My Research and Study Journey

          Next Generation of Quantum Algorithms and Materials Figure: Quantum Computer Simulations It is anticipated that quantum computing would fundamentally alter how researchers approach challenging computational issues. These computers are created to address significant problems in fundamental scientific fields like quantum chemistry. Quantum computing is still particularly sensitive to environmental disturbances and noise at this point in its development. As a result, quantum bits, or qubits, lose information when they become out of sync, a process known as decoherence, which makes quantum computing "noisy." Researchers at Pacific Northwest National Laboratory (PNNL) are creating simulations that offer a glimpse into how quantum computers function in order to get around the constraints of current quantum computers. According to PNNL computer scientist Ang Li, "the quantum states of quantum systems, like qubits, would collapse when we try to directly examine the act

Figure: A Model Structure-Neural Network Process Researchers from Los Alamos National Laboratory proposed incorporating more quantum mechanics mathematics into the structure of machine learning predictions in a new study published in Proceedings of the National Academy of Sciences. The machine learning model predicts an effective Hamiltonian matrix based on the specific positions of atoms within a molecule, which describes the various possible electronic states and their associated energies. The machine learning-based approach makes predictions at a much lower computational cost than traditional quantum chemistry simulations. It allows for quantitatively precise predictions of material properties, interpretable insight into the nature of chemical bonding between atoms, and can be used to predict other complex phenomena, such as how the system will respond to perturbations like light-matter interactions. The method also outperforms traditional machine learning models in terms of accurac

  One of the important emerging technologies for the twenty-first century is quantum computing. Even the most advanced supercomputers cannot match their potential. They have shown to be effective tools, especially for solving challenging computational problems that go beyond the capabilities of traditional hardware. Quantum chemistry is one interesting area for quantum computing, where it can be used to, for instance, solve the electronic Schrödinger equation and forecast the atomic composition of substances or molecules. Computer simulations are crucial in research to address these problems. On conventional computers, this is only partially doable with numerical approaches, though. Figure: Quantum Computer can revolutionize Chemistry Now that huge molecule simulations can be efficiently carried out on quantum computers, researchers at Paderborn University should be able to determine the energies and nuclear forces of these large molecules. The researchers concentrate on parallelizatio

The phosphodiester link between the tyrosine residue of topoisomerase I and the 3′-phosphate of DNA is hydrolyzed by the DNA repair enzyme tyrosyl-DNA phosphodiesterase I (Tdp1), which is conserved throughout eukaryotes. A fully quantum mechanical, geometrically constrained model is proposed and used to study atomic-level aspects of the Tdp1 process. The crystal structure of human Tdp1 inhibited by vanadate serves as the structural underpinning for the computer model (hTdp1, Protein Data Bank entry 1RFF). To gather thermodynamic and kinetic information about the catalytic pathway, including the phosphoryl transfer and subsequent hydrolysis, density functional theory computations are employed. A five-coordinate phosphorane intermediate associative phosphoryl transfer mechanism is suggested by the location of transition states and intermediates along the reaction coordinate. Similar to phospholipase D theoretical and experimental results.

Human histidine triad nucleotide-binding protein 1 (hHint1) acts as a hydrolase, breaking down substrates linked to phosphoramidites. To better understand the mechanism of hHint1-catalyzed phosphoramidite hydrolysis, DFT computational studies on a 228-atom cluster model of the enzyme were performed. The following steps comprise the overall proposed mechanism: (a) proton transfer from protonated His114 to form a protonated methyl amino group (2); (b) a protonated Penta-coordinated methyl phosphorodiamidate intermediate (3) formed by nitrogen attack of His112 on the phosphorus of the methyl phosphoramidite substrate via an associative intermediate; and (c) amine (RNH2) dissociation and formation of  (e) an interchange associative transition state creates a temporary tetra-coordinate phosphoryl intermediate (e) an interchange associative transition state generates a temporary tetra-coordinate phosphoryl intermediate (6); (f) the formation of a hydrolyzed nucleotide (7) that then transfers

            Quantum Mechanical Enzyme Model Design RINRUS Webster Group RINRUS will be the foundation for better and more automated cheminformatics-based enzyme model design. Models designed by RINRUS with only 200-300 atoms have been shown to converge. The Residue Interaction Network Residue Selector (RINRUS) toolkit is being developed to use interatomic contact network data for automated, rational residue selection and QM-cluster model generation. A series of QM-cluster models were built using RINRUS. For a total of 550 models, the reactant, product, and transition state of the methyl transfer reaction were computed, and the resulting free energies of activation and reaction were used to evaluate model convergence. For the simulation of enzyme active sites using quantum mechanics, reactive species, surrounding residues, solvent, or coenzymes involved in microenvironment creation. Ad hoc model design frequently hinders the efficiency, accuracy, and replicability of enzyme simulations.

                              Computational Biochemistry Research in Bangladesh The mathematical description of chemistry is what theoretical chemistry is. When a mathematical method is sufficiently developed to be automated for computer implementation, it is referred to as computational chemistry. Computational chemistry is a natural extension of theoretical chemistry, whose traditional role involves developing conceptual understanding and quantitative characterization for the chemical sciences, particularly at the atomic and molecular levels. Over the last few decades, there has been a tremendous development of user-friendly computational chemistry software that has made molecular calculations accessible to a wide range of users, such that molecular modeling can now be performed in any laboratory or classroom. The continuous rapid increase in the power of individual PCs is providing comparatively low-cost alternatives to costly experimental studies. Computational chemistry programs a

                                    Computational Chemistry: Past, Present, and Future Specialized computational chemistry packages have forever changed the landscape of chemical and materials science by providing tools to support and guide experimental efforts as well as predict atomic and electronic properties. Electronic structure packages have played a special role in this regard, modeling complex chemical and materials processes using first-principles-driven methodologies. The rapid development of computing technologies and the tremendous increase in computational power over the last few decades have provided a unique opportunity to study complex transformations using sophisticated and predictive many-body techniques that describe the correlated behavior of electrons in molecular and condensed phase systems at various levels of theory. Novel parallel algorithms that enabled these simulations were able to take advantage of computational resources to address the polynomial scaling p

Atomistic Force Fields to Study Proteins Figure: Different Methods Showing Cost of Calculation Protein structural stabilities, intermolecular binding affinities, and enzymatic rates are all affected by the high concentration of macromolecules in the crowded cellular interior. Furthermore, various structural biology methods, such as NMR or various spectroscopies, typically involve samples with a high protein concentration. However, due to the high sampling requirements, the accuracy of classical molecular dynamics (MD) simulations in capturing protein behavior at high concentrations is still largely unknown. In this research wild-type (folded) and oxidatively damaged (unfolded) forms of villin headpiece have been studied.  The experiment begins by running a large number of simulations with multiple protein molecules in the simulation box, using GROMOS 45a3 and 54a7 force fields, as well as various types of electrostatics treatment and solution ionic strengths. Surprisingly, despite havi