Computational Methods and Tools in Antimicrobial Peptide Research.

The evolution of antibiotic-resistant bacteria is an ongoing and troubling development that has increased the number of diseases and infections that risk going untreated. There is an urgent need to develop alternative strategies and treatments to address this issue. One class of molecules that is attracting significant interest is that of antimicrobial peptides (AMPs). Their design and development has been aided considerably by the applications of molecular models, and we review these here. These methods include the use of tools to explore the relationships between their structures, dynamics, and functions and the increasing application of machine learning and molecular dynamics simulations. This review compiles resources such as AMP databases, AMP-related web servers, and commonly used techniques, together aimed at aiding researchers in the area toward complementing experimental studies with computational approaches.

Computational Design of Macrocyclic Binders of S100B(ßß): Novel Peptide Theranostics.

S100B(ßß) proteins are a family of multifunctional proteins that are present in several tissues and regulate a wide variety of cellular processes. Their altered expression levels have been associated with several human diseases, such as cancer, inflammatory disorders and neurodegenerative conditions, and hence are of interest as a therapeutic target and a biomarker. Small molecule inhibitors of S100B(ßß) have achieved limited success. Guided by the wealth of available experimental structures of S100B(ßß) in complex with diverse peptides from various protein interacting partners, we combine comparative structural analysis and molecular dynamics simulations to design a series of peptides and their analogues (stapled) as S100B(ßß) binders. The stapled peptides were subject to in silico mutagenesis experiments, resulting in optimized analogues that are predicted to bind to S100B(ßß) with high affinity, and were also modified with imaging agents to serve as diagnostic tools. These stapled peptides can serve as theranostics, which can be used to not only diagnose the levels of S100B(ßß) but also to disrupt the interactions of S100B(ßß) with partner proteins which drive disease progression, thus serving as novel therapeutics.

Macrocyclization of an all-d linear α-helical peptide imparts cellular permeability.

Peptide-based molecules hold great potential as targeted inhibitors of intracellular protein-protein interactions (PPIs). Indeed, the vast diversity of chemical space conferred through their primary, secondary and tertiary structures allows these molecules to be applied to targets that are typically deemed intractable via small molecules. However, the development of peptide therapeutics has been hindered by their limited conformational stability, proteolytic sensitivity and cell permeability. Several contemporary peptide design strategies are aimed at addressing these issues. Strategic macrocyclization through optimally placed chemical braces such as olefinic hydrocarbon crosslinks, commonly referred to as staples, may improve peptide properties by (i) restricting conformational freedom to improve target affinities, (ii) improving proteolytic resistance, and (iii) enhancing cell permeability. As a second strategy, molecules constructed entirely from d-amino acids are hyper-resistant to proteolytic cleavage, but generally lack conformational stability and membrane permeability. Since neither approach is a complete solution, we have combined these strategies to identify the first examples of all-d α-helical stapled and stitched peptides. As a template, we used a recently reported all d-linear peptide that is a potent inhibitor of the p53-Mdm2 interaction, but is devoid of cellular activity. To design both stapled and stitched all-d-peptide analogues, we used computational modelling to predict optimal staple placement. The resultant novel macrocyclic all d-peptide was determined to exhibit increased α-helicity, improved target binding, complete proteolytic stability and, most notably, cellular activity.

DStabilize: A Web Resource to Generate Mirror Images of Biomolecules.

Peptides comprising D-amino acids have been shown to be resistant to proteolysis. This makes them potential candidates as probes of cellular interactions, notably protein-biomolecule interactions. However, the empirical conversion of the amino acids that constitute a peptide from L-forms to D-forms will result in abrogation of the normal interactions made by the L-amino acids due to side-chain orientation changes that are associated with the changes in chirality. These interactions can be preserved by reversing the sequence of the D-peptide. We present a web server (http://dstabilize.bii.a-star.edu.sg/) that allows users to convert between L-proteins and D-proteins and for sequence reversal of D-peptides, along with the capability of performing other empirical geometric transforms. This resource allows the user to generate structures of interest easily for subsequent in silico processing.

Comparison of Charge Derivation Methods Applied to Amino Acid Parameterization.

When using non-natural amino acids in computational simulations of proteins, it is necessary to ensure appropriate parameterization of the new amino acids toward the creation of appropriate input files. In particular, the charges on the atoms may have to be derived de novo and ad hoc for the new species. As there are many variables in the charge derivation process, an investigation was devised to compare different approaches and determine their effect on simulations. This was done with the purpose to identify the methods which produced results compatible with the existing parameters. It was found in this study that all analyzed charge derivation methods reproduce with sufficient accuracy the literature values and can be used with confidence when parameterizing novel species.