Towards best practices in low-dimensional semi-supervised latent Bayesian optimization for the design of antimicrobial peptides

TL;DR

This paper investigates latent Bayesian optimization for antimicrobial peptide design, emphasizing interpretability, dimensionality reduction, and organizing latent spaces to improve search efficiency and scientific understanding.

Contribution

It introduces a theoretical framework for using dimensionally-reduced latent spaces and property-based organization to enhance peptide design optimization.

Findings

Dimensionally-reduced latent spaces are more interpretable.

Organizing latent spaces by physicochemical relevance improves optimization.

Using physicochemical properties can enhance search efficiency in peptide design.

Abstract

Generative deep learning techniques have demonstrated an impressive capacity for tackling biomolecular design problems in recent years. Despite their high performance, however, they still suffer from a lack of interpretability and rigorous quantification of associated search spaces, which are necessary to unlock their full potential for scientific inquiry beyond efficient design. An area in which they are of particular interest is in the design of antimicrobial peptides, which 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 and comparatively small amount of experimental information. In this work, we perform a theoretical investigation of latent Bayesian optimization for searching through peptide sequence spaces, with a focus on antimicrobial peptides. We investigate (1) whether searching through a dimensionally-reduced variant of the latent design space may facilitate optimization, (2) how organizing latent spaces by differing amounts of more and less relevant information may improve the efficiency of arriving at an optimal peptide design, and (3) the interpretability of the spaces. We find that employing a dimensionally-reduced version of the latent space is more interpretable and can be advantageous, while the use of less-relevant but more easily-computable physicochemical properties is advantageous to latent space organization in certain contexts and the use of more-relevant but sparser properties associated with the latent Bayesian objective function is advantageous in others. This work lays crucial groundwork for biophysically-motivated peptide design procedures, with an especial focus on antimicrobial peptides.