A monomeric mycobacteriophage immunity repressor utilizes two domains to recognize an asymmetric DNA sequence

SEA-PHAGES and SEA-GENES: Advancing Virology and Science Education

Research opportunities for undergraduate students are strongly advantageous, but implementation at a large scale presents numerous challenges. The enormous diversity of the bacteriophage population and a supportive programmatic structure provide opportunities to engage early-career undergraduates in phage discovery, genomics, and genetics. The Science Education Alliance (SEA) is an inclusive Research-Education Community (iREC) providing centralized programmatic support for students and faculty without prior experience in virology at institutions from community colleges to research-active universities to participate in two course-based projects, SEA-PHAGES (SEA Phage Hunters Advancing Genomic and Evolutionary Science) and SEA-GENES (SEA Gene-function Exploration by a Network of Emerging Scientists). Since 2008, the SEA has supported more than 50,000 undergraduate researchers who have isolated more than 23,000 bacteriophages of which more than 4,500 are fully sequenced and annotated. Students have functionally characterized hundreds of phage genes, and the phage collection has fueled the therapeutic use of phages for treatment of Mycobacterium infections. Participation in the SEA promotes student persistence in science education, and its inclusivity promotes a more equitable scientific community.

Mycobacteriophage Alexphander Gene 94 Encodes an Essential dsDNA-Binding Protein during Lytic Infection

Mycobacteriophages are viruses that specifically infect bacterial species within the genera Mycobacterium and Mycolicibacterium. Over 2400 mycobacteriophages have been isolated on the host Mycolicibacterium smegmatis and sequenced. This wealth of genomic data indicates that mycobacteriophage genomes are diverse, mosaic, and contain numerous (35-60%) genes for which there is no predicted function based on sequence similarity to characterized orthologs, many of which are essential to lytic growth. To fully understand the molecular aspects of mycobacteriophage-host interactions, it is paramount to investigate the function of these genes and gene products. Here we show that the temperate mycobacteriophage, Alexphander, makes stable lysogens with a frequency of 2.8%. Alexphander gene 94 is essential for lytic infection and encodes a protein predicted to contain a C-terminal MerR family helix-turn-helix DNA-binding motif (HTH) and an N-terminal DinB/YfiT motif, a putative metal-binding motif found in stress-inducible gene products. Full-length and C-terminal gp94 constructs form high-order nucleoprotein complexes on 100-500 base pair double-stranded DNA fragments and full-length phage genomic DNA with little sequence discrimination for the DNA fragments tested. Maximum gene 94 mRNA levels are observed late in the lytic growth cycle, and gene 94 is transcribed in a message with neighboring genes 92 through 96. We hypothesize that gp94 is an essential DNA-binding protein for Alexphander during lytic growth. We proposed that gp94 forms multiprotein complexes on DNA through cooperative interactions involving its HTH DNA-binding motif at sites throughout the phage chromosome, facilitating essential DNA transactions required for lytic propagation.

Accurate structure prediction of biomolecular interactions with AlphaFold 3

The introduction of AlphaFold 21 has spurred a revolution in modelling the structure of proteins and their interactions, enabling a huge range of applications in protein modelling and design2-6. Here we describe our AlphaFold 3 model with a substantially updated diffusion-based architecture that is capable of predicting the joint structure of complexes including proteins, nucleic acids, small molecules, ions and modified residues. The new AlphaFold model demonstrates substantially improved accuracy over many previous specialized tools: far greater accuracy for protein-ligand interactions compared with state-of-the-art docking tools, much higher accuracy for protein-nucleic acid interactions compared with nucleic-acid-specific predictors and substantially higher antibody-antigen prediction accuracy compared with AlphaFold-Multimer v.2.37,8. Together, these results show that high-accuracy modelling across biomolecular space is possible within a single unified deep-learning framework.

Brewing COFFEE: A Sequence-Specific Coarse-Grained Energy Function for Simulations of DNA-Protein Complexes

DNA-protein interactions are pervasive in a number of biophysical processes ranging from transcription and gene expression to chromosome folding. To describe the structural and dynamic properties underlying these processes accurately, it is important to create transferable computational models. Toward this end, we introduce Coarse-grained Force Field for Energy Estimation, COFFEE, a robust framework for simulating DNA-protein complexes. To brew COFFEE, we integrated the energy function in the self-organized polymer model with side-chains for proteins and the three interaction site model for DNA in a modular fashion, without recalibrating any of the parameters in the original force-fields. A unique feature of COFFEE is that it describes sequence-specific DNA-protein interactions using a statistical potential (SP) derived from a data set of high-resolution crystal structures. The only parameter in COFFEE is the strength (λDNAPRO) of the DNA-protein contact potential. For an optimal choice of λDNAPRO, the crystallographic B-factors for DNA-protein complexes with varying sizes and topologies are quantitatively reproduced. Without any further readjustments to the force-field parameters, COFFEE predicts scattering profiles that are in quantitative agreement with small-angle X-ray scattering experiments, as well as chemical shifts that are consistent with NMR. We also show that COFFEE accurately describes the salt-induced unraveling of nucleosomes. Strikingly, our nucleosome simulations explain the destabilization effect of ARG to LYS mutations, which do not alter the balance of electrostatic interactions but affect chemical interactions in subtle ways. The range of applications attests to the transferability of COFFEE, and we anticipate that it would be a promising framework for simulating DNA-protein complexes at the molecular length-scale.

Phage Milagro: a platform for engineering a broad host range virulent phage for Burkholderia

IMPORTANCE

Burkholderia infections are a significant concern in people with CF and other immunocompromising disorders, and are difficult to treat with conventional antibiotics due to their inherent drug resistance. Bacteriophages, or bacterial viruses, are now seen as a potential alternative therapy for these infections, but most of the naturally occurring phages are temperate and have narrow host ranges, which limit their utility as therapeutics. Here we describe the temperate Burkholderia phage Milagro and our efforts to engineer this phage into a potential therapeutic by expanding the phage host range and selecting for phage mutants that are strictly virulent. This approach may be used to generate new therapeutic agents for treating intractable infections in CF patients.

Brewing COFFEE: A sequence-specific coarse-grained energy function for simulations of DNA-protein complexes

DNA-protein interactions are pervasive in a number of biophysical processes ranging from transcription, gene expression, to chromosome folding. To describe the structural and dynamic properties underlying these processes accurately, it is important to create transferable computational models. Toward this end, we introduce Coarse grained force field for energy estimation, COFFEE, a robust framework for simulating DNA-protein complexes. To brew COFFEE, we integrated the energy function in the Self-Organized Polymer model with Side Chains for proteins and the Three Interaction Site model for DNA in a modular fashion, without re-calibrating any of the parameters in the original force-fields. A unique feature of COFFEE is that it describes sequence-specific DNA-protein interactions using a statistical potential (SP) derived from a dataset of high-resolution crystal structures. The only parameter in COFFEE is the strength (λ D N A P R O ) of the DNA-protein contact potential. For an optimal choice of λ D N A P R O , the crystallographic B-factors for DNA-protein complexes, with varying sizes and topologies, are quantitatively reproduced. Without any further readjustments to the force-field parameters, COFFEE predicts the scattering profiles that are in quantitative agreement with SAXS experiments as well as chemical shifts that are consistent with NMR. We also show that COFFEE accurately describes the salt-induced unraveling of nucleosomes. Strikingly, our nucleosome simulations explain the destabilization effect of ARG to LYS mutations, which does not alter the balance of electrostatic interactions, but affects chemical interactions in subtle ways. The range of applications attests to the transferability of COFFEE, and we anticipate that it would be a promising framework for simulating DNA-protein complexes at the molecular length-scale.

Using AlphaFold Predictions in Viral Research

Elucidation of the tertiary structure of proteins is an important task for biological and medical studies. AlphaFold, a modern deep-learning algorithm, enables the prediction of protein structure to a high level of accuracy. It has been applied in numerous studies in various areas of biology and medicine. Viruses are biological entities infecting eukaryotic and procaryotic organisms. They can pose a danger for humans and economically significant animals and plants, but they can also be useful for biological control, suppressing populations of pests and pathogens. AlphaFold can be used for studies of molecular mechanisms of viral infection to facilitate several activities, including drug design. Computational prediction and analysis of the structure of bacteriophage receptor-binding proteins can contribute to more efficient phage therapy. In addition, AlphaFold predictions can be used for the discovery of enzymes of bacteriophage origin that are able to degrade the cell wall of bacterial pathogens. The use of AlphaFold can assist fundamental viral research, including evolutionary studies. The ongoing development and improvement of AlphaFold can ensure that its contribution to the study of viral proteins will be significant in the future.