Quantum annealing of a hard combinatorial problem

Projecte Final de Màster Oficial fet en col.laboració amb el Departament de Física Fonamental, Facultat de Física,Universitat de BarcelonaWe present the numerical results obtained using quantum annealing (QA) in a hard combinatorial problem: the coloring problem (q-COL) of an Erd˝os-R´enyi graph. We first propose a quantum coloring Hamiltonian, natural extension of q-COL, based on the quantum Ising model in a transverse field. We then test several QA schemes and find the one that solves the highest number of graphs in the smallest number of iterations. Our results suggest that the computation time of QA scales exponentially in the size and it does not improve the results obtained by thermal annealing (TA) for q-COL

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

Quantum annealing of a hard combinatorial problem

Projecte Final de Màster Oficial fet en col.laboració amb el Departament de Física Fonamental, Facultat de Física,Universitat de BarcelonaWe present the numerical results obtained using quantum annealing (QA) in a hard combinatorial problem: the coloring problem (q-COL) of an Erd˝os-R´enyi graph. We first propose a quantum coloring Hamiltonian, natural extension of q-COL, based on the quantum Ising model in a transverse field. We then test several QA schemes and find the one that solves the highest number of graphs in the smallest number of iterations. Our results suggest that the computation time of QA scales exponentially in the size and it does not improve the results obtained by thermal annealing (TA) for q-COL

On the way to real time protein-ligand sampling

Protein-ligand binding free energy is one of the keystones of drug design, and developing a fast method to calculate it would have great impact in personalized medicine. However, it is a daunting task for computational methods, since the conformational space is rugged, having a lot of metastable states that hinder the exploration. Using PELE and an adaptive sampling scheme, one can quickly get thermodynamic properties by traversing the conformational space on a simulation time scale (24h). We show the performance on a new benchmark of a series of different families of proteins and ligands with a large range of binding free energy differences (about 8 kcal/mol)

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

Binding free energy and ligand orientation calculations using a Monte Carlo method with Markov Sate analysis

Computing binding free energies has great implications in drug design. Using PELE technique, it has been shown that one can get quick and accurate estimations by means of a Markov state model3. We improved our methodology to compute faster binding relative free energy differences, mainly by analysis reducing the sampled region. This possibility opens a way in all-atom drug lead optimization by efficiently scoring a list of potential candidates in terms of binding affinities (approximately in 24hours), while still modeling accurately the protein-drug induced fit. Furthermore, we added information of the ligand orientation allowing us to obtain a better insight of the entrance mechanism. First, we show benchmark results - a series of benzamidine-like inhibitors in trypsin. Then, we apply our method to a more realistic scenario: the binding to a glucocorticoid receptor, and we show the performance for a new benchmark with a larger range of binding free energies (~14 kcal/mol). Simulations are obtained with our new in-house code PELE++, an improvement over the technique presented in references [1,2], (paper in preparation)

Phase-Only Filtering for Comparison of Functional Neuroimaging Time Sequences

From the image processing literature, we know that the phase information is often significantly more important than amplitude in preserving the features of a visual scene. A modified version of the traditional matched filter has been computer simulated and tested. The modification consists of using only the phase function and setting the amplitude function to unity; a so-called Phase-Only Matched Filter (POMF). This thesis presents a new method to match two different spatio-temporal movies using the POMF. The aim of this project is to implement a Phase-Only filter, which automatically give us the degree of similarity between two different functional neuroimaging time sequences. Practical results are presented for this application. All the properties of this filter have been tested. The results are compared in terms of correlation peak height, relation main-secondary peak and signal/noise ratio

768-km Multi-Stage Ultra-Trail Case Study-Muscle Damage, Biochemical Alterations and Strength Loss on Lower Limbs

A series of case studies aimed to evaluate muscular fatigue in running a 768-km ultra-trail race in 11 days. Four non-professional athletes (four males) were enrolled. Muscle damage blood biomarkers (creatine kinase (CK), lactodeshydrogenase (LDH), aspartate transaminase (AST) and alanine aminotransferase (ALT) and lower limb strength were evaluated by using Bosco jumps test; squat jump (SJ), countermovement jump (CMJ) and Abalakov jump (ABA) were assessed before (pre), after the race (post) and for two and nine days during the recovery period (rec2 and rec9), respectively. Results showed: pre-post SJ = −28%, CMJ = −36% and ABA = −21%. Values returned to basal during rec9: SJ = −1%, CMJ = −2% or even exceeded pre-values ABA = +3%. On the contrary, muscle damage blood biomarkers values increased at post; CK = +888%, LDH = +172%, AST = +167% and ALT = +159% and the values returned gradually to baseline at rec9 except for AST = +226% and ALT = +103% which remained higher. Nonparametric bivariate Spearman’s test showed strong correlations (Rs ≥ 0.8) between some jumps and muscle damage biomarkers at post (SJ-LDH Rs = 0.80, SJ-AST Rs = 0.8, ABA-LD H Rs = 0.80 and ABA-AST Rs = 0.80), at rec2 (SJ-CK Rs = 0.80 and SJ-ALT Rs = 0.80) and even during rec9 (ABA-CK). Similarly, some parameters such as accumulated elevation and training volume showed a strong correlation with LDH values after finishing the ultra-trail race. The alteration induced by completing an ultra-trail event in the muscle affects lower limb strength and may in some circumstances result in serious medical conditions including post- exertional rhabdomyolysis

Enhancing backbone sampling in Monte Carlo simulations using Internal Coordinates Normal Mode Analysis

Normal mode methods are becoming a popular alternative to sample the conformational landscape of proteins. In this study, we describe the implementation of an internal coordinate normal mode analysis method and its application in exploring protein flexibility by using the Monte Carlo method PELE. This new method alternates two different stages, a perturbation of the backbone through the application of torsional normal modes, and a resampling of the side chains. We have evaluated the new approach using two test systems, ubiquitin and c-Src kinase, and the differences to the original ANM method are assessed by comparing both results to reference molecular dynamics simulations. The results suggest that the sampled phase space in the internal coordinate approach is closer to the molecular dynamics phase space than the one coming from a Cartesian coordinate anisotropic network model. In addition, the new method shows a great speedup (∼∼5-7x), making it a good candidate for future normal mode implementations in Monte Carlo methods.The authors thank D. E. Shaw Research lab. for providing the kinase MD coordinates and Dr. López Blanco for sharing the code developed in his thesis and for providing useful comments. This work was supported by the CTQ-48287-R projects of the Spanish Ministry of Economy and Competitiveness (MINECO) and the grant SEV-2011-00067 of Severo Ochoa Program, awarded by the Spanish Government.Peer ReviewedPostprint (author's final draft

A simplified implementation of the stationary liquid mass balance method for on-line OUR monitoring in animal cell cultures

This is the peer reviewed version of the following article: [Fontova, A. , Lecina, M. , López‐Repullo, J. , Martínez‐Monge, I. , Comas, P. , Bragós, R. and Cairó, J. J. (2018), A simplified implementation of the stationary liquid mass balance method for on‐line OUR monitoring in animal cell cultures. J. Chem. Technol. Biotechnol. doi:10.1002/jctb.5551], which has been published in final form at [doi:10.1002/jctb.5551]. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.BACKGROUND: Compared with other methods, the stationary liquid mass balance method for oxygen uptake rate (OUR) determination offers advantages in terms of estimation accuracy and reduction of stress. However, the need for sophisticated instrumentation, like mass flow controllers and gas analysers, has historically limited wider implementation of such a method. In this paper, a new simplified method based on inexpensive valves for the continuous estimation of OUR in animal cell cultures is evaluated. The determination of OUR values is based on accurate operation of the dissolved oxygen (DO) control loop and monitoring of its internal variables. RESULTS: The method developed was tested empirically in 2¿L bioreactor HEK293 batch cultures. OUR profiles obtained by a dynamic method, global mass balance method and the developed simplified method were monitored and compared. The results show how OUR profile obtained with the proposed method better follows the off-line cell density determination. The OUR estimation frequency was also increased, improving the method capabilities and applications. The theoretical rationale of the method was extended to the sensitivity analysis which was analytically and numerically approached. CONCLUSIONS: The results showed the proposed method to be not only cheap, but also a reliable alternative to monitor the metabolic activity in bioreactors in many biotechnological processes, being a useful tool for high cell density culture strategies implementation based on OUR monitoring.Peer ReviewedPostprint (published version