Jeffrey Skolnick

Scoring function for automated assessment of protein structure template quality

We have developed a new scoring function, the template modeling score (TM‐score), to assess the quality of protein structure templates and predicted full‐length models by extending the approaches used in Global Distance Test (GDT) 1 and MaxSub. 2 First, a protein size‐dependent scale is exploited to eliminate the inherent protein size dependence of the previous scores and appropriately account for random protein structure pairs. Second, rather than setting specific distance cutoffs and calculating only the fractions with errors below the cutoff, all residue pairs in alignment/modeling are evaluated in the proposed score. For comparison of various scoring functions, we have constructed a large‐scale benchmark set of structure templates for 1489 small to medium size proteins using the threading program PROSPECTOR_3 and built the full‐length models using MODELLER and TASSER. The TM‐score of the initial threading alignments, compared to the GDT and MaxSub scoring functions, shows a much stronger correlation to the quality of the final full‐length models. The TM‐score is further exploited as an assessment of all ‘new fold’ targets in the recent CASP5 experiment and shows a close coincidence with the results of human‐expert visual assessment. These data suggest that the TM‐score is a useful complement to the fully automated assessment of protein structure predictions. The executable program of TM‐score is freely downloadable at http://bioinformatics.buffalo.edu/TM‐score. Proteins 2004.

SPICKER: A clustering approach to identify near-native protein folds

We have developed SPICKER, a simple and efficient strategy to identify near‐native folds by clustering protein structures generated during computer simulations. In general, the most populated clusters tend to be closer to the native conformation than the lowest energy structures. To assess the generality of the approach, we applied SPICKER to 1489 representative benchmark proteins ≤200 residues that cover the PDB at the level of 35% sequence identity; each contains up to 280,000 structure decoys generated using the recently developed TASSER (Threading ASSembly Refinement) algorithm. The best of the top five identified folds has a root‐mean‐square deviation from native (RMSD) in the top 1.4% of all decoys. For 78% of the proteins, the difference in RMSD from native to the identified models and RMSD from native to the absolutely best individual decoy is below 1 Å; the majority belong to the targets with converged conformational distributions. Although native fold identification from divergent decoy structures remains a challenge, our overall results show significant improvement over our previous clustering algorithms.

A distance-dependent atomic knowledge-based potential for improved protein structure selection

A heavy atom distance‐dependent knowledge‐based pairwise potential has been developed. This statistical potential is first evaluated and optimized with the native structure z‐scores from gapless threading. The potential is then used to recognize the native and near‐native structures from both published decoy test sets, as well as decoys obtained from our groups protein structure prediction program. In the gapless threading test, there is an average z‐score improvement of 4 units in the optimized atomic potential over the residue‐based quasichemical potential. Examination of the z‐scores for individual pairwise distance shells indicates that the specificity for the native protein structure is greatest at pairwise distances of 3.5–6.5 Å, i.e., in the first solvation shell. On applying the current atomic potential to test sets obtained from the web, composed of native protein and decoy structures, the current generation of the potential performs better than residue‐based potentials as well as the other published atomic potentials in the task of selecting native and near‐native structures. This newly developed potential is also applied to structures of varying quality generated by our groups protein structure prediction program. The current atomic potential tends to pick lower RMSD structures than do residue‐based contact potentials. In particular, this atomic pairwise interaction potential has better selectivity especially for near‐native structures. As such, it can be used to select near‐native folds generated by structure prediction algorithms as well as for protein structure refinement. Proteins 2001;44:223–232.

Simulations of the folding of a globular protein

Dynamic Monte Carlo simulations of the folding of a globular protein, apoplastocyanin, have been undertaken in the context of a new lattice model of proteins that includes both side chains and a-carbon backbone atoms and that can approximate native conformations at the level of 2 angstroms (root mean square) or better. Starting from random-coil unfolded states, the model apoplastocyanin was folded to a native conformation that is topologically similar to the real protein. The present simulations used a marginal propensity for local secondary structure consistent with but by no means enforcing the native conformation and a full hydrophobicity scale in which any nonbonded pair of side chains could interact. These molecules folded through a punctuated on-site mechanism of assembly where folding initiated at or near one of the turns ultimately found in the native conformation. Thus these simulations represent a partial solution to the globular-protein folding problem.

MULTIPROSPECTOR: an algorithm for the prediction of protein-protein interactions by multimeric threading.

In this postgenomic era, the ability to identify protein–protein interactions on a genomic scale is very important to assist in the assignment of physiological function. Because of the increasing number of solved structures involving protein complexes, the time is ripe to extend threading to the prediction of quaternary structure. In this spirit, a multimeric threading approach has been developed. The approach is comprised of two phases. In the first phase, traditional threading on a single chain is applied to generate a set of potential structures for the query sequences. In particular, we use our recently developed threading algorithm, PROSPECTOR. Then, for those proteins whose template structures are part of a known complex, we rethread on both partners in the complex and now include a protein–protein interfacial energy. To perform this analysis, a database of multimeric protein structures has been constructed, the necessary interfacial pairwise potentials have been derived, and a set of empirical indicators to identify true multimers based on the threading Z‐score and the magnitude of the interfacial energy have been established. The algorithm has been tested on a benchmark set comprised of 40 homodimers, 15 heterodimers, and 69 monomers that were scanned against a protein library of 2478 structures that comprise a representative set of structures in the Protein Data Bank. Of these, the method correctly recognized and assigned 36 homodimers, 15 heterodimers, and 65 monomers. This protocol was applied to identify partners and assign quaternary structures of proteins found in the yeast database of interacting proteins. Our multimeric threading algorithm correctly predicts 144 interacting proteins, compared to the 56 (26) cases assigned by PSI‐BLAST using a (less) permissive E‐value of 1 (0.01). Next, all possible pairs of yeast proteins have been examined. Predictions (n = 2865) of protein–protein interactions are made; 1138 of these 2865 interactions have counterparts in the Database of Interacting Proteins. In contrast, PSI‐BLAST made 1781 predictions, and 1215 have counterparts in DIP. An estimation of the false‐negative rate for yeast‐predicted interactions has also been provided. Thus, a promising approach to help assist in the assignment of protein–protein interactions on a genomic scale has been developed. Proteins 2002;49:350–364.

Crowding and hydrodynamic interactions likely dominate in vivo macromolecular motion

To begin to elucidate the principles of intermolecular dynamics in the crowded environment of cells, employing Brownian dynamics (BD) simulations, we examined possible mechanism(s) responsible for the great reduction in diffusion constants of macromolecules in vivo from that at infinite dilution. In an Escherichia coli cytoplasm model comprised of 15 different macromolecule types at physiological concentrations, BD simulations of molecular-shaped and equivalent sphere representations were performed with a soft repulsive potential. At cellular concentrations, the calculated diffusion constant of GFP is much larger than experiment, with no significant shape dependence. Next, using the equivalent sphere system, hydrodynamic interactions (HI) were considered. Without adjustable parameters, the in vivo experimental GFP diffusion constant was reproduced. Finally, the effects of nonspecific attractive interactions were examined. The reduction in diffusivity is very sensitive to macromolecular radius with the motion of the largest macromolecules dramatically slowed down; this is not seen if HI dominate. In addition, long-lived clusters involving the largest macromolecules form if attractions dominate, whereas HI give rise to significant, size independent intermolecular dynamic correlations. These qualitative differences provide a testable means of differentiating the importance of HI vs. nonspecific attractive interactions on macromolecular motion in cells.

A threading-based method (FINDSITE) for ligand-binding site prediction and functional annotation.

The detection of ligand-binding sites is often the starting point for protein function identification and drug discovery. Because of inaccuracies in predicted protein structures, extant binding pocket-detection methods are limited to experimentally solved structures. Here, FINDSITE, a method for ligand-binding site prediction and functional annotation based on binding-site similarity across groups of weakly homologous template structures identified from threading, is described. For crystal structures, considering a cutoff distance of 4 Å as the hit criterion, the success rate is 70.9% for identifying the best of top five predicted ligand-binding sites with a ranking accuracy of 76.0%. Both high prediction accuracy and ability to correctly rank identified binding sites are sustained when approximate protein models (<35% sequence identity to the closest template structure) are used, showing a 67.3% success rate with 75.5% ranking accuracy. In practice, FINDSITE tolerates structural inaccuracies in protein models up to a rmsd from the crystal structure of 8–10 Å. This is because analysis of weakly homologous protein models reveals that about half have a rmsd from the native binding site <2 Å. Furthermore, the chemical properties of template-bound ligands can be used to select ligand templates associated with the binding site. In most cases, FINDSITE can accurately assign a molecular function to the protein model.

Structure Modeling of All Identified G Protein–Coupled Receptors in the Human Genome

G protein–coupled receptors (GPCRs), encoded by about 5% of human genes, comprise the largest family of integral membrane proteins and act as cell surface receptors responsible for the transduction of endogenous signal into a cellular response. Although tertiary structural information is crucial for function annotation and drug design, there are few experimentally determined GPCR structures. To address this issue, we employ the recently developed threading assembly refinement (TASSER) method to generate structure predictions for all 907 putative GPCRs in the human genome. Unlike traditional homology modeling approaches, TASSER modeling does not require solved homologous template structures; moreover, it often refines the structures closer to native. These features are essential for the comprehensive modeling of all human GPCRs when close homologous templates are absent. Based on a benchmarked confidence score, approximately 820 predicted models should have the correct folds. The majority of GPCR models share the characteristic seven-transmembrane helix topology, but 45 ORFs are predicted to have different structures. This is due to GPCR fragments that are predominantly from extracellular or intracellular domains as well as database annotation errors. Our preliminary validation includes the automated modeling of bovine rhodopsin, the only solved GPCR in the Protein Data Bank. With homologous templates excluded, the final model built by TASSER has a global Cα root-mean-squared deviation from native of 4.6 Å, with a root-mean-squared deviation in the transmembrane helix region of 2.1 Å. Models of several representative GPCRs are compared with mutagenesis and affinity labeling data, and consistent agreement is demonstrated. Structure clustering of the predicted models shows that GPCRs with similar structures tend to belong to a similar functional class even when their sequences are diverse. These results demonstrate the usefulness and robustness of the in silico models for GPCR functional analysis. All predicted GPCR models are freely available for noncommercial users on our Web site (http://www.bioinformatics.buffalo.edu/GPCR).

Structural genomics and its importance for gene function analysis

Structural genomics projects aim to solve the experimental structures of all possible protein folds. Such projects entail a conceptual shift from traditional structural biology in which structural information is obtained on known proteins to one in which the structure of a protein is determined first and the function assigned only later. Whereas the goal of converting protein structure into function can be accomplished by traditional sequence motif-based approaches, recent studies have shown that assignment of a proteins biochemical function can also be achieved by scanning its structure for a match to the geometry and chemical identity of a known active site. Importantly, this approach can use low-resolution structures provided by contemporary structure prediction methods. When applied to genomes, structural information (either experimental or predicted) is likely to play an important role in high-throughput function assignment.

TASSER: An automated method for the prediction of protein tertiary structures in CASP6†

The recently developed TASSER (Threading/ASSembly/Refinement) method is applied to predict the tertiary structures of all CASP6 targets. TASSER is a hierarchical approach that consists of template identification by the threading program PROSPECTOR_3, followed by tertiary structure assembly via rearranging continuous template fragments. Assembly occurs using parallel hyperbolic Monte Carlo sampling under the guide of an optimized, reduced force field that includes knowledge‐based statistical potentials and spatial restraints extracted from threading alignments. Models are automatically selected from the Monte Carlo trajectories in the low‐temperature replicas using the clustering program SPICKER. For all 90 CASP targets/domains, PROSPECTOR_3 generates initial alignments with an average root‐mean‐square deviation (RMSD) to native of 8.4 Å with 79% coverage. After TASSER reassembly, the average RMSD decreases to 5.4 Å over the same aligned residues; the overall cumulative TM‐score increases from 39.44 to 52.53. Despite significant improvements over the PROSPECTOR_3 template alignment observed in all target categories, the overall quality of the final models is essentially dictated by the quality of threading templates: The average TM‐scores of TASSER models in the three categories are, respectively, 0.79 [comparative modeling (CM), 43 targets/domains], 0.47 [fold recognition (FR), 37 targets/domains], and 0.30 [new fold (NF), 10 targets/domains]. This highlights the need to develop novel (or improved) approaches to identify very distant targets as well as better NF algorithms. Proteins 2005;Suppl 7:91–98.