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PubMed2006

Utilizing peptide structures as keys for unlocking challenging targets.

Three-dimensional structures of protein targets have proven to be extremely valuable for modern drug design and discovery. For cases where the structure of the protein is unattainable, such as G-protein coupled receptors (GPCRs), structural information on active ligands is still useful and helpful for deciphering the geometrical and chemical features of the active site. Peptides, constructed from easy-to-form amide backbones and featuring variable side-chains, have an inherent advantage in generating rapid quantitative structure-activity relationships (QSAR). Given the fact that peptides are natural ligands for many protein targets, structural investigation of a series of related peptides, typically carried out via nuclear magnetic resonance (NMR), can result in an accurate pharmacophore model. Such a model can be used for virtual screening, and to assist design of second-generation peptidomimetics with improved properties and design of non-peptidic leads. In this article, we will review examples in which a structural approach utilizing peptide ligands was employed to obtain a better understanding of the target active site. We will focus on cases where such information supplied guidance toward the discovery of small molecule ligands.

Drug Evaluation, Preclinical↗
PubMed2004

Rational design of potent and selective VLA-4 inhibitors and their utility in the treatment of asthma.

Asthma, a chronic inflammatory disease of the airways, is a significant burden on our healthcare system. There is high unmet need for treatments directed towards the underlying causes of the disease. The cell surface integrin VLA-4 (very late antigen-4; alpha4beta1; CD49d/CD29) plays an important role in the trafficking of white blood cells to sites of inflammation and represents an exciting target for the development of novel anti-inflammatory drugs for the treatment of asthma. Here, we review our efforts to use rational design to identify potent, selective inhibitors of VLA-4. We describe the discovery of a series of potent VLA-4 inhibitors through the addition of a novel N-terminal organic cap to a tetrapeptide VLA-4 binding motif 4-((N'-2-methylphenyl)uriedo)phenylacetyl-Leu-Asp-Val-Pro ; Kd = 70 pM), and rationalize their structure-activity relationships using 3D-QSAR. Also, we show our rational peptidomimetic design strategy using "template hopping" from the gpIIb/IIIa integrin antagonist field, and also a novel virtual screening strategy. Two series have been developed, one that has high selectivity for the activated over the non-activated state of the receptor, and the other which is non-selective inhibiting both activated and non-activated VLA-4. Both series are highly selective for VLA-4 versus against other integrin family members. These inhibitors show promise in the treatment of asthma, based upon efficacy in a sheep model of asthma, where they inhibit both the early and late-phase responses to asthma and also block hypersensitivity.

Amides↗
PubMed2005

Potency-scaled partitioning in descriptor spaces with increasing dimensionality.

Partitioning algorithms are described that operate in chemical reference spaces formed by combinations of binary-transformed molecular descriptors and aim at the identification of potent hits in ligand-based virtual screening. One of these approaches depends on mapping of consensus positions of compound activity sets in descriptor spaces followed by step-wise extension of the dimensionality of these spaces and re-mapping of activity-dependent consensus positions. Dimension extension is carried out to increase the discriminatory power of descriptor combinations and distinguish database compounds from potential hits. This method was originally named Dynamic Mapping of Consensus positions (DMC) and subsequently extended in order to take different potency levels of known active molecules into account and increase the probability of recognizing potent database hits. The extension was accomplished by adding potency scaling to DMC calculations, and the resulting approach was termed POT-DMC. Results of comparisons of DMC and POT-DMC calculations on different classes of active compounds with substantially varying potency levels support the validity of the POT-DMC approach.

Algorithms↗
PubMed2005

Hierarchical docking of databases of multiple ligand conformations.

Ligand flexibility is an important problem in molecular docking and virtual screening. To address this challenge, we investigate a hierarchical pre-organization of multiple conformations of small molecules. Such organization of pre-calculated conformations removes the exploration of ligand conformational space from the docking calculation and allows for concise representation of what can be thousands of conformations. The hierarchy also recognizes and prunes incompatible conformations early in the calculation, eliminating redundant calculations of fit. We investigate the method by docking the MDL Drug Data Report (MDDR), an annotated database of 100,000 molecules, into apo and holo forms of seven unrelated targets. This annotated database allows us to track the ranking of tens to hundreds of annotated ligands in each of the docking systems. The binding sites and database are prepared in an automated fashion in an attempt to remove some human bias from the calculations. Many thousands of explicit and implicit ligand conformations may be docked in calculations not much longer than required for single conformer docking. As long as internal energies are not considered, recombination with the hierarchy is additive as the number of degrees of freedom is increased. Molecules with even millions of conformations can be docked in a few minutes on a single desktop computer.

Computer Simulation↗
PubMed2006

Structure-function based design of small molecule inhibitors targeting Rho family GTPases.

Rho GTPases of the Ras superfamily are involved in the regulation of multiple cell functions and have been implicated in the pathology of various human diseases including cancer. They are attractive drug targets in future targeted therapy. A wealth of structure-function information made available by high resolution structures and mutagenesis studies has laid out the foundation for the derivation of a mechanism-based targeting strategy. Here we describe the rational design and characterizations of a first generation Rac-specific small molecule inhibitor. Based on the structure-function information of Rac interaction with GEFs, in a computer based Virtual Screening we have identified NSC23766, a highly soluble and membrane permeable compound, as a specific inhibitor of a subset of GEF binding to Rac and therefore Rac activation. In fibroblast cells NSC23766 inhibited Rac1 GTP-loading without affecting Cdc42 or RhoA activity and suppressed the Rac-GEF, Tiam1, and oncogenic Ras induced cell growth and transformation. NSC23766 also potently inhibited the prostate PC-3 cancer cell proliferation and invasion induced by Rac hyperactivation. Intraperitoneal administration of NSC23766 to laboratory mice resulted in effective Rac GTPase suppression and hematopoietic stem cell mobilization from the bone marrow to the peripheral blood, similar to the effects of genetically targeted disruption of Rac GTPases in the animals. A co-crystal structure of NSC23766 bound to Rac1 provided further insight for future medicinal chemistry modification and improvement of this lead Rac-specific inhibitor. Thus, structure-function based rational design may represent a new avenue for generating lead small molecule inhibitors of Ras superfamily GTPases that are useful for modulating pathological conditions in which the small GTPase deregulation may play a role.

Animals↗
PubMed2004

The FlexX database docking environment--rational extraction of receptor based pharmacophores.

We present an integrated docking environment that allows for iterative and interactive detailed analysis of many docking solutions. All docking information is stored in an ORACLE database. New scoring schemes (e.g. target-specific scoring functions) as well as various types of filters can be easily defined and tested within this environment. As an example application we investigated the validity of the following hypothesis: If a docking procedure can lead to enrichments significantly better than random then a bias towards (partially) correct placements should be detectable. Such bias in terms of a preference for certain interacting groups within the active site can be used to select a set of receptor-based pharmacophore constraints, which in turn might be used to enhance the docking procedure. As a proof of concept for this approach we performed docking studies on three targets: thrombin, the cyclin-dependent kinase 2 (CDK2) and the angiotensin converting enzyme (ACE). We docked a set of known active compounds with standard FlexX and derived three sets of target-specific receptor-based pharmacophore constraints by statistical analysis of the predicted placements. Applying these receptor-based constraints in a virtual screening protocol utilizing FlexX-Pharm led to significantly improved enrichments.

Calibration↗
PubMed2005

Autocorrelation of molecular electrostatic potential surface properties combined with partial least squares analysis as alternative attractive tool to generate ligand-based 3D-QSARs.

A database of 106 human A3 adenosine receptor antagonists was used to derive two alternative PLS models: one starting from CoMFA descriptors and the other starting from the autocorrelation descriptors. The peculiarity of this work is the introduction of autocorrelation vectors as molecular descriptors for the PLS analysis. The autocorrelation allows comparing molecules (and their properties) with different structures and with different spatial orientation without any previous alignment. In particular, Molecular Electrostatic Potential (MEP) was the property computed and its information encoded in autocorrelation vectors. The 3D spatial distribution and the values of the electrostatic potential is in fact largely responsible for the binding of a substrate to its receptor binding site. Validation was done with an external test set and the results of the two models were compared. Interestingly, our preliminary results seem to indicate that this new alternative approach could robustly compete with the already well consolidated CoMFA approach. In particular, we have suggested that it could be a very interesting tool to filter large structural database in several virtual screening applications.

Adenosine A3 Receptor Antagonists↗
PubMed2026

Large language models in bioinformatics: a comprehensive survey.

The emergence of foundation models with trillion-level parameters has redefined the landscape of artificial intelligence. Various fields are developing their own large-scale models, which can solve many problems within the field and improve work efficiency. Biological large-scale models are a cross-disciplinary research field that combines mathematics, computer science, and biology, aiming to simulate and understand the structure, function, and dynamic changes of biological systems through the establishment of complex computational models. This field covers multiple levels such as biological pathways, population dynamics, protein folding, etc., providing us with tools for deep exploration of the mysteries of life and applications in medicine, ecology, and other fields. This article reviews the background and research status of biological large-scale models, and discusses future directions. Large language models (LLMs) and other large-scale foundation models have rapidly advanced in recent years, enabling powerful representation learning and generation across text, sequences, and multimodal data. In bioinformatics and biomedicine, these models are increasingly used to analyze genomic sequences, infer protein properties and structures, support drug discovery, and integrate heterogeneous biomedical evidence. This survey reviews the basic principles of LLMs and summarizes representative applications in (i) gene and genome sequence analysis, (ii) protein structure and function prediction, and (iii) drug design, including virtual screening and personalized medicine. We also discuss emerging multi-model modeling approaches, as well as key challenges such as data quality and privacy, interpretability, generalization to new organisms and tasks, and responsible deployment in health-related settings. Finally, we outline future directions for developing reliable, scalable, and explainable bioinformatics foundation models.

bioinformatics↗
PubMedDate not supplied

Image-navigated endoscopic surgery: introduction of the face-mounted display system with picture-in-picture capabilities.

BACKGROUND/AIMS: The face-mounted display system with picture-in-picture capabilities was evaluated for clinical usefulness in endoscopic surgery. METHODOLOGY: The device mounts two crystal liquid color panels, being shaped like a pair of glasses. It can provide us with an ultra-large virtual screen of the endoscopic view. Furthermore, a picture-in-picture capability can materialize a multi-image environment. Twenty-five patients were enrolled in this study. RESULTS: The view obtained by the surgeon consisted of the FMD screen of the upper two-thirds and the direct view of the lower one-third. The surgeon could concentrate more on the procedure, because he could face the target organ regardless of the display position. No complications attributable to the device were encountered. With a multi-image environment, endoscopic procedure was executed without taking one's eyes off the operative field. CONCLUSIONS: The face-mounted display system will conduct us to another sphere of endoscopic surgery, i.e., an image-navigated endoscopic surgery.

Cholecystectomy, Laparoscopic↗
PubMed2002

The use of chemical design tools to transform proteomics data into drug candidates.

The Human Genome Project has fueled the massive information-driven growth of genomics and proteomics and promises to deliver new insights into biology and medicine. Since proteins represent the majority of drug targets, these molecules are the focus of activity in pharmaceutical and biotechnology organizations. In this article, we describe the processes by which computational drug design may be used to exploit protein structural information to create virtual small molecules that may become novel medicines. Experimental protein structure determination, site exploration, and virtual screening provide a foundation for small molecule generation in silico, thus creating the bridge between proteomics and drug discovery.

Combinatorial Chemistry Techniques↗
PubMed2002

Designing selective COX-2 inhibitors: molecular modeling approaches.

Molecular modeling approaches have been successfully used to gain insight into the molecular mechanism of selective cyclooxygenase-2 (COX-2) inhibition. These approaches are based on X-ray structure data and results from structure-activity relationships and site-directed mutagenesis experiments. The recognition process of substrates and inhibitors by COX isoenzymes can be visualized by applying algorithms to describe local properties on the enzyme surface, thus allowing key differences in structure to be studied that may confer differential sensitivity to inhibitors. The virtual screening techniques for COX isoenzymes are rapidly improving, and the search for new selective COX-2 inhibitors has been stimulated by the tremendous success of meloxicam, celecoxib and rofecoxib in the pharmaceutical market and their potential use in new indications.

Animals↗
PubMed2002

Using X-ray crystallography in drug discovery.

One of the most dramatic benefits of using crystallography in drug discovery has been the development of the structure-based drug design (SBDD) cycle: structure, analysis, synthesis, testing and back to structure. This review covers recent examples of SBDD to illustrate how this design cycle has evolved from its early implementation. The dimension of the cycle has increased to include the consideration of structures related to the target, optimization of global drug parameters and binding affinity. Other technologies, such as combinatorial chemistry and virtual screening, have been integrated into the design cycle. In addition, novel new ways of directly using the crystal have been developed.

Animals↗
PubMed2001

The impact of new technologies on drug design.

The Cutting Edge Approaches to Drug Design symposium, held March 13, 2001, in London, U.K., was organized by the Royal Society of Chemistry's Molecular Modelling Group and the Structural Biology Group of the Biochemical Society. The symposium was aimed primarily at medicinal chemists keen to discover how cutting edge methods can accelerate drug design. The talks were aimed at the nonspecialist, and topics included target finding, virtual screening, informatics in lead optimization and in silico pharmacokinetic profiling. Several examples demonstrated the power of these methods.

Journal Article↗
PubMed2003

Support vector machine applications in bioinformatics.

The support vector machine (SVM) approach represents a data-driven method for solving classification tasks. It has been shown to produce lower prediction error compared to classifiers based on other methods like artificial neural networks, especially when large numbers of features are considered for sample description. In this review, the theory and main principles of the SVM approach are outlined, and successful applications in traditional areas of bioinformatics research are described. Current developments in techniques related to the SVM approach are reviewed which might become relevant for future functional genomics and chemogenomics projects. In a comparative study, we developed neural network and SVM models to identify small organic molecules that potentially modulate the function of G-protein coupled receptors. The SVM system was able to correctly classify approximately 90% of the compounds in a cross-validation study yielding a Matthews correlation coefficient of 0.78. This classifier can be used for fast filtering of compound libraries in virtual screening applications.

Algorithms↗
PubMed2004

Structure-based generation of viable leads from small combinatorial libraries.

Parallel synthesis and structure-aided design have begun to converge in the process of drug discovery. Virtual screening using X-ray crystal structures of therapeutic targets to front-load a high-throughput screen or to establish a tractable collection for lower throughput assays has become a standard practice in many lead generation settings. The application of similar techniques for increasing the likelihood of including active compounds in a focused combinatorial library is a natural progression that is beginning to be recognized. In this review, we will cover recent reports of small, focused libraries designed for specific therapeutic targets, and for which X-ray crystallographic data is available.

Combinatorial Chemistry Techniques↗
PubMed2004

A new topological descriptors based model for predicting intestinal epithelial transport of drugs in Caco-2 cell culture.

PURPOSE: Quantitative Structure-Permeability Relationships (QSPerR) of the intestinal permeability across the (Caco-2) cells monolayer could be obtained by the application of new molecular descriptors. METHOD: A novel topologic-molecular approach to computer molecular design ( TOMOCOMD-CARDD ) has been used to estimate the intestinal-epithelial transport of drug in Caco-2 cell culture. RESULTS: The Permeability Coefficients in Caco-2 cells (P) for 33 structurally diverse drugs were well described using quadratic indices of the molecular pseudograph's atom adjacency matrix as molecular descriptors. A quantitative model that discriminates the high-absorption compounds from those with moderate-poor absorption was obtained for the training data set, showing a global classification of 87.87%. In addition, two QSPerR models, through a multiple linear regression, were obtained to predict the P [apical to basolateral (AP-->BL) and basolateral to apical (BL-->AP)]. A leave- n -out and leave- one -out cross-validation procedure revealed that the discriminant and regression models respectively, had a good predictability. Furthermore, others 18 drugs were selected as a test set in order to assess the predictive power of the models and the accuracy of the final prediction was similar to achieve for the data set. Besides, the use of both regression models, in a combinative way, is possible to predict the Permeability Directional Ratio (PDR, BL-->AP/AP-->BL) value. The found models were used in virtual screening of drug intestinal permeability and a relationship between calculated P and percentage of human intestinal absorption for several compounds was established. Furthermore, this approximation permits us to obtain a good explanation of the experiment based on the molecular structural features. CONCLUSIONS: These results suggest that the proposed method is able to predict the P values and it proved to be a good tool for studying the oral absorption of drug candidates during the drug development process.

Biological Transport↗
PubMed2005

Targeting the phosphatidylinositol-3 kinase/Akt pathway for the treatment of cancer.

Phosphatidylinositol-3 kinase (PI3K)/Akt is overactivated in a wide range of tumor types, and this triggers a cascade of responses, from cell growth and proliferation to survival, motility, epithelial-mesenchymal transition and angiogenesis. Therefore, this pathway presents an exciting target for molecular therapeutics. In addition, ectopic expression of PI3K or Akt, especially constitutively activated PI3K (p110alpha) or Akt, is sufficient to induce the oncogenic transformation of cells and tumor formation in transgenic mice, as well as the development of chemoresistance. Inhibition of PI3K/Akt signaling induces apoptosis and inhibits the growth of tumor cells that have elevated Akt levels. The dependence of certain tumors on PI3K/Akt signaling for survival and growth has wide implications for cancer therapy, offering the potential for preferential tumor cell killing. In the past few years, a number of inhibitors of the Akt pathway have been identified by combinatorial chemistry, high-throughput and virtual screening, or traditional medicinal chemistry. This review focuses on ongoing translational efforts to therapeutically target the PI3K/Akt pathway.

Animals↗
PubMed2006

Circular fingerprints: flexible molecular descriptors with applications from physical chemistry to ADME.

Circular fingerprints -- the representation of molecular structures by atom neighborhoods -- have been applied to a wide range of applications, such as similarity searching and the prediction of absorption, distribution, metabolism, excretion and toxicity properties. In recent years there has been a surge in applications resulting from the superior performance of circular fingerprints in comparative studies. This feature examines the nature of circular fingerprints as well as their applications, including virtual screening, metabolism prediction and the estimation of pK((a)) constants.

Algorithms↗