Yuedong Yang

Improving protein fold recognition and template-based modeling by employing probabilistic-based matching between predicted one-dimensional structural properties of query and corresponding native properties of templates

MOTIVATION In recent years, development of a single-method fold-recognition server lags behind consensus and multiple template techniques. However, a good consensus prediction relies on the accuracy of individual methods. This article reports our efforts to further improve a single-method fold recognition technique called SPARKS by changing the alignment scoring function and incorporating the SPINE-X techniques that make improved prediction of secondary structure, backbone torsion angle and solvent accessible surface area. RESULTS The new method called SPARKS-X was tested with the SALIGN benchmark for alignment accuracy, Lindahl and SCOP benchmarks for fold recognition, and CASP 9 blind test for structure prediction. The method is compared to several state-of-the-art techniques such as HHPRED and BoostThreader. Results show that SPARKS-X is one of the best single-method fold recognition techniques. We further note that incorporating multiple templates and refinement in model building will likely further improve SPARKS-X. AVAILABILITY The method is available as a SPARKS-X server at http://sparks.informatics.iupui.edu/

Specific interactions for ab initio folding of protein terminal regions with secondary structures

Proteins fold into unique three‐dimensional structures by specific, orientation‐dependent interactions between amino acid residues. Here, we extract orientation‐dependent interactions from protein structures by treating each polar atom as a dipole with a direction. The resulting statistical energy function successfully refolds 13 out of 16 fully unfolded secondary‐structure terminal regions of 10–23 amino acid residues in 15 small proteins. Dissecting the orientation‐dependent energy function reveals that the orientation preference between hydrogen‐bonded atoms is not enough to account for the structural specificity of proteins. The result has significant implications on the theoretical and experimental searches for specific interactions involved in protein folding and molecular recognition between proteins and other biologically active molecules. Proteins 2008.

SPINE X: improving protein secondary structure prediction by multistep learning coupled with prediction of solvent accessible surface area and backbone torsion angles.

Accurate prediction of protein secondary structure is essential for accurate sequence alignment, three‐dimensional structure modeling, and function prediction. The accuracy of ab initio secondary structure prediction from sequence, however, has only increased from around 77 to 80% over the past decade. Here, we developed a multistep neural‐network algorithm by coupling secondary structure prediction with prediction of solvent accessibility and backbone torsion angles in an iterative manner. Our method called SPINE X was applied to a dataset of 2640 proteins (25% sequence identity cutoff) previously built for the first version of SPINE and achieved a 82.0% accuracy based on 10‐fold cross validation (Q3). Surpassing 81% accuracy by SPINE X is further confirmed by employing an independently built test dataset of 1833 protein chains, a recently built dataset of 1975 proteins and 117 CASP 9 targets (critical assessment of structure prediction techniques) with an accuracy of 81.3%, 82.3% and 81.8%, respectively. The prediction accuracy is further improved to 83.8% for the dataset of 2640 proteins if the DSSP assignment used above is replaced by a more consistent consensus secondary structure assignment method. Comparison to the popular PSIPRED and CASP‐winning structure‐prediction techniques is made. SPINE X predicts number of helices and sheets correctly for 21.0% of 1833 proteins, compared to 17.6% by PSIPRED. It further shows that SPINE X consistently makes more accurate prediction in helical residues (6%) without over prediction while PSIPRED makes more accurate prediction in coil residues (3–5%) and over predicts them by 7%. SPINE X Server and its training/test datasets are available at http://sparks.informatics.iupui.edu/

Community-wide assessment of protein-interface modeling suggests improvements to design methodology

The CAPRI (Critical Assessment of Predicted Interactions) and CASP (Critical Assessment of protein Structure Prediction) experiments have demonstrated the power of community-wide tests of methodology in assessing the current state of the art and spurring progress in the very challenging areas of protein docking and structure prediction. We sought to bring the power of community-wide experiments to bear on a very challenging protein design problem that provides a complementary but equally fundamental test of current understanding of protein-binding thermodynamics. We have generated a number of designed protein-protein interfaces with very favorable computed binding energies but which do not appear to be formed in experiments, suggesting that there may be important physical chemistry missing in the energy calculations. A total of 28 research groups took up the challenge of determining what is missing: we provided structures of 87 designed complexes and 120 naturally occurring complexes and asked participants to identify energetic contributions and/or structural features that distinguish between the two sets. The community found that electrostatics and solvation terms partially distinguish the designs from the natural complexes, largely due to the nonpolar character of the designed interactions. Beyond this polarity difference, the community found that the designed binding surfaces were, on average, structurally less embedded in the designed monomers, suggesting that backbone conformational rigidity at the designed surface is important for realization of the designed function. These results can be used to improve computational design strategies, but there is still much to be learned; for example, one designed complex, which does form in experiments, was classified by all metrics as a nonbinder.

Improving prediction of secondary structure, local backbone angles, and solvent accessible surface area of proteins by iterative deep learning

Direct prediction of protein structure from sequence is a challenging problem. An effective approach is to break it up into independent sub-problems. These sub-problems such as prediction of protein secondary structure can then be solved independently. In a previous study, we found that an iterative use of predicted secondary structure and backbone torsion angles can further improve secondary structure and torsion angle prediction. In this study, we expand the iterative features to include solvent accessible surface area and backbone angles and dihedrals based on Cα atoms. By using a deep learning neural network in three iterations, we achieved 82% accuracy for secondary structure prediction, 0.76 for the correlation coefficient between predicted and actual solvent accessible surface area, 19° and 30° for mean absolute errors of backbone φ and ψ angles, respectively, and 8° and 32° for mean absolute errors of Cα-based θ and τ angles, respectively, for an independent test dataset of 1199 proteins. The accuracy of the method is slightly lower for 72 CASP 11 targets but much higher than those of model structures from current state-of-the-art techniques. This suggests the potentially beneficial use of these predicted properties for model assessment and ranking.

Ab initio folding of terminal segments with secondary structures reveals the fine difference between two closely related all-atom statistical energy functions.

One of the common methods for assessing energy functions of proteins is selection of native or near‐native structures from decoys. This is an efficient but indirect test of the energy functions because decoy structures are typically generated either by sampling procedures or by a separate energy function. As a result, these decoys may not contain the global minimum structure that reflects the true folding accuracy of the energy functions. This paper proposes to assess energy functions by ab initio refolding of fully unfolded terminal segments with secondary structures while keeping the rest of the proteins fixed in their native conformations. Global energy minimization of these short unfolded segments, a challenging yet tractable problem, is a direct test of the energy functions. As an illustrative example, refolding terminal segments is employed to assess two closely related all‐atom statistical energy functions, DFIRE (distance‐scaled, finite, ideal‐gas reference state) and DOPE (discrete optimized protein energy). We found that a simple sequence‐position dependence contained in the DOPE energy function leads to an intrinsic bias toward the formation of helical structures. Meanwhile, a finer statistical treatment of short‐range interactions yields a significant improvement in the accuracy of segment refolding by DFIRE. The updated DFIRE energy function yields success rates of 100% and 67%, respectively, for its ability to sample and fold fully unfolded terminal segments of 15 proteins to within 3.5 Å global root‐mean‐squared distance from the corresponding native structures. The updated DFIRE energy function is available as DFIRE 2.0 upon request.

Structural insights into the histone H1-nucleosome complex

Significance Linker H1 histones control the accessibility of linker DNA between two neighbor nucleosomes to DNA-binding proteins and regulate chromatin folding. We investigated the structure of the H1–nucleosome complex through a combination of multidimensional nuclear magnetic resonance spectroscopy, site-directed mutagenesis-isothermal-titration calorimetry and computational design/modeling. The results lead to a unique structural model for the globular domain of H1 in complex with the nucleosome that contains residue-level information and have implications for the dynamics of chromatin in vivo. In addition, our approach will be useful for testing the hypothesis that the globular domain of H1 variants might have distinct binding geometries within the nucleosome, and thereby contribute to the heterogeneity of chromatin structure. Linker H1 histones facilitate formation of higher-order chromatin structures and play important roles in various cell functions. Despite several decades of effort, the structural basis of how H1 interacts with the nucleosome remains elusive. Here, we investigated Drosophila H1 in complex with the nucleosome, using solution nuclear magnetic resonance spectroscopy and other biophysical methods. We found that the globular domain of H1 bridges the nucleosome core and one 10-base pair linker DNA asymmetrically, with its α3 helix facing the nucleosomal DNA near the dyad axis. Two short regions in the C-terminal tail of H1 and the C-terminal tail of one of the two H2A histones are also involved in the formation of the H1–nucleosome complex. Our results lead to a residue-specific structural model for the globular domain of the Drosophila H1 in complex with the nucleosome, which is different from all previous experiment-based models and has implications for chromatin dynamics in vivo.

Predicting continuous local structure and the effect of its substitution for secondary structure in fragment-free protein structure prediction

Local structures predicted from protein sequences are used extensively in every aspect of modeling and prediction of protein structure and function. For more than 50 years, they have been predicted at a low-resolution coarse-grained level (e.g., three-state secondary structure). Here, we combine a two-state classifier with real-value predictor to predict local structure in continuous representation by backbone torsion angles. The accuracy of the angles predicted by this approach is close to that derived from NMR chemical shifts. Their substitution for predicted secondary structure as restraints for ab initio structure prediction doubles the success rate. This result demonstrates the potential of predicted local structure for fragment-free tertiary-structure prediction. It further implies potentially significant benefits from using predicted real-valued torsion angles as a replacement for or supplement to the secondary-structure prediction tools used almost exclusively in many computational methods ranging from sequence alignment to function prediction.

Structure-based prediction of RNA-binding domains and RNA-binding sites and application to structural genomics targets

Mechanistic understanding of many key cellular processes often involves identification of RNA binding proteins (RBPs) and RNA binding sites in two separate steps. Here, they are predicted simultaneously by structural alignment to known protein–RNA complex structures followed by binding assessment with a DFIRE-based statistical energy function. This method achieves 98% accuracy and 91% precision for predicting RBPs and 93% accuracy and 78% precision for predicting RNA-binding amino-acid residues for a large benchmark of 212 RNA binding and 6761 non-RNA binding domains (leave-one-out cross-validation). Additional tests revealed that the method makes no false positive prediction from 311 DNA binding domains but correctly detects six domains binding with both DNA and RNA. In addition, it correctly identified 31 of 75 unbound RNA-binding domains with 92% accuracy and 65% precision for predicted binding residues and achieved 86% success rate in its application to SCOP RNA binding domain superfamily (Structural Classification Of Proteins). It further predicts 25 targets as RBPs in 2076 structural genomics targets: 20 of 25 predicted ones (80%) are putatively RNA binding. The superior performance over existing methods indicates the importance of dividing structures into domains, using a Z-score to measure relative structural similarity, and a statistical energy function to measure protein–RNA binding affinity.

Aging affects contrast response functions and adaptation of middle temporal visual area neurons in rhesus monkeys

In the present study we studied the effects of aging on the coding of contrast in area V1 (primary visual cortex) and MT (middle temporal visual area) of the macaque monkey using single-neuron in vivo electrophysiology. Our results show that both MT and V1 neurons in old monkeys are less sensitive to contrast than those in young monkeys. Generally, contrast sensitivity is affected by aging more severely in MT cells than in V1 cells. Specifically, MT cells were affected more severely than motion direction selective V1 cells. Particularly, we found that MT neurons in old monkeys exhibited enhanced maximum visual responses, higher levels of spontaneous activity and decreased signal-to-noise ratios. In addition, we also found age-related changes in neuronal adaptation to visual motion in MT. Compared with young animals, the contrast gain of MT neurons in old monkeys is less affected, but the response gain by adaptation of MT neurons is more affected. Our results suggest that there may be an anomalous visual processing in both the magnocellular and parvocellular pathways. The neural changes described here are consistent with an age-related degeneration of intracortical inhibition and could underlie some deficits in visual function during normal aging.