Esteban López-Camacho

Solving molecular flexible docking problems with metaheuristics

Graphical abstractDisplay Omitted HighlightsFour algorithms (two GAs, PSO, and DE) are used to solve molecular docking problems.A set of 83 docking instances is generated based on real existing molecules.A thorough experimentation with statistical assessment of results is performed.Relevant solutions are analyzed from the point of view of their biological meaning. The main objective of the molecular docking problem is to find a conformation between a small molecule (ligand) and a receptor molecule with minimum binding energy. The quality of the docking score depends on two factors: the scoring function and the search method being used to find the lowest binding energy solution. In this context, AutoDock 4.2 is a popular C++ software package in the bioinformatics community providing both elements, including two genetic algorithms, one of them endowed with a local search strategy. This paper principally focuses on the search techniques for solving the docking problem. In using the AutoDock 4.2 scoring function, the approach in this study is twofold. On the one hand, a number of four metaheuristic techniques are analyzed within an extensive set of docking problems, looking for the best technique according to the quality of the binding energy solutions. These techniques are thoroughly evaluated and also compared with popular well-known docking algorithms in AutoDock 4.2. The metaheuristics selected are: generational and a steady-state Genetic Algorithm, Differential Evolution, and Particle Swarm Optimization. On the other hand, a C++ version of the jMetal optimization framework has been integrated inside AutoDock 4.2, so that all the algorithms included in jMetal are readily available to solve docking problems. The experiments reveal that Differential Evolution obtains the best overall results, even outperforming other existing algorithms specifically designed for molecular docking.

jMetalCpp: optimizing molecular docking problems with a C++ metaheuristic framework

MOTIVATION Molecular docking is a method for structure-based drug design and structural molecular biology, which attempts to predict the position and orientation of a small molecule (ligand) in relation to a protein (receptor) to produce a stable complex with a minimum binding energy. One of the most widely used software packages for this purpose is AutoDock, which incorporates three metaheuristic techniques. We propose the integration of AutoDock with jMetalCpp, an optimization framework, thereby providing both single- and multi-objective algorithms that can be used to effectively solve docking problems. RESULTS The resulting combination of AutoDock + jMetalCpp allows users of the former to easily use the metaheuristics provided by the latter. In this way, biologists have at their disposal a richer set of optimization techniques than those already provided in AutoDock. Moreover, designers of metaheuristic techniques can use molecular docking for case studies, which can lead to more efficient algorithms oriented to solving the target problems. AVAILABILITY AND IMPLEMENTATION  jMetalCpp software adapted to AutoDock is freely available as a C++ source code at http://khaos.uma.es/AutodockjMetal/.

Solving molecular docking problems with multi-objective metaheuristics.

Molecular docking is a hard optimization problem that has been tackled in the past with metaheuristics, demonstrating new and challenging results when looking for one objective: the minimum binding energy. However, only a few papers can be found in the literature that deal with this problem by means of a multi-objective approach, and no experimental comparisons have been made in order to clarify which of them has the best overall performance. In this paper, we use and compare, for the first time, a set of representative multi-objective optimization algorithms applied to solve complex molecular docking problems. The approach followed is focused on optimizing the intermolecular and intramolecular energies as two main objectives to minimize. Specifically, these algorithms are: two variants of the non-dominated sorting genetic algorithm II (NSGA-II), speed modulation multi-objective particle swarm optimization (SMPSO), third evolution step of generalized differential evolution (GDE3), multi-objective evolutionary algorithm based on decomposition (MOEA/D) and S-metric evolutionary multi-objective optimization (SMS-EMOA). We assess the performance of the algorithms by applying quality indicators intended to measure convergence and the diversity of the generated Pareto front approximations. We carry out a comparison with another reference mono-objective algorithm in the problem domain (Lamarckian genetic algorithm (LGA) provided by the AutoDock tool). Furthermore, the ligand binding site and molecular interactions of computed solutions are analyzed, showing promising results for the multi-objective approaches. In addition, a case study of application for aeroplysinin-1 is performed, showing the effectiveness of our multi-objective approach in drug discovery.

Sharing and executing linked data queries in a collaborative environment

MOTIVATION Life Sciences have emerged as a key domain in the Linked Data community because of the diversity of data semantics and formats available through a great variety of databases and web technologies. Thus, it has been used as the perfect domain for applications in the web of data. Unfortunately, bioinformaticians are not exploiting the full potential of this already available technology, and experts in Life Sciences have real problems to discover, understand and devise how to take advantage of these interlinked (integrated) data. RESULTS In this article, we present Bioqueries, a wiki-based portal that is aimed at community building around biological Linked Data. This tool has been designed to aid bioinformaticians in developing SPARQL queries to access biological databases exposed as Linked Data, and also to help biologists gain a deeper insight into the potential use of this technology. This public space offers several services and a collaborative infrastructure to stimulate the consumption of biological Linked Data and, therefore, contribute to implementing the benefits of the web of data in this domain. Bioqueries currently contains 215 query entries grouped by database and theme, 230 registered users and 44 end points that contain biological Resource Description Framework information. AVAILABILITY The Bioqueries portal is freely accessible at http://bioqueries.uma.es. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

kpath: integration of metabolic pathway linked data

In the last few years, the Life Sciences domain has experienced a rapid growth in the amount of available biological databases. The heterogeneity of these databases makes data integration a challenging issue. Some integration challenges are locating resources, relationships, data formats, synonyms or ambiguity. The Linked Data approach partially solves the heterogeneity problems by introducing a uniform data representation model. Linked Data refers to a set of best practices for publishing and connecting structured data on the Web. This article introduces kpath, a database that integrates information related to metabolic pathways. kpath also provides a navigational interface that enables not only the browsing, but also the deep use of the integrated data to build metabolic networks based on existing disperse knowledge. This user interface has been used to showcase relationships that can be inferred from the information available in several public databases. Database URL: The public Linked Data repository can be queried at http://sparql.kpath.khaos.uma.es using the graph URI “www.khaos.uma.es/metabolic-pathways-app”. The GUI providing navigational access to kpath database is available at http://browser.kpath.khaos.uma.es.

A New Multi-objective Approach for Molecular Docking Based on RMSD and Binding Energy

Ligand-protein docking is an optimization problem based on predicting the position of a ligand with the lowest binding energy in the active site of the receptor. Molecular docking problems are traditionally tackled with single-objective, as well as with multi-objective approaches, to minimize the binding energy. In this paper, we propose a novel multi-objective formulation that considers: the Root Mean Square Deviation (RMSD) difference in the coordinates of ligands and the binding (intermolecular) energy, as two objectives to evaluate the quality of the ligand-protein interactions. To determine the kind of Pareto front approximations that can be obtained, we have selected a set of representative multi-objective algorithms such as NSGA-II, SMPSO, GDE3, and MOEA/D. Their performances have been assessed by applying two main quality indicators intended to measure convergence and diversity of the fronts. In addition, a comparison with LGA, a reference single-objective evolutionary algorithm for molecular docking (AutoDock) is carried out. In general, SMPSO shows the best overall results in terms of energy and RMSD (value lower than 2A for successful docking results). This new multi-objective approach shows an improvement over the ligand-protein docking predictions that could be promising in in silico docking studies to select new anticancer compounds for therapeutic targets that are multidrug resistant.

Multi-objective ligand-protein docking with particle swarm optimizers

Abstract In the last years, particle swarm optimizers have emerged as prominent search methods to solve the molecular docking problem. A new approach to address this problem consists in a multi-objective formulation, minimizing the intermolecular energy and the Root Mean Square Deviation (RMSD) between the atom coordinates of the co-crystallized and the predicted ligand conformations. In this paper, we analyze the performance of a set of multi-objective particle swarm optimization variants based on different archiving and leader selection strategies, in the scope of molecular docking. The conducted experiments involve a large set of 75 molecular instances from the Protein Data Bank database (PDB) characterized by different sizes of HIV-protease inhibitors. The main motivation is to provide molecular biologists with unbiased conclusions concerning which algorithmic variant should be used in drug discovery. Our study confirms that the multi-objective particle swarm algorithms SMPSOhv and MPSO/D show the best overall performance. An analysis of the resulting molecular ligand conformations, in terms of binding site and molecular interactions, is also performed to validate the solutions found, from a biological point of view.

A Study of Archiving Strategies in Multi-objective PSO for Molecular Docking

Molecular docking is a complex optimization problem aimed at predicting the position of a ligand molecule in the active site of a receptor with the lowest binding energy. This problem can be formulated as a bi-objective optimization problem by minimizing the binding energy and the Root Mean Square Deviation (RMSD) difference in the coordinates of ligands. In this context, the SMPSO multi-objective swarm-intelligence algorithm has shown a remarkable performance. SMPSO is characterized by having an external archive used to store the non-dominated solutions and also as the basis of the leader selection strategy. In this paper, we analyze several SMPSO variants based on different archiving strategies in the scope of a benchmark of molecular docking instances. Our study reveals that the SMPSOhv, which uses an hypervolume contribution based archive, shows the overall best performance.

Re-constructing Hidden Semantic Data Models by Querying SPARQL Endpoints

Linked Open Data community is constantly producing new repositories that store information from different domains. The data included in these repositories follow the rules proposed by the W3C community, based on standards such as Resource Description Framework RDF and the SPARQL query language. The main advantage of this approach is the possibility of external developers accessing the data from their applications. This advantage is also one of the main challenges of this new technology due to the cost of exploring how the data is structured in a given repository in order to construct SPARQL queries to retrieve useful information. According to the reviewed literature, there are no applications to reconstruct the underlying semantic data models from an SPARQL endpoint. In this paper, we present an application for the reconstruction of the data model as an OWL Ontology Web Language ontology. This application, available as Open Source at http://github.com/estebanpua/ontology-endpoint-extraction uses a set of SPARQL queries to discover the classes and the object and data properties for a given RDF database. A web application interface has also been implemented for users to browse through classes, properties of the ontology generated from the data structure http://khaos.uma.es/oee. The ontologies generated by this application can help users to understand how the information is semantically organized, making easier the design of SPARQL queries.