in terms of selectivity, affinity, and their ability to target "undruggable" proteins that are associated with a wide range of pathologies. Despite their importance, current molecular design capabilities that inform medicinal chemistry decisions on peptide programs are limited. More specifically, there are unmet needs for structure−activity relationship (SAR) analysis and visualization of linear, cyclic, and cross-linked peptides containing nonnatural motifs, which are widely used in drug discovery. To bridge this gap, we developed PepSeA (Peptide Sequence Alignment and Visualization), an open-source, freely available package of sequence-based tools (https://github.com/Merck/PepSeA). PepSeA enables multiple sequence alignment of non-natural amino acids and enhanced visualization with the hierarchical editing language for macromolecules (HELM). Via stepwise SAR analysis of a ChEMBL peptide data set, we demonstrate the utility of PepSeA to accelerate decision making in lead optimization campaigns in pharmaceutical setting. PepSeA represents an initial attempt to expand cheminformatics capabilities for therapeutic peptides and to enable rapid and more efficient design-make-test cycles.

Protein engineering is the discipline of developing useful proteins for applications in research, therapeutic, and industrial processes by modification of naturally occurring proteins or by invention of de novo proteins. Modern protein engineering relies on the ability to rapidly generate and screen diverse libraries of mutant proteins. However, design of mutant libraries is typically hampered by scale and complexity, necessitating development of advanced automation and optimization tools that can improve efficiency and accuracy. At present, automated library design tools are functionally limited or not freely available. To address these issues, we developed Mutation Maker, an open source mutagenic oligo design software for large-scale protein engineering experiments. Mutation Maker is not only specifically tailored to multisite random and directed mutagenesis protocols, but also pioneers bespoke mutagenic oligo design for de novo gene synthesis workflows. Enabled by a novel bundle of orchestrated heuristics, optimization, constraint-satisfaction and backtracking algorithms, Mutation Maker offers a versatile toolbox for gene diversification design at industrial scale. Supported by in silico simulations and compelling experimental validation data, Mutation Maker oligos produce diverse gene libraries at high success rates irrespective of genes or vectors used. Finally, Mutation Maker was created as an extensible platform on the notion that directed evolution techniques will continue to evolve and revolutionize current and future-oriented applications.