The subunit interfaces of oligomeric enzymes are conserved to a similar extent to the overall protein sequences

Catalysis and Electron Transfer in De Novo Designed Metalloproteins

One of the hallmark advances in our understanding of metalloprotein function is showcased in our ability to design new, non-native, catalytically active protein scaffolds. This review highlights progress and milestone achievements in the field of de novo metalloprotein design focused on reports from the past decade with special emphasis on de novo designs couched within common subfields of bioinorganic study: heme binding proteins, monometal- and dimetal-containing catalytic sites, and metal-containing electron transfer sites. Within each subfield, we highlight several of what we have identified as significant and important contributions to either our understanding of that subfield or de novo metalloprotein design as a discipline. These reports are placed in context both historically and scientifically. General suggestions for future directions that we feel will be important to advance our understanding or accelerate discovery are discussed.

In silico Approaches for the Design and Optimization of Interfering Peptides Against Protein-Protein Interactions

Large contact surfaces of protein-protein interactions (PPIs) remain to be an ongoing issue in the discovery and design of small molecule modulators. Peptides are intrinsically capable of exploring larger surfaces, stable, and bioavailable, and therefore bear a high therapeutic value in the treatment of various diseases, including cancer, infectious diseases, and neurodegenerative diseases. Given these promising properties, a long way has been covered in the field of targeting PPIs via peptide design strategies. In silico tools have recently become an inevitable approach for the design and optimization of these interfering peptides. Various algorithms have been developed to scrutinize the PPI interfaces. Moreover, different databases and software tools have been created to predict the peptide structures and their interactions with target protein complexes. High-throughput screening of large peptide libraries against PPIs; "hotspot" identification; structure-based and off-structure approaches of peptide design; 3D peptide modeling; peptide optimization strategies like cyclization; and peptide binding energy evaluation are among the capabilities of in silico tools. In the present study, the most recent advances in the field of in silico approaches for the design of interfering peptides against PPIs will be reviewed. The future perspective of the field and its advantages and limitations will also be pinpointed.

The rational discovery of multipurpose inhibitors of the ornithine decarboxylase

Metabolic reprograming is a hallmark of cancer, and the polyamine metabolic network is dysregulated in many cancers. Ornithine decarboxylase (ODC) is a rate-limiting enzyme for polyamine synthesis in the polyamine metabolic network. In many cancer cells, ODC is over-expressed, so this enzyme has been an attracting anti-cancer drug target. In the catalysis axis (pathway), ODC converts ornithine to putrescine. Meanwhile, ODC's activity is regulated by protein-protein interactions (PPIs), including the ODC-OAZ1-AZIN1 PPI axis and its monomer-dimer equilibrium. Previous studies showed that when ODC's activity is inhibited, the PPIs might counteract the inhibition efficiency. Therefore, we proposed that multipurpose inhibitors that can simultaneously inhibit ODC's activity and perturb the PPIs would be very valuable as drug candidates and molecular tools. To discover multipurpose ODC inhibitors, we established a computational pipeline by combining positive screening and negative screening. We used this pipeline for the forward screening of multipurpose ligands that might inhibit ODC's activity, block ODC-OAZ1 interaction and enhance ODC non-functional dimerization. With a combination of different experimental assays, we identified three multipurpose ODC inhibitors. At last, we showed that one of these inhibitors is a promising drug candidate. This work demonstrated that our computational pipeline is useful for discovering multipurpose ODC inhibitors, and multipurpose inhibitors would be very valuable. Similar with ODC, there are a lot of proteins in human proteome that act as both enzymes and PPI components. Therefore, this work is not only presenting new molecular tools for polyamine study, but also providing potential insights and protocols for discovering multipurpose inhibitors to target more important protein targets.

Chemometric Models of Differential Amino Acids at the Navα and Navβ Interface of Mammalian Sodium Channel Isoforms

(1) Background: voltage-gated sodium channels (Na_vs) are integral membrane proteins that allow the sodium ion flux into the excitable cells and initiate the action potential. They comprise an α (Na_vα) subunit that forms the channel pore and are coupled to one or more auxiliary β (Na_vβ) subunits that modulate the gating to a variable extent. (2) Methods: after performing homology in silico modeling for all nine isoforms (Na_v1.1α to Na_v1.9α), the Na_vα and Na_vβ protein-protein interaction (PPI) was analyzed chemometrically based on the primary and secondary structures as well as topological or spatial mapping. (3) Results: our findings reveal a unique isoform-specific correspondence between certain segments of the extracellular loops of the Na_vα subunits. Precisely, loop S5 in domain I forms part of the PPI and assists Na_vβ1 or Na_vβ3 on all nine mammalian isoforms. The implied molecular movements resemble macroscopic springs, all of which explains published voltage sensor effects on sodium channel fast inactivation in gating. (4) Conclusions: currently, the specific functions exerted by the Na_vβ1 or Na_vβ3 subunits on the modulation of Na_vα gating remain unknown. Our work determined functional interaction in the extracellular domains on theoretical grounds and we propose a schematic model of the gating mechanism of fast channel sodium current inactivation by educated guessing.

Protein-protein binding supersites

The lack of a deep understanding of how proteins interact remains an important roadblock in advancing efforts to identify binding partners and uncover the corresponding regulatory mechanisms of the functions they mediate. Understanding protein-protein interactions is also essential for designing specific chemical modifications to develop new reagents and therapeutics. We explored the hypothesis of whether protein interaction sites serve as generic biding sites for non-cognate protein ligands, just as it has been observed for small-molecule-binding sites in the past. Using extensive computational docking experiments on a test set of 241 protein complexes, we found that indeed there is a strong preference for non-cognate ligands to bind to the cognate binding site of a receptor. This observation appears to be robust to variations in docking programs, types of non-cognate protein probes, sizes of binding patches, relative sizes of binding patches and full-length proteins, and the exploration of obligate and non-obligate complexes. The accuracy of the docking scoring function appears to play a role in defining the correct site. The frequency of interaction of unrelated probes recognizing the binding interface was utilized in a simple prediction algorithm that showed accuracy competitive with other state of the art methods.

The choice of sequence homologs included in multiple sequence alignments has a dramatic impact on evolutionary conservation analysis

Motivation

The analysis of sequence conservation patterns has been widely utilized to identify functionally important (catalytic and ligand-binding) protein residues for over a half-century. Despite decades of development, on average state-of-the-art non-template-based functional residue prediction methods must predict ∼25% of a protein's total residues to correctly identify half of the protein's functional site residues. The overwhelming proportion of false positives results in reported 'F-Scores' of ∼0.3. We investigated the limits of current approaches, focusing on the so-far neglected impact of the specific choice of homologs included in multiple sequence alignments (MSAs).

Results

The limits of conservation-based functional residue prediction were explored by surveying the binding sites of 1023 proteins. A straightforward conservation analysis of MSAs composed of randomly selected homologs sampled from a PSI-BLAST search achieves average F-Scores of ∼0.3, a performance matching that reported by state-of-the-art methods, which often consider additional features for the prediction in a machine learning setting. Interestingly, we found that a simple combinatorial MSA sampling algorithm will in almost every case produce an MSA with an optimal set of homologs whose conservation analysis reaches average F-Scores of ∼0.6, doubling state-of-the-art performance. We also show that this is nearly at the theoretical limit of possible performance given the agreement between different binding site definitions. Additionally, we showcase the progress in this direction made by Selection of Alignment by Maximal Mutual Information (SAMMI), an information-theory-based approach to identifying biologically informative MSAs. This work highlights the importance and the unused potential of optimally composed MSAs for conservation analysis.

Supplementary information

Supplementary data are available at Bioinformatics online.

Trypanosomatid Deoxyhypusine Synthase Activity Is Dependent on Shared Active-Site Complementation between Pseudoenzyme Paralogs

Trypanosoma brucei is a neglected tropical disease endemic to Africa. The polyamine spermidine is essential for post-translational hypusine modification of eukaryotic initiation factor 5A (eIF5A), which is catalyzed by deoxyhypusine synthase (TbDHS). In trypanosomatids, deoxyhypusine synthase (DHS) activity is dependent on heterotetramer formation between two paralogs, DHSc and DHSp, both with minimal activity on their own due to missing catalytic residues. We determined the X-ray structure of TbDHS showing a single functional shared active site is formed at the DHSc/DHSp heterodimer interface, with deficiencies in one subunit complemented by the other. Each heterodimer contains two NAD⁺ binding sites, one housed in the functional catalytic site and the second bound in a remnant dead site that lacks key catalytic residues. Functional analysis of these sites by site-directed mutagenesis identified long-range contributions to the catalytic site from the dead site. Differences between trypanosomatid and human DHS that could be exploited for drug discovery were identified.

Structure based sequence analysis of viral and cellular protein assemblies

It is well accepted that, in general, protein structural similarity is strongly related to the amino acid sequence identity. To analyze in great detail the correlation, distribution and variation levels of conserved residues in the protein structure, we analyzed all available high-resolution structural data of 5245 cellular complex-forming proteins and 293 spherical virus capsid proteins (VCPs). We categorized and compare them in terms of protein structural regions. In all cases, the buried core residues are the most conserved, followed by the residues at the protein-protein interfaces. The solvent-exposed surface shows greater sequence variations. Our results provide evidence that cellular monomers and VCPs could be two extremes in the quaternary structural space, with cellular dimers and oligomers in between. Moreover, based on statistical analysis, we detected a distinct group of icosahedral virus families whose capsid proteins seem to evolve much slower than the rest of the protein complexes analyzed in this work.

Computational prediction of protein interfaces: A review of data driven methods

Reliably pinpointing which specific amino acid residues form the interface(s) between a protein and its binding partner(s) is critical for understanding the structural and physicochemical determinants of protein recognition and binding affinity, and has wide applications in modeling and validating protein interactions predicted by high-throughput methods, in engineering proteins, and in prioritizing drug targets. Here, we review the basic concepts, principles and recent advances in computational approaches to the analysis and prediction of protein-protein interfaces. We point out caveats for objectively evaluating interface predictors, and discuss various applications of data-driven interface predictors for improving energy model-driven protein-protein docking. Finally, we stress the importance of exploiting binding partner information in reliably predicting interfaces and highlight recent advances in this emerging direction.

Algorithmic approaches to protein-protein interaction site prediction

Interaction sites on protein surfaces mediate virtually all biological activities, and their identification holds promise for disease treatment and drug design. Novel algorithmic approaches for the prediction of these sites have been produced at a rapid rate, and the field has seen significant advancement over the past decade. However, the most current methods have not yet been reviewed in a systematic and comprehensive fashion. Herein, we describe the intricacies of the biological theory, datasets, and features required for modern protein-protein interaction site (PPIS) prediction, and present an integrative analysis of the state-of-the-art algorithms and their performance. First, the major sources of data used by predictors are reviewed, including training sets, evaluation sets, and methods for their procurement. Then, the features employed and their importance in the biological characterization of PPISs are explored. This is followed by a discussion of the methodologies adopted in contemporary prediction programs, as well as their relative performance on the datasets most recently used for evaluation. In addition, the potential utility that PPIS identification holds for rational drug design, hotspot prediction, and computational molecular docking is described. Finally, an analysis of the most promising areas for future development of the field is presented.

ECMIS: computational approach for the identification of hotspots at protein-protein interfaces

BACKGROUND

Various methods have been developed to computationally predict hotspot residues at novel protein-protein interfaces. However, there are various challenges in obtaining accurate prediction. We have developed a novel method which uses different aspects of protein structure and sequence space at residue level to highlight interface residues crucial for the protein-protein complex formation.

RESULTS

ECMIS (Energetic Conservation Mass Index and Spatial Clustering) algorithm was able to outperform existing hotspot identification methods. It was able to achieve around 80% accuracy with incredible increase in sensitivity and outperforms other existing methods. This method is even sensitive towards the hotspot residues contributing only small-scale hydrophobic interactions.

CONCLUSION

Combination of diverse features of the protein viz. energy contribution, extent of conservation, location and surrounding environment, along with optimized weightage for each feature, was the key for the success of the algorithm. The academic version of the algorithm is available at http://caps.ncbs.res.in/download/ECMIS/ECMIS.zip.

Catalysis by a de novo zinc-mediated protein interface: implications for natural enzyme evolution and rational enzyme engineering

Here we show that a recent computationally designed zinc-mediated protein interface is serendipitously capable of catalyzing carboxyester and phosphoester hydrolysis. Although the original motivation was to design a de novo zinc-mediated protein-protein interaction (called MID1-zinc), we observed in the homodimer crystal structure a small cleft and open zinc coordination site. We investigated if the cleft and zinc site at the designed interface were sufficient for formation of a primitive active site that can perform hydrolysis. MID1-zinc hydrolyzes 4-nitrophenyl acetate with a rate acceleration of 10(5) and a k(cat)/K(M) of 630 M(-1) s(-1) and 4-nitrophenyl phosphate with a rate acceleration of 10(4) and a k(cat)/K(M) of 14 M(-1) s(-1). These rate accelerations by an unoptimized active site highlight the catalytic power of zinc and suggest that the clefts formed by protein-protein interactions are well-suited for creating enzyme active sites. This discovery has implications for protein evolution and engineering: from an evolutionary perspective, three-coordinated zinc at a homodimer interface cleft represents a simple evolutionary path to nascent enzymatic activity; from a protein engineering perspective, future efforts in de novo design of enzyme active sites may benefit from exploring clefts at protein interfaces for active site placement.

A QUANTITATIVE ANALYSIS OF INTERFACIAL AMINO ACID CONSERVATION IN PROTEIN-PROTEIN HETERO COMPLEXES

A long-standing question in molecular biology is whether interfaces of protein-protein complexes are more conserved than the rest of the protein surfaces. Although it has been reported that conservation can be used as an indicator for predicting interaction sites on proteins, there are recent reports stating that the interface regions are only slightly more conserved than the rest of the protein surfaces, with conservation signals not being statistically significant enough for predicting protein-protein binding sites. In order to properly address these controversial reports we have studied a set of 28 well resolved hetero complex structures of proteins that consists of transient and non-transient complexes. The surface positions were classified into four conservation classes and the conservation index of the surface positions was quantitatively analyzed. The results indicate that the surface density of highly conserved positions is significantly higher in the protein-protein interface regions compared with the other regions of the protein surface. However, the average conservation index of the patches in the interface region is not significantly higher compared with other surface regions of the protein structures. This finding demonstrates that the number of conserved residue positions is a more appropriate indicator for predicting protein-protein binding sites than the average conservation index in the interacting region. We have further validated our findings on a set of 59 benchmark complex structures. Furthermore, an analysis of 19 complexes of antigen-antibody interactions shows that there is no conservation of amino acid positions in the interacting regions of these complexes, as expected, with the variable region of the immunoglobulins interacting mostly with the antigens. Interestingly, antigen interacting regions also have a higher number of non-conserved residue positions in the interacting region than the rest of the protein surface.

Improving the prediction of protein binding sites by combining heterogeneous data and Voronoi diagrams

BACKGROUND

Protein binding site prediction by computational means can yield valuable information that complements and guides experimental approaches to determine the structure of protein complexes. Predictions become even more relevant and timely given the current resolution of protein interaction maps, where there is a very large and still expanding gap between the available information on: (i) which proteins interact and (ii) how proteins interact. Proteins interact through exposed residues that present differential physicochemical properties, and these can be exploited to identify protein interfaces.

RESULTS

Here we present VORFFIP, a novel method for protein binding site prediction. The method makes use of broad set of heterogeneous data and defined of residue environment, by means of Voronoi Diagrams that are integrated by a two-steps Random Forest ensemble classifier. Four sets of residue features (structural, energy terms, sequence conservation, and crystallographic B-factors) used in different combinations together with three definitions of residue environment (Voronoi Diagrams, sequence sliding window, and Euclidian distance) have been analyzed in order to maximize the performance of the method.

CONCLUSIONS

The integration of different forms information such as structural features, energy term, evolutionary conservation and crystallographic B-factors, improves the performance of binding site prediction. Including the information of neighbouring residues also improves the prediction of protein interfaces. Among the different approaches that can be used to define the environment of exposed residues, Voronoi Diagrams provide the most accurate description. Finally, VORFFIP compares favourably to other methods reported in the recent literature.

HomPPI: a class of sequence homology based protein-protein interface prediction methods

BACKGROUND

Although homology-based methods are among the most widely used methods for predicting the structure and function of proteins, the question as to whether interface sequence conservation can be effectively exploited in predicting protein-protein interfaces has been a subject of debate.

RESULTS

We studied more than 300,000 pair-wise alignments of protein sequences from structurally characterized protein complexes, including both obligate and transient complexes. We identified sequence similarity criteria required for accurate homology-based inference of interface residues in a query protein sequence.Based on these analyses, we developed HomPPI, a class of sequence homology-based methods for predicting protein-protein interface residues. We present two variants of HomPPI: (i) NPS-HomPPI (Non partner-specific HomPPI), which can be used to predict interface residues of a query protein in the absence of knowledge of the interaction partner; and (ii) PS-HomPPI (Partner-specific HomPPI), which can be used to predict the interface residues of a query protein with a specific target protein.Our experiments on a benchmark dataset of obligate homodimeric complexes show that NPS-HomPPI can reliably predict protein-protein interface residues in a given protein, with an average correlation coefficient (CC) of 0.76, sensitivity of 0.83, and specificity of 0.78, when sequence homologs of the query protein can be reliably identified. NPS-HomPPI also reliably predicts the interface residues of intrinsically disordered proteins. Our experiments suggest that NPS-HomPPI is competitive with several state-of-the-art interface prediction servers including those that exploit the structure of the query proteins. The partner-specific classifier, PS-HomPPI can, on a large dataset of transient complexes, predict the interface residues of a query protein with a specific target, with a CC of 0.65, sensitivity of 0.69, and specificity of 0.70, when homologs of both the query and the target can be reliably identified. The HomPPI web server is available at http://homppi.cs.iastate.edu/.

CONCLUSIONS

Sequence homology-based methods offer a class of computationally efficient and reliable approaches for predicting the protein-protein interface residues that participate in either obligate or transient interactions. For query proteins involved in transient interactions, the reliability of interface residue prediction can be improved by exploiting knowledge of putative interaction partners.

Beauty is in the eye of the beholder: proteins can recognize binding sites of homologous proteins in more than one way

Understanding the mechanisms of protein-protein interaction is a fundamental problem with many practical applications. The fact that different proteins can bind similar partners suggests that convergently evolved binding interfaces are reused in different complexes. A set of protein complexes composed of non-homologous domains interacting with homologous partners at equivalent binding sites was collected in 2006, offering an opportunity to investigate this point. We considered 433 pairs of protein-protein complexes from the ABAC database (AB and AC binary protein complexes sharing a homologous partner A) and analyzed the extent of physico-chemical similarity at the atomic and residue level at the protein-protein interface. Homologous partners of the complexes were superimposed using Multiprot, and similar atoms at the interface were quantified using a five class grouping scheme and a distance cut-off. We found that the number of interfacial atoms with similar properties is systematically lower in the non-homologous proteins than in the homologous ones. We assessed the significance of the similarity by bootstrapping the atomic properties at the interfaces. We found that the similarity of binding sites is very significant between homologous proteins, as expected, but generally insignificant between the non-homologous proteins that bind to homologous partners. Furthermore, evolutionarily conserved residues are not colocalized within the binding sites of non-homologous proteins. We could only identify a limited number of cases of structural mimicry at the interface, suggesting that this property is less generic than previously thought. Our results support the hypothesis that different proteins can interact with similar partners using alternate strategies, but do not support convergent evolution.

A combinatorial approach to detect coevolved amino acid networks in protein families of variable divergence

Communication between distant sites often defines the biological role of a protein: amino acid long-range interactions are as important in binding specificity, allosteric regulation and conformational change as residues directly contacting the substrate. The maintaining of functional and structural coupling of long-range interacting residues requires coevolution of these residues. Networks of interaction between coevolved residues can be reconstructed, and from the networks, one can possibly derive insights into functional mechanisms for the protein family. We propose a combinatorial method for mapping conserved networks of amino acid interactions in a protein which is based on the analysis of a set of aligned sequences, the associated distance tree and the combinatorics of its subtrees. The degree of coevolution of all pairs of coevolved residues is identified numerically, and networks are reconstructed with a dedicated clustering algorithm. The method drops the constraints on high sequence divergence limiting the range of applicability of the statistical approaches previously proposed. We apply the method to four protein families where we show an accurate detection of functional networks and the possibility to treat sets of protein sequences of variable divergence.

Identification of computational hot spots in protein interfaces: combining solvent accessibility and inter-residue potentials improves the accuracy

MOTIVATION

Hot spots are residues comprising only a small fraction of interfaces yet accounting for the majority of the binding energy. These residues are critical in understanding the principles of protein interactions. Experimental studies like alanine scanning mutagenesis require significant effort; therefore, there is a need for computational methods to predict hot spots in protein interfaces.

RESULTS

We present a new intuitive efficient method to determine computational hot spots based on conservation (C), solvent accessibility [accessible surface area (ASA)] and statistical pairwise residue potentials (PP) of the interface residues. Combination of these features is examined in a comprehensive way to study their effect in hot spot detection. The predicted hot spots are observed to match with the experimental hot spots with an accuracy of 70% and a precision of 64% in Alanine Scanning Energetics Database (ASEdb), and accuracy of 70% and a precision of 73% in Binding Interface Database (BID). Several machine learning methods are also applied to predict hot spots. Performance of our empirical approach exceeds learning-based methods and other existing hot spot prediction methods. Residue occlusion from solvent in the complexes and pairwise potentials are found to be the main discriminative features in hot spot prediction.

CONCLUSION

Our empirical method is a simple approach in hot spot prediction yet with its high accuracy and computational effectiveness. We believe that this method provides insights for the researchers working on characterization of protein binding sites and design of specific therapeutic agents for protein interactions.

AVAILABILITY

The list of training and test sets are available as Supplementary Data at http://prism.ccbb.ku.edu.tr/hotpoint/supplement.doc.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Progress and challenges in predicting protein-protein interaction sites

The identification of protein-protein interaction sites is an essential intermediate step for mutant design and the prediction of protein networks. In recent years a significant number of methods have been developed to predict these interface residues and here we review the current status of the field. Progress in this area requires a clear view of the methodology applied, the data sets used for training and testing the systems, and the evaluation procedures. We have analysed the impact of a representative set of features and algorithms and highlighted the problems inherent in generating reliable protein data sets and in the posterior analysis of the results. Although it is clear that there have been some improvements in methods for predicting interacting sites, several major bottlenecks remain. Proteins in complexes are still under-represented in the structural databases and in particular many proteins involved in transient complexes are still to be crystallized. We provide suggestions for effective feature selection, and make it clear that community standards for testing, training and performance measures are necessary for progress in the field.

Residue conservation in viral capsid assembly

To evaluate the evolutionary constraints placed on viral proteins by the structure and assembly of the capsid, we calculate Shannon entropies in the aligned sequences of 45 polypeptide chains in 32 icosahedral viruses, and relate these entropies to the residue location in the three-dimensional structure of the capsids. Three categories of residues have entropies lower than the chain average implying that they are better conserved than average: residues that are buried within a subunit (the protein core), residues that contain atoms buried at an interface between subunits (the interface core), and residues that contribute to several such interfaces. The interface core is also conserved in homomeric proteins and in transient protein-protein complexes, which have only one interface whereas capsids have many. In capsids, the subunit interfaces implicate most of the polypeptide chain: on average, 66% of the capsid residues are at an interface, 34% at more than one, and 47% at the interface core. Nevertheless, we observe that the degree of residue conservation can vary widely between interfaces within a capsid and between regions within an interface. The interfaces and regions of interfaces that show a low sequence variability are likely to play major roles in the self-assembly of the capsid, with implications on its mechanism that we discuss taking adeno-associated virus as an example.

Structures of the N47A and E109Q mutant proteins of pyruvoyl-dependent arginine decarboxylase from Methanococcus jannaschii

Pyruvoyl-dependent arginine decarboxylase (PvlArgDC) catalyzes the first step of the polyamine-biosynthetic pathway in plants and some archaebacteria. The pyruvoyl group of PvlArgDC is generated by an internal autoserinolysis reaction at an absolutely conserved serine residue in the proenzyme, resulting in two polypeptide chains. Based on the native structure of PvlArgDC from Methanococcus jannaschii, the conserved residues Asn47 and Glu109 were proposed to be involved in the decarboxylation and autoprocessing reactions. N47A and E109Q mutant proteins were prepared and the three-dimensional structure of each protein was determined at 2.0 A resolution. The N47A and E109Q mutant proteins showed reduced decarboxylation activity compared with the wild-type PvlArgDC. These residues may also be important for the autoprocessing reaction, which utilizes a mechanism similar to that of the decarboxylation reaction.

Structural deformation upon protein-protein interaction: a structural alphabet approach

Background

In a number of protein-protein complexes, the 3D structures of bound and unbound partners significantly differ, supporting the induced fit hypothesis for protein-protein binding.

Results

In this study, we explore the induced fit modifications on a set of 124 proteins available in both bound and unbound forms, in terms of local structure. The local structure is described thanks to a structural alphabet of 27 structural letters that allows a detailed description of the backbone. Using a control set to distinguish induced fit from experimental error and natural protein flexibility, we show that the fraction of structural letters modified upon binding is significantly greater than in the control set (36% versus 28%). This proportion is even greater in the interface regions (41%). Interface regions preferentially involve coils. Our analysis further reveals that some structural letters in coil are not favored in the interface. We show that certain structural letters in coil are particularly subject to modifications at the interface, and that the severity of structural change also varies. These information are used to derive a structural letter substitution matrix that summarizes the local structural changes observed in our data set. We also illustrate the usefulness of our approach to identify common binding motifs in unrelated proteins.

Conclusion

Our study provides qualitative information about induced fit. These results could be of help for flexible docking.

Perturbation of the dimer interface of triosephosphate isomerase and its effect on Trypanosoma cruzi

BACKGROUND

Chagas disease affects around 18 million people in the American continent. Unfortunately, there is no satisfactory treatment for the disease. The drugs currently used are not specific and exert serious toxic effects. Thus, there is an urgent need for drugs that are effective. Looking for molecules to eliminate the parasite, we have targeted a central enzyme of the glycolytic pathway: triosephosphate isomerase (TIM). The homodimeric enzyme is catalytically active only as a dimer. Because there are significant differences in the interface of the enzymes from the parasite and humans, we searched for small molecules that specifically disrupt contact between the two subunits of the enzyme from Trypanosoma cruzi but not those of TIM from Homo sapiens (HTIM), and tested if they kill the parasite.

METHODOLOGY/PRINCIPAL FINDINGS

Dithiodianiline (DTDA) at nanomolar concentrations completely inactivates recombinant TIM of T. cruzi (TcTIM). It also inactivated HTIM, but at concentrations around 400 times higher. DTDA was also tested on four TcTIM mutants with each of its four cysteines replaced with either valine or alanine. The sensitivity of the mutants to DTDA was markedly similar to that of the wild type. The crystal structure of the TcTIM soaked in DTDA at 2.15 A resolution, and the data on the mutants showed that inactivation resulted from alterations of the dimer interface. DTDA also prevented the growth of Escherichia coli cells transformed with TcTIM, had no effect on normal E. coli, and also killed T. cruzi epimastigotes in culture.

CONCLUSIONS/SIGNIFICANCE

By targeting on the dimer interface of oligomeric enzymes from parasites, it is possible to discover small molecules that selectively thwart the life of the parasite. Also, the conformational changes that DTDA induces in the dimer interface of the trypanosomal enzyme are unique and identify a region of the interface that could be targeted for drug discovery.

On Itinerant Water Molecules and Detectability of Protein–Protein Interfaces through Comparative Analysis of Homologues

We discuss the question of which residues are sufficiently important for protein-protein interaction to be under notable evolutionary pressure. Its interest stems from the applicability of this knowledge in the reverse direction, to detect a protein-protein interface on a single protomer, starting from the rate of mutation of participating residues. Using the analysis of trajectories produced by the molecular dynamics simulations, we suggest that, in the case of water soluble proteins, a large fraction of evolutionarily privileged residues can be found by considering the dynamic behavior of the protein interface and by looking for residues which exchange water molecules with the bulk of the solvent outstandingly slowly (tentatively termed "dry residues"). We show that the dry interface residues are better conserved across homologues than the generic "geometric footprint" and can be quite reliably detected through comparative analysis of protein homologues, without strong dependence on the choice of method. Furthermore, we show that dry residues distinguish themselves through a set of biophysical properties consistent with the known mechanisms of protein oligomerization: their compositional shift toward nonpolar, overlap, and co-location with residues exhibiting low mobility, their two- to threefold increased propensity over the rest of the geometric footprint to form hydrogen bonds, and four- to almost tenfold increased likelihood to participate in formation of salt bridges. These properties, consistently, help understand the observed increase in the evolutionary pressure that dry residues experience.

X-ray structure of Paramecium bursaria Chlorella virus arginine decarboxylase: insight into the structural basis for substrate specificity

The group IV pyridoxal-5'-phosphate (PLP)-dependent decarboxylases belong to the beta/alpha barrel structural family, and include enzymes with substrate specificity for a range of basic amino acids. A unique homolog of this family, the Paramecium bursaria Chlorella virus arginine decarboxylase (cvADC), shares about 40% amino acid sequence identity with the eukaryotic ornithine decarboxylases (ODCs). The X-ray structure of cvADC has been solved to 1.95 and 1.8 A resolution for the free and agmatine (product)-bound enzymes. The global structural differences between cvADC and eukaryotic ODC are minimal (rmsd of 1.2-1.4 A); however, the active site has significant structural rearrangements. The key "specificity element," is identified as the 310-helix that contains and positions substrate-binding residues such as E296 cvADC (D332 in T. brucei ODC). In comparison to the ODC structures, the 310-helix in cvADC is shifted over 2 A away from the PLP cofactor, thus accommodating the larger arginine substrate. Within the context of this conserved fold, the protein is designed to be flexible in the positioning and amino acid sequence of the 310-helix, providing a mechanism to evolve different substrate preferences within the family without large structural rearrangements. Also, in the structure, the "K148-loop" (homologous to the "K169-loop" of ODC) is observed in a closed, substrate-bound conformation for the first time. Apparently the K148 loop is a mobile loop, analogous to those observed in triose phosphate isomerase and tryptophan synthetase. In conjunction with prior structural studies these data predict that this loop adopts different conformations throughout the catalytic cycle, and that loop movement may be kinetically linked to the rate-limiting step of product release.

Computational analyses of the surface properties of protein-protein interfaces

Several potential applications of structural biology depend on discovering how one macromolecule might recognize a partner. Experiment remains the best way to answer this question, but computational tools can contribute where this fails. In such cases, structures may be studied to identify patches of exposed residues that have properties common to interaction surfaces and the locations of these patches can serve as the basis for further modelling or for further experimentation. To date, interaction surfaces have been proposed on the basis of unusual physical properties, unusual propensities for particular amino-acid types or an unusually high level of sequence conservation. Using the CXXSurface toolkit, developed as a part of the CCP4MG program, a suite of tools to analyse the properties of surfaces and their interfaces in complexes has been prepared and applied. These tools have enabled the rapid analysis of known complexes to evaluate the distribution of (i) hydrophobicity, (ii) electrostatic complementarity and (iii) sequence conservation in authentic complexes, so as to assess the extent to which these properties may be useful indicators of probable biological function.

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.

The many faces of protein-protein interactions: A compendium of interface geometry

A systematic classification of protein-protein interfaces is a valuable resource for understanding the principles of molecular recognition and for modelling protein complexes. Here, we present a classification of domain interfaces according to their geometry. Our new algorithm uses a hybrid approach of both sequential and structural features. The accuracy is evaluated on a hand-curated dataset of 416 interfaces. Our hybrid procedure achieves 83% precision and 95% recall, which improves the earlier sequence-based method by 5% on both terms. We classify virtually all domain interfaces of known structure, which results in nearly 6,000 distinct types of interfaces. In 40% of the cases, the interacting domain families associate in multiple orientations, suggesting that all the possible binding orientations need to be explored for modelling multidomain proteins and protein complexes. In general, hub proteins are shown to use distinct surface regions (multiple faces) for interactions with different partners. Our classification provides a convenient framework to query genuine gene fusion, which conserves binding orientation in both fused and separate forms. The result suggests that the binding orientations are not conserved in at least one-third of the gene fusion cases detected by a conventional sequence similarity search. We show that any evolutionary analysis on interfaces can be skewed by multiple binding orientations and multiple interaction partners. The taxonomic distribution of interface types suggests that ancient interfaces common to the three major kingdoms of life are enriched by symmetric homodimers. The classification results are online at http://www.scoppi.org.

Insights into protein-protein interfaces using a Bayesian network prediction method

Identifying the interface between two interacting proteins provides important clues to the function of a protein, and is becoming increasing relevant to drug discovery. Here, surface patch analysis was combined with a Bayesian network to predict protein-protein binding sites with a success rate of 82% on a benchmark dataset of 180 proteins, improving by 6% on previous work and well above the 36% that would be achieved by a random method. A comparable success rate was achieved even when evolutionary information was missing, a further improvement on our previous method which was unable to handle incomplete data automatically. In a case study of the Mog1p family, we showed that our Bayesian network method can aid the prediction of previously uncharacterised binding sites and provide important clues to protein function. On Mog1p itself a putative binding site involved in the SLN1-SKN7 signal transduction pathway was detected, as was a Ran binding site, previously characterized solely by conservation studies, even though our automated method operated without using homologous proteins. On the remaining members of the family (two structural genomics targets, and a protein involved in the photosystem II complex in higher plants) we identified novel binding sites with little correspondence to those on Mog1p. These results suggest that members of the Mog1p family bind to different proteins and probably have different functions despite sharing the same overall fold. We also demonstrated the applicability of our method to drug discovery efforts by successfully locating a number of binding sites involved in the protein-protein interaction network of papilloma virus infection. In a separate study, we attempted to distinguish between the two types of binding site, obligate and non-obligate, within our dataset using a second Bayesian network. This proved difficult although some separation was achieved on the basis of patch size, electrostatic potential and conservation. Such was the similarity between the two interacting patch types, we were able to use obligate binding site properties to predict the location of non-obligate binding sites and vice versa.

Statistical analysis and prediction of protein-protein interfaces

Predicting protein-protein interfaces from a three-dimensional structure is a key task of computational structural proteomics. In contrast to geometrically distinct small molecule binding sites, protein-protein interface are notoriously difficult to predict. We generated a large nonredundant data set of 1494 true protein-protein interfaces using biological symmetry annotation where necessary. The data set was carefully analyzed and a Support Vector Machine was trained on a combination of a new robust evolutionary conservation signal with the local surface properties to predict protein-protein interfaces. Fivefold cross validation verifies the high sensitivity and selectivity of the model. As much as 97% of the predicted patches had an overlap with the true interface patch while only 22% of the surface residues were included in an average predicted patch. The model allowed the identification of potential new interfaces and the correction of mislabeled oligomeric states.

Structure-function studies of two synthetic anti-vascular endothelial growth factor Fabs and comparison with the Avastin Fab

In the quest to discover new research tools and to develop better agents in the fight against cancer, two antibodies, G6 and B20-4, were isolated from synthetic antibody phage libraries. Unlike the AVASTINtrade mark antibody, a recently approved agent for the treatment of patients with colorectal cancer, B20-4 and G6 bind and block both human and murine vascular endothelial growth factor (VEGF). Here we have analyzed and compared the binding epitopes on VEGF for these three antibodies using alanine-scanning mutagenesis and structural analyses. The epitopes recognized by both synthetic antibodies are conserved between human and mouse VEGF, and they match closely to the receptor epitopes both structurally and functionally. In contrast, the Avastin epitope overlaps minimally with the receptor binding surface and centers around a residue that is not conserved in mouse. Our structural and functional analyses elucidate the cross-species reactivity of all three antibodies and emphasize the potential advantages of antibody generation using phage display as the resulting antibodies do not depend on sequence differences across species and preferentially target natural protein-protein interaction surfaces.

Protein oligomerization: how and why

A large fraction of cellular proteins are oligomeric. Protein oligomerization may often be an advantageous feature from the perspective of protein evolution and has probably evolved by a variety of mechanisms. The study of protein oligomerization may provide insights into the early protein environment and the evolution of modern proteins. Oligomeric mini-proteins, short peptides with discrete protein-like structures, may serve as valuable models for understanding features of protein oligomerization.

A structure and evolution-guided Monte Carlo sequence selection strategy for multiple alignment-based analysis of proteins

MOTIVATION

Various multiple sequence alignment-based methods have been proposed to detect functional surfaces in proteins, such as active sites or protein interfaces. The effect that the choice of sequences has on the conclusions of such analysis has seldom been discussed. In particular, no method has been discussed in terms of its ability to optimize the sequence selection for the reliable detection of functional surfaces.

RESULTS

Here we propose, for the case of proteins with known structure, a heuristic Metropolis Monte Carlo strategy to select sequences from a large set of homologues, in order to improve detection of functional surfaces. The quantity guiding the optimization is the clustering of residues which are under increased evolutionary pressure, according to the sample of sequences under consideration. We show that we can either improve the overlap of our prediction with known functional surfaces in comparison with the sequence similarity criteria of selection or match the quality of prediction obtained through more elaborate non-structure based-methods of sequence selection. For the purpose of demonstration we use a set of 50 homodimerizing enzymes which were co-crystallized with their substrates and cofactors.

Localization of protein-binding sites within families of proteins

We address the question of whether or not the positions of protein-binding sites on homologous protein structures are conserved irrespective of the identities of their binding partners. First, for each domain family in the Structural Classification of Proteins (SCOP), protein-binding sites are extracted from our comprehensive database of structurally defined binary domain interactions (PIBASE). Second, the binding sites within each family are superposed using a structural alignment of its members. Finally, the degree of localization of binding sites within each family is quantified by comparing it with localization expected by chance. We found that 72% of the 1847 SCOP domain families in PIBASE have binding sites with localization values greater than expected by chance. Moreover, 554 (30%) of these families have localizations that are statistically significant (i.e., more than four standard deviations away from the mean expected by chance). In contrast, only 144 (8%) families have significantly low localization. The absence of a significant correlation of the binding site localization with the average sequence and structural conservations in a family suggests that localization can be helpful for describing the functional diversity of protein-protein interactions, complementing measures of sequence and structural conservation. Consideration of the binding site localization may also result in spatial restraints for the modeling of protein assembly structures.

Structure, function, and evolution of transient and obligate protein-protein interactions

Recent analyses of high-throughput protein interaction data coupled with large-scale investigations of evolutionary properties of interaction networks have left some unanswered questions. To what extent do protein interactions act as constraints during evolution of the protein sequence? How does the type of interaction, specifically transient or obligate, play into these constraints? Are the mutations in the binding site of an interacting protein correlated with mutations in the binding site of its partner? We address these and other questions by relying on a carefully curated dataset of protein complex structures. Results point to the importance of distinguishing between transient and obligate interactions. We conclude that residues in the interfaces of obligate complexes tend to evolve at a relatively slower rate, allowing them to coevolve with their interacting partners. In contrast, the plasticity inherent in transient interactions leads to an increased rate of substitution for the interface residues and leaves little or no evidence of correlated mutations across the interface.

REVCOM: a robust Bayesian method for evolutionary rate estimation

MOTIVATION

Evolutionary conservation estimated from a multiple sequence alignment is a powerful indicator of the functional significance of a residue and helps to predict active sites, ligand binding sites, and protein interaction interfaces. Many algorithms that calculate conservation work well, provided an accurate and balanced alignment is used. However, such a strong dependence on the alignment makes the results highly variable. We attempted to improve the conservation prediction algorithm by making it more robust and less sensitive to (1) local alignment errors, (2) overrepresentation of sequences in some branches and (3) occasional presence of unrelated sequences.

RESULTS

A novel method is presented for robust constrained Bayesian estimation of evolutionary rates that avoids overfitting independent rates and satisfies the above requirements. The method is evaluated and compared with an entropy-based conservation measure on a set of 1494 protein interfaces. We demonstrated that approximately 62% of the analyzed protein interfaces are more conserved than the remaining surface at the 5% significance level. A consistent method to incorporate alignment reliability is proposed and demonstrated to reduce arbitrary variation of calculated rates upon inclusion of distantly related or unrelated sequences into the alignment.

Inactivation of triosephosphate isomerase from Trypanosoma cruzi by an agent that perturbs its dimer interface

We characterized by crystallographic, calorimetric and biochemical methods the action of a low molecular weight compound, 3-(2-benzothiazolylthio)-1-propanesulfonic acid (compound 8) that binds to the dimer interface of triosephosphate isomerase from Trypanosoma cruzi (TcTIM) and thereby abolishes its function with a high level of selectivity. The kinetics of TcTIM inactivation by the agent and isothermal titration calorimetry experiments showed that the binding of two molecules of the compound per enzyme is needed for inactivation. The binding of the first molecule is endothermic, and that of the second exothermic. Crystals of TcTIM in complex with one molecule of the inactivating agent that diffracted to a resolution of 2A were obtained. The compound is at the dimer interface at less than 4A from residues of the two subunits. Compound 8 is more effective at low than at high protein concentrations, indicating that it perturbs the association between the two TcTIM monomers. Calorimetric and kinetic data of experiments in which TcTIM was added to a solution of the inactivating agent showed that at low concentrations of the compound, inactivation is limited by binding, whereas at high concentrations of the agent, the events that follow binding become rate-limiting. The portion of the interface of TcTIM that binds the benzothiazole derivative and its equivalent region in human TIM differs in amino acid composition and hydrophobic packing. Thus, we show that by focusing on protein-protein interfaces, it is possible to discover low molecular weight compounds that are selective for enzymes from parasites.

Are protein-protein interfaces more conserved in sequence than the rest of the protein surface?

Protein interfaces are thought to be distinguishable from the rest of the protein surface by their greater degree of residue conservation. We test the validity of this approach on an expanded set of 64 protein-protein interfaces using conservation scores derived from two multiple sequence alignment types, one of close homologs/orthologs and one of diverse homologs/paralogs. Overall, we find that the interface is slightly more conserved than the rest of the protein surface when using either alignment type, with alignments of diverse homologs showing marginally better discrimination. However, using a novel surface-patch definition, we find that the interface is rarely significantly more conserved than other surface patches when using either alignment type. When an interface is among the most conserved surface patches, it tends to be part of an enzyme active site. The most conserved surface patch overlaps with 39% (+/- 28%) and 36% (+/- 28%) of the actual interface for diverse and close homologs, respectively. Contrary to results obtained from smaller data sets, this work indicates that residue conservation is rarely sufficient for complete and accurate prediction of protein interfaces. Finally, we find that obligate interfaces differ from transient interfaces in that the former have significantly fewer alignment gaps at the interface than the rest of the protein surface, as well as having buried interface residues that are more conserved than partially buried interface residues.

Apparent dependence of protein evolutionary rate on number of interactions is linked to biases in protein-protein interactions data sets

Background

Several studies have suggested that proteins that interact with more partners evolve more slowly. The strength and validity of this association has been called into question. Here we investigate how biases in high-throughput protein–protein interaction studies could lead to a spurious correlation.

Results

We examined the correlation between evolutionary rate and the number of protein–protein interactions for sets of interactions determined by seven different high-throughput methods in Saccharomyces cerevisiae. Some methods have been shown to be biased towards counting more interactions for abundant proteins, a fact that could be important since abundant proteins are known to evolve more slowly. We show that the apparent tendency for interactive proteins to evolve more slowly varies directly with the bias towards counting more interactions for abundant proteins. Interactions studies with no bias show no correlation between evolutionary rate and the number of interactions, and the one study biased towards counting fewer interactions for abundant proteins actually suggests that interactive proteins evolve more rapidly. In all cases, controlling for protein abundance significantly decreases the observed correlation between interactions and evolutionary rate. Finally, we disprove the hypothesis that small data set size accounts for the failure of some interactions studies to show a correlation between evolutionary rate and the number of interactions.

Conclusions

The only correlation supported by a careful analysis of the data is between evolutionary rate and protein abundance. The reported correlation between evolutionary rate and protein–protein interactions cannot be separated from the biases of some protein–protein interactions studies to count more interactions for abundant proteins.

Role of bulk and of interface contacts in the behavior of lattice model dimeric proteins

Some dimeric proteins first fold and then dimerize (three-state dimers) while others first dimerize and then fold (two-state dimers). Within the framework of a minimal lattice model, we can distinguish between sequences following one or the other mechanism on the basis of the distribution of the ground state energy between bulk and interface contacts. The topology of contacts is very different for the bulk than for the interface: while the bulk displays a rich network of interactions, the dimer interface is built up of a set of essentially independent contacts. Consequently, the two sets of interactions play very different roles both, in the folding and in the evolutionary history of the protein. Three-state dimers, where a large fraction of energy is concentrated in few contacts buried in the bulk, and where the relative contact energy of interface contacts is considerably smaller than that associated with bulk contacts, fold according to a hierarchical pathway controlled by local elementary structures, as also happens in the folding of single-domain monomeric proteins. On the other hand, two-state dimers display a relative contact energy of interface contacts, which is larger than the corresponding quantity associated with the bulk. In this case, the assembly of the interface stabilizes the system and leads the two chains to fold. The specific properties of three-state dimers acquired through evolution are expected to be more robust than those of two-state dimers; a fact that has consequences on proteins connected with viral diseases.

The constraints protein-protein interactions place on sequence divergence

Structural analyses on a small number of protein families have shown that residues in protein interfaces are more conserved than average amino acid residues. This is also true of other ligand-binding and active site residues. This raises the question whether protein interactions place additional constraints on sequence divergence beyond this general background of functional restrictions on all different types of proteins. In order to investigate this, the sequence identities of Saccharomyces cerevisiae (SC) proteins to their Schizosaccharomyces pombe (SP) orthologues were used as a measure of sequence divergence. The SC proteins were divided into those in stable complexes, those that participate in transient interactions and the remaining proteins. All types of proteins can undergo extensive divergence: all three sequence identity distributions range from less than 20 to over 90%. However, overall, protein interactions do place additional constraints on sequence divergence and the distributions differ significantly: proteins not known to be involved in interactions have an average sequence identity of 38% while this value is 46% for proteins in stable complexes. Proteins that have transient interactions are intermediate between the two, with an average sequence identity of 41%. This trend is independent of whether the proteins are involved in informational functions (transcription, translation and replication) or not and of protein dispensability.

Design and folding of dimeric proteins

In a similar way in which the folding of single-domain proteins provides an important test in the study of self-organization, the folding of homodimers constitutes a basic challenge in the quest for the mechanisms that are the basis of biological recognition. Dimerization is studied by following the evolution of two identical 20-letter amino acid chains within the framework of a lattice model and using Monte Carlo simulations. It is found that when design (evolution pressure) selects few, strongly interacting (conserved) amino acids to control the process, a three-state folding scenario follows, where the monomers first fold forming the halves of the eventual dimeric interface independently of each other, and then dimerize ("lock and key" kind of association). On the other hand, if design distributes the control of the folding process on a large number of (conserved) amino acids, a two-state folding scenario ensues, where dimerization takes place at the beginning of the process, resulting in an "induced type" of association. Making use of conservation patterns of families of analogous dimers, it is possible to compare the model predictions with the behavior of real proteins. It is found that theory provides an overall account of the experimental findings.

Protein-DNA interactions: amino acid conservation and the effects of mutations on binding specificity

We investigate the conservation of amino acid residue sequences in 21 DNA-binding protein families and study the effects that mutations have on DNA-sequence recognition. The observations are best understood by assigning each protein family to one of three classes: (i) non-specific, where binding is independent of DNA sequence; (ii) highly specific, where binding is specific and all members of the family target the same DNA sequence; and (iii) multi-specific, where binding is also specific, but individual family members target different DNA sequences. Overall, protein residues in contact with the DNA are better conserved than the rest of the protein surface, but there is a complex underlying trend of conservation for individual residue positions. Amino acid residues that interact with the DNA backbone are well conserved across all protein families and provide a core of stabilising contacts for homologous protein-DNA complexes. In contrast, amino acid residues that interact with DNA bases have variable levels of conservation depending on the family classification. In non-specific families, base-contacting residues are well conserved and interactions are always found in the minor groove where there is little discrimination between base types. In highly specific families, base-contacting residues are highly conserved and allow member proteins to recognise the same target sequence. In multi-specific families, base-contacting residues undergo frequent mutations and enable different proteins to recognise distinct target sequences. Finally, we report that interactions with bases in the target sequence often follow (though not always) a universal code of amino acid-base recognition and the effects of amino acid mutations can be most easily understood for these interactions.

Crystal structure of the Escherichia coli RNA degradosome component enolase

The crystal structure of Escherichia coli enolase (EC 4.2.1.11, phosphopyruvate hydratase), which is a component of the RNA degradosome, has been determined at 2.5 A. There are four molecules in the asymmetric unit of the C2 cell, and in one of the molecules, flexible loops close onto the active site. This closure mimics the conformation of the substrate-bound intermediate. A comparison of the structure of the E. coli enolase with the eukaryotic enolase structures available (lobster and yeast) indicates a high degree of conservation of the hydrophobic core and the subunit interface of this homodimeric enzyme. The dimer interface is enriched in charged residues compared with other protein homodimers, which may explain our observations from analytical ultracentrifugation that dimerisation is affected by ionic strength. The putative role of enolase in the RNA degradosome is discussed; although it was not possible to ascribe a specific role to it, a structural role is possible.

A method to predict residues conferring functional differences between related proteins: application to MAP kinase pathways

Physicochemical properties are potentially useful in predicting functional differences between aligned protein subfamilies. We present a method that considers physicochemical properties from ancestral sequences predicted to have given rise to the subfamilies of interest by gene duplication. Comparison between two map kinases subfamilies, p38 and ERK, revealed a region that had an excess of change in properties after gene duplication followed by conservation within the two subfamilies. This region corresponded to that experimentally defined as important for substrate and pathway specificity. The derived scores for the region of interest were found to differ significantly in their distribution compared to the rest of the protein when the Kolmogorov-Smirnov test was applied (p = 0.005). Thus, the incorporation of ancestral physicochemical properties is useful in predicting functional differences between protein subfamilies. In addition, the method was applied to the MKK and MAPK components of the p38 and JNK pathways. These proteins showed a similar pattern in their evolution and regions predicted to confer functional differences are discussed.