Class A scavenger receptors, including scavenger receptor A and macrophage receptor with collagenous structure, are surface proteins that bind modified endogenous ligands and bacterial components. As these receptors play a key role in innate immunity, they are associated with a range of infectious diseases such as pneumonia. Thus, understanding the physical properties of these receptors will provide insight for the development of novel therapeutics. Macrophage receptor with collagenous structure is amongst the least well-characterized members of this family and is expressed in macrophages to mediate pathogen recognition. Our investigation addresses the development of an efficient and highly accurate bioinformatics technique through combinatorial usage of Aligned Pattern Clustering, and Multiple Sequence Alignment Pattern Retrieval Program for novel motif discovery in the macrophage scavenger receptor. By utilizing scavenger receptor A and its validated motifs as a model, we successfully validated the feasibility of this bioinformatics technique with a minimum sensitivity of 54% and positive predictive value of 60%. As a subsequent validation of the newly discovered motifs, the top sub-sequences of macrophage receptor with collagenous structure, selected by both programs, were analyzed for their biological functionality. Here, we propose that RGRAE, VFCRMLG, EDAGVE and WGTICDD motifs in the scavenger receptor cysteine-rich domain play an important role in pathogen recognition.