Fuzzy virtual ligands for virtual screening

A new method to bridge the gap between ligand and receptor-based methods in virtual screening (VS) is presented. We introduce a structure-derived virtual ligand (VL) model as an extension to a previously published pseudo-ligand technique [1]: LIQUID [2] fuzzy pharmacophore virtual screening is combined with grid-based protein binding site predictions of PocketPicker [3]. This approach might help reduce bias introduced by manual selection of binding site residues and introduces pocket shape information to the VL. It allows for a combination of several protein structure models into a single "fuzzy" VL representation, which can be used to scan screening compound collections for ligand structures with a similar potential pharmacophore. PocketPicker employs an elaborate grid-based scanning procedure to determine buried cavities and depressions on the protein's surface. Potential binding sites are represented by clusters of grid probes characterizing the shape and accessibility of a cavity. A rule-based system is then applied to project reverse pharmacophore types onto the grid probes of a selected pocket. The pocket pharmacophore types are assigned depending on the properties and geometry of the protein residues surrounding the pocket with regard to their relative position towards the grid probes. LIQUID is used to cluster representative pocket probes by their pharmacophore types describing a fuzzy VL model. The VL is encoded in a correlation vector, which can then be compared to a database of pre-calculated ligand models. A retrospective screening using the fuzzy VL and several protein structures was evaluated by ten fold cross-validation with ROC-AUC and BEDROC metrics, obtaining a significant enrichment of actives. Future work will be devoted to prospective screening using a novel protein target of Helicobacter pylori and compounds from commercial providers

PocketPicker: analysis of ligand binding-sites with shape descriptors

Background Identification and evaluation of surface binding-pockets and occluded cavities are initial steps in protein structure-based drug design. Characterizing the active site's shape as well as the distribution of surrounding residues plays an important role for a variety of applications such as automated ligand docking or in situ modeling. Comparing the shape similarity of binding site geometries of related proteins provides further insights into the mechanisms of ligand binding. Results We present PocketPicker, an automated grid-based technique for the prediction of protein binding pockets that specifies the shape of a potential binding-site with regard to its buriedness. The method was applied to a representative set of protein-ligand complexes and their corresponding apo-protein structures to evaluate the quality of binding-site predictions. The performance of the pocket detection routine was compared to results achieved with the existing methods CAST, LIGSITE, LIGSITEcs, PASS and SURFNET. Success rates PocketPicker were comparable to those of LIGSITEcs and outperformed the other tools. We introduce a descriptor that translates the arrangement of grid points delineating a detected binding-site into a correlation vector. We show that this shape descriptor is suited for comparative analyses of similar binding-site geometry by examining induced-fit phenomena in aldose reductase. This new method uses information derived from calculations of the buriedness of potential binding-sites. Conclusions The pocket prediction routine of PocketPicker is a useful tool for identification of potential protein binding-pockets. It produces a convenient representation of binding-site shapes including an intuitive description of their accessibility. The shape-descriptor for automated classification of binding-site geometries can be used as an additional tool complementing elaborate manual inspections

PocketGraph : graph representation of binding site volumes

The representation of small molecules as molecular graphs is a common technique in various fields of cheminformatics. This approach employs abstract descriptions of topology and properties for rapid analyses and comparison. Receptor-based methods in contrast mostly depend on more complex representations impeding simplified analysis and limiting the possibilities of property assignment. In this study we demonstrate that ligand-based methods can be applied to receptor-derived binding site analysis. We introduce the new method PocketGraph that translates representations of binding site volumes into linear graphs and enables the application of graph-based methods to the world of protein pockets. The method uses the PocketPicker algorithm for characterization of binding site volumes and employs a Growing Neural Gas procedure to derive graph representations of pocket topologies. Self-organizing map (SOM) projections revealed a limited number of pocket topologies. We argue that there is only a small set of pocket shapes realized in the known ligand-receptor complexes

Analysis of shape, properties and "druggability" of protein binding pockets

Kenntnisse über die dreidimensionale Struktur therapeutisch relevanter Zielproteine bieten wertvolle Informationen für den rationalen Wirkstoffentwurf. Die stetig wachsende Zahl aufgeklärter Kristallstrukturen von Proteinen ermöglicht eine qualitative und quantitative rechnergestützte Untersuchung von spezifischen Protein-Liganden Wechselwirkungen. Im Rahmen dieser Arbeit wurden neue Algorithmen für die Identifikation und den Ähnlichkeitsvergleich von Proteinbindetaschen und ihren Eigenschaften entwickelt und in dem Programm PocketomePicker zusammengefasst. Die Software gliedert sich in die Routinen PocketPicker, PocketShapelets und PocketGraph. Ferner wurde in dieser Arbeit die Methode ReverseLIQUID reimplementiert und im Rahmen einer Kooperation für das strukturbasierte Virtuelle Screening angewendet. Die genannten Methoden und ihre wissenschaftliche Anwendungen sollte hier zusammengefasst werden: Die Methode PocketPicker ermöglicht die Vorhersage potentieller Bindetaschen auf Proteinoberflächen. Diese Technik implementiert einen geometrischen Ansatz auf Basis „künstlicher Gitter“ zur Identifikation zusammenhängender vergrabener Bereiche der Proteinoberfläche als Orte möglicher Ligandenbindestellen. Die Methode erreicht eine korrekte Vorhersage der tatsächlichen Bindetasche für 73 % der Einträge eines repräsentativen Datensatzes von Proteinstrukturen. Für 90 % der Proteinstrukturen wird die tatsächlich Ligandenbindestelle unter den drei wahrscheinlichsten vorhergesagten Taschen gefunden. PocketPicker übertrifft die Vorhersagequalität anderer etablierter Algorithmen und ermöglicht Taschenidentifikationen auf apo-Strukturen ohne signifikante Einbußen des Vorhersageerfolges. Andere Verfahren weisen deutlich eingeschränkte Ergebnisse bei der Anwendung auf apo-Strukturen auf. PocketPicker erlaubt den alignmentfreien Ähnlichkeitsvergleich von Bindetaschenfor-men durch die Kodierung berechneter Bindevolumen als Korrelationsdeskriptoren. Dieser Ansatz wurde erfolgreich für Funktionsvorhersage von Bindetaschen aus Homologiemodellen von APOBEC3C und Glutamat Dehydrogenase des Malariaerregers Plasmodium falciparum angewendet. Diese beiden Projekte wurden in Zusammenarbeit mit Kollaborationspartnern durchgeführt. Zudem wurden PocketPicker Korrelationsdeskriptoren erfolgreich für die automatisierte Konformationsanalyse der enzymatischen Tasche von Aldose Reduktase angewendet. Für detaillierte Analysen der Form und der physikochemischen Eigenschaften von Proteinbindetaschen wurde in dieser Arbeit die Methode PocketShapelets entwickelt. Diese Technik ermöglicht strukturelle Alignments von extrahierten Bindevolumen durch Zerlegungen der Oberfläche von Proteinbindetaschen. Die Überlagerung gelingt durch die Identifikation strukturell ähnlicher Oberflächenkurvaturen zweier Taschen. PocketShapelets wurde erfolgreich zur Analyse funktioneller Ähnlichkeit von Bindetaschen verwendet, die auf Betrachtungen physikochemischer Eigenschaften basiert. Zur Analyse der topologischen Vielfalt von Bindetaschengeometrien wurde in dieser Arbeit die Methode PocketGraph entwickelt. Dieser Ansatz nutzt das Konzept des sog. „Wachsenden Neuronalen Gases“ aus dem Bereich des maschinellen Lernens für eine automatische Extraktion des strukturellen Aufbaus von Bindetaschen. Ferner ermöglicht diese Methode die Zerlegung einer Bindestelle in ihre Subtaschen. Die von PocketPicker charakterisierten Taschenvolumen bilden die Grundlage für die Methode ReverseLIQUID. Dieses Programm wurde in dieser Arbeit weiterentwickelt und im Rahmen einer Kooperation zur Identifikation eines Inhibitors der Serinprotease HtrA des Erregers Helicobacter pylori verwendet. Mit ReverseLIQUID konnte ein strukturbasiertes Pharmakophormodell für das Virtuelle Screening erstellt werden. Dieser Ansatz ermöglichte die Identifikation einer Substanz mit niedrig mikromolarer Affinität gegenüber der Zielstruktur.Knowledge of the three-dimensional structure therapeutically relevant target proteins provides valuable information for rational drug design. The constantly increasing numbers of available crystal structures enable qualitative and quantitative analysis of specific protein-ligand interactions in silico. In this work novel algorithms for the identification and the comparison of protein binding sites and their properties were developed and combined in the program PocketomePicker. The software combines the routines PocketPicker, PocketShapelets and PocketGraph. Furthermore, the method ReverseLIQUID was re-implemented in this work and used for the structure-based virtual screening with a cooperation partner. The programs and their scientific applications are summarized here: The method PocketPicker is designed for the prediction of potential binding sites on protein surfaces. The technique implements a geometric approach based on the concept of “artificial grids” for the identification of continuous buried regions of the protein surface that might act as potential ligand binding sites. The method yields correct predications of the actual binding site for 73 % of the entries in a representative data set of protein structures. For 90 % of the proteins the actual binding site is found among the top three predicted binding pockets. PocketPicker exceeds the predictive quality of other established algorithms and enables correct binding site identifications on apo structures without significant drops of the prediction success. This is not achieved by other programs. PocketPicker enables alignment-free comparisons of binding site shapes by encoding extracted binding volumes as correlation vectors. This approach was used for successful predictions of binding site functionality for homology models of APOBEC3C and glutamate dehydrogenase of the malaria pathogen Plasmodium falciparum. These projects were carried out with collaboration partners. Furthermore, PocketPicker correlation descriptors were used for automated analysis of binding site conformations of aldose reductase active sites. The method PocketShapelets was implemented in this work for detailed analysis of shapes and physicochemical properties of protein binding sites. This approach enables structural alignments of extracted binding volumes by surface decomposition of protein binding sites. The structural superposition is achieved by identification of structurally similar surface curvatures of different binding pockets. PocketShapelets was successfully used for the analysis of functional similarity of binding sites based on observations of physicochemical properties. PocketGraph was developed for the analysis of the structural diversity of binding site geometries. This approach uses the “Growing Neural Gas” concept used in machine learning for an automated extraction of the structural organization of binding sites. Furthermore, the method enables the decomposition of binding sites into subpockets. The pocket volumes characterized by PocketPicker are the foundation of another program called ReverseLIQUID. This method was refined in this work and used for the identification of a Helicobacter pylori serine protease HtrA inhibitor. This project was performed with a collaboration partner. A receptor-based pharmacophore model was derived using ReverseLIQUID and used for virtual screening. This approach led to the identification of a substance with low micromolar affinity towards the target protein

A Modular Fibrinogen Model that Captures the Stress-Strain Behavior of Fibrin Fibers

We tested what to our knowledge is a new computational model for fibrin fiber mechanical behavior. The model is composed of three distinct elements: the folded fibrinogen core as seen in the crystal structure, the unstructured α-C connector, and the partially folded α-C domain. Previous studies have highlighted the importance of all three regions and how they may contribute to fibrin fiber stress-strain behavior. Yet no molecular model has been computationally tested that takes into account the individual contributions of all these regions. Constant velocity, steered molecular dynamics studies at 0.025 Å/ps were conducted on the folded fibrinogen core and the α-C domain to determine their force-displacement behavior. A wormlike chain model with a persistence length of 0.8 nm (Kuhn length = 1.6 nm) was used to model the mechanical behavior of the unfolded α-C connector. The three components were combined to calculate the total stress-strain response, which was then compared to experimental data. The results show that the three-component model successfully captures the experimentally determined stress-strain behavior of fibrin fibers. The model evinces the key contribution of the α-C domains to fibrin fiber stress-strain behavior. However, conversion of the α-helical coiled coils to β-strands, and partial unfolding of the protein, may also contribute

Anti-CD38 antibody therapy for patients with relapsed/refractory multiple myeloma: differential mechanisms of action and recent clinical trial outcomes.

CD38 is a transmembrane glycoprotein that functions both as a receptor and an ectoenzyme, playing key roles in the regulation of calcium signaling and migration of immune cells to tumor microenvironments. High expression on multiple myeloma (MM) cells and limited expression on normal cells makes CD38 an ideal target for the treatment of MM patients. Two monoclonal antibodies directed at CD38, isatuximab and daratumumab, are available for use in patients with relapsed and/or refractory MM (RRMM); daratumumab is also approved in newly diagnosed MM and light-chain amyloidosis. Clinical experience has shown that anti-CD38 antibody therapy is transforming treatment of MM owing to its anti-myeloma efficacy and manageable safety profile. Isatuximab and daratumumab possess similarities and differences in their mechanisms of action, likely imparted by their binding to distinct, non-overlapping epitopes on the CD38 molecule. In this review, we present the mechanistic properties of these two antibodies and outline available evidence on their abilities to induce adaptive immune responses and modulate the bone marrow niche in MM. Further, we discuss differences in regulatory labeling between these two agents and analyze recent key clinical trial results, including evidence in patients with underlying renal impairment and other poor prognostic factors. Finally, we describe the limited existing evidence for the use of isatuximab or daratumumab after disease progression on prior anti-CD38 mono- or combination therapy, highlighting the need for additional clinical evaluations to define optimal anti-CD38 antibody therapy selection and sequencing in RRMM

a randomized, open, multicenter phase III trial of lenalidomide/dexamethasone versus lenalidomide/dexamethasone plus subsequent autologous stem cell transplantation and lenalidomide maintenance in patients with relapsed multiple myeloma

Background Despite novel therapeutic agents, most multiple myeloma (MM) patients eventually relapse. Two large phase III trials have shown significantly improved response rates (RR) of lenalidomide/dexamethasone compared with placebo/dexamethasone in relapsed MM (RMM) patients. These results have led to the approval of lenalidomide for RMM patients and lenalidomide/dexamethasone has since become a widely accepted second-line treatment. Furthermore, in RMM patients consolidation with high-dose chemotherapy plus autologous stem cell transplantation has been shown to significantly increase progression free survival (PFS) as compared to cyclophosphamide in a phase III trial. The randomized prospective ReLApsE trial is designed to evaluate PFS after lenalidomide/dexamethasone induction, high-dose chemotherapy consolidation plus autologous stem cell transplantation and lenalidomide maintenance compared with the well-established lenalidomide/dexamethasone regimen in RMM patients. Methods/Design ReLApsE is a randomized, open, multicenter phase III trial in a planned study population of 282 RMM patients. All patients receive three lenalidomide/dexamethasone cycles and - in absence of available stem cells from earlier harvesting - undergo peripheral blood stem cell mobilization and harvesting. Subsequently, patients in arm A continue on consecutive lenalidomide/dexamethasone cycles, patients in arm B undergo high dose chemotherapy plus autologous stem cell transplantation followed by lenalidomide maintenance until discontinuation criteria are met. Therapeutic response is evaluated after the 3rd (arm A + B) and the 5th lenalidomide/dexamethasone cycle (arm A) or 2 months after autologous stem cell transplantation (arm B) and every 3 months thereafter (arm A + B). After finishing the study treatment, patients are followed up for survival and subsequent myeloma therapies. The expected trial duration is 6.25 years from first patient in to last patient out. The primary endpoint is PFS, secondary endpoints include overall survival (OS), RR, time to best response and the influence of early versus late salvage high dose chemotherapy plus autologous stem cell transplantation on OS. Discussion This phase III trial is designed to evaluate whether high dose chemotherapy plus autologous stem cell transplantation and lenalidomide maintenance after lenalidomide/dexamethasone induction improves PFS compared with the well-established continued lenalidomide/dexamethasone regimen in RMM patients. Trial registration: ISRCTN16345835 (date of registration 2010-08-24)

The Effects of Temperature on Clot Microstructure and Strength in Healthy Volunteers

BACKGROUND: Anesthesia, critical illness, and trauma are known to alter thermoregulation, which can potentially affect coagulation and clinical outcome. This in vitro preclinical study explores the relationship between temperature change and hemostasis using a recently validated viscoelastic technique. We hypothesize that temperature change will cause significant alterations in the microstructural properties of clot. METHODS: We used a novel viscoelastic technique to identify the gel point of the blood. The gel point identifies the transition of the blood from a viscoelastic liquid to a viscoelastic solid state. Furthermore, identification of the gel point provides 3 related biomarkers: the elastic modulus at the gel point, which is a measure of clot elasticity; the time to the gel point (TGP), which is a measure of the time required to form the clot; and the fractal dimension of the clot at the gel point, df, which quantifies the microstructure of the clot. The gel point measurements were performed in vitro on whole blood samples from 136 healthy volunteers over a temperature range of 27°C to 43°C. RESULTS: There was a significant negative correlation between increases in temperature, from 27°C to 43°C, and TGP (r = −0.641, P 37°C. CONCLUSIONS: This study demonstrates that the gel point technique can identify alterations in clot microstructure because of changes in temperature. This was demonstrated in slower-forming clots with less structural complexity as temperature is decreased. We also found that significant changes in clot microstructure occurred when the temperature was ≤32°C

α−α Cross-Links Increase Fibrin Fiber Elasticity and Stiffness

Fibrin fibers, which are ∼100 nm in diameter, are the major structural component of a blood clot. The mechanical properties of single fibrin fibers determine the behavior of a blood clot and, thus, have a critical influence on heart attacks, strokes, and embolisms. Cross-linking is thought to fortify blood clots; though, the role of α–α cross-links in fibrin fiber assembly and their effect on the mechanical properties of single fibrin fibers are poorly understood. To address this knowledge gap, we used a combined fluorescence and atomic force microscope technique to determine the stiffness (modulus), extensibility, and elasticity of individual, uncross-linked, exclusively α–α cross-linked (γQ398N/Q399N/K406R fibrinogen variant), and completely cross-linked fibrin fibers. Exclusive α–α cross-linking results in 2.5× stiffer and 1.5× more elastic fibers, whereas full cross-linking results in 3.75× stiffer, 1.2× more elastic, but 1.2× less extensible fibers, as compared to uncross-linked fibers. On the basis of these results and data from the literature, we propose a model in which the α-C region plays a significant role in inter- and intralinking of fibrin molecules and protofibrils, endowing fibrin fibers with increased stiffness and elasticity

DSMM XI study: dose definition for intravenous cyclophosphamide in combination with bortezomib/dexamethasone for remission induction in patients with newly diagnosed myeloma

A clinical trial was initiated to evaluate the recommended dose of cyclophosphamide in combination with bortezomib and dexamethasone as induction treatment before stem cell transplantation for younger patients with newly diagnosed multiple myeloma (MM). Thirty patients were treated with three 21-day cycles of bortezomib 1.3 mg/m2 on days 1, 4, 8, and 11 plus dexamethasone 40 mg on the day of bortezomib injection and the day after plus cyclophosphamide at 900, 1,200, or 1,500 mg/m2 on day 1. The maximum tolerated dose of cyclophosphamide was defined as 900 mg/m2. At this dose level, 92% of patients achieved at least a partial response. The overall response rate [complete response (CR) plus partial response (PR)] across all dose levels was 77%, with a 10% CR rate. No patient experienced progressive disease. The most frequent adverse events were hematological and gastrointestinal toxicities as well as neuropathy. The results suggest that bortezomib in combination with cyclophosphamide at 900 mg/m2 and dexamethasone is an effective induction treatment for patients with newly diagnosed MM that warrants further investigation