Rachael A. Mansbach

Jyler Menard, R. A. Mansbach

Antimicrobial peptides (AMPs) are a promising class of therapeutics to treat bacterial infections. Discovering and designing such peptides is difficult because of the vast number of possible sequences of amino acids. Deep generative models, such as variational autoencoders, have shown value in peptide design due to their ability to model sequence space with a continuous-valued latent space. Although such models have already been used to great effect in biomolecular design, they still suffer from a lack of interpretability and rigorous quantification of latent space quality as a search space. We investigate (1) whether further compression of the design space via dimensionality reduction may facilitate optimization, (2) the interpretability of the spaces, and (3) how organizing latent spaces with physicochemical properties may improve the efficiency of optimizing antimicrobial activity. We find that further reduction of the latent space via dimensionality reduction can be advantageous when organizing the space with more relevant information at data availability, that using the dimensionality reduction search space can be more interpretable, and that we can organize the latent space with different physicochemical properties even at different percentages of available labels.

Kirill Shmilovich, Rachael A. Mansbach, Hythem Sidky et al.

Electronically-active organic molecules have demonstrated great promise as novel soft materials for energy harvesting and transport. Self-assembled nanoaggregates formed from $π$-conjugated oligopeptides composed of an aromatic core flanked by oligopeptide wings offer emergent optoelectronic properties within a water soluble and biocompatible substrate. Nanoaggregate properties can be controlled by tuning core chemistry and peptide composition, but the sequence-structure-function relations remain poorly characterized. In this work, we employ coarse-grained molecular dynamics simulations within an active learning protocol employing deep representational learning and Bayesian optimization to efficiently identify molecules capable of assembling pseudo-1D nanoaggregates with good stacking of the electronically-active $π$-cores. We consider the DXXX-OPV3-XXXD oligopeptide family, where D is an Asp residue and OPV3 is an oligophenylene vinylene oligomer (1,4-distyrylbenzene), to identify the top performing XXX tripeptides within all 20$^3$ = 8,000 possible sequences. By direct simulation of only 2.3% of this space, we identify molecules predicted to exhibit superior assembly relative to those reported in prior work. Spectral clustering of the top candidates reveals new design rules governing assembly. This work establishes new understanding of DXXX-OPV3-XXXD assembly, identifies promising new candidates for experimental testing, and presents a computational design platform that can be generically extended to other peptide-based and peptide-like systems.