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Template modeling score

Template modeling score is a chemistry topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Template modeling score rather than just read about it. In short: In bioinformatics, the template modeling score or TM-score is a measure of similarity between two protein structures. The TM-score is intended as a more accurate measure of the global similarity of full-length protein structures than the often used RMSD measure.

Key takeaways

  • Template modeling score belongs to chemistry; place it in that map before memorising details.
  • Learn the definition first, then one example that makes the definition concrete.
  • Connect Template modeling score to a quantity you can measure, compute or draw — that is where exam questions come from.
  • Reproduce the core statement of Template modeling score from memory before moving on to harder problems.

Reference excerpt

In bioinformatics, the template modeling score or TM-score is a measure of similarity between two protein structures. The TM-score is intended as a more accurate measure of the global similarity of full-length protein structures than the often used RMSD measure. The TM-score indicates the similarity between two structures by a score between ( 0 , 1 ] {\displaystyle (0,1]} , where 1 indicates a perfect match between two structures (thus the higher the better). Generally scores below 0.20 corresponds to randomly chosen unrelated proteins whereas structures with a score higher than 0.5 assume roughly the same fold. A quantitative study

shows that proteins of TM-score = 0.5 have a posterior probability of 37% in the same CATH topology family and of 13% in the same SCOP fold family. The probabilities increase rapidly when TM-score > 0.5. The TM-score is designed to be independent of protein lengths.

The TM-score equation TM-score between two protein structures (e.g., a template structure and a target structure) is defined by

TM-score = max [ 1 L target ∑ i L common 1 1 + ( d i d 0 ( L target ) ) 2 ] {\displaystyle {\text{TM-score}}=\max \left[{\frac {1}{L_{\text{target}}}}\sum _{i}^{L_{\text{common}}}{\frac {1}{1+\left({\frac {d_{i}}{d_{0}(L_{\text{target}})}}\right)^{2}}}\right]}

where L target {\displaystyle L_{\text{target}}} is the length of the amino acid sequence of the target protein, and L common {\displaystyle L_{\text{common}}} is the number of residues that appear in both the template and target structures.

d i {\displaystyle d_{i}} is the distance between the i {\displaystyle i} th pair of residues in the template and target structures, and

d 0 ( L target ) = 1.24 L target − 15 3 − 1.8 {\displaystyle d_{0}(L_{\text{target}})=1.24{\sqrt[{3}]{L_{\text{target}}-15}}-1.8} is a distance scale that normalizes distances. The maximum is taken over all possible structure superpositions of the model and template (or some sample thereof). When comparing two protein structures that have the same residue order, L common {\displaystyle L_{\text{common}}} reads from the C-alpha order number of the structure files (i.e., Column 23-26 in Protein Data Bank (file format)). When comparing two protein structures that have different sequences and/or different residue orders, a structural alignment is usually performed first, and TM-score is then calculated on the commonly aligned residues from the structural alignment.

Other measures An often used structural similarity measure is root-mean-square deviation (RMSD). Because RMSD = ∑ i = 1 L d i 2 / L {\displaystyle ={\sqrt {\sum _{i=1}^{L}d_{i}^{2}/{L}}}}

is calculated as an average of distance error ( d i {\displaystyle d_{i}} ) with equal weight over all residue pairs, a large local error on a few residue pairs can result in a quite large RMSD. On the other hand, by putting d i {\displaystyle d_{i}} in the denominator, TM-score naturally weights smaller distance errors more strongly than larger distance errors. Therefore, TM-score value is more sensitive to the global structural similarity rather than to the local structural errors, compared to RMSD. Another advantage of TM-score is the introduction of the scale

… excerpt ends here. Continue reading the full article.

Worked examples

Example 1 — a first encounter with Template modeling score

Start with the simplest possible case. Write down what Template modeling score claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In chemistry, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Template modeling score before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Template modeling score ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Template modeling score

In research
Template modeling score appears in chemistry research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry
Engineering practice reuses Template modeling score in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom
Template modeling score is common in secondary-school and first-year university syllabi. It links to neighbouring topics Bioinformatics, Computational chemistry, so understanding it makes those chapters shorter.
In everyday life
Look for Template modeling score outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Template modeling score in 20 minutes

  1. Read the reference excerpt below once, without taking notes.
  2. Close the page and write down what Template modeling score means in your own words.
  3. Compare your version with the excerpt and mark what you missed.
  4. Work through the three examples above with pen and paper.
  5. Explain Template modeling score out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Template modeling score in simple terms?

In bioinformatics, the template modeling score or TM-score is a measure of similarity between two protein structures. The TM-score is intended as a more accurate measure of the global similarity of full-length protein structures than the often used RMSD measure.

Why does Template modeling score matter?

Because it connects several chemistry ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Template modeling score?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Template modeling score.

Tags

  • Bioinformatics
  • Computational chemistry

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