New approaches to identification of bacterial pathogens by surface enhanced laser desorption/ionization time of flight mass spectrometry in concert with artificial neural networks, with special reference to Neisseria gonorrhoeae

Abstract

Surface enhanced laser desorption/ionization-time of flight mass spectrometry (SELDI-TOF MS) has been applied in large numbers of oncological studies but the microbiological field has not been extensively explored to date. This paper describes the application of SELDI-TOF MS in concert with a multi-layer perceptron artificial neural network (ANN) with a back propagation algorithm for the identification of Neisseria gonorrhoeae. N. gonorrhoeae, the aetiological agent of gonorrhoea, is the second most common sexually transmitted disease in the UK and USA. Analysis of over 350 strains of N. gonorrhoeae and closely related species by SELDI-TOF MS facilitated the design of an ANN model and revealed 20 ion peak descriptors of positive, negative and secondary nature that were paramount for the identification of the pathogen. The model performed with over 96 % efficiency when based on these 20 ion peak descriptors and exhibited a sensitivity of 95.7 % and a specificity of 97.1 %, with an area under the curve value of 0.996. The technology has the potential to link several ANN models for a comprehensive rapid identification platform for clinically important pathogens.