Rational enzyme engineering of heme peroxidases through biophysical and biochemical modeling

[eng] Enzymes are proteins that catalyze biochemical reactions and their use report multiple advantages, as they can be very selective, low polluting (biodegradable), cheap and allow working in mild conditions compared with traditional non enzymatic processes. Despite their enormous benefits, their applications at the industrial level are still limited, mainly due to low productivity, low substrate tolerance (too specifics) and poor resistance to the industrial conditions, and for this reason, developing enhanced enzymes by means of enzyme engineering is a central research field nowadays. Notably, the application of computational chemistry in the field of enzyme engineering is increasing due to improvements in hardware and software. Moreover, this process is fast and low-priced and therefore, profitable for the application to the real problems that face industry. Therefore, motivated by this progress, the main goal of this thesis is the development of computational strategies that allow designing and evaluating modifications in enzymes, also aiming to obtain results quickly and inexpensively. This purpose was reached by the combination of different in silico methodologies that were further supported by experimental data in an interactive feedback process. As a result of this thesis, the enzymatic process in heme peroxidases was first satisfactorily described by dividing the process into two steps (from the ligand diffusion to the chemical reaction), using a combination of different computational techniques. The first step, which involves the protein/ligand recognition, was characterized with different molecular mechanics based techniques (MD, Docking and MC-PELE). On the other hand, the chemical reaction (including bond formation and electron transfer) was reproduced using QM based methods by means of energy calculation, spin density characterization, e-coupling calculations and QM/MM e-pathways descriptions. Following this procedure, the oxidation of veratryl alcohol by the enzyme lignin peroxidase was also characterized. Moreover, regarding the e-coupling calculations, a server to compute this vale faster and easy was developed. In the second part of the thesis, the results demonstrated that our protocol could reliably describe and predict enzymatic functions, not only in native enzymes but also in mutated ones, which results were in agreement with experimental data. For example, the structural implications over the reactivity in manganese peroxidase and its engineered variant obtained by cutting the last terminal residues were identified and characterized by the combination of Monte Carlo simulations (PELE) and electronic coupling calculations. The pH resistance in the mutant 2-1B (which was obtained experimentally by random directed evolution) in contrast with the wild type versatile peroxidase, were also rationalized by molecular dynamics, where the residues in the heme environment presented different conformation due to the mutations introduced, resulting in different pH resistance. Interestingly, the last part of the thesis was centered of engineering heme peroxidases. We engineered a peroxidase from in silico predictions to elucidate the long range electron transfer processes involved in the oxidation of the substrate veratryl alcohol by the enzyme versatile peroxidase. In this work we identify the key residues involved in the process, with further applications in engineering enhanced enzymes. Moreover, an enhanced manganese peroxidase mutant from a complete computational study was designed. First, the ligand diffusion study allowed finding the key aminoacids in the substrate/enzyme recognition and binding. Then, the chemical reaction in terms of the oxidation probability and kinetic constant for the proposed mutant were estimated, and the results were in agreement with experimental data. Therefore, the work of this thesis probed that computational biophysics and biochemistry are promising and valuable tools for enzyme engineering. In particular, in the field of rational design of heme peroxidases, they provide relevant information about the enzymatic mechanism and allow designing new enzymes, as well as checking their improvement/worsening, in an efficient way.[spa] Las enzimas son proteínas que catalizan reacciones bioquímicas y cuyo uso aporta múltiples ventajas, ya que son en general muy selectivas, poco contaminantes (biodegradables), baratas y permiten trabajar en condiciones suaves, en comparación con los procesos tradicionales no enzimáticos. A pesar de sus enormes beneficios, sus aplicaciones a nivel industrial son todavía limitadas, debido principalmente a la baja productividad, baja tolerancia al sustrato (demasiado específicos) y una escasa resistencia a las condiciones industriales en general, y por esta razón el desarrollo de enzimas mejoradas es un campo de investigación muy importante hoy en día. En particular, la aplicación de la química computacional en el campo de la ingeniería de enzimas está en aumento debido a las mejoras en hardware y software. Motivado por este progreso, el objetivo principal de esta tesis es el desarrollo de estrategias de cálculo que, mediante la combinación de diferentes metodologías in silico permitan diseñar y evaluar modificaciones en las enzimas, centrándonos en la obtención de resultados de forma rápida y económica. La primera parte de la tesis está centrada en la descripción del mecanismo enzimático entendido como un proceso de dos pasos que incluyen la difusión ligando y la reacción química, mediante una combinación de diferentes técnicas computacionales. El primer paso, que implica el reconocimiento de la proteína / ligando, se caracterizó con diferentes técnicas basadas en la mecánica molecular (dinámica molecular, docking y Monte Carlo- PELE). Por otro lado, la reacción química (incluyendo la formación de enlaces y la transferencia de electrones) se simuló usando métodos basados en mecánica cuántica por medio de cálculos de energía, la caracterización del spin o cálculos de acoplamiento electrónico. Por ejemplo, siguiendo este procedimiento, se caracterizó la oxidación de alcohol veratrílico por medio de la enzima lignin peroxidasa. Además, con el objetivo de poder calcular los acoplamientos electrónicos de una manera más rápida y fácil, se desarrolló un servidor web: ecoupling server. En la segunda parte de la tesis, los resultados demostraron que el protocolo anterior podría describir funciones enzimáticas no sólo en las especies nativas sino también en las variantes mutadas. Por ejemplo, se identificaron las implicaciones estructurales de la reactividad en una manganeso peroxidasa de la subfamilia larga y su variante modificada obtenida mediante la reducción de los últimos residuos terminales gracias al estudio de simulaciones de Monte Carlo (PELE) y cálculos de acoplamiento electrónico. Además, la resistencia a pH ácido en el mutante 2-1B (que se había obtenido previamente por evolución dirigida al azar) se comparó con la especie nativa y también se racionalizó por dinámica molecular, donde se observó que los residuos del entorno del hemo presentaban diferente conformación debido a las mutaciones introducidas, resultando en una diferente resistencia a pH ácido. La última parte de la tesis se centra en la ingeniería racional de hemo peroxidasas. A partir de predicciones in silico se diseñaron variantes de peroxidasa versátil para tratar de entender los procesos de transferencia electrónica de largo alcance que participan en la oxidación del sustrato de alcohol veratrílico, mediante la identificación de los residuos intermedios involucrados en el proceso. Además, a partir de un estudio computacional completo, se diseñó un mutante mejorado de manganeso peroxidasa, cuyos valores cinéticos estimados computacionalmente se encontraban de acuerdo con los resultados experimentales. En conclusión, en esta tesis se ilustra cómo los métodos biofísicos y bioquímicos computacionales son herramientas prometedoras y valiosas para la ingeniería de enzimas, en particular en el campo del diseño racional

Pneumonia treated in the internal medicine department: Focus on healthcare-associated pneumonia

Patients with pneumonia treated in the internal medicine department (IMD) are often at risk of healthcare-associated pneumonia (HCAP). The importance of HCAP is controversial. We invited physicians from 72 IMDs to report on all patients with pneumonia hospitalized in their department during 2weeks (one each in January and June 2010) to compare HCAP with community-acquired pneumonia (CAP) and hospital-acquired pneumonia (HAP). We analysed 1002 episodes of pneumonia: 58.9% were CAP, 30.6% were HCAP and 10.4% were HAP. A comparison between CAP, HCAP and HAP showed that HCAP patients were older (77, 83 and 80.5years; p<0.001), had poorer functional status (Barthel 100, 30 and 65; p<0.001) and had more risk factors for aspiration pneumonia (18, 50 and 34%; p<0.001). The frequency of testing to establish an aetiological diagnosis was lower among HCAP patients (87, 72 and 79; p<0.001), as was adherence to the therapeutic recommendations of guidelines (70, 23 and 56%; p<0.001). In-hospital mortality increased progressively between CAP, HCAP and HAP (8, 19 and 27%; p<0.001). Streptococcus pneumoniae was the main pathogen in CAP and HCAP. Pseudomonas aeruginosa and methicillin-resistant Staphylococcus aureus (MRSA) caused 17 and 12.3% of HCAP. In patients with a confirmed aetiological diagnosis, the independent risk factors for pneumonia due do difficult-to-treat microorganisms (Enterobacteriaceae, P. aeruginosa or MRSA) were HCAP, chronic obstructive pulmonary diseases and higher Port Severity Index. Our data confirm the importance of maintaining high awareness of HCAP among patients treated in IMDs, because of the different aetiologies, therapy requirements and prognosis of this population. © 2011 The Authors. Clinical Microbiology and Infection © 2011 European Society of Clinical Microbiology and Infectious Diseases

Erratum to: Guidelines for the use and interpretation of assays for monitoring autophagy (3rd edition) (Autophagy, 12, 1, 1-222, 10.1080/15548627.2015.1100356

non present