The lysosomal degradation pathway of autophagy has a crucial role in defence against infection, neurodegenerative disorders, cancer and ageing. Accordingly, agents that induce autophagy may have ...broad therapeutic applications. One approach to developing such agents is to exploit autophagy manipulation strategies used by microbial virulence factors. Here we show that a peptide, Tat-beclin 1-derived from a region of the autophagy protein, beclin 1, which binds human immunodeficiency virus (HIV)-1 Nef-is a potent inducer of autophagy, and interacts with a newly identified negative regulator of autophagy, GAPR-1 (also called GLIPR2). Tat-beclin 1 decreases the accumulation of polyglutamine expansion protein aggregates and the replication of several pathogens (including HIV-1) in vitro, and reduces mortality in mice infected with chikungunya or West Nile virus. Thus, through the characterization of a domain of beclin 1 that interacts with HIV-1 Nef, we have developed an autophagy-inducing peptide that has potential efficacy in the treatment of human diseases.
Functional non-coding (fnc)RNAs are nucleotide sequences of varied lengths, structures, and mechanisms that ubiquitously influence gene expression and translation, genome stability and dynamics, and ...human health and disease. Here, to shed light on their functional determinants, we seek to exploit the evolutionary record of variation and divergence read from sequence comparisons. The approach follows the phylogenetic Evolutionary Trace (ET) paradigm, first developed and extensively validated on proteins. We assigned a relative rank of importance to every base in a study of 1070 functional RNAs, including the ribosome, and observed evolutionary patterns strikingly similar to those seen in proteins, namely, (1) the top-ranked bases clustered in secondary and tertiary structures. (2) In turn, these clusters mapped functional regions for catalysis, binding proteins and drugs, post-transcriptional modification, and deleterious mutations. (3) Moreover, the quantitative quality of these clusters correlated with the identification of functional regions. (4) As a result of this correlation, smoother structural distributions of evolutionary important nucleotides improved functional site predictions. Thus, in practice, phylogenetic analysis can broadly identify functional determinants in RNA sequences and functional sites in RNA structures, and reveal details on the basis of RNA molecular functions. As example of application, we report several previously undocumented and potentially functional ET nucleotide clusters in the ribosome. This work is broadly relevant to studies of structure-function in ribonucleic acids. Additionally, this generalization of ET shows that evolutionary constraints among sequence, structure, and function are similar in structured RNA and proteins. RNA ET is currently available as part of the ET command-line package, and will be available as a web-server.
Functional selectivity of G-protein-coupled receptors is believed to originate from ligand-specific conformations that activate only subsets of signaling effectors. In this study, to identify ...molecular motifs playing important roles in transducing ligand binding into distinct signaling responses, we combined in silico evolutionary lineage analysis and structure-guided site-directed mutagenesis with large-scale functional signaling characterization and non-negative matrix factorization clustering of signaling profiles. Clustering based on the signaling profiles of 28 variants of the β2-adrenergic receptor reveals three clearly distinct phenotypical clusters, showing selective impairments of either the Gi or βarrestin/endocytosis pathways with no effect on Gs activation. Robustness of the results is confirmed using simulation-based error propagation. The structural changes resulting from functionally biasing mutations centered around the DRY, NPxxY, and PIF motifs, selectively linking these micro-switches to unique signaling profiles. Our data identify different receptor regions that are important for the stabilization of distinct conformations underlying functional selectivity.
The neuromodulator dopamine signals through the dopamine D2 receptor (D₂R) to modulate central nervous system functions through diverse signal transduction pathways. D₂R is a prominent target for ...drug treatments in disorders where dopamine function is aberrant, such as schizophrenia. D₂R signals through distinct G-protein and β-arrestin pathways, and drugs that are functionally selective for these pathways could have improved therapeutic potential. How D₂R signals through the two pathways is still not well defined, and efforts to elucidate these pathways have been hampered by the lack of adequate tools for assessing the contribution of each pathway independently. To address this, Evolutionary Trace was used to produce D₂R mutants with strongly biased signal transduction for either the G-protein or β-arrestin interactions. These mutants were used to resolve the role of G proteins and β-arrestins in D₂R signaling assays. The results show that D₂R interactions with the two downstream effectors are dissociable and that G-protein signaling accounts for D₂R canonical MAP kinase signaling cascade activation, whereas β-arrestin only activates elements of this cascade under certain conditions. Nevertheless, when expressed in mice in GABAergic medium spiny neurons of the striatum, the β-arrestin–biased D₂R caused a significant potentiation of amphetamine-induced locomotion, whereas the G proteinbiased D₂R had minimal effects. The mutant receptors generated here provide a molecular tool set that should enable a better definition of the individual roles of G-protein and β-arrestin signaling pathways in D₂R pharmacology, neurobiology, and associated pathologies.
The structural basis of allosteric signaling in G protein-coupled receptors (GPCRs) is important in guiding design of therapeutics and understanding phenotypic consequences of genetic variation. The ...Evolutionary Trace (ET) algorithm previously proved effective in redesigning receptors to mimic the ligand specificities of functionally distinct homologs. We now expand ET to consider mutual information, with validation in GPCR structure and dopamine D2 receptor (D2R) function. The new algorithm, called ET-MIp, identifies evolutionarily relevant patterns of amino acid covariations. The improved predictions of structural proximity and D2R mutagenesis demonstrate that ET-MIp predicts functional interactions between residue pairs, particularly potency and efficacy of activation by dopamine. Remarkably, although most of the residue pairs chosen for mutagenesis are neither in the binding pocket nor in contact with each other, many exhibited functional interactions, implying at-a-distance coupling. The functional interaction between the coupled pairs correlated best with the evolutionary coupling potential derived from dopamine receptor sequences rather than with broader sets of GPCR sequences. These data suggest that the allosteric communication responsible for dopamine responses is resolved by ET-MIp and best discerned within a short evolutionary distance. Most double mutants restored dopamine response to wild-type levels, also suggesting that tight regulation of the response to dopamine drove the coevolution and intramolecular communications between coupled residues. Our approach provides a general tool to identify evolutionary covariation patterns in small sets of close sequence homologs and to translate them into functional linkages between residues.
A large panel of methods exists that aim to identify residues with critical impact on protein function based on evolutionary signals, sequence and structure information. However, it is not clear to ...what extent these different methods overlap, and if any of the methods have higher predictive potential compared to others when it comes to, in particular, the identification of catalytic residues (CR) in proteins. Using a large set of enzymatic protein families and measures based on different evolutionary signals, we sought to break up the different components of the information content within a multiple sequence alignment to investigate their predictive potential and degree of overlap.
Our results demonstrate that the different methods included in the benchmark in general can be divided into three groups with a limited mutual overlap. One group containing real-value Evolutionary Trace (rvET) methods and conservation, another containing mutual information (MI) methods, and the last containing methods designed explicitly for the identification of specificity determining positions (SDPs): integer-value Evolutionary Trace (ivET), SDPfox, and XDET. In terms of prediction of CR, we find using a proximity score integrating structural information (as the sum of the scores of residues located within a given distance of the residue in question) that only the methods from the first two groups displayed a reliable performance. Next, we investigated to what degree proximity scores for conservation, rvET and cumulative MI (cMI) provide complementary information capable of improving the performance for CR identification. We found that integrating conservation with proximity scores for rvET and cMI achieved the highest performance. The proximity conservation score contained no complementary information when integrated with proximity rvET. Moreover, the signal from rvET provided only a limited gain in predictive performance when integrated with mutual information and conservation proximity scores. Combined, these observations demonstrate that the rvET and cMI scores add complementary information to the prediction system.
This work contributes to the understanding of the different signals of evolution and also shows that it is possible to improve the detection of catalytic residues by integrating structural and higher order sequence evolutionary information with sequence conservation.
Precision medicine is an emerging field with hopes to improve patient treatment and reduce morbidity and mortality. To these ends, computational approaches have predicted associations among genes, ...chemicals and diseases. Such efforts, however, were often limited to using just some available association types. This lowers prediction coverage and, since prior evidence shows that integrating heterogeneous data is likely beneficial, it may limit accuracy. Therefore, we systematically tested whether using more association types improves prediction.
We study multimodal networks linking diseases, genes and chemicals (drugs) by applying three diffusion algorithms and varying information content. Ten-fold cross-validation shows that these networks are internally consistent, both within and across association types. Also, diffusion methods recovered missing edges, even if all the edges from an entire mode of association were removed. This suggests that information is transferable between these association types. As a realistic validation, time-stamped experiments simulated the predictions of future associations based solely on information known prior to a given date. The results show that many future published results are predictable from current associations. Moreover, in most cases, using more association types increases prediction coverage without significantly decreasing sensitivity and specificity. In case studies, literature-supported validation shows that these predictions mimic human-formulated hypotheses. Overall, this study suggests that diffusion over a more comprehensive multimodal network will generate more useful hypotheses of associations among diseases, genes and chemicals, which may guide the development of precision therapies.
Code and data are available at https://github.com/LichtargeLab/multimodal-network-diffusion.
Supplementary data are available at Bioinformatics online.
► Evolutionary patterns in sequences, structures, and phylogenomic classifications can predict some aspects of protein function. ► These patterns can be global in nature, such as in folds and ...profiles, or local, such as in motifs and templates. ► The Evolutionary Trace (ET) integrates in a single framework the analysis of these different types of functionally relevant patterns. ► ET residues cluster in structures and map out catalytic sites, binding interfaces and allosteric pathways and their specificity determinants.
With genomic data skyrocketing, their biological interpretation remains a serious challenge. Diverse computational methods address this problem by pointing to the existence of recurrent patterns among sequence, structure, and function. These patterns emerge naturally from evolutionary variation, natural selection, and divergence—the defining features of biological systems—and they identify molecular events and shapes that underlie specificity of function and allosteric communication. Here we review these methods, and the patterns they identify in case studies and in proteome-wide applications, to infer and rationally redesign function.
Scientific progress depends on formulating testable hypotheses informed by the literature. In many domains, however, this model is strained because the number of research papers exceeds human ...readability. Here, we developed computational assistance to analyze the biomedical literature by reading PubMed abstracts to suggest new hypotheses. The approach was tested experimentally on the tumor suppressor p53 by ranking its most likely kinases, based on all available abstracts. Many of the best-ranked kinases were found to bind and phosphorylate p53 (P value = 0.005), suggesting six likely p53 kinases so far. One of these, NEK2, was studied in detail. A known mitosis promoter, NEK2 was shown to phosphorylate p53 at Ser315 in vitro and in vivo and to functionally inhibit p53. These bona fide validations of text-based predictions of p53 phosphorylation, and the discovery of an inhibitory p53 kinase of pharmaceutical interest, suggest that automated reasoning using a large body of literature can generate valuable molecular hypotheses and has the potential to accelerate scientific discovery.
To determine the structural origins of diverse ligand response specificities among metabotropic glutamate receptors (mGluRs), we combined computational approaches with mutagenesis and ligand response ...assays to identify specificity-determining residues in the group I receptor, mGluR1, and the group III receptors, mGluR4 and mGluR7. Among these, mGluR1 responds to l-glutamate effectively, whereas it binds weakly to another endogenous ligand, l-serine-O-phosphate (l-SOP), which antagonizes the effects of l-glutamate. In contrast, mGluR4 has in common with other group III mGluR that it is activated with higher potency and efficacy by l-SOP. mGluR7 differs from mGluR4 and other group III mGluR in that l-glutamate and l-SOP activate it with low potency and efficacy. Enhanced versions of the evolutionary trace (ET) algorithm were used to identify residues that when swapped between mGluR1 and mGluR4 increased the potency of l-SOP inhibition relative to the potency of l-glutamate activation in mGluR1 mutants and others that diminished the potency/efficacy of l-SOP for mGluR4 mutants. In addition, combining ET identified swaps from mGluR4 with one identified by computational docking produced mGluR7 mutants that respond with dramatically enhanced potency/efficacy to l-SOP. These results reveal that an early functional divergence between group I/II and group III involved variation at positions primarily at allosteric sites located outside of binding pockets, whereas a later divergence within group III occurred through sequence variation both at the ligand-binding pocket and at loops near the dimerization interface and interlobe hinge region. They also demonstrate the power of ET for identifying allosteric determinants of evolutionary importance.
Background:l-Glutamate and l-serine-O-phosphate (l-SOP) differentially regulate subtypes of metabotropic glutamate receptor (mGluR).
Results: Evolutionary trace identified residues whose swapping between mGluR1 or mGluR7 and mGluR4 altered responses as predicted.
Conclusion: Determinants for early and later functional divergence of mGluRs were identified.
Significance: The structural and evolutionary origins of divergent mGluR specificities may aid in design of new therapeutics.
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