Structure and Function of the T4 Spackle Protein Gp61.3

More than just pattern recognition: Prediction of uncommon protein structure features by AI methods

The CASP14 experiment demonstrated the extraordinary structure modeling capabilities of artificial intelligence (AI) methods. That result has ignited a fierce debate about what these methods are actually doing. One of the criticisms has been that the AI does not have any sense of the underlying physics but is merely performing pattern recognition. Here, we address that issue by analyzing the extent to which the methods identify rare structural motifs. The rationale underlying the approach is that a pattern recognition machine tends to choose the more frequently occurring motifs, whereas some sense of subtle energetic factors is required to choose infrequently occurring ones. To reduce the possibility of bias from related experimental structures and to minimize the effect of experimental errors, we examined only CASP14 target protein crystal structures determined to a resolution limit better than 2 Å, which lacked significant amino acid sequence homology to proteins of known structure. In those experimental structures and in the corresponding models, we track cis peptides, π-helices, 310-helices, and other small 3D motifs that occur in the PDB database at a frequency of lower than 1% of total amino acid residues. The best-performing AI method, AlphaFold2, captured these uncommon structural elements exquisitely well. All discrepancies appeared to be a consequence of crystal environment effects. We propose that the neural network learned a protein structure potential of mean force, enabling it to correctly identify situations where unusual structural features represent the lowest local free energy because of subtle influences from the atomic environment.

APTC-EC-2A: A Lytic Phage Targeting Multidrug Resistant E. coli Planktonic Cells and Biofilms

Escherichia coli (E. coli) are common bacteria that colonize the human and animal gastrointestinal tract, where they help maintain a balanced microbiome. However, some E. coli strains are pathogenic and can cause serious infectious diseases and life-threatening complications. Due to the overuse of antibiotics and limited development of novel antibiotics, the emergence of antibiotic-resistant strains has threatened modern medicine, whereby common infections can become lethal. Phage therapy has once again attracted interest in recent years as an alternative treatment option to antibiotics for severe infections with antibiotic-resistant strains. The aim of this study was to isolate and characterize phage against multi-drug resistant E. coli isolated from clinical samples and hospital wastewater. For phage isolation, wastewater samples were collected from The Queen Elizabeth Hospital (Adelaide, SA, Australia) followed by phage enrichment as required. Microbiological assays, electron microscopy and genomic sequencing were carried out to characterize the phage. From the 10 isolated E. coli phages, E. coli phage APTC-EC-2A was the most promising and could lyse 6/7 E. coli clinical isolates. APTC-EC-2A was stable at a broad pH range (3–11) and could lyse the host E. coli at temperatures ranging between 30–50 °C. Furthermore, APTC-EC-2A could kill E. coli in planktonic and biofilm form. Electron microscopy and genomic sequencing indicated the phage to be from the Myoviridae family and of lytic nature. In conclusion, the newly isolated phage APTC-EC-2A has the desired properties that support its potential for development as a therapeutic agent against therapy refractory E. coli infections.