Interpreting linear support vector machine models with heat map molecule coloring

<p>Abstract</p> <p>Background</p> <p>Model-based virtual screening plays an important role in the early drug discovery stage. The outcomes of high-throughput screenings are a valuable source for machine learning algorithms to infer such models. Besides a strong performance, the interpretability of a machine learning model is a desired property to guide the optimization of a compound in later drug discovery stages. Linear support vector machines showed to have a convincing performance on large-scale data sets. The goal of this study is to present a heat map molecule coloring technique to interpret linear support vector machine models. Based on the weights of a linear model, the visualization approach colors each atom and bond of a compound according to its importance for activity.</p> <p>Results</p> <p>We evaluated our approach on a toxicity data set, a chromosome aberration data set, and the maximum unbiased validation data sets. The experiments show that our method sensibly visualizes structure-property and structure-activity relationships of a linear support vector machine model. The coloring of ligands in the binding pocket of several crystal structures of a maximum unbiased validation data set target indicates that our approach assists to determine the correct ligand orientation in the binding pocket. Additionally, the heat map coloring enables the identification of substructures important for the binding of an inhibitor.</p> <p>Conclusions</p> <p>In combination with heat map coloring, linear support vector machine models can help to guide the modification of a compound in later stages of drug discovery. Particularly substructures identified as important by our method might be a starting point for optimization of a lead compound. The heat map coloring should be considered as complementary to structure based modeling approaches. As such, it helps to get a better understanding of the binding mode of an inhibitor.</p

Identifying allosteric fluctuation transitions between different protein conformational states as applied to Cyclin Dependent Kinase 2

BACKGROUND: The mechanisms underlying protein function and associated conformational change are dominated by a series of local entropy fluctuations affecting the global structure yet are mediated by only a few key residues. Transitional Dynamic Analysis (TDA) is a new method to detect these changes in local protein flexibility between different conformations arising from, for example, ligand binding. Additionally, Positional Impact Vertex for Entropy Transfer (PIVET) uses TDA to identify important residue contact changes that have a large impact on global fluctuation. We demonstrate the utility of these methods for Cyclin-dependent kinase 2 (CDK2), a system with crystal structures of this protein in multiple functionally relevant conformations and experimental data revealing the importance of local fluctuation changes for protein function. RESULTS: TDA and PIVET successfully identified select residues that are responsible for conformation specific regional fluctuation in the activation cycle of Cyclin Dependent Kinase 2 (CDK2). The detected local changes in protein flexibility have been experimentally confirmed to be essential for the regulation and function of the kinase. The methodologies also highlighted possible errors in previous molecular dynamic simulations that need to be resolved in order to understand this key player in cell cycle regulation. Finally, the use of entropy compensation as a possible allosteric mechanism for protein function is reported for CDK2. CONCLUSION: The methodologies embodied in TDA and PIVET provide a quick approach to identify local fluctuation change important for protein function and residue contacts that contributes to these changes. Further, these approaches can be used to check for possible errors in protein dynamic simulations and have the potential to facilitate a better understanding of the contribution of entropy to protein allostery and function

A physicochemical descriptor-based scoring scheme for effective and rapid filtering of kinase-like chemical space

<p>Abstract</p> <p>Background</p> <p>The current chemical space of known small molecules is estimated to exceed 10⁶⁰structures. Though the largest physical compound repositories contain only a few tens of millions of unique compounds, virtual screening of databases of this size is still difficult. In recent years, the application of physicochemical descriptor-based profiling, such as Lipinski's rule-of-five for drug-likeness and Oprea's criteria of lead-likeness, as early stage filters in drug discovery has gained widespread acceptance. In the current study, we outline a kinase-likeness scoring function based on known kinase inhibitors.</p> <p>Results</p> <p>The method employs a collection of 22,615 known kinase inhibitors from the ChEMBL database. A kinase-likeness score is computed using statistical analysis of nine key physicochemical descriptors for these inhibitors. Based on this score, the kinase-likeness of four publicly and commercially available databases, i.e., National Cancer Institute database (NCI), the Natural Products database (NPD), the National Institute of Health's Molecular Libraries Small Molecule Repository (MLSMR), and the World Drug Index (WDI) database, is analyzed. Three of these databases, i.e., NCI, NPD, and MLSMR are frequently used in the virtual screening of kinase inhibitors, while the fourth WDI database is for comparison since it covers a wide range of known chemical space. Based on the kinase-likeness score, a kinase-focused library is also developed and tested against three different kinase targets selected from three different branches of the human kinome tree.</p> <p>Conclusions</p> <p>Our proposed methodology is one of the first that explores how the narrow chemical space of kinase inhibitors and its relevant physicochemical information can be utilized to build kinase-focused libraries and prioritize pre-existing compound databases for screening. We have shown that focused libraries generated by filtering compounds using the kinase-likeness score have, on average, better docking scores than an equivalent number of randomly selected compounds. Beyond library design, our findings also impact the broader efforts to identify kinase inhibitors by screening pre-existing compound libraries. Currently, the NCI library is the most commonly used database for screening kinase inhibitors. Our research suggests that other libraries, such as MLSMR, are more kinase-like and should be given priority in kinase screenings.</p

A Novel Protein Kinase-Like Domain in a Selenoprotein, Widespread in the Tree of Life

Selenoproteins serve important functions in many organisms, usually providing essential oxidoreductase enzymatic activity, often for defense against toxic xenobiotic substances. Most eukaryotic genomes possess a small number of these proteins, usually not more than 20. Selenoproteins belong to various structural classes, often related to oxidoreductase function, yet a few of them are completely uncharacterised

Ethical issues in autologous stem cell transplantation (ASCT) in advanced breast cancer: A systematic literature review

BACKGROUND: An effectiveness assessment on ASCT in locally advanced and metastatic breast cancer identified serious ethical issues associated with this intervention. Our objective was to systematically review these aspects by means of a literature analysis. METHODS: We chose the reflexive Socratic approach as the review method using Hofmann's question list, conducted a comprehensive literature search in biomedical, psychological and ethics bibliographic databases and screened the resulting hits in a 2-step selection process. Relevant arguments were assembled from the included articles, and were assessed and assigned to the question list. Hofmann's questions were addressed by synthesizing these arguments. RESULTS: Of the identified 879 documents 102 included arguments related to one or more questions from Hofmann's question list. The most important ethical issues were the implementation of ASCT in clinical practice on the basis of phase-II trials in the 1990s and the publication of falsified data in the first randomized controlled trials (Bezwoda fraud), which caused significant negative effects on recruiting patients for further clinical trials and the doctor-patient relationship. Recent meta-analyses report a marginal effect in prolonging disease-free survival, accompanied by severe harms, including death. ASCT in breast cancer remains a stigmatized technology. Reported health-related-quality-of-life data are often at high risk of bias in favor of the survivors. Furthermore little attention has been paid to those patients who were dying. CONCLUSIONS: The questions were addressed in different degrees of completeness. All arguments were assignable to the questions. The central ethical dimensions of ASCT could be discussed by reviewing the published literature

Pharmacol. Ther.

Protein kinases catalyze the phosphotransfer reaction fundamental to most signaling and regulatory processes in the eukaryotic cell. Absolute control of individual protein kinase activity is, therefore, of utmost importance to signaling fidelity in the cell. Mechanisms for activity modulation, including complete and reversible inactivation, have been shown by crystal structures of many active and inactive protein kinases. The structures of inactivated kinases, compared with those of active and catalytically competent kinases such as the protein kinase A catalytic subunit, highlight recurring structural alterations among a set of elements of the catalytic kinase core. These 'activity modulation sites' apparently comprise the principal evolved mechanisms for control of enzyme activity in the catalytic domain. In combination, they enable diverse physiological regulatory mechanisms operative for most protein kinases. Identification and characterization of these sites should impact strategies for discovery and design of target-specific therapeutic drugs as the range of structural variations for specific kinases becomes known. The principle site, the ATP-binding pocket, is the target of many physiological regulators and also most experimental or therapeutic inhibitors, which typically block it in a competitive or allosteric fashion. Co-crystallization studies with protein kinase A and other kinases have revealed binding features of several classes of protein kinase inhibitors. Ligand-induced structural changes are common and tend to optimize buried surface areas. The ability to optimize binding energies arising from the hydrophobic effect creates a logarithmic dependence of binding energy on buried surface areas. Exceptions to this rule arise for specific inhibitor classes, and possibly also as artifacts of structure determination. (C) 2002 Elsevier Science Inc. All rights reserved