computer science

STRIDE (algorithm)

STRIDE (algorithm) is a computer science 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 STRIDE (algorithm) rather than just read about it. In short: In protein structure, STRIDE (Structural identification) is an algorithm for the assignment of protein secondary structure elements given the atomic coordinates of the protein, as defined by X-ray crystallography, protein NMR, or another protein structure determination method. In addition to the hydrogen bond criteria used by the more common DSSP algorithm, the STRIDE assignment criteria also include dihedral angle…

Key takeaways

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

Reference excerpt

In protein structure, STRIDE (Structural identification) is an algorithm for the assignment of protein secondary structure elements given the atomic coordinates of the protein, as defined by X-ray crystallography, protein NMR, or another protein structure determination method. In addition to the hydrogen bond criteria used by the more common DSSP algorithm, the STRIDE assignment criteria also include dihedral angle potentials. As such, its criteria for defining individual secondary structures are more complex than those of DSSP. The STRIDE energy function contains a hydrogen-bond term containing a Lennard-Jones-like 8-6 distance-dependent potential and two angular dependence factors reflecting the planarity of the optimized hydrogen bond geometry. The criteria for individual secondary structural elements, which are divided into the same groups as those reported by DSSP, also contain statistical probability factors derived from empirical examinations of solved structures with visually assigned secondary structure elements extracted from the Protein Data Bank. Although DSSP is the older method and continues to be the most commonly used, the original STRIDE definition reported it to give a more satisfactory structural assignment in at least 70% of cases. In particular, STRIDE was observed to correct for the propensity of DSSP to assign shorter secondary structures than would be assigned by an expert crystallographer, usually due to the minor local variations in structure that are most common near the termini of secondary structure elements. Using a sliding-window method to smooth variations in assignment of single terminal residues, current implementations of STRIDE and DSSP are reported to agree in up to 95.4% of cases. Both STRIDE and DSSP, among other common secondary structure assignment methods, are believed to underpredict pi helices.

Worked examples

Example 1 — a first encounter with STRIDE (algorithm)

Start with the simplest possible case. Write down what STRIDE (algorithm) claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In computer science, 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 STRIDE (algorithm) 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 STRIDE (algorithm) 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 STRIDE (algorithm)

In research: STRIDE (algorithm) appears in computer science 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 STRIDE (algorithm) 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: STRIDE (algorithm) is common in secondary-school and first-year university syllabi. It links to neighbouring topics Protein structure, so understanding it makes those chapters shorter.
In everyday life: Look for STRIDE (algorithm) 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 STRIDE (algorithm) in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what STRIDE (algorithm) means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain STRIDE (algorithm) out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is STRIDE (algorithm) in simple terms?

Why does STRIDE (algorithm) matter?

Because it connects several computer science 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 STRIDE (algorithm)?

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 STRIDE (algorithm).