Inter-residue Contacts Research Articles

The evolution of logic circuits for the purpose of protein contact map prediction.

Predicting protein structure from sequence remains a major open problem in protein biochemistry. One component of predicting complete structures is the prediction of inter-residue contact patterns (contact maps). Here, we discuss protein contact map prediction by machine learning. We describe a novel method for contact map prediction that uses the evolution of logic circuits. These logic circuits operate on feature data and output whether or not two amino acids in a protein are in contact or not. We show that such a method is feasible, and in addition that evolution allows the logic circuits to be trained on the dataset in an unbiased manner so that it can be used in both contact map prediction and the selection of relevant features in a dataset.

Mechanical Unfolding Studies on Single-Domain SUMO and Multi-Domain Periplasmic Binding Proteins

Protein mechanics is a key component of many cellular and sub-cellular processes. The current review focuses on recent studies from our laboratory that probe the effect of sequence on the mechanical stability of structurally similar proteins and the unfolding mechanisms of multi-domain periplasmic binding proteins. Ubiquitin and small ubiquitin-related modifiers (SUMOs) are structurally similar and possess different mechanical stabilities, ubiquitin being stronger than SUMOs as revealed from their unfolding forces. These differences are plausibly due to the variation in number of inter-residue contacts. The unfolding potential widths determined from the pulling speed-dependent studies revealed that SUMOs are mechanically more flexible than ubiquitin. This flexibility of SUMOs plays a role in ligand binding and our single-molecule studies on SUMO interaction with SUMO binding motifs (SBMs) have shown that ligand binding decreases the SUMO flexibility and increases its mechanical stability. Studies on multi-domain periplasmic binding proteins have revealed that the unfolding energy landscape of these proteins is complex and they follow kinetic partitioning between two-state and multiple three-state pathways.

Coupled Residue-Residue Dynamics in Protein Allosteric Mechanisms

Although the relationship between structure and function in biomolecules is well established, it is not always adequate to provide a complete understanding of biomolecular function. The dynamical fluctuations of biomolecules can also play an essential role in function. Detailed understanding of how conformational dynamics orchestrates function in allosteric regulation of recognition and catalysis at atomic resolution remains ambiguous. The overarching goal is to understand how biomolecular dynamics are coupled to function by using atomistic molecular simulations to complement experiments. In this talk, we will discuss computational studies on members of a ubiquitous family of enzymes that catalyze peptidyl-prolyl bonds and regulate many sub-cellular processes. We analyze large amount of time-dependent multi-dimensional data with a coarse-grained approach and map key dynamical features within individual macrostates by defining dynamics in terms of residue-residue contacts. The effects of substrate binding are observed to be largely sensed at a location over 15 A from the active site, implying its importance in allostery. Using NMR experiments, we confirm that a dynamic cluster of residues in this distal region is directly coupled to the active site. Furthermore, the dynamical network of inter-residue contacts is coupled and temporally dispersed. Mapping these dynamical features and the coupling of dynamics to function has crucial ramifications in understanding allosteric regulation in enzymes and proteins in general.

Alpha Helices Are More Robust to Mutations than Beta Strands.

The rapidly increasing amount of data on human genetic variation has resulted in a growing demand to identify pathogenic mutations computationally, as their experimental validation is currently beyond reach. Here we show that alpha helices and beta strands differ significantly in their ability to tolerate mutations: helices can accumulate more mutations than strands without change, due to the higher numbers of inter-residue contacts in helices. This results in two patterns: a) the same number of mutations causes less structural change in helices than in strands; b) helices diverge more rapidly in sequence than strands within the same domains. Additionally, both helices and strands are significantly more robust than coils. Based on this observation we show that human missense mutations that change secondary structure are more likely to be pathogenic than those that do not. Moreover, inclusion of predicted secondary structure changes shows significant utility for improving upon state-of-the-art pathogenicity predictions.

Introducing a Clustering Step in a Consensus Approach for the Scoring of Protein-Protein Docking Models.

Correctly scoring protein-protein docking models to single out native-like ones is an open challenge. It is also an object of assessment in CAPRI (Critical Assessment of PRedicted Interactions), the community-wide blind docking experiment. We introduced in the field the first pure consensus method, CONSRANK, which ranks models based on their ability to match the most conserved contacts in the ensemble they belong to. In CAPRI, scorers are asked to evaluate a set of available models and select the top ten ones, based on their own scoring approach. Scorers’ performance is ranked based on the number of targets/interfaces for which they could provide at least one correct solution. In such terms, blind testing in CAPRI Round 30 (a joint prediction round with CASP11) has shown that critical cases for CONSRANK are represented by targets showing multiple interfaces or for which only a very small number of correct solutions are available. To address these challenging cases, CONSRANK has now been modified to include a contact-based clustering of the models as a preliminary step of the scoring process. We used an agglomerative hierarchical clustering based on the number of common inter-residue contacts within the models. Two criteria, with different thresholds, were explored in the cluster generation, setting either the number of common contacts or of total clusters. For each clustering approach, after selecting the top (most populated) ten clusters, CONSRANK was run on these clusters and the top-ranked model for each cluster was selected, in the limit of 10 models per target. We have applied our modified scoring approach, Clust-CONSRANK, to SCORE_SET, a set of CAPRI scoring models made recently available by CAPRI assessors, and to the subset of homodimeric targets in CAPRI Round 30 for which CONSRANK failed to include a correct solution within the ten selected models. Results show that, for the challenging cases, the clustering step typically enriches the ten top ranked models in native-like solutions. The best performing clustering approaches we tested indeed lead to more than double the number of cases for which at least one correct solution can be included within the top ten ranked models.

Conformational Shift of a β-Hairpin Peptide upon Complex Formation with an Oligo-proline Peptide Studied by Mass Spectrometry.

So-called super-secondary structures such as the β-hairpin, studied here, form an intermediate hierarchy between secondary and tertiary structures of proteins. Their sequence-derived 'pure' peptide backbone conformation is combined with 'remote' interstrand or interresidue contacts reminiscent of the 3D-structure of full-length proteins. This renders them ideally suited for studying potential nucleation sites of protein folding reactions as well as intermolecular interactions. But β-hairpins do not merely serve as model systems; their unique structure characteristics warrant a central role in structural studies on their own. In this study we applied photo cross-linking in combination with high-resolution mass spectrometry and computational modeling as well as with ion mobility-mass spectrometry to elucidate these structural properties. Using variants of a known β-hairpin representative, the so-called trpzip peptide and its ligands, we found evidence for a conformational transition of the β-hairpin and its impact on ligand binding.

Structural Polymorphism of Alzheimer's β-Amyloid Fibrils as Controlled by an E22 Switch: A Solid-State NMR Study.

The amyloid-β (Aβ) peptide of Alzheimer's disease (AD) forms polymorphic fibrils on the micrometer and molecular scales. Various fibril growth conditions have been identified to cause polymorphism, but the intrinsic amino acid sequence basis for this polymorphism has been unclear. Several single-site mutations in the center of the Aβ sequence cause different disease phenotypes and fibrillization properties. The E22G (Arctic) mutant is found in familial AD and forms protofibrils more rapidly than wild-type Aβ. Here, we use solid-state NMR spectroscopy to investigate the structure, dynamics, hydration and morphology of Arctic E22G Aβ40 fibrils. (13)C, (15)N-labeled synthetic E22G Aβ40 peptides are studied and compared with wild-type and Osaka E22Δ Aβ40 fibrils. Under the same fibrillization conditions, Arctic Aβ40 exhibits a high degree of polymorphism, showing at least four sets of NMR chemical shifts for various residues, while the Osaka and wild-type Aβ40 fibrils show a single or a predominant set of chemical shifts. Thus, structural polymorphism is intrinsic to the Arctic E22G Aβ40 sequence. Chemical shifts and inter-residue contacts obtained from 2D correlation spectra indicate that one of the major Arctic conformers has surprisingly high structural similarity with wild-type Aβ42. (13)C-(1)H dipolar order parameters, (1)H rotating-frame spin-lattice relaxation times and water-to-protein spin diffusion experiments reveal substantial differences in the dynamics and hydration of Arctic, Osaka and wild-type Aβ40 fibrils. Together, these results strongly suggest that electrostatic interactions in the center of the Aβ peptide sequence play a crucial role in the three-dimensional fold of the fibrils, and by inference, fibril-induced neuronal toxicity and AD pathogenesis.

In silico predicted structural and functional insights of all missense mutations on 2B domain of K1/K10 causing genodermatoses.

The K1 and K10 associated genodermatoses are characterized by clinical symptoms of mild to severe redness, blistering and hypertrophy of the skin. In this paper, we set out to computationally investigate the structural and functional effects of missense mutations on the 2B domain of K1/K10 heterodimer and its consequences in disease phenotype. We modeled the structure of the K1/K10 heterodimer based on crystal structures for the human homolog K5/K14 heterodimer, and identified that the missense mutations exert their effects on stability and assembly competence of the heterodimer by altering physico-chemical properties, interatomic interactions, and inter-residue atomic contacts. Comparative structural analysis between all the missense mutations and SNPs showed that the location and physico-chemical properties of the substituted amino acid are significantly correlated with phenotypic variations. In particular, we find evidence that a particular SNP (K10, p.E443K) is a pathogenic nsSNP which disrupts formation of the hydrophobic core and destabilizes the heterodimer through the loss of interatomic interactions. Our study is the first comprehensive report analyzing the mutations located on 2B domain of K1/K10 heterodimeric coiled-coil complex.

Dynamical network of residue–residue contacts reveals coupled allosteric effects in recognition, catalysis, and mutation

Detailed understanding of how conformational dynamics orchestrates function in allosteric regulation of recognition and catalysis remains ambiguous. Here, we simulate CypA using multiple-microsecond-long atomistic molecular dynamics in explicit solvent and carry out NMR experiments. We analyze a large amount of time-dependent multidimensional data with a coarse-grained approach and map key dynamical features within individual macrostates by defining dynamics in terms of residue-residue contacts. The effects of substrate binding are observed to be largely sensed at a location over 15 Å from the active site, implying its importance in allostery. Using NMR experiments, we confirm that a dynamic cluster of residues in this distal region is directly coupled to the active site. Furthermore, the dynamical network of interresidue contacts is found to be coupled and temporally dispersed, ranging over 4 to 5 orders of magnitude. Finally, using network centrality measures we demonstrate the changes in the communication network, connectivity, and influence of CypA residues upon substrate binding, mutation, and during catalysis. We identify key residues that potentially act as a bottleneck in the communication flow through the distinct regions in CypA and, therefore, as targets for future mutational studies. Mapping these dynamical features and the coupling of dynamics to function has crucial ramifications in understanding allosteric regulation in enzymes and proteins, in general.

R2C: improving ab initio residue contact map prediction using dynamic fusion strategy and Gaussian noise filter.

Inter-residue contacts in proteins dictate the topology of protein structures. They are crucial for protein folding and structural stability. Accurate prediction of residue contacts especially for long-range contacts is important to the quality of ab inito structure modeling since they can enforce strong restraints to structure assembly. In this paper, we present a new Residue-Residue Contact predictor called R2C that combines machine learning-based and correlated mutation analysis-based methods, together with a two-dimensional Gaussian noise filter to enhance the long-range residue contact prediction. Our results show that the outputs from the machine learning-based method are concentrated with better performance on short-range contacts; while for correlated mutation analysis-based approach, the predictions are widespread with higher accuracy on long-range contacts. An effective query-driven dynamic fusion strategy proposed here takes full advantages of the two different methods, resulting in an impressive overall accuracy improvement. We also show that the contact map directly from the prediction model contains the interesting Gaussian noise, which has not been discovered before. Different from recent studies that tried to further enhance the quality of contact map by removing its transitive noise, we designed a new two-dimensional Gaussian noise filter, which was especially helpful for reinforcing the long-range residue contact prediction. Tested on recent CASP10/11 datasets, the overall top L/5 accuracy of our final R2C predictor is 17.6%/15.5% higher than the pure machine learning-based method and 7.8%/8.3% higher than the correlated mutation analysis-based approach for the long-range residue contact prediction. http://www.csbio.sjtu.edu.cn/bioinf/R2C/Contact:hbshen@sjtu.edu.cn Supplementary data are available at Bioinformatics online.

Analysis of the interface variability in NMR structure ensembles of protein-protein complexes.

NMR structures consist in ensembles of conformers, all satisfying the experimental restraints, which exhibit a certain degree of structural variability. We analyzed here the interface in NMR ensembles of protein-protein heterodimeric complexes and found it to span a wide range of different conservations. The different exhibited conservations do not simply correlate with the size of the systems/interfaces, and are most probably the result of an interplay between different factors, including the quality of experimental data and the intrinsic complex flexibility. In any case, this information is not to be missed when NMR structures of protein-protein complexes are analyzed; especially considering that, as we also show here, the first NMR conformer is usually not the one which best reflects the overall interface. To quantify the interface conservation and to analyze it, we used an approach originally conceived for the analysis and ranking of ensembles of docking models, which has now been extended to directly deal with NMR ensembles. We propose this approach, based on the conservation of the inter-residue contacts at the interface, both for the analysis of the interface in whole ensembles of NMR complexes and for the possible selection of a single conformer as the best representative of the overall interface. In order to make the analyses automatic and fast, we made the protocol available as a web tool at: https://www.molnac.unisa.it/BioTools/consrank/consrank-nmr.html.

Long-Range Conformational Response of a PDZ Domain to Ligand Binding and Release: A Molecular Dynamics Study.

The binding of a ligand to a protein may induce long-range structural or dynamical changes in the biomacromolecule even at sites physically well separated from the binding pocket. A system for which such behavior has been widely discussed is the PDZ2 domain of human tyrosine phosphatase 1E. Here, we present results from equilibrium trajectories of the PDZ2 domain in the free and ligand-bound state, as well as nonequilibrium simulations of the relaxation of PDZ2 after removal of its peptide ligand. The study reveals changes in inter-residue contacts, backbone dihedral angles, and C(α) positions upon ligand release. Our findings show a long-range conformational response of the PDZ2 domain to ligand release in the form of a collective shift of the secondary structure elements α2, β2, β3, α1-β4, and the C terminal loop relative to the rest of the protein away from the N-terminus, and a shift of the loops β2-β3 and β1-β2 in the opposite direction. The shifts lead to conformational changes in the backbone, especially in the β2-β3 loop but also in the β5-α2 and the α2-β6 loop, and are accompanied by changes of inter-residue contacts mainly within the β2-β3 loop as well as between the α2 helix and other segments. The residues showing substantial changes of inter-residue contacts, backbone conformations, or C(α) positions are considered "key residues" for the long-range conformational response of PDZ2. By comparing these residues with various sets of residues highlighted by previous studies of PDZ2, we investigate the statistical correlation of the various approaches. Interestingly, we find a considerable correlation of our findings with several works considering structural changes but no significant correlations with approaches considering energy flow or networks based on inter-residue energies.

Contact- and distance-based principal component analysis of protein dynamics.

To interpret molecular dynamics simulations of complex systems, systematic dimensionality reduction methods such as principal component analysis (PCA) represent a well-established and popular approach. Apart from Cartesian coordinates, internal coordinates, e.g., backbone dihedral angles or various kinds of distances, may be used as input data in a PCA. Adopting two well-known model problems, folding of villin headpiece and the functional dynamics of BPTI, a systematic study of PCA using distance-based measures is presented which employs distances between Cα-atoms as well as distances between inter-residue contacts including side chains. While this approach seems prohibitive for larger systems due to the quadratic scaling of the number of distances with the size of the molecule, it is shown that it is sufficient (and sometimes even better) to include only relatively few selected distances in the analysis. The quality of the PCA is assessed by considering the resolution of the resulting free energy landscape (to identify metastable conformational states and barriers) and the decay behavior of the corresponding autocorrelation functions (to test the time scale separation of the PCA). By comparing results obtained with distance-based, dihedral angle, and Cartesian coordinates, the study shows that the choice of input variables may drastically influence the outcome of a PCA.

Analysis of inter-residue contacts reveals folding stabilizers in P-loops of potassium, sodium, and TRPV channels.

The family of P-loop channels includes potassium, sodium, calcium, cyclic nucleotide-gated and TRPV channels, as well as ionotropic glutamate receptors. Despite vastly different physiological and pharmacological properties, the channels have structurally conserved folding of the pore domain. Furthermore, crystallographic data demonstrate surprisingly similar mutual disposition of transmembrane and membrane-diving helices. To understand determinants of this conservation, here we have compared available high-resolution structures of sodium, potassium, and TRPV1 channels. We found that some residues, which are in matching positions of the sequence alignment, occur in different positions in the 3D alignment. Surprisingly, we found 3D mismatches in well-packed P-helices. Analysis of energetics of individual residues in Monte Carlo minimized structures revealed cyclic patterns of energetically favorable inter- and intra-subunit contacts of P-helices with S6 helices. The inter-subunit contacts are rather conserved in all the channels, whereas the intra-subunit contacts are specific for particular types of the channels. Our results suggest that these residue-residue contacts contribute to the folding stabilization. Analysis of such contacts is important for structural and phylogenetic studies of homologous proteins.

An Efficient Labelling Approach to Harness Backbone and Side‐Chain Protons in 1H‐Detected Solid‐State NMR Spectroscopy

Abstract1H‐detection can greatly improve spectral sensitivity in biological solid‐state NMR (ssNMR), thus allowing the study of larger and more complex proteins. However, the general requirement to perdeuterate proteins critically curtails the potential of 1H‐detection by the loss of aliphatic side‐chain protons, which are important probes for protein structure and function. Introduced herein is a labelling scheme for 1H‐detected ssNMR, and it gives high quality spectra for both side‐chain and backbone protons, and allows quantitative assignments and aids in probing interresidual contacts. Excellent 1H resolution in membrane proteins is obtained, the topology and dynamics of an ion channel were studied. This labelling scheme will open new avenues for the study of challenging proteins by ssNMR.

An Efficient Labelling Approach to Harness Backbone and Side-Chain Protons in (1) H-Detected Solid-State NMR Spectroscopy.

1H-detection can greatly improve spectral sensitivity in biological solid-state NMR (ssNMR), thus allowing the study of larger and more complex proteins. However, the general requirement to perdeuterate proteins critically curtails the potential of 1H-detection by the loss of aliphatic side-chain protons, which are important probes for protein structure and function. Introduced herein is a labelling scheme for 1H-detected ssNMR, and it gives high quality spectra for both side-chain and backbone protons, and allows quantitative assignments and aids in probing interresidual contacts. Excellent 1H resolution in membrane proteins is obtained, the topology and dynamics of an ion channel were studied. This labelling scheme will open new avenues for the study of challenging proteins by ssNMR.

Structural origin of slow diffusion in protein folding.

Experimental, theoretical, and computational studies of small proteins suggest that interresidue contacts not present in the folded structure play little or no role in the self-assembly mechanism. Non-native contacts can, however, influence folding kinetics by introducing additional local minima that slow diffusion over the global free-energy barrier between folded and unfolded states. Here, we combine single-molecule fluorescence with all-atom molecular dynamics simulations to discover the structural origin for the slow diffusion that markedly decreases the folding rate for a designed α-helical protein. Our experimental determination of transition path times and our analysis of the simulations point to non-native salt bridges between helices as the source, which provides a quantitative glimpse of how specific intramolecular interactions influence protein folding rates by altering dynamics and not activation free energies.

Codon-level information improves predictions of inter-residue contacts in proteins by correlated mutation analysis

Methods for analysing correlated mutations in proteins are becoming an increasingly powerful tool for predicting contacts within and between proteins. Nevertheless, limitations remain due to the requirement for large multiple sequence alignments (MSA) and the fact that, in general, only the relatively small number of top-ranking predictions are reliable. To date, methods for analysing correlated mutations have relied exclusively on amino acid MSAs as inputs. Here, we describe a new approach for analysing correlated mutations that is based on combined analysis of amino acid and codon MSAs. We show that a direct contact is more likely to be present when the correlation between the positions is strong at the amino acid level but weak at the codon level. The performance of different methods for analysing correlated mutations in predicting contacts is shown to be enhanced significantly when amino acid and codon data are combined.

Analysis and Ranking of Protein-Protein Docking Models Using Inter-Residue Contacts and Inter-Molecular Contact Maps.

In view of the increasing interest both in inhibitors of protein-protein interactions and in protein drugs themselves, analysis of the three-dimensional structure of protein-protein complexes is assuming greater relevance in drug design. In the many cases where an experimental structure is not available, protein-protein docking becomes the method of choice for predicting the arrangement of the complex. However, reliably scoring protein-protein docking poses is still an unsolved problem. As a consequence, the screening of many docking models is usually required in the analysis step, to possibly single out the correct ones. Here, making use of exemplary cases, we review our recently introduced methods for the analysis of protein complex structures and for the scoring of protein docking poses, based on the use of inter-residue contacts and their visualization in inter-molecular contact maps. We also show that the ensemble of tools we developed can be used in the context of rational drug design targeting protein-protein interactions.

ICOSA: A Distance-Dependent, Orientation-Specific Coarse-Grained Contact Potential for Protein Structure Modeling

The relative distance and orientation in contacting residue pairs plays a significant role in protein folding and stabilization. We hereby devise a new knowledge-based, coarse-grained contact potential, so-called ICOSA, by correlating inter-residue contact distance and orientation in evaluating pair-wise inter-residue interactions. The rationale of our approach is to establish icosahedral local coordinates to estimate the statistical residue contact distributions in all spherical triangular shells within a sphere. We extend the theory of finite ideal gas reference state to icosahedral local coordinates. ICOSA incorporates long-range contact interactions, which is critical to ICOSA sensitivity and is justified in statistical rigor. With only backbone atoms information, ICOSA is at least comparable to all-atom, fine-grained potentials such as Rosetta, DFIRE, I-TASSER, and OPUS in discriminating near-natives from misfold protein conformations in the Rosetta and I-TASSER protein decoy sets. ICOSA also outperforms a set of widely used coarse-grained potentials and is comparable to all-atom, fine-grained potentials in identifying CASP10 models.