PepSeA: Peptide Sequence Alignment and Visualization Tools to Enable Lead Optimization.

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 non-natural 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.

Mutations at hypothetical binding site 2 in insulin and insulin-like growth factors 1 and 2 result in receptor- and hormone-specific responses.

Information on how insulin and insulin-like growth factors 1 and 2 (IGF-1 and -2) activate insulin receptors (IR-A and -B) and the IGF-1 receptor (IGF-1R) is crucial for understanding the difference in the biological activities of these peptide hormones. Cryo-EM studies have revealed that insulin uses its binding sites 1 and 2 to interact with IR-A and have identified several critical residues in binding site 2. However, mutagenesis studies suggest that Ile-A10, Ser-A12, Leu-A13, and Glu-A17 also belong to insulin's site 2. Here, to resolve this discrepancy, we mutated these insulin residues and the equivalent residues in IGFs. Our findings revealed that equivalent mutations in the hormones can result in differential biological effects and that these effects can be receptor-specific. We noted that the insulin positions A10 and A17 are important for its binding to IR-A and IR-B and IGF-1R and that A13 is important only for IR-A and IR-B binding. The IGF-1/IGF-2 positions 51/50 and 54/53 did not appear to play critical roles in receptor binding, but mutations at IGF-1 position 58 and IGF-2 position 57 affected the binding. We propose that IGF-1 Glu-58 interacts with IGF-1R Arg-704 and belongs to IGF-1 site 1, a finding supported by the NMR structure of the less active Asp-58-IGF-1 variant. Computational analyses indicated that the aforementioned mutations can affect internal insulin dynamics and inhibit adoption of a receptor-bound conformation, important for binding to receptor site 1. We provide a molecular model and alternative hypotheses for how the mutated insulin residues affect activity.

mRNAid, an open-source platform for therapeutic mRNA design and optimization strategies.

Recent COVID-19 vaccines unleashed the potential of mRNA-based therapeutics. A common bottleneck across mRNA-based therapeutic approaches is the rapid design of mRNA sequences that are translationally efficient, long-lived and non-immunogenic. Currently, an accessible software tool to aid in the design of such high-quality mRNA is lacking. Here, we present mRNAid, an open-source platform for therapeutic mRNA optimization, design and visualization that offers a variety of optimization strategies for sequence and structural features, allowing one to customize desired properties into their mRNA sequence. We experimentally demonstrate that transcripts optimized by mRNAid have characteristics comparable with commercially available sequences. To encompass additional aspects of mRNA design, we experimentally show that incorporation of certain uridine analogs and untranslated regions can further enhance stability, boost protein output and mitigate undesired immunogenicity effects. Finally, this study provides a roadmap for rational design of therapeutic mRNA transcripts.

Cis-to- Trans Isomerization of Azobenzene Derivatives Studied with Transition Path Sampling and Quantum Mechanical/Molecular Mechanical Molecular Dynamics.

Azobenzene-based molecular photoswitches are becoming increasingly important for the development of photoresponsive, functional soft-matter material systems. Upon illumination with light, fast interconversion between a more stable trans and a metastable cis configuration can be established resulting in pronounced changes in conformation, dipole moment or hydrophobicity. A rational design of functional photosensitive molecules with embedded azo moieties requires a thorough understanding of isomerization mechanisms and rates, especially the thermally activated relaxation. For small azo derivatives considered in the gas phase or simple solvents, Eyring's classical transition state theory (TST) approach yields useful predictions for trends in activation energies or corresponding half-life times of the cis isomer. However, TST or improved theories cannot easily be applied when the azo moiety is part of a larger molecular complex or embedded into a heterogeneous environment, where a multitude of possible reaction pathways may exist. In these cases, only the sampling of an ensemble of dynamic reactive trajectories (transition path sampling, TPS) with explicit models of the environment may reveal the nature of the processes involved. In the present work we show how a TPS approach can conveniently be implemented for the phenomenon of relaxation-isomerization of azobenzenes starting with the simple examples of pure azobenzene and a push-pull derivative immersed in a polar (DMSO) and apolar (toluene) solvent. The latter are represented explicitly at a molecular mechanical (MM) and the azo moiety at a quantum mechanical (QM) level. We demonstrate for the push-pull azobenzene that path sampling in combination with the chosen QM/MM scheme produces the expected change in isomerization pathway from inversion to rotation in going from a low to a high permittivity (explicit) solvent model. We discuss the potential of the simulation procedure presented for comparative calculation of reaction rates and an improved understanding of activated states.