Song Liu

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.

A structural basis for the unequal sensitivity of the major cardiac and liver gap junctions to intracellular acidification: the carboxyl tail length

The regulation of junctional conductance (Gi) of the major cardiac (connexin43; Cx43) and liver (connexin32; Cx32) gap junction proteins by intracellular hydrogen ion concentration (pH; pHi), as well as well as that of a truncation mutant of Cx43 (M257) with 125 amino acids deleted from the COOH terminus, was characterized in pairs of Xenopus laevis oocytes expressing homologous channels. Oocytes were injected with 40 nl mRNAs (2 micrograms/microliters) encoding the respective proteins; subsequently, cells were stripped, paired, and incubated for 20-24 h. Gj was measured in oocyte pairs using the dual electrode voltage-clamp technique, while pHi was recorded simultaneously in the unstimulated cell by means of a proton-selective microelectrode. Because initial experiments showed that the pH-sensitive microelectrode responded more appropriately to acetate than to CO2 acidification, oocytes expressing Cx32 and wild type and mutant Cx43 were exposed to a sodium acetate saline, which was balanced to various levels of pH using NaOH and HCl. pH was changed in a stepwise manner, and quasi-steady-state Gj -pHi relationships were constructed from data collected at each step after both Gj and pHi had reached their respective asymptotic values. A moderate but significant increase of Gj was observed in Cx43 pairs as pHi decreased from 7.2 to 6.8. In both Cx32 and M257 pairs, Gj increased significantly over a wider pH range (i.e., between 7.2 and 6.3). Further acidification reversibly reduced Gj to zero in all oocyte pairs. Pooled data for the individual connexins obtained during uncoupling were fitted by the Hill equation; apparent 50%-maximum (pK;pKa) values were 6.6 and 6.1 for Cx43 and Cx32, respectively, and Hill coefficients were 4.2 for Cx43 and 6.2 for Cx32. Like Cx32, M257 had a more acidic pKa (6.1) and steeper Hill coefficient (6.0) than wild type Cx43. The pKa and Hill coefficient of M257 were very similar to those of Cx32. These experiments provide the first direct comparison of the effects of acidification on Gj in oocyte pairs expressing Cx43 or Cx32. The results indicate that structural differences in the connexins are the basis for their unequal sensitivity to intracellular acidification in vivo. The data further suggest that a common pH gating mechanism may exist between amino acid residues 1 and 256 in both Cx32 and Cx43. However, the longer carboxyl tail of Cx43 relative to Cx32 or M257 provides additional means to facilitate acidification-induced gating; its presence shifts the pKa from 6.1 (Cx32 and M257) to 6.6 (Cx43) in the conductance of these channels.

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.

An accurate, residue-level, pair potential of mean force for folding and binding based on the distance-scaled, ideal-gas reference state

Structure prediction on a genomic scale requires a simplified energy function that can efficiently sample the conformational space of polypeptide chains. A good energy function at minimum should discriminate native structures against decoys. Here, we show that a recently developed, residue‐specific, all‐atom knowledge‐based potential (167 atomic types) based on distance‐scaled, finite ideal‐gas reference state (DFIRE‐all‐atom) can be substantially simplified to 20 residue types located at side‐chain center of mass (DFIRE‐SCM) without a significant change in its capability of structure discrimination. Using 96 standard multiple decoy sets, we show that there is only a small reduction (from 80% to 78%) in success rate of ranking native structures as the top 1. The success rate is higher than two previously developed, all‐atom distance‐dependent statistical pair potentials. Applied to structure selections of 21 docking decoys without modification, the DFIRE‐SCM potential is 29% more successful in recognizing native complex structures than an all‐atom statistical potential trained by a database of dimeric interfaces. The potential also achieves 92% accuracy in distinguishing true dimeric interfaces from artificial crystal interfaces. In addition, the DFIRE potential with the Cα positions as the interaction centers recognizes 123 native structures out of a comprehensive 125‐protein TOUCHSTONE decoy set in which each protein has 24,000 decoys with only Cα positions. Furthermore, the performance by DFIRE‐SCM on newly established 25 monomeric and 31 docking Rosetta‐decoy sets is comparable to (or better than in the case of monomeric decoy sets) that of a recently developed, all‐atom Rosetta energy function enhanced with an orientation‐dependent hydrogen bonding potential.

A comparison of RNA-Seq and high-density exon array for detecting differential gene expression between closely related species

RNA-Seq has emerged as a revolutionary technology for transcriptome analysis. In this article, we report a systematic comparison of RNA-Seq and high-density exon array for detecting differential gene expression between closely related species. On a panel of human/chimpanzee/rhesus cerebellum RNA samples previously examined by the high-density human exon junction array (HJAY) and real-time qPCR, we generated 48.68 million RNA-Seq reads. Our results indicate that RNA-Seq has significantly improved gene coverage and increased sensitivity for differentially expressed genes compared with the high-density HJAY array. Meanwhile, we observed a systematic increase in the RNA-Seq error rate for lowly expressed genes. Specifically, between-species DEGs detected by array/qPCR but missed by RNA-Seq were characterized by relatively low expression levels, as indicated by lower RNA-Seq read counts, lower HJAY array expression indices and higher qPCR raw cycle threshold values. Furthermore, this issue was not unique to between-species comparisons of gene expression. In the RNA-Seq analysis of MicroArray Quality Control human reference RNA samples with extensive qPCR data, we also observed an increase in both the false-negative rate and the false-positive rate for lowly expressed genes. These findings have important implications for the design and data interpretation of RNA-Seq studies on gene expression differences between and within species.

Accurate and efficient loop selections by the DFIRE-based all-atom statistical potential.

The conformations of loops are determined by the water‐mediated interactions between amino acid residues. Energy functions that describe the interactions can be derived either from physical principles (physical‐based energy function) or statistical analysis of known protein structures (knowledge‐based statistical potentials). It is commonly believed that statistical potentials are appropriate for coarse‐grained representation of proteins but are not as accurate as physical‐based potentials when atomic resolution is required. Several recent applications of physical‐based energy functions to loop selections appear to support this view. In this article, we apply a recently developed DFIRE‐based statistical potential to three different loop decoy sets (RAPPER, Jacobson, and Forrest‐Woolf sets). Together with a rotamer library for side‐chain optimization, the performance of DFIRE‐based potential in the RAPPER decoy set (385 loop targets) is comparable to that of AMBER/GBSA for short loops (two to eight residues). The DFIRE is more accurate for longer loops (9 to 12 residues). Similar trend is observed when comparing DFIRE with another physical‐based OPLS/SGB‐NP energy function in the large Jacobson decoy set (788 loop targets). In the Forrest‐Woolf decoy set for the loops of membrane proteins, the DFIRE potential performs substantially better than the combination of the CHARMM force field with several solvation models. The results suggest that a single‐term DFIRE‐statistical energy function can provide an accurate loop prediction at a fraction of computing cost required for more complicate physical‐based energy functions. A Web server for academic users is established for loop selection at the softwares/services section of the Web site http://theory.med.buffalo.edu/.

Fold recognition by concurrent use of solvent accessibility and residue depth

Recognizing the structural similarity without significant sequence identity (called fold recognition) is the key for bridging the gap between the number of known protein sequences and the number of structures solved. Previously, we developed a fold‐recognition method called SP3 which combines sequence‐derived sequence profiles, secondary‐structure profiles and residue‐depth dependent, structure‐derived sequence profiles. The use of residue‐depth‐dependent profiles makes SP3 one of the best automatic predictors in CASP 6. Because residue depth (RD) and solvent accessible surface area (solvent accessibility) are complementary in describing the exposure of a residue to solvent, we test whether or not incorporation of solvent‐accessibility profiles into SP3 could further increase the accuracy of fold recognition. The resulting method, called SP4, was tested in SALIGN benchmark for alignment accuracy and Lindahl, LiveBench 8 and CASP7 blind prediction for fold recognition sensitivity and model‐structure accuracy. For remote homologs, SP4 is found to consistently improve over SP3 in the accuracy of sequence alignment and predicted structural models as well as in the sensitivity of fold recognition. Our result suggests that RD and solvent accessibility can be used concurrently for improving the accuracy and sensitivity of fold recognition. The SP4 server and its local usage package are available on http://sparks.informatics.iupui.edu/SP4. Proteins 2007.

SP5: Improving Protein Fold Recognition by Using Torsion Angle Profiles and Profile-Based Gap Penalty Model

How to recognize the structural fold of a protein is one of the challenges in protein structure prediction. We have developed a series of single (non-consensus) methods (SPARKS, SP2, SP3, SP4) that are based on weighted matching of two to four sequence and structure-based profiles. There is a robust improvement of the accuracy and sensitivity of fold recognition as the number of matching profiles increases. Here, we introduce a new profile-profile comparison term based on real-value dihedral torsion angles. Together with updated real-value solvent accessibility profile and a new variable gap-penalty model based on fractional power of insertion/deletion profiles, the new method (SP5) leads to a robust improvement over previous SP method. There is a 2% absolute increase (5% relative improvement) in alignment accuracy over SP4 based on two independent benchmarks. Moreover, SP5 makes 7% absolute increase (22% relative improvement) in success rate of recognizing correct structural folds, and 32% relative improvement in model accuracy of models within the same fold in Lindahl benchmark. In addition, modeling accuracy of top-1 ranked models is improved by 12% over SP4 for the difficult targets in CASP 7 test set. These results highlight the importance of harnessing predicted structural properties in challenging remote-homolog recognition. The SP5 server is available at http://sparks.informatics.iupui.edu.

What is a desirable statistical energy function for proteins and how can it be obtained

Can one obtain a physical energy function for proteins from statistical analysis of protein structures? A direct answer to this question is likely “no”. A less demanding question is whether one can produce a statistical energy function that has the desirable features of a physical-based energy function. Such a desirable energy function would be founded on a physical basis with few or no adjustable parameters, reproduce the known physical characters of amino acid residues, be mostly database independent and transferable, and, more importantly, reasonably accurate in various applications. In this review, we show how such a desirable energy function can be obtained via introducing a simple physical-based reference state called DRIRE (Distance-scaled, Finite, Ideal-gas Reference state).

Docking prediction using biological information, ZDOCK sampling technique, and clustering guided by the DFIRE statistical energy function

We entered the CAPRI experiment during the middle of Round 4 and have submitted predictions for all 6 targets released since then. We used the following procedures for docking prediction: (1) the identification of possible binding region(s) of a target based on known biological information, (2) rigid‐body sampling around the binding region(s) by using the docking program ZDOCK, (3) ranking of the sampled complex conformations by employing the DFIRE‐based statistical energy function, (4) clustering based on pairwise root‐mean‐square distance and the DFIRE energy, and (5) manual inspection and relaxation of the side‐chain conformations of the top‐ranked structures by geometric constraint. Reasonable predictions were made for 4 of the 6 targets. The best fraction of native contacts within the top 10 models are 89.1% for Target 12, 54.3% for Target 13, 29.3% for Target 14, and 94.1% for Target 18. The origin of successes and failures is discussed. Proteins 2005;60:314–318.