Self-assembling polyhedral protein biomaterials have gained attention as engineering targets owing to their naturally evolved sophisticated functions, ranging from protecting macromolecules from the environment to spatially controlling biochemical reactions. Precise computational design of de novo protein polyhedra is possible through two main types of approaches: methods from first principles, using physical and geometrical rules, and more recent data-driven methods based on artificial intelligence (AI), including deep learning (DL). Here, we retrospect first principle-and AI-based approaches for designing finite polyhedral protein assemblies, as well as advances in the structure prediction of such assemblies. We further highlight the possible applications of these materials and explore how the presented approaches can be combined to overcome current challenges and to advance the design of functional protein-based biomaterials.
Acinetobactor baumannii is Gram negative bacillus is an opportunistic pathogen responsible for worldwide nosocomial infections. Certain strains of this multidrug resistant bacteria are resistant to available antibiotics and the development of new active antimicrobial and antibiofilm agents are the need of the present. The biofilm formation depends on the filamentous organelle pili which are assembled by the CsuA/BABCDE chaperon usher system. TheCsu C protein of the chaperone usher system in Acenitobacter baumannii is a potential target for the design of drugs and antimicrobials. Plant obtained natural products have proven to be effective compounds with unique properties, thus making them safer drug candidates and efficient antibiotic adjuvants. This paper demonstrates the molecular interaction of Csu C with five phytochemical compounds namely berberine, ellagic acid, eugenol, carvacrol and curcumin. The molecular docking and virtual screening results revealed all compounds to exhibit good docking scores. Among the five compounds tested, ellagic acid showed the most effective binding activity -8.8 kJ with Csu C indicating its ability to inhibit the formation of Acenitobacter biofilms. The high docking score of ellagic acid suggested that this compound may bind the active site of Csu C effectively and the important amino acid residues in the active region of the protein should be considered while designing drugs that can effectively inhibit the biofilm formation in multidrug resistant strains of Acinetobacter baumannii.
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