Angela D. Wilkins

Identification of a candidate therapeutic autophagy-inducing peptide

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.

An AT-hook domain in MeCP2 determines the clinical course of Rett syndrome and related disorders.

Mutations in the X-linked MECP2 cause Rett syndrome, a devastating neurological disorder typified by a period of apparently normal development followed by loss of cognitive and psychomotor skills. Data from rare male patients suggest symptom onset and severity can be influenced by the location of the mutation, with amino acids 270 and 273 marking the difference between neonatal encephalopathy and death, on the one hand, and survival with deficits on the other. We therefore generated two mouse models expressing either MeCP2-R270X or MeCP2-G273X. The mice developed phenotypes at strikingly different rates and showed differential ATRX nuclear localization within the nervous system, over time, coinciding with phenotypic progression. We discovered that MeCP2 contains three AT-hook-like domains over a stretch of 250 amino acids, like HMGA DNA-bending proteins; one conserved AT-hook is disrupted in MeCP2-R270X, lending further support to the notion that one of MeCP2s key functions is to alter chromatin structure.

Elucidation of G-protein and β-arrestin functional selectivity at the dopamine D2 receptor

Significance The dopamine D2 receptor (D2R), a G protein-coupled receptor, can initiate signaling events through both activation of G proteins and interactions with β-arrestins. To begin to understand the contribution of these events in the physiology of the dopamine system, D2R was mutated to be functionally selective for each signaling pathway. The engineered receptors are functional in vitro and in vivo. Furthermore, both functions are essentially dissociable and mediate different physiological and pharmacological responses. These tools provide an additional but previously unavailable approach to elucidate the role of biased signaling in the multiple physiological actions of the dopamine system. 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 protein-biased 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.

Automated hypothesis generation based on mining scientific literature

Keeping up with the ever-expanding flow of data and publications is untenable and poses a fundamental bottleneck to scientific progress. Current search technologies typically find many relevant documents, but they do not extract and organize the information content of these documents or suggest new scientific hypotheses based on this organized content. We present an initial case study on KnIT, a prototype system that mines the information contained in the scientific literature, represents it explicitly in a queriable network, and then further reasons upon these data to generate novel and experimentally testable hypotheses. KnIT combines entity detection with neighbor-text feature analysis and with graph-based diffusion of information to identify potential new properties of entities that are strongly implied by existing relationships. We discuss a successful application of our approach that mines the published literature to identify new protein kinases that phosphorylate the protein tumor suppressor p53. Retrospective analysis demonstrates the accuracy of this approach and ongoing laboratory experiments suggest that kinases identified by our system may indeed phosphorylate p53. These results establish proof of principle for automated hypothesis generation and discovery based on text mining of the scientific literature.

Single nucleotide variations: Biological impact and theoretical interpretation

Genome‐wide association studies (GWAS) and whole‐exome sequencing (WES) generate massive amounts of genomic variant information, and a major challenge is to identify which variations drive disease or contribute to phenotypic traits. Because the majority of known disease‐causing mutations are exonic non‐synonymous single nucleotide variations (nsSNVs), most studies focus on whether these nsSNVs affect protein function. Computational studies show that the impact of nsSNVs on protein function reflects sequence homology and structural information and predict the impact through statistical methods, machine learning techniques, or models of protein evolution. Here, we review impact prediction methods and discuss their underlying principles, their advantages and limitations, and how they compare to and complement one another. Finally, we present current applications and future directions for these methods in biological research and medical genetics.

Disentangling evolutionary signals: conservation, specificity determining positions and coevolution. Implication for catalytic residue prediction.

BackgroundA 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.ResultsOur 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.ConclusionsThis 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.

Evolution: a guide to perturb protein function and networks.

Protein interactions give rise to networks that control cell fate in health and disease; selective means to probe these interactions are therefore of wide interest. We discuss here Evolutionary Tracing (ET), a comparative method to identify protein functional sites and to guide experiments that selectively block, recode, or mimic their amino acid determinants. These studies suggest, in principle, a scalable approach to perturb individual links in protein networks.

The use of evolutionary patterns in protein annotation

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.

Regulation of Ras localization and cell transformation by evolutionarily conserved palmitoyltransferases.

ABSTRACT Ras can act on the plasma membrane (PM) to mediate extracellular signaling and tumorigenesis. To identify key components controlling Ras PM localization, we performed an unbiased screen to seek Schizosaccharomyces pombe mutants with reduced PM Ras. Five mutants were found with mutations affecting the same gene, S. pombe erf2 (sp-erf2), encoding sp-Erf2, a palmitoyltransferase, with various activities. sp-Erf2 localizes to the trans-Golgi compartment, a process which is mediated by its third transmembrane domain and the Erf4 cofactor. In fission yeast, the human ortholog zDHHC9 rescues the phenotypes of sp-erf2 null cells. In contrast, expressing zDHHC14, another sp-Erf2-like human protein, did not rescue Ras1 mislocalization in these cells. Importantly, ZDHHC9 is widely overexpressed in cancers. Overexpressing ZDHHC9 promotes, while repressing it diminishes, Ras PM localization and transformation of mammalian cells. These data strongly demonstrate that sp-Erf2/zDHHC9 palmitoylates Ras proteins in a highly selective manner in the trans-Golgi compartment to facilitate PM targeting via the trans-Golgi network, a role that is most certainly critical for Ras-driven tumorigenesis.

Intramolecular allosteric communication in dopamine D2 receptor revealed by evolutionary amino acid covariation

Significance Characterizing relationships among protein structure, function, and evolution requires understanding the evolutionary constraints on each constituent residue of a protein. Previous studies have shown that structural information can be retrieved from evolutionary residue covariation in protein families. However, whether the evolutionary history in protein sequences informs on functional interactions between nonadjacent residues has been unclear. Here, we developed a method that uses evolutionary amino acid covariation to infer functionally coupled residue pairs in the dopamine D2 receptor. We discovered functional coupling between residue pairs that have coevolved mainly to control responses to dopamine and maintain them at wild-type levels. Our findings demonstrate the possibility of extracting the networks of intramolecular allosteric communication from evolutionary residue covariation patterns. 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.