Interchain Contacts Research Articles

Limits and potential of combined folding and docking.

MotivationIn the last decade, de novo protein structure prediction accuracy for individual proteins has improved significantly by utilising deep learning (DL) methods for harvesting the co-evolution information from large multiple sequence alignments (MSAs). The same approach can, in principle, also be used to extract information about evolutionary-based contacts across protein–protein interfaces. However, most earlier studies have not used the latest DL methods for inter-chain contact distance prediction. This article introduces a fold-and-dock method based on predicted residue-residue distances with trRosetta.ResultsThe method can simultaneously predict the tertiary and quaternary structure of a protein pair, even when the structures of the monomers are not known. The straightforward application of this method to a standard dataset for protein–protein docking yielded limited success. However, using alternative methods for generating MSAs allowed us to dock accurately significantly more proteins. We also introduced a novel scoring function, PconsDock, that accurately separates 98% of correctly and incorrectly folded and docked proteins. The average performance of the method is comparable to the use of traditional, template-based or ab initio shape-complementarity-only docking methods. Moreover, the results of conventional and fold-and-dock approaches are complementary, and thus a combined docking pipeline could increase overall docking success significantly. This methodology contributed to the best model for one of the CASP14 oligomeric targets, H1065. Availability and implementationAll scripts for predictions and analysis are available from https://github.com/ElofssonLab/bioinfo-toolbox/ and https://gitlab.com/ElofssonLab/benchmark5/. All models joined alignments, and evaluation results are available from the following figshare repository https://doi.org/10.6084/m9.figshare.14654886.v2.Supplementary information Supplementary data are available at Bioinformatics online.

Distance-based reconstruction of protein quaternary structures from inter-chain contacts.

Predicting the quaternary structure of protein complex is an important problem. Inter‐chain residue‐residue contact prediction can provide useful information to guide the ab initio reconstruction of quaternary structures. However, few methods have been developed to build quaternary structures from predicted inter‐chain contacts. Here, we develop the first method based on gradient descent optimization (GD) to build quaternary structures of protein dimers utilizing inter‐chain contacts as distance restraints. We evaluate GD on several datasets of homodimers and heterodimers using true/predicted contacts and monomer structures as input. GD consistently performs better than both simulated annealing and Markov Chain Monte Carlo simulation. Starting from an arbitrarily quaternary structure randomly initialized from the tertiary structures of protein chains and using true inter‐chain contacts as input, GD can reconstruct high‐quality structural models for homodimers and heterodimers with average TM‐score ranging from 0.92 to 0.99 and average interface root mean square distance from 0.72 Å to 1.64 Å. On a dataset of 115 homodimers, using predicted inter‐chain contacts as restraints, the average TM‐score of the structural models built by GD is 0.76. For 46% of the homodimers, high‐quality structural models with TM‐score ≥ 0.9 are reconstructed from predicted contacts. There is a strong correlation between the quality of the reconstructed models and the precision and recall of predicted contacts. Only a moderate precision or recall of inter‐chain contact prediction is needed to build good structural models for most homodimers. Moreover, GD improves the quality of quaternary structures predicted by AlphaFold2 on a Critical Assessment of Techniques for Protein Structure Prediction–Critical Assessments of Predictions of Interactions dataset.

DeepComplex: A Web Server of Predicting Protein Complex Structures by Deep Learning Inter-chain Contact Prediction and Distance-Based Modelling.

Proteins interact to form complexes. Predicting the quaternary structure of protein complexes is useful for protein function analysis, protein engineering, and drug design. However, few user-friendly tools leveraging the latest deep learning technology for inter-chain contact prediction and the distance-based modelling to predict protein quaternary structures are available. To address this gap, we develop DeepComplex, a web server for predicting structures of dimeric protein complexes. It uses deep learning to predict inter-chain contacts in a homodimer or heterodimer. The predicted contacts are then used to construct a quaternary structure of the dimer by the distance-based modelling, which can be interactively viewed and analysed. The web server is freely accessible and requires no registration. It can be easily used by providing a job name and an email address along with the tertiary structure for one chain of a homodimer or two chains of a heterodimer. The output webpage provides the multiple sequence alignment, predicted inter-chain residue-residue contact map, and predicted quaternary structure of the dimer. DeepComplex web server is freely available at http://tulip.rnet.missouri.edu/deepcomplex/web_index.html

Protein oligomer modeling guided by predicted interchain contacts in CASP14.

For CASP14, we developed deep learning‐based methods for predicting homo‐oligomeric and hetero‐oligomeric contacts and used them for oligomer modeling. To build structure models, we developed an oligomer structure generation method that utilizes predicted interchain contacts to guide iterative restrained minimization from random backbone structures. We supplemented this gradient‐based fold‐and‐dock method with template‐based and ab initio docking approaches using deep learning‐based subunit predictions on 29 assembly targets. These methods produced oligomer models with summed Z‐scores 5.5 units higher than the next best group, with the fold‐and‐dock method having the best relative performance. Over the eight targets for which this method was used, the best of the five submitted models had average oligomer TM‐score of 0.71 (average oligomer TM‐score of the next best group: 0.64), and explicit modeling of inter‐subunit interactions improved modeling of six out of 40 individual domains (ΔGDT‐TS > 2.0).

Molecular dynamics simulation for polyethylene crystallization: Effect of long chain branches

The influence of long branches on crystallization behavior has been studied by means of molecular dynamics simulations. Using two systems: polyethylene (PE) with long branches (LCB-PE) and PE without long branches (linear-PE) with the same molecular weight, we have examined the crystallization behavior of the two systems by molecular dynamics simulation. This paper explains the influence of long branches on the isothermal crystallization process and the non-isothermal crystallization process with similar initial interchain contact fraction (ICF) in terms of final ICF, crystal regions, crystallinity, concentration of tie chains and energy. It is found that the crystallization process is classified as two stages: the nucleation stage and the crystal growth stage. The existence of long branches is favorable for the first stage while unfavorable for the second stage. Knots that act as crystalline defects are excluded from the lamella, resulting in decreasing in regularity and crystallinity of molecular chains. From the perspective of potential energy and non-bond energy, LCB-PE has lower energy than linear-PE in the nucleation stage while the energy of linear-PE is lower than that of LCB-PE in the second stage. In short, the long branched chains inhibit the crystallization process.

DNCON2_Inter: predicting interchain contacts for homodimeric and homomultimeric protein complexes using multiple sequence alignments of monomers and deep learning

Deep learning methods that achieved great success in predicting intrachain residue-residue contacts have been applied to predict interchain contacts between proteins. However, these methods require multiple sequence alignments (MSAs) of a pair of interacting proteins (dimers) as input, which are often difficult to obtain because there are not many known protein complexes available to generate MSAs of sufficient depth for a pair of proteins. In recognizing that multiple sequence alignments of a monomer that forms homomultimers contain the co-evolutionary signals of both intrachain and interchain residue pairs in contact, we applied DNCON2 (a deep learning-based protein intrachain residue-residue contact predictor) to predict both intrachain and interchain contacts for homomultimers using multiple sequence alignment (MSA) and other co-evolutionary features of a single monomer followed by discrimination of interchain and intrachain contacts according to the tertiary structure of the monomer. We name this tool DNCON2_Inter. Allowing true-positive predictions within two residue shifts, the best average precision was obtained for the Top-L/10 predictions of 22.9% for homodimers and 17.0% for higher-order homomultimers. In some instances, especially where interchain contact densities are high, DNCON2_Inter predicted interchain contacts with 100% precision. We also developed Con_Complex, a complex structure reconstruction tool that uses predicted contacts to produce the structure of the complex. Using Con_Complex, we show that the predicted contacts can be used to accurately construct the structure of some complexes. Our experiment demonstrates that monomeric multiple sequence alignments can be used with deep learning to predict interchain contacts of homomeric proteins.

Accurate prediction of inter-protein residue-residue contacts for homo-oligomeric protein complexes.

Protein–protein interactions play a fundamental role in all cellular processes. Therefore, determining the structure of protein–protein complexes is crucial to understand their molecular mechanisms and develop drugs targeting the protein–protein interactions. Recently, deep learning has led to a breakthrough in intra-protein contact prediction, achieving an unusual high accuracy in recent Critical Assessment of protein Structure Prediction (CASP) structure prediction challenges. However, due to the limited number of known homologous protein–protein interactions and the challenge to generate joint multiple sequence alignments of two interacting proteins, the advances in inter-protein contact prediction remain limited. Here, we have proposed a deep learning model to predict inter-protein residue–residue contacts across homo-oligomeric protein interfaces, named as DeepHomo. Unlike previous deep learning approaches, we integrated intra-protein distance map and inter-protein docking pattern, in addition to evolutionary coupling, sequence conservation, and physico-chemical information of monomers. DeepHomo was extensively tested on both experimentally determined structures and realistic CASP-Critical Assessment of Predicted Interaction (CAPRI) targets. It was shown that DeepHomo achieved a high precision of >60% for the top predicted contact and outperformed state-of-the-art direct-coupling analysis and machine learning-based approaches. Integrating predicted inter-chain contacts into protein–protein docking significantly improved the docking accuracy on the benchmark dataset of realistic homo-dimeric targets from CASP-CAPRI experiments. DeepHomo is available at http://huanglab.phys.hust.edu.cn/DeepHomo/

FLIPPER: Predicting and Characterizing Linear Interacting Peptides in the Protein Data Bank

A large fraction of peptides or protein regions are disordered in isolation and fold upon binding. These regions, also called MoRFs, SLiMs or LIPs, are often associated with signaling and regulation processes. However, despite their importance, only a limited number of examples are available in public databases and their automatic detection at the proteome level is problematic. Here we present FLIPPER, an automatic method for the detection of structurally linear sub-regions or peptides that interact with another chain in a protein complex. FLIPPER is a random forest classification that takes the protein structure as input and provides the propensity of each amino acid to be part of a LIP region. Models are built taking into consideration structural features such as intra- and inter-chain contacts, secondary structure, solvent accessibility in both bound and unbound state, structural linearity and chain length. FLIPPER is accurate when evaluated on non-redundant independent datasets, 99% precision and 99% sensitivity on PixelDB-25 and 87% precision and 88% sensitivity on DIBS-25. Finally, we used FLIPPER to process the entire Protein Data Bank and identified different classes of LIPs based on different binding modes and partner molecules. We provide a detailed description of these LIP categories and show that a large fraction of these regions are not detected by disorder predictors. All FLIPPER predictions are integrated in the MobiDB 4.0 database.

Heat-induced degradation of fibrils: Exponential vs logistic kinetics.

The degradation of fibrils under the influence of thermal fluctuations was studied experimentally by various groups around the world. In the first set of experiments, it was shown that the decay of fibril content, which can be measured by the ThT fluorescence assay, obeys a bi-exponential function. In the second series of experiments, it was demonstrated that when the monomers separated from the aggregate are not recyclable, the time dependence of the number of monomers belonging to the dominant cluster is described by a single-exponential function if the fraction of bound chains becomes less than a certain threshold. Note that the time dependence of the fraction of bound chains can be measured by tryptophan fluorescence. To understand these interesting experimental results, we developed a phenomenological theory and performed molecular simulation. According to our theory and simulations using the lattice and all-atom models, the time dependence of bound chains is described by a logistic function, which slowly decreases at short time scales but becomes a single exponential function at large time scales. The results, obtained by using lattice and all-atom simulations, ascertained that the time dependence of the fibril content can be described by a bi-exponential function that decays faster than the logistic function on short time scales. We have uncovered the molecular mechanism for the distinction between the logistic and bi-exponential behavior. Since the dissociation of the chain from the fibrils requires the breaking of a greater number of inter-chain contacts as compared to the breaking of the beta sheet structure, the decrease in the number of connected chains is slower than the fibril content. Therefore, the time dependence of the aggregate size is logistic, while the two-exponential behavior is preserved for the content of fibrils. Our results are in agreement with the results obtained in both sets of experiments.

Nearly Isotropic Conjugated Polymer Aggregates with Efficient Local Exciton Diffusion

To bridge the gap between single-molecule and bulk thin film studies of organic semiconductors and understand the influence of molecular structure on photophysical properties across scales, we prepared and characterized aggregates containing a few to more than a thousand single chains via well-controlled solvent vapor swelling of an inert polymeric matrix containing the conjugated polymer poly[2-methoxy-5-(2-ethylhexyloxy)-1,4-phenylenevinylene] (MEH-PPV). The single chains were initially immobilized in conformations with few interchain contacts, which can support exciton funneling on the single-molecule level. We show that aggregates prepared via solvent swelling and templated on such molecules result in largely isotropic aggregates. Analysis of aggregate polarization anisotropy, photoluminescence transient decay, and photoluminescence spectra as a function of aggregate size suggests that in these aggregates exciton diffusion is locally enhanced upon initial aggregation but does not reach extended length...

Liquid-Liquid Phase Separation Is Driven by Large-Scale Conformational Unwinding and Fluctuations of Intrinsically Disordered Protein Molecules.

Liquid-liquid phase separation occurs via a multitude of transient, noncovalent, and intermolecular interactions resulting in phase transition of intrinsically disordered proteins/regions (IDPs/IDRs) and other biopolymers into mesoscopic, dynamic, nonstoichiometric, and supramolecular condensates. Here we present a unique case to demonstrate that unusual conformational expansion events coupled with solvation and fluctuations drive phase separation of tau, an IDP associated with Alzheimer's disease. Using intramolecular excimer emission as a powerful proximity readout, we show the unraveling of polypeptide chains within the protein-rich interior environment that can promote critical interchain contacts. Using highly sensitive picosecond time-resolved fluorescence depolarization measurements, we directly capture rapid large-amplitude torsional fluctuations in the extended chains that can control the relay of making-and-breaking of noncovalent intermolecular contacts maintaining the internal fluidity. The interplay of these key molecular parameters can be of prime importance in modulating the mesoscale material property of liquid-like condensates and their maturation into pathological gel-like and solid-like aggregates.

An Insight into molecular structure and properties of flexible amorphous polymers: A molecular dynamics simulation approach

ABSTRACTVisualization of polymer molecules by molecular dynamics simulation remains a challenging area in molecular modeling, as it involves a number of factors like type of force field, simulation time, simulation steps, and so forth. In our present study, we have used the condensed‐phase optimized molecular potentials for atomistic simulation studies (COMPASS) force field, which is specific for polymers and organic molecules, in order to visualize the macromolecular chains of various flexible amorphous polymers: natural rubber, polybutadiene rubber, styrene–butadiene rubber, nitrile rubber, polychloroprene rubber, and fluoroelastomer. The study covered a wide range from nonpolar rubbers to polar rubbers, and the COMPASS force field was promising to understand the local structure and the packing of the chains inside the simulation box. The distance between different adjacent pairs of carbon atoms within the polymer chains was discussed in detail from intramolecular radial distribution function and represented pictorially in the polymer chains. Various bond angles were also examined for further details. In addition, interchain contacts, glass‐transition temperature, and solubility parameter were predicted and compared with the experimental values. The diffusive characteristic of the chains was assessed by mean square displacement which in turn described the polymer chain mobility. In essence, the present study is expected to aid in the vivid conceptualization of molecular orientation of various polymers and would help in predicting various physical properties. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2019, 136, 47457

Semiconducting polymer blends that exhibit stable charge transport at high temperatures.

Although high-temperature operation (i.e., beyond 150°C) is of great interest for many electronics applications, achieving stable carrier mobilities for organic semiconductors at elevated temperatures is fundamentally challenging. We report a general strategy to make thermally stable high-temperature semiconducting polymer blends, composed of interpenetrating semicrystalline conjugated polymers and high glass-transition temperature insulating matrices. When properly engineered, such polymer blends display a temperature-insensitive charge transport behavior with hole mobility exceeding 2.0 cm2/V·s across a wide temperature range from room temperature up to 220°C in thin-film transistors.

Beating the heat by blending

Polymers Charge carriers move through semiconductor polymers by hopping transport. In principle, these polymers should be more conductive at higher temperatures. In practice, conductivity drops at high temperatures because interchain contacts are disrupted, which limits potential applications. Gumyusenge et al. now show that appropriate blending of a semicrystalline conjugated polymer with an insulating polymer that has a high glass-transition temperature creates a morphology that stabilizes a network of semiconductor channels. High charge conductivity was maintained in these materials up to 220°C. Science , this issue p. [1131][1] [1]: /lookup/doi/10.1126/science.aau0759

Entanglement Reduction Induced by Geometrical Confinement in Polymer Thin Films

We report simulation results on melts of entangled linear polymers confined in a free-standing thin film. We study how the geometric constraints imposed by the confinement alter the entanglement state of the system compared to the equivalent bulk system using various observables. We find that the confinement compresses the chain conformation uniaxially, decreasing the volume pervaded by the chain, which in turn reduces the number of the accessible inter-chain contact that could lead to entanglements. This local and non-uniform effect depends on the position of the chain within the film. We also test a recently presented theory that predicts how the number of entanglements decreases with geometrical confinement.

Structure of aspartate β-semialdehyde dehydrogenase from Francisella tularensis.

Aspartate β-semialdehyde dehydrogenase (ASADH) is an enzyme involved in the diaminopimelate pathway of lysine biosynthesis. It is essential for the viability of many pathogenic bacteria and therefore has been the subject of considerable research for the generation of novel antibiotic compounds. This manuscript describes the first structure of ASADH from Francisella tularensis, the causative agent of tularemia and a potential bioterrorism agent. The structure was determined at 2.45 Å resolution and has a similar biological assembly to other bacterial homologs. ASADH is known to be dimeric in bacteria and have extensive interchain contacts, which are thought to create a half-sites reactivity enzyme. ASADH from higher organisms shows a tetrameric oligomerization, which also has implications for both reactivity and regulation. This work analyzes the apo form of F. tularensis ASADH, as well as the binding of the enzyme to its cofactor NADP+.

Molecular mechanism of reflectin’s tunable biophotonic control: Opportunities and limitations for new optoelectronics

Discovery that reflectin proteins fill the dynamically tunable Bragg lamellae in the reflective skin cells of certain squids has prompted efforts to design new reflectin-inspired systems for dynamic photonics. But new insights into the actual role and mechanism of action of the reflectins constrain and better define the opportunities and limitations for rationally designing optical systems with reflectin-based components. We and our colleagues have discovered that the reflectins function as a signal-controlled molecular machine, regulating an osmotic motor that tunes the thickness, spacing, and refractive index of the tunable, membrane-bound Bragg lamellae in the iridocytes of the loliginid squids. The tunable reflectin proteins, characterized by a variable number of highly conserved peptide domains interspersed with positively charged linker segments, are restricted in intra- and inter-chain contacts by Coulombic repulsion. Physiologically, this inhibition is progressively overcome by charge-neutralization resulting from acetylcholine (neurotransmitter)-induced, site-specific phosphorylation, triggering the simultaneous activation and progressive tuning of reflectance from red to blue. Details of this process have been resolved through in vitro analyses of purified recombinant reflectins, controlling charge-neutralization by pH-titration or mutation as surrogates for the in vivo phosphorylation. Results of these analyses have shown that neutralization overcoming the Coulombic inhibition reversibly and cyclably triggers condensation and secondary folding of the reflectins, with the emergence of previously cryptic, phase-segregated hydrophobic domains enabling hierarchical assembly. This tunable, reversible, and cyclable assembly regulates the Gibbs-Donnan mediated osmotic shrinking or swelling of the Bragg lamellae that tunes the brightness and color of reflected light. Our most recent studies have revealed a direct relationship between the extent of charge neutralization and the size of the reflectin assemblies, further explaining the synergistic effects on the intensity and wavelength of reflected light. Mutational analyses show that the “switch” controlling reflectins’ structural transitions is distributed along the protein, while detailed comparisons of the sequences and structures of the recently evolved tunable reflectins to those of their ancestral, non-tunable homologs are helping to identify the specific structural determinants governing tunability.

Conjugated Polyelectrolyte-Based New Strategy for in Situ Detection of Carbon Dioxide.

A conjugated polymer centered on fluorene and 2,1,3-benzothia-diazole (PFBT) is prepared for sensing CO2 in situ with high sensitivity and low background. Upon introducing CO2, the weaker electrostatic repulsion and stronger hydrophobic interactions between neighboring PFBT molecules enhance the interchain contacts compared to that without CO2, leading to the energy transfer from fluorene to 2,1,3-benzothia-diazole sites and the emission color shift from blue to green, which is sensitive to sensing CO2 in atmospheric air with a content of ∼400 ppm. Importantly, PFBT is employed to monitor photosynthesis and respiration upon cycling day and night in situ.

Factors responsible for the aggregation behavior of hydrophobic polyelectrolyte PEA in aqueous solution studied by molecular dynamics simulations

Self-association (i.e. interchain aggregation) behavior of atactic poly(ethacrylic acid) PEA in dilute aqueous solution as function of degree-of-neutralization by Na+ counter-ions (i.e. charge fraction f) was investigated by molecular dynamics simulations. Aggregation is found to occur in the range 0≤f≤0.7 in agreement with experimental results compared at specified polymer concentration Cp=0.36mol/l in dilute solution. The macromolecular solution was characterized and analysed for radius-of-gyration, torsion angle distribution, inter and intra-molecular hydrogen bonds, radial distribution functions of intermolecular and inter-atomic pairs, inter-chain contacts and solvation enthalpy. The PEA chains form aggregate through attractive inter-chain interaction via hydrogen bonding, in the range f<0.7, in agreement with experimental observation. The numbers of inter-chain contacts decreases with f. A critical structural transition occurs at f=0.7, observed via simulations for the first time, in Rg as well as inter-chain H-bonds. The inter-chain distance increases with f due to repulsive interactions between COO− groups on the chains. PEA-PEA electrostatic interactions dominant solvation enthalpy. The PEA solvation enthalpy becomes increasingly favorable with increase in f. The transition enthalpy change, in going from uncharged (acid) state to fully charged state (f=1) is unfavorable towards aggregate formation.

Inhibition of GNNQQNY prion peptide aggregation by trehalose: a mechanistic view.

Deposition of amyloid fibrils is the seminal event in the pathogenesis of numerous neurodegenerative diseases. The formation of this amyloid assembly is the manifestation of a cascade of structural transitions including toxic oligomer formation in the early stages of aggregation. Thus a viable therapeutic strategy involves the use of small molecular ligands to interfere with this assembly. In this perspective, we have explored the kinetics of aggregate formation of the fibril forming GNNQQNY peptide fragment from the yeast prion protein SUP35 using multiple all atom MD simulations with explicit solvent and provided mechanistic insights into the way trehalose, an experimentally known aggregation inhibitor, modulates the aggregation pathway. The results suggest that the assimilation process is impeded by different barriers at smaller and larger oligomeric sizes: the initial one being easily surpassed at higher temperatures and peptide concentrations. The kinetic profile demonstrates that trehalose delays the aggregation process by increasing both these activation barriers, specifically the latter one. It increases the sampling of small-sized aggregates that lack the beta sheet conformation. Analysis reveals that the barrier in the growth of larger stable oligomers causes the formation of multiple stable small oligomers which then fuse together bimolecularly. The PCA of 26 properties was carried out to deconvolute the events within the temporary lag phases, which suggested dynamism in lags involving an increase in interchain contacts and burial of SASA. The predominant growth route is monomer addition, which changes to condensation on account of a large number of depolymerisation events in the presence of trehalose. The favourable interaction of trehalose specifically with the sidechain of the peptide promotes crowding of trehalose molecules in its vicinity - the combination of both these factors imparts the observed behaviour. Furthermore, increasing trehalose concentration leads to faster expulsion of water molecules than interpeptide interactions. These expelled water molecules have larger translational movement, suggesting an entropy factor to favor the assembly process. Different conformations observed under this condition suggest the role of water molecules in guiding the morphology of the aggregates as well. A similar scenario exists on increasing peptide concentration.