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Preface from the Authors

  • Page ID
    74783
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    Philosophy

    Writing a textbook is an enormous task. This is especially true for a biochemistry text, given the field's inherently interdisciplinary nature. Our insight into the biochemical bases for the biological world has exploded over time. Somehow that that has to be presented in and integrated into a textbook, which attempts to explain life from a structure/function perspective. 

    Any book by default reflects the strengths, weaknesses, interests and biases of the writers. We have tried to select and explain topics that are perhaps most difficult for students to understand, but even those choices reflect in part the difficulties that authors have had to overcome throughout the years in their own understanding of the field. At the same time, we see our students struggling with the same content and concepts that we did as students. There is broad consensus among biochemists about which concepts are most difficult. Loertscher et al have described these as threshold concepts and most are fundamental concepts in chemistry. Examples include Gibbs free energy, the difference between rapid equilibrium and the steady state, the thermodynamics of macromolecular structure formation, the physical basis of interactions, and in addition biomolecular visualization literacy.

    At the same time, the biological complexities of the emergent properties of the cell, organs and organisms from a myriad of interacting molecules in space and time is truly humbling. Such complexity must increasingly be probed by mathematical models and the use of machine learning and artificial intelligence. Hence we have not shied away from the inclusion of mathematics and modeling of complex interconnected pathways. This is the future of biochemistry. Some material might be presented in too little depth for some. Again this reflects our backgrounds and experiences. Likewise, some material might be presented in too much depth for faculty adopters and students. We don’t view this as a problem since users can pick and choose the content they wish. We chose to write a text that extends students and is challenging.

    No one can know all of biology and all of chemistry (an obvious statement). Each of us is predominately stronger in one field than the other. Think of yourself looking at the field of biochemistry with glasses containing a "biology-centric" lense and a "chemistry-centric" lense. We all have a dominant eye, which sees the world more clearly. Which lense is most comfortable to you as you study biochemistry?

    2lense.svg

    If you have ever tried to see the world through glasses containing just one lense, the world looks distorted and incomplete. The same with biochemistry. It will become clearer to you if you correct your vision to include both a biological and chemistry lense. Adding even more lenses, including mathematics, computer sciences and physics, would offer a much better and clearer understanding (think of the compound lense of the fly)!  In addition, most biochemistry texts might be considered protein centric and give less attention to other classes of molecules such as lipids and nucleic acids. Coverage of the latter topics might seem underrepresented in this book in the eyes of a lipid or nucleio acid chemist.

    Fundamentals of Biochemistry is a classical textbook given its coverage and depth. It follows the arrangements and sequence of typical commercial textbooks to allow easier adoption by faculty and for ease of use by students.  We have tried to not focus too much on human biochemistry, which is most likely emphasized in many biochemistry courses.  Yet in reality, students who take biochemistry are often in a pre-medical track.  We hope to populate chapters with problems based on the research literature to enable a more problem-solving approach.  

    We have deliberately chosen to incorporate graphs and some text from published materials that can be shared through Creative Common licenses. It would not have been possible to create a text without using such materials, as we did not have a publishing company supporting our work. We have done our best to attribute materials not directly made by us to the original authors. We apologize if we neglected to give attribution to those who sources we have used. The complexities of writing a book over many years in what free time we could devote to the project increases the likelihood of such omissions.  In reality, we view ourselves as authors (of original materials) and compilers (of materials licensed through Creative Commons) of this textbook.

    This book, as with any first edition text, will contain many mistakes. We hope these most are in the form of typos and grammatical errors. Given our backgrounds and the limitations of our own knowledge, there will also likely be conceptual mistakes. We ask for your forgiveness for these and ask you to help correct them. 

    Key Features

    This text covers a full year sequence of biochemistry and will be available in Fall 2022. The book could serve traditional and ACS-certified majors, traditional and ASBMB-certified majors, as well as students taking a one-semester course. Characteristic of all LibreText books, Fundamentals of Biochemistry will be open to everyone via the internet and completely free, as all should have access to knowledge. It is comprehensive and topical, as new materials are easily added. The text is divided into four "books" and given the number of image and interactive models, it is best used online.  Each chapter and section can be printed as a PDF file if needed.  The LibreText collections of books have served 223 million students and saved them $31 million, an undue burden for our students and society.

    An online text has some advantages in addition to cost over hardback textbooks. Chief among these are imbedded interactivity and ease of updating. Here are some of the differentiating features of Fundamentals of Biochemistry:

    • Interactive 2D mathematical graphs with sliders that allow users to change mathematical constants with immediate update of graphs;
    • Interactive molecular models using iCn3D (https://www.ncbi.nlm.nih.gov/Structure/icn3d/full.html) that allows students to view and interact with rendered structures;
    • Imbedded VCell (https://vcell.org/) models that allow users to display time course graphs for simple to complex reactions;
    • Animations to accompany the VCell progress curve graphs;
    • Chapter sections on metabolic control analyses;
    • Constant addition of new materials such as RNA glycans, SARS-CoV-2 structures, etc.
    • Extension chapters on Abiotic Origins of Life and Quantum Biochemistry (which we will begin writing in September 2022).

    Interactive mathematical graphs

    These offer clear advantages over static graphs (which we also choose to incorporate). Key constants in the mathematical equation can be changed using sliders with updated graphs shown in real time. For example, users can change rate constants, dissociation constants, and other parameter in way that change the apparent form of the graph. This will make the constants less abstract and give users insight into their fundamental meaning. An example is the graph for the concentration of the macromolecule:ligand complex (ML) vs free ligand (L) shown below. L0 and Mare the total ligand and macromolecule concentrations, respectively.

    If \(L_0 \ll M_0\), then the equations simplifies to:

    \[ML = \dfrac{(M_0)(L_0)}{K_D + L} \nonumber \] 

    This is a graph for a hyperbola. If \(K_D \gg L\) (equivalent to L << KD ) then the equation becomes 

    \begin{equation}
    [\mathrm{ML}]=\left(\frac{\mathrm{M}_{0}}{\mathrm{~K}_{\mathrm{D}}}\right) \mathrm{L}
    \end{equation}

    Under these conditions, ML is a linear function of L. Now change the slider for KD in the interactive graph and see how the initial hyperbola changes into an apparently linear function when L << KD

    Now if a reader who had fully explored this interactive graph conducted an actual binding analysis in the lab and got a linear plot of ML vs L, they might immediately recognize that they need to increase the ligand concentration to cover the full binding curve.

    Use of iCn3Ds

    iCn3Ds interactive molecular models are found in most chapters and sections. These models have been rendered to illustrate key points related to structure/function and are not presented just as attractive images. It is important tat users interact with each models to enhance their understanding of the structure and function. Users can either click on the image and see an interactive popup model of the structure or use a link to go to a larger models in a separate window. Mouse instructions are given on each page of the popup window. 

    An example of an interactive iCn3D model presented in the book is the human nucleosome (3afa). Each iCn3D structure includes a Protein Data Bank (PDB) 4 alphanumeric code. We choose not to directly include iCn3D interactive models directly in the page. The size of the structure and the script necessary to produce the desired rendering make the load time for the page too long (especially when more than one model is presents in a given page. Instead, users can click on the image and a popup of the fully rendered model will appear. The user can modify the rendered model by clicking the top menu bar icon (=) and selecting from the dropdown menu. This requires some knowledge of the iCn3D GUI. Alteratively, the user can click the link in the legend underneath the image to get an expanded new window of the model directly through the full iCn3D user webpage. 

    Explore the model of the nucleosome below. This structure show how doubled-stranded DNA is packed around an octamer of pairs of 4 basic proteins called histones that are found in the eukaryotic nucleus. One member of each pair of histones is shown in cartoon rendering, while the other member of the pair is shown in the same color but in spacefill rendering. Each strand of DNA is shown in a different shade of gray.

    human nucleosome (3afa).png

    NIH_NCBI_iCn3D_Banner.svg Figure \(\PageIndex{10}\): Human nucleosome (3afa). (Copyright; author via source).
    Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...B2SwQHYDLj4BJ6

    Simply viewing the image will not lead to enhanced insight into the structure. Users should hover over key groups and rotate the model to help identify interactions. Labeled and interactive computer models offer key advantages over static 2D images. Using additional menu items to further interrogate the structure over and above the rendered image offered will lead to increased insight.

    A note on images: Many of the static structures of biomolecules are made with Pymol. The rest are made with iCn3D. Effort has been made to use colors optimal to those who are color-blind.

    Imbedded Virtual Cell (VCell) models 

    VCell (Virtual Cell) is platform for the mathematical modeling of cell biological systems. Our use of it is to display product vs time progress curves for simple to more complicated reactions. It can do so much more than that, however. For example, it can be used to model concentration of all metabolites in full metabolic and signal transduction pathways. We introduce it in this book to allow users to alter rate, enzyme kinetic, and dissociation constants, etc and see the effects on the output of simple to more complex reactions. Given the complexity of the biological world and its interactions, mathematical modeling of biological processes is essential to fully understanding it. Most readers to do not have the mathematical and computer skills to create and run models on their own. Vcell does that for you. We will explain in general the mathematical equations used in Vcell to model many reactions throughout this book. We hope that interested readers will become more familiar with Vcell and explore it for their own uses in courses and eventually in this own research.

    Modeling is very important especially when our intuitions are insufficient to account for observed results. Take for example the reactions of three enzymes involved in cell signaling in the mitogen activate protein kinase (MAPK) cascade system as illustrated in the scheme below. (We will explore this in more detail later in the text).

    MAPKCascadeFigure.svg

    A protein kinase is an enzyme that covalently transfers a terminal phosphate (P) from ATP to a protein. Line 1 shows one protein kinase, MAPKKK, that is phosphorylated (by another kinase not shown) to become MAPKKK-P. MAPKKK-P in turn is an active protein kinase that doubly phosphorylates MAPKK (line 2) to form MAPKK-PP. MAPKK-PP is in turn yet another downstream kinase, which doubly phosphorylates MAPK to form MAPK-PP. These phosphorylation reactions are shown in green in the reaction diagram. If you plot the concentration of MAPKKK (the starting enzyme with a cellular concentration set to 100 nM and MAPK-PP (initial concentration of MAPK is 300 nM) vs time for a specific set of conditions, you get the very predictable graph shown in the left panel below. However, if you allow the final product of the cascade (MAPK-PP), to inhibit the very first reaction in the cascade (Rx 1 forward, red blunt arrow), you get the remarkable oscillatory graph shown in the right panel of the figure.

     

    MAPKCascade_Intuition_PrefaceGraphs.svg

    You can actually run the simulatin by clicking "SUBMIT OMEX" below.

    Vcell OMEX-SBML file

    Model: 

    OscillationsMAPKCascadeVCellModel.png

    Reference: Kholodenko2000___Ultrasensitivity_and_negative_feedback_bring_oscillations_in_MAPK_cascade

    There is simply no way to predict the observed oscillatory behavior for the system from simple intuition as was possible for the reaction scheme without feedback inhibition. Vcell allows these types of calculations, which greatly informs our understanding of biological interactions in complicated biological pathways. (The Virtual Cell was developed with funding from the National Institute of General Medical Sciences (NIGMS) as a Biomedical Technology Research Resource at the Center for Cell Analysis and Modeling (CCAM). 

    Animations

    Science is really difficult for most students. Let's pick chemistry as an example and consider one of its simplest reactions, that of hydrogen chloride and water, to form the hydronium ion and chloride. We know such a mixture as hydrochloric acid. Chemists use every resource available to them to understand and explain reactions. These encompass the macroscopic, nanoscopic, mathematical and symbolic realms, as shown in the figure below.

    chemnanomacrosymbolicmath.svg

    Some need words to best understand a difficult concept. Others need images. Most need multiple modalities. For that reason we have incorporated animations of relevant physical and chemical reactions for which we also have imbedded Vcell models. These animations were created by Hui Liu (Anne) and Shraddha Nayak from Janet Iwasa's lab. In addition we have incorporate metabolic flux animations created by Shraddha for key metabolic pathways.

    We hope that all the different learning modalities we incorporate into Fundamentals of Biochemistry will help users comprehend this amazing yet difficult field.

    What is still needed

    The text will be available for adoption by Fall 2022. We continue to need help in writing key features and in a continual process of updating, especially in these areas:

    • end-of-chapter and chapter-imbedded problems (hopefully based on research literature)
    • mechanisms of your favorite enzymes or topics you feel not presented well in other texts
    • inclusive and diverse content
    • climate change, health disparities, addiction, etc.

    If you are interested in assisting in any of these areas, please contact Henry Jakubowski at hjakubowski@csbsju.edu

    Acknowledgments

    We greatly appreciate the help of Leslie Loew, Ion Moreau, Jonathan Karr, Bilal Shaikh and Henry Agnew in integrating Vcell into Fundamentals of Biochemistry. Likewise we greatly appreciate the help of Hui Liu (Anne), Shraddha Nayak and Janet Iwasa in creating the animations that accompany the VCell and other models.

    References

    1. VCell :
    • Schaff, J., C. C. Fink, B. Slepchenko, J. H. Carson, and L. M. Loew. 1997. A general computational framework for modeling cellular structure and function. Biophysical journal 73:1135-1146. PMC1181013
    • Cowan, A. E., Moraru, II, J. C. Schaff, B. M. Slepchenko, and L. M. Loew. 2012. Spatial modeling of cell signaling networks. Methods Cell Biol 110:195-221. PMC3288182

    2. https://upload.wikimedia.org/wikiped...nannotated.svg

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