Electrostatic-field and surface-shape similarity for virtual screening and pose prediction.

We introduce a new method for rapid computation of 3D molecular similarity that combines electrostatic field comparison with comparison of molecular surface-shape and directional hydrogen-bonding preferences (called "eSim"). Rather than employing heuristic "colors" or user-defined molecular feature types to represent conformation-dependent molecular electrostatics, eSim calculates the similarity of the electrostatic fields of two molecules (in addition to shape and hydrogen-bonding). We present detailed virtual screening performance data on the standard 102 target DUD-E set. In its moderately fast screening mode, eSim running on a single computing core is capable of processing over 60 molecules per second. In this mode, eSim performed significantly better than all alternate methods for which full DUD-E data were available (mean ROC area of 0.74, p [Formula: see text], by paired t-test, compared with the best performing alternate method). In addition, for 92 targets of the DUD-E set where multiple ligand-bound crystal structures were available, screening performance was assessed using alternate ligands or sets thereof (in their bound poses) as similarity targets. Using the joint alignment of five ligands for each protein target, mean ROC area exceeded 0.82 for the 92 targets. Design-focused application of ligand similarity methods depends on accurate predictions of geometric molecular relationships. We comprehensively assessed pose prediction accuracy by curating nearly 400,000 bound ligand pose pairs across the DUD-E targets. Overall, beginning from agnostic initial poses, we observed an 80% success rate for RMSD [Formula: see text] Å  among the top 20 predicted eSim poses. These examples were split roughly 50/50 into cases with high direct atomic overlap (where a shared scaffold exists between a pair) and low direct atomic overlap (where where a ligand pair has dissimilar scaffolds but largely occupies the same space). Within the high direct atomic overlap subset, the pose prediction success rate was 93%. For the more challenging subset (where dissimilar scaffolds are to be aligned), the success rate was 70%. The eSim approach enables both large-scale screening and rational design of ligands and is rooted in physically meaningful, non-heuristic, molecular comparisons

Cribado virtual mediante un algoritmo evolutivo global paralelo

Las técnicas de cribado virtual proporcionan predicciones sobre la bioactividad y la toxicidad de los fármacos y su actividad en nuevas enfermedades. Para lograr este objetivo, es necesario incorporar descriptores adecuados para obtener información específica sobre los compuestos según la base de datos de origen. En particular, nos centramos en el descriptor de similitud de forma. Recientemente, se ha propuesto una nueva estrategia para la comparación de la forma molecular. Se trata de un algoritmo evolutivo de optimización basado en subpoblaciones que necesita grandes tamaños de poblaciones para explorar ampliamente el espacio de búsqueda y por tanto obtener buenas soluciones. Esto se traduce directamente en tiempos computacionales mayores y más cantidad de recursos necesarios. En vista de esta situación, en este trabajo, se ha paralelizado el algoritmo de optimización. Se ha llevado a cabo un estudio computacional para analizar el nuevo método paralelo en términos de eficiencia y efectividad. El uso de varios procesadores, y por lo tanto más recursos computacionales, nos permite acelerar el procedimiento de comparación de la forma molecular

DIA-DB : a database and web server for the prediction of diabetes drugs

The DIA-DB is a web server for the prediction of diabetes drugs that uses two different and complementary approaches: (a) comparison by shape similarity against a curated database of approved antidiabetic drugs and experimental small molecules and (b) inverse virtual screening of the input molecules chosen by the users against a set of therapeutic protein targets identified as key elements in diabetes. As a proof of concept DIA-DB was successfully applied in an integral workflow for the identification of the antidiabetic chemical profile in a complex crude plant extract. To this end, we conducted the extraction and LC-MS based chemical profile analysis of Sclerocarya birrea and subsequently utilized this data as input for our server. The server is open to all users, registration is not necessary, and a detailed report with the results of the prediction is sent to the user by email once calculations are completed. This is a novel public domain database and web server specific for diabetes drugs and can be accessed online through http://bio-hpc.eu/software/dia-db/.http://pubs.acs.org/journal/jcics1/about.htmlhj2021BiochemistryGeneticsMicrobiology and Plant Patholog

Desarrollo de herramientas bioinformáticas fácilmente usables y accesibles vía web con aplicabilidad general en contextos farmacológicos, agrícolas, nutracéuticos y cosméticos.

El cribado virtual (virtual screening, VS) es una técnica computacional, empleada frecuentemente en bioinformática, cuyo objetivo es reducir un espacio químico de gran tamaño y complejidad en otro más reducido y manejable. Para esta tarea, el cribado virtual basado en ligandos (ligand-based virtual screening, LBVS) se ha convertido en una alternativa eficiente frente a otras técnicas más complejas computacionalmente tales como la Dinámica Molecular, puesto que permite encontrar rápidamente los compuestos más prometedores a un bajo coste computacional. En los últimos años, con el desarrollo del big data y de los paradigmas de supercomputación, han emergido numerosos servidores web que prestan servicios de LBVS y que aprovechan la computación de alto rendimiento (high performance computing, HPC) para mejorar sus prestaciones. Pero, a pesar de que la mejora del rendimiento es un punto importante, el principal factor de calidad de estos servidores sigue siendo la fiabilidad de sus predicciones. Esta tesis pretende analizar las características de los servidores de LBVS actuales, prestando especial atención a las tecnologías que emplean y al rendimiento que, en términos computacionales, extraen de las plataformas HPC. Una vez obtenidas las conclusiones de dicho análisis, el objetivo es desarrollar una herramienta web que solvente las debilidades de los servidores existentes aplicando técnicas no estudiadas hasta ahora en el campo de LBVS. Como resultado, se presentan los estudios teóricos realizados y la herramienta web BRUSELAS (Balanced, Rapid and Unrestricted Server for Extensive Ligand-Aimed Screening), la cual está disponible, de manera totalmente gratuita, en http://bio-hpc.eu/software/Bruselas. Como principales características diferenciadoras de BRUSELAS destacan la utilización de funciones de consenso para combinar las predicciones de varios algoritmos de similitud y farmacofóricos, el uso de descriptores moleculares y palabras clave para crear librerías dinámicamente y el filtrado de los resultados mediante filtros moleculares. Además, se ha implementado un potente módulo de análisis de resultados que permite su procesado tanto online como offline, mediante la conocida herramienta PyMOL, y la visualización de la salida generada por cada algoritmo de similitud. La nueva herramienta se ha aplicado a casos de estudio prácticos, como la búsqueda de anticoagulantes sanguíneos y de posibles fármacos para terapias contra el cáncer. Las tareas ejecutadas han dado lugar a interesantes resultados teóricos que pueden servir como base para la experimentación en etapas posteriores, ya sea in vitro o in vivo. En conclusión, se puede afirmar que BRUSELAS puede ser una herramienta muy útil en la etapa de búsqueda de compuestos candidatos a fármacos. Además, BRUSELAS proporciona a los usuarios nuevas funcionalidades que otros servidores web no proporcionan a través de una interfaz amigable y sin necesidad de tener grandes conocimientos en informática. Los resultados acumulados confirman que es una arquitectura fiable en cuanto a la calidad de las predicciones, y que tiene un rendimiento comparable al de otros servidores similares. Por lo tanto, BRUSELAS puede ser de gran ayuda en futuros estudios, ya sea utilizada de manera individual o colaborando con otras técnicas computacionalmente más costosas (p.ej. docking).Agricultura y VeterinariaMedicin

Advances in the Development of Shape Similarity Methods and Their Application in Drug Discovery

Molecular similarity is a key concept in drug discovery. It is based on the assumption that structurally similar molecules frequently have similar properties. Assessment of similarity between small molecules has been highly effective in the discovery and development of various drugs. Especially, two-dimensional (2D) similarity approaches have been quite popular due to their simplicity, accuracy and efficiency. Recently, the focus has been shifted toward the development of methods involving the representation and comparison of three-dimensional (3D) conformation of small molecules. Among the 3D similarity methods, evaluation of shape similarity is now gaining attention for its application not only in virtual screening but also in molecular target prediction, drug repurposing and scaffold hopping. A wide range of methods have been developed to describe molecular shape and to determine the shape similarity between small molecules. The most widely used methods include atom distance-based methods, surface-based approaches such as spherical harmonics and 3D Zernike descriptors, atom-centered Gaussian overlay based representations. Several of these methods demonstrated excellent virtual screening performance not only retrospectively but also prospectively. In addition to methods assessing the similarity between small molecules, shape similarity approaches have been developed to compare shapes of protein structures and binding pockets. Additionally, shape comparisons between atomic models and 3D density maps allowed the fitting of atomic models into cryo-electron microscopy maps. This review aims to summarize the methodological advances in shape similarity assessment highlighting advantages, disadvantages and their application in drug discovery

Enhancing Molecular Shape Comparison by Weighted Gaussian Functions

Shape comparing technologies based on Gaussian functions have been widely used in virtual screening of drug discovery. For efficiency, most of them adopt the First Order Gaussian Approximation (FOGA), in which the shape density of a molecule is represented as a simple sum of all individual atomic shape densities. In the current work, the effectiveness and error in shape similarity calculated by such an approximation are carefully analyzed. A new approach, which is called the Weighted Gaussian Algorithm (WEGA), is proposed to improve the accuracy of the first order approximation. The new approach significantly improves the accuracy of molecular volumes and reduces the error of shape similarity calculations by 37% using the hard-sphere model as the reference. The new algorithm also keeps the simplicity and efficiency of the FOGA. A program based on the new method has been implemented for molecular overlay and shape-based virtual screening. With improved accuracy for shape similarity scores, the new algorithm also improves virtual screening results, particularly when a shape-feature combo scoring function is used