PMC:1867812 / 20760-24329 JSONTXT

{"target":"https://pubannotation.org/docs/sourcedb/PMC/sourceid/1867812","sourcedb":"PMC","sourceid":"1867812","source_url":"https://www.ncbi.nlm.nih.gov/pmc/1867812","text":"Identifying Maximally Connected Bit-patterns\nEvery bit in a contact map has eight neighbor bits. For an edge position, we assume its out-of-boundary positions contain 0. In a contact map, a connected bit-pattern is a collection of bit-1 positions, where for each 1, at least one of its neighbors is 1. Correspondingly, we define a maximally-connected bit-pattern (also referred to as a bit-pattern in this article) to be a connected pattern p where every neighbor bit not in p is 0. We apply a simple region growth algorithm to identify all the maximally-connected patterns in each contact map within the two series of contact maps, corresponding to the two folding trajectories of BBA5. Altogether, we identified 352 maximally-connected bit-patterns in such contact maps. For the GSGS folding data, a total of 50,572 unique bit-patterns are constructed. We then represent each identified bit-pattern as a 6-tuple feature vector consisting of the following attributes:\n• Height: the number of rows contained in the pattern's Minimum Bounding Rectangle (MBR).\n• Width: the number of columns in the pattern's MBR.\n• NumOnes: the number of 1s in the pattern.\n• Slope: the general linear distribution trend of all the 1s in the pattern within its MBR. To compute the angle of a connected pattern we use the least-squares method to estimate the slope of a linear regression line. For a pattern containing n 1s, we denote the positions of the 1s as: (x1, y1)...(xn, yn). The least-squares method then estimates the slope β1 as: β1=∑i=1n((xi−x¯)∗(yi−y¯))/∑i=1n((xi−x¯)2) MathType@MTEF@5@5@+=feaafiart1ev1aaatCvAUfKttLearuWrP9MDH5MBPbIqV92AaeXatLxBI9gBaebbnrfifHhDYfgasaacH8akY=wiFfYdH8Gipec8Eeeu0xXdbba9frFj0=OqFfea0dXdd9vqai=hGuQ8kuc9pgc9s8qqaq=dirpe0xb9q8qiLsFr0=vr0=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@5A04@\n• xStdDev: the standard deviation of all the 1s' x-coordinates (this quantifies how the 1s spread along the x dimension).\n• yStdDev: the standard deviation of all the 1s' y-coordinates.\nNote that this feature vector captures the main geometric properties of a bit-pattern.\nAs discussed in the literature [18-21], non-local patterns (where bit-patterns are one type of non-local patterns,) in contact maps can effectively capture the secondary structure of proteins. Our previous work [6,7] demonstrated that by characterizing the spatial relationship among the above described bit-patterns, one can construct structural signatures for proteins of different classes or folds. In the context of protein folding, we have observed that the above-defined bit-patterns are also capable of capturing a wide range of local 3D structural motifs. They can even approximately measure the strength of secondary structure propensity in a conformation. For instance, we have identified bit-patterns that correspond to \"premature\" α-helices and native-like α-helices respectively. Henceforth, we refer to the 3D structure formed by all the participating residues of a bit-pattern as the 3D motif of the bit-pattern. The relationship between bit-patterns and 3D motifs will be further discussed in the next section.","divisions":[{"label":"title","span":{"begin":0,"end":44}},{"label":"p","span":{"begin":45,"end":968}},{"label":"p","span":{"begin":969,"end":1058}},{"label":"p","span":{"begin":1059,"end":1111}},{"label":"p","span":{"begin":1112,"end":1155}},{"label":"p","span":{"begin":1156,"end":2269}},{"label":"p","span":{"begin":2270,"end":2391}},{"label":"p","span":{"begin":2392,"end":2455}},{"label":"p","span":{"begin":2456,"end":2542}}],"tracks":[{"project":"2_test","denotations":[{"id":"17407611-9806935-1692924","span":{"begin":2575,"end":2577},"obj":"9806935"}],"attributes":[{"subj":"17407611-9806935-1692924","pred":"source","obj":"2_test"}]}],"config":{"attribute types":[{"pred":"source","value type":"selection","values":[{"id":"2_test","color":"#93c5ec","default":true}]}]}}