Cooperativity in Bacterial Membrane Association Controls the Synergistic Activities of Antimicrobial Peptides

Understanding Antimicrobial Peptide Synergy: Differential Binding Interactions and Their Impact on Membrane Integrity

Research on antimicrobial peptides (AMPs) has been conducted as a solution to overcome antibiotic resistance. In particular, the synergistic effect that appears when two or more AMPs are used in combination has been observed. To find an effective synergistic combination, it is necessary to understand the underlying mechanism. However, a consistent explanation for this phenomenon has not yet been provided due to limitations in experimentally determining or predicting the structure of the heteroaggregates formed by the interactions between different AMPs and the interaction of the aggregate surface with the lipid membrane surface. In this study, we conducted molecular dynamics simulations for two heterogeneous aggregates of melittin-indolicidin and pexiganan-indolicidin to observe their structures in the solution phase and their interactions with the lipid membrane. We aimed to determine how the surfaces of these aggregates interact with the lipid membrane. Due to the different amino acid residue sequence characteristics of melittin and pexiganan, we found that when the two AMPs bind to indolicidin, they form aggregates with completely different structural characteristics. Accordingly, the sequence characteristics of pexiganan, which exhibits a relatively unstable structure compared to melittin in aqueous solution or on lipid membranes, allow for a more stable interaction with the lipid membrane when forming aggregates with indolicidin, effectively inhibiting the integrity of the lipid membranes. We also found that the amino acid residues forming the surface of the AMP aggregate show differential binding strengths to different lipid species forming the lipid membrane, thereby disrupting the membrane in a way that weakens its integrity. Through this, we provided insight into the basic principle of how the synergistic effect of AMPs occurs.

QSAR Reveals Decreased Lipophilicity of Polar Residues Determines the Selectivity of Antimicrobial Peptide Activity

Antimicrobial resistance has increased rapidly, causing daunting morbidity and mortality rates worldwide. Antimicrobial peptides (AMPs) have emerged as promising alternatives to traditional antibiotics due to their broad range of targets and low tendency to elicit resistance. However, potent antimicrobial activity is often accompanied by excessive cytotoxicity toward host cells, leading to a halt in AMP therapeutic development. Here, we present multivariate analyses that correlate 28 peptide properties to the activity and toxicity of 46 diverse African-derived AMPs and identify the negative lipophilicity of polar residues as an essential physiochemical property for selective antimicrobial activity. Twenty-seven active AMPs are identified, of which the majority are of scorpion or frog origin. Of these, thirteen are novel with no previously reported activities. Principal component analysis and quantitative structure-activity relationships (QSAR) reveal that overall hydrophobicity, lipophilicity, and residue side chain surface area affect the antimicrobial and cytotoxic activity of an AMP. This has been well documented previously, but the present QSAR analysis additionally reveals that a decrease in the lipophilicity, contributed by those amino acids classified as polar, confers selectivity for a peptide to pathogen over mammalian cells. Furthermore, an increase in overall peptide charge aids selectivity toward Gram-negative bacteria and fungi, while selectivity toward Gram-positive bacteria is obtained through an increased number of small lipophilic residues. Finally, a conservative increase in peptide size in terms of sequence length and molecular weight also contributes to improved activity without affecting toxicity. Our findings suggest a novel approach for the rational design or modification of existing AMPs to increase pathogen selectivity and enhance therapeutic potential.

Differences in Relevant Physicochemical Properties Correlate with Synergistic Activity of Antimicrobial Peptides

With the urgent need for new medical approaches due to increased bacterial resistance to antibiotics, antimicrobial peptides (AMPs) have been considered as potential treatments for infections. Experiments indicate that combinations of several types of AMPs might be even more effective at inhibiting bacterial growth with reduced toxicity and a lower likelihood of inducing bacterial resistance. The molecular mechanisms of AMP-AMP synergistic antimicrobial activity, however, remain not well understood. Here, we present a theoretical approach that allows us to relate the physicochemical properties of AMPs and their antimicrobial cooperativity. It utilizes correlation and bioinformatics analysis. A concept of physicochemical similarity is introduced, and it is found that less similar AMPs with respect to certain physicochemical properties lead to greater synergy because of their complementary antibacterial actions. The analysis of correlations between the similarity and the antimicrobial properties allows us to effectively separate synergistic from nonsynergistic AMP pairs. Our theoretical approach can be used for the rational design of more effective AMP combinations for specific bacterial targets, for clarifying the mechanisms of bacterial elimination, and for a better understanding of cooperativity phenomena in biological systems.

Increasing Heterogeneity in Antimicrobial Peptide Combinations Enhances Their Synergistic Activities

Antimicrobial peptides (AMPs) are short biopolymers produced by living organisms as an immune system defense against infections. They have been considered as potential alternatives to conventional antibiotics. Experiments suggest that combining several types of different AMPs might enhance their antimicrobial activity more effectively than using single-component AMPs. However, a clear understanding of the underlying microscopic mechanisms is still lacking. We present a theoretical investigation of antibacterial cooperativity mechanisms involving several types of AMPs. It is argued that synergy results from intermolecular interactions when the presence of one type of AMP stimulates the association of another type of AMP to bacteria. It is found that increasing the number of different AMPs in the mixtures increases the number of such interactions, making them more efficient in eliminating infections. Our theoretical framework provides valuable insights into the mechanisms of antimicrobial action.

Predicting Antimicrobial Activity for Untested Peptide-Based Drugs Using Collaborative Filtering and Link Prediction

The increase of bacterial resistance to currently available antibiotics has underlined the urgent need to develop new antibiotic drugs. Antimicrobial peptides (AMPs), alone or in combination with other peptides and/or existing antibiotics, have emerged as promising candidates for this task. However, given that there are thousands of known AMPs and an even larger number can be synthesized, it is impossible to comprehensively test all of them using standard wet lab experimental methods. These observations stimulated an application of machine-learning methods to identify promising AMPs. Currently, machine learning studies combine very different bacteria without considering bacteria-specific features or interactions with AMPs. In addition, the sparsity of current AMP data sets disqualifies the application of traditional machine-learning methods or makes the results unreliable. Here, we present a new approach, featuring neighborhood-based collaborative filtering, to predict with high accuracy a given bacteria's response to untested AMPs based on similarities between bacterial responses. Furthermore, we also developed a complementary bacteria-specific link prediction approach that can be used to visualize networks of AMP-antibiotic combinations, enabling us to propose new combinations that are likely to be effective.

Bacteria-Specific Feature Selection for Enhanced Antimicrobial Peptide Activity Predictions Using Machine-Learning Methods

There are several classes of short peptide molecules, known as antimicrobial peptides (AMPs), which are produced during the immune responses of living organisms against various infections. In recent years, substantial progress has been achieved in applying machine-learning methods to predict the activities of AMPs against bacteria. In most investigated cases, however, the outcome is not bacterium-specific since the specific features of bacteria, such as chemical composition and structure of membranes, are not considered. To overcome this problem, we developed a new computational approach that allowed us to train several supervised machine-learning models using a specific set of data associated with peptides targeting E. coli bacteria. LASSO regression and Support Vector Machine techniques have been utilized to select, among more than 1500 physicochemical descriptors, the most important features that can be used to classify a peptide as antimicrobial or ineffective against E. coli. We then performed the classification of active versus inactive AMPs using the Support Vector classifiers, Logistic Regression, and Random Forest methods. This computational study allows us to make recommendations of how to design more efficient antibacterial drug therapies.