Yaoqi Zhou

Distance-scaled, finite ideal-gas reference state improves structure-derived potentials of mean force for structure selection and stability prediction

The distance‐dependent structure‐derived potentials developed so far all employed a reference state that can be characterized as a residue (atom)‐averaged state. Here, we establish a new reference state called the distance‐scaled, finite ideal‐gas reference (DFIRE) state. The reference state is used to construct a residue‐specific all‐atom potential of mean force from a database of 1011 nonhomologous (less than 30% homology) protein structures with resolution less than 2 Å. The new all‐atom potential recognizes more native proteins from 32 multiple decoy sets, and raises an average Z‐score by 1.4 units more than two previously developed, residue‐specific, all‐atom knowledge‐based potentials. When only backbone and Cβ atoms are used in scoring, the performance of the DFIRE‐based potential, although is worse than that of the all‐atom version, is comparable to those of the previously developed potentials on the all‐atom level. In addition, the DFIRE‐based all‐atom potential provides the most accurate prediction of the stabilities of 895 mutants among three knowledge‐based all‐atom potentials. Comparison with several physical‐based potentials is made.

Interpreting the folding kinetics of helical proteins

The detailed mechanism of protein folding is one of the major problems in structural biology. Its solution is of practical as well as fundamental interest because of its possible role in utilizing the many sequences becoming available from genomic analysis. Although the Levinthal paradox (namely, that a polypeptide chain can find its unique native state in spite of the astronomical number of configurations in the denatured state) has been resolved, the reasons for the differences in the folding behaviour of individual proteins remain to be elucidated. Here a Cα-based three-helix-bundle-like protein model with a realistic thermodynamic phase diagram is used to calculate several hundred folding trajectories. By varying a single parameter, the difference between the strength of native and non-native contacts, folding is changed from a diffusion–collision mechanism to one that involves simultaneous collapse and partial secondary-structure formation, followed by reorganization to the native structure. Non-obligatory intermediates are important in the former, whereas there is an obligatory on-pathway intermediate in the latter. Our results provide a basis for understanding the range of folding behaviour that is observed in helical proteins.

Fold recognition by combining sequence profiles derived from evolution and from depth-dependent structural alignment of fragments.

Recognizing structural similarity without significant sequence identity has proved to be a challenging task. Sequence‐based and structure‐based methods as well as their combinations have been developed. Here, we propose a fold‐recognition method that incorporates structural information without the need of sequence‐to‐structure threading. This is accomplished by generating sequence profiles from protein structural fragments. The structure‐derived sequence profiles allow a simple integration with evolution‐derived sequence profiles and secondary‐structural information for an optimized alignment by efficient dynamic programming. The resulting method (called SP3) is found to make a statistically significant improvement in both sensitivity of fold recognition and accuracy of alignment over the method based on evolution‐derived sequence profiles alone (SP) and the method based on evolution‐derived sequence profile and secondary structure profile (SP2). SP3 was tested in SALIGN benchmark for alignment accuracy and Lindahl, PROSPECTOR 3.0, and LiveBench 8.0 benchmarks for remote‐homology detection and model accuracy. SP3 is found to be the most sensitive and accurate single‐method server in all benchmarks tested where other methods are available for comparison (although its results are statistically indistinguishable from the next best in some cases and the comparison is subjected to the limitation of time‐dependent sequence and/or structural library used by different methods.). In LiveBench 8.0, its accuracy rivals some of the consensus methods such as ShotGun‐INBGU, Pmodeller3, Pcons4, and ROBETTA. SP3 fold‐recognition server is available on http://theory.med.buffalo.edu. Proteins 2005.

Protein binding site prediction using an empirical scoring function

Most biological processes are mediated by interactions between proteins and their interacting partners including proteins, nucleic acids and small molecules. This work establishes a method called PINUP for binding site prediction of monomeric proteins. With only two weight parameters to optimize, PINUP produces not only 42.2% coverage of actual interfaces (percentage of correctly predicted interface residues in actual interface residues) but also 44.5% accuracy in predicted interfaces (percentage of correctly predicted interface residues in the predicted interface residues) in a cross validation using a 57-protein dataset. By comparison, the expected accuracy via random prediction (percentage of actual interface residues in surface residues) is only 15%. The binding sites of the 57-protein set are found to be easier to predict than that of an independent test set of 68 proteins. The average coverage and accuracy for this independent test set are 30.5 and 29.4%, respectively. The significant gain of PINUP over expected random prediction is attributed to (i) effective residue-energy score and accessible-surface-area-dependent interface-propensity, (ii) isolation of functional constraints contained in the conservation score from the structural constraints through the combination of residue-energy score (for structural constraints) and conservation score and (iii) a consensus region built on top-ranked initial patches.

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/

Single-body residue-level knowledge-based energy score combined with sequence-profile and secondary structure information for fold recognition

An elaborate knowledge‐based energy function is designed for fold recognition. It is a residue‐level single‐body potential so that highly efficient dynamic programming method can be used for alignment optimization. It contains a backbone torsion term, a buried surface term, and a contact‐energy term. The energy score combined with sequence profile and secondary structure information leads to an algorithm called SPARKS (Sequence, secondary structure Profiles and Residue‐level Knowledge‐based energy Score) for fold recognition. Compared with the popular PSI‐BLAST, SPARKS is 21% more accurate in sequence‐sequence alignment in ProSup benchmark and 10%, 25%, and 20% more sensitive in detecting the family, superfamily, fold similarities in the Lindahl benchmark, respectively. Moreover, it is one of the best methods for sensitivity (the number of correctly recognized proteins), alignment accuracy (based on the MaxSub score), and specificity (the average number of correctly recognized proteins whose scores are higher than the first false positives) in LiveBench 7 among more than twenty servers of non‐consensus methods. The simple algorithm used in SPARKS has the potential for further improvement. This highly efficient method can be used for fold recognition on genomic scales. A web server is established for academic users on http://theory.med.buffalo.edu. Proteins 2004.

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.

A physical reference state unifies the structure-derived potential of mean force for protein folding and binding

Extracting knowledge‐based statistical potential from known structures of proteins is proved to be a simple, effective method to obtain an approximate free‐energy function. However, the different compositions of amino acid residues at the core, the surface, and the binding interface of proteins prohibited the establishment of a unified statistical potential for folding and binding despite the fact that the physical basis of the interaction (water‐mediated interaction between amino acids) is the same. Recently, a physical state of ideal gas, rather than a statistically averaged state, has been used as the reference state for extracting the net interaction energy between amino acid residues of monomeric proteins. Here, we find that this monomer‐based potential is more accurate than an existing all‐atom knowledge‐based potential trained with interfacial structures of dimers in distinguishing native complex structures from docking decoys (100% success rate vs. 52% in 21 dimer/trimer decoy sets). It is also more accurate than a recently developed semiphysical empirical free‐energy functional enhanced by an orientation‐dependent hydrogen‐bonding potential in distinguishing native state from Rosetta docking decoys (94% success rate vs. 74% in 31 antibody–antigen and other complexes based on Z score). In addition, the monomer potential achieved a 93% success rate in distinguishing true dimeric interfaces from artificial crystal interfaces. More importantly, without additional parameters, the potential provides an accurate prediction of binding free energy of protein–peptide and protein–protein complexes (a correlation coefficient of 0.87 and a root‐mean‐square deviation of 1.76 kcal/mol with 69 experimental data points). This work marks a significant step toward a unified knowledge‐based potential that quantitatively captures the common physical principle underlying folding and binding. A Web server for academic users, established for the prediction of binding free energy and the energy evaluation of the protein–protein complexes, may be found at http://theory.med.buffalo.edu. Proteins 2004.

Equilibrium thermodynamics of homopolymers and clusters: Molecular dynamics and Monte Carlo simulations of systems with square-well interactions

The thermodynamics of homopolymers and clusters with square-well interactions of up to 64 particles are studied with constant-temperature discontinuous molecular dynamics ~DMD! simulations; for comparison Monte Carlo ~MC! simulations are also reported. Homopolymers composed of more than five beads are found to exhibit two or more equilibrium transitions. In the long chain limit, these multiple transitions correspond to gas-to-liquid, liquid-to-solid, and solid-to-solid transitions. In particular, the liquid-to-solid-like disorder-to-order transition for isolated 32mers and 64mers is strongly first order ~bimodal energy distribution! at the reduced square-well diameter l51.5. As l decreases from 1.5 to 1.3, the bimodal distribution becomes unimodal. The use of Lindemann’s rule for solids indicates that the structure formed right below the liquid-to-solid transition temperature has a solid core but a liquid surface. Comparing the homopolymer results with those for square-well clusters indicates that the bonding constraint in homopolymers increases the temperatures of transitions but decreases their strength. The solid structure of an isolated 64mer is nearly identical to that of a cluster of 64 beads. Possible approaches to the experimental observation of the solid-state for an isolated chain are discussed.

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/