Technical Bulletin

The use of computers for predicting the structures and properties of biomolecules has closely paralleled computer development since the 1950s, and has been one of the core areas of theoretical or computational chemistry for the past 30 years. Initially, the focus was on force-field-based methodologies for studying the structures, dynamics, and interactions of biomolecules as such, and the development of accurate models for the main biological solvent, water. With the emergence of accurate quantum chemical techniques suitable for studying (from a quantum chemistry perspective) large systems, density functional theory entered the stage in the 1990s as the key approach for investigating enzymatic mechanisms or properties and reactions of small, but biologically relevant, molecules. The combined use of these tools, so-called QM/MM and lately QM/MM-MD techniques enables precise descriptions of biological phenomena and reactions.

With the exponential increase in data to be analyzed, obtained through the introduction of automated whole-genome and protein sequencing techniques, the field of bioinformatics rapidly emerged in the early 2000s from the pioneering laborious mapping and comparison of protein and gene sequences in molecular biology, via an intense phase, which to a large extent can be viewed as ‘database mining’ and the development of efficient computer-based algorithms, into a science of its own, which today has reached a high level of maturity and sophistication. Tools in bioinformatics are nowadays used with great success in structural biology, computational chemistry, genetics, molecular biology, the pharmaceutical industry, pharmacology, and more. The technical bulletin of bioinformatics and computer science included herein focus on protein structure determination (often referred to as homology modelling), and the tools of database screening and prediction used in drug design.

Here, a brief outline of simulation techniques are given, focusing on the interface between biology and medicinal chemistry; that is molecular mechanics/molecular dynamics to explore the evolution of a system, homology modelling to determine protein structures, and the use of bioinformatics tools such as molecular docking and pharmacophores in drug design. The aim is to provide a brief introduction to a vast and rapidly growing field.

* For Research Use Only.
Principles of Molecular Docking

Principles of molecular docking Molecular docking is a structure-based drug design method that predicts the binding mode and affinity by studying the interaction of organic small molecule ligands with biological macromolecular receptors. Molecular docking methods have a wide range of applications in the fields of enzymology research and drug design. Since the Kuntz team at […]

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Selection Method of Protein Crystal Structure

When using the CD ComputaBio computing platform, many users have questions about how to choose a protein crystal structure. This article will share some experience on this topic. Each standard has a scope of application. We only discuss here the principles and methods for selecting protein crystal structures for molecular docking. 1. Determine the protein […]

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FBDD Drug Discovery Technology Inventory

FBDD (Fragment-based drug design) is a new method of drug discovery that combines random screening and structure-based drug design. Since it was proposed in 1981, with the continuous improvement of fragment design, screening and optimization technologies, FBDD has gradually moved from theory to practice and has become one of the mainstream drug discovery technologies today. […]

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Quantum Chemistry

Quantum chemistry is a branch of theoretical chemistry and a basic science that applies the basic principles and methods of quantum mechanics to study chemical problems. The research scope includes the structure and performance of stable and unstable molecules and the relationship between structure and performance; the interaction between molecules; the collision and interaction between […]

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Molecular Mechanics

Molecular mechanics is a method of calculating molecular structure and energy with the help of experience and semi-empirical parameters based on the theory of classical mechanics, also known as force field method. The basic idea of this method is to treat a molecule as a collection of atoms held together by elastic force. If these […]

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Current Problems and Challenges Facing Coarse-Grained

Further expansion of the research system Although the coarse-grained method has been successful for many systems, some important systems, especially biological macromolecular systems, have yet to be developed. Biological macromolecular systems, such as DNA, protein, etc., have the characteristics of spatial inhomogeneity, long time characteristic scale, and multiple scales. It makes it difficult for basic […]

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Time Control of Molecular Simulation

The length of the equilibrium simulation time depends on the relaxation period of the property of interest. To obtain the statistical average of the properties of interest, it is necessary to sample 3-10 times in different relaxation periods. For example, for the vibration of the key, a period of 10 fs per vibration counts as […]

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Data-Driven Computational Protein Design

Computational protein design can generate proteins that are not found in nature that adopt desired structures and perform new functions. Although it is theoretically possible to design proteins from scratch, the actual success comes from the use of a large amount of data describing the sequence, structure, and function of existing proteins and their variants. […]

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Comparison of Machine Learning and Classical Force Field in Peptide Simulation

Compared with the classical force field, the new machine learning (ML) force field does not describe the energy of the molecule in a parameterized way, but uses the AI framework to learn the energy of the atom in the corresponding chemical environment, and compare the energy of each atom in the molecular structure. The local […]

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Challenges Faced by Drug Design Based on AI Technology

Artificial intelligence can play a key role in drug discovery, especially artificial neural networks, such as deep neural networks or recurrent networks, driving the development of this field. Many applications in property or activity prediction, such as physical chemistry and ADMET properties, quantitative structure-property relationship (QSPR) or quantitative structure-activity relationship (QSAR), support this application. Artificial […]

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Overview of the Antibody Drug Discovery

Antibody drug discovery Monoclonal antibodiesIn 1975, British scientist Milstein and French scientist Kohler fused mouse B lymphocytes with tumor cells to form hybridoma cells. The first generation of monoclonal antibody (MAb) was born. This antibody has high specificity, uniform properties, and is easy to produce in large quantities, which brings new hope for the treatment […]

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Computational Antibody Design

Antibody calculation design In recent years, as the research and development of China's biological drugs, especially antibody drugs, has become more and more popular, the application of computational simulation technology to innovative biological drugs has also become a means for major companies to gradually rise. Computational Antibody Design is increasingly used in the design of […]

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Antigen-Antibody Interaction

The interaction of antigen and antibody is the basis of immunochemical technology. As an effective research tool, antibodies need to know how the antibody binds to the corresponding antigen. The characteristics of the interaction between antigen and antibody mainly have the following three points: 1. Specificity of antigen and antibody binding The antigenic determinant and […]

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MD Simulation Accuracy Improvement

Introductions Force fields in molecular dynamics simulations have been the subject of extensive research in the past decades, and force fields play a key role in capturing the precise physical and chemical properties of materials. Artificial neural network potentials (ANNPs) trained using density flooding theory (DFT) datasets have been shown to reproduce these properties well […]

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