What can machine learning do for antimicrobial peptides, and what can antimicrobial peptides do for machine learning?

Abstract

Antimicrobial peptides (AMPs) are a diverse class of well-studied membrane-permeating peptides with important functions in innate host defense. In this short review, we provide a historical overview of AMPs, summarize previous applications of machine learning to AMPs, and discuss the results of our studies in the context of the latest AMP literature. Much work has been recently done in leveraging computational tools to design new AMP candidates with high therapeutic efficacies for drug-resistant infections. We show that machine learning on AMPs can be used to identify essential physico-chemical determinants of AMP functionality, and identify and design peptide sequences to generate membrane curvature. In a broader scope, we discuss the implications of our findings for the discovery of membrane-active peptides in general, and uncovering membrane activity in new and existing peptide taxonomies.

Keywords: amphiphilic peptides; antimicrobial peptides; machine learning; membrane curvature.

Figure 1.

AMPs and their mechanisms of action. (a) Examples of cationic AMPs: LL-37 [18] (i, PDB ID: 2K6O), magainin [19] (ii, PDB ID: 2MAG) and melittin [20] (iii, PDB ID: 1MLT, 2MLT). Cationic residues are coloured blue and hydrophobic residues are coloured white. Structures were taken from the Protein Data Bank and visualized in VMD. Proposed mechanisms of AMP antimicrobial activity include the ‘barrel-stave’ model (b), the ‘carpet’ model (c) and the ‘toroidal-pore’ model (d). Reproduced with permission from [5,14,21].

Figure 2.

The SVM detects the ability of peptides to generate NGC. (a) We observe a lack of correlation between distance to margin σ and antimicrobial potency (MIC) of known AMPs against S. aureus (R_Spearman = −0.060 [−0.154, 0.034], p = 0.187). (b) Using SAXS, we observe that test peptides derived from machine learning generate NGC. (i) Shows the 3D topology of a Pn3m cubic phase induced by test peptides in model membranes. (ii) Illustrates the concept of NGC with positive curvature (+) in one principal direction and negative curvature (−) in the orthogonal direction. (c) We find that σ correlates strongly with the ability to generate membrane curvature (R_Spearman = 0.653 [0.234, 0.891], p = 0.006). Adapted from data in [1].

Figure 3.

Machine learning model learns amphipathicity of membrane-permeating helices. (a) Helical wheel plot demonstrating four physico-chemical descriptors that describe amphipathicity (see nos. 2, 3, 6 and 8 in table 1). Residues spaced apart by two, nine and 30 positions along the helix likely have opposite character (red lines), while residues spaced apart by four positions likely have similar character (green line). (b) Mean hydrophobic moments (measure of amphipathicity) of test peptides derived from machine learning are similar to those of known AMPs. (c) Positive, statistically significant correlation between NGC generated and amphipathicity of test peptides (R_Spearman = 0.680 [0.259, 0.856], p = 0.0038). (d) Tested peptides were classified into two groups based on the angle (θ) subtended by the hydrophobic face. Simplified diagrams of helix cross-sections depict the different widths of the polar (upper) and hydrophobic (lower) faces for each of the two groups, θ > 120° and θ ≤ 120°. For a subset of the tested peptides, the membrane activity in terms of |〈K〉| (units of 10⁻⁴ Å⁻²) and σ are listed alongside the mean hydrophobic moment 〈μH〉 (units of kcal mol⁻¹), mean hydrophobicity 〈H〉 (units of kcal/mol) and charge z at pH 7.4. Adapted from data in [1].

Figure 4.

Pareto-optimal sequences and newly discovered curvature-generating sequences generally follow the saddle-splay selection rule. Physico-chemically restricted (a) and physico-chemically unrestricted (b) Pareto-optimal sequences follow the saddle-splay selection rule denoted by the AMP database (black). (c) Tested peptides (stems) demonstrate NGC generating ability regardless of their placement along the saddle-splay selection rule (black circles). |〈K〉| has units of units of 10⁻⁴ Å⁻². Adapted from data in [1].

Figure 5.

Diverse families of membrane curvature-generating peptides discovered from a directed search of the unknown peptide sequence space. Plot of discovered peptides and proteins from the Protein Data Bank on a sequence map obtained from a Monte Carlo search of unknown peptide sequence space (a) and on the saddle-splay selection rule (b). Adapted from data in [1].