Relapsed acute lymphoblastic leukemia-specific mutations in NT5C2 cluster into hotspots driving intersubunit stimulation

Characterizing and predicting ccRCC-causing missense mutations in Von Hippel-Lindau disease

BACKGROUND

Mutations within the Von Hippel-Lindau (VHL) tumor suppressor gene are known to cause VHL disease, which is characterized by the formation of cysts and tumors in multiple organs of the body, particularly clear cell renal cell carcinoma (ccRCC). A major challenge in clinical practice is determining tumor risk from a given mutation in the VHL gene. Previous efforts have been hindered by limited available clinical data and technological constraints.

METHODS

To overcome this, we initially manually curated the largest set of clinically validated VHL mutations to date, enabling a robust assessment of existing predictive tools on an independent test set. Additionally, we comprehensively characterized the effects of mutations within VHL using in silico biophysical tools describing changes in protein stability, dynamics and affinity to binding partners to provide insights into the structure-phenotype relationship. These descriptive properties were used as molecular features for the construction of a machine learning model, designed to predict the risk of ccRCC development as a result of a VHL missense mutation.

RESULTS

Analysis of our model showed an accuracy of 0.81 in the identification of ccRCC-causing missense mutations, and a Matthew's Correlation Coefficient of 0.44 on a non-redundant blind test, a significant improvement in comparison to the previous available approaches.

CONCLUSION

This work highlights the power of using protein 3D structure to fully explore the range of molecular and functional consequences of genomic variants. We believe this optimized model will better enable its clinical implementation and assist guiding patient risk stratification and management.

AI-Driven Enhancements in Drug Screening and Optimization

The greatest challenge in drug discovery remains the high rate of attrition across the different phases of the process, which cost the industry billions of dollars every year. While all phases remain crucial to ensure pharmaceutical-level safety, quality, and efficacy of the end product, streamlining these efforts toward compounds with success potential is pivotal for a more efficient and cost-effective process. The use of artificial intelligence (AI) within the pharmaceutical industry aims at just this, and has applications in preclinical screening for biological activity, optimization of pharmacokinetic properties for improved drug formulation, early toxicity prediction which reduces attrition, and pre-emptively screening for genetic changes in the biological target to improve therapeutic longevity. Here, we present a series of in silico tools that address these applications in small molecule development and describe how they can be embedded within the current pharmaceutical development pipeline.

Using Graph-Based Signatures to Guide Rational Antibody Engineering

Antibodies are essential experimental and diagnostic tools and as biotherapeutics have significantly advanced our ability to treat a range of diseases. With recent innovations in computational tools to guide protein engineering, we can now rationally design better antibodies with improved efficacy, stability, and pharmacokinetics. Here, we describe the use of the mCSM web-based in silico suite, which uses graph-based signatures to rapidly identify the structural and functional consequences of mutations, to guide rational antibody engineering to improve stability, affinity, and specificity.

Pharmacologic Inhibition of NT5C2 Reverses Genetic and Nongenetic Drivers of 6-MP Resistance in Acute Lymphoblastic Leukemia

Low-intensity maintenance therapy with 6-mercaptopurine (6-MP) limits the occurrence of acute lymphoblastic leukemia (ALL) relapse and is central to the success of multiagent chemotherapy protocols. Activating mutations in the 5'-nucleotidase cytosolic II (NT5C2) gene drive resistance to 6-MP in over 35% of early relapse ALL cases. Here we identify CRCD2 as a first-in-class small-molecule NT5C2 nucleotidase inhibitor broadly active against leukemias bearing highly prevalent relapse-associated mutant forms of NT5C2 in vitro and in vivo. Importantly, CRCD2 treatment also enhanced the cytotoxic activity of 6-MP in NT5C2 wild-type leukemias, leading to the identification of NT5C2 Ser502 phosphorylation as a novel NT5C2-mediated mechanism of 6-MP resistance in this disease. These results uncover an unanticipated role of nongenetic NT5C2 activation as a driver of 6-MP resistance in ALL and demonstrate the potential of NT5C2 inhibitor therapy for enhancing the efficacy of thiopurine maintenance therapy and overcoming resistance at relapse.

SIGNIFICANCE

Relapse-associated NT5C2 mutations directly contribute to relapse in ALL by driving resistance to chemotherapy with 6-MP. Pharmacologic inhibition of NT5C2 with CRCD2, a first-in-class nucleotidase inhibitor, enhances the cytotoxic effects of 6-MP and effectively reverses thiopurine resistance mediated by genetic and nongenetic mechanisms of NT5C2 activation in ALL. This article is highlighted in the In This Issue feature, p. 2483.

Systematic evaluation of computational tools to predict the effects of mutations on protein stability in the absence of experimental structures

Changes in protein sequence can have dramatic effects on how proteins fold, their stability and dynamics. Over the last 20 years, pioneering methods have been developed to try to estimate the effects of missense mutations on protein stability, leveraging growing availability of protein 3D structures. These, however, have been developed and validated using experimentally derived structures and biophysical measurements. A large proportion of protein structures remain to be experimentally elucidated and, while many studies have based their conclusions on predictions made using homology models, there has been no systematic evaluation of the reliability of these tools in the absence of experimental structural data. We have, therefore, systematically investigated the performance and robustness of ten widely used structural methods when presented with homology models built using templates at a range of sequence identity levels (from 15% to 95%) and contrasted performance with sequence-based tools, as a baseline. We found there is indeed performance deterioration on homology models built using templates with sequence identity below 40%, where sequence-based tools might become preferable. This was most marked for mutations in solvent exposed residues and stabilizing mutations. As structure prediction tools improve, the reliability of these predictors is expected to follow, however we strongly suggest that these factors should be taken into consideration when interpreting results from structure-based predictors of mutation effects on protein stability.

The amazing cN-II, the enzyme that keeps us busy

cN-II is a cytosolic 5'-nucleotidase with preference for IMP and GMP over AMP. The enzyme has been extensively studied over the last 20-30 years both for its enzymatic activity, structure, role in nucleotide metabolism and in cell biology, as well as in diseases. With the aim of highlighting the complexity of the enzyme, I will, as during PP21, present work from our group and others working on cN-II and its various roles and not give an exhaustive overview of new data.

mmCSM-PPI: predicting the effects of multiple point mutations on protein-protein interactions

Protein-protein interactions play a crucial role in all cellular functions and biological processes and mutations leading to their disruption are enriched in many diseases. While a number of computational methods to assess the effects of variants on protein-protein binding affinity have been proposed, they are in general limited to the analysis of single point mutations and have been shown to perform poorly on independent test sets. Here, we present mmCSM-PPI, a scalable and effective machine learning model for accurately assessing changes in protein-protein binding affinity caused by single and multiple missense mutations. We expanded our well-established graph-based signatures in order to capture physicochemical and geometrical properties of multiple wild-type residue environments and integrated them with substitution scores and dynamics terms from normal mode analysis. mmCSM-PPI was able to achieve a Pearson's correlation of up to 0.75 (RMSE = 1.64 kcal/mol) under 10-fold cross-validation and 0.70 (RMSE = 2.06 kcal/mol) on a non-redundant blind test, outperforming existing methods. Our method is freely available as a user-friendly and easy-to-use web server and API at http://biosig.unimelb.edu.au/mmcsm_ppi.

Distinguishing between PTEN clinical phenotypes through mutation analysis

Phosphate and tensin homolog on chromosome ten (PTEN) germline mutations are associated with an overarching condition known as PTEN hamartoma tumor syndrome. Clinical phenotypes associated with this syndrome range from macrocephaly and autism spectrum disorder to Cowden syndrome, which manifests as multiple noncancerous tumor-like growths (hamartomas), and an increased predisposition to certain cancers. It is unclear, however, the basis by which mutations might lead to these very diverse phenotypic outcomes. Here we show that, by considering the molecular consequences of mutations in PTEN on protein structure and function, we can accurately distinguish PTEN mutations exhibiting different phenotypes. Changes in phosphatase activity, protein stability, and intramolecular interactions appeared to be major drivers of clinical phenotype, with cancer-associated variants leading to the most drastic changes, while ASD and non-pathogenic variants associated with more mild and neutral changes, respectively. Importantly, we show via saturation mutagenesis that more than half of variants of unknown significance could be associated with disease phenotypes, while over half of Cowden syndrome mutations likely lead to cancer. These insights can assist in exploring potentially important clinical outcomes delineated by PTEN variation.

CD73 and cN-II regulate the cellular response to chemotherapeutic and hypoxic stress in lung adenocarcinoma cells

BACKGROUND

Cytosolic 5'-nucleotidase II (cN-II) and ecto-5'-nucleotidase (CD73) are enzymes involved in the nucleotide metabolism by dephosphorylating nucleoside monophosphates. Both enzymes are involved in cancer by modifying anticancer drug activity, cancer cell biology and immune modulation.

METHODS

We have modified lung cancer cells (NCI-H292) to become deficient for either or both enzymes using the CRISPR/Cas9 technique, and studied the implication of the two enzymes in the cellular response to different stress condition i.e. chemotherapeutic agents, hypoxia and nucleotide stress.

RESULTS

Our results show that there is no significant role of these enzymes in cell proliferation under hypoxic stress. Similarly, cN-II and CD73 are not involved in wound healing ability under CoCl₂-mediated HIF-1α stabilization. Furthermore, our results show that CD73-deficiency is associated with increased apoptosis in response to 1600 μM adenosine, decreased sensitivity to mitomycin and enhanced sensitivity to vincristine. cN-II deficiency increased in vivo tumor growth and sensitivity to vincristine and mitomycin C.

CONCLUSIONS

Our study gives new insights into the biological roles of cN-II and CD73 under stress conditions in this particular cancer cell line. Further experiments will help deciphering the molecular mechanisms underlying the observed differences.

Promising genes and variants to reduce chemotherapy adverse effects in acute lymphoblastic leukemia

Almost two decades ago, the sequencing of the human genome and high throughput technologies came to revolutionize the clinical and therapeutic approaches of patients with complex human diseases. In acute lymphoblastic leukemia (ALL), the most frequent childhood malignancy, these technologies have enabled to characterize the genomic landscape of the disease and have significantly improved the survival rates of ALL patients. Despite this, adverse reactions from treatment such as toxicity, drug resistance and secondary tumors formation are still serious consequences of chemotherapy, and the main obstacles to reduce ALL-related mortality. It is well known that germline variants and somatic mutations in genes involved in drug metabolism impact the efficacy of drugs used in oncohematological diseases therapy. So far, a broader spectrum of clinically actionable alterations that seems to be crucial for the progression and treatment response have been identified. Although these results are promising, it is necessary to put this knowledge into the clinics to help physician make medical decisions and generate an impact in patients' health. This review summarizes the gene variants and clinically actionable mutations that modify the efficacy of antileukemic drugs. Therefore, knowing their genetic status before treatment is critical to reduce severe adverse effects, toxicities and life-threatening consequences in ALL patients.

mCSM-membrane: predicting the effects of mutations on transmembrane proteins

Significant efforts have been invested into understanding and predicting the molecular consequences of mutations in protein coding regions, however nearly all approaches have been developed using globular, soluble proteins. These methods have been shown to poorly translate to studying the effects of mutations in membrane proteins. To fill this gap, here we report, mCSM-membrane, a user-friendly web server that can be used to analyse the impacts of mutations on membrane protein stability and the likelihood of them being disease associated. mCSM-membrane derives from our well-established mutation modelling approach that uses graph-based signatures to model protein geometry and physicochemical properties for supervised learning. Our stability predictor achieved correlations of up to 0.72 and 0.67 (on cross validation and blind tests, respectively), while our pathogenicity predictor achieved a Matthew's Correlation Coefficient (MCC) of up to 0.77 and 0.73, outperforming previously described methods in both predicting changes in stability and in identifying pathogenic variants. mCSM-membrane will be an invaluable and dedicated resource for investigating the effects of single-point mutations on membrane proteins through a freely available, user friendly web server at http://biosig.unimelb.edu.au/mcsm_membrane.

mCSM-AB2: guiding rational antibody design using graph-based signatures

MOTIVATION

A lack of accurate computational tools to guide rational mutagenesis has made affinity maturation a recurrent challenge in antibody (Ab) development. We previously showed that graph-based signatures can be used to predict the effects of mutations on Ab binding affinity.

RESULTS

Here we present an updated and refined version of this approach, mCSM-AB2, capable of accurately modelling the effects of mutations on Ab-antigen binding affinity, through the inclusion of evolutionary and energetic terms. Using a new and expanded database of over 1800 mutations with experimental binding measurements and structural information, mCSM-AB2 achieved a Pearson's correlation of 0.73 and 0.77 across training and blind tests, respectively, outperforming available methods currently used for rational Ab engineering.

AVAILABILITY AND IMPLEMENTATION

mCSM-AB2 is available as a user-friendly and freely accessible web server providing rapid analysis of both individual mutations or the entire binding interface to guide rational antibody affinity maturation at http://biosig.unimelb.edu.au/mcsm_ab2.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

DynaMut2: Assessing changes in stability and flexibility upon single and multiple point missense mutations

Predicting the effect of missense variations on protein stability and dynamics is important for understanding their role in diseases, and the link between protein structure and function. Approaches to estimate these changes have been proposed, but most only consider single‐point missense variants and a static state of the protein, with those that incorporate dynamics are computationally expensive. Here we present DynaMut2, a web server that combines Normal Mode Analysis (NMA) methods to capture protein motion and our graph‐based signatures to represent the wildtype environment to investigate the effects of single and multiple point mutations on protein stability and dynamics. DynaMut2 was able to accurately predict the effects of missense mutations on protein stability, achieving Pearson's correlation of up to 0.72 (RMSE: 1.02 kcal/mol) on a single point and 0.64 (RMSE: 1.80 kcal/mol) on multiple‐point missense mutations across 10‐fold cross‐validation and independent blind tests. For single‐point mutations, DynaMut2 achieved comparable performance with other methods when predicting variations in Gibbs Free Energy (ΔΔG) and in melting temperature (ΔT_m). We anticipate our tool to be a valuable suite for the study of protein flexibility analysis and the study of the role of variants in disease. DynaMut2 is freely available as a web server and API at http://biosig.unimelb.edu.au/dynamut2.

Identifying Genotype-Phenotype Correlations via Integrative Mutation Analysis

Mutations in protein-coding regions can lead to large biological changes and are associated with genetic conditions, including cancers and Mendelian diseases, as well as drug resistance. Although whole genome and exome sequencing help to elucidate potential genotype-phenotype correlations, there is a large gap between the identification of new variants and deciphering their molecular consequences. A comprehensive understanding of these mechanistic consequences is crucial to better understand and treat diseases in a more personalized and effective way. This is particularly relevant considering estimates that over 80% of mutations associated with a disease are incorrectly assumed to be causative. A thorough analysis of potential effects of mutations is required to correctly identify the molecular mechanisms of disease and enable the distinction between disease-causing and non-disease-causing variation within a gene. Here we present an overview of our integrative mutation analysis platform, which focuses on refining the current genotype-phenotype correlation methods by using the wealth of protein structural information.

mmCSM-AB: guiding rational antibody engineering through multiple point mutations

While antibodies are becoming an increasingly important therapeutic class, especially in personalized medicine, their development and optimization has been largely through experimental exploration. While there have been many efforts to develop computational tools to guide rational antibody engineering, most approaches are of limited accuracy when applied to antibody design, and have largely been limited to analysing a single point mutation at a time. To overcome this gap, we have curated a dataset of 242 experimentally determined changes in binding affinity upon multiple point mutations in antibody-target complexes (89 increasing and 153 decreasing binding affinity). Here, we have shown that by using our graph-based signatures and atomic interaction information, we can accurately analyse the consequence of multi-point mutations on antigen binding affinity. Our approach outperformed other available tools across cross-validation and two independent blind tests, achieving Pearson's correlations of up to 0.95. We have implemented our new approach, mmCSM-AB, as a web-server that can help guide the process of affinity maturation in antibody design. mmCSM-AB is freely available at http://biosig.unimelb.edu.au/mmcsm_ab/.

Subclonal NT5C2 mutations are associated with poor outcomes after relapse of pediatric acute lymphoblastic leukemia

Activating mutations in cytosolic 5'-nucleotidase II (NT5C2) are considered to drive relapse formation in acute lymphoblastic leukemia (ALL) by conferring purine analog resistance. To examine the clinical effects of NT5C2 mutations in relapsed ALL, we analyzed NT5C2 in 455 relapsed B-cell precursor ALL patients treated within the ALL-REZ BFM 2002 relapse trial using sequencing and sensitive allele-specific real-time polymerase chain reaction. We detected 110 NT5C2 mutations in 75 (16.5%) of 455 B-cell precursor ALL relapses. Two-thirds of relapses harbored subclonal mutations and only one-third harbored clonal mutations. Event-free survival after relapse was inferior in patients with relapses with clonal and subclonal NT5C2 mutations compared with those without (19% and 25% vs 53%, P < .001). However, subclonal, but not clonal, NT5C2 mutations were associated with reduced event-free survival in multivariable analysis (hazard ratio, 1.89; 95% confidence interval, 1.28-2.69; P = .001) and with an increased rate of nonresponse to relapse treatment (subclonal 32%, clonal 12%, wild type 9%, P < .001). Nevertheless, 27 (82%) of 33 subclonal NT5C2 mutations became undetectable at the time of nonresponse or second relapse, and in 10 (71%) of 14 patients subclonal NT5C2 mutations were undetectable already after relapse induction treatment. These results show that subclonal NT5C2 mutations define relapses associated with high risk of treatment failure in patients and at the same time emphasize that their role in outcome is complex and goes beyond mutant NT5C2 acting as a targetable driver during relapse progression. Sensitive, prospective identification of NT5C2 mutations is warranted to improve the understanding and treatment of this aggressive ALL relapse subtype.

A Comprehensive Computational Platform to Guide Drug Development Using Graph-Based Signature Methods

High-throughput computational techniques have become invaluable tools to help increase the overall success, process efficiency, and associated costs of drug development. By designing ligands tailored to specific protein structures in a disease of interest, an understanding of molecular interactions and ways to optimize them can be achieved prior to chemical synthesis. This understanding can help direct crucial chemical and biological experiments by maximizing available resources on higher quality leads. Moreover, predicting molecular binding affinity within specific biological contexts, as well as ligand pharmacokinetics and toxicities, can aid in filtering out redundant leads early on within the process. We describe a set of computational tools which can aid in drug discovery at different stages, from hit identification (EasyVS) to lead optimization and candidate selection (CSM-lig, mCSM-lig, Arpeggio, pkCSM). Incorporating these tools along the drug development process can help ensure that candidate leads are chemically and biologically feasible to become successful and tractable drugs.

mCSM-PPI2: predicting the effects of mutations on protein-protein interactions

Protein-protein Interactions are involved in most fundamental biological processes, with disease causing mutations enriched at their interfaces. Here we present mCSM-PPI2, a novel machine learning computational tool designed to more accurately predict the effects of missense mutations on protein-protein interaction binding affinity. mCSM-PPI2 uses graph-based structural signatures to model effects of variations on the inter-residue interaction network, evolutionary information, complex network metrics and energetic terms to generate an optimised predictor. We demonstrate that our method outperforms previous methods, ranking first among 26 others on CAPRI blind tests. mCSM-PPI2 is freely available as a user friendly webserver at http://biosig.unimelb.edu.au/mcsm_ppi2/.

Genetics and mechanisms of NT5C2-driven chemotherapy resistance in relapsed ALL

Mutations in the cytosolic 5' nucleotidase II (NT5C2) gene drive resistance to thiopurine chemotherapy in relapsed acute lymphoblastic leukemia (ALL). Mechanistically, NT5C2 mutant proteins have increased nucleotidase activity as a result of altered activating and autoregulatory switch-off mechanisms. Leukemias with NT5C2 mutations are chemoresistant to 6-mercaptopurine yet show impaired proliferation and self-renewal. Direct targeting of NT5C2 or inhibition of compensatory pathways active in NT5C2 mutant cells may antagonize the emergence of NT5C2 mutant clones driving resistance and relapse in ALL.

Exploring Protein Supersecondary Structure Through Changes in Protein Folding, Stability, and Flexibility

The ability to predict how mutations affect protein structure, folding, and flexibility can elucidate the molecular mechanisms leading to disruption of supersecondary structures, the emergence of phenotypes, as well guiding rational protein engineering. The advent of fast and accurate computational tools has enabled us to comprehensively explore the landscape of mutation effects on protein structures, prioritizing mutations for rational experimental validation.Here we describe the use of two complementary web-based in silico methods, DUET and DynaMut, developed to infer the effects of mutations on folding, stability, and flexibility and how they can be used to explore and interpret these effects on protein supersecondary structures.

Emerging Role of Purine Metabolizing Enzymes in Brain Function and Tumors

The growing evidence of the involvement of purine compounds in signaling, of nucleotide imbalance in tumorigenesis, the discovery of purinosome and its regulation, cast new light on purine metabolism, indicating that well known biochemical pathways may still surprise. Adenosine deaminase is important not only to preserve functionality of immune system but also to ensure a correct development and function of central nervous system, probably because its activity regulates the extracellular concentration of adenosine and therefore its function in brain. A lot of work has been done on extracellular 5′-nucleotidase and its involvement in the purinergic signaling, but also intracellular nucleotidases, which regulate the purine nucleotide homeostasis, play unexpected roles, not only in tumorigenesis but also in brain function. Hypoxanthine guanine phosphoribosyl transferase (HPRT) appears to have a role in the purinosome formation and, therefore, in the regulation of purine synthesis rate during cell cycle with implications in brain development and tumors. The final product of purine catabolism, uric acid, also plays a recently highlighted novel role. In this review, we discuss the molecular mechanisms underlying the pathological manifestations of purine dysmetabolisms, focusing on the newly described/hypothesized roles of cytosolic 5′-nucleotidase II, adenosine kinase, adenosine deaminase, HPRT, and xanthine oxidase.

Nucleobase and Nucleoside Analogues: Resistance and Re-Sensitisation at the Level of Pharmacokinetics, Pharmacodynamics and Metabolism

Antimetabolites, in particular nucleobase and nucleoside analogues, are cytotoxic drugs that, starting from the small field of paediatric oncology, in combination with other chemotherapeutics, have revolutionised clinical oncology and transformed cancer into a curable disease. However, even though combination chemotherapy, together with radiation, surgery and immunotherapy, can nowadays cure almost all types of cancer, we still fail to achieve this for a substantial proportion of patients. The understanding of differences in metabolism, pharmacokinetics, pharmacodynamics, and tumour biology between patients that can be cured and patients that cannot, builds the scientific basis for rational therapy improvements. Here, we summarise current knowledge of how tumour-specific and patient-specific factors can dictate resistance to nucleobase/nucleoside analogues, and which strategies of re-sensitisation exist. We revisit well-established hurdles to treatment efficacy, like the blood-brain barrier and reduced deoxycytidine kinase activity, but will also discuss the role of novel resistance factors, such as SAMHD1. A comprehensive appreciation of the complex mechanisms that underpin the failure of chemotherapy will hopefully inform future strategies of personalised medicine.

Structure and Mechanisms of NT5C2 Mutations Driving Thiopurine Resistance in Relapsed Lymphoblastic Leukemia

Activating mutations in the cytosolic 5'-nucleotidase II gene NT5C2 drive resistance to 6-mercaptopurine in acute lymphoblastic leukemia. Here we demonstrate that constitutively active NT5C2 mutations K359Q and L375F reconfigure the catalytic center for substrate access and catalysis in the absence of allosteric activator. In contrast, most relapse-associated mutations, which involve the arm segment and residues along the surface of the inter-monomeric cavity, disrupt a built-in switch-off mechanism responsible for turning off NT5C2. In addition, we show that the C-terminal acidic tail lost in the Q523X mutation functions to restrain NT5C2 activation. These results uncover dynamic mechanisms of enzyme regulation targeted by chemotherapy resistance-driving NT5C2 mutations, with important implications for the development of NT5C2 inhibitor therapies.

Kinact: a computational approach for predicting activating missense mutations in protein kinases

Protein phosphorylation is tightly regulated due to its vital role in many cellular processes. While gain of function mutations leading to constitutive activation of protein kinases are known to be driver events of many cancers, the identification of these mutations has proven challenging. Here we present Kinact, a novel machine learning approach for predicting kinase activating missense mutations using information from sequence and structure. By adapting our graph-based signatures, Kinact represents both structural and sequence information, which are used as evidence to train predictive models. We show the combination of structural and sequence features significantly improved the overall accuracy compared to considering either primary or tertiary structure alone, highlighting their complementarity. Kinact achieved a precision of 87% and 94% and Area Under ROC Curve of 0.89 and 0.92 on 10-fold cross-validation, and on blind tests, respectively, outperforming well established tools (P < 0.01). We further show that Kinact performs equally well on homology models built using templates with sequence identity as low as 33%. Kinact is freely available as a user-friendly web server at http://biosig.unimelb.edu.au/kinact/.