PubMed · 763311
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B J Sullivan. 1979-03-29. IgA protease.. https://doi.org/10.1056/nejm197903293001319
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Protein-based antibacterials such as bacteriophage endolysins offer a targeted therapeutic strategy against Gram-positive pathogens. However, prioritizing the most effective candidates from the large sequence diversity available remains a significant challenge. Here we present a standardized computational-experimental benchmarking framework that evaluates seven phage-derived endolysin variants (E1, E2, E3, E7, E10, E12, and E15) identified from Bacillus genomes. We combined molecular docking and residue-level interaction mapping against muramyl dipeptide (MDP), a minimal conserved peptidoglycan motif, with 1000-ns molecular dynamics simulations, MM/PBSA binding free-energy estimation, and matched functional inhibition assays against Staphylococcus aureus and Micrococcus luteus. Computational analyses revealed generally favorable MDP recognition across variants, albeit with notable differences in contact patterns and complex stability profiles. Experimental screening identified E2 as the most potent antibacterial agent against both species, while E7 and E1 performed strongly in selected computational metrics. Integrated analysis showed only modest correlations between computational descriptors of fragment recognition/stability and observed antibacterial performance. This study establishes a practical comparative benchmarking platform for endolysin candidate prioritization, nominates E2 and E7 as promising candidates for further development, and highlights E1 as a potential structural scaffold for rational engineering, while explicitly demonstrating both the utility and the current limitations of using minimal peptidoglycan fragments as proxies for full cell-wall recognition in lysin benchmarking.
We have noted consistent structural similarities among unrelated proteases. In comparison with other proteins of similar size, proteases have smaller than average surface areas, smaller radii of gyration, and higher C(alpha) densities. These findings imply that proteases are, as a group, more tightly packed than other proteins. There are also notable differences in secondary structure content between these two groups of proteins: proteases have fewer helices and more loops. We speculate that both high packing density and low alpha-helical content coevolved in proteases to avoid autolysis. By using the structural parameters that seem to show some separation between proteases and nonproteases, a neural network has been trained to predict protease function with over 86% accuracy. Moreover, it is possible to identify proteases whose folds were not represented during training. Similar structural analyses may be useful for identifying other classes of proteins and may be of great utility for categorizing the flood of structures soon to flow from structural genomics initiatives.
Investigators have been studying the expression and activity of proteases in the final steps of tumor progression, invasion and metastasis, for the past 30 years. Recent studies, however, indicate that proteases are involved earlier in progression, e.g., in tumor growth both at the primary and metastatic sites. Extracellular proteases may co-operatively influence matrix degradation and tumor cell invasion through proteolytic cascades, with individual proteases having distinct roles in tumor growth, invasion, migration and angiogenesis. In this review, we use cathepsin B as an example to examine the involvement of proteases in tumor progression and metastasis. We discuss the effect of interactions among tumor cells, stromal cells, and the extracellular matrix on the regulation of protease expression. Further elucidation of the role of proteases in cancer will allow us to design more effective inhibitors and novel protease-based drugs for clinical use.