The targets of CAPRI rounds 6-12

Six protein-protein complexes and two homodimeric proteins involved in a variety of biological processes were offered as targets to CAPRI by crystallographers in Rounds 6-12. CAPRI predictor groups had to predict their structure by docking the free proteins, which they did with a degree of success that depended largely on the amplitude of the conformation changes. In one case at least, the prediction pointed to alternative possibilities of interactions in the crystal of a complex, showing that docking methods have value even when there is an experimental structure.     

Assessing predictions of protein-protein interaction: the CAPRI experiment

The Critical Assessment of PRedicted Interactions (CAPRI) experiment was designed in 2000 to test protein docking algorithms in blind predictions of the structure of protein-protein complexes. In four years, 17 complexes offered by crystallographers as targets prior to publication, have been subjected to structure prediction by docking their two components. Models of these complexes were submitted by predictor groups and assessed by comparing their geometry to the X-ray structure and by evaluating the quality of the prediction of the regions of interaction and of the pair wise residue contacts. Prediction was successful on 12 of the 17 targets, most of the failures being due to large conformation changes that the algorithms could not cope with. Progress in the prediction quality observed in four years indicates that the experiment is a powerful incentive to develop new procedures that allow for flexibility during docking and incorporate nonstructural information. We therefore call upon structural biologists who study protein-protein complexes to provide targets for further rounds of CAPRI predictions.     

The targets of CAPRI rounds 3-5

Ten protein-protein complexes have been offered by X-ray crystallographers as targets for structure prediction in Rounds 3-5 of the CAPRI experiment. They illustrate molecular recognition in several domains of biology: enzyme regulation, antigen-antibody recognition, signal transduction, and oligomer assembly. The targets presented various degrees of difficulty to the predictors, depending on their status (bound when components were taken from the complex, unbound when coming from independent structures of the free proteins), the amplitude of conformation changes, and the amount of biological information available. Predictors produced high-quality models of 6 of the targets, good models of 3 others, and failed only in 1 case, where the conformation change was particularly large. This result demonstrates significant progress relative to earlier rounds of CAPRI.     

Assessing the fitness landscape revolution

According to Pigliucci and Kaplan, there is a revolution underway in how we understand fitness landscapes. Recent models suggest that a perennial problem in these landscapes—how to get from one peak across a fitness valley to another peak—is, in fact, non-existent. In this paper I assess the structure and the extent of Pigliucci and Kaplan’s proposed revolution and argue for two points. First, I provide an alternative interpretation of what underwrites this revolution, motivated by some recent work on model-based science. Second, I show that the implications of this revolution need to carefully assessed depending on question being asked, for peak-shifting is not central to all evolutionary questions that fitness landscapes have been used to explore.

Modeling side-chains using molecular dynamics improve recognition of binding region in CAPRI targets

The CAPRI-II experiment added an extra level of complexity to the problem of predicting protein-protein interactions by including 5 targets for which participants had to build or complete the 3-dimensional (3D) structure of either the receptor or ligand based on the structure of a close homolog. In this article, we describe how modeling key side-chains using molecular dynamics (MD) in explicit solvent improved the recognition of the binding region of a free energy- based computational docking method. In particular, we show that MD is able to predict with relatively high accuracy the rotamer conformation of the anchor side-chains important for molecular recognition as suggested by Rajamani et al. (Proc Natl Acad Sci USA 2004;101:11287-11292). As expected, the conformations are some of the most common rotamers for the given residue, while latch side-chains that undergo induced fit upon binding are forced into less common conformations. Using these models as starting conformations in conjunction with the rigid-body docking server ClusPro and the flexible docking algorithm SmoothDock, we produced valuable predictions for 6 of the 9 targets in CAPRI-II, missing only the 3 targets that underwent significant structural rearrangements upon binding. We also show that our free energy- based scoring function, consisting of the sum of van der Waals, Coulombic electrostatic with a distance-dependent dielectric, and desolvation free energy successfully discriminates the nativelike conformation of our submitted predictions. The latter emphasizes the critical role that thermodynamics plays on our methodology, and validates the generality of the algorithm to predict protein interactions.     

HADDOCK versus HADDOCK: new features and performance of HADDOCK2.0 on the CAPRI targets

Here we present version 2.0 of HADDOCK, which incorporates considerable improvements and new features. HADDOCK is now able to model not only protein-protein complexes but also other kinds of biomolecular complexes and multi-component (N > 2) systems. In the absence of any experimental and/or predicted information to drive the docking, HADDOCK now offers two additional ab initio docking modes based on either random patch definition or center-of-mass restraints. The docking protocol has been considerably improved, supporting among other solvated docking, automatic definition of semi-flexible regions, and inclusion of a desolvation energy term in the scoring scheme. The performance of HADDOCK2.0 is evaluated on the targets of rounds 4-11, run in a semi-automated mode using the original information we used in our CAPRI submissions. This enables a direct assessment of the progress made since the previous versions. Although HADDOCK performed very well in CAPRI (65% and 71% success rates, overall and for unbound targets only, respectively), a substantial improvement was achieved with HADDOCK2.0.     

Exploring the energy landscape of the genetic code

New insights into the arrangement of the genetic code table, based on the analysis of the physico-chemical properties of its molecular constituents, are reported in this paper. It will be demonstrated that the code has a twofold symmetry that is not apparent from the conventional code table, but becomes apparent when the codon-anticodon energies are listed for each triplet. The evolutionary development of the current code based on single base replacement mutations (transitions) from an 'iso-energetic' degenerated subset of 16 of the 64 codons is discussed. The energy landscape of all 64 codons is presented. A detailed analysis of the energy changes due to mutations in the 3rd, 1st or 2nd position of a codon reveals that the modern genetic code is highly robust. Changes come in small discrete steps that can be quantified in relation to the thermal noise of the system. The relation of the individual codon to its neighbours in the rearranged codon table can be completely understood based on thermodynamic considerations.     

Sampling the conformational energy landscape of a hyperthermophilic protein by engineering key substitutions

Proteins exist as a dynamic ensemble of interconverting substates, which defines their conformational energy landscapes. Recent work has indicated that mutations that shift the balance between conformational substates (CSs) are one of the main mechanisms by which proteins evolve new functions. In the present study, we probe this assertion by examining phenotypic protein adaptation to extreme conditions, using the allosteric tetrameric lactate dehydrogenase (LDH) from the hyperthermophilic bacterium Thermus thermophilus (Tt) as a model enzyme. In the presence of fructose 1, 6 bis-phosphate (FBP), allosteric LDHs catalyze the conversion of pyruvate to lactate with concomitant oxidation of nicotinamide adenine dinucleotide, reduced form (NADH). The catalysis involves a structural transition between a low-affinity inactive "T-state" and a high-affinity active "R-state" with bound FBP. During this structural transition, two important residues undergo changes in their side chain conformations. These are R171 and H188, which are involved in substrate and FBP binding, respectively. We designed two mutants of Tt-LDH with one ("1-Mut") and five ("5-Mut") mutations distant from the active site and characterized their catalytic, dynamical, and structural properties. In 1-Mut Tt-LDH, without FBP, the K(m)(Pyr) is reduced compared with that of the wild type, which is consistent with a complete shifting of the CS equilibrium of H188 to that observed in the R-state. By contrast, the CS populations of R171, k(cat) and protein stability are little changed. In 5-Mut Tt-LDH, without FBP, K(m)(Pyr) approaches the values it has with FBP and becomes almost temperature independent, k(cat) increases substantially, and the CS populations of R171 shift toward those of the R-state. These changes are accompanied by a decrease in protein stability at higher temperature, which is consistent with an increased flexibility at lower temperature. Together, these results show that the thermal properties of an enzyme can be strongly modified by only a few or even a single mutation, which serve to alter the equilibrium and, hence, the relative populations of functionally important native-state CSs, without changing the nature of the CSs themselves. They also provide insights into the types of mutational pathways by which protein adaptation to temperature is achieved.     

Probing the energy landscape of the membrane protein bacteriorhodopsin

The folding and stability of transmembrane proteins is a fundamental and unsolved biological problem. Here, single bacteriorhodopsin molecules were mechanically unfolded from native purple membranes using atomic force microscopy and force spectroscopy. The energy landscape of individual transmembrane alpha helices and polypeptide loops was mapped by monitoring the pulling speed dependence of the unfolding forces and applying Monte Carlo simulations. Single helices formed independently stable units stabilized by a single potential barrier. Mechanical unfolding of the helices was triggered by 3.9-7.7 A extension, while natural unfolding rates were of the order of 10(-3) s(-1). Besides acting as individually stable units, helices associated pairwise, establishing a collective potential barrier. The unfolding pathways of individual proteins reflect distinct pulling speed-dependent unfolding routes in their energy landscapes. These observations support the two-stage model of membrane protein folding in which alpha helices insert into the membrane as stable units and then assemble into the functional protein.     

Topological properties of the energy landscape of small peptides

The topology of the potential energy landscape (PEL) underlying the dynamics of a two dimensional off-lattice model for a heteropolymer is analyzed for different sequences of amino-acids. A statistical characterization of the metastable minima and first-order saddles of the PEL highlights structural differences in the landscape of good and bad folding sequences and provides insight on the chain dynamics during folding.     

Topological properties of the energy landscape of small peptides

In several works it has been shown that the interplay between short range and long range interactions, mimicking the hydrophobic effect, leads to the formation of the typical secondary structures in proteins, alpha-helices and beta-sheets. In this work we study in detail how the general properties of the energy landscape emerge in a model that presents both components. In this regard it proves useful a sort of perturbative approach. In our model many features of the energy landscape in absence of long range interaction can be determined analytically. The comparison between the energy landscape of this reduced model to that of the complete model gives interesting insight on the role of long range interactions.     

Large-scale characteristics of the energy landscape in protein-protein interactions

Characterization of intermolecular energy landscapes in protein-protein interactions is important for understanding the mechanisms of these interactions as well as for designing better protein docking methods. A simplified representation of the landscape was used for a systematic study of its large-scale characteristics in a large nonredundant dataset of protein complexes. The focus of the study is on the basic features of the low-resolution energy basins and their distribution on the landscape. The results clearly show that, in general, the number of such basins is small, these basins are well formed, correlated with actual binding modes, and the pattern of basins distribution depends on the type of the complex. For docking studies, the results suggest that adequate starting points for the structural refinement are detectable by low-resolution procedures and the number of such starting points is relatively small.     

Binding free energy landscape of domain-peptide interactions

Peptide recognition domains (PRDs) are ubiquitous protein domains which mediate large numbers of protein interactions in the cell. How these PRDs are able to recognize peptide sequences in a rapid and specific manner is incompletely understood. We explore the peptide binding process of PDZ domains, a large PRD family, from an equilibrium perspective using an all-atom Monte Carlo (MC) approach. Our focus is two different PDZ domains representing two major PDZ classes, I and II. For both domains, a binding free energy surface with a strong bias toward the native bound state is found. Moreover, both domains exhibit a binding process in which the peptides are mostly either bound at the PDZ binding pocket or else interact little with the domain surface. Consistent with this, various binding observables show a temperature dependence well described by a simple two-state model. We also find important differences in the details between the two domains. While both domains exhibit well-defined binding free energy barriers, the class I barrier is significantly weaker than the one for class II. To probe this issue further, we apply our method to a PDZ domain with dual specificity for class I and II peptides, and find an analogous difference in their binding free energy barriers. Lastly, we perform a large number of fixed-temperature MC kinetics trajectories under binding conditions. These trajectories reveal significantly slower binding dynamics for the class II domain relative to class I. Our combined results are consistent with a binding mechanism in which the peptide C terminal residue binds in an initial, rate-limiting step.     

Direct measurement of protein energy landscape roughness

The energy landscape of proteins is thought to have an intricate, corrugated structure. Such roughness should have important consequences on the folding and binding kinetics of proteins, as well as on their equilibrium fluctuations. So far, no direct measurement of protein energy landscape roughness has been made. Here, we combined a recent theory with single-molecule dynamic force spectroscopy experiments to extract the overall energy scale of roughness epsilon for a complex consisting of the small GTPase Ran and the nuclear transport receptor importin-beta. The results gave epsilon > 5k(B)T, indicating a bumpy energy surface, which is consistent with the ability of importin-beta to accommodate multiple conformations and to interact with different, structurally distinct ligands.     

Assessing the effect of landscape features on pond colonisation by an elusive amphibian invader using environmental DNA

1. Environmental DNA (eDNA) is becoming an essential tool for detecting aquatic invasive species and investigating their spread. Surprisingly, this technique has been very rarely used to investigate habitat selection, site occupancy, and colonisation despite its higher capacity to detect many species.
2. The African clawed frog (Xenopus laevis) is a principally aquatic amphibian introduced in several continents from South Africa. In western France, no recent systematic survey of the invasion range has been attempted, mainly because of the elusive nature of the species. Furthermore, the influence of landscape features on invasion has never been investigated, even if adults and juveniles are known to disperse overland and along river networks.
3. Using presence–absence data generated by an eDNA survey conducted across the known invasion front of X. laevis in western France, we aimed to determine whether and how the landscape features surrounding a pond influence the probability that a pond is colonised.
4. Xenopus laevis was detected well beyond the formerly known invasive distribution and at the outward end of some transects, suggesting that we did not reach the actual invasion front in these parts of the range. The landscape variables that best predicted the presence of X. laevis in a pond were topographic wetness index and grass cover within a buffer of 250 m.
5. Higher values of both topographic wetness index and grass cover were negatively related to the occurrence probability. The effects of these two variables more likely to reflect dispersal behaviour than habitat preferences at the pond scale.
6. By combining the high detection probability of eDNA survey techniques and a landscape ecology approach, we may gain valuable insight into the colonisation process of water bodies by elusive invasive species. Such information is crucial to prevent access to specific sites and locate invasion front areas where connectivity can be disrupted, thus increasing the effectiveness of management countermeasures.