4.7.11: Biomolecular Visualization - Conceptions and Misconceptions

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Learning Goals (ChatGPT o1, 1/27/25)

Below is a series of learning goals designed for junior and senior biochemistry majors based on the provided information:

1. Relate Structure to Function and Reactivity:

   • Explain how the three-dimensional structure of a molecule dictates its physical properties (melting point, boiling point, solubility) and chemical reactivity (acid–base behavior, redox properties, nucleophilicity/electrophilicity).
   • Connect classical representations (e.g., Lewis structures) with modern molecular orbital theory to describe bonding and reactivity.

2. Differentiate Visualization Representations:

   • Identify various molecular renderings (line, stick, ball-and-stick, spacefill, surface, and cartoon) and articulate the type of structural and functional information each provides.
   • Evaluate the strengths and limitations of each representation in conveying details such as atom connectivity, bond angles, steric hindrance, and molecular surface features.

3. Interpret Structural Data from Experimental Methods:

   • Describe how techniques like X-ray crystallography, NMR spectroscopy, and cryo-EM are used to determine biomolecular structures and discuss the inherent limitations (e.g., static snapshots, missing hydrogen atoms).
   • Explain how computational visualization tools are essential for interpreting and “fleshing out” structural data, especially for large and complex biomolecules.

4. Apply Concepts from Small Molecules to Macromolecules:

   • Use the example of oleic acid to illustrate how small molecule renderings can inform our understanding of molecular shape, polarity, and function.
   • Extend these ideas to proteins (e.g., titin, superoxide dismutase) by discussing how different renderings help reveal features like hydrophobic cores, active sites, and metal ion binding.

5. Analyze Functional Implications of Surface Properties:

   • Interpret electrostatic potential surfaces to understand how charge distribution contributes to enzyme function, such as the attraction of a negatively charged substrate (e.g., superoxide in superoxide dismutase).
   • Discuss how the visualization of metal ions and co-factors (e.g., Cu and Zn in superoxide dismutase) is critical for deducing catalytic mechanisms.

6. Integrate Static and Dynamic Views of Protein Structure:

   • Appreciate that while crystallographic structures offer static images, protein dynamics (bond stretching, angle bending, torsional rotations) are vital for understanding real-life function.
   • Recognize the role of computational methods (like molecular dynamics) in bridging the gap between static structures and dynamic behavior.

7. Critically Evaluate Structural Models and Data:

   • Assess potential sources of error in structural models (e.g., missing hydrogen atoms, steric clashes) and consider strategies to resolve these issues in both experimental and computational settings.
   • Discuss how multiple visualizations provide complementary insights and can prevent misconceptions about molecular structure and function.

By mastering these learning goals, students will be able to interpret various molecular renderings critically, understand the experimental and computational methods used to derive them, and apply these insights to explain how molecular structure underpins biological function.

Structure determines everything in biology and chemistry. Since you learned to represent molecules with Lewis structures, it's been drilled into you that the structure of a molecule determines its physical and chemical properties. Physical properties would include melting points, boiling points, and solubility. In contrast, chemical properties include acid/base, redox, precipitation, and general chemical reactivity determined by the presence of Lewis/Brønsted acids/bases and nucleophiles/electrophiles. More modern analyses of reactivity would include molecular orbital theory descriptions of bonding.

We can't see molecules but make inferences from data (x-ray crystallography, NMR spectroscopy, and cryo-EM) about the structure of a molecule (atom type, atom/bond connectivity, and geometry). As molecules get larger (consider the muscle protein titin, also called connectin, with a molecular weight of around 3.8 million), we must use computer visualization to understand its structure and infer its resulting function and activity. As with small molecules, we can render the molecule in different ways to better understand the attributes that confer function and activity. We ask students to view a biomolecule and infer its properties from the rendering without giving them devoted attention and instruction on how to do that.

Small molecules

Let's start with a small molecule like oleic acid, a long chain carboxylic acid with 18 carbon atoms and one cis (Z) double bond between carbons 9 and 10, as shown in Figure \(\PageIndex{1}\).

Figure \(\PageIndex{1}\): Line drawing of oleic acid

Table \(\PageIndex{1}\) below shows multiple ways to render the molecule. Each rendering offers insight into the molecule's function/activity, but might, at the same time, leave students struggling to interpret it and reinforce or instill misconceptions. Each representation below shows the very same molecule. The top row shows representations without H atoms; the bottom row shows those with H atoms.

line	stick	ball sphere	sphere	surface

Table \(\PageIndex{1}\):Different renderings of oleic acid

Here are some important things to remember about biomolecular structures, including both small and large molecules:

• Structures obtained using X-ray crystallography are constructed from relative electron densities calculated from diffraction patterns. Computer programs calculate the structure based on these electron density maps and known bond lengths, bond angles, and atom types. Most of the structures used in this book are available in the Protein Data Bank. The structures seen in computer models are visualized data and may contain errors (missing atoms, steric clashes, incorrect atom assignments), although structural refinement techniques minimize such problems.
• Structures derived from X-ray and cryomicroscopy analyses are static and represent only one conformation from a large ensemble. As you learned from studying simple molecules in organic chemistry, bond length and angle changes continually occur within molecules. Bonds connecting two atoms can stretch, angles connecting three atoms can bend, and the torsional angle around the center bond in a four-atom, three-bond system can rotate to form eclipsed and staggered (gauche and anti) conformers.
• PDB structures obtained by X-ray crystallography contain no H atoms as they are too small and contain too few electrons to diffract/scatter X-rays. So get used to adding them in your mind when you see a structure. Programs are used to calculate and show H bonds between a slightly positive H atom on an O or N atom in a protein and another slightly negative Os or Ns on the same or different molecule. The H bonds are often shown between N and O atoms. You should look at the atoms involved and the distance between them, and visualize a hydrogen atom connected to one of them. Figure \(\PageIndex{2}\) shows an example of an H bond between two base pairs in a DNA molecule.

Figure \(\PageIndex{2}\): Hydrogen bonds between two base pairs of DNA, with one showing actual hydrogen atoms

• Double bonds or likely resonance structures are not typically shown in PDB structures.
• Line, ball-and-stick, and stick renderings are useful for showing atom connectivity and bond angles. However, they are not particularly useful for showing how atom size might affect a molecule's structure and properties. This type of information is better demonstrated when spacefill renderings that show the sizes of the atoms (based on their Van der Waals radii) are used or when the surface of the molecule, calculated from the contact surface created between the van der Waals surface of the atoms and a rolling probe (often an O atom mimicking water) is displayed.

Large molecules

As molecules get larger, line, stick, and ball-and-stick representations become increasingly useless. New ways of visualizing the molecular structure are needed. The importance of multiple renderings to clarify structure/function relationships becomes apparent when you wish to understand protein structure. Various renderings of the protein superoxide dismutase (2sod) are shown in Table \(\PageIndex{2}\) below.

line	stick	cartoon	sphere	surface

Table \(\PageIndex{2}\): Multiple renderings of the protein superoxide dismutase

• The same features and limitations described above for small molecules apply to large ones. It is best to omit most atoms and use mixed renderings within a single display to reveal the biomolecule's crucial structural features. The cartoon representation of superoxide dismutase shows a single tiny alpha helix (red) and many beta strands (yellow). All side chain and backbone atoms have been removed. The green line traces the backbone of amino acids not involved in secondary structure. The Cu and Zn ions are shown as spheres.

Another type of surface rendering, the electrostatic potential surface, is beneficial. Figure \(\PageIndex{3}\) shows the electrostatic potential surface of superoxide dismutase taken from two different angles after simply rotating the protein.

Figure \(\PageIndex{3}\): Electrostatic potential surface of superoxidase dismutase from two perspectives

The red represents minimal (most negative) potential. This part of the structure would be enriched in slightly negative Os and Ns or wholly negative Os (i.e., have the highest electron density). Blue represents the positive potential centered in regions containing a slight or full positive charge (I, the lowest electron density). This enzyme binds superoxide, O₂^-, a toxic free radical reduction product of dioxygen, \(\ce{O2}\). It catalyzes this reaction:

\[\ce{2 O2^{-} + 2H^{+} → O2 + H2O}. \nonumber \]

The enzyme can effectively scavenge superoxide in its vicinity as the negative superoxide is drawn into the active site with the \(\ce{Cu}\) and \(\ce{Zn}\) atoms by the positive potential surrounding the active site, enhancing the typical diffusion encounter rate of the reactant with the enzyme pocket.

Figure \(\PageIndex{4}\) shows an interactive iCn3D model of the electrostatic potential of superoxidase dismutase (2sod).

Figure \(\PageIndex{4}\): Electrostatic potential of superoxidase dismutase (2sod) (Copyright; author via source).
Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...QQy2hAXehWLBW8

Summary

In this chapter, we explore the foundational concept that “structure determines function”—a principle that spans from small molecules to large proteins. We begin with the idea that a molecule’s Lewis structure dictates its physical properties (melting/boiling points, solubility) and its chemical reactivity (acid/base, redox, precipitation). Modern approaches, including molecular orbital theory, further refine our understanding of chemical bonding.

Because we cannot directly see molecules, scientists rely on experimental techniques such as X-ray crystallography, NMR spectroscopy, and cryo-EM to infer molecular structures. These methods provide detailed information about atom types, connectivity, and geometry. As the complexity of molecules increases—as in the case of gigantic proteins like titin (with a molecular weight ≈ 3.8 million)—computer visualization becomes essential for understanding how structure influences function.

For small molecules like oleic acid, various rendering techniques (line drawings, stick models, ball-and-stick, spacefill, and surface representations) highlight different aspects of molecular structure. For example, line models emphasize connectivity and bond angles, while spacefill models reveal the size and shape of atoms as defined by their van der Waals radii. These diverse visualizations help us infer how the structure might influence the molecule's function and reactivity.

When we turn to larger biomolecules, such as the enzyme superoxide dismutase (SOD), additional rendering techniques become useful. Cartoon renderings, which simplify the protein by showing its secondary structure elements (like alpha helices and beta strands), help reveal the overall folding pattern. In contrast, electrostatic potential surfaces illustrate the distribution of charge over the protein’s surface, thereby shedding light on how SOD attracts and binds its substrate—superoxide—via complementary charge interactions.

Overall, this chapter underscores that while experimental structures are invaluable, they are snapshots of dynamic systems. Each rendering provides distinct insights into the molecular determinants of biological function, underscoring that the interplay between structure and dynamics is central to understanding both small-molecule and macromolecular behavior in biochemistry.