Finding Protein Sequence Motifs--Methods And Applications

National Library Of Medicine

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Abstract

The rapid accumulation of genome sequences and protein structures during the last decade has been paralleled by major advances in sequence database search methods as well as protein structure prediction. The powerful Position-Specific Iterating BLAST (PSI-BLAST) method developed at the NCBI forms the basis of our work on protein motif analysis. In addition, Hidden Markov Models (HMM), protein profile-against-profile comparison implemented in the HHSearch method, protein structure comparison methods, homology modeling of protein structure and genome context analysis were extensively and increasingly applied. Furthermore, custom libraries of protein domain profiles as well as computational pipelines for novel domain identification have been developed and applied. Lately, these methods for protein motif search are being complemented by deep learning computational methods. During the year under review, we have continued and expanded our investigation of the proteins domains, particularly, those that are encoded in the genomes of viruses of prokaryotes and eukaryotes as well as domains involved in the defense of bacteria against viruses. In particular, in collaboration with the laboratory of professor Feng Zhang at the Broad Institute of MIT and Harvard, we explored protein domain involved in bacterial innate immunity. Many organisms have evolved specialized immune pattern-recognition receptors, including nucleotide-binding oligomerization domain-like receptors (NLRs) of the STAND superfamily that are ubiquitous in plants, animals, and fungi. Although the roles of NLRs in eukaryotic immunity are well established, it is unknown whether prokaryotes use similar defense mechanisms. Our joint work with the Zhang laboratory showed that antiviral STAND (Avs) homologs in bacteria and archaea detect hallmark viral proteins, triggering Avs tetramerization and the activation of diverse N-terminal effector domains, including DNA endonucleases, to abrogate infection. Cryo-electron microscopy reveals that Avs sensor domains recognize conserved folds, active-site residues, and enzyme ligands, allowing a single Avs receptor to detect a wide variety of viruses. These findings extend the paradigm of pattern recognition of pathogen-specific proteins across all three domains of life. Bacteria and archaea produce an enormous diversity of modified peptides that are involved in various forms of inter-microbial conflicts or communication. A vast class of such peptides are Ribosomally synthesized, Postranslationally modified Peptides (RiPPs), and a major group of RiPPs are graspetides, so named after ATP-grasp ligases that catalyze the formation of lactam and lactone linkages in these peptides. The diversity of graspetides, the multiple proteins encoded in the respective Biosynthetic Gene Clusters (BGCs) and their evolution have not been studied in full detail. We performed a comprehensive analysis of the graspetide-encoding BGCs and report a variety of novel graspetide groups as well as ancillary proteins implicated in graspetide biosynthesis and expression. We compiled a comprehensive, manually curated set of graspetides that includes 174 families including 115 new families with distinct patterns of amino acids implicated in macrocyclization and further modification, roughly tripling the known graspetide diversity. We derived signature motifs for the leader regions of graspetide precursors that could be used to facilitate graspetide prediction. Graspetide biosynthetic gene clusters and specific precursors were identified in bacterial divisions not previously known to encode RiPPs, in particular, the parasitic and symbiotic bacteria of the Candidate phyla radiation. We identified Bacteroides-specific biosynthetic gene clusters (BGC) that include remarkable diversity of graspetides encoded in the same loci which predicted to be modified by the same ATP-grasp ligase. We studied in details evolution of recently characterized chryseoviridin BGCs and showed that duplication and horizonal gene exchange both contribute to the diversification of the graspetides during evolution. This work demonstrated previously unsuspected diversity of graspetide sequences, even those associated with closely related ATP-grasp enzymes. Several previously unnoticed families of proteins associated with graspetide biosynthetic gene clusters are identified. The results of this work substantially expand the known diversity of RiPPs and can be harnessed to further advance approaches for their identification. Additionally, we explored epistatic interactions mutations in the receptor-binding domain (RBD) of SARS-CoV-2 spike (S) protein. The interface between the RBD and the host receptor (ACE2) overlaps the binding site of principal neutralizing antibodies (NAb), limiting the repertoire of viable mutations. Nonetheless, variants with multiple RBD mutations have risen to dominance. Nonadditive, epistatic relationships among RBD mutations are apparent, and assessing the impact of such epistasis on the mutational landscape, particularly the risk of vaccine escape, is crucial. We employed protein structure modeling using Rosetta to compare the effects of all single mutants at the RBD-NAb and RBD-ACE2 interfaces for the wild type and Delta, Gamma, and Omicron variants. Overall, epistasis at the RBD interface appears to be limited, and the effects of most multiple mutations are additive. Epistasis at the Delta variant interface weakly stabilizes NAb interaction relative to ACE2 interaction, whereas in Gamma, epistasis more substantially destabilizes NAb interaction. Despite bearing many more RBD mutations, the epistatic landscape of Omicron closely resembles that of Gamma. Thus, although Omicron poses new risks not observed with Delta, structural constraints on the RBD appear to hamper continued evolution toward more complete vaccine escape. The modest ensemble of mutations relative to the wild type that are currently known to reduce vaccine efficacy is likely to contain the majority of all possible escape mutations for future variants, predicting the continued efficacy of the existing vaccines.