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Szczepaniak, K.

Publications and source records attributed to Szczepaniak, K..

2 recordsLinked to original sources

Protein modularity in phages is extensive and associated with functions linked to core replication machinery and host tropism determinants

Biological modularity enhances evolutionary adaptability by allowing rearrangement of functional components. One striking example are bacterial viruses (phages). They exhibit extensive genomic modularity by being built of independent functional modules that evolve separately and combine in various ways, making them astoundingly diverse. While multiple studies have investigated genomic modularity in phages, less attention has been given to protein modularity--proteins having distinct building blocks or domains that can evolve and recombine, enhancing functional and genetic diversity. To better understand the impact of protein modularity on viral evolution, we quantified it by detecting instances of domain mosaicism, defined as a homologous fragment sharing between two otherwise unrelated proteins. We used highly sensitive homology detection to quantify domain mosaicism between pairs of 133,574 representative phage proteins and to understand its relationship with functional diversity in phage genomes. We found that diverse functional classes often shared homologous domains. This phenomenon was often linked to protein modularity, particularly in receptor-binding proteins, endolysins and DNA polymerases. We also identified multiple instances of recent diversification via exchange and gain/loss of domains in receptor-binding proteins, neck passage structures, endolysins and some members of the core replication machinery. Diversification via protein fragment exchange often transcended distant taxonomic and ecological borders. We argue that the ongoing diversification via shuffling of protein domains associated with those functions is reflective of co-evolutionary arms race and the resulting diversifying selection to overcome multiple mechanisms of bacterial resistance against phages.

evolutionary biology↗

Graph neural networks and sequence embeddings enable the prediction and design of the cofactor specificity of Rossmann fold proteins

The Rossmann fold enzymes are involved in essential biochemical pathways such as nucleotide and amino acid metabolism. Their functioning relies on interaction with cofactors, small nucleoside-based compounds specifically recognized by a conserved {beta}{beta} motif shared by all Rossmann fold proteins. While Rossmann methyltransferases recognize only a single cofactor type, the S-Adenosylmethionine (SAM), the oxidoreductases, depending on the family, bind nicotinamide (NAD, NADP) or flavin-based (FAD) cofactors. In this study, we show that despite its short length, the {beta}{beta} motif unambiguously defines the specificity towards the cofactor. Following this observation, we trained two complementary deep learning models for the prediction of the cofactor specificity based on the sequence and structural features of the {beta}{beta} motif. A benchmark on two independent test sets, one containing {beta}{beta} motifs bearing no resemblance to those of the training set, and the other comprising 38 experimentally confirmed cases of rational design of the cofactor specificity, revealed the nearly perfect performance of the two methods. The Rossmann-toolbox protocols can be accessed via the webserver at https://lbs.cent.uw.edu.pl/rossmann-toolbox and are available as a Python package at https://github.com/labstructbioinf/rossmann-toolbox. Key pointsO_LIThe Rossmann fold encompasses a multitude of diverse enzymes involved in most of the essential cellular pathways C_LIO_LIProteins belonging to the Rossmann fold co-evolved with their nucleoside-based cofactors and require them for the functioning C_LIO_LIManipulating the cofactor specificity is an important step in the process of enzyme engineering C_LIO_LIWe developed an end-to-end pipeline for the prediction and design of the cofactor specificity of the Rossmann fold proteins C_LIO_LIOwing to the utilization of deep learning approaches the pipeline achieved nearly perfect accuracy C_LI

bioinformatics↗