Optimización multi-objetivo en las ciencias de la vida.

Para conseguir este objetivo, en lugar de intentar incorporar nuevos algoritmos directamente en el código fuente de AutoDock, se utilizó un framework orientado a la resolución de problemas de optimización con metaheurísticas. Concretamente, se usó jMetal, que es una librería de código libre basada en Java. Ya que AutoDock está implementado en C++, se desarrolló una versión en C++ de jMetal (posteriormente distribuida públicamente). De esta manera, se consiguió integrar ambas herramientas (AutoDock 4.2 y jMetal) para optimizar la energía libre de unión entre compuesto químico y receptor. Después de disponer de una amplia colección de metaheurísticas implementadas en jMetalCpp, se realizó un detallado estudio en el cual se aplicaron un conjunto de metaheurísticas para optimizar un único objetivo minimizando la energía libre de unión, el cual es el resultado de la suma de todos los términos de energía de la función objetivo de energía de AutoDock 4.2. Por lo tanto, cuatro metaheurísticas tales como dos variantes de algoritmo genético gGA (Algoritmo Genético generacional) y ssGA (Algoritmo Genético de estado estacionario), DE (Evolución Diferencial) y PSO (Optimización de Enjambres de Partículas) fueron aplicadas para resolver el problema del acoplamiento molecular. Esta fase se dividió en dos subfases en las que se usaron dos conjuntos de instancias diferentes, utilizando como receptores HIV-proteasas con cadenas laterales de aminoacidos flexibles y como ligandos inhibidores HIV-proteasas flexibles. El primer conjunto de instancias se usó para un estudio de configuración de parámetros de los algoritmos y el segundo para comparar la precisión de las conformaciones ligando-receptor obtenidas por AutoDock y AutoDock+jMetalCpp. La siguiente fase implicó aplicar una formulación multi-objetivo para resolver problemas de acoplamiento molecular dados los resultados interesantes obtenidos en estudios previos existentes en los que dos objetivos como la energía intermolecular y la energía intramolecular fueron minimizados. Por lo tanto, se comparó y analizó el rendimiento de un conjunto de metaheurísticas multi-objetivo mediante la resolución de complejos flexibles de acoplamiento molecular minimizando la energía inter- e intra-molecular. Estos algoritmos fueron: NSGA-II (Algoritmo Genético de Ordenación No dominada) y su versión de estado estacionario (ssNSGA-II), SMPSO (Optimización Multi-objetivo de Enjambres de Partículas con Modulación de Velocidad), GDE3 (Tercera versión de la Evolución Diferencial Generalizada), MOEA/D (Algoritmo Evolutivo Multi-Objetivo basado en la Decomposición) y SMS-EMOA (Optimización Multi-objetivo Evolutiva con Métrica S). Después de probar enfoques multi-objetivo ya existentes, se probó uno nuevo. En concreto, el uso del RMSD como un objetivo para encontrar soluciones similares a la de la solución de referencia. Se replicó el estudio previo usando este conjunto diferente de objetivos. Por último, se analizó de forma detallada el algoritmo que obtuvo mejores resultados en los estudios previos. En concreto, se realizó un estudio de variantes del SMPSO minimizando la energía intermolecular y el RMSD. Este estudio proporcionó algunas pistas sobre cómo nuevos algoritmos basados en SMPSO pueden ser adaptados para mejorar los resultados de acoplamiento molecular para aquellas simulaciones que involucren ligandos y receptores flexibles. Esta tesis demuestra que la inclusión de técnicas metaheurísticas de jMetalCpp en la herramienta de acoplamiento molecular AutoDock incrementa las posibilidades a los usuarios de ámbito biológico cuando resuelven el problema del acoplamiento molecular. El uso de técnicas de optimización mono-objetivo diferentes aparte de aquéllas ampliamente usadas en las comunidades de acoplamiento molecular podría dar lugar a soluciones de mayor calidad. En nuestro caso de estudio mono-objetivo, el algoritmo de evolución diferencial obtuvo mejores resultados que aquellos obtenidos por AutoDock. También se propone diferentes enfoques multi-objetivo para resolver el problema del acoplamiento molecular, tales como la decomposición de los términos de la energía de unión o el uso del RMSD como un objetivo. Finalmente, se demuestra que el SMPSO, una metaheurística de optimización multi-objetivo de enjambres de partículas, es una técnica remarcable para resolver problemas de acoplamiento molecular cuando se usa un enfoque multi-objetivo, obteniendo incluso mejores soluciones que las técnicas mono-objetivo.Las herramientas de acoplamiento molecular han llegado a ser bastante eficientes en el descubrimiento de fármacos y en el desarrollo de la investigación de la industria farmacéutica. Estas herramientas se utilizan para elucidar la interacción de una pequeña molécula (ligando) y una macro-molécula (diana) a un nivel atómico para determinar cómo el ligando interactúa con el sitio de unión de la proteína diana y las implicaciones que estas interacciones tienen en un proceso bioquímico dado. En el desarrollo computacional de las herramientas de acoplamiento molecular los investigadores de este área se han centrado en mejorar los componentes que determinan la calidad del software de acoplamiento molecular: 1) la función objetivo y 2) los algoritmos de optimización. La función objetivo de energía se encarga de proporcionar una evaluación de las conformaciones entre el ligando y la proteína calculando la energía de unión, que se mide en kcal/mol. En esta tesis, se ha usado AutoDock, ya que es una de las herramientas de acoplamiento molecular más citada y usada, y cuyos resultados son muy precisos en términos de energía y valor de RMSD (desviación de la media cuadrática). Además, se ha seleccionado la función de energía de AutoDock versión 4.2, ya que permite realizar una mayor cantidad de simulaciones realistas incluyendo flexibilidad en el ligando y en las cadenas laterales de los aminoácidos del receptor que están en el sitio de unión. Se han utilizado algoritmos de optimización para mejorar los resultados de acoplamiento molecular de AutoDock 4.2, el cual minimiza la energía libre de unión final que es la suma de todos los términos de energía de la función objetivo de energía. Dado que encontrar la solución óptima en el acoplamiento molecular es un problema de gran complejidad y la mayoría de las veces imposible, se suelen utilizar algoritmos no exactos como las metaheurísticas, para así obtener soluciones lo suficientemente buenas en un tiempo razonable

Minimum Population Search, an Application to Molecular Docking

Computer modeling of protein-ligand interactions is one of the most important phases in a drug design process. Part of the process involves the optimization of highly multi-modal objective (scoring) functions. This research presents the Minimum Population Search heuristic as an alternative for solving these global unconstrained optimization problems. To determine the effectiveness of Minimum Population Search, a comparison with seven state-of-the-art search heuristics is performed. Being specifically designed for the optimization of large scale multi-modal problems, Minimum Population Search achieves excellent results on all of the tested complexes, especially when the amount of available function evaluations is strongly reduced. A first step is also made toward the design of hybrid algorithms based on the exploratory power of Minimum Population Search. Computational results show that hybridization leads to a further improvement in performance

Computational structure‐based drug design: Predicting target flexibility

The role of molecular modeling in drug design has experienced a significant revamp in the last decade. The increase in computational resources and molecular models, along with software developments, is finally introducing a competitive advantage in early phases of drug discovery. Medium and small companies with strong focus on computational chemistry are being created, some of them having introduced important leads in drug design pipelines. An important source for this success is the extraordinary development of faster and more efficient techniques for describing flexibility in three‐dimensional structural molecular modeling. At different levels, from docking techniques to atomistic molecular dynamics, conformational sampling between receptor and drug results in improved predictions, such as screening enrichment, discovery of transient cavities, etc. In this review article we perform an extensive analysis of these modeling techniques, dividing them into high and low throughput, and emphasizing in their application to drug design studies. We finalize the review with a section describing our Monte Carlo method, PELE, recently highlighted as an outstanding advance in an international blind competition and industrial benchmarks.We acknowledge the BSC-CRG-IRB Joint Research Program in Computational Biology. This work was supported by a grant from the Spanish Government CTQ2016-79138-R.J.I. acknowledges support from SVP-2014-068797, awarded by the Spanish Government.Peer ReviewedPostprint (author's final draft

On the role of metaheuristic optimization in bioinformatics

Metaheuristic algorithms are employed to solve complex and large-scale optimization problems in many different fields, from transportation and smart cities to finance. This paper discusses how metaheuristic algorithms are being applied to solve different optimization problems in the area of bioinformatics. While the text provides references to many optimization problems in the area, it focuses on those that have attracted more interest from the optimization community. Among the problems analyzed, the paper discusses in more detail the molecular docking problem, the protein structure prediction, phylogenetic inference, and different string problems. In addition, references to other relevant optimization problems are also given, including those related to medical imaging or gene selection for classification. From the previous analysis, the paper generates insights on research opportunities for the Operations Research and Computer Science communities in the field of bioinformatics

Exploration of Reaction Pathways and Chemical Transformation Networks

For the investigation of chemical reaction networks, the identification of all relevant intermediates and elementary reactions is mandatory. Many algorithmic approaches exist that perform explorations efficiently and automatedly. These approaches differ in their application range, the level of completeness of the exploration, as well as the amount of heuristics and human intervention required. Here, we describe and compare the different approaches based on these criteria. Future directions leveraging the strengths of chemical heuristics, human interaction, and physical rigor are discussed.Comment: 48 pages, 4 figure

Gradient based optimization in ligand-receptor docking

In this work, we compared six global search heuristics and two scoring functions in the field of ligand-receptor docking. A new way for the gradient based minimization of a ligand whose position in space is defined by translation, orientation and a set of torsional flexible angles was implemented and thoroughly tested. The default local search method of a Lamarckian genetic algorithm was replaced by our novel gradient based approach and the new hybrid was compared to non-gradient global search heuristics. Finally, we present our docking program BALLDock, in which we incorporated our findings.In der vorliegenden Arbeit wurden sechs populationsbasierte Optmierungsheuristiken und zwei Scoring-Funktionen im Hinblick auf ihre Leistungsfähigkeit im Bereich Ligand-Rezeptor Docking miteinander verglichen. Parallel dazu wurde eine neuer Ansatz entwickelt, der die lokale, gradientenbasierte Optimierung partiell flexibler Moleküle, deren Position und Konformation durch Translation, Orientierung und eine Anzahl flexibler Bindungswinkel definiert ist, erlaubt. Danach wurde die gradientenfreie Methode zur lokalen Optimierung eines Lamarck genetischen Algorithmus durch das neuartige gradientbasierte Verfahren ersetzt und dessen Einfluss auf die Ergebnisse der globalen Suchheuristik analysiert. Abschließend wird das Dockingprogramm BALLDock vorgestellt, in das die neu gewonnenen Erkenntnisse einflossen

Insights into the Development of Chemotherapeutics Targeting PFKFB Enzymes

The PFKFB enzymes control the primary checkpoint in the glycolytic pathway and are implicated in a multitude of diseases: from cancer, to schizophrenia, to diabetes, and heart disease. The inducible isoform, PFKFB3, is known to be associated with the upregulation of glycolysis in many cancers. The first study within this work investigates the potential for using tier-based approaches of virtual screening to target small molecule kinases, with PFKFB3 serving as a case study. For this investigation, bioactive compounds for PFKFB3 were identified from a compound library of 1364 compounds via high-throughput screening, with bioactive compounds being further characterized as either competitive or non-competitive for F6P. Using the F6P-competitive compounds, several structure based docking programs were assessed individually and in conjunction with a pharmacophore screening. The results showed that the tiered virtual screening approach, using pharmacophore screening in addition to structure-based docking, improved enrichments rates in 80% of cases, reduced CPU costs up to 7-fold, and lessened variability among different structure-based docking methods. The second study investigates the structural and kinetic characteristics of citrate inhibition on the heart PFKFB isoenzyme, PFKFB2. High levels of citrate, an intermediate of the TCA cycle, signify an abundance of biosynthetic precursors and that additional glucose need not be degraded for this purpose. Previous studies have noted that citrate acts as an important negative feed-back mechanism to limit glycolytic activity by inhibiting PFKFB enzymes, yet the structural and mechanistic details of citrate’s inhibition had not been determined. To study the molecular basis for citrate inhibition, the three-dimensional structures of the human and bovine PFKFB2 orthologues were solved, each in complex with citrate. For both cases, citrate primarily occupied the binding site of Fructose-6-phosphate (F6P), competitively blocking F6P from binding. Additionally, a carboxy arm of citrate extended into the γ-phosphate binding site of ATP, sterically and electrostatically blocking the catalytic binding mode for ATP. In the human orthologue, which utilized AMPPNP as an ATP analogue, conformational changes were observed in the 2-kinase domain as well as the binding mode for AMPPNP. This study gives new insights as to how the citrate-mediate negative feedback loop influences glycolytic flux through PFKFB enzymes

Adaptive simulations, towards interactive protein-ligand modeling

Modeling the dynamic nature of protein-ligand binding with atomistic simulations is one of the main challenges in computational biophysics, with important implications in the drug design process. Although in the past few years hardware and software advances have significantly revamped the use of molecular simulations, we still lack a fast and accurate ab initio description of the binding mechanism in complex systems, available only for up-to-date techniques and requiring several hours or days of heavy computation. Such delay is one of the main limiting factors for a larger penetration of protein dynamics modeling in the pharmaceutical industry. Here we present a game-changing technology, opening up the way for fast reliable simulations of protein dynamics by combining an adaptive reinforcement learning procedure with Monte Carlo sampling in the frame of modern multi-core computational resources. We show remarkable performance in mapping the protein-ligand energy landscape, being able to reproduce the full binding mechanism in less than half an hour, or the active site induced fit in less than 5 minutes. We exemplify our method by studying diverse complex targets, including nuclear hormone receptors and GPCRs, demonstrating the potential of using the new adaptive technique in screening and lead optimization studies.We thank Drs Anders Hogner and Christoph Grebner, from AstraZeneca, and Jorge Estrada, from BSC, for fruitful discussions and feedback on the manuscript. We acknowledge the BSC-CRG-IRB Joint Research Program in Computational Biology. This work was supported by the CTQ2016-79138-R grant from the Spanish Government. D.L. acknowledges the support of SEV-2011-00067, awarded by the Spanish Government.Peer ReviewedPostprint (published version

De novo design of multi-domain metalloenzymes

The course of evolution required the recombination of protein domains to perform ever-growing complex functions. The presence of an additional domain in a multi-domain protein expands, alters, or modulates the functionality with respect to the isolated one-domain protein (1). In particular, small molecule binding domains have shown a strong propensity to form multi-domain proteins and regulate enzymatic, transport, and signal-transducing domains (2). This modulation is referred to as allostery (from Greek, other solid body), as the properties of a functional site are affected by a small molecule bound to a distinctive protein site (3). Taking inspiration from Nature, artificial proteins have been engineered combining different domains to develop bioinspired molecular machines, able to respond to external stimuli (4). This Ph.D. project, born from the collaboration of the Artificial Metallo-Enzyme Group and the DeGradoLab, was devoted to the development of a multi-domain protein. This represents the first example of an artificial multi-domain protein, in which allostery was designed completely from scratch (5,6). DF (Due Ferri), a diiron phenol oxidase domain, and PS (Porphyrin-binding Sequence), a zinc porphyrin binding domain, were selected as starting proteins to be combined and give DFP (Due Ferri Porphyrin).7 The multiple junctions were exploited to link the two domains, and obtain a more extensive structural coupling between them. While the two metalloproteins present the same kind of domain, the two four-helix bundles are characterized by different geometrical parameters. Therefore, a structural-based methodology was firstly developed in order to identify the best colocalization and helical junctions to accommodate the changes in interhelical separation and registry between the bundles. The x-ray structure of the first analogue, DFP1, was determined, bound to its metal cofactors. The superposition of the 120 residues comprising binding sites gave an excellent fit to the design model, with an overall backbone RMSD of less than 1.4 Å. However, DFP1 was designed to maximize structural stability with a tight and uniform packing, which hindered the access to organic substrates at the DF domain and, thus, its functional characterization. The channel-lining residues of the dimetal-binding site in DF domain were mutated in Gly residues to create a pocket for a substrate. The introduction of helix-breaking residues, that gave oligomerization promiscuity, required also the mutation of DF loop, leading to the final candidate DFP3. An extensive spectroscopic characterization was performed to investigate the functional properties of the multi-domain proteins. DFP3 was demonstrated to bind the designed zinc porphyrin ZnP (Zn-meso-(trifluoromethyl)porphin) at the PS domain with nanomolar affinity. The strong negative Cotton Effect in the ZnP Soret region confirmed the tight and single-mode binding in the rigid asymmetric protein core. On the other side of the multi-domain metalloprotein, cobalt binding experiments confirmed the preservation of the DF penta-coordinating environment. Indeed, the dizinc form was able to stabilize the semiquinone form of 3,5-ditertbutylcatechol/quinone couple, and DFP3 showed ferroxidase and phenoloxidase activities. Although these reactivities were still present upon ZnP binding, a modulation effect was observed. The catalytic characterization of 4-aminophenol oxidation demonstrated a Michaelis-Menten mechanism in the phenoloxidase activity, and high-lightened a 4-fold tighter Km and a 7-fold decrease in kcat upon binding of ZnP. Molecular Dynamics simulations suggested that the presence of ZnP restrains the conformational freedom of a second-shell Tyr, that have been previously shown to largely affect the reactivity of the diiron center. Subsequently, the binding fitness of the zinc porphyrin was changed to investigate the bidirectionality of the allosteric regulation. In the presence of the different zinc porphyrin ZnDP (ZnDP, Zn-Deuteroporphyrin IX), DFP3 resulted to be more flexible, as demonstrated by thermal and chemical denaturations. Nevertheless, the dizinc center continued to stabilize the seminiquinone, and the ferroxidase and phenol oxidase activities were still modulated by the presence of ZnDP. DFP3 showed an excellent affinity for ZnDP, only one order lower in magnitude compared to the designed ZnP. More importantly, the ZnDP affinity was modulated by the presence of zinc bound to DFP3, showing a 3-fold decrease in KD, and demonstrating the presence of a back-regulation. In final instance, the photosensitizing properties of zinc porphyrin-DFP3 complexes were tested in the oxidation of the biological redox cofactor NADH. The photocatalytic characterization highlighted the paramount role of the protein scaffold not only in increasing the reaction rate, but also in protecting the zinc porphyrins from highly reactive species. The lower binding fitness DFP3 towards ZnDP hindered this protection, enabling a major permeability of these species and leading to the zinc porphyrin photobleaching. Although only a preliminary characterization of photocatalysis has been performed, the high reactivity and versatility of such systems are a promising starting point for the de novo design of artificial photosystems for the storage of light energy in chemical fuels.   References (1) Bashton, M. & Chothia, C. The Generation of New Protein Functions by the Combination of Domains. Structure 15, 85–99 (2007). (2) Anantharaman, V., Koonin, E. V. & Aravind, L. Regulatory potential, phyletic distribution and evolution of ancient, intracellular small-molecule-binding domains11Edited by F. Cohen. J. Mol. Biol. 307, 1271–1292 (2001). (3) Monod, J., Wyman, J. & Changeux, J.-P. On the nature of allosteric transitions: A plausible model. J. Mol. Biol. 12, 88–118 (1965). (4) Ostermeier, M. Engineering allosteric protein switches by domain insertion. Protein Eng. Des. Sel. 18, 359–364 (2005). (5) Researchers design allosteric protein from scratch. Chemical & Engineering News https://cen.acs.org/biological-chemistry/Researchers-design-allosteric-protein-scratch/98/i48. 6. Pirro, F. et al. Allosteric cooperation in a de novo-designed two-domain protein. Proc. Natl. Acad. Sci. 117, 33246–33253 (2020). (7) Lombardi, A., Pirro, F., Maglio, O., Chino, M. & DeGrado, W. F. De Novo Design of Four-Helix Bundle Metalloproteins: One Scaffold, Diverse Reactivities. Acc. Chem. Res. 52, 1148–1159 (2019)