基于蛋白质受体的药物分子计算机辅助设计策略

药物分子计算机辅助设计是一种在计算机或者理论上通过构建具有一定潜在药理活性的新化学实体的分子模拟方法。近十几年来,高通量组学技术的快速发展为生物和化学药物分子设计提供了良好的数据支撑和研究契机。另外,现代社会对生物制药合理性以及作用机理理解的要求越来越高,行业普遍要求药物需要有高效、无毒或者低毒以及靶向性强等特点。随着越来越多与药物靶点相关的蛋白质结构通过实验方法解析出来,基于蛋白质受体的药物分子设计方法可行性进一步提高,其方法也变得越来越重要。基于蛋白质受体的药物分子设计方法,一般是以蛋白质以及配体的三维结构出发进行分析,这让药物分子先导物的发现更加理性化。随着相关实验数据的积累以及深度学习等算法的发展,从而可以进行更加科学的药物分子设计,这在一定程度上加快了新药研发的进程,并更有利于探索相应的分子机理。本文对基于蛋白质受体的药物分子设计方法的常用策略进行系统的回顾、总结和展望。     

Towards a personalized digital patient for diagnosis and therapy guided by image

Recent advances in computer science and medical imaging allow the design of new computational models of the patient which are used to assist physicians. These models, whose parameters are optimized to fit in vivo acquired images, from cells to an entire body, are designed to better quantify the observations (computer aided diagnosis), to simulate the evolution of a pathology (computer aided prognosis), to plan and simulate an intervention to optimize its effects (computer aided therapy), therefore addressing some of the major challenges of medicine of 21(st) century.     

Signaling protein networks as targets of new antineoplastic drugs

In-depth analysis of molecular regulatory networks in cancer holds the promise of improved knowledge of the pathophysiology of tumor cells so that it will become possible to design a detailed molecular tumor taxonomy. This knowledge will also offer new opportunities for the identification and validation of key molecular tumor targets to be exploited for novel therapeutic approaches. Some signaling proteins have already been identified as such, e.g. c-Myc, Cyclin D1, Bcl-XL, kinases and some nuclear receptors. This has led to the successful development of a few function-modulatory drugs (Glivec, SERM, Iressa), providing proof-of-principle of the validity of this approach. Further developments are likely to derive from "-omic" approaches, aimed at the understanding of signaling networks and of the mechanism of action of newfound lead molecules. High-throughput screening of small drug-like molecules from combinatorial chemical libraries or from microbial extracts will identify novel, "intelligent" drug candidates. An additional medicinal chemistry strategy (via 40-50 unit rosary-bead chains) has the potential to be much more effective than small molecules in interfering with protein-protein interactions. This may lead to considerably higher selectivity and effectiveness compared with historical approaches in drug discovery.     

Recent progress in general force fields of small molecules

Recent advances in computational hardware and free energy algorithms enable a broader application of molecular simulation of binding interactions between receptors and small-molecule ligands. The underlying molecular mechanics force fields (FFs) for small molecules have also achieved advancements in accuracy, user-friendliness, and speed during the past several years (2018–2020). Besides the expansion of chemical space coverage of ligand-like molecules among major popular classical additive FFs and polarizable FFs, new charge models have been proposed for better accuracy and transferability, new chemical perception of avoiding predefined atom types have been applied, and new automated parameterization toolkits, including machine learning approaches, have been developed for users’ convenience.     

Computer aided analysis for biosensing and screening

Biosensors with animal and microbial cells immobilized close to the tip of a membrane electrode have been developed for chemical and drug testing. Our experimental results show that biosensors can be used for drug screening and to provide useful information about various cell-chemical interactions. A computer aided analysis (CAA) software package is being developed here using the biosensor for various screening purposes. This software package enables us to use a computer to analyze the biosensor dynamic responses. Computer simulation and parameter estimation techniques are used to select the best model and to describe the biochemical and pharmacologic effects of various chemicals and drugs on different cell lines.     

Towards structure-based protein drug design

Structure-based drug design for chemical molecules has been widely used in drug discovery in the last 30 years. Many successful applications have been reported, especially in the field of virtual screening based on molecular docking. Recently, there has been much progress in fragment-based as well as de novo drug discovery. As many protein-protein interactions can be used as key targets for drug design, one of the solutions is to design protein drugs based directly on the protein complexes or the target structure. Compared with protein-ligand interactions, protein-protein interactions are more complicated and present more challenges for design. Over the last decade, both sampling efficiency and scoring accuracy of protein-protein docking have increased significantly. We have developed several strategies for structure-based protein drug design. A grafting strategy for key interaction residues has been developed and successfully applied in designing erythropoietin receptor-binding proteins. Similarly to small-molecule design, we also tested de novo protein-binder design and a virtual screen of protein binders using protein-protein docking calculations. In comparison with the development of structure-based small-molecule drug design, we believe that structure-based protein drug design has come of age.     

Recent advances in single molecule studies of nucleosomes

As the fundamental packing units of DNA in eukaryotes, nucleosomes play a central role in governing DNA accessibility in a variety of cellular processes. Our understanding of the mechanisms underlying this complex regulation has been aided by unique structural and dynamic perspectives offered by single molecule techniques. Recent years have witnessed remarkable advances achieved using these techniques, including the generation of a detailed histone-DNA energy landscape, elucidation of nucleosome disassembly processes, and real-time monitoring of molecular motors interacting with nucleosomes. These and other highlights of single molecule nucleosome studies will be discussed in this review.     

Chemical language models for de novo drug design: Challenges and opportunities

Generative deep learning is accelerating de novo drug design, by allowing the generation of molecules with desired properties on demand. Chemical language models – which generate new molecules in the form of strings using deep learning – have been particularly successful in this endeavour. Thanks to advances in natural language processing methods and interdisciplinary collaborations, chemical language models are expected to become increasingly relevant in drug discovery. This minireview provides an overview of the current state-of-the-art of chemical language models for de novo design, and analyses current limitations, challenges, and advantages. Finally, a perspective on future opportunities is provided.     

Active site model of cytochrome P-450 LM2

Based on (i) a detailed analysis of the physicochemical properties of selected benzphetamine derived substrates and (ii) the identification of Tyr-380 as active site residue trans to thiolate theoretical studies (computer aided molecular design) revealed a model of the substrate binding site of cytochrome P-450 LM2. The results indicate that substrates with a butterfly-like bulky conformation exhibit the highest intrinsic activity. Those substrates which preferably exist in an extended conformation are sterically hindered to intensively interact with the binding site which is demonstrated by computer graphics.     

Analysis of multiple compound–protein interactions reveals novel bioactive molecules

The discovery of novel bioactive molecules advances our systems‐level understanding of biological processes and is crucial for innovation in drug development. For this purpose, the emerging field of chemical genomics is currently focused on accumulating large assay data sets describing compound–protein interactions (CPIs). Although new target proteins for known drugs have recently been identified through mining of CPI databases, using these resources to identify novel ligands remains unexplored. Herein, we demonstrate that machine learning of multiple CPIs can not only assess drug polypharmacology but can also efficiently identify novel bioactive scaffold‐hopping compounds. Through a machine‐learning technique that uses multiple CPIs, we have successfully identified novel lead compounds for two pharmaceutically important protein families, G‐protein‐coupled receptors and protein kinases. These novel compounds were not identified by existing computational ligand‐screening methods in comparative studies. The results of this study indicate that data derived from chemical genomics can be highly useful for exploring chemical space, and this systems biology perspective could accelerate drug discovery processes.     

Sitting above the maze: recent model discoveries in molecular science

The 2013 Nobel Prize in Chemistry has convinced the world that how important the role that computational sciences play in chemical and materials sciences. In this review, computational methods and rational molecule design, including quantum mechanics and molecular mechanics methods, have been applied to study electronic structures and the interactions in a number of important applications at molecular level. The applications which include bioactive compounds, drug candidates and photoactive molecules at Swinburne University in the past several years are discussed. The research is in close collaboration with world class experimental groups from spectroscopy, organic and medicinal synthesis laboratories and most recently to γ-ray spectroscopy as well as other theory groups in the world. Ionisation spectra of biomolecules and bioactive compounds including amino acids, DNA bases, cyclic dipeptides, drug candidates, complexes and photoactive molecules are discussed. Most recent projects such as infrared spectral studies of ferrocene, rational design of organic dyes in solar cell applications, and recent development in γ-ray spectra of positron annihilation in molecules are highlighted.     

Chemical genetics strategies for identification of molecular targets

Chemical genetics is an emerging field that can be used to study the interactions of chemical compounds, including natural products, with proteins. Usually, the identification of molecular targets is the starting point for studying a drug’s mechanism of action and this has been a crucial step in understanding many biological processes. While a great variety of target identification methods have been developed over the last several years, there are still many bioactive compounds whose target proteins have not yet been revealed because no routine protocols can be adopted. This review contains information concerning the most relevant principles of chemical genetics with special emphasis on the different genomic and proteomic approaches used in forward chemical genetics to identify the molecular targets of the bioactive compounds, the advantages and disadvantages of each and a detailed list of successful examples of molecular targets identified with these approaches.     

Viral proteomics.

Viruses have long been studied not only for their pathology and associated disease but also as model systems for molecular processes and as tools for identifying important cellular regulatory proteins and pathways. Recent advances in mass spectrometry methods coupled with the development of proteomic approaches have greatly facilitated the detection of virion components, protein interactions in infected cells, and virally induced changes in the cellular proteome, resulting in a more comprehensive understanding of viral infection. In addition, a rapidly increasing number of high-resolution structures for viral proteins have provided valuable information on the mechanism of action of these proteins as well as aided in the design and understanding of specific inhibitors that could be used in antiviral therapies. In this paper, we discuss proteomic studies conducted on all eukaryotic viruses and bacteriophages, covering virion composition, viral protein structures, virus-virus and virus-host protein interactions, and changes in the cellular proteome upon viral infection.     

The neuroecology of chemical defenses

Chemicals are a frequent means whereby organisms defend themselves against predators, competitors, parasites, microbes, and other potentially harmful organisms. Much progress has been made in understanding how a phylogenetic diversity of organisms living in a variety of environments uses chemical defenses. Chief among these advances is determining the molecular identity of defensive chemicals and the roles they play in shaping interactions between individuals. Some progress has been made in deciphering the molecular, cellular, and systems level mechanisms underlying these interactions, as well as how these interactions can lead to structuring of communities and even ecosystems. The neuroecological approach unifies practices and principles from these diverse disciplines and at all scales as it attempts to explain in a single conceptual framework the abundances of organisms and the distributions of species within natural habitats. This article explores the neuroecology of chemical defenses with a focus on aquatic organisms and environments. We review the concept of molecules of keystone significance, including examples of how saxitoxin and tetrodotoxin can shape the organization and dynamics of marine and riparian communities, respectively. We also describe the current status and future directions of a topic of interest to our research group-the use of ink by marine molluscs, especially sea hares, in their defense. We describe a diversity of molecules and mechanisms mediating the protective effects of sea hares' ink, including use as chemical defenses against predators and as alarm cues toward conspecifics, and postulate that some defensive molecules may function as molecules of keystone significance. Finally, we propose future directions for studying the neuroecology of the chemical defenses of sea hares and their molluscan relatives, the cephalopods.     

Protein interaction network for Alzheimer's disease using computational approach

Alzheimer''s disease (AD) is the most common form of dementia. It is the sixth leading cause of death in old age people. Despite recent advances in the field of drug design, the medical treatment for the disease is purely symptomatic and hardly effective. Thus there is a need to understand the molecular mechanism behind the disease in order to improve the drug aspects of the disease. We provided two contributions in the field of proteomics in drug design. First, we have constructed a protein-protein interaction network for Alzheimer''s disease reviewed proteins with 1412 interactions predicted among 969 proteins. Second, the disease proteins were given confidence scores to prioritize and then analyzed for their homology nature with respect to paralogs and homologs. The homology persisted with the mouse giving a basis for drug design phase. The method will create a new drug design technique in the field of bioinformatics by linking drug design process with protein-protein interactions via signal pathways. This method can be improvised for other diseases in future.     

Using computer-aided drug design and medicinal chemistry strategies in the fight against diabetes

The aim of this work is to present a simple, practical and efficient protocol for drug design, in particular Diabetes, which includes selection of the illness, good choice of a target as well as a bioactive ligand and then usage of various computer aided drug design and medicinal chemistry tools to design novel potential drug candidates in different diseases. We have selected the validated target dipeptidyl peptidase IV (DPP-IV), whose inhibition contributes to reduce glucose levels in type 2 diabetes patients. The most active inhibitor with complex X-ray structure reported was initially extracted from the BindingDB database. By using molecular modification strategies widely used in medicinal chemistry, besides current state-of-the-art tools in drug design (including flexible docking, virtual screening, molecular interaction fields, molecular dynamics, ADME and toxicity predictions), we have proposed 4 novel potential DPP-IV inhibitors with drug properties for Diabetes control, which have been supported and validated by all the computational tools used herewith.     

A comprehensive examination of the contributions to the binding entropy of protein–ligand complexes

One of the most important requirements in computer‐aided drug design is the ability to reliably evaluate the binding free energies. However, the process of ligand binding is very complex because of the intricacy of the interrelated processes that are difficult to predict and quantify. In fact, the deeper understanding of the origin of the observed binding free energies requires the ability to decompose these free energies to their contributions from different interactions. Furthermore, it is important to evaluate the relative entropic and enthalpic contributions to the overall free energy. Such an evaluation is useful for assessing temperature effects and exploring specialized options in enzyme design. Unfortunately, calculations of binding entropies have been much more challenging than calculations of binding free energies. This work is probably the first to present microscopic evaluation of all of the relevant components to the binding entropy, namely configurational, polar solvation, and hydrophobic entropies. All of these contributions are evaluated by the restraint release approach. The calculated results shed an interesting light on major compensation effects in both the solvation and hydrophobic effect and, despite some overestimate, can provide very useful insight. This study also helps in analyzing some problems with the widely used molecular mechanics/Poisson‐Boltzmann surface area approach. Proteins 2010. © 2010 Wiley‐Liss, Inc.     

Probing molecular processes in live cells by quantitative multidimensional microscopy

Modern light microscopy has become a most powerful analytical tool for studying molecular processes in live cells. Recent advances in sample preparation, microscope design and image processing allow the generation of "multidimensional" data, simultaneously reporting the three-dimensional distribution and concentrations of several different molecules within cells and tissues at multiple time points with sub-micron spatial resolution and sub-second temporal resolution. Thus, molecular interactions and processes that were approached by biochemical analyses in vitro can now be directly monitored in live cells. Here, we address different aspects of multidimensional microscopy and, in particular, image quantification and the characterization of molecular dynamics, as applied to the study of cell adhesion.     

How does a key fit a flexible lock? Structure and dynamics in receptor function

The preceding five years have brought remarkable advances in our understanding of the primary structure of drug receptors. The roles of certain amino acid residues in binding drugs and effecting receptor function have been proposed. As even more detailed structures become available, the goal of rational design of drug molecules based on predicted fits between the drug and its receptor will be near at hand. Although none of the classical receptors has yet yielded to X-ray crystallographic analysis, the methods of molecular biology facilitate the production of the large amounts of these rare proteins necessary for crystallization. Receptor proteins share one fundamental characteristic with allosterically regulated enzymes. Both have the structural flexibility that allows information to be transmitted to distant parts of the molecule. We will discuss recent observations about receptor structure and the dynamic nature of drug receptors, and pose questions about the significance of receptor dynamics for drug design.     

Orthogonal chemical genetic approaches for unraveling signaling pathways

While chemical genetic approach uses small molecules to probe protein functions in cells or organisms, orthogonal chemical genetics refers to strategies that utilize reengineered protein-small molecule interfaces, to alter specificities, in order to probe their functions. The advantage of orthogonal chemical genetics is that the changes at the interfaces are generally so minute that it goes undetected by natural processes, and thus depicts a true physiological picture of biological phenomenon. This review highlights the recent advances in the area of orthogonal chemical genetics, especially those designed to probe signaling processes. Dynamic protein-protein and enzyme-substrate interactions following stimuli form the foundation of signal transduction. These processes not only break spatial and temporal boundaries between interacting proteins, but also impart distinct regulatory properties by creating functional diversity at the interfaces. Functional and temporal modulation of these dynamic interactions by specific chemical probes provides extremely powerful tools to initiate, ablate, decouple and deconvolute different components of a signaling pathway at multiple stages. Not surprisingly, multiple receptor-ligand reengineering approaches have been developed in the last decade to selectively manipulate these transient interactions with the aim of unraveling signaling events. However, given the diversity of protein-protein interactions and novel chemical genetic probes developed to perturb these processes, a short review cannot do adequate justice to all aspects of signaling. For this reason, this review focuses on some orthogonal chemical-genetic strategies that are developed to study signaling processes involving enzyme-substrate interactions.