An Evolutionary Approach to Drug-Design Using Quantam Binary Particle Swarm Optimization Algorithm

The present work provides a new approach to evolve ligand structures which represent possible drug to be docked to the active site of the target protein. The structure is represented as a tree where each non-empty node represents a functional group. It is assumed that the active site configuration of the target protein is known with position of the essential residues. In this paper the interaction energy of the ligands with the protein target is minimized. Moreover, the size of the tree is difficult to obtain and it will be different for different active sites. To overcome the difficulty, a variable tree size configuration is used for designing ligands. The optimization is done using a quantum discrete PSO. The result using fixed length and variable length configuration are compared.Comment: 4 pages, 6 figures (Published in IEEE SCEECS 2012). arXiv admin note: substantial text overlap with arXiv:1205.641

Lower-energy conformers search of TPP-1 polypeptide via hybrid particle swarm optimization and genetic algorithm

Low-energy conformation search on biological macromolecules remains a challenge in biochemical experiments and theoretical studies. Finding efficient approaches to minimize the energy of peptide structures is critically needed for researchers either studying peptide-protein interactions or designing peptide drugs. In this study, we aim to develop a heuristic-based algorithm to efficiently minimize a promising PD-L1 inhibiting polypeptide, TPP-1, and build its low-energy conformer pool to advance its subsequent structure optimization and molecular docking studies. Through our study, we find that, using backbone dihedral angles as the decision variables, both PSO and GA can outperform other existing heuristic approaches in optimizing the structure of Met-enkephalin, a benchmarking pentapeptide for evaluating the efficiency of conformation optimizers. Using the established algorithm pipeline, hybridizing PSO and GA minimized TPP-1 structure efficiently and a low-energy pool was built with an acceptable computational cost (a couple days using a single laptop). Remarkably, the efficiency of hybrid PSO-GA is hundreds-fold higher than the conventional Molecular Dynamic simulations running under the force filed. Meanwhile, the stereo-chemical quality of the minimized structures was validated using Ramachandran plot. In summary, hybrid PSO-GA minimizes TPP-1 structure efficiently and yields a low-energy conformer pool within a reasonably short time period. Overall, our approach can be extended to biochemical research to speed up the peptide conformation determinations and hence can facilitate peptide-involved drug development

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

Application of Signal Processing and Soft Computing To Genomics

A major challenge for genomic research is to establish a relationship among sequences,structures and function of genes. In addition processing and analyzing this information are of prime importance. Basically genes are repositories for protein coding information and proteins in turn are responsible for most of the important biological functions in all cells. These in turn gives rise to analysis of DNA sequences in proteins, designing of various drugs for genetic diseases. This thesis deals with the applications of signal processing and soft computing algorithms to the field of genomics and proteinomics. Diseases like SARS and Migraine have been modeled using these tools and potential druggable compounds have been proposed which are better than the previous available drugs. Protein structural classes have been identified more accurately based on Genetic Algorithm and Particle Swarm Optimization.Better and efficient methods like Sliding-DFT and Adaptive AR Modeling were proposed to identify Protein coding regions in genes. The proposed methods showed better results as compared to existing methods

Hybrid protein-polymer nanoparticles loaded with cisplatin: synthesis and characterization

Nowadays, many research related tovhybrid materials and the advances in Reversible-Deactivation Radical Polymerization (RDRP) techniques have enabled the development of responsive materials. These compounds respond to specific stimuli and have been integrating many research projects involving different drug delivery systems. In particular, hybrid conjugates based on protein−polymer have been integrating different formulations already approved by the Food and Drug Administration. In general, protein-polymer conjugates can increase the drug plasmatic half-life, altering the drug biodistribution profile and opening the possibility to reduce the dose administrated, which is a relevant advantage for patients. In this work, poly (N-vinylcaprolactam) (PNVCL) and poly (2-dimethylamino-ethyl methacrylate) (PDMAEMA) polymers were grafted to the surface of a protein model, the bovine serum albumin (BSA), by grafting-from approach, using the Atom Transfer Radical Polymerization (ATRP) technique. Firstly, a macroinitiator (BSA-MI) was successfully obtained and characterized by Sodium dodecyl sulfate polyacrylamide gel electrophoresis and Matrix-Assisted Laser Desorption Ionization Time of Flight Mass Spectrometry by modifying lysine groups present in the BSA. Then, the BSA-PNVCLco-PDMAEMA hybrid was synthesized using BSA-MI as an initiator. The conjugate production was evaluated, revealing significant changes in the nanoparticles’ molecular mass and zeta potential . Additionally, it is demonstrated that altering the monomers' ratio can further adjust the lower critical solution temperature (LCST) of the protein-polymer conjugates. The results indicate the obtaining of a BSA-PNVCL-coPDMAEMA able to encapsulate approximately 1.9 mg of cisplatin for each 1 mg of the hybrid, making this conjugate a very promising hybrid material with desirable properties for a possible application in smart drug delivery systems

Systems biology approaches to the computational modelling of trypanothione metabolism in Trypanosoma brucei

This work presents an advanced modelling procedure, which applies both structural modelling and kinetic modelling approaches to the trypanothione metabolic network in the bloodstream form of Trypanosoma brucei, the parasite responsible for African Sleeping sickness. Trypanothione has previously been identified as an essential compound for parasitic protozoa, however the underlying metabolic processes are poorly understood. Structural modelling allows the study of the network metabolism in the absence of sufficient quantitative information of target enzymes. Using this approach we examine the essential features associated with the control and regulation of intracellular trypanothione level. The first detailed kinetic model of the trypanothione metabolic network is developed, based on a critical review of the relevant scientific papers. Kinetic modelling of the network focuses on understanding the effect of anti-trypanosomal drug DFMO and examining other enzymes as potential targets for anti-trypanosomal chemotherapy. We also consider the inverse problem of parameter estimation when the system is defined with non-linear differential equations. The performance of a recently developed population-based PSwarm algorithm that has not yet been widely applied to biological problems is investigated and the problem of parameter estimation under conditions such as experimental noise and lack of information content is illustrated using the ERK signalling pathway. We propose a novel multi-objective optimization algorithm (MoPSwarm) for the validation of perturbation-based models of biological systems, and perform a comparative study to determine the factors crucial to the performance of the algorithm. By simultaneously taking several, possibly conflicting aspects into account, the problem of parameter estimation arising from non-informative experimental measurements can be successfully overcome. The reliability and efficiency of MoPSwarm is also tested using the ERK signalling pathway and demonstrated in model validation of the polyamine biosynthetic pathway of the trypanothione network. It is frequently a problem that models of biological systems are based on a relatively small amount of experimental information and that extensive in vivo observations are rarely available. To address this problem, we propose a new and generic methodological framework guided by the principles of Systems Biology. The proposed methodology integrates concepts from mathematical modelling and system identification to enable physical insights about the system to be accounted for in the modelling procedure. The framework takes advantage of module-based representation and employs PSwarm and our proposed multi-objective optimization algorithm as the core of this framework. The methodological framework is employed in the study of the trypanothione metabolic network, specifically, the validation of the model of the polyamine biosynthetic pathway. Good agreements with several existing data sets are obtained and new predictions about enzyme kinetics and regulatory mechanisms are generated, which could be tested by in vivo approaches

The doctoral research abstracts. Vol:8 2015 / Institute of Graduate Studies, UiTM

Foreword: THIRTY FIRST October 2015 marks the celebration of 47 PhD doctorates receiving their scroll during UiTM 83rd Convocation Ceremony. This date is significant to UiTM since it is an official indication of 47 more scholarly contributions to the world of knowledge and innovation through the novelty of their research. To date UiTM has contributed 471 producers of knowledge through their doctoral research ranging from the field of Science and Technology, Business and Administration, and Social Science and Humanities. This Doctoral Abstracts epitomizes knowledge par excellence and a form of tribute to the 47 doctorates whose achievement we proudly celebrate. To the graduands, your success in achieving the highest academic qualification has demonstrated that you have indeed engineered your destiny well. The action of registering for a PhD program was not by chance but by choice. It was a choice made to realise your self-actualization level that is the highest level in Maslow’s Hierarchy of Needs, while at the same time unleashing your potential in the scholarly research. Do not forget that life is a treasure and that its contents continue to be a mystery, thus, your journey of discovery through research has not come to an end but rather, is just the beginning. Enjoy life through your continuous discovery of knowledge, and spearhead innovation while you are at it. Make your alma mater proud through this continuous discovery as alumni of UiTM. As you soar upwards in your career, my advice will be to continuously be humble and ‘plant’ your feet firmly on the ground. Congratulations once again and may you carry UiTM as ‘Sentiasa di Hatiku’. Tan Sri Dato’ Sri Prof Ir Dr Sahol Hamid Abu Bakar, FASc, PEng Vice Chancellor Universiti Teknologi MAR

Advances in High-Throughput Analysis: Automated Radiochemical Separations and Nanopillar based Separations and Field Enhanced Spectroscopy

Often the need to analyze a large number of samples coincide with critical time consternates. At such times, the implementation of high-throughput technologies is paramount. In this work we explore some viable pathways for high-throughput analysis and develop advancements in novel forms of detection of materials that are vital in the environmental, biological as well as national security arenas. Through the use of new protocols with high sensitivity and specificity as well as simplified chemical processing and sample preparation we aim to allow for improved throughput, fieldable detection, and rapid data acquisition of extensive sample sets. The methods developed in this work focus on unique platforms of the collection and analysis and combine them with automation and portability. Foremost, analytes of interest must be selectively isolated and concentrated by chemical and/or mechanical processes. Secondly, spectroscopic and physical properties are exploited and enhanced by employing viable detection platforms. Finally, automation and field portability are implemented through a combination of optimized robotics, minimized chemical preparation and/or unique lab on a chip type platforms. Presented are two sub areas of research. One focuses on the automation of a time consuming solid phase extraction process that is coupled to inductively coupled plasma mass spectrometry increasing sample throughput by orders of magnitude. The second focused on the fabrication and use of silicon nanopillars as a platform for separations and enhanced optical analysis. Each section of work focuses on the development of a practical, accessible, and deployable methods of analysis