Ligand scaffold hopping combining 3D maximal substructure search and molecular similarity

International audienceBACKGROUND: Virtual screening methods are now well established as effective to identify hit and lead candidates and are fully integrated in most drug discovery programs. Ligand-based approaches make use of physico-chemical, structural and energetics properties of known active compounds to search large chemical libraries for related and novel chemotypes. While 2D-similarity search tools are known to be fast and efficient, the use of 3D-similarity search methods can be very valuable to many research projects as integration of "3D knowledge" can facilitate the identification of not only related molecules but also of chemicals possessing distant scaffolds as compared to the query and therefore be more inclined to scaffolds hopping. To date, very few methods performing this task are easily available to the scientific community. RESULTS: We introduce a new approach (LigCSRre) to the 3D ligand similarity search of drug candidates. It combines a 3D maximum common substructure search algorithm independent on atom order with a tunable description of atomic compatibilities to prune the search and increase its physico-chemical relevance. We show, on 47 experimentally validated active compounds across five protein targets having different specificities, that for single compound search, the approach is able to recover on average 52% of the co-actives in the top 1% of the ranked list which is better than gold standards of the field. Moreover, the combination of several runs on a single protein target using different query active compounds shows a remarkable improvement in enrichment. Such Results demonstrate LigCSRre as a valuable tool for ligand-based screening. CONCLUSION: LigCSRre constitutes a new efficient and generic approach to the 3D similarity screening of small compounds, whose flexible design opens the door to many enhancements. The program is freely available to the academics for non-profit research at: http://bioserv.rpbs.univ-paris-diderot.fr/LigCSRre.html

Assessing the geometric diversity of cytochrome P450 ligand conformers by hierarchical clustering with a stop criterion.

International audienceAn algorithm is presented, which exhibits a computed number of rigid conformers of an input small molecule, covering the geometric diversity in the conformational space, with minimal structural redundancy. The algorithm calls a conformer generator, then performs an agglomerative hierarchical clustering with the modified clustering gain as the stop criterion. The number of classes is computed without an arbitrary parameter. A representative conformer is selected in each class, and nonrepresentative conformers are discarded. For illustration, the algorithm has been applied on a database containing 70 ligands of the cytochrome CYP 3A4, showing that the structural flexibility of each ligand is indeed handled via a small number of its representative conformers. The method is valid for all small molecules

Query and mining in biological databases

Ph.DDOCTOR OF PHILOSOPH

Automatic whole heart segmentation based on image registration

Whole heart segmentation can provide important morphological information of the heart, potentially enabling the development of new clinical applications and the planning and guidance of cardiac interventional procedures. This information can be extracted from medical images, such as these of magnetic resonance imaging (MRI), which is becoming a routine modality for the determination of cardiac morphology. Since manual delineation is labour intensive and subject to observer variation, it is highly desirable to develop an automatic method. However, automating the process is complicated by the large shape variation of the heart and limited quality of the data. The aim of this work is to develop an automatic and robust segmentation framework from cardiac MRI while overcoming these difficulties. The main challenge of this segmentation is initialisation of the substructures and inclusion of shape constraints. We propose the locally affine registration method (LARM) and the freeform deformations with adaptive control point status to tackle the challenge. They are applied to the atlas propagation based segmentation framework, where the multi-stage scheme is used to hierarchically increase the degree of freedom. In this segmentation framework, it is also needed to compute the inverse transformation for the LARM registration. Therefore, we propose a generic method, using Dynamic Resampling And distance Weighted interpolation (DRAW), for inverting dense displacements. The segmentation framework is validated on a clinical dataset which includes nine pathologies. To further improve the nonrigid registration against local intensity distortions in the images, we propose a generalised spatial information encoding scheme and the spatial information encoded mutual information (SIEMI) registration. SIEMI registration is applied to the segmentation framework to improve the accuracy. Furthermore, to demonstrate the general applicability of SIEMI registration, we apply it to the registration of cardiac MRI, brain MRI, and the contrast enhanced MRI of the liver. SIEMI registration is shown to perform well and achieve significantly better accuracy compared to the registration using normalised mutual information

¿Son los moduladores positivos de adrenomedulina nuevos inhibidores de metaloproteasas de la matriz?

Matrix metalloproteinases (MMPs), are a family of structurally related zinc containing enzymes that play a major role in the breakdown of connective tissue and therefore, are targets for therapeutic inhibitors in many inflammatory, malignant, and degenerative diseases. On the other hand, it has been recently demonstrated that one of these enzymes, MMP-2, a type IV collagenase, termed gelatinase A, cleaves the angiogenic peptide adrenomedullin (AM) (1). AM is a peptide hormone that plays a critical role in several diseases such as diabetes, hypertension and cancer. In a High Throughput Screening (HTS) carried out at the National Cancer Institute (NCI), a series of AM modulators were identified, with an interesting hypotensive activity (2). In order to shed light into the mechanism of action of these interesting compounds, we have hypothesized that they may be affecting the biodisponibility of AM in the blood stream by inhibiting the MMP-2 protease activity. In the present work, we present a theoretical study, making use of molecular mechanics, docking and virtual screening techniques, with the aim of demonstrating this hypothesis. Biological evaluation of MMP-2 inhibition by some selected compounds, followed the computational work, leading us to propose a structurally new type of MMP-2 inhibitors, with possible interest as anticancer and antiangiogenic agents.Las metaloproteasas de la matriz (MMPs) pertenecen a la familia de enzimas que contienen zinc y juegan un papel predominante en la degradación del tejido conectivo. Por ello se consideran dianas terapéuticas para procesos de inflamación y enfermedades malignas y degenerativas. Por otro lado, se ha demostrado recientemente que un miembro de esta familia, MMP-2, una colagenasa de tipo IV también conocida como gelatinasa A, es capaz de degradar un péptido angiogénico denominado adrenomedulina (AM) (1). AM es una hormona peptídica que desarrolla un papel importante en diversas patologías como diabetes, hipertensión y cáncer. Se ha identificado mediante un cribado de alto rendimiento (HTS) de la colección de compuestos del Instituto Nacional del Cáncer (NCI), una serie de moduladores con interesante actividad hipotensora (2). El mecanismo de acción de estos moduladores es desconocido y nosotros proponemos que pueden afectar a la biodisponibilidad de la AM en el torrente sanguíneo por medio de la inhibición de la actividad de la MMP-2. En este trabajo presentamos un estudio teórico que hace uso de técnicas como mecánica molecular, docking y Cribado Virtual con el objetivo de demostrar esa hipótesis. A continuación del estudio computacional se llevó a cabo la evaluación biológica de algunos compuestos, permitiéndonos proponer un nuevo tipo de ZBG que puede ser interesante para el diseño de nuevos inhibidores de MMPS, con interés como agentes anticancerosos y antiangiogénicos

Geometric Expression Invariant 3D Face Recognition using Statistical Discriminant Models

Currently there is no complete face recognition system that is invariant to all facial expressions. Although humans find it easy to identify and recognise faces regardless of changes in illumination, pose and expression, producing a computer system with a similar capability has proved to be particularly di cult. Three dimensional face models are geometric in nature and therefore have the advantage of being invariant to head pose and lighting. However they are still susceptible to facial expressions. This can be seen in the decrease in the recognition results using principal component analysis when expressions are added to a data set. In order to achieve expression-invariant face recognition systems, we have employed a tensor algebra framework to represent 3D face data with facial expressions in a parsimonious space. Face variation factors are organised in particular subject and facial expression modes. We manipulate this using single value decomposition on sub-tensors representing one variation mode. This framework possesses the ability to deal with the shortcomings of PCA in less constrained environments and still preserves the integrity of the 3D data. The results show improved recognition rates for faces and facial expressions, even recognising high intensity expressions that are not in the training datasets. We have determined, experimentally, a set of anatomical landmarks that best describe facial expression e ectively. We found that the best placement of landmarks to distinguish di erent facial expressions are in areas around the prominent features, such as the cheeks and eyebrows. Recognition results using landmark-based face recognition could be improved with better placement. We looked into the possibility of achieving expression-invariant face recognition by reconstructing and manipulating realistic facial expressions. We proposed a tensor-based statistical discriminant analysis method to reconstruct facial expressions and in particular to neutralise facial expressions. The results of the synthesised facial expressions are visually more realistic than facial expressions generated using conventional active shape modelling (ASM). We then used reconstructed neutral faces in the sub-tensor framework for recognition purposes. The recognition results showed slight improvement. Besides biometric recognition, this novel tensor-based synthesis approach could be used in computer games and real-time animation applications

COMPUTATIONALLY AIDED RATIONAL DESIGN, SYNTHESIS AND EVALUATION OF PFKFB3 LIGANDS FOR ATHEROSCLEROTIC PLAQUE STABILISATION

Several types of cells actively involved in atherosclerosis undergo a metabolic reprogramming that comprises an accelerated glycolytic flux to the detriment of mitochondrial oxidative phosphorylation. This phenomenon, known as the Warburg effect, allows cells to produce ample energy and biomass with negligible oxygen consumption, resulting in uncontrolled high-speed proliferation. This phenotypic dysfunction underlies both inflammation and angiogenesis, two processes that are foundation of the pathological behaviour of the plaque and promote its destabilisation. The preponderant kinase domain of inducible PFKFB3 enzyme catalyses the synthesis of F2,6P, which simultaneously promotes glycolysis and inhibits gluconeogenesis. PFKFB3 has been found to be one of the most abundant and overly expressed isoenzymes in the Warburg effect, suggesting its key role in the pathology. From a clinical perspective, PFKFB3 represents an emerging biological target, and its inhibition would be an innovative therapeutic strategy for the treatment of atherosclerotic lesions. Our study revolves around the identification of new PFKFB3 kinase inhibitors. The rational design was carried out with the aid of computational methods, based on the 3D-structures of both PFKFB3 protein and known active ligands. In particular, two classes of inhibitors were used as reference ligands for the development of two distinct virtual screening workflows that led to the identification of two generations of candidate inhibitors. The first hierarchical workflow consisted of a pharmacophore-based library filtration, followed by molecular docking and molecular dynamic simulations, whereas the second consisted of a ligand- optimisation process based on the design of a focussed synthesisable library of analogues that were submitted to molecular docking along with a concomitant similarity search for scaffold hopping. The first- and the second-generation candidate compounds, which were predicted to have good in silico affinity towards PFKFB3, were synthesised and biologically evaluated through kinase assay, providing further insights about these selected drug-like molecules and new structure-activity relationship information

Interactive maximal common 3D substructure searching with the combined SDM/RMS algorithm

International audienceAn interactive procedure calling iteratively the sorted distances matrix (SDM) algorithm and the root mean square (RMS) algorithm performs the spatial alignment of two three-dimensional sets of points, and outputs the maximal common subset with the pairwise correspondence between the two common regions