Can an SVM predict which amino acids touch in a folded protein?
A support vector machine using many sequence-derived features predicted residue contacts better than the previous leading method, though accuracy remained low overall.
Source
Improved residue contact prediction using support vector machines and a large feature set
Study at a glance
- Design
- Computational / modelling — RBF-kernel SVM classifies residue pairs as in contact or not; trained and tested on the same split as CMAPpro, then assessed on CASP7 de novo domains.
- N
- N=48 · 48 test proteins (485 training proteins); the CASP7 comparison used 13 released de novo domains.
- Population
- Non-redundant protein chains (under 25% pairwise sequence identity) and CASP7 targets
- Outcome
- Accuracy (specificity) and coverage (sensitivity) of predicted medium- and long-range residue contacts
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What they did
The authors trained SVMcon, a support vector machine with a Gaussian (RBF) kernel, to decide whether two residues at least six positions apart in a protein sequence lie within 8 Å of each other. Each residue pair was described by hundreds of features: sequence profiles in windows around both residues, predicted secondary structure and solvent accessibility, pairwise mutual information, contact potentials and whole-protein composition. They trained on 485 proteins, tested on 48 against CMAPpro, and entered the blind CASP7 competition.
What they found
At the point where sensitivity equals specificity SVMcon reached 27.1%, about four points higher than CMAPpro and roughly nine times a random guess. Contacts in proteins with beta-sheets were predicted more accurately than those in all-alpha proteins. In CASP7 it had 27.7% accuracy at a sequence separation of at least 12, second among the eight predictors compared.
The limits
What it doesn't show
Even the best predictions are mostly wrong at longer ranges, so this is a step toward, not a solution for, building 3D structures from contacts. The CASP7 set was only 13 domains, and the authors warn against over-interpreting the ranking and note their evaluation differs slightly from the official one. Training took days, so only a crude feature-removal check was possible, and the paper does not test whether the predicted contacts actually improve 3D modelling.
Key terms
- Residue contact
- Two amino acids whose alpha-carbons are within 8 Å of each other in the folded protein.
- Support vector machine
- A classifier that finds the boundary with the widest margin between classes, possibly after mapping inputs to a higher-dimensional space with a kernel.
- RBF kernel
- A similarity function that decays with the squared distance between two inputs, letting an SVM draw curved decision boundaries.
- Break-even point
- The decision threshold where sensitivity equals specificity, used to compare predictors on equal footing.
- CASP
- A blind community experiment where methods predict protein structures before the true structures are released.
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Quiz yourself
What kind of model is SVMcon?
Common questions
Why only predict contacts six or more residues apart?
Short-range contacts are largely explained by local secondary structure; the informative, hard cases are medium- and long-range pairs.
Why compare with a coin-flip baseline?
Contacts are rare, so a random guesser tuned to the base rate shows how much real signal the model adds; SVMcon was about nine times better.
Why did the authors throw away most negative examples?
Non-contacts vastly outnumber contacts, so they kept a random 5% of negatives in training to balance the classes.
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