Fast dynamics perturbation analysis for prediction of protein functional sites

Background  

We present a fast version of the dynamics perturbation analysis (DPA) algorithm to predict functional sites in protein structures. The original DPA algorithm finds regions in proteins where interactions cause a large change in the protein conformational distribution, as measured using the relative entropy D _x . Such regions are associated with functional sites.     

Quantifying allosteric effects in proteins

In allosteric regulation, protein activity is altered when ligand binding causes changes in the protein conformational distribution. Little is known about which aspects of protein design lead to effective allosteric regulation, however. To increase understanding of the relation between protein structure and allosteric effects, we have developed theoretical tools to quantify the influence of protein-ligand interactions on probability distributions of reaction rates and protein conformations. We define the rate divergence, Dk, and the allosteric potential, Dx, as the Kullback-Leibler divergence between either the reaction-rate distributions or protein conformational distributions with and without the ligand bound. We then define Dx as the change in the conformational distribution of the combined protein/ligand system, derive Dx in the harmonic approximation, and identify contributions from 3 separate terms: the first term, D[stackxomega], results from changes in the eigenvalue spectrum; the second term, D[stackxDeltax], results from changes in the mean conformation; and the third term, Dxv, corresponds to changes in the eigenvectors. Using normal modes analysis, we have calculated these terms for a natural interaction between lysozyme and the ligand tri-N-acetyl-D-glucosamine, and compared them with calculations for a large number of simulated random interactions. The comparison shows that interactions in the known binding-site are associated with large values of Dxv. The results motivate using allosteric potential calculations to predict functional binding sites on proteins, and suggest the possibility that, in Nature, effective ligand interactions occur at intrinsic control points at which binding induces a relatively large change in the protein conformational distribution.     

Predicting permanent and transient protein–protein interfaces

Protein–protein interactions (PPIs) are involved in diverse functions in a cell. To optimize functional roles of interactions, proteins interact with a spectrum of binding affinities. Interactions are conventionally classified into permanent and transient, where the former denotes tight binding between proteins that result in strong complexes, whereas the latter compose of relatively weak interactions that can dissociate after binding to regulate functional activity at specific time point. Knowing the type of interactions has significant implications for understanding the nature and function of PPIs. In this study, we constructed amino acid substitution models that capture mutation patterns at permanent and transient type of protein interfaces, which were found to be different with statistical significance. Using the substitution models, we developed a novel computational method that predicts permanent and transient protein binding interfaces (PBIs) in protein surfaces. Without knowledge of the interacting partner, the method uses a single query protein structure and a multiple sequence alignment of the sequence family. Using a large dataset of permanent and transient proteins, we show that our method, BindML+, performs very well in protein interface classification. A very high area under the curve (AUC) value of 0.957 was observed when predicted protein binding sites were classified. Remarkably, near prefect accuracy was achieved with an AUC of 0.991 when actual binding sites were classified. The developed method will be also useful for protein design of permanent and transient PBIs. © Proteins 2013. © 2012 Wiley Periodicals, Inc.     

Modeling protein–nucleic acid complexes with extremely large conformational changes using Flex-LZerD

Proteins and nucleic acids are key components in many processes in living cells, and interactions between proteins and nucleic acids are often crucial pathway components. In many cases, large flexibility of proteins as they interact with nucleic acids is key to their function. To understand the mechanisms of these processes, it is necessary to consider the 3D atomic structures of such protein–nucleic acid complexes. When such structures are not yet experimentally determined, protein docking can be used to computationally generate useful structure models. However, such docking has long had the limitation that the consideration of flexibility is usually limited to small movements or to small structures. We previously developed a method of flexible protein docking which could model ordered proteins which undergo large-scale conformational changes, which we also showed was compatible with nucleic acids. Here, we elaborate on the ability of that pipeline, Flex-LZerD, to model specifically interactions between proteins and nucleic acids, and demonstrate that Flex-LZerD can model more interactions and types of conformational change than previously shown.     

Text mining improves prediction of protein functional sites

We present an approach that integrates protein structure analysis and text mining for protein functional site prediction, called LEAP-FS (Literature Enhanced Automated Prediction of Functional Sites). The structure analysis was carried out using Dynamics Perturbation Analysis (DPA), which predicts functional sites at control points where interactions greatly perturb protein vibrations. The text mining extracts mentions of residues in the literature, and predicts that residues mentioned are functionally important. We assessed the significance of each of these methods by analyzing their performance in finding known functional sites (specifically, small-molecule binding sites and catalytic sites) in about 100,000 publicly available protein structures. The DPA predictions recapitulated many of the functional site annotations and preferentially recovered binding sites annotated as biologically relevant vs. those annotated as potentially spurious. The text-based predictions were also substantially supported by the functional site annotations: compared to other residues, residues mentioned in text were roughly six times more likely to be found in a functional site. The overlap of predictions with annotations improved when the text-based and structure-based methods agreed. Our analysis also yielded new high-quality predictions of many functional site residues that were not catalogued in the curated data sources we inspected. We conclude that both DPA and text mining independently provide valuable high-throughput protein functional site predictions, and that integrating the two methods using LEAP-FS further improves the quality of these predictions.     

An accurate,sensitive, and scalable method to identify functional sites in protein structures

Functional sites determine the activity and interactions of proteins and as such constitute the targets of most drugs. However, the exponential growth of sequence and structure data far exceeds the ability of experimental techniques to identify their locations and key amino acids. To fill this gap we developed a computational Evolutionary Trace method that ranks the evolutionary importance of amino acids in protein sequences. Studies show that the best-ranked residues form fewer and larger structural clusters than expected by chance and overlap with functional sites, but until now the significance of this overlap has remained qualitative. Here, we use 86 diverse protein structures, including 20 determined by the structural genomics initiative, to show that this overlap is a recurrent and statistically significant feature. An automated ET correctly identifies seven of ten functional sites by the least favorable statistical measure, and nine of ten by the most favorable one. These results quantitatively demonstrate that a large fraction of functional sites in the proteome may be accurately identified from sequence and structure. This should help focus structure-function studies, rational drug design, protein engineering, and functional annotation to the relevant regions of a protein.     

Structure/function analysis of the PII signal transduction protein of Escherichia coli: genetic separation of interactions with protein receptors.

The PII protein, encoded by glnB, is known to interact with three bifunctional signal transducing enzymes (uridylyltransferase/uridylyl-removing enzyme, adenylyltransferase, and the kinase/phosphatase nitrogen regulator II [NRII or NtrB]) and three small-molecule effectors, glutamate, 2-ketoglutarate, and ATP. We constructed 15 conservative alterations of PII by site-specific mutagenesis of glnB and also isolated three random glnB mutants affecting nitrogen regulation. The abilities of the 18 altered PII proteins to interact with the PII receptors and the small-molecule effectors 2-ketoglutarate and ATP were examined by using purified components. Results with certain mutants suggested that the specificity for the various protein receptors was altered; other mutations affected the interaction with all three receptors and the small-molecule effectors to various extents. The apex of the large solvent-exposed T loop of the PII protein (P. D. Carr, E. Cheah, P. M. Suffolk, S. G. Vasudevan, N. E. Dixon, and D. L. Ollis, Acta Crytallogr. Sect. D 52:93-104, 1996), which includes the site of PII modification, was not required for the binding of small-molecule effectors but was necessary for the interaction with all three receptors. Mutations altering residues of this loop or affecting the nearby B loop of PII, which line a cleft between monomers in the trimeric PII, affected the interactions with protein receptors and the binding of small-molecule ligands. Thus, our results support the predictions made from structural studies that the exposed loops of PII and cleft formed at their interface are the sites of regulatory interactions.     

Coupling Protein Side-Chain and Backbone Flexibility Improves the Re-design of Protein-Ligand Specificity

Interactions between small molecules and proteins play critical roles in regulating and facilitating diverse biological functions, yet our ability to accurately re-engineer the specificity of these interactions using computational approaches has been limited. One main difficulty, in addition to inaccuracies in energy functions, is the exquisite sensitivity of protein–ligand interactions to subtle conformational changes, coupled with the computational problem of sampling the large conformational search space of degrees of freedom of ligands, amino acid side chains, and the protein backbone. Here, we describe two benchmarks for evaluating the accuracy of computational approaches for re-engineering protein-ligand interactions: (i) prediction of enzyme specificity altering mutations and (ii) prediction of sequence tolerance in ligand binding sites. After finding that current state-of-the-art “fixed backbone” design methods perform poorly on these tests, we develop a new “coupled moves” design method in the program Rosetta that couples changes to protein sequence with alterations in both protein side-chain and protein backbone conformations, and allows for changes in ligand rigid-body and torsion degrees of freedom. We show significantly increased accuracy in both predicting ligand specificity altering mutations and binding site sequences. These methodological improvements should be useful for many applications of protein – ligand design. The approach also provides insights into the role of subtle conformational adjustments that enable functional changes not only in engineering applications but also in natural protein evolution.     

Investigation of cathepsin D–mAb interactions using a combined experimental and computational tool set

Cathepsin D has been identified as a challenge to remove in downstream bioprocessing of monoclonal antibodies (mAbs) due to interactions with some mAbs. This study focused on investigating the mechanisms of interaction between cathepsin D and two industrial mAbs using a combined experimental and computational approach. Surface plasmon resonance was used to study the impact of pH and salt concentration on these protein–protein interactions. While salt had a moderate effect on the interactions with one of the mAbs, the other mAb demonstrated highly salt-dependent association behavior. Cathepsin D binding to the mAbs was also seen to be highly pH dependent, with operation at pH 9 resulting in a significant decrease in the binding affinity. Protein–protein docking simulations identified three interaction sites on both mAbs; near the complementarity determining region (CDR), in the hinge, and in the C_H3 domain. In contrast, only one face of cathepsin D was identified to interact with all the three sites on the mAbs. Surface property analysis revealed that the binding regions on the mAbs contained strong hydrophobic clusters and were predominantly negatively charged. In contrast, the binding site on cathepsin D was determined to be highly positively charged and hydrophobic, indicating that these protein–protein interactions were likely due to a combination of hydrophobic and electrostatic interactions. Finally, covalent crosslinking coupled with mass spectrometry was used to validate the docking predictions and to further investigate the regions of interaction involved in mAb–cathepsin D binding. A strong agreement was observed between the two approaches, and the CDR loops were identified to be important for cathepsin D interactions. This study establishes a combined experimental and computational platform that can be used to probe mAb–host cell protein (HCP) interactions of importance in biomanufacturing.     

Host pathogen protein interactions predicted by comparative modeling

Pathogens have evolved numerous strategies to infect their hosts, while hosts have evolved immune responses and other defenses to these foreign challenges. The vast majority of host-pathogen interactions involve protein-protein recognition, yet our current understanding of these interactions is limited. Here, we present and apply a computational whole-genome protocol that generates testable predictions of host-pathogen protein interactions. The protocol first scans the host and pathogen genomes for proteins with similarity to known protein complexes, then assesses these putative interactions, using structure if available, and, finally, filters the remaining interactions using biological context, such as the stage-specific expression of pathogen proteins and tissue expression of host proteins. The technique was applied to 10 pathogens, including species of Mycobacterium, apicomplexa, and kinetoplastida, responsible for "neglected" human diseases. The method was assessed by (1) comparison to a set of known host-pathogen interactions, (2) comparison to gene expression and essentiality data describing host and pathogen genes involved in infection, and (3) analysis of the functional properties of the human proteins predicted to interact with pathogen proteins, demonstrating an enrichment for functionally relevant host-pathogen interactions. We present several specific predictions that warrant experimental follow-up, including interactions from previously characterized mechanisms, such as cytoadhesion and protease inhibition, as well as suspected interactions in hypothesized networks, such as apoptotic pathways. Our computational method provides a means to mine whole-genome data and is complementary to experimental efforts in elucidating networks of host-pathogen protein interactions.     

Long- and short-range interactions in native protein structures are consistent/minimally frustrated in sequence space

We show that long- and short-range interactions in almost all protein native structures are actually consistent with each other for coarse-grained energy scales; specifically we mean the long-range inter-residue contact energies and the short-range secondary structure energies based on peptide dihedral angles, which are potentials of mean force evaluated from residue distributions observed in protein native structures. This consistency is observed at equilibrium in sequence space rather than in conformational space. Statistical ensembles of sequences are generated by exchanging residues for each of 797 protein native structures with the Metropolis method. It is shown that adding the other category of interaction to either the short- or long-range interactions decreases the means and variances of those energies for essentially all protein native structures, indicating that both interactions consistently work by more-or-less restricting sequence spaces available to one of the interactions. In addition to this consistency, independence by these interaction classes is also indicated by the fact that there are almost no correlations between them when equilibrated using both interactions and significant but small, positive correlations at equilibrium using only one of the interactions. Evidence is provided that protein native sequences can be regarded approximately as samples from the statistical ensembles of sequences with these energy scales and that all proteins have the same effective conformational temperature. Designing protein structures and sequences to be consistent and minimally frustrated among the various interactions is a most effective way to increase protein stability and foldability.     

Conformational changes in DNA‐binding proteins: Relationships with precomplex features and contributions to specificity and stability

Both Proteins and DNA undergo conformational changes in order to form functional complexes and also to facilitate interactions with other molecules. These changes have direct implications for the stability and specificity of the complex, as well as the cooperativity of interactions between multiple entities. In this work, we have extensively analyzed conformational changes in DNA‐binding proteins by superimposing DNA‐bound and unbound pairs of protein structures in a curated database of 90 proteins. We manually examined each of these pairs, unified the authors' annotations, and summarized our observations by classifying conformational changes into six structural categories. We explored a relationship between conformational changes and functional classes, binding motifs, target specificity, biophysical features of unbound proteins, and stability of the complex. In addition, we have also investigated the degree to which the intrinsic flexibility can explain conformational changes in a subset of 52 proteins with high quality coordinate data. Our results indicate that conformational changes in DNA‐binding proteins contribute significantly to both the stability of the complex and the specificity of targets recognized by them. We also conclude that most conformational changes occur in proteins interacting with specific DNA targets, even though unbound protein structures may have sufficient information to interact with DNA in a nonspecific manner. Proteins 2014; 82:841–857. © 2013 Wiley Periodicals, Inc.     

DNA curving and bending in protein–DNA recognition

Most biological events are regulated at the molecular level by site-specific associations between specialized proteins and DNA. These associations may bring distal regions of the genome into functional contact or may lead to the formation of large multisubunit complexes capable of regulating highly site-specific transactional events. It is now believed that sequence-specific protein-DNA recognition and the ability of certain proteins to compete for multiple binding sites is regulated at several levels by the local structure and conformation of the binding partners. These encompass the microstructure of DNA, including its curvature, bending and flexing as well as conformational lability in the DNA-binding domains of the proteins. Possible mechanisms for binding specificity are discussed in the context of specific nucleoprotein systems with particular emphasis given to the roles of DNA conformations in these interactions.     

Prediction of functional sites by analysis of sequence and structure conservation

We present a method for prediction of functional sites in a set of aligned protein sequences. The method selects sites which are both well conserved and clustered together in space, as inferred from the 3D structures of proteins included in the alignment. We tested the method using 86 alignments from the NCBI CDD database, where the sites of experimentally determined ligand and/or macromolecular interactions are annotated. In agreement with earlier investigations, we found that functional site predictions are most successful when overall background sequence conservation is low, such that sites under evolutionary constraint become apparent. In addition, we found that averaging of conservation values across spatially clustered sites improves predictions under certain conditions: that is, when overall conservation is relatively high and when the site in question involves a large macromolecular binding interface. Under these conditions it is better to look for clusters of conserved sites than to look for particular conserved sites.     

The nature of the free energy barriers to two-state folding

We introduce a simple procedure to analyze the temperature dependence of the folding and unfolding rates of two-state proteins. We start from the simple transition-state-like rate expression: k = D(eff)exp(-DeltaG(TS)/RT), in which upper and lower bounds for the intra-chain effective diffusion coefficient (D(eff)) are obtained empirically using the timescales of elementary processes in protein folding. From the changes in DeltaG(TS) as a function of temperature, we calculate enthalpies and heat capacities of activation, together with the more elusive entropies of activation. We then estimate the conformational entropy of the transition state by extrapolation to the temperature at which the solvation entropy vanishes by cancellation between polar and apolar terms. This approach is based on the convergence temperatures for the entropy of solvating apolar (approximately 385 K) and polar groups (approximately 335 K), the assumption that the structural properties of the transition state are somewhere in between the unfolded and folded states, and the established relationship between observed heat capacity and solvent accessibility.1 To circumvent the lack of structural information about transition states, we use the empirically determined heat capacities of activation as constraints to identify the extreme values of the transition state conformational entropy that are consistent with experiment. The application of this simple approach to six two-state folding proteins for which there is temperature-dependent data available in the literature provides important clues about protein folding. For these six proteins, we obtain an average equilibrium cost in conformational entropy of -4.3 cal x mol(-1)K(-1)per residue, which is in close agreement to previous empirical and computational estimates of the same quantity. Furthermore, we find that all these proteins have a conformationally diverse transition state, with more than half of the conformational entropy of the unfolded state. In agreement with predictions from theory and computer simulations, the transition state signals the change from a regime dominated by loss in conformational entropy to one driven by the gain in stabilization free energy (i.e., including protein interactions and solvation effects). Moreover, the height of the barrier is determined by how much stabilization free energy is realized at that point, which is related to the relative contribution of local versus non-local interactions. A remarkable observation is that the fraction of conformational entropy per residue that is present in the transition state is very similar for the six proteins in this study. Based on this commonality, we propose that the observed change in thermodynamic regime is connected to a change in the pattern of structure formation: from one driven by formation of pairwise interactions to one dominated by coupling of the networks of interactions involved in forming the protein core. In this framework, the barrier to two-state folding is crossed when the folding protein reaches a "critical native density" that allows expulsion of remaining interstitial water and consolidation of the core. The principle of critical native density should be general for all two-state proteins, but can accommodate different folding mechanisms depending on the particularities of the structure and sequence.     

Exploring the conformational space of protein loops using a mean field technique with MOLS sampling

We have recently developed a computational technique that uses mutually orthogonal Latin square sampling to explore the conformational space of oligopeptides in an exhaustive manner. In this article, we report its use to analyze the conformational spaces of 120 protein loop sequences in proteins, culled from the PDB, having the length ranging from 5 to 10 residues. The force field used did not have any information regarding the sequences or structures that flanked the loop. The results of the analyses show that the native structure of the loop, as found in the PDB falls at one of the low energy points in the conformational landscape of the sequences. Thus, a large portion of the structural determinants of the loop may be considered intrinsic to the sequence, regardless of either adjacent sequences or structures, or the interactions that the atoms of the loop make with other residues in the protein or in neighboring proteins.     

Dynamic Autoinhibition of the HMGB1 Protein via Electrostatic Fuzzy Interactions of Intrinsically Disordered Regions

Highly negatively charged segments containing only aspartate or glutamate residues (“D/E repeats”) are found in many eukaryotic proteins. For example, the C-terminal 30 residues of the HMGB1 protein are entirely D/E repeats. Using nuclear magnetic resonance (NMR), fluorescence, and computational approaches, we investigated how the D/E repeats causes the autoinhibition of HMGB1 against its specific binding to cisplatin-modified DNA. By varying ionic strength in a wide range (40–900 mM), we were able to shift the conformational equilibrium between the autoinhibited and uninhibited states toward either of them to the full extent. This allowed us to determine the macroscopic and microscopic equilibrium constants for the HMGB1 autoinhibition at various ionic strengths. At a macroscopic level, a model involving the autoinhibited and uninhibited states can explain the salt concentration-dependent binding affinity data. Our data at a microscopic level show that the D/E repeats and other parts of HMGB1 undergo electrostatic fuzzy interactions, each of which is weaker than expected from the macroscopic autoinhibitory effect. This discrepancy suggests that the multivalent nature of the fuzzy interactions enables strong autoinhibition at a macroscopic level despite the relatively weak intramolecular interaction at each site. Both experimental and computational data suggest that the D/E repeats interact preferentially with other intrinsically disordered regions (IDRs) of HMGB1. We also found that mutations mimicking post-translational modifications relevant to nuclear export of HMGB1 can moderately modulate DNA-binding affinity, possibly by impacting the autoinhibition. This study illuminates a functional role of the fuzzy interactions of D/E repeats.     

Calcium-Dependent Interaction of Calmodulin with Human 80S Ribosomes and Polyribosomes

Ribosomes are the protein factories of every living cell. The process of protein translation is highly complex and tightly regulated by a large number of diverse RNAs and proteins. Earlier studies indicate that Ca(2+) plays a role in protein translation. Calmodulin (CaM), a ubiquitous Ca(2+)-binding protein, regulates a large number of proteins participating in many signaling pathways. Several 40S and 60S ribosomal proteins have been identified to interact with CaM, and here, we report that CaM binds with high affinity to 80S ribosomes and polyribosomes in a Ca(2+)-dependent manner. No binding is observed in buffer with 6 mM Mg(2+) and 1 mM EGTA that chelates Ca(2+), suggesting high specificity of the CaM-ribosome interaction dependent on the Ca(2+) induced conformational change of CaM. The interactions between CaM and ribosomes are inhibited by synthetic peptides comprising putative CaM-binding sites in ribosomal proteins S2 and L14. Using a cell-free in vitro translation system, we further found that these synthetic peptides are potent inhibitors of protein synthesis. Our results identify an involvement of CaM in the translational activity of ribosomes.     

Protein/DNA interactions involving ATF/AP1-, CCAAT-, and HiNF-D-related factors in the human H3-ST519 histone promoter: cross-competition with transcription regulatory sites in cell cycle controlled H4 and H1 histone genes.

Protein/DNA interactions of the H3-ST519 histone gene promoter were analyzed in vitro. Using several assays for sequence specificity, we established binding sites for ATF/AP1-, CCAAT-, and HiNF-D related DNA binding proteins. These binding sites correlate with two genomic protein/DNA interaction domains previously established for this gene. We show that each of these protein/DNA interactions has a counterpart in other histone genes: H3-ST519 and H4-F0108 histone genes interact with ATF- and HiNF-D related binding activities, whereas H3-ST519 and H1-FNC16 histone genes interact with the same CCAAT-box binding activity. These factors may function in regulatory coupling of the expression of different histone gene classes. We discuss these results within the context of established and putative protein/DNA interaction sites in mammalian histone genes. This model suggests that heterogeneous permutations of protein/DNA interaction elements, which involve both general and cell cycle regulated DNA binding proteins, may govern the cellular competency to express and coordinately control multiple distinct histone genes.