Simulasi Penambatan Molekuler Senyawa Kompleks Besi Terhadap Protein Hemofor sebagai Kandidat Fotosensitizer pada Terapi Fotodinamika

Resistensi antibiotika muncul sebagai polemik yang dapat mempengaruhi kesehatan manusia. Kemajuan teknologi membuka peluang dalam penemuan molekul senyawa baru yang mampu mencegah perkembangan mikroba patogen, seperti Pseudomonas aeruginosa yang resisten terhadap beberapa jenis antibiotika. Terapi fotodinamika dengan memanfaatkan penggunaan fotosensitizer yang berasal dari senyawa yang membentuk kompleks dengan besi merupakan salah satu pendekatan alternatif untuk mengatasi penyakit infeksi dengan risiko resistensi mikroba yang lebih rendah. Penelitian yang dilakukan secara in silico ini bertujuan untuk mengamati, mengeksplorasi, dan mengevaluasi mekanisme aksi berbasis struktural dari molekul senyawa yang membentuk kompleks dengan besi, yaitu besi-ftalosianina dan besi-salofen terhadap protein hemofor HasAp serta pengaruh molekularnya terhadap bagian situs aktif pengikatan dari protein hemofor HasR. Identifikasi interaksi molekuler dan afinitas antara molekul senyawa besi-ftalosianina dan besi-salofen terhadap protein hemofor HasAp dilakukan dengan simulasi ligan-protein docking mempergunakan software MGLTools 1.5.6 yang dilengkapi dengan AutoDock 4.2. Di samping itu, dilakukan juga simulasi protein-protein docking terhadap sistem kompleks ligan-protein untuk memastikan pengaruh molekularnya terhadap bagian situs aktif pengikatan dari protein hemofor HasR dengan mempergunakan software PatchDock. Berdasarkan simulasi ligan-protein docking diperoleh hasil bahwa senyawa besi-ftalosianina memiliki afinitas paling baik terhadap kedua protein hemofor HasAp, dengan nilai energi bebas pengikatan masing-masing sebesar −68,45 kJ/mol dan −65,23 kJ/mol. Menariknya, hasil simulasi protein-protein docking antara kompleks molekul senyawa besi-ftalosianina dan protein hemofor HasAp-besi-ftalosianina terhadap protein hemofor HasR memiliki nilai energi kontak atom yang positif sebesar 556,56 kJ/mol. Dari penelitian ini dapat diprediksikan bahwa perbedaan struktur molekul senyawa yang membentuk kompleks dengan besi mampu mempengaruhi mekanisme aksi berbasis structural terhadap protein hemofor target

Structural evidence of quercetin multi-target bioactivity:A reverse virtual screening strategy

The ubiquitous flavonoid quercetin is broadly recognized for showing diverse biological and health-promoting effects, such as anti-cancer, anti-inflammatory and cytoprotective activities. The therapeutic potential of quercetin and similar compounds for preventing such diverse oxidative stress-related pathologies has been generally attributed to their direct antioxidant properties. Nevertheless, accumulated evidence indicates that quercetin is also able to interact with multiple cellular targets influencing the activity of diverse signaling pathways. Even though there are a number of well-established protein targets such as phosphatidylinositol 3 kinase and xanthine oxidase, there remains a lack of a comprehensive knowledge of the potential mechanisms of action of quercetin and its target space. In the present work we adopted a reverse screening strategy based on ligand similarity (SHAFTS) and target structure (idTarget, LIBRA) resulting in a set of predicted protein target candidates. Furthermore, using this method we corroborated a broad array of previously experimentally tested candidates among the predicted targets, supporting the suitability of this screening approach. Notably, all of the predicted target candidates belonged to two main protein families, protein kinases and poly [ADP-ribose] polymerases. They also included key proteins involved at different points within the same signaling pathways or within interconnected signaling pathways, supporting a pleiotropic, multilevel and potentially synergistic mechanism of action of quercetin. In this context we highlight the value of quercetin's broad target profile for its therapeutic potential in diseases like inflammation, neurodegeneration and cancer

Correcting the impact of docking pose generation error on binding affinity prediction

International audienceAbstractBackgroundPose generation error is usually quantified as the difference between the geometry of the pose generated by the docking software and that of the same molecule co-crystallised with the considered protein. Surprisingly, the impact of this error on binding affinity prediction is yet to be systematically analysed across diverse protein-ligand complexes.ResultsAgainst commonly-held views, we have found that pose generation error has generally a small impact on the accuracy of binding affinity prediction. This is also true for large pose generation errors and it is not only observed with machine-learning scoring functions, but also with classical scoring functions such as AutoDock Vina. Furthermore, we propose a procedure to correct a substantial part of this error which consists of calibrating the scoring functions with re-docked, rather than co-crystallised, poses. In this way, the relationship between Vina-generated protein-ligand poses and their binding affinities is directly learned. As a result, test set performance after this error-correcting procedure is much closer to that of predicting the binding affinity in the absence of pose generation error (i.e. on crystal structures). We evaluated several strategies, obtaining better results for those using a single docked pose per ligand than those using multiple docked poses per ligand.ConclusionsBinding affinity prediction is often carried out on the docked pose of a known binder rather than its co-crystallised pose. Our results suggest than pose generation error is in general far less damaging for binding affinity prediction than it is currently believed. Another contribution of our study is the proposal of a procedure that largely corrects for this error. The resulting machine-learning scoring function is freely available at http://istar.cse.cuhk.edu.hk/rf-score-4.tgzand http://ballester.marseille.inserm.fr/rf-score-4.tgz

GPU Accelerated Quantum Virtual Screening: Application for the Natural Inhibitors of New Dehli Metalloprotein (NDM-1)

Quantum mechanical approaches for the massive computation on large biological system such as virtual screening in drug design and development have presented a challenge to computational chemists for many years. In this study, we demonstrated that by taking advantage of rapid growth of GPU-based hardware and software (i.e., teraChem), it is feasible to perform virtual screening of a refined chemical library at quantum mechanical level in order to identify the lead compounds with improved accuracy, especially for the drug targets such as metalloproteins in which significant charge transfer and polarization occur amongst the metal ions and their coordinated amino acids. Our calculations predicted four nature compounds (i.e., Curcumin, Catechin, menthol, and Ferulic acid) as the suitable inhibitors for antibiotics resistance against New Delhi Metallo-β-lactamase-1 (NDM-1). Molecular orbitals (MOs) of the QM region of metal ions and their coordinated residues indicate that the bridged hydroxide ion delocalized the electron over the Zn-OH-Zn group at HOMO, different from MOs when the OH− is not presented in NDM-1. This indicates that the bridged hydroxide ion plays an important role in the design of antibiotics and other inhibitors targeting the metalloproteins

Disorder-to-helix conformational conversion of the human immunomodulatory peptide LL-37 induced by antiinflammatory drugs, food dyes and some metabolites

The human antimicrobial and immunomodulatory peptide LL-37 is ubiquitously expressed and secreted by epithelial cells of mucosal surfaces including the gastrointestinal tract, the primary absorption site of orally administered drugs and food components. Besides antimicrobial properties, LL-37 also contributes to the pathophysiology of various diseases such as ulcerative colitis, Crohn's disease and cancer. The non-covalent association of antiinflammatory drugs, porphyrin pigments, bile salts and food dyes to the peptide was uncovered and evaluated by circular dichroism (CD) spectroscopy. These agents induce the disorder-to-order conformational transition of the natively unstructured LL-37 leading to its helical folding. Even in the presence of chloride, where LL-37 is partially folded, these small molecules were able to rise the α-helix content. CD titration data indicated positive cooperativity between the ligand molecules accommodated to the peptide chain resulting in multimeric complexes with apparent dissociation constants ranged from 2 to 500 μM. Computational docking suggested the prominent role of the Lys8-Arg19 segment of LL-37 in the accommodation of ligand molecules, governed principally by salt bridges and H-bonding. Since pleiotropic biological functions of LL-37 are strongly conformation-dependent it could be anticipated that folding inducer compounds may modulate its in vivo actions and of related cationic peptides

Empirical Scoring Functions for Structure-Based Virtual Screening: Applications, Critical Aspects, and Challenges

Structure-based virtual screening (VS) is a widely used approach that employs the knowledge of the three-dimensional structure of the target of interest in the design of new lead compounds from large-scale molecular docking experiments. Through the prediction of the binding mode and affinity of a small molecule within the binding site of the target of interest, it is possible to understand important properties related to the binding process. Empirical scoring functions are widely used for pose and affinity prediction. Although pose prediction is performed with satisfactory accuracy, the correct prediction of binding affinity is still a challenging task and crucial for the success of structure-based VS experiments. There are several efforts in distinct fronts to develop even more sophisticated and accurate models for filtering and ranking large libraries of compounds. This paper will cover some recent successful applications and methodological advances, including strategies to explore the ligand entropy and solvent effects, training with sophisticated machine-learning techniques, and the use of quantum mechanics. Particular emphasis will be given to the discussion of critical aspects and further directions for the development of more accurate empirical scoring functions

Robust Scoring Functions for Protein-Ligand Interactions with Quantum Chemical Charge Models

Ordinary least-squares (OLS) regression has been used widely for constructing the scoring functions for protein–ligand interactions. However, OLS is very sensitive to the existence of outliers, and models constructed using it are easily affected by the outliers or even the choice of the data set. On the other hand, determination of atomic charges is regarded as of central importance, because the electrostatic interaction is known to be a key contributing factor for biomolecular association. In the development of the AutoDock4 scoring function, only OLS was conducted, and the simple Gasteiger method was adopted. It is therefore of considerable interest to see whether more rigorous charge models could improve the statistical performance of the AutoDock4 scoring function. In this study, we have employed two well-established quantum chemical approaches, namely the restrained electrostatic potential (RESP) and the Austin- model 1-bond charge correction (AM1-BCC) methods, to obtain atomic partial charges, and we have compared how different charge models affect the performance of AutoDock4 scoring functions. In combination with robust regression analysis and outlier exclusion, our new protein–ligand free energy regression model with AM1-BCC charges for ligands and Amber 99SB charges for proteins achieve lowest root-mean- squared error of 1.637 kcal/mol for the training set of 147 complexes and 2.176 kcal/mol for the external test set of 1427 complexes. The assessment for binding pose prediction with the 100 external decoy sets indicates very high success rate of 87% with the criteria of predicted root-mean- squared deviation of less than 2 Å. The success rates and statistical performance of our robust scoring functions are only weakly class-dependent (hydrophobic, hydrophilic, or mixed)

Robust Scoring Functions for Protein–Ligand Interactions with Quantum Chemical Charge Models

Ordinary least-squares (OLS) regression has been used widely for constructing the scoring functions for protein–ligand interactions. However, OLS is very sensitive to the existence of outliers, and models constructed using it are easily affected by the outliers or even the choice of the data set. On the other hand, determination of atomic charges is regarded as of central importance, because the electrostatic interaction is known to be a key contributing factor for biomolecular association. In the development of the AutoDock4 scoring function, only OLS was conducted, and the simple Gasteiger method was adopted. It is therefore of considerable interest to see whether more rigorous charge models could improve the statistical performance of the AutoDock4 scoring function. In this study, we have employed two well-established quantum chemical approaches, namely the restrained electrostatic potential (RESP) and the Austin-model 1-bond charge correction (AM1-BCC) methods, to obtain atomic partial charges, and we have compared how different charge models affect the performance of AutoDock4 scoring functions. In combination with robust regression analysis and outlier exclusion, our new protein–ligand free energy regression model with AM1-BCC charges for ligands and Amber99SB charges for proteins achieve lowest root-mean-squared error of 1.637 kcal/mol for the training set of 147 complexes and 2.176 kcal/mol for the external test set of 1427 complexes. The assessment for binding pose prediction with the 100 external decoy sets indicates very high success rate of 87% with the criteria of predicted root-mean-squared deviation of less than 2 Å. The success rates and statistical performance of our robust scoring functions are only weakly class-dependent (hydrophobic, hydrophilic, or mixed)

Glicomiméticos de ácido hialurónico como terapia adyuvante contra patologías tumorales

Fil: Modenutti, Carlos Pablo. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Buenos Aires, ArgentinaA pesar de los grandes avances que existen en el conocimiento del cáncer de mama,\nlos principales tratamientos sistémicos que se utilizan para combatir la enfermedad\navanzada son la hormonoterapia y la quimioterapia. Esta ultima estrategia, si bien es\nefectiva en la mayoría de los casos, suele ser nociva para el paciente.\nEl Ácido Hialurónico (AH) esta compuesto por unidades repetidas de disacáridos de Dglucurónico\ny N-acetil-D-glucosamina, unidos mediante enlaces alternados B1-3 y B1-4.\nLa mayoría de los procesos en los cuales el AH se encuentra involucrado están mediados\npor su principal receptor, CD44, una glicoproteína de membrana que se encuentra\npresente en muchos tipos celulares.\nSe ha demostrado que los oligosacáridos de Ácido Hialurónico (oAH) tienen la\ncapacidad de inhibir la proliferación celular (e inclusive, en algunos casos, de inducir\napoptosis) en diferentes modelos de patologías tumorales, como ser las lineas celulares\nde cáncer de colon, de distintos linfomas y de cáncer de mama. Ademas, cuando se\nadministran en combinación con fármacos antitumorales de uso convencional como la\nDoxorrubicina en lineas celulares cultivadas in vitro, son capaces de actuar de forma\nsinérgica, permitiendo una reducción de la dosis del quimioterápico.\nEstas propiedades de los oAH, los posicionan como buenos candidatos para su\nadministración conjunta con quimioterápicos en una terapia adyuvante. Pero los oAH\npresentan dos grandes desventajas. La primera, es que al ser un componente del\norganismo, su metabolismo se encuentra estrechamente regulado, a tal punto que su vida\nmedia en la circulación sanguínea es de apenas un par de minutos. El otro es su costo de\nproducción elevado. Es por ello, que la búsqueda de compuestos con propiedades\nequivalentes a las de los oAH surge como una prometedora linea de investigación.\nUno de los desafíos más grandes de la química medicinal es el empleo de\noligosacáridos como medicamentos. Los avances en la comprensión funcional de las\ninteracciones proteína-carbohidrato han permitido el desarrollo de una nueva clase de\nfármacos, conocidos como fármacos glicomiméticos.\nEstos compuestos bioactivos son capaces de imitar la función de los hidratos de\ncarbono pero carecen de las propiedades no deseadas de los mismos (propiedades\nfarmacocinéticas y farmacodinámicas insuficientes, baja actividad y permeabilidad a los\ntejidos, escasa estabilidad y vida media en suero).\nLa visión detallada de las interacciones carbohidrato-proteína que se requiere para el\ndiseño de esta clase de moléculas es provista en general por la cristalografía y la RMN.\nPero no siempre se puede contar con este tipo de información experimental y es por ello\nque el empleo de simulaciones computacionales para obtener información estructural de\nbiomoléculas en proyecto de desarrollo de fármacos es cada vez mas difundido en la\nactualidad.\nLas herramientas bioinformaticas que se emplean en la búsqueda y optimización de\nnuevos fármacos, fueron concebidas con una perspectiva utilitaria multipropósito, por lo\nque en algunos casos, los resultados obtenidos no son tan confiables. Es por ello que la\nadecuación de dichas herramientas a un sistema particular, es habitual dentro del proceso\nde descubrimiento de nuevos compuestos.\nTeniendo en cuenta estos antecedentes, nos propusimos como objetivo de identificar\ncompuestos naturales capaces de actuar como glicomiméticos de oAH. Para ello,\nrealizamos un análisis detallado de las propiedades moleculares de los oAH como así\ntambién de los principales características que determinan su interacción con CD44\nmediante el empleo de simulaciones de Dinámica Molecular. Luego de una\ncaracterización rigurosa, identificamos que el tipo de enlaces alternados (B1-3 y B1-4) y\nque la presencia del azúcar N-Acetilglucosamina eran de suma importancia.\nA partir de estos datos y luego de una búsqueda en bases de datos de compuestos\nnaturales, identificamos dos polímeros que cumplían con la características de presentar\nenlaces alternados (Liquenina y Xilano) y uno compuesto exclusivamente por NAcetilglucosamina\n(Quitina).\nUna vez identificados los compuestos, procedimos a la construcción de los complejos\ncorrespondientes con CD44, con el fin de evaluar la afinidad relativa del receptor por cada\nuno de ellos. Debido a que las herramientas disponibles para la predicción de estructuras\nproteína-carbohidrato presentan un grado muy bajo de precisión y exactitud, se desarrollo\nun método alternativo al que se emplea habitualmente con el programa de docking\nAutodock (CADM), basado en la estructura del solvente en el sitio de reconocimiento para\ncarbohidratos (CBS), al cual denominamos WSBDM.\nUtilizando el WSBDM, obtuvimos complejos entre CD44 y tetrasacáridos de cada uno\nde los compuestos seleccionados. Luego realizamos simulaciones de Dinámica Molecular\ncon el fin de caracterizar la estabilidad de los oligosacáridos el CBS de CD44 y estimar la\nafinidad relativa por receptor a partir del calculo de energía libre de unión con un método\nde punto final (MMPB-SA). Los resultados indican que solo los oligosacáridos de\nLiquenina (oLi), tendrían una afinidad significativa por CD44.\nPor ultimo, decidimos evaluar in vitro la actividad de estos compuestos. Los resultados\nde los ensayos del efecto de los oligosacáridos, indican que solo los oLi serian capaces\ndisminuir de forma significativa la proliferación celular, en concordancia con los resultados\nobtenidos in silico. Por otro lado, y mas interesante aun, cuando los oligosacáridos son\nadministrados en combinación con Doxorrubicina, tanto los oligosacárido de Liquenina\ncomo los de Xilano, son capaces de potenciar la actividad del quimioterápico. Estos\nresultados indicarían que quizás estén ejerciendo su efecto por una vía independiente de\nCD44. Estos resultados in vitro constituyen solo una prueba de concepto de los hallazgos\nrealizados in silico, pero constituyen un estimulo a futuras investigaciones en el tema