Abstract
Beta-lactamase (β-lactamase) produced by different bacteria confers resistance against β-lactam-containing drugs. The gene encoding β-lactamase is plasmid-borne and can easily be transferred from one bacterium to another during conjugation. By such transformations, the recipient also acquires resistance against the drugs of the β-lactam family. β-Lactam antibiotics play a vital significance in clinical treatment of disastrous diseases like soft tissue infections, gonorrhoea, skin infections, urinary tract infections, and bronchitis. Herein, we report a prediction classifier named as βLact-Pred for the identification of β-lactamase proteins. The computational model uses the primary amino acid sequence structure as its input. Various metrics are derived from the primary structure to form a feature vector. Experimentally determined data of positive and negative beta-lactamases are collected and transformed into feature vectors. An operating algorithm based on the artificial neural network is used by integrating the position relative features and sequence statistical moments in PseAAC for training the neural networks. The results for the proposed computational model were validated by employing numerous types of approach, i.e., self-consistency testing, jackknife testing, cross-validation, and independent testing. The overall accuracy of the predictor for self-consistency, jackknife testing, cross-validation, and independent testing presents 99.76%, 96.07%, 94.20%, and 91.65%, respectively, for the proposed model. Stupendous experimental results demonstrated that the proposed predictor “βLact-Pred” has surpassed results from the existing methods.
Highlights
Methods and MaterialsTo develop a vigorous computational model, it is prerequisite to acknowledge an accurate and explicit scale dataset for the sake of training and testing the model
Muhammad Adeel Ashraf,1 Yaser Daanial Khan,1 Bilal Shoaib,2,3 Muhammad Adnan Khan,4 Faheem Khan,5 and T
The recipient acquires resistance against the drugs of the β-lactam family. β-Lactam antibiotics play a vital significance in clinical treatment of disastrous diseases like soft tissue infections, gonorrhoea, skin infections, urinary tract infections, and bronchitis
Summary
To develop a vigorous computational model, it is prerequisite to acknowledge an accurate and explicit scale dataset for the sake of training and testing the model. Reverse position relative index matrix (RPRIM) is used to extract hidden features from protein sequences which have the ambiguity of homologous sequences. Like PRIM, the dimension of the RPRIM matrix is curtailed by computing the three moments, i.e., raw, central, and Hahn. Frequency matrix did not contain the information related to the occurrence of amino acid residues in a polypeptide chain. Accumulative absolute position incidence vector (AAPIV) is used to compute the information related to the position of amino acid residue in the polypeptide chain. A vector with 20 elements in which each component encompasses a numerical ordered value to represent the amino acid position relevant information from the residue [20]. Ese methods help in extracting position and composition relative features from the amino acid sequence which is a very pivotal aspect while dealing with proteins. The frequency of amino acids in molecule, position relative occurrence of amino acids, composition of a specific peptide, and absolute positioning of residues
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