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“ Peptides from the top quartile (3-48) were compared to peptides from the 2nd (24,897—24,944) and 3rd (74,655—74,680) quartiles. Columns headings: peptide ID indicates the control Bac2A or the test peptide by rank number. Columns give MIC values in “M and g/mL in parentheses, measured in 3—5 replicates for A, P. aeruginosa wild type strain H103; B, C, D, P. aeruginosa multidrug-resistant strains from Brazil 9, 198, and 213, respectively; E, F, G, P. aeruginosa Liverpool epidemic strains LES400, H1030, and H1027, respectively; H, P. maltophilia ATCC13637; I, constitutive class C chromosomal /3-lactamase expressing E. cloacae 218R; J, K, extended-spectrum /-lactamase-producing (ESBL) E. coli (clinical strains 63103 and 64771); L, M, ESBL-resistant K. pneumonia (clinical strains 61962 and 63575); N, S. aureus ATCC25923; O, methicillin- resistant S. aureus strain C623; P, E. faecalis ATCC29212; Q, R, vancomycin-resistant E. faecalis [clinical isolates w61950 (VanA) and £43559 (VanB)]; S,T, vancomycin-resistant E. faecium [clinical isolates mic80 (VanA) and t62764 (VanB)]. Table 3. Activities against Multiresistant Superbugs of Selected Peptides Predicted through QSAR Analysis Compared to the Peptide Bac2A“

Table 3 “ Peptides from the top quartile (3-48) were compared to peptides from the 2nd (24,897—24,944) and 3rd (74,655—74,680) quartiles. Columns headings: peptide ID indicates the control Bac2A or the test peptide by rank number. Columns give MIC values in “M and g/mL in parentheses, measured in 3—5 replicates for A, P. aeruginosa wild type strain H103; B, C, D, P. aeruginosa multidrug-resistant strains from Brazil 9, 198, and 213, respectively; E, F, G, P. aeruginosa Liverpool epidemic strains LES400, H1030, and H1027, respectively; H, P. maltophilia ATCC13637; I, constitutive class C chromosomal /3-lactamase expressing E. cloacae 218R; J, K, extended-spectrum /-lactamase-producing (ESBL) E. coli (clinical strains 63103 and 64771); L, M, ESBL-resistant K. pneumonia (clinical strains 61962 and 63575); N, S. aureus ATCC25923; O, methicillin- resistant S. aureus strain C623; P, E. faecalis ATCC29212; Q, R, vancomycin-resistant E. faecalis [clinical isolates w61950 (VanA) and £43559 (VanB)]; S,T, vancomycin-resistant E. faecium [clinical isolates mic80 (VanA) and t62764 (VanB)]. Table 3. Activities against Multiresistant Superbugs of Selected Peptides Predicted through QSAR Analysis Compared to the Peptide Bac2A“

Related Figures (7)

Figure 1. General workflow for QSAR modeling of antimicrobial peptides. The modeling proceeded in two phases, learning and prediction. In the learning phase (left column), the 3D structures of experimentally tested peptides were estimated, and the QSAR descriptors were calculated on the basis of these structures. Artificial neural networks (ANNs) were trained to classify peptides as active or inactive on the basis of the descripto1 values and experimental activity level. ANNs involve a nonlinear transformation of the inputs and are organized in three layers, with connections and function inspired by natural neuron connections (center column). In the prediction phase (right column), the activities of virtual peptides were predicted using calculated descriptor values (based on estimated 3D structures) as input to the trained ANNs.
Figure 1. General workflow for QSAR modeling of antimicrobial peptides. The modeling proceeded in two phases, learning and prediction. In the learning phase (left column), the 3D structures of experimentally tested peptides were estimated, and the QSAR descriptors were calculated on the basis of these structures. Artificial neural networks (ANNs) were trained to classify peptides as active or inactive on the basis of the descripto1 values and experimental activity level. ANNs involve a nonlinear transformation of the inputs and are organized in three layers, with connections and function inspired by natural neuron connections (center column). In the prediction phase (right column), the activities of virtual peptides were predicted using calculated descriptor values (based on estimated 3D structures) as input to the trained ANNs.
Figure 2. Transmission electron micrographs (TEM) and scanning electron micrographs (SEM) of P. aeruginosa. TEM and SEM are shown for control untreated (TEM A, SEM A) and Bac2A-treated (TEM B, SEM B,C). Bac2A concentration was at the MIC. Bacteria were incubated with Bac2A for 1 h at 37 °C before fixation and preparation for TEM or SEM. Scale bar is 100 nm (TEM A and B, SEM C) or 500 nm (SEM A and B).
Figure 2. Transmission electron micrographs (TEM) and scanning electron micrographs (SEM) of P. aeruginosa. TEM and SEM are shown for control untreated (TEM A, SEM A) and Bac2A-treated (TEM B, SEM B,C). Bac2A concentration was at the MIC. Bacteria were incubated with Bac2A for 1 h at 37 °C before fixation and preparation for TEM or SEM. Scale bar is 100 nm (TEM A and B, SEM C) or 500 nm (SEM A and B).
Figure 3. Luminescence profile of a dilution series for three peptides- .The luminescences for three peptides having high, medium (control peptide), and low activity are shown. Luminescence and concentrations were scaled to maximum of 1.0. The X-axis value equivalent to the point where the horizontal line at luminescence of 0.5 crossed the fitted curves indicated the relative ICs) value for each peptide.
Figure 3. Luminescence profile of a dilution series for three peptides- .The luminescences for three peptides having high, medium (control peptide), and low activity are shown. Luminescence and concentrations were scaled to maximum of 1.0. The X-axis value equivalent to the point where the horizontal line at luminescence of 0.5 crossed the fitted curves indicated the relative ICs) value for each peptide.
“Numbers of peptides with various levels of antibacterial activity are shown. Q1, top of Ist quartile; Q2, top of 2nd quartile; Q3, bottom of 3rd quartile; Q4, bottom of 4th quartile. Rel ICso is the relative ICso, the ratio of the ICs» for the experimental peptide to the ICs) of Bac2A. Peptides of which the highest concentration failed to reduce the luminescence by at least 50% were identified as inactive. Table 1. Activities of Peptides from Training Sets and Quartiles in the 100,000 Test Set*
“Numbers of peptides with various levels of antibacterial activity are shown. Q1, top of Ist quartile; Q2, top of 2nd quartile; Q3, bottom of 3rd quartile; Q4, bottom of 4th quartile. Rel ICso is the relative ICso, the ratio of the ICs» for the experimental peptide to the ICs) of Bac2A. Peptides of which the highest concentration failed to reduce the luminescence by at least 50% were identified as inactive. Table 1. Activities of Peptides from Training Sets and Quartiles in the 100,000 Test Set*
Table 2. Predicted Activity Rank and Experimental ICs) Values for Selected Test Peptides® “ Forty peptides are shown from the 200 total candidate peptides. Hydrophobic moment is given using the Eisenberg scale.
Table 2. Predicted Activity Rank and Experimental ICs) Values for Selected Test Peptides® “ Forty peptides are shown from the 200 total candidate peptides. Hydrophobic moment is given using the Eisenberg scale.
Related topics:
Artificial IntelligenceOrganic ChemistryMachine LearningQuantitative Structure Activity RelationshipAnti-Bacterial AgentsAntimicrobial Cationic PeptidesMicrobial Sensitivity Tests
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