Studying protein-ligand interactions using a Monte Carlo procedure

[eng] Biomolecular simulations have been widely used in the study of protein-ligand interactions; comprehending the mechanisms involved in the prediction of binding affinities would have a significant repercussion in the pharmaceutical industry. Notwithstanding the intrinsic difficulty of sampling the phase space, hardware and methodological developments make computer simulations a promising candidate in the resolution of biophysically relevant problems. In this context, the objective of the thesis is the development of a protocol that permits studying protein-ligand interactions, in view to be applied in drug discovery pipelines. The author contributed to the rewriting PELE, our Monte Carlo sampling procedure, using good practices of software development. These involved testing, improving the readability, modularity, encapsulation, maintenance and version control, just to name a few. Importantly, the recoding resulted in a competitive cutting-edge software that is able to integrate new algorithms and platforms, such as new force fields or a graphical user interface, while being reliable and efficient. The rest of the thesis is built upon this development. At this point, we established a protocol of unbiased all-atom simulations using PELE, often combined with Markov (state) Models (MSM) to characterize the energy landscape exploration. In the thesis, we have shown that PELE is a suitable tool to map complex mechanisms in an accurate and efficient manner. For example, we successfully conducted studies of ligand migration in prolyl oligopeptidases and nuclear hormone receptors (NHRs). Using PELE, we could map the ligand migration and binding pathway in such complex systems in less than 48 hours. On the other hand, with this technique we often run batches of 100s of simulations to reduce the wall-clock time. MSM is a useful technique to join these independent simulations in a unique statistical model, as individual trajectories only need to characterize the energy landscape locally, and the global characterization can be extracted from the model. We successfully applied the combination of these two methodologies to quantify binding mechanisms and estimate the binding free energy in systems involving NHRs and tyorsinases. However, this technique represents a significant computational effort. To reduce the computational load, we developed a new methodology to overcome the sampling limitations caused by the ruggedness of the energy landscape. In particular, we used a procedure of iterative simulations with adaptive spawning points based on reinforcement learning ideas. This permits sampling binding mechanisms at a fraction of the cost, and represents a speedup of an order of magnitude in complex systems. Importantly, we show in a proof-of-concept that it can be used to estimate absolute binding free energies. Overall, we hope that the methodologies presented herein help streamline the drug design process.[spa] Las simulaciones biomoleculares se han usado ampliamente en el estudio de interacciones proteína-ligando. Comprender los mecanismos involucrados en la predicción de afinidades de unión tiene una gran repercusión en la industria farmacéutica. A pesar de las dificultades intrínsecas en el muestreo del espacio de fases, mejoras de hardware y metodológicas hacen de las simulaciones por ordenador un candidato prometedor en la resolución de problemas biofísicos con alta relevancia. En este contexto, el objetivo de la tesis es el desarrollo de un protocolo que introduce un estudio más eficiente de las interacciones proteína-ligando, con vistas a diseminar PELE, un procedimiento de muestreo de Monte Carlo, en el diseño de fármacos. Nuestro principal foco ha sido sobrepasar las limitaciones de muestreo causadas por la rugosidad del paisaje de energías, aplicando nuestro protocolo para hacer analsis detallados a nivel atomístico en receptores nucleares de hormonas, receptores acoplados a proteínas G, tirosinasas y prolil oligopeptidasas, en colaboración con una compañía farmacéutica y de varios laboratorios experimentales. Con todo ello, esperamos que las metodologías presentadas en esta tesis ayuden a mejorar el diseño de fármacos

Multifunctional nanocarriers for lung drug delivery

Nanocarriers have been increasingly proposed for lung drug delivery applications. The strategy of combining the intrinsic and more general advantages of the nanostructures with specificities that improve the therapeutic outcomes of particular clinical situations is frequent. These include the surface engineering of the carriers by means of altering the material structure (i.e., chemical modifications), the addition of specific ligands so that predefined targets are reached, or even the tuning of the carrier properties to respond to specific stimuli. The devised strategies are mainly directed at three distinct areas of lung drug delivery, encompassing the delivery of proteins and protein-based materials, either for local or systemic application, the delivery of antibiotics, and the delivery of anticancer drugs-the latter two comprising local delivery approaches. This review addresses the applications of nanocarriers aimed at lung drug delivery of active biological and pharmaceutical ingredients, focusing with particular interest on nanocarriers that exhibit multifunctional properties. A final section addresses the expectations regarding the future use of nanocarriers in the area.UID/Multi/04326/2019; PD/BD/137064/2018info:eu-repo/semantics/publishedVersio

Molecular Communication for Quorum Sensing Inspired Cooperative Drug Delivery

A cooperative drug delivery system is proposed, where quorum sensing (QS), a density-dependent bacterial behavior coordination mechanism, is employed by synthetic bacterium-based nanomachines (B-NMs) for controllable drug delivery. In our proposed system, drug delivery is only triggered when there are enough QS molecules, which in turn only happens when there are enough B-NMs. This makes the proposed system can be used to achieve a high release rate of drug molecules from a high number of B-NMs when the population density of B-NMs may not be known. Analytical expressions for i) the expected activation probability of the B-NM due to randomly-distributed B-NMs and ii) the expected aggregate absorption rate of drug molecules due to randomly-distributed QS activated B-NMs are derived. Analytical results are verified by particle-based simulations. The derived results can help to predict and control the impact of environmental factors (e.g. diffusion coefficient and degradation rate) on the absorption rate of drug molecules since rigorous diffusion-based molecular channels are considered. Our results show that the activation probability at the B-NM increases as this B-NM is located closer to the center of the B-NM population and the aggregate absorption rate of the drug molecules non-linearly increases as the population density increases.Comment: 9 pages; 9 figure

Washington University Record, February 20, 1997

https://digitalcommons.wustl.edu/record/1749/thumbnail.jp

Molecular dynamics and virtual screening approaches in drug discovery

Computer-aided drug discovery (CADD) methods are now routinely used in the preclinical phase of drug development. Powerful high-performance computing facilities and the extremely fast CADD methods constantly scale up the coverage of drug-like chemical space achievable in rational drug development. In this thesis, CADD approaches were applied to address several early-phase drug discovery problems. Namely, small molecule binding site detection on a novel target protein, virtual screening (VS) of molecular databases, and characterization of small molecule interactions with metabolic enzymes were studied. Various CADD methods, including molecular dynamics (MD) simulations in mixed solvents, molecular docking, and binding free energy calculations, were employed. Co-solvent MD simulations detected biologically relevant binding sites and provided guidance for screening potential protein-protein interaction inhibitors for a crucial protein of the SARS-CoV-2. VS with fragment- and negative image-based (F-NIB) models identified three active and structurally novel inhibitors of the putative drug target phosphodiesterase 10A. MD simulations and docking provided detailed insights on the effects of active site structural flexibility and variation on the binding and resultant metabolism of small molecules with the cytochrome P450 enzymes. The results presented in this thesis contribute to the increasing evidence that supports employment and further development of CADD approaches in drug discovery. Ultimately, rational drug development coupled with CADD may enable higher quality drug candidates to the human studies in the future, reducing the risk of financially and temporally costly clinical failure. KEYWORDS: Structure-based drug development, Computer-aided drug discovery (CADD), Molecular dynamics (MD) simulation, Virtual screening (VS), Fragmentand negative image-based (F-NIB) model, Structure-activity relationship (QSAR), Cytochrome P450 ligand binding predictionMolekyylidynamiikka- ja virtuaaliseulontamenetelmät lääkeaine-etsinnässä Tietokoneavusteista lääkeaine-etsintää käytetään nykyisin yleisesti prekliinisessä lääketutkimuksessa. Suurteholaskenta ja äärimmäisen nopeat tietokoneavusteiset lääkeaine-etsintämenetelmät mahdollistavat jatkuvasti kattavamman lääkkeenkaltaisten molekyylien kemiallisen avaruuden seulonnan. Tässä väitöskirjassa tietokonepohjaisia menetelmiä hyödynnettiin lääketutkimuksen prekliiniseen vaiheeseen liittyvissä tyypillisissä tutkimusongelmissa. Työhön kuului pienmolekyylien sitoutumisalueiden tunnistus uuden kohdeproteiinin rakenteesta, molekyylitietokantojen virtuaaliseulonta sekä pienmolekyylien ja metabolian entsyymien välisten vuorovaikutusten tietokonemallinnus. Työssä käytettiin useita tietokoneavusteisen lääkeaine-etsinnän menetelmiä, sisältäen molekyylidynamiikkasimulaatiot (MD-simulaatiot) vaihtuvissa liuottimissa, molekulaarisen telakoinnin ja sitoutumisenergian laskennan. Orgaanisen liuottimen ja veden sekoituksessa tehdyt MD-simulaatiot tunnistivat biologisesti merkittäviä sitoutumisalueita SARS-CoV-2:n tärkeästä proteiinista ja ohjasivat infektioon liittyvän proteiini-proteiinivuorovaikutuksen potentiaalisten estäjien etsintää. Virtuaaliseulonnalla tunnistettiin kolme rakenteellisesti uudenlaista tunnetun lääkekehityskohteen, fosfodiesteraasi 10A:n, estäjää hyödyntäen fragmentti- ja negatiivikuvamalleja. MD-simulaatiot ja telakointi tuottivat yksityiskohtaista tietoa sytokromi P450 entsyymien aktiivisen kohdan rakenteen jouston ja muutosten vaikutuksesta pienmolekyylien sitoutumiseen ja metaboliaan. Tämän väitöskirjan tulokset tukevat kasvavaa todistusaineistoa tietokoneavusteisen lääkeaine-etsinnän käytön ja kehityksen hyödyllisyydestä prekliinisessä lääketutkimuksessa. Tietokoneavusteinen lääkeaine-etsintä voi lopulta mahdollistaa korkeampilaatuisten lääkekandidaattien päätymisen ihmiskokeisiin, pienentäen taloudellisesti ja ajallisesti kalliin kliinisen tutkimuksen epäonnistumisen riskiä. AVAINSANAT: Rakennepohjainen lääkeainekehitys, Tietokoneavusteinen lääkeaine-etsintä, Molekyylidynamiikkasimulaatio (MD-simulaatio), Virtuaaliseulonta, Fragmentti- ja negatiivikuvamalli, Rakenne-aktiivisuussuhde, Sytokromi P450 ligandien sitoutumisen ennustu

Uncertainty quantification in classical molecular dynamics

Molecular dynamics simulation is now a widespread approach for understanding complex systems on the atomistic scale. It finds applications from physics and chemistry to engineering, life and medical science. In the last decade, the approach has begun to advance from being a computer-based means of rationalizing experimental observations to producing apparently credible predictions for a number of real-world applications within industrial sectors such as advanced materials and drug discovery. However, key aspects concerning the reproducibility of the method have not kept pace with the speed of its uptake in the scientific community. Here, we present a discussion of uncertainty quantification for molecular dynamics simulation designed to endow the method with better error estimates that will enable it to be used to report actionable results. The approach adopted is a standard one in the field of uncertainty quantification, namely using ensemble methods, in which a sufficiently large number of replicas are run concurrently, from which reliable statistics can be extracted. Indeed, because molecular dynamics is intrinsically chaotic, the need to use ensemble methods is fundamental and holds regardless of the duration of the simulations performed. We discuss the approach and illustrate it in a range of applications from materials science to ligand-protein binding free energy estimation. This article is part of the theme issue 'Reliability and reproducibility in computational science: implementing verification, validation and uncertainty quantification in silico'

Molecular-level characterization of the interaction between monolayer-protected Au nanoparticles and model lipid membranes and proteins

In this thesis we study the interactions of monolayer-protected gold nanoparticles with proteins and model lipid membranes. Gold nanoparticles are a paradigm in biological applications of nanomaterials, thanks to their peculiar physico-chemical properties; among these, the possibility to bind different kinds of molecules (ligands) to gold atoms in clusters via thiol bonds is particularly important for biomedical use of nanoparticles. In fact, the ligand shell is what nanoparticles expose to the biological environment in which they are used. In in vivo applications, nanoparticles come in contact with many biological molecules before being delivered to cells, which are the target of most of biological applications of nanoparticles. Among them, proteins are particularly relevant since they can irreversibly bind to nanoparticles, thus changing the surface they expose to the biological environment. In this thesis we use molecular dynamics simulations to study how different ligands can influence the interaction of nanoparticles with the most abundant protein in blood, human serum albumin. We test two zwitterionic ligands with different hydrophobic content and we find that ligand conformation, which is affected by hydrophobicity, promotes different adhesion strengths between nanoparticles and albumin. We then study the interaction of nanoparticles with a model cell membrane, the first barrier they encounter in cell-targeted applications. We use molecular dynamics simulations to study the mechanism of interaction between a negatively charged nanoparticle and a neutral lipid membrane. We find that the process develops in three stages which involves the adsorption on the membrane surface and the progressive penetration in the membrane core of the nanoparticle. Finally, we study the influence of the sign of the charge on nanoparticles on their interaction with a model membrane. We use experiments of fluorescent dye-leakage from neutral liposomes to probe the effect of positively and negatively charged nanoparticles on the model membrane of the lipid vesicles. In particular, we find that both anionic and cationic nanoparticles behave similarly in the interaction with a zwitterionic lipid membrane. We use molecular dynamics simulations to support the experimental findings and observe that anionic and cationic nanoparticles share similar interaction processes and energetics