scholarly journals Activity of Antimicrobial Peptides Decreases with Increased Cell Membrane Crossing Free Energy Cost

2018 ◽  
Author(s):  
Rongfeng Zou ◽  
Xiaomin Zhu ◽  
Yaoquan Tu ◽  
Junchen Wu ◽  
Markita P. Landry
Keyword(s):  
Free Energy ◽  
Antibacterial Activity ◽  
Cell Membrane ◽  
Antimicrobial Peptides ◽  
Intermolecular Interactions ◽  
Energy Cost ◽  
Bacterial Resistance ◽  
Free Energy Calculations ◽  
Intramolecular Interactions ◽  
Promising Alternative

ABSTRACTAntimicrobial peptides (AMPs) are a promising alternative to mitigating bacterial infections in light of increasing bacterial resistance to antibiotics. However, predicting, understanding, and controlling the antibacterial activity of AMPs remains a significant challenge. While peptide intramolecular interactions are known to modulate AMP antimi-crobial activity, peptide intermolecular interactions remain elusive in their impact on peptide bioactivity. Herein, we test the relationship between AMP intermolecular interactions and antibacterial efficacy by controlling AMP intermolecular hydrophobic and hydrogen bonding interactions. Molecular dynamics simulations and Gibbs free energy calculations in concert with experimental assays show that increasing intermolecular interactions via inter-peptide aggregation increases the energy cost for the peptide to cross the bacterial cell membrane, which in turn decreases the AMP antibacterial activity. Our findings provide a route for predicting and controlling the antibacterial activity of AMPs against Gramnegative bacteria via reductions of intermolecular AMP interactions.

scholarly journals Antimicrobial Peptides: Mechanisms of Action and Resistance

2016 ◽  
Vol 96 (3) ◽  
pp. 254-260 ◽  
Author(s):  
B. Bechinger ◽  
S.-U. Gorr
Keyword(s):  
Cell Wall ◽  
Cell Membrane ◽  
Antimicrobial Peptides ◽  
Bacterial Resistance ◽  
Rare Event ◽  
Mechanisms Of Action ◽  
Current Evidence ◽  
Aggregation State ◽  
Host Defense Peptides ◽  
Bacterial Membranes

More than 40 antimicrobial peptides and proteins (AMPs) are expressed in the oral cavity. These AMPs have been organized into 6 functional groups, 1 of which, cationic AMPs, has received extensive attention in recent years for their promise as potential antibiotics. The goal of this review is to describe recent advances in our understanding of the diverse mechanisms of action of cationic AMPs and the bacterial resistance against these peptides. The recently developed peptide GL13K is used as an example to illustrate many of the discussed concepts. Cationic AMPs typically exhibit an amphipathic conformation, which allows increased interaction with negatively charged bacterial membranes. Peptides undergo changes in conformation and aggregation state in the presence of membranes; conversely, lipid conformation and packing can adapt to the presence of peptides. As a consequence, a single peptide can act through several mechanisms depending on the peptide’s structure, the peptide:lipid ratio, and the properties of the lipid membrane. Accumulating evidence shows that in addition to acting at the cell membrane, AMPs may act on the cell wall, inhibit protein folding or enzyme activity, or act intracellularly. Therefore, once a peptide has reached the cell wall, cell membrane, or its internal target, the difference in mechanism of action on gram-negative and gram-positive bacteria may be less pronounced than formerly assumed. While AMPs should not cause widespread resistance due to their preferential attack on the cell membrane, in cases where specific protein targets are involved, the possibility exists for genetic mutations and bacterial resistance. Indeed, the potential clinical use of AMPs has raised the concern that resistance to therapeutic AMPs could be associated with resistance to endogenous host-defense peptides. Current evidence suggests that this is a rare event that can be overcome by subtle structural modifications of an AMP.

scholarly journals Activity of Antimicrobial Peptide Aggregates Decreases with Increased Cell Membrane Embedding Free Energy Cost

Biochemistry ◽  
2018 ◽  
Vol 57 (18) ◽  
pp. 2606-2610 ◽  
Author(s):  
Rongfeng Zou ◽  
Xiaomin Zhu ◽  
Yaoquan Tu ◽  
Junchen Wu ◽  
Markita P. Landry
Keyword(s):  
Free Energy ◽  
Cell Membrane ◽  
Antimicrobial Peptide ◽  
Energy Cost

scholarly journals Antimicrobial Peptides with Antibacterial Activity against Vancomycin-Resistant Staphylococcus aureus Strains: Classification, Structures, and Mechanisms of Action

2021 ◽  
Vol 22 (15) ◽  
pp. 7927
Author(s):  
Isabella Hernández-Aristizábal ◽  
Iván Darío Ocampo-Ibáñez
Keyword(s):  
Staphylococcus Aureus ◽  
Antibacterial Activity ◽  
Antimicrobial Peptides ◽  
Structural Characteristics ◽  
Modern Medicine ◽  
Promising Alternative ◽  
Resistant Strains ◽  
Vancomycin Resistant ◽  
Considerable Morbidity ◽  
Conventional Antibiotics

The emergence of bacteria resistant to conventional antibiotics is of great concern in modern medicine because it renders ineffectiveness of the current empirical antibiotic therapies. Infections caused by vancomycin-resistant Staphylococcus aureus (VRSA) and vancomycin-intermediate S. aureus (VISA) strains represent a serious threat to global health due to their considerable morbidity and mortality rates. Therefore, there is an urgent need of research and development of new antimicrobial alternatives against these bacteria. In this context, the use of antimicrobial peptides (AMPs) is considered a promising alternative therapeutic strategy to control resistant strains. Therefore, a wide number of natural, artificial, and synthetic AMPs have been evaluated against VRSA and VISA strains, with great potential for clinical application. In this regard, we aimed to present a comprehensive and systematic review of research findings on AMPs that have shown antibacterial activity against vancomycin-resistant and vancomycin-intermediate resistant strains and clinical isolates of S. aureus, discussing their classification and origin, physicochemical and structural characteristics, and possible action mechanisms. This is the first review that includes all peptides that have shown antibacterial activity against VRSA and VISA strains exclusively.

Large-Scale Assessment of Binding Free Energy Calculations in Active Drug Discovery Projects

2020 ◽  
Author(s):  
Christina Schindler ◽  
Hannah Baumann ◽  
Andreas Blum ◽  
Dietrich Böse ◽  
Hans-Peter Buchstaller ◽  
...  
Keyword(s):  
Free Energy ◽  
Drug Discovery ◽  
Large Scale ◽  
Active Drug ◽  
Binding Free Energy ◽  
Free Energy Calculations ◽  
Energy Calculation ◽  
Large Scale Assessment ◽  
New Public ◽  
Binding Free Energy Calculations

Here we present an evaluation of the binding affinity prediction accuracy of the free energy calculation method FEP+ on internal active drug discovery projects and on a large new public benchmark set.<br>

Automated Assessment of Binding Affinity via Alchemical Free Energy Calculations

2020 ◽  
Author(s):  
Maximilian Kuhn ◽  
Stuart Firth-Clark ◽  
Paolo Tosco ◽  
Antonia S. J. S. Mey ◽  
Mark Mackey ◽  
...  
Keyword(s):  
Free Energy ◽  
Protein Structures ◽  
Free Energy Calculations ◽  
Distinct Advantage ◽  
Binding Affinities ◽  
Structure Based Drug Design ◽  
Energy Calculations ◽  
Kendall’S Τ ◽  
Experimental Values ◽  
Better Than

Free energy calculations have seen increased usage in structure-based drug design. Despite the rising interest, automation of the complex calculations and subsequent analysis of their results are still hampered by the restricted choice of available tools. In this work, an application for automated setup and processing of free energy calculations is presented. Several sanity checks for assessing the reliability of the calculations were implemented, constituting a distinct advantage over existing open-source tools. The underlying workflow is built on top of the software Sire, SOMD, BioSimSpace and OpenMM and uses the AMBER14SB and GAFF2.1 force fields. It was validated on two datasets originally composed by Schrödinger, consisting of 14 protein structures and 220 ligands. Predicted binding affinities were in good agreement with experimental values. For the larger dataset the average correlation coefficient Rp was 0.70 ± 0.05 and average Kendall’s τ was 0.53 ± 0.05 which is broadly comparable to or better than previously reported results using other methods. <br>

SAMPL7 TrimerTrip Host-Guest Binding Poses and Binding Affinities from Spherical-Coordinates-Biased Simulations

2020 ◽  
Author(s):  
Zhaoxi Sun
Keyword(s):  
Free Energy ◽  
Computational Modelling ◽  
Free Energy Calculations ◽  
Energy Estimates ◽  
Binding Affinities ◽  
Spherical Coordinates ◽  
Collective Variables ◽  
Guest Binding ◽  
Starting Point ◽  
Sampl Challenge

Host-guest binding remains a major challenge in modern computational modelling. The newest 7<sup>th</sup> statistical assessment of the modeling of proteins and ligands (SAMPL) challenge contains a new series of host-guest systems. The TrimerTrip host binds to 16 structurally diverse guests. Previously, we have successfully employed the spherical coordinates as the collective variables coupled with the enhanced sampling technique metadynamics to enhance the sampling of the binding/unbinding event, search for possible binding poses and predict the binding affinities in all three host-guest binding cases of the 6<sup>th</sup> SAMPL challenge. In this work, we employed the same protocol to investigate the TrimerTrip host in the SAMPL7 challenge. As no binding pose is provided by the SAMPL7 host, our simulations initiate from randomly selected configurations and are proceeded long enough to obtain converged free energy estimates and search for possible binding poses. The predicted binding affinities are in good agreement with the experimental reference, and the obtained binding poses serve as a nice starting point for end-point or alchemical free energy calculations.

Automated, Accurate, and Scalable Relative Protein-Ligand Binding Free Energy Calculations using Lambda Dynamics

2020 ◽  
Author(s):  
E. Prabhu Raman ◽  
Thomas J. Paul ◽  
Ryan L. Hayes ◽  
Charles L. Brooks III
Keyword(s):  
Free Energy ◽  
Ligand Binding ◽  
Binding Affinity ◽  
Binding Free Energy ◽  
Computational Cost ◽  
Combinatorial Libraries ◽  
Free Energy Calculations ◽  
Lead Optimization ◽  
Efficient Estimation ◽  
Lead Compound

<p>Accurate predictions of changes to protein-ligand binding affinity in response to chemical modifications are of utility in small molecule lead optimization. Relative free energy perturbation (FEP) approaches are one of the most widely utilized for this goal, but involve significant computational cost, thus limiting their application to small sets of compounds. Lambda dynamics, also rigorously based on the principles of statistical mechanics, provides a more efficient alternative. In this paper, we describe the development of a workflow to setup, execute, and analyze Multi-Site Lambda Dynamics (MSLD) calculations run on GPUs with CHARMm implemented in BIOVIA Discovery Studio and Pipeline Pilot. The workflow establishes a framework for setting up simulation systems for exploratory screening of modifications to a lead compound, enabling the calculation of relative binding affinities of combinatorial libraries. To validate the workflow, a diverse dataset of congeneric ligands for seven proteins with experimental binding affinity data is examined. A protocol to automatically tailor fit biasing potentials iteratively to flatten the free energy landscape of any MSLD system is developed that enhances sampling and allows for efficient estimation of free energy differences. The protocol is first validated on a large number of ligand subsets that model diverse substituents, which shows accurate and reliable performance. The scalability of the workflow is also tested to screen more than a hundred ligands modeled in a single system, which also resulted in accurate predictions. With a cumulative sampling time of 150ns or less, the method results in average unsigned errors of under 1 kcal/mol in most cases for both small and large combinatorial libraries. For the multi-site systems examined, the method is estimated to be more than an order of magnitude more efficient than contemporary FEP applications. The results thus demonstrate the utility of the presented MSLD workflow to efficiently screen combinatorial libraries and explore chemical space around a lead compound, and thus are of utility in lead optimization.</p>

Reaction-based Enumeration, Active Learning, and Free Energy Calculations to Rapidly Explore Synthetically Tractable Chemical Space and Optimize Potency of Cyclin Dependent Kinase 2 Inhibitors

2019 ◽  
Author(s):  
Kyle Konze ◽  
Pieter Bos ◽  
Markus Dahlgren ◽  
Karl Leswing ◽  
Ivan Tubert-Brohman ◽  
...  
Keyword(s):  
Free Energy ◽  
Drug Discovery ◽  
Active Learning ◽  
Large Scale ◽  
Chemical Space ◽  
Population Based ◽  
Free Energy Calculations ◽  
Computational Technique ◽  
Cyclin Dependent Kinase ◽  
Energy Calculations

We report a new computational technique, PathFinder, that uses retrosynthetic analysis followed by combinatorial synthesis to generate novel compounds in synthetically accessible chemical space. Coupling PathFinder with active learning and cloud-based free energy calculations allows for large-scale potency predictions of compounds on a timescale that impacts drug discovery. The process is further accelerated by using a combination of population-based statistics and active learning techniques. Using this approach, we rapidly optimized R-groups and core hops for inhibitors of cyclin-dependent kinase 2. We explored greater than 300 thousand ideas and identified 35 ligands with diverse commercially available R-groups and a predicted IC<sub>50</sub> < 100 nM, and four unique cores with a predicted IC<sub>50</sub> < 100 nM. The rapid turnaround time, and scale of chemical exploration, suggests that this is a useful approach to accelerate the discovery of novel chemical matter in drug discovery campaigns.

Reaction-based Enumeration, Active Learning, and Free Energy Calculations to Rapidly Explore Synthetically Tractable Chemical Space and Optimize Potency of Cyclin Dependent Kinase 2 Inhibitors

2019 ◽  
Author(s):  
Kyle Konze ◽  
Pieter Bos ◽  
Markus Dahlgren ◽  
Karl Leswing ◽  
Ivan Tubert-Brohman ◽  
...  
Keyword(s):  
Free Energy ◽  
Drug Discovery ◽  
Active Learning ◽  
Large Scale ◽  
Chemical Space ◽  
Population Based ◽  
Free Energy Calculations ◽  
Computational Technique ◽  
Cyclin Dependent Kinase ◽  
Energy Calculations

We report a new computational technique, PathFinder, that uses retrosynthetic analysis followed by combinatorial synthesis to generate novel compounds in synthetically accessible chemical space. Coupling PathFinder with active learning and cloud-based free energy calculations allows for large-scale potency predictions of compounds on a timescale that impacts drug discovery. The process is further accelerated by using a combination of population-based statistics and active learning techniques. Using this approach, we rapidly optimized R-groups and core hops for inhibitors of cyclin-dependent kinase 2. We explored greater than 300 thousand ideas and identified 35 ligands with diverse commercially available R-groups and a predicted IC<sub>50</sub> < 100 nM, and four unique cores with a predicted IC<sub>50</sub> < 100 nM. The rapid turnaround time, and scale of chemical exploration, suggests that this is a useful approach to accelerate the discovery of novel chemical matter in drug discovery campaigns.

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