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Zoran Obradovic

Publications and source records attributed to Zoran Obradovic.

6 recordsLinked to original sources

Protein flexibility and intrinsic disorder.

Comparisons were made among four categories of protein flexibility: (1) low-B-factor ordered regions, (2) high-B-factor ordered regions, (3) short disordered regions, and (4) long disordered regions. Amino acid compositions of the four categories were found to be significantly different from each other, with high-B-factor ordered and short disordered regions being the most similar pair. The high-B-factor (flexible) ordered regions are characterized by a higher average flexibility index, higher average hydrophilicity, higher average absolute net charge, and higher total charge than disordered regions. The low-B-factor regions are significantly enriched in hydrophobic residues and depleted in the total number of charged residues compared to the other three categories. We examined the predictability of the high-B-factor regions and developed a predictor that discriminates between regions of low and high B-factors. This predictor achieved an accuracy of 70% and a correlation of 0.43 with experimental data, outperforming the 64% accuracy and 0.32 correlation of predictors based solely on flexibility indices. To further clarify the differences between short disordered regions and ordered regions, a predictor of short disordered regions was developed. Its relatively high accuracy of 81% indicates considerable differences between ordered and disordered regions. The distinctive amino acid biases of high-B-factor ordered regions, short disordered regions, and long disordered regions indicate that the sequence determinants for these flexibility categories differ from one another, whereas the significantly-greater-than-chance predictability of these categories from sequence suggest that flexible ordered regions, short disorder, and long disorder are, to a significant degree, encoded at the primary structure level.

Amino Acid Sequence↗

Flavors of protein disorder.

Intrinsically disordered proteins are characterized by long regions lacking 3-D structure in their native states, yet they have been so far associated with 28 distinguishable functions. Previous studies showed that protein predictors trained on disorder from one type of protein often achieve poor accuracy on disorder of proteins of a different type, thus indicating significant differences in sequence properties among disordered proteins. Important biological problems are identifying different types, or flavors, of disorder and examining their relationships with protein function. Innovative use of computational methods is needed in addressing these problems due to relative scarcity of experimental data and background knowledge related to protein disorder. We developed an algorithm that partitions protein disorder into flavors based on competition among increasing numbers of predictors, with prediction accuracy determining both the number of distinct predictors and the partitioning of the individual proteins. Using 145 variously characterized proteins with long (>30 amino acids) disordered regions, 3 flavors, called V, C, and S, were identified by this approach, with the V subset containing 52 segments and 7743 residues, C containing 39 segments and 3402 residues, and S containing 54 segments and 5752 residues. The V, C, and S flavors were distinguishable by amino acid compositions, sequence locations, and biological function. For the sequences in SwissProt and 28 genomes, their protein functions exhibit correlations with the commonness and usage of different disorder flavors, suggesting different flavor-function sets across these protein groups. Overall, the results herein support the flavor-function approach as a useful complement to structural genomics as a means for automatically assigning possible functions to sequences.

Algorithms↗

Predicting intrinsic disorder from amino acid sequence.

Blind predictions of intrinsic order and disorder were made on 42 proteins subsequently revealed to contain 9,044 ordered residues, 284 disordered residues in 26 segments of length 30 residues or less, and 281 disordered residues in 2 disordered segments of length greater than 30 residues. The accuracies of the six predictors used in this experiment ranged from 77% to 91% for the ordered regions and from 56% to 78% for the disordered segments. The average of the order and disorder predictions ranged from 73% to 77%. The prediction of disorder in the shorter segments was poor, from 25% to 66% correct, while the prediction of disorder in the longer segments was better, from 75% to 95% correct. Four of the predictors were composed of ensembles of neural networks. This enabled them to deal more efficiently with the large asymmetry in the training data through diversified sampling from the significantly larger ordered set and achieve better accuracy on ordered and long disordered regions. The exclusive use of long disordered regions for predictor training likely contributed to the disparity of the predictions on long versus short disordered regions, while averaging the output values over 61-residue windows to eliminate short predictions of order or disorder probably contributed to the even greater disparity for three of the predictors. This experiment supports the predictability of intrinsic disorder from amino acid sequence.

Amino Acids↗

Improved amino acid flexibility parameters.

Protein molecules exhibit varying degrees of flexibility throughout their three-dimensional structures, with some segments showing little mobility while others may be so disordered as to be unresolvable by techniques such as X-ray crystallography. Atomic displacement parameters, or B-factors, from X-ray crystallographic studies give an experimentally determined indication of the degree of mobility in a protein structure. To provide better estimators of amino acid flexibility, we have examined B-factors from a large set of high-resolution crystal structures. Because of the differences among structures, it is necessary to normalize the B-factors. However, many proteins have segments of unusually high mobility, which must be accounted for before normalization can be performed. Accordingly, a median-based method from quality control studies was used to identify outliers. After removal of outliers from, and normalization of, each protein chain, the B-factors were collected for each amino acid in the set. It was found that the distribution of normalized B-factors followed a Gumbel, or extreme value distribution, and the location parameter, or mode, of this distribution was used as an estimator of flexibility for the amino acid. These new parameters have a higher correlation with experimentally determined B-factors than parameters from earlier methods.

Amino Acids↗

Improving sequence alignments for intrinsically disordered proteins.

Here we analyze sequence alignments for intrinsically disordered proteins. For 55 disordered protein families we measure the performance of different scoring matrices and propose one adjusted to disordered regions. An iterative algorithm of realigning sequences and recalculating matrices is designed and tested. For each matrix we also test a wide range of gap penalties. Results show an improvement in the ability to detect and discriminate related disordered proteins whose average sequence identity with the other family members is below 50%.

Amino Acid Sequence↗