David B. Ascher

mCSM: predicting the effects of mutations in proteins using graph-based signatures

Motivation: Mutations play fundamental roles in evolution by introducing diversity into genomes. Missense mutations in structural genes may become either selectively advantageous or disadvantageous to the organism by affecting protein stability and/or interfering with interactions between partners. Thus, the ability to predict the impact of mutations on protein stability and interactions is of significant value, particularly in understanding the effects of Mendelian and somatic mutations on the progression of disease. Here, we propose a novel approach to the study of missense mutations, called mCSM, which relies on graph-based signatures. These encode distance patterns between atoms and are used to represent the protein residue environment and to train predictive models. To understand the roles of mutations in disease, we have evaluated their impacts not only on protein stability but also on protein–protein and protein–nucleic acid interactions. Results: We show that mCSM performs as well as or better than other methods that are used widely. The mCSM signatures were successfully used in different tasks demonstrating that the impact of a mutation can be correlated with the atomic-distance patterns surrounding an amino acid residue. We showed that mCSM can predict stability changes of a wide range of mutations occurring in the tumour suppressor protein p53, demonstrating the applicability of the proposed method in a challenging disease scenario. Availability and implementation: A web server is available at http://structure.bioc.cam.ac.uk/mcsm. Contact: dpires@dcc.ufmg.br; tom@cryst.bioc.cam.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.

DUET: a server for predicting effects of mutations on protein stability using an integrated computational approach

Cancer genome and other sequencing initiatives are generating extensive data on non-synonymous single nucleotide polymorphisms (nsSNPs) in human and other genomes. In order to understand the impacts of nsSNPs on the structure and function of the proteome, as well as to guide protein engineering, accurate in silicomethodologies are required to study and predict their effects on protein stability. Despite the diversity of available computational methods in the literature, none has proven accurate and dependable on its own under all scenarios where mutation analysis is required. Here we present DUET, a web server for an integrated computational approach to study missense mutations in proteins. DUET consolidates two complementary approaches (mCSM and SDM) in a consensus prediction, obtained by combining the results of the separate methods in an optimized predictor using Support Vector Machines (SVM). We demonstrate that the proposed method improves overall accuracy of the predictions in comparison with either method individually and performs as well as or better than similar methods. The DUET web server is freely and openly available at http://structure.bioc.cam.ac.uk/duet.

pkCSM: Predicting small-molecule pharmacokinetic and toxicity properties using graph-based signatures

Drug development has a high attrition rate, with poor pharmacokinetic and safety properties a significant hurdle. Computational approaches may help minimize these risks. We have developed a novel approach (pkCSM) which uses graph-based signatures to develop predictive models of central ADMET properties for drug development. pkCSM performs as well or better than current methods. A freely accessible web server (http://structure.bioc.cam.ac.uk/pkcsm), which retains no information submitted to it, provides an integrated platform to rapidly evaluate pharmacokinetic and toxicity properties.

Identification and characterization of a new cognitive enhancer based on inhibition of insulin-regulated aminopeptidase

Approximately one‐quarter of people over the age of 65 are estimated to suffer some form of cognitive impairment, underscoring the need for effec tive cognitive‐enhancing agents. Insulin‐regulated ami nopeptidase (IRAP) is potentially an innovative tar get for the development of cognitive enhancers, as its peptide inhibitors exhibit memory‐enhancing effects in both normal and memory‐impaired rodents. Using a homology model of the catalytic domain of IRAP and virtual screening, we have identified a class of nonpeptide, small‐molecule inhibitors of IRAP. Structure‐based computational development of an initial “hit” resulted in the identification of two divergent families of compounds. Subsequent medicinal chemistry performed on the highest affinity compound produced inhibitors with nanomolar affinities (Ki 20‐700 nM) for IRAP. In vivo efficacy of one of these inhibitors was demonstrated in rats with an acute dose (1 nmol in 1 μl) administered into the lateral ventricles, improving performance in both spatial working and recognition memory paradigms. We have identified a family of specific IRAP inhibi tors that is biologically active which will be useful both in understanding the physiological role of IRAP and potentially in the development of clinically useful cogni tive enhancers. Notably, this study also provides unequiv ocal proof of principal that inhibition of IRAP results in memory enhancement.— Albiston, A. L., Morton, C. J., Ng, H. L., Pham, V., Yeatman, H. R., Ye, S., Ruani, N., Fernando, R. N., De Bundel, D., Ascher, D. B., Men delsohn, F. A. O., Parker, M. W., Chai, S. Y. Identification and characterization of a new cognitive enhancer based on inhibition of insulin‐regulated aminopeptidase. FASEB J. 22, 4209–4217 (2008)

Development of cognitive enhancers based on inhibition of insulin-regulated aminopeptidase

The peptides angiotensin IV and LVV-hemorphin 7 were found to enhance memory in a number of memory tasks and reverse the performance deficits in animals with experimentally induced memory loss. These peptides bound specifically to the enzyme insulin-regulated aminopeptidase (IRAP), which is proposed to be the site in the brain that mediates the memory effects of these peptides. However, the mechanism of action is still unknown but may involve inhibition of the aminopeptidase activity of IRAP, since both angiotensin IV and LVV-hemorphin 7 are competitive inhibitors of the enzyme. IRAP also has another functional domain that is thought to regulate the trafficking of the insulin-responsive glucose transporter GLUT4, thereby influencing glucose uptake into cells. Although the exact mechanism by which the peptides enhance memory is yet to be elucidated, IRAP still represents a promising target for the development of a new class of cognitive enhancing agents.

Potent hepatitis C inhibitors bind directly to NS5A and reduce its affinity for RNA

Hepatitis C virus (HCV) infection affects more than 170 million people. The high genetic variability of HCV and the rapid development of drug-resistant strains are driving the urgent search for new direct-acting antiviral agents. A new class of agents has recently been developed that are believed to target the HCV protein NS5A although precisely where they interact and how they affect function is unknown. Here we describe an in vitro assay based on microscale thermophoresis and demonstrate that two clinically relevant inhibitors bind tightly to NS5A domain 1 and inhibit RNA binding. Conversely, RNA binding inhibits compound binding. The compounds bind more weakly to known resistance mutants L31V and Y93H. The compounds do not affect NS5A dimerisation. We propose that current NS5A inhibitors act by favouring a dimeric structure of NS5A that does not bind RNA.

Studies of glutathione transferase P1-1 bound to a platinum(IV)-based anticancer compound reveal the molecular basis of its activation.

Platinum-based cancer drugs, such as cisplatin, are highly effective chemotherapeutic agents used extensively for the treatment of solid tumors. However, their effectiveness is limited by drug resistance, which, in some cancers, has been associated with an overexpression of pi class glutathione S-transferase (GST P1-1), an important enzyme in the mercapturic acid detoxification pathway. Ethacraplatin (EA-CPT), a trans-Pt(IV) carboxylate complex containing ethacrynate ligands, was designed as a platinum cancer metallodrug that could also target cytosolic GST enzymes. We previously reported that EA-CPT was an excellent inhibitor of GST activity in live mammalian cells compared to either cisplatin or ethacrynic acid. In order to understand the nature of the drug-protein interactions between EA-CPT and GST P1-1, and to obtain mechanistic insights at a molecular level, structural and biochemical investigations were carried out, supported by molecular modeling analysis using quantum mechanical/molecular mechanical methods. The results suggest that EA-CPT preferentially docks at the dimer interface at GST P1-1 and subsequent interaction with the enzyme resulted in docking of the ethacrynate ligands at both active sites (in the H-sites), with the Pt moiety remaining bound at the dimer interface. The activation of the inhibitor by its target enzyme and covalent binding accounts for the strong and irreversible inhibition of enzymatic activity by the platinum complex.

Flexibility and small pockets at protein–protein interfaces: New insights into druggability

The transient assembly of multiprotein complexes mediates many aspects of cell regulation and signalling in living organisms. Modulation of the formation of these complexes through targeting protein–protein interfaces can offer greater selectivity than the inhibition of protein kinases, proteases or other post-translational regulatory enzymes using substrate, co-factor or transition state mimetics. However, capitalising on protein–protein interaction interfaces as drug targets has been hindered by the nature of interfaces that tend to offer binding sites lacking the well-defined large cavities of classical drug targets. In this review we posit that interfaces formed by concerted folding and binding (disorder-to-order transitions on binding) of one partner and other examples of interfaces where a protein partner is bound through a continuous epitope from a surface-exposed helix, flexible loop or chain extension may be more tractable for the development of “orthosteric”, competitive chemical modulators; these interfaces tend to offer small-volume but deep pockets and/or larger grooves that may be bound tightly by small chemical entities. We discuss examples of such protein–protein interaction interfaces for which successful chemical modulators are being developed.

PEGylation of interferon α2 improves lymphatic exposure after subcutaneous and intravenous administration and improves antitumour efficacy against lymphatic breast cancer metastases.

The efficacy of protein-based therapeutics with indications in the treatment of lymphatic diseases is expected to be improved by enhancing lymphatic disposition. This study was therefore aimed at examining whether PEGylation can usefully be applied to improve the lymphatic uptake of interferon α2 and whether this ultimately translates into improved therapeutic efficacy against lymph-resident cancer. The lymphatic pharmacokinetics of interferon α2b (IFN, 19kDa) and PEGylated interferon α2b (IFN-PEG12, 31kDa) or α2a (IFN-PEG40, 60kDa) was examined in thoracic lymph duct cannulated rats. IFN was poorly absorbed from the SC injection site (Fabs 36%) and showed little uptake into lymph after SC or IV administration (≤1%). In contrast, IFN-PEG12 was efficiently absorbed from the SC injection site (Fabs 82%) and approximately 20% and 8% of the injected dose was recovered in thoracic lymph over 30h after SC or IV administration respectively. IFN-PEG40, however, was incompletely absorbed from the SC injection site (Fabs 23%) and showed similar lymphatic access after SC administration to IFN-PEG12 (21%). The recovery of IFN-PEG40 in thoracic lymph after IV administration, however, was significantly greater (29%) when compared to IV IFN-PEG12. The anti-tumour efficacy of interferon against axillary metastases of a highly lymph-metastatic variant of human breast MDA-MB-231 carcinoma was significantly increased by SC administration of lymph-targeted IFN-PEG12 when compared to the administration of IFN on the ipsilateral side to the axillary metastasis. Optimal PEGylation may therefore represent a viable approach to improving the lymphatic disposition and efficacy of therapeutic proteins against lymphatic diseases.

Mycobacterium tuberculosis whole genome sequencing and protein structure modelling provides insights into anti-tuberculosis drug resistance

BackgroundCombating the spread of drug resistant tuberculosis is a global health priority. Whole genome association studies are being applied to identify genetic determinants of resistance to anti-tuberculosis drugs. Protein structure and interaction modelling are used to understand the functional effects of putative mutations and provide insight into the molecular mechanisms leading to resistance.MethodsTo investigate the potential utility of these approaches, we analysed the genomes of 144 Mycobacterium tuberculosis clinical isolates from The Special Programme for Research and Training in Tropical Diseases (TDR) collection sourced from 20 countries in four continents. A genome-wide approach was applied to 127 isolates to identify polymorphisms associated with minimum inhibitory concentrations for first-line anti-tuberculosis drugs. In addition, the effect of identified candidate mutations on protein stability and interactions was assessed quantitatively with well-established computational methods.ResultsThe analysis revealed that mutations in the genes rpoB (rifampicin), katG (isoniazid), inhA-promoter (isoniazid), rpsL (streptomycin) and embB (ethambutol) were responsible for the majority of resistance observed. A subset of the mutations identified in rpoB and katG were predicted to affect protein stability. Further, a strong direct correlation was observed between the minimum inhibitory concentration values and the distance of the mutated residues in the three-dimensional structures of rpoB and katG to their respective drugs binding sites.ConclusionsUsing the TDR resource, we demonstrate the usefulness of whole genome association and convergent evolution approaches to detect known and potentially novel mutations associated with drug resistance. Further, protein structural modelling could provide a means of predicting the impact of polymorphisms on drug efficacy in the absence of phenotypic data. These approaches could ultimately lead to novel resistance mutations to improve the design of tuberculosis control measures, such as diagnostics, and inform patient management.