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Insight regarding how diverse enzymatic functions and reactions have evolved from ancestral scaffolds is fundamental to understanding chemical and evolutionary biology, and for the exploitation of enzymes for biotechnology. We undertook an extensive computational analysis using a unique and comprehensive combination of tools that include large-scale phylogenetic reconstruction to determine the sequence, structural, and functional relationships of the functionally diverse flavin mononucleotide-dependent nitroreductase (NTR) superfamily (>24,000 sequences from all domains of life, 54 structures, and >10 enzymatic functions). Our results suggest an evolutionary model in which contemporary subgroups of the superfamily have diverged in a radial manner from a minimal flavin-binding scaffold. We identified the structural design principle for this divergence: Insertions at key positions in the minimal scaffold that, combined with the fixation of key residues, have led to functional specialization. These results will aid future efforts to delineate the emergence of functional diversity in enzyme superfamilies, provide clues for functional inference for superfamily members of unknown function, and facilitate rational redesign of the NTR scaffold.

Many enzymes that catalyze protein post-translational modifications can specifically modify multiple target proteins. However, little is known regarding the molecular basis and evolution of multispecificity in these enzymes. Here, we used a combined bioinformatics and experimental approaches to investigate the evolution of multispecificity in the sirtuin-1 (SIRT1) deacetylase. Guided by bioinformatics analysis of SIRT1 orthologs and substrates, we identified and examined important amino acid substitutions that have occurred during the evolution of sirtuins in Metazoa and Fungi. We found that mutation of human SIRT1 at these positions, based on sirtuin orthologs from Fungi, could alter its substrate specificity. These substitutions lead to reduced activity toward K382 acetylated p53 protein, which is only present in Metazoa, without affecting the high activity toward the conserved histone substrates. Results from ancestral sequence reconstruction are consistent with a model in which ancestral sirtuin proteins exhibited multispecificity, suggesting that the multispecificity of some metazoan sirtuins, such as hSIRT1, could be a relatively ancient trait.

Many protein-protein interactions are mediated by domain-motif interaction, where a domain in one protein binds a short linear motif in its interacting partner. Such interactions are often involved in key cellular processes, necessitating their tight regulation. A common strategy of the cell to control protein function and interaction is by post-translational modifications of specific residues, especially phosphorylation. Indeed, there are motifs, such as SH2-binding motifs, in which motif phosphorylation is required for the domain-motif interaction. On the contrary, there are other examples where motif phosphorylation prevents the domain-motif interaction. Here we present a large-scale integrative analysis of experimental human data of domain-motif interactions and phosphorylation events, demonstrating an intriguing coupling between the two. We report such coupling for SH3, PDZ, SH2 and WW domains, where residue phosphorylation within or next to the motif is implied to be associated with switching on or off domain binding. For domains that require motif phosphorylation for binding, such as SH2 domains, we found coupled phosphorylation events other than the ones required for domain binding. Furthermore, we show that phosphorylation might function as a double switch, concurrently enabling interaction of the motif with one domain and disabling interaction with another domain. Evolutionary analysis shows that co-evolution of the motif and the proximal residues capable of phosphorylation predominates over other evolutionary scenarios, in which the motif appeared before the potentially phosphorylated residue, or vice versa. Our findings provide strengthening evidence for coupled interaction-regulation units, defined by a domain-binding motif and a phosphorylated residue.

Compilations of domain-domain interactions based on solved structures suggest there are distinct domain pairs that are used repeatedly in different protein contexts to mediate protein-protein interactions. However, not all protein pairs with the corresponding domains that can potentially mediate interaction do interact, even when they are colocalized and coexpressed. It is conceivable that there are structural and sequence features, below the domain level, that play a role in determining the potential of domains to mediate protein-protein interactions. Here, we discover such features by comparing domains that, on the one hand, mediate homodimerization of proteins and, on the other, occur in different proteins that are documented as monomers. Intriguingly, this comparison uncovered surface loops that can be considered as determinants of the interactions. There are enabling loops, which mediate the domain interactions, and disabling loops that prevent the interactions. The presence of the enabling/disabling loops is consistent with the fulfillment/prevention of the interaction and is highly preserved in evolution. This suggests that, along with the preservation of structural elements that enable interaction, evolution maintains elements intended to prevent unwanted interactions. The enabling and disabling loops discovered in this study have implications in prediction of protein-protein interactions, by pointing to the protein regions that determine the interaction. Our results extend the hierarchy of attributes that collectively establish the modularity of domain-mediated protein-protein interactions.

Metabolic pathways in eubacteria and archaea often are encoded by operons and/or gene clusters (genome neighborhoods) that provide important clues for assignment of both enzyme functions and metabolic pathways. We describe a bioinformatic approach (genome neighborhood network; GNN) that enables large scale prediction of the in vitro enzymatic activities and in vivo physiological functions (metabolic pathways) of uncharacterized enzymes in protein families. We demonstrate the utility of the GNN approach by predicting in vitro activities and in vivo functions in the proline racemase superfamily (PRS; InterPro IPR008794). The predictions were verified by measuring in vitro activities for 51 proteins in 12 families in the PRS that represent ~85% of the sequences; in vitro activities of pathway enzymes, carbon/nitrogen source phenotypes, and/or transcriptomic studies confirmed the predicted pathways. The synergistic use of sequence similarity networks3 and GNNs will facilitate the discovery of the components of novel, uncharacterized metabolic pathways in sequenced genomes. DNA molecules are polymers in which four nucleotides—guanine, adenine, thymine, and cytosine—are arranged along a sugar backbone. The sequence of these four nucleotides along the DNA strand determines the genetic code of the organism, and can be deciphered using various genome sequencing techniques. Microbial genomes are particularly easy to sequence as they contain fewer than several million nucleotides, compared with the 3 billion or so nucleotides that are present in the human genome. Reading a genome sequence is straight forward, but predicting the physiological functions of the proteins encoded by the genes in the sequence can be challenging. In a process called genome annotation, the function of protein is predicted by comparing the relevant gene to the genes of proteins with known functions. However, microbial genomes and proteins are hugely diverse and over 50% of the microbial genomes that have been sequenced have not yet been related to any physiological function. With thousands of microbial genomes waiting to be deciphered, large scale approaches are needed. Zhao et al. take advantage of a particular characteristic of microbial genomes. DNA sequences that code for two proteins required for the same task tend to be closer to each other in the genome than two sequences that code for unrelated functions. Operons are an extreme example; an operon is a unit of DNA that contains several genes that are expressed as proteins at the same time. Zhao et al. have developed a bioinformatic method called the genome neighbourhood network approach to work out the function of proteins based on their position relative to other proteins in the genome. When applied to the proline racemase superfamily (PRS), which contains enzymes with similar sequences that can catalyze three distinct chemical reactions, the new approach was able to assign a function to the majority of proteins in a public database of PRS enzymes, and also revealed new members of the PRS family. Experiments confirmed that the proteins behaved as predicted. The next challenge is to develop the genome neighbourhood network approach so that it can be applied to more complex systems.

The cytosolic glutathione transferase (cytGST) superfamily comprises more than 13,000 nonredundant sequences found throughout the biosphere. Their key roles in metabolism and defense against oxidative damage have led to thousands of studies over several decades. Despite this attention, little is known about the physiological reactions they catalyze and most of the substrates used to assay cytGSTs are synthetic compounds. A deeper understanding of relationships across the superfamily could provide new clues about their functions. To establish a foundation for expanded classification of cytGSTs, we generated similarity-based subgroupings for the entire superfamily. Using the resulting sequence similarity networks, we chose targets that broadly covered unknown functions and report here experimental results confirming GST-like activity for 82 of them, along with 37 new 3D structures determined for 27 targets. These new data, along with experimentally known GST reactions and structures reported in the literature, were painted onto the networks to generate a global view of their sequence-structure-function relationships. The results show how proteins of both known and unknown function relate to each other across the entire superfamily and reveal that the great majority of cytGSTs have not been experimentally characterized or annotated by canonical class. A mapping of taxonomic classes across the superfamily indicates that many taxa are represented in each subgroup and highlights challenges for classification of superfamily sequences into functionally relevant classes. Experimental determination of disulfide bond reductase activity in many diverse subgroups illustrate a theme common for many reaction types. Finally, sequence comparison between an enzyme that catalyzes a reductive dechlorination reaction relevant to bioremediation efforts with some of its closest homologs reveals differences among them likely to be associated with evolution of this unusual reaction. Interactive versions of the networks, associated with functional and other types of information, can be downloaded from the Structure-Function Linkage Database (SFLD; http://sfld.rbvi.ucsf.edu).

Predicting which mutations proteins tolerate while maintaining their structure and function has important applications for modeling fundamental properties of proteins and their evolution; it also drives progress in protein design. Here we develop a computational model to predict the tolerated sequence space of HIV-1 protease reachable by single mutations. We assess the model by comparison to the observed variability in more than 50,000 HIV-1 protease sequences, one of the most comprehensive datasets on tolerated sequence space. We then extend the model to a second protein, reverse transcriptase. The model integrates multiple structural and functional constraints acting on a protein and uses ensembles of protein conformations. We find the model correctly captures a considerable fraction of protease and reverse-transcriptase mutational tolerance and shows comparable accuracy using either experimentally determined or computationally generated structural ensembles. Predictions of tolerated sequence space afforded by the model provide insights into stability-function tradeoffs in the emergence of resistance mutations and into strengths and limitations of the computational model.

Affinity tagging, mass spectroscopy and a tailor-made scoring system are used to identify 497 high-confidence interactions between human proteins and human immunodeficiency virus proteins. Interactions between human and HIV proteins Nevan Krogan and colleagues report a global analysis of human proteins that interact with the 18 proteins expressed by HIV-1. Using affinity tagging and mass spectrometry combined with a new quantitative scoring system and a high level of validation by co-immunoprecipitation, they identify 497 HIV–human protein–protein interactions, providing new insights into host proteins that could play a part in HIV replication. Functional validation of a few of these hits revealed a number of new factors that inhibit HIV replication, including EIF3d, which is cleaved by HIV protease, and DESP and HEAT1, which interact with integrase and inhibit integration. Human immunodeficiency virus (HIV) has a small genome and therefore relies heavily on the host cellular machinery to replicate. Identifying which host proteins and complexes come into physical contact with the viral proteins is crucial for a comprehensive understanding of how HIV rewires the host’s cellular machinery during the course of infection. Here we report the use of affinity tagging and purification mass spectrometry 1 , 2 , 3 to determine systematically the physical interactions of all 18 HIV-1 proteins and polyproteins with host proteins in two different human cell lines (HEK293 and Jurkat). Using a quantitative scoring system that we call MiST, we identified with high confidence 497 HIV–human protein–protein interactions involving 435 individual human proteins, with ∼40% of the interactions being identified in both cell types. We found that the host proteins hijacked by HIV, especially those found interacting in both cell types, are highly conserved across primates. We uncovered a number of host complexes targeted by viral proteins, including the finding that HIV protease cleaves eIF3d, a subunit of eukaryotic translation initiation factor 3. This host protein is one of eleven identified in this analysis that act to inhibit HIV replication. This data set facilitates a more comprehensive and detailed understanding of how the host machinery is manipulated during the course of HIV infection.

We introduce a new novel dataset named PiNUI: Protein interactions with Nearly Uniform Imbalance. PiNUI is a dataset of Protein-Protein Interactions (PPI) specifically designed for Machine Learning (ML) applications that offer a higher degree of representativeness of real-world PPI tasks compared to existing ML-ready PPI datasets. We achieve such by increasing the data size and quality, and minimizing the sampling bias of negative interactions. We demonstrate that models trained on PiNUI almost always outperform those trained on conventional PPI datasets when evaluated on various general PPI tasks using external test sets.Competing Interest StatementThe authors have declared no competing interest.Footnotes* https://linktr.ee/geoffroy.shiru