Surface mediated cooperative interactions of drugs enhance mechanical forces for antibiotic action

The alarming increase of pathogenic bacteria that are resistant to multiple antibiotics is now recognized as a major health issue fuelling demand for new drugs. Bacterial resistance is often caused by molecular changes at the bacterial surface, which alter the nature of specific drug-target interactions. Here, we identify a novel mechanism by which drug-target interactions in resistant bacteria can be enhanced. We examined the surface forces generated by four antibiotics; vancomycin, ristomycin, chloroeremomycin and oritavancin against drug-susceptible and drug-resistant targets on a cantilever and demonstrated significant differences in mechanical response when drug-resistant targets are challenged with different antibiotics although no significant differences were observed when using susceptible targets. Remarkably, the binding affinity for oritavancin against drug-resistant targets (70 nM) was found to be 11,000 times stronger than for vancomycin (800 μM), a powerful antibiotic used as the last resort treatment for streptococcal and staphylococcal bacteria including methicillin-resistant Staphylococcus aureus (MRSA). Using an exactly solvable model, which takes into account the solvent and membrane effects, we demonstrate that drug-target interactions are strengthened by pronounced polyvalent interactions catalyzed by the surface itself. These findings further enhance our understanding of antibiotic mode of action and will enable development of more effective therapies.


Supplementary
n/a K surf value not available.       Surface plasmon resonance (SPR) sensor chip functionalization procedure. We applied the same cantilever functionalization procedure to the SPR sensor chip functionalization. The plain

Supplementary
Au-coated SPR chips were incubated in 100 µl ethanolic solutions of alkanethiol SAMs of VSR, VRR and PEG at a total concentration of 1 µM for 20 min, washed three times with ethanol and buffer solution before use.

Statistical data analysis
The differential stress measurements obtained from cantilever chips are typically associated with multiple parameters including number of repeated measurements, concentration and the number of cantilevers where each array has eight individual cantilevers. The statistical analysis was found to be essential for replacing a vast quantity of data with numbers such as the averages and standard deviations. To obtain the statistical summary of the differential stresses of drug susceptible (VSR) and drug resistant (VRR) targets exposed to antibiotics in each concentration, we employed a range of formulae. To express the arithmetic mean of the differential equilibrium stress data (σ eq ), all the differential stress data (σ diff ) in each concentration were added up and then divided by the total number (n) of experiments using the expression To calculate the standard deviation of the stress data (σ), we used the following expression Subsequently, the standard error (SE) was calculated based on the standard deviation of the stress data using the expression To determine the confidence intervals where the ranges of values include the true distribution of the stress data, we performed statistical analysis using commercial IBM SPSS Statistics software (IBM Corporation) and the results are summarized in the Supplementary So that Here [N] is the total number of molecules in the solution and K 1 is the complex aggregation constant where the dimensionality is the inverse of the dissociation constant of the tabulated values in Table S1.

Single binding mechanism
For this case, we consider that the empty sites (n Here n s is the total number of binding sites on the surface, n o is the total number of surface binding sites occupied by molecules, n m is the total number of bound monovalent ligands at a surface and K 2 is the surface ligand-target binding strength for ligand molecules binding monovalently.

Multiple binding mechanisms
Equations (7), (8) and (9) (10) where θ = (n o /n s ) is the fraction of the surface occupied by the binding ligands and  =  max θ.

Case II: Effect of surface on polyvalent interactions
In this case we propose that the near-membrane layer rather than solvent effects are dominant factors in determining pharmacological activities of drugs. This is because membrane receptors are polyvalent and such polyvalence certainly should contribute to the increased binding affinity when going from solution to surface targets. Thus, the subsequent strengthening of surface interactions can be defined by an additional five equilibrium equations as summarized.
Here n m BL is the total number of monovalent ligands at a surface boundary layer and n p BL is the corresponding number if they are polyvalent. K 4 is the surface complex aggregation constant. K 5 is the surface ligand-target binding strength, when ligand molecules follow monovalent binding mechanism and K 6 is the corresponding constant if they undergo multivalent interactions.
Equations (11)-(15) can be solved analytically to yield Here  and  are renormalized binding coefficients when ligand molecules follow monovalent binding or multivalent interactions and are defined as 2K K K K K K and

Deductions
If we consider only monovalent binding, equation (17) (3) and (6) in the main text.

Definition of a near membrane layer
To define a near membrane layer effect, we used the Debye screening theory such that when a charged molecule is placed in a buffer solution, the electrostatic interactions can cause counterions to surround the molecule, partially offsetting the molecular charge. The simple approximation of the resulting screened electric potential from a point charge Q is