PINT: Protein-protein Interactions Thermodynamic Database

DbPTM 3.0: an informative resource for investigating substrate site specificity and functional association of protein post-translational modifications

Protein modification is an extremely important post-translational regulation that adjusts the physical and chemical properties, conformation, stability and activity of a protein; thus altering protein function. Due to the high throughput of mass spectrometry (MS)-based methods in identifying site-specific post-translational modifications (PTMs), dbPTM (http://dbPTM.mbc.nctu.edu.tw/) is updated to integrate experimental PTMs obtained from public resources as well as manually curated MS/MS peptides associated with PTMs from research articles. Version 3.0 of dbPTM aims to be an informative resource for investigating the substrate specificity of PTM sites and functional association of PTMs between substrates and their interacting proteins. In order to investigate the substrate specificity for modification sites, a newly developed statistical method has been applied to identify the significant substrate motifs for each type of PTMs containing sufficient experimental data. According to the data statistics in dbPTM, >60% of PTM sites are located in the functional domains of proteins. It is known that most PTMs can create binding sites for specific protein-interaction domains that work together for cellular function. Thus, this update integrates protein–protein interaction and domain–domain interaction to determine the functional association of PTM sites located in protein-interacting domains. Additionally, the information of structural topologies on transmembrane (TM) proteins is integrated in dbPTM in order to delineate the structural correlation between the reported PTM sites and TM topologies. To facilitate the investigation of PTMs on TM proteins, the PTM substrate sites and the structural topology are graphically represented. Also, literature information related to PTMs, orthologous conservations and substrate motifs of PTMs are also provided in the resource. Finally, this version features an improved web interface to facilitate convenient access to the resource.

SKEMPI: a Structural Kinetic and Energetic database of Mutant Protein Interactions and its use in empirical models

MOTIVATION

Empirical models for the prediction of how changes in sequence alter protein-protein binding kinetics and thermodynamics can garner insights into many aspects of molecular biology. However, such models require empirical training data and proper validation before they can be widely applied. Previous databases contained few stabilizing mutations and no discussion of their inherent biases or how this impacts model construction or validation.

RESULTS

We present SKEMPI, a database of 3047 binding free energy changes upon mutation assembled from the scientific literature, for protein-protein heterodimeric complexes with experimentally determined structures. This represents over four times more data than previously collected. Changes in 713 association and dissociation rates and 127 enthalpies and entropies were also recorded. The existence of biases towards specific mutations, residues, interfaces, proteins and protein families is discussed in the context of how the data can be used to construct predictive models. Finally, a cross-validation scheme is presented which is capable of estimating the efficacy of derived models on future data in which these biases are not present.

AVAILABILITY

The database is available online at http://life.bsc.es/pid/mutation_database/.

Combinatorial complexity and compositional drift in protein interaction networks

The assembly of molecular machines and transient signaling complexes does not typically occur under circumstances in which the appropriate proteins are isolated from all others present in the cell. Rather, assembly must proceed in the context of large-scale protein-protein interaction (PPI) networks that are characterized both by conflict and combinatorial complexity. Conflict refers to the fact that protein interfaces can often bind many different partners in a mutually exclusive way, while combinatorial complexity refers to the explosion in the number of distinct complexes that can be formed by a network of binding possibilities. Using computational models, we explore the consequences of these characteristics for the global dynamics of a PPI network based on highly curated yeast two-hybrid data. The limited molecular context represented in this data-type translates formally into an assumption of independent binding sites for each protein. The challenge of avoiding the explicit enumeration of the astronomically many possibilities for complex formation is met by a rule-based approach to kinetic modeling. Despite imposing global biophysical constraints, we find that initially identical simulations rapidly diverge in the space of molecular possibilities, eventually sampling disjoint sets of large complexes. We refer to this phenomenon as "compositional drift". Since interaction data in PPI networks lack detailed information about geometric and biological constraints, our study does not represent a quantitative description of cellular dynamics. Rather, our work brings to light a fundamental problem (the control of compositional drift) that must be solved by mechanisms of assembly in the context of large networks. In cases where drift is not (or cannot be) completely controlled by the cell, this phenomenon could constitute a novel source of phenotypic heterogeneity in cell populations.

A "lookup table" schema for synthetic biological patterning

A schema is proposed by which the three-dimensional structure and temporal development of a biological organism might be encoded and implemented via a genetic "lookup table". In the schema, diffusive morphogen gradients and/or the global concentration of a quickly diffusing signal index sets of kinase genes having promoters with logarithmically diminished affinity for the signal. Specificity of indexing is enhanced via concomitant expression of phosphatases undoing phosphorylation by "neighboring" kinases of greater affinity. Combinations of thus-selected kinases in turn jointly activate, via multiple phosphorylation, a particular enzyme from a virtual, multi-dimensional array thereof, at locations and times specified within the "lookup table". In principle, such a scheme could be employed to specify arbitrary gross anatomy, surface pigmentation, and/or developmental sequencing, extending the burgeoning toolset of the nascent field of synthetic morphology. A model of two-dimensional surface coloration using this scheme is specified, and LabVIEW software for its exploration is described and made available.

Physical limits on cooperative protein-DNA binding and the kinetics of combinatorial transcription regulation

Much of the complexity observed in gene regulation originates from cooperative protein-DNA binding. Although studies of the target search of proteins for their specific binding sites on the DNA have revealed design principles for the quantitative characteristics of protein-DNA interactions, no such principles are known for the cooperative interactions between DNA-binding proteins. We consider a simple theoretical model for two interacting transcription factor (TF) species, searching for and binding to two adjacent target sites hidden in the genomic background. We study the kinetic competition of a dimer search pathway and a monomer search pathway, as well as the steady-state regulation function mediated by the two TFs over a broad range of TF-TF interaction strengths. Using a transcriptional AND-logic as exemplary functional context, we identify the functionally desirable regime for the interaction. We find that both weak and very strong TF-TF interactions are favorable, albeit with different characteristics. However, there is also an unfavorable regime of intermediate interactions where the genetic response is prohibitively slow.

Measuring dissociation rate constants of protein complexes through subunit exchange: experimental design and theoretical modeling

Protein complexes are dynamic macromolecules that constantly dissociate into, and simultaneously are assembled from, free subunits. Dissociation rate constants, k_off, provide structural and functional information on protein complexes. However, because all existing methods for measuring k_off require high-quality purification and specific modifications of protein complexes, dissociation kinetics has only been studied for a small set of model complexes. Here, we propose a new method, called Metabolically-labeled Affinity-tagged Subunit Exchange (MASE), to measure k_off using metabolic stable isotope labeling, affinity purification and mass spectrometry. MASE is based on a subunit exchange process between an unlabeled affinity-tagged variant and a metabolically-labeled untagged variant of a complex. The subunit exchange process was modeled theoretically for a heterodimeric complex. The results showed that k_off determines, and hence can be estimated from, the observed rate of subunit exchange. This study provided the theoretical foundation for future experiments that can validate and apply the MASE method.

Conformational flexibility in the allosteric regulation of human UDP-α-D-glucose 6-dehydrogenase

UDP-α-D-xylose (UDX) acts as a feedback inhibitor of human UDP-α-D-glucose 6-dehydrogenase (hUGDH) by activating an unusual allosteric switch, the Thr131 loop. UDX binding induces the Thr131 loop to translate ~5 Å through the protein core, changing packing interactions and rotating a helix (α6(136-144)) to favor the formation of an inactive hexameric complex. But how does to conformational change occur given the steric packing constraints of the protein core? To answer this question, we deleted Val132 from the Thr131 loop to approximate an intermediate state in the allosteric transition. The 2.3 Å resolution crystal structure of the deletion construct (Δ132) reveals an open conformation that relaxes steric constraints and facilitates repacking of the protein core. Sedimentation velocity studies show that the open conformation stabilizes the Δ132 construct as a hexamer with point group symmetry 32, similar to that of the active complex. In contrast, the UDX-inhibited enzyme forms a lower-symmetry, horseshoe-shaped hexameric complex. We show that the Δ132 and UDX-inhibited structures have similar hexamer-building interfaces, suggesting that the hinge-bending motion represents a path for the allosteric transition between the different hexameric states. On the basis of (i) main chain flexibility and (ii) a model of the conformational change, we propose that hinge bending can occur as a concerted motion between adjacent subunits in the high-symmetry hexamer. We combine these results in a structurally detailed model for allosteric feedback inhibition and substrate--product exchange during the catalytic cycle.

The structure, molecular dynamics, and energetics of centrin-melittin complex

Centrin is a calcium binding protein (CaBP) belonging to the EF-hand superfamily. As with other proteins within this family, centrin is a calcium sensor with multiple biological target proteins. We chose to study Chlamydomonas reinhardtii centrin (Crcen) and its interaction with melittin (MLT) as a model for CaBP complexes due to its amphipathic properties. Our goal was to determine the molecular interactions that lead to centrin-MLT complex formation, their relative stability, and the conformational changes associated with the interaction, when compared to the single components. For this, we determined the thermodynamic parameters that define Crcen-MLT complex formation. Two-dimensional infrared (2D IR) correlation spectroscopy were used to study the amide I', I'*, and side chain bands for (13)C-Crcen, MLT, and the (13)C-Crcen-MLT complex. This approach resulted in the determination of MLT's increased helicity, while centrin was stabilized within the complex. Herein we provide the first complete molecular description of centrin-MLT complex formation and the dissociation process. Also, discussed is the first structure of a CaBP-MLT complex by X-ray crystallography, which shows that MLT has a different binding orientation than previously characterized centrin-bound peptides. Finally, all of the experimental results presented herein are consistent with centrin maintaining an extended conformation while interacting with MLT. The molecular implications of these results are: (1) the recognition of hydrophobic contacts as requirements for initial binding, (2) minimum electrostatic interactions within the C-terminal end of the peptide, and (3) van der Waals interactions within MLTs N-terminal end are required for complex formation.

Direct inference of protein-DNA interactions using compressed sensing methods

Compressed sensing has revolutionized signal acquisition, by enabling complex signals to be measured with remarkable fidelity using a small number of so-called incoherent sensors. We show that molecular interactions, e.g., protein-DNA interactions, can be analyzed in a directly analogous manner and with similarly remarkable results. Specifically, mesoscopic molecular interactions act as incoherent sensors that measure the energies of microscopic interactions between atoms. We combine concepts from compressed sensing and statistical mechanics to determine the interatomic interaction energies of a molecular system exclusively from experimental measurements, resulting in a "de novo" energy potential. In contrast, conventional methods for estimating energy potentials are based on theoretical models premised on a priori assumptions and extensive domain knowledge. We determine the de novo energy potential for pairwise interactions between protein and DNA atoms from (i) experimental measurements of the binding affinity of protein-DNA complexes and (ii) crystal structures of the complexes. We show that the de novo energy potential can be used to predict the binding specificity of proteins to DNA with approximately 90% accuracy, compared to approximately 60% for the best performing alternative computational methods applied to this fundamental problem. This de novo potential method is directly extendable to other biomolecule interaction domains (enzymes and signaling molecule interactions) and to other classes of molecular interactions.

Role of packing defects in the evolution of allostery and induced fit in human UDP-glucose dehydrogenase

Allosteric feedback inhibition is the mechanism by which metabolic end products regulate their own biosynthesis by binding to an upstream enzyme. Despite its importance in controlling metabolism, there are relatively few allosteric mechanisms understood in detail. This is because allostery does not have an identifiable structural motif, making the discovery of new allosteric enzymes a difficult process. The lack of a conserved motif implies that the evolution of each allosteric mechanism is unique. Here we describe an atypical allosteric mechanism in human UDP-α-d-glucose 6-dehydrogenase (hUGDH) based on an easily acquired and identifiable structural attribute: packing defects in the protein core. In contrast to classic allostery, the active and allosteric sites in hUGDH are present as a single, bifunctional site. Using two new crystal structures, we show that binding of the feedback inhibitor, UDP-α-d-xylose, elicits a distinct induced-fit response; a buried loop translates ∼4 Å along and rotates ∼180° about the main chain axis, requiring surrounding side chains to repack. This allosteric transition is facilitated by packing defects, which negate the steric conformational restraints normally imposed by the protein core. Sedimentation velocity studies show that this repacking favors the formation of an inactive hexameric complex with unusual symmetry. We present evidence that hUGDH and the unrelated enzyme dCTP deaminase have converged to very similar atypical allosteric mechanisms using the same adaptive strategy, the selection for packing defects. Thus, the selection for packing defects is a robust mechanism for the evolution of allostery and induced fit.

A structure-based benchmark for protein-protein binding affinity

We have assembled a nonredundant set of 144 protein-protein complexes that have high-resolution structures available for both the complexes and their unbound components, and for which dissociation constants have been measured by biophysical methods. The set is diverse in terms of the biological functions it represents, with complexes that involve G-proteins and receptor extracellular domains, as well as antigen/antibody, enzyme/inhibitor, and enzyme/substrate complexes. It is also diverse in terms of the partners' affinity for each other, with K(d) ranging between 10(-5) and 10(-14) M. Nine pairs of entries represent closely related complexes that have a similar structure, but a very different affinity, each pair comprising a cognate and a noncognate assembly. The unbound structures of the component proteins being available, conformation changes can be assessed. They are significant in most of the complexes, and large movements or disorder-to-order transitions are frequently observed. The set may be used to benchmark biophysical models aiming to relate affinity to structure in protein-protein interactions, taking into account the reactants and the conformation changes that accompany the association reaction, instead of just the final product.

Large-scale prediction of human protein-protein interactions from amino acid sequence based on latent topic features

Protein-protein interaction (PPI) is at the core of the entire interactomic system of any living organism. Although there are many human protein-protein interaction links being experimentally determined, the number is still relatively very few compared to the estimation that there are ∼300,000 protein-protein interactions in human beings. Hence, it is still urgent and challenging to develop automated computational methods to accurately and efficiently predict protein-protein interactions. In this paper, we propose a novel hierarchical LDA-RF (latent dirichlet allocation-random forest) model to predict human protein-protein interactions from protein primary sequences directly, which is featured by a high success rate and strong ability for handling large-scale data sets by digging the hidden internal structures buried into the noisy amino acid sequences in low dimensional latent semantic space. First, the local sequential features represented by conjoint triads are constructed from sequences. Then the generative LDA model is used to project the original feature space into the latent semantic space to obtain low dimensional latent topic features, which reflect the hidden structures between proteins. Finally, the powerful random forest model is used to predict the probability for interaction of two proteins. Our results show that the proposed latent topic feature is very promising for PPI prediction and could also become a powerful strategy to deal with many other bioinformatics problems. As a web server, LDA-RF is freely available at http://www.csbio.sjtu.edu.cn/bioinf/LR_PPI for academic use.

Structure and properties of a complex of α-synuclein and a single-domain camelid antibody

The aggregation of the intrinsically disordered protein α-synuclein to form fibrillar amyloid structures is intimately associated with a variety of neurological disorders, most notably Parkinson's disease. The molecular mechanism of α-synuclein aggregation and toxicity is not yet understood in any detail, not least because of the paucity of structural probes through which to study the behavior of such a disordered system. Here, we describe an investigation involving a single-domain camelid antibody, NbSyn2, selected by phage display techniques to bind to α-synuclein, including the exploration of its effects on the in vitro aggregation of the protein under a variety of conditions. We show using isothermal calorimetric methods that NbSyn2 binds specifically to monomeric α-synuclein with nanomolar affinity and by means of NMR spectroscopy that it interacts with the four C-terminal residues of the protein. This latter finding is confirmed by the determination of a crystal structure of NbSyn2 bound to a peptide encompassing the nine C-terminal residues of α-synuclein. The NbSyn2:α-synuclein interaction is mediated mainly by side-chain interactions while water molecules cross-link the main-chain atoms of α-synuclein to atoms of NbSyn2, a feature we believe could be important in intrinsically disordered protein interactions more generally. The aggregation behavior of α-synuclein at physiological pH, including the morphology of the resulting fibrillar structures, is remarkably unaffected by the presence of NbSyn2 and indeed we show that NbSyn2 binds strongly to the aggregated as well as to the soluble forms of α-synuclein. These results give strong support to the conjecture that the C-terminal region of the protein is not directly involved in the mechanism of aggregation and suggest that binding of NbSyn2 could be a useful probe for the identification of α-synuclein aggregation in vitro and possibly in vivo.

Optimality of mutation and selection in germinal centers

The population dynamics theory of B cells in a typical germinal center could play an important role in revealing how affinity maturation is achieved. However, the existing models encountered some conflicts with experiments. To resolve these conflicts, we present a coarse-grained model to calculate the B cell population development in affinity maturation, which allows a comprehensive analysis of its parameter space to look for optimal values of mutation rate, selection strength, and initial antibody-antigen binding level that maximize the affinity improvement. With these optimized parameters, the model is compatible with the experimental observations such as the ∼100-fold affinity improvements, the number of mutations, the hypermutation rate, and the “all or none” phenomenon. Moreover, we study the reasons behind the optimal parameters. The optimal mutation rate, in agreement with the hypermutation rate in vivo, results from a tradeoff between accumulating enough beneficial mutations and avoiding too many deleterious or lethal mutations. The optimal selection strength evolves as a balance between the need for affinity improvement and the requirement to pass the population bottleneck. These findings point to the conclusion that germinal centers have been optimized by evolution to generate strong affinity antibodies effectively and rapidly. In addition, we study the enhancement of affinity improvement due to B cell migration between germinal centers. These results could enhance our understanding of the functions of germinal centers.

The antibodies in our immune system could efficiently improve their abilities in recognizing new antigens. This is done with the help of proliferation, mutation and selection of B cells which carry antibodies, but we have difficulties in developing a quantitative description of this adaptation process which is consistent with the various aspects of experimental observations. Based on the knowledge from experiments, here we present a theoretical model to calculate the numbers of B cells with different antigen recognizing abilities all the time, and look for the best possible design that improves the antigen recognizing ability most efficiently. We find that the best possible design is consistent with the experimental observations, pointing to the conclusion that the immune system has been optimized in evolution. We then study the trade-offs leading to the optimization of the design. The results will not only improve our understanding of the functions in immune system, but also reveal the design principles behind the details. In addition, the study enhances our understanding of the population dynamics in evolution.

Energy based approach for understanding the recognition mechanism in protein-protein complexes

Protein-protein interactions play an essential role in the regulation of various cellular processes. Understanding the recognition mechanism of protein-protein complexes is a challenging task in molecular and computational biology. In this work, we have developed an energy based approach for identifying the binding sites and important residues for binding in protein-protein complexes. The new approach is different from the traditional distance based contacts in which the repulsive interactions are treated as binding sites as well as the contacts within a specific cutoff have been treated in the same way. We found that the residues and residue-pairs with charged and aromatic side chains are important for binding. These residues influence to form cation-, electrostatic and aromatic interactions. Our observation has been verified with the experimental binding specificity of protein-protein complexes and found good agreement with experiments. Based on these results we have proposed a novel mechanism for the recognition of protein-protein complexes: the charged and aromatic residues in receptor and ligand initiate recognition by making suitable interactions between them; the neighboring hydrophobic residues assist the stability of complex along with other hydrogen bonding partners by the polar residues. Further, the propensity of residues in the binding sites of receptors and ligands, atomic contributions and the influence on secondary structure will be discussed.

Proteomic and biochemical analyses unveil tight interaction of ataxin-3 with tubulin

Ataxin-3 consists of an N-terminal globular Josephin domain and an unstructured C-terminal region containing a stretch of consecutive glutamines that triggers an inherited neurodegenerative disorder, spinocerebellar ataxia type 3, when its length exceeds a critical threshold. The pathology results from protein misfolding and intracellular accumulation of fibrillar amyloid-like aggregates. Plenty of work has been carried out to elucidate the protein's physiological role(s), which has shown that ataxin-3 is multifunctional; it acts as a transcriptional repressor, and also has polyubiquitin-binding/ubiquitin-hydrolase activity. In addition, a recent report shows that it participates in sorting misfolded protein to aggresomes, close to the microtubule-organizing center. Since a thorough understanding of the protein's physiological role(s) requires the identification of all the molecular partners interacting with ataxin-3, we pursued this goal by taking advantage of two-dimensional chromatography coupled to tandem mass spectrometry. We found that different ataxin-3 constructs, including the sole Josephin domain, bound alpha- and beta-tubulin from soluble rat brain extracts. Coimmunoprecipitation experiments confirmed this interaction. Also, normal ataxin-3 overexpressed in COS7 cultured cells partially colocalized with microtubules, whereas an expanded variant only occasionally did so, probably due to aggregation. Furthermore, by surface plasmon resonance we determined a dissociation constant of 50-70nM between ataxin-3 and tubulin dimer, which strongly supports the hypothesis of a direct interaction of this protein with microtubules in vivo. These findings suggest an involvement of ataxin-3 in directing aggregated protein to aggresomes, and shed light on the mode of interaction among the different molecular partners participating in the process.

Empirical estimation of the energetic contribution of individual interface residues in structures of protein-protein complexes

We report a simple algorithm to scan interfaces in protein-protein complexes for identifying binding 'hot spots'. The change in side-chain solvent accessible area (DeltaASA) of interface residues has been related to change in binding energy due to mutating interface residues to Ala (DeltaDeltaG (X --> ALA)) based on two criteria-hydrogen bonding across the interface and location in the interface core-both of which are major determinants in specific, high-affinity binding. These relationships are used to predict the energetic contribution of individual interface residues. The predictions are tested against 462 experimental X --> ALA mutations from 28 interfaces with an average unsigned error of 1.04 kcal/mol. More than 80% of interface hot spots (with experimental DeltaDeltaG > or = 2 kcal/mol) could be identified as being energetically important. From the experimental values, Asp, Lys, Tyr and Trp are found to contribute most of the binding energy, burying >45 A2 on average. The method described here would be useful to understand and interfere with protein interactions by assessing the energetic importance of individual interface residues.

Structural models in the assessment of protein druggability based on HTS data

Insights on the potential of target proteins to bind small molecules with high affinity can be derived from the knowledge of their three-dimensional structural details especially of their binding pockets. The present study uses high-throughput screening (HTS) results on various targets, to obtain mathematical predictive models in which a minimal set of structural parameters significantly contributing to the hit rates or the affinity of the protein binding pockets for small molecular entities, is identified. An emphasis is given to focus on target variation aspect of the data by consideration of commonly tested compounds against the HTS targets. We identify 'four-parameter' models with R (2), [Formula: see text], SEE, and LOO q (2) values of 0.70, 0.60, 0.27 and 0.50, respectively, or better. We demonstrate through cross-validation exercises that our regression models apply well on varied data sets. Thus we can use these models to estimate hit rates for HTS campaigns and thereby assign priority to drug targets before they undergo such resource intense experimental screening and follow-up.

Fluctuations in mass-action equilibrium of protein binding networks

We consider two types of fluctuations in the mass-action equilibrium in protein binding networks. The first type is driven by slow changes in total concentrations of interacting proteins. The second type (spontaneous) is caused by quickly decaying thermodynamic deviations away from equilibrium. We investigate the effects of network connectivity on fluctuations by comparing them to scenarios in which the interacting pair is isolated from the network and analytically derives bounds on fluctuations. Collective effects are shown to sometimes lead to large amplification of spontaneous fluctuations. The strength of both types of fluctuations is positively correlated with the complex connectivity and negatively correlated with complex concentration. Our general findings are illustrated using a curated network of protein interactions and multiprotein complexes in baker's yeast, with empirical protein concentrations.

Volume-based solvation models out-perform area-based models in combined studies of wild-type and mutated protein-protein interfaces

BACKGROUND

Empirical binding models have previously been investigated for the energetics of protein complexation (DeltaG models) and for the influence of mutations on complexation (i.e. differences between wild-type and mutant complexes, DeltaDeltaG models). We construct binding models to directly compare these processes, which have generally been studied separately.

RESULTS

Although reasonable fit models were found for both DeltaG and DeltaDeltaG cases, they differ substantially. In a dataset curated for the absence of mainchain rearrangement upon binding, non-polar area burial is a major determinant of DeltaG models. However this DeltaG model does not fit well to the data for binding differences upon mutation. Burial of non-polar area is weighted down in fitting of DeltaDeltaG models. These calculations were made with no repacking of sidechains upon complexation, and only minimal packing upon mutation. We investigated the consequences of more extensive packing changes with a modified mean-field packing scheme. Rather than emphasising solvent exposure with relatively extended sidechains, rotamers are selected that exhibit maximal packing with protein. This provides solvent accessible areas for proteins that are much closer to those of experimental structures than the more extended sidechain regime. The new packing scheme increases changes in non-polar burial for mutants compared to wild-type proteins, but does not substantially improve agreement between DeltaG and DeltaDeltaG binding models.

CONCLUSION

We conclude that solvent accessible area, based on modelled mutant structures, is a poor correlate for DeltaDeltaG upon mutation. A simple volume-based, rather than solvent accessibility-based, model is constructed for DeltaG and DeltaDeltaG systems. This shows a more consistent behaviour. We discuss the efficacy of volume, as opposed to area, approaches to describe the energetic consequences of mutations at interfaces. This knowledge can be used to develop simple computational screens for binding in comparative modelled interfaces.

Constraints imposed by non-functional protein-protein interactions on gene expression and proteome size

Crowded intracellular environments present a challenge for proteins to form functional specific complexes while reducing non-functional interactions with promiscuous non-functional partners. Here we show how the need to minimize the waste of resources to non-functional interactions limits the proteome diversity and the average concentration of co-expressed and co-localized proteins. Using the results of high-throughput Yeast 2-Hybrid experiments, we estimate the characteristic strength of non-functional protein–protein interactions. By combining these data with the strengths of specific interactions, we assess the fraction of time proteins spend tied up in non-functional interactions as a function of their overall concentration. This allows us to sketch the phase diagram for baker's yeast cells using the experimentally measured concentrations and subcellular localization of their proteins. The positions of yeast compartments on the phase diagram are consistent with our hypothesis that the yeast proteome has evolved to operate closely to the upper limit of its size, whereas keeping individual protein concentrations sufficiently low to reduce non-functional interactions. These findings have implication for conceptual understanding of intracellular compartmentalization, multicellularity and differentiation.

Computer-based screening of functional conformers of proteins

A long-standing goal in biology is to establish the link between function, structure, and dynamics of proteins. Considering that protein function at the molecular level is understood by the ability of proteins to bind to other molecules, the limited structural data of proteins in association with other bio-molecules represents a major hurdle to understanding protein function at the structural level. Recent reports show that protein function can be linked to protein structure and dynamics through network centrality analysis, suggesting that the structures of proteins bound to natural ligands may be inferred computationally. In the present work, a new method is described to discriminate protein conformations relevant to the specific recognition of a ligand. The method relies on a scoring system that matches critical residues with central residues in different structures of a given protein. Central residues are the most traversed residues with the same frequency in networks derived from protein structures. We tested our method in a set of 24 different proteins and more than 260,000 structures of these in the absence of a ligand or bound to it. To illustrate the usefulness of our method in the study of the structure/dynamics/function relationship of proteins, we analyzed mutants of the yeast TATA-binding protein with impaired DNA binding. Our results indicate that critical residues for an interaction are preferentially found as central residues of protein structures in complex with a ligand. Thus, our scoring system effectively distinguishes protein conformations relevant to the function of interest.

Proteins participate in most of the doings of the cells through a variety of interactions. There is an intimate relationship between the function of a protein and its three-dimensional structure, but understanding this relationship remains an unsolved problem, in part due to the limited information on protein structures bound to other biological molecules. On the other hand, thousands of protein structures in the unbound or free form, are made public every year and these differ from those of the bound structures. How to predict the protein structure in the bound form may assist researchers in understanding the structure/function relationship. Here we report that protein structures bound to other molecules tend to present, as central amino acids, those that are critical for binding other molecules. This feature allowed us to identify the protein structures known to be involved in protein interactions from a screening of thousands of structures derived from the free form.

Propagation of large concentration changes in reversible protein-binding networks

We study how the dynamic equilibrium of the reversible protein-protein-binding network in yeast Saccharomyces cerevisiae responds to large changes in abundances of individual proteins. The magnitude of shifts between free and bound concentrations of their immediate and more distant neighbors in the network is influenced by such factors as the network topology, the distribution of protein concentrations among its nodes, and the average binding strength. Our primary conclusion is that, on average, the effects of a perturbation are strongly localized and exponentially decay with the network distance away from the perturbed node, which explains why, despite globally connected topology, individual functional modules in such networks are able to operate fairly independently. We also found that under specific favorable conditions, realized in a significant number of paths in the yeast network, concentration perturbations can selectively propagate over considerable network distances (up to four steps). Such "action-at-a-distance" requires high concentrations of heterodimers along the path as well as low free (unbound) concentration of intermediate proteins.