Memory optimization and performance study of protein/DNA-ligand interaction software for HPC clusters

This master's thesis aims to improve the memory requirements and efficiency of the PELE software, developed by BSC, on High-Performance Computing (HPC) systems. The research focuses on analyzing the behavior of PELE on HPC systems to detect areas of improvement and optimize its efficiency. The primary objectives of this thesis are to significantly reduce the memory usage of PELE, replace legacy MPI communication models, and port the software to an ARM-based architecture. The first objective of the thesis is to investigate the current memory usage of PELE and identify the areas where it can be reduced. This study will involve analyzing the code and profiling the performance of PELE on HPC systems to identify memory leaks and inefficient data structures. The results will be used to propose modifications to the software's code to reduce its memory footprint. The second objective is to replace the legacy MPI communication models with more efficient communication models. This study will involve analyzing the communication patterns of PELE and identifying areas where the current models are not optimal. The proposed improvements will include implementing new communication models and optimizing the existing ones to reduce communication overhead and improve the software's scalability. Furthermore, we have made a proposal outside of local PELE changes to improve the global performance of the Adaptive PELE data-flow, which is a common workflow that uses PELE. Finally, the thesis aims to port the PELE software to an ARM-based architecture. This study will involve analyzing the software's code and identifying any platform-specific dependencies. The proposed modifications will ensure that the software can run on ARM-based HPC systems efficiently. In conclusion, this master's thesis aims to improve the efficiency of the PELE software on HPC systems by reducing its memory usage, replacing legacy MPI communication models, and porting it to an ARM-based architecture. The research will involve analyzing the software's code, profiling its performance on HPC systems, and proposing modifications to optimize its performance. The proposed improvements will make PELE more efficient and scalable, making it suitable for use in large-scale simulations and scientific research

Hydration free energies in the FreeSolv database calculated with polarized iterative Hirshfeld charges

Computer simulations of biomolecular systems often use force fields, which are combinations of simple empirical atom-based functions to describe the molecular interactions. Even though polarizable force fields give a more detailed description of intermolecular interactions, nonpolarizable force fields, developed several decades ago, are often still preferred because of their reduced computation cost. Electrostatic interactions play a major role in biomolecular systems and are therein described by atomic point charges. In this work, we address the performance of different atomic charges to reproduce experimental hydration free energies in the FreeSolv database in combination with the GAFF force field. Atomic charges were calculated by two atoms-in-molecules approaches, Hirshfeld-I and Minimal Basis Iterative Stockholder (MBIS). To account for polarization effects, the charges were derived from the solute’s electron density computed with an implicit solvent model, and the energy required to polarize the solute was added to the free energy cycle. The calculated hydration free energies were analyzed with an error model, revealing systematic errors associated with specific functional groups or chemical elements. The best agreement with the experimental data is observed for the AM1-BCC and the MBIS atomic charge methods. The latter includes the solvent polarization and presents a root-mean-square error of 2.0 kcal mol–1 for the 613 organic molecules studied. The largest deviation was observed for phosphorus-containing molecules and the molecules with amide, ester and amine functional groups

Protein Hydrogen Exchange, Dynamics, and Function

Models derived from X-ray crystallography can give the impression that proteins are rigid structures with little mobility. NMR ensembles may suggest a more dynamic picture, but even these represent a rather narrow range of possibilities close to the lowest energy state. In reality proteins participate in a wide range of dynamics from the subtle and rapid sidechain dynamics that occur in nanoseconds in the PDZ signaling domain to the large and slow rearrangement of secondary structure that takes days in the mitotic checkpoint protein Mad2. Between these extremes are motions on time scales typically associated with protein function, such as those in SNase monitored by hydrogen exchange. The dynamic character of several protein systems, including PDZ domain, Calmodulin, SNase, and Mad2, were explored using a variety of biophysical techniques. This broad investigation demonstrates the dynamic variability between and within proteins. The study of PDZ and Calmodulin illustrates how a computational technique can recapitulate experimental results and provide additional insight into signal transduction. The case of SNase shows that HX NMR data can be exploited to reveal protein dynamics with unprecedented detail. The Mad2 system highlighted some of the pitfalls associated with this technique and some alternative strategies for investigating protein dynamics

Targeting Tight Junctions in Nanomedicine: a Molecular Modeling Perspective

Molecular Dynamics Simulations of Claudin Paracellular Channel

Numerical Methods for Electronic Structure Calculations of Materials

This is the published version. Copyright 2010 Society for Industrial and Applied MathematicsThe goal of this article is to give an overview of numerical problems encountered when determining the electronic structure of materials and the rich variety of techniques used to solve these problems. The paper is intended for a diverse scientific computing audience. For this reason, we assume the reader does not have an extensive background in the related physics. Our overview focuses on the nature of the numerical problems to be solved, their origin, and the methods used to solve the resulting linear algebra or nonlinear optimization problems. It is common knowledge that the behavior of matter at the nanoscale is, in principle, entirely determined by the Schrödinger equation. In practice, this equation in its original form is not tractable. Successful but approximate versions of this equation, which allow one to study nontrivial systems, took about five or six decades to develop. In particular, the last two decades saw a flurry of activity in developing effective software. One of the main practical variants of the Schrödinger equation is based on what is referred to as density functional theory (DFT). The combination of DFT with pseudopotentials allows one to obtain in an efficient way the ground state configuration for many materials. This article will emphasize pseudopotential-density functional theory, but other techniques will be discussed as well

Molecular Dynamics Simulation

Condensed matter systems, ranging from simple fluids and solids to complex multicomponent materials and even biological matter, are governed by well understood laws of physics, within the formal theoretical framework of quantum theory and statistical mechanics. On the relevant scales of length and time, the appropriate ‘first-principles’ description needs only the Schroedinger equation together with Gibbs averaging over the relevant statistical ensemble. However, this program cannot be carried out straightforwardly—dealing with electron correlations is still a challenge for the methods of quantum chemistry. Similarly, standard statistical mechanics makes precise explicit statements only on the properties of systems for which the many-body problem can be effectively reduced to one of independent particles or quasi-particles. [...

A Multi-Scale Molecular Dynamic Approach to the Study of the Outer Membrane of the Bacteria Psudomonas Aeruginosa PA01 and the Biocide Chlorhexidine

The introductory chapters of this thesis contains an explanation to the methods and basic theory of the molecular dynamics approach. Together with the appendix section, in which a step by step tutorial how to set up and run basic simulations using the gromacs software is presented, this thesis can serve as an introductory aid in performing molecular dynamics simulations. In the research portion of this thesis, I provide several uses for the molecular dynamics approach applied to the biocide chlorhexidine as well as the study of membranes, including a mimic of the bacteria membrane of Pseudomonas Aeruginosa PA01. The motivation for this research was previous work done in our lab which determined that chlorhexidine has a high affinity for DMPC and found the depth at which it resides in a model DMPC membrane. From this information, an all-atom representation of chlorhexidine was made, which was proven to reproduce the experimental results. While we learned much about chlorhexidine in a model DMPC membrane, this study lacked the destruction of the membrane as well as the study of chlorhexidine in a biologically relevant membrane. For these reasons coarse grained versions of the all-atom chlorhexidine models as well as a new lipopolysaccharide molecule was created. With the coarse grained model of chlorhexidine and the ability to create a bacterial membrane mimic, the study of chlorhexidine and other antibacterial agents can be further studied

Computational Modeling and Design of Protein and Polymeric Nano-Assemblies

Advances in nanotechnology have the potential to utilize biological and polymeric systems to address fundamental scientific and societal issues, including molecular electronics and sensors, energy-relevant light harvesting, â??greenâ?? catalysis, and environmental cleanup. In many cases, synthesis and fabrication are well within grasp, but designing such systems requires simultaneous consideration of large numbers of degrees of freedom including structure, sequence, and functional properties. In the case of protein design, even simply considering amino acid identity scales exponentially with the protein length. This work utilizes computational techniques to develop a fundamental, molecularly detailed chemical and physical understanding to investigate and design such nano-assemblies. Throughout, we leverage a probabilistic computational design approach to guide the identification of protein sequences that fold to predetermined structures with targeted function. The statistical methodology is encapsulated in a computational design platform, recently reconstructed with improvements in speed and versatility, to estimate site-specific probabilities of residues through the optimization of an effective sequence free energy. This provides an information-rich perspective on the space of possible sequences which is able to harness the incorporation of new constraints that fit design objectives. The approach is applied to the design and modeling of protein systems incorporating non-biological cofactors, namely (i) an aggregation prone peptide assembly to bind uranyl and (ii) a protein construct to encapsulate a zinc porphyrin derivative with unique photo-physical properties. Additionally, molecular dynamics simulations are used to investigate purely synthetic assemblies of (iii) highly charged semiconducting polymers that wrap and disperse carbon nanotubes. Free energy calculations are used to explore the factors that lead to observed polymer-SWNT super-structures, elucidating well-defined helical structures; for chiral derivatives, the simulations corroborate a preference for helical handedness observed in TEM and AFM data. The techniques detailed herein, demonstrate how advances in computational chemistry allot greater control and specificity in the engineering of novel nano-materials and offer the potential to greatly advance applications of these systems