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Molecular recognition forms the basis of all protein interactions, and therefore is crucial for maintaining biological functions and pathways. It can be governed by many factors, but in case of proteins and peptides, the amino acids sequences of the interacting entities play a huge role. It is molecular recognition that…
Molecular recognition forms the basis of all protein interactions, and therefore is crucial for maintaining biological functions and pathways. It can be governed by many factors, but in case of proteins and peptides, the amino acids sequences of the interacting entities play a huge role. It is molecular recognition that helps a protein identify the correct sequences residues necessary for an interaction, among the vast number of possibilities from the combinatorial sequence space. Therefore, it is fundamental to study how the interacting amino acid sequences define the molecular interactions of proteins. In this work, sparsely sampled peptide sequences from the combinatorial sequence space were used to study the molecular recognition observed in proteins, especially monoclonal antibodies. A machine learning based approach was used to study the molecular recognition characteristics of 11 monoclonal antibodies, where a neural network (NN) was trained on data from protein binding experiments performed on high-throughput random-sequence peptide microarrays. The use of random-sequence microarrays allowed for the peptides to be sparsely sampled from sequence space. Post-training, a sequence vs. binding relationship was deduced by the NN, for each antibody. This in silico relationship was then extended to larger libraries of random peptides, as well as to the biologically relevant sequences (target antigens, and proteomes). The NN models performed well in predicting the pertinent interactions for 6 out of the 11 monoclonal antibodies, in all aspects. The interactions of the other five monoclonal antibodies could not be predicted well by the models, due to their poor recognition of the residues that were omitted from the array. Furthermore, NN predicted sequence vs. binding relationships for 3 other proteins were experimentally probed using surface plasmon resonance (SPR). This was done to explore the relationship between the observed and predicted binding to the arrays and the observed binding on different assay platforms. It was noted that there was a general motif dependent correlation between predicted and SPR-measured binding. This study also indicated that a combined reiterative approach using in silico and in vitro techniques is a powerful tool for optimizing the selectivity of the protein-binding peptides.
