Abstract
Antimicrobial peptides (AMPs), especially antibacterial peptides, have been widely investigated as potential alternatives to antibiotic-based therapies. Indeed, naturally occurring and synthetic AMPs have shown promising results against a series of clinically relevant bacteria. Even so, this class of antimicrobials has continuously failed clinical trials at some point, highlighting the importance of AMP optimization. In this context, the computer-aided design of AMPs has put together crucial information on chemical parameters and bioactivities in AMP sequences, thus providing modes of prediction to evaluate the antibacterial potential of a candidate sequence before synthesis. Quantitative structure-activity relationship (QSAR) computational models, for instance, have greatly contributed to AMP sequence optimization aimed at improved biological activities. In addition to machine-learning methods, the de novo design, linguistic model, pattern insertion methods, and genetic algorithms, have shown the potential to boost the automated design of AMPs. However, how successful have these approaches been in generating effective antibacterial drug candidates? Bearing this in mind, this review will focus on the main computational strategies that have generated AMPs with promising activities against pathogenic bacteria, as well as anti-infective potential in different animal models, including sepsis and cutaneous infections. Moreover, we will point out recent studies on the computer-aided design of antibiofilm peptides. As expected from automated design strategies, diverse candidate sequences with different structural arrangements have been generated and deposited in databases. We will, therefore, also discuss the structural diversity that has been engendered.
Highlights
Peptides can be produced as part of the host defense system during infections (Hancock and Scott, 2000)
Different computational methods have been used to predict and generate optimized Antimicrobial peptides (AMPs) sequences (Table 1), a crucial question remains: are we generating effective drug candidates? Here we have focused on the primary computational methodologies applied for computationally designing AMPs (Figure 1) and analyze how effective these new drug candidates have been against bacteria, biofilms and animal infection models
This study introduces a promising strategy for the computer-aided design of improved antibiofilm peptides, it is worth noting that the modeling strategy used only classifies peptide candidates as “active” or “inactive,” but it does not consider antibiofilm potency
Summary
Peptides can be produced as part of the host defense system during infections (Hancock and Scott, 2000). A fitness function, which is often ruled by a machine-learning method when sufficient training data are available, provides guidance for AMP design toward regions in sequence space aiming at a higher predicted biological activity (e.g., antibacterial) (Fjell et al, 2012).
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