Common physical basis of macromolecule-binding sites in proteins

ProteinsPlus: a web portal for structure analysis of macromolecules

With currently more than 126 000 publicly available structures and an increasing growth rate, the Protein Data Bank constitutes a rich data source for structure-driven research in fields like drug discovery, crop science and biotechnology in general. Typical workflows in these areas involve manifold computational tools for the analysis and prediction of molecular functions. Here, we present the ProteinsPlus web server that offers a unified easy-to-use interface to a broad range of tools for the early phase of structure-based molecular modeling. This includes solutions for commonly required pre-processing tasks like structure quality assessment (EDIA), hydrogen placement (Protoss) and the search for alternative conformations (SIENA). Beyond that, it also addresses frequent problems as the generation of 2D-interaction diagrams (PoseView), protein-protein interface classification (HyPPI) as well as automatic pocket detection and druggablity assessment (DoGSiteScorer). The unified ProteinsPlus interface covering all featured approaches provides various facilities for intuitive input and result visualization, case-specific parameterization and download options for further processing. Moreover, its generalized workflow allows the user a quick familiarization with the different tools. ProteinsPlus also stores the calculated results temporarily for future request and thus facilitates convenient result communication and re-access. The server is freely available at http://proteins.plus.

Removal of the Side Chain at the Active-Site Serine by a Glycine Substitution Increases the Stability of a Wide Range of Serine β-Lactamases by Relieving Steric Strain

Serine β-lactamases are bacterial enzymes that hydrolyze β-lactam antibiotics. They utilize an active-site serine residue as a nucleophile, forming an acyl-enzyme intermediate during hydrolysis. In this study, thermal denaturation experiments as well as X-ray crystallography were performed to test the effect of substitution of the catalytic serine with glycine on protein stability in serine β-lactamases. Six different enzymes comprising representatives from each of the three classes of serine β-lactamases were examined, including TEM-1, CTX-M-14, and KPC-2 of class A, P99 of class C, and OXA-48 and OXA-163 of class D. For each enzyme, the wild type and a serine-to-glycine mutant were evaluated for stability. The glycine mutants all exhibited enhanced thermostability compared to that of the wild type. In contrast, alanine substitutions of the catalytic serine in TEM-1, OXA-48, and OXA-163 did not alter stability, suggesting removal of the Cβ atom is key to the stability increase associated with the glycine mutants. The X-ray crystal structures of P99 S64G, OXA-48 S70G and S70A, and OXA-163 S70G suggest that removal of the side chain of the catalytic serine releases steric strain to improve enzyme stability. Additionally, analysis of the torsion angles at the nucleophile position indicates that the glycine mutants exhibit improved distance and angular parameters of the intrahelical hydrogen bond network compared to those of the wild-type enzymes, which is also consistent with increased stability. The increased stability of the mutants indicates that the enzyme pays a price in stability for the presence of a side chain at the catalytic serine position but that the cost is necessary in that removal of the serine drastically impairs function. These findings support the stability-function hypothesis, which states that active-site residues are optimized for substrate binding and catalysis but that the requirements for catalysis are often not consistent with the requirements for optimal stability.

Identifying RNA-binding residues based on evolutionary conserved structural and energetic features

Increasing numbers of protein structures are solved each year, but many of these structures belong to proteins whose sequences are homologous to sequences in the Protein Data Bank. Nevertheless, the structures of homologous proteins belonging to the same family contain useful information because functionally important residues are expected to preserve physico-chemical, structural and energetic features. This information forms the basis of our method, which detects RNA-binding residues of a given RNA-binding protein as those residues that preserve physico-chemical, structural and energetic features in its homologs. Tests on 81 RNA-bound and 35 RNA-free protein structures showed that our method yields a higher fraction of true RNA-binding residues (higher precision) than two structure-based and two sequence-based machine-learning methods. Because the method requires no training data set and has no parameters, its precision does not degrade when applied to 'novel' protein sequences unlike methods that are parameterized for a given training data set. It was used to predict the 'unknown' RNA-binding residues in the C-terminal RNA-binding domain of human CPEB3. The two predicted residues, F430 and F474, were experimentally verified to bind RNA, in particular F430, whose mutation to alanine or asparagine nearly abolished RNA binding. The method has been implemented in a webserver called DR_bind1, which is freely available with no login requirement at http://drbind.limlab.ibms.sinica.edu.tw.

Localized frustration and binding-induced conformational change in recognition of 5S RNA by TFIIIA zinc finger

Protein TFIIIA is composed of nine tandemly arranged Cys2His2 zinc fingers. It can bind either to the 5S RNA gene as a transcription factor or to the 5S RNA transcript as a chaperone. Although structural and biochemical data provided valuable information on the recognition between the TFIIIIA and the 5S DNA/RNA, the involved conformational motions and energetic factors contributing to the binding affinity and specificity remain unclear. In this work, we conducted MD simulations and MM/GBSA calculations to investigate the binding-induced conformational changes in the recognition of the 5S RNA by the central three zinc fingers of TFIIIA and the energetic factors that influence the binding affinity and specificity at an atomistic level. Our results revealed drastic interdomain conformational changes between these three zinc fingers, involving the exposure/burial of several crucial DNA/RNA binding residues, which can be related to the competition between DNA and RNA for the binding of TFIIIA. We also showed that the specific recognition between finger 4/finger 6 and the 5S RNA introduces frustrations to the nonspecific interactions between finger 5 and the 5S RNA, which may be important to achieve optimal binding affinity and specificity.

Protein-Protein Docking Using EMAP in CHARMM and Support Vector Machine: Application to Ab/Ag Complexes

In this work, we have (i) evaluated the ability of the EMAP method implemented in the CHARMM program to generate the correct conformation of Ab/Ag complex structures and (ii) developed a support vector machine (SVM) classifier to detect native conformations among the thousands of refined Ab/Ag configurations using the individual components of the binding free energy based on a thermodynamic cycle as input features in training the SVM. Tests on 24 Ab/Ag complexes from the protein-protein docking benchmark version 3.0 showed that based on CAPRI evaluation criteria, EMAP could generate medium-quality native conformations in each case. Furthermore, the SVM classifier could rank medium/high-quality native conformations mostly in the top six among the thousands of refined Ab/Ag configurations. Thus, Ab-Ag docking can be performed using different levels of protein representations, from grid-based (EMAP) to polar hydrogen (united-atom) to all-atom representation within the same program. The scripts used and the trained SVM are available at the www.charmm.org forum script repository.

Impact of mutations on the allosteric conformational equilibrium

Allostery in a protein involves effector binding at an allosteric site that changes the structure and/or dynamics at a distant, functional site. In addition to the chemical equilibrium of ligand binding, allostery involves a conformational equilibrium between one protein substate that binds the effector and a second substate that less strongly binds the effector. We run molecular dynamics simulations using simple, smooth energy landscapes to sample specific ligand-induced conformational transitions, as defined by the effector-bound and effector-unbound protein structures. These simulations can be performed using our web server (http://salilab.org/allosmod/). We then develop a set of features to analyze the simulations and capture the relevant thermodynamic properties of the allosteric conformational equilibrium. These features are based on molecular mechanics energy functions, stereochemical effects, and structural/dynamic coupling between sites. Using a machine-learning algorithm on a data set of 10 proteins and 179 mutations, we predict both the magnitude and the sign of the allosteric conformational equilibrium shift by the mutation; the impact of a large identifiable fraction of the mutations can be predicted with an average unsigned error of 1k(B)T. With similar accuracy, we predict the mutation effects for an 11th protein that was omitted from the initial training and testing of the machine-learning algorithm. We also assess which calculated thermodynamic properties contribute most to the accuracy of the prediction.

DR_bind: a web server for predicting DNA-binding residues from the protein structure based on electrostatics, evolution and geometry

DR_bind is a web server that automatically predicts DNA-binding residues, given the respective protein structure based on (i) electrostatics, (ii) evolution and (iii) geometry. In contrast to machine-learning methods, DR_bind does not require a training data set or any parameters. It predicts DNA-binding residues by detecting a cluster of conserved, solvent-accessible residues that are electrostatically stabilized upon mutation to Asp⁻/Glu⁻. The server requires as input the DNA-binding protein structure in PDB format and outputs a downloadable text file of the predicted DNA-binding residues, a 3D visualization of the predicted residues highlighted in the given protein structure, and a downloadable PyMol script for visualization of the results. Calibration on 83 and 55 non-redundant DNA-bound and DNA-free protein structures yielded a DNA-binding residue prediction accuracy/precision of 90/47% and 88/42%, respectively. Since DR_bind does not require any training using protein–DNA complex structures, it may predict DNA-binding residues in novel structures of DNA-binding proteins resulting from structural genomics projects with no conservation data. The DR_bind server is freely available with no login requirement at http://dnasite.limlab.ibms.sinica.edu.tw.

Hidden relationship between conserved residues and locally conserved phosphate-binding structures in NAD(P)-binding proteins

A one-dimensional (1D) motif usually comprises conserved essential residues involved in catalysis, ligand binding, or maintaining a specific structure. However, it cannot be easily detected in proteins with low sequence identity because it is difficult to (1) identify protein sequences suspected to contain the motif, and (2) align sequences with little sequence identity to spot the conserved residues. Here, we present a strategy for discovering phosphate-binding 1D motifs in NAD(P)-binding proteins sharing low sequence identity that overcomes these two hurdles by determining all distinct locally conserved pyrophosphate-binding structures and aligning the same-length sequences comprising each of these structures to identify the conserved residues. We show that the sequence motifs derived from the distinct pyrophosphate-binding structures yield different numbers/spacing of conserved Gly residues. We also show that they depend on the side chain orientations and cofactor type (NAD or NADP). Thus, sequence motifs derived from local similarity of backbone structures without consideration of the cofactor type and/or side chain orientations would reduce their reliability in annotating protein function from sequence alone. The three-dimensional (3D) and 1D motifs comprising conserved residues in nonredundant proteins reveal hidden relationships between the protein structure/function and sequence as well as protein-cofactor interactions.

The role of RNA in mammalian prion protein conversion

Prion diseases remain a challenge to modern science in the 21st century because of their capacity for transmission without an encoding nucleic acid. PrP(Sc), the infectious and alternatively folded form of the PrP prion protein, is capable of self-replication, using PrP(C), the properly folded form of PrP, as a template. This process is associated with neuronal death and the clinical manifestation of prion-based diseases. Unfortunately, little is known about the mechanisms that drive this process. Over the last decade, the theory that a nucleic acid, such as an RNA molecule, might be involved in the process of prion structural conversion has become more widely accepted; such a nucleic acid would act as a catalyst rather than encoding genetic information. Significant amounts of data regarding the interactions of PrP with nucleic acids have created a new foundation for understanding prion conversion and the transmission of prion diseases. Our knowledge has been enhanced by the characterization of a large group of RNA molecules known as non-coding RNAs, which execute a series of important cellular functions, from transcriptional regulation to the modulation of neuroplasticity. The RNA-binding properties of PrP along with the competition with other polyanions, such as glycosaminoglycans and nucleic acid aptamers, open new avenues for therapy.

Computational methods for prediction of protein-RNA interactions

Understanding the molecular mechanism of protein-RNA recognition and complex formation is a major challenge in structural biology. Unfortunately, the experimental determination of protein-RNA complexes by X-ray crystallography and nuclear magnetic resonance spectroscopy (NMR) is tedious and difficult. Alternatively, protein-RNA interactions can be predicted by computational methods. Although less accurate than experimental observations, computational predictions can be sufficiently accurate to prompt functional hypotheses and guide experiments, e.g. to identify individual amino acid or nucleotide residues. In this article we review 10 methods for predicting protein-RNA interactions, seven of which predict RNA-binding sites from protein sequences and three from structures. We also developed a meta-predictor that uses the output of top three sequence-based primary predictors to calculate a consensus prediction, which outperforms all the primary predictors. In order to fully cover the software for predicting protein-RNA interactions, we also describe five methods for protein-RNA docking. The article highlights the strengths and shortcomings of existing methods for the prediction of protein-RNA interactions and provides suggestions for their further development.

From face to interface recognition: a differential geometric approach to distinguish DNA from RNA binding surfaces

Protein nucleic acid interactions play a critical role in all steps of the gene expression pathway. Nucleic acid (NA) binding proteins interact with their partners, DNA or RNA, via distinct regions on their surface that are characterized by an ensemble of chemical, physical and geometrical properties. In this study, we introduce a novel methodology based on differential geometry, commonly used in face recognition, to characterize and predict NA binding surfaces on proteins. Applying the method on experimentally solved three-dimensional structures of proteins we successfully classify double-stranded DNA (dsDNA) from single-stranded RNA (ssRNA) binding proteins, with 83% accuracy. We show that the method is insensitive to conformational changes that occur upon binding and can be applicable for de novo protein-function prediction. Remarkably, when concentrating on the zinc finger motif, we distinguish successfully between RNA and DNA binding interfaces possessing the same binding motif even within the same protein, as demonstrated for the RNA polymerase transcription-factor, TFIIIA. In conclusion, we present a novel methodology to characterize protein surfaces, which can accurately tell apart dsDNA from an ssRNA binding interfaces. The strength of our method in recognizing fine-tuned differences on NA binding interfaces make it applicable for many other molecular recognition problems, with potential implications for drug design.

A structural-alphabet-based strategy for finding structural motifs across protein families

Proteins with insignificant sequence and overall structure similarity may still share locally conserved contiguous structural segments; i.e. structural/3D motifs. Most methods for finding 3D motifs require a known motif to search for other similar structures or functionally/structurally crucial residues. Here, without requiring a query motif or essential residues, a fully automated method for discovering 3D motifs of various sizes across protein families with different folds based on a 16-letter structural alphabet is presented. It was applied to structurally non-redundant proteins bound to DNA, RNA, obligate/non-obligate proteins as well as free DNA-binding proteins (DBPs) and proteins with known structures but unknown function. Its usefulness was illustrated by analyzing the 3D motifs found in DBPs. A non-specific motif was found with a ‘corner’ architecture that confers a stable scaffold and enables diverse interactions, making it suitable for binding not only DNA but also RNA and proteins. Furthermore, DNA-specific motifs present ‘only’ in DBPs were discovered. The motifs found can provide useful guidelines in detecting binding sites and computational protein redesign.

Redesign of high-affinity nonspecific nucleases with altered sequence preference

It is of crucial importance to elucidate the underlying principles that govern the binding affinity and selectivity between proteins and DNA. Here we use the nuclease domain of Colicin E7 (nColE7) as a model system to generate redesigned nucleases with improved DNA-binding affinities. ColE7 is a bacterial toxin, bearing a nonspecific endonuclease domain with a preference for hydrolyzing DNA phosphodiester bonds at the 3'O-side after thymine and adenine; i.e., it prefers Thy and Ade at the -1 site. Using systematic computational screening, six nColE7 mutants were predicted to bind DNA with high affinity. Five of the redesigned single-point mutants were constructed and purified, and four mutants had a 3- to 5-fold higher DNA binding affinity than wild-type nColE7 as measured by fluorescence kinetic assays. Moreover, three of the designed mutants, D493N, D493Q, and D493R, digested DNA with an increased preference for guanine at +3 sites compared to the wild-type enzyme, as shown by DNA footprint assays. X-ray structure determination of the ColE7 mutant D493Q-DNA complex in conjunction with structural and free energy decomposition analyses provides a physical basis for the improved protein-DNA interactions: Replacing D493 at the protein-DNA interface with an amino acid residue that can maintain the native hydrogen bonds removes the unfavorable electrostatic repulsion between the negatively charged carboxylate and DNA phosphate groups. These results show that computational screening combined with biochemical, structural, and free energy analyses provide a useful means for generating redesigned nucleases with a higher DNA-binding affinity and altered sequence preferences in DNA cleavage.

Optimizing protein stability in vivo

Identifying mutations that stabilize proteins is challenging because most substitutions are destabilizing. In addition to being of immense practical utility, the ability to evolve protein stability in vivo may indicate how evolution has formed today's protein sequences. Here we describe a genetic selection that directly links the in vivo stability of proteins to antibiotic resistance. It allows the identification of stabilizing mutations within proteins. The large majority of mutants selected for improved antibiotic resistance are stabilized both thermodynamically and kinetically, indicating that similar principles govern stability in vivo and in vitro. The approach requires no prior structural or functional knowledge and allows selection for stability without a need to maintain function. Mutations that enhance thermodynamic stability of the protein Im7 map overwhelmingly to surface residues involved in binding to colicin E7, showing how the evolutionary pressures that drive Im7-E7 complex formation have compromised the stability of the isolated Im7 protein.

Structural bases for stability-function tradeoffs in antibiotic resistance

Preorganization of enzyme active sites for substrate recognition typically comes at a cost to the stability of the folded form of the protein; consequently, enzymes can be dramatically stabilized by substitutions that attenuate the size and preorganization "strain" of the active site. How this stability-activity tradeoff constrains enzyme evolution has remained less certain, and it is unclear whether one should expect major stability insults as enzymes mutate towards new activities or how these new activities manifest structurally. These questions are both germane and easy to study in beta-lactamases, which are evolving on the timescale of years to confer resistance to an ever-broader spectrum of beta-lactam antibiotics. To explore whether stability is a substantial constraint on this antibiotic resistance evolution, we investigated extended-spectrum mutants of class C beta-lactamases, which had evolved new activity versus third-generation cephalosporins. Five mutant enzymes had between 100-fold and 200-fold increased activity against the antibiotic cefotaxime in enzyme assays, and the mutant enzymes all lost thermodynamic stability (from 1.7 kcal mol(-)(1) to 4.1 kcal mol(-)(1)), consistent with the stability-function hypothesis. Intriguingly, several of the substitutions were 10-20 A from the catalytic serine; the question of how they conferred extended-spectrum activity arose. Eight structures, including complexes with inhibitors and extended-spectrum antibiotics, were determined by X-ray crystallography. Distinct mechanisms of action, including changes in the flexibility and ground-state structures of the enzyme, are revealed for each mutant. These results explain the structural bases for the antibiotic resistance conferred by these substitutions and their corresponding decrease in protein stability, which will constrain the evolution of new antibiotic resistance.

Prediction of hot spot residues at protein-protein interfaces by combining machine learning and energy-based methods

BACKGROUND

Alanine scanning mutagenesis is a powerful experimental methodology for investigating the structural and energetic characteristics of protein complexes. Individual amino-acids are systematically mutated to alanine and changes in free energy of binding (DeltaDeltaG) measured. Several experiments have shown that protein-protein interactions are critically dependent on just a few residues ("hot spots") at the interface. Hot spots make a dominant contribution to the free energy of binding and if mutated they can disrupt the interaction. As mutagenesis studies require significant experimental efforts, there is a need for accurate and reliable computational methods. Such methods would also add to our understanding of the determinants of affinity and specificity in protein-protein recognition.

RESULTS

We present a novel computational strategy to identify hot spot residues, given the structure of a complex. We consider the basic energetic terms that contribute to hot spot interactions, i.e. van der Waals potentials, solvation energy, hydrogen bonds and Coulomb electrostatics. We treat them as input features and use machine learning algorithms such as Support Vector Machines and Gaussian Processes to optimally combine and integrate them, based on a set of training examples of alanine mutations. We show that our approach is effective in predicting hot spots and it compares favourably to other available methods. In particular we find the best performances using Transductive Support Vector Machines, a semi-supervised learning scheme. When hot spots are defined as those residues for which DeltaDeltaG >or= 2 kcal/mol, our method achieves a precision and a recall respectively of 56% and 65%.

CONCLUSION

We have developed an hybrid scheme in which energy terms are used as input features of machine learning models. This strategy combines the strengths of machine learning and energy-based methods. Although so far these two types of approaches have mainly been applied separately to biomolecular problems, the results of our investigation indicate that there are substantial benefits to be gained by their integration.