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Structural characteristics of novel protein folds.

ABSTRACT: Folds are the basic building blocks of protein structures. Understanding the emergence of novel protein folds is an important step towards understanding the rules governing the evolution of protein structure and function and for developing tools for protein structure modeling and design. We explored the frequency of occurrences of an exhaustively classified library of supersecondary structural elements (Smotifs), in protein structures, in order to identify features that would define a fold as novel compared to previously known structures. We found that a surprisingly small set of Smotifs is sufficient to describe all known folds. Furthermore, novel folds do not require novel Smotifs, but rather are a new combination of existing ones. Novel folds can be typified by the inclusion of a relatively higher number of rarely occurring Smotifs in their structures and, to a lesser extent, by a novel topological combination of commonly occurring Smotifs. When investigating the structural features of Smotifs, we found that the top 10% of most frequent ones have a higher fraction of internal contacts, while some of the most rare motifs are larger, and contain a longer loop region.

SUBMITTER: Fernandez-Fuentes N

PROVIDER: S-EPMC2858679 | biostudies-literature | 2010 Apr

REPOSITORIES: biostudies-literature

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Structural characteristics of novel protein folds.

Fernandez-Fuentes Narcis N Dybas Joseph M JM Fiser Andras A

PLoS computational biology 20100422 4

Folds are the basic building blocks of protein structures. Understanding the emergence of novel protein folds is an important step towards understanding the rules governing the evolution of protein structure and function and for developing tools for protein structure modeling and design. We explored the frequency of occurrences of an exhaustively classified library of supersecondary structural elements (Smotifs), in protein structures, in order to identify features that would define a fold as no ...[more]

PMID: 20421995

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Project description:Viruses with large, double-stranded DNA genomes captured the majority of their genes from their hosts at different stages of evolution. The origins of many virus genes are readily detected through significant sequence similarity with cellular homologs. In particular, this is the case for virus enzymes, such as DNA and RNA polymerases or nucleotide kinases, that retain their catalytic activity after capture by an ancestral virus. However, a large fraction of virus genes have no readily detectable cellular homologs, meaning that their origins remain enigmatic. We explored the potential origins of such proteins that are encoded in the genomes of orthopoxviruses, a thoroughly studied virus genus that includes major human pathogens. To this end, we used AlphaFold2 to predict the structures of all 214 proteins that are encoded by orthopoxviruses. Among the proteins of unknown provenance, structure prediction yielded clear indications of origin for 14 of them and validated several inferences that were previously made via sequence analysis. A notable emerging trend is the exaptation of enzymes from cellular organisms for nonenzymatic, structural roles in virus reproduction that is accompanied by the disruption of catalytic sites and by an overall drastic divergence that precludes homology detection at the sequence level. Among the 16 orthopoxvirus proteins that were found to be inactivated enzyme derivatives are the poxvirus replication processivity factor A20, which is an inactivated NAD-dependent DNA ligase; the major core protein A3, which is an inactivated deubiquitinase; F11, which is an inactivated prolyl hydroxylase; and more similar cases. For nearly one-third of the orthopoxvirus virion proteins, no significantly similar structures were identified, suggesting exaptation with subsequent major structural rearrangement that yielded unique protein folds. IMPORTANCE Protein structures are more strongly conserved in evolution than are amino acid sequences. Comparative structural analysis is particularly important for inferring the origins of viral proteins that typically evolve at high rates. We used a powerful protein structure modeling method, namely, AlphaFold2, to model the structures of all orthopoxvirus proteins and compared them to all available protein structures. Multiple cases of recruitment of host enzymes for structural roles in viruses, accompanied by the disruption of catalytic sites, were discovered. However, many viral proteins appear to have evolved unique structural folds.

Project description:Although massive data is quickly accumulating on protein sequence and structure, there is a small and limited number of protein architectural types (or structural folds). This study is addressing the following question: how well could one reveal underlying sequence-structure relationships and design protein sequences for an arbitrary, potentially novel, structural fold? In response to the question, we have developed novel deep generative models, namely, semisupervised gcWGAN (guided, conditional, Wasserstein Generative Adversarial Networks). To overcome training difficulties and improve design qualities, we build our models on conditional Wasserstein GAN (WGAN) that uses Wasserstein distance in the loss function. Our major contributions include (1) constructing a low-dimensional and generalizable representation of the fold space for the conditional input, (2) developing an ultrafast sequence-to-fold predictor (or oracle) and incorporating its feedback into WGAN as a loss to guide model training, and (3) exploiting sequence data with and without paired structures to enable a semisupervised training strategy. Assessed by the oracle over 100 novel folds not in the training set, gcWGAN generates more successful designs and covers 3.5 times more target folds compared to a competing data-driven method (cVAE). Assessed by sequence- and structure-based predictors, gcWGAN designs are physically and biologically sound. Assessed by a structure predictor over representative novel folds, including one not even part of basis folds, gcWGAN designs have comparable or better fold accuracy yet much more sequence diversity and novelty than cVAE. The ultrafast data-driven model is further shown to boost the success of a principle-driven de novo method (RosettaDesign), through generating design seeds and tailoring design space. In conclusion, gcWGAN explores uncharted sequence space to design proteins by learning generalizable principles from current sequence-structure data. Data, source codes, and trained models are available at https://github.com/Shen-Lab/gcWGAN.

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Structural characteristics of novel protein folds.

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Structural characteristics of novel protein folds.

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