Structures Of Protein Complexes Research Articles

A gated graph transformer for protein complex structure quality assessment and its performance in CASP15.

Proteins interact to form complexes to carry out essential biological functions. Computational methods such as AlphaFold-multimer have been developed to predict the quaternary structures of protein complexes. An important yet largely unsolved challenge in protein complex structure prediction is to accurately estimate the quality of predicted protein complex structures without any knowledge of the corresponding native structures. Such estimations can then be used to select high-quality predicted complex structures to facilitate biomedical research such as protein function analysis and drug discovery. In this work, we introduce a new gated neighborhood-modulating graph transformer to predict the quality of 3D protein complex structures. It incorporates node and edge gates within a graph transformer framework to control information flow during graph message passing. We trained, evaluated and tested the method (called DProQA) on newly-curated protein complex datasets before the 15th Critical Assessment of Techniques for Protein Structure Prediction (CASP15) and then blindly tested it in the 2022 CASP15 experiment. The method was ranked 3rd among the single-model quality assessment methods in CASP15 in terms of the ranking loss of TM-score on 36 complex targets. The rigorous internal and external experiments demonstrate that DProQA is effective in ranking protein complex structures. The source code, data, and pre-trained models are available at https://github.com/jianlin-cheng/DProQA.

Deep Local Analysis deconstructs protein-protein interfaces and accurately estimates binding affinity changes upon mutation.

The spectacular recent advances in protein and protein complex structure prediction hold promise for reconstructing interactomes at large-scale and residue resolution. Beyond determining the 3D arrangement of interacting partners, modeling approaches should be able to unravel the impact of sequence variations on the strength of the association. In this work, we report on Deep Local Analysis, a novel and efficient deep learning framework that relies on a strikingly simple deconstruction of protein interfaces into small locally oriented residue-centered cubes and on 3D convolutions recognizing patterns within cubes. Merely based on the two cubes associated with the wild-type and the mutant residues, DLA accurately estimates the binding affinity change for the associated complexes. It achieves a Pearson correlation coefficient of 0.735 on about 400 mutations on unseen complexes. Its generalization capability on blind datasets of complexes is higher than the state-of-the-art methods. We show that taking into account the evolutionary constraints on residues contributes to predictions. We also discuss the influence of conformational variability on performance. Beyond the predictive power on the effects of mutations, DLA is a general framework for transferring the knowledge gained from the available non-redundant set of complex protein structures to various tasks. For instance, given a single partially masked cube, it recovers the identity and physicochemical class of the central residue. Given an ensemble of cubes representing an interface, it predicts the function of the complex. Source code and models are available at http://gitlab.lcqb.upmc.fr/DLA/DLA.git.

Nuclear lamina strain states revealed by intermolecular force biosensor

Nuclear lamins have been considered an important structural element of the nucleus. The nuclear lamina is thought both to shield DNA from excessive mechanical forces and to transmit mechanical forces onto the DNA. However, to date there is not yet a technical approach to directly measure mechanical forces on nuclear lamins at the protein level. To overcome this limitation, we developed a nanobody-based intermolecular tension FRET biosensor capable of measuring the mechanical strain of lamin filaments. Using this sensor, we were able to show that the nuclear lamina is subjected to significant force. These forces are dependent on nuclear volume, actomyosin contractility, functional LINC complex, chromatin condensation state, cell cycle, and EMT. Interestingly, large forces were also present on nucleoplasmic lamins, indicating that these lamins may also have an important mechanical role in the nucleus. Overall, we demonstrate that the nanobody-based approach allows construction of biosensors for complex protein structures for mechanobiology studies.

Multiscale Topological Indices for the Quantitative Prediction of SARS CoV-2 Binding Affinity Change upon Mutations.

The Coronavirus disease 2019 (COVID-19) has affected people's lives and the development of the global economy. Biologically, protein-protein interactions between SARS-CoV-2 surface spike (S) protein and human ACE2 protein are the key mechanism behind the COVID-19 disease. In this study, we provide insights into interactions between the SARS-CoV-2 S-protein and ACE2, and propose topological indices to quantitatively characterize the impact of mutations on binding affinity changes (ΔΔG). In our model, a series of nested simplicial complexes and their related adjacency matrices at various different scales are generated from a specially designed filtration process, based on the 3D structures of spike-ACE2 protein complexes. We develop a set of multiscale simplicial complexes-based topological indices, for the first time. Unlike previous graph network models, which give only a qualitative analysis, our topological indices can provide a quantitative prediction of the binding affinity change caused by mutations and achieve great accuracy. In particular, for mutations that happened at specifical amino acids, such as Polar amino acids or Arginine amino acids, the correlation between our topological gravity model index and binding affinity change, in terms of Pearson correlation coefficient, can be higher than 0.8. As far as we know, this is the first time multiscale topological indices have been used in the quantitative analysis of protein-protein interactions.

Discriminating physiological from non-physiological interfaces in structures of protein complexes: A community-wide study.

Reliably scoring and ranking candidate models of protein complexes and assigning their oligomeric state from the structure of the crystal lattice represent outstanding challenges. A community-wide effort was launched to tackle these challenges. The latest resources on protein complexes and interfaces were exploited to derive a benchmark dataset consisting of 1677 homodimer protein crystal structures, including a balanced mix of physiological and non-physiological complexes. The non-physiological complexes in the benchmark were selected to bury a similar or larger interface area than their physiological counterparts, making it more difficult for scoring functions to differentiate between them. Next, 252 functions for scoring protein-protein interfaces previously developed by 13 groups were collected and evaluated for their ability to discriminate between physiological and non-physiological complexes. A simple consensus score generated using the best performing score of each of the 13 groups, and a cross-validated Random Forest (RF) classifier were created. Both approaches showed excellent performance, with an area under the Receiver Operating Characteristic (ROC) curve of 0.93 and 0.94, respectively, outperforming individual scores developed by different groups. Additionally, AlphaFold2 engines recalled the physiological dimers with significantly higher accuracy than the non-physiological set, lending support to the reliability of our benchmark dataset annotations. Optimizing the combined power of interface scoring functions and evaluating it on challenging benchmark datasets appears to be a promising strategy.

The role of ion channels in the vital functions of the cell

The cell ion channels are complex protein structures with molecular systems of opening, closing, selectivity, inactivation and regulation. The purpose of this review is to generalize and systematize literature data on the structural and functional characteristics of ion channels and methods for studying their activity. Violations of their activity can lead to a change in the functioning of the cell and the whole organism as a whole, therefore, further study of the structural and physiological characteristics of ion channels is promising and relevant.

Correspondence between functional scores from deep mutational scans and predicted effects on protein stability.

Many methodologically diverse computational methods have been applied to the growing challenge of predicting and interpreting the effects of protein variants. As many pathogenic mutations have a perturbing effect on protein stability or intermolecular interactions, one highly interpretable approach is to use protein structural information to model the physical impacts of variants and predict their likely effects on protein stability and interactions. Previous efforts have assessed the accuracy of stability predictors in reproducing thermodynamically accurate values and evaluated their ability to distinguish between known pathogenic and benign mutations. Here, we take an alternate approach, and explore how well stability predictor scores correlate with functional impacts derived from deep mutational scanning (DMS) experiments. In this work, we compare the predictions of 9 protein stability-based tools against mutant protein fitness values from 49 independent DMS datasets, covering 170,940 unique single amino acid variants. We find that FoldX and Rosetta show the strongest correlations with DMS-based functional scores, similar to their previous top performance in distinguishing between pathogenic and benign variants. For both methods, performance is considerably improved when considering intermolecular interactions from protein complex structures, when available. Furthermore, using these two predictors, we derive a 'Foldetta' consensus score, which improves upon the performance of both, and manages to match dedicated variant effect predictors in reflecting variant functional impacts. Finally, we also highlight that predicted stability effects show consistently higher correlations with certain DMS experimental phenotypes, particularly those based upon protein abundance, and, in certain cases, can significantly outcompete sequence-based variant effect prediction methodologies for predicting functional scores from DMS experiments. This article is protected by copyright. All rights reserved.

Improved the heterodimer protein complex prediction with protein language models.

AlphaFold-Multimer has greatly improved the protein complex structure prediction, but its accuracy also depends on the quality of the multiple sequence alignment (MSA) formed by the interacting homologs (i.e. interologs) of the complex under prediction. Here we propose a novel method, ESMPair, that can identify interologs of a complex using protein language models. We show that ESMPair can generate better interologs than the default MSA generation method in AlphaFold-Multimer. Our method results in better complex structure prediction than AlphaFold-Multimer by a large margin (+10.7% in terms of the Top-5 best DockQ), especially when the predicted complex structures have low confidence. We further show that by combining several MSA generation methods, we may yield even better complex structure prediction accuracy than Alphafold-Multimer (+22% in terms of the Top-5 best DockQ). By systematically analyzing the impact factors of our algorithm we find that the diversity of MSA of interologs significantly affects the prediction accuracy. Moreover, we show that ESMPair performs particularly well on complexes in eucaryotes.

Structure Relaxation Approximation (SRA) for Elucidation of Protein Structures from Ion Mobility Measurements (II). Protein Complexes.

Characterizing structures of protein complexes and their disease-related aberrations is essential to understanding molecular mechanisms of many biological processes. Electrospray ionization coupled with hybrid ion mobility/mass spectrometry (ESI-IM/MS) methods offer sufficient sensitivity, sample throughput, and dynamic range to enable systematic structural characterization of proteomes. However, because ESI-IM/MS characterizes ionized protein systems in the gas phase, it generally remains unclear to what extent the protein ions characterized by IM/MS have retained their solution structures. Here, we discuss the first application of our computational structure relaxation approximation [Bleiholder, C.; et al. J. Phys. Chem. B 2019, 123 (13), 2756-2769] to assign structures of protein complexes in the range from ∼16 to ∼60 kDa from their "native" IM/MS spectra. Our analysis shows that the computed IM/MS spectra agree with the experimental spectra within the errors of the methods. The structure relaxation approximation (SRA) indicates that native backbone contacts appear largely retained in the absence of solvent for the investigated protein complexes and charge states. Native contacts between polypeptide chains of the protein complex appear to be retained to a comparable extent as contacts within a folded polypeptide chain. Our computations also indicate that the hallmark "compaction" often observed for protein systems in native IM/MS measurements appears to be a poor indicator of the extent to which native residue-residue interactions are lost in the absence of solvent. Further, the SRA indicates that structural reorganization of the protein systems in IM/MS measurements appears driven largely by remodeling of the protein surface that increases its hydrophobic content by approximately 10%. For the systems studied here, this remodeling of the protein surface appears to occur mainly by structural reorganization of surface-associated hydrophilic amino acid residues not associated with β-strand secondary structure elements. Properties related to the internal protein structure, as assessed by void volume or packing density, appear unaffected by remodeling of the surface. Taken together, the structural reorganization of the protein surface appears to be generic in nature and to sufficiently stabilize protein structures to render them metastable on the time scale of IM/MS measurements.

Unraveling the mechanisms of cardiolipin function: The role of oxidative polymerization of unsaturated acyl chains

Cardiolipin is a unique phospholipid of the inner mitochondrial membrane (IMM) as well as in bacteria. It performs several vital functions such as resisting osmotic rupture and stabilizing the supramolecular structure of large membrane proteins, like ATP synthases and respirasomes. The process of cardiolipin biosynthesis results in the production of immature cardiolipin. A subsequent step is required for its maturation when its acyl groups are replaced with unsaturated acyl chains, primarily linoleic acid. Linoleic acid is the major fatty acid of cardiolipin across all organs and tissues, except for the brain. Linoleic acid is not synthesized by mammalian cells. It has the unique ability to undergo oxidative polymerization at a moderately accelerated rate compared to other unsaturated fatty acids. This property can enable cardiolipin to form covalently bonded net-like structures essential for maintaining the complex geometry of the IMM and gluing the quaternary structure of large IMM protein complexes. Unlike triglycerides, phospholipids possess only two covalently linked acyl chains, which constrain their capacity to develop robust and complicated structures through oxidative polymerization of unsaturated acyl chains. Cardiolipin, on the other hand, has four fatty acids at its disposal to form covalently bonded polymer structures. Despite its significance, the oxidative polymerization of cardiolipin has been overlooked due to the negative perception surrounding biological oxidation and methodological difficulties. Here, we discuss an intriguing hypothesis that oxidative polymerization of cardiolipin is essential for the structure and function of cardiolipin in the IMM in physiological conditions. In addition, we highlight current challenges associated with the identification and characterization of oxidative polymerization of cardiolipin in vivo. Altogether, the study provides a better understanding of the structural and functional role of cardiolipin in mitochondria.

In the Age of Machine Learning Cryo-EM Research is Still Necessary: A Path toward Precision Medicine.

Machine learning has proven useful in analyzing complex biological data and has greatly influenced the course of research in structural biology and precision medicine. Deep neural network models oftentimes fail to predict the structure of complex proteins and are heavily dependent on experimentally determined structures for their training and validation. Single-particle cryogenic electron microscopy (cryoEM) is also advancing the understanding of biology and will be needed to complement these models by continuously supplying high-quality experimentally validated structures for improvements in prediction quality. In this perspective, the significance of structure prediction methods is highlighted, butthe authors also ask, what if these programs cannot accurately predict a protein structure important for preventing disease? The role of cryoEM is discussed to help fill the gaps left by artificial intelligence predictive models in resolving targetable proteins and protein complexes that will pave the way for personalized therapeutics.

Prospects and Limitations of High-Resolution Single-Particle Cryo-Electron Microscopy

Single particle cryo-electron microscopy (cryo-EM) has matured into a robust method for the determination of biological macromolecule structures in the past decade, complementing X-ray crystallography and nuclear magnetic resonance. Constant methodological improvements in both cryo-EM hardware and image processing software continue to contribute to an exponential growth in the number of structures solved annually. In this review, we provide a historical view of the many steps that were required to make cryo-EM a successful method for the determination of high-resolution protein complex structures. We further discuss aspects of cryo-EM methodology that are the greatest pitfalls challenging successful structure determination to date. Lastly, we highlight and propose potential future developments that would improve the method even further in the near future.

PAE viewer: a webserver for the interactive visualization of the predicted aligned error for multimer structure predictions and crosslinks.

The development of AlphaFold for protein structure prediction has opened a new era in structural biology. This is even more the case for AlphaFold-Multimer for the prediction of protein complexes. The interpretation of these predictions has become more important than ever, but it is difficult for the non-specialist. While an evaluation of the prediction quality is provided for monomeric protein predictions by the AlphaFold Protein Structure Database, such a tool is missing for predicted complex structures. Here, we present the PAE Viewer webserver (http://www.subtiwiki.uni-goettingen.de/v4/paeViewerDemo), an online tool for the integrated visualization of predicted protein complexes using a 3D structure display combined with an interactive representation of the Predicted Aligned Error (PAE). This metric allows an estimation of the quality of the prediction. Importantly, our webserver also allows the integration of experimental cross-linking data which helps to interpret the reliability of the structure predictions. With the PAE Viewer, the user obtains a unique online tool which for the first time allows the intuitive evaluation of the PAE for protein complex structure predictions with integrated crosslinks.

Spatial Layouts of Low-Entropy Hydration Shells Guide Protein Binding.

Protein-protein binding enables orderly biological self-organization and is therefore considered a miracle of nature. Protein‒protein binding is driven by electrostatic forces, hydrogen bonding, van der Waals force, and hydrophobic interactions. Among these physical forces, only hydrophobic interactions can be considered long-range intermolecular attractions between proteins due to the electrostatic shielding of surrounding water molecules. Low-entropy hydration shells around proteins drive hydrophobic attraction among them that essentially coordinate protein‒protein binding. Here, an innovative method is developed for identifying low-entropy regions of hydration shells of proteins by screening off pseudohydrophilic groups on protein surfaces and revealing that large low-entropy regions of the hydration shells typically cover the binding sites of individual proteins. According to an analysis of determined protein complex structures, shape matching between a large low-entropy hydration shell region of a protein and that of its partner at the binding sites is revealed as a universal law. Protein‒protein binding is thus found to be mainly guided by hydrophobic collapse between the shape-matched low-entropy hydration shells that is verified by bioinformatics analyses of hundreds of structures of protein complexes, which cover four test systems. A simple algorithm is proposed to accurately predict protein binding sites.

Targeted proteomics-driven computational modeling of the mouse macrophage toll-like receptor signaling pathway

Abstract The Toll-like receptor (TLR) signaling pathway is crucial for the initiation of effective immune responses. Computational modeling of biological pathways can test mechanistic hypotheses about how regulation is achieved and why it sometimes fails, causing disease (e.g., sepsis). In this investigation, mass spectrometry (LC-MS) and computational modeling are being integrated to develop a model of the TLR4 pathway. TLR4 pathway proteins within mouse macrophages were quantitated using targeted LC-MS and stable-isotope labeled internal (phospho)peptide standards (136 unmodified peptides and 29 phosphopeptides for 54 (phospho)proteins). AlphaFold-Multimer was used to predict protein complex structures, Rosetta was used to idealize and relax the structures, and Simulation of Diffusional Association (SDA) and TransComp were used to estimate protein-protein association rates. Simmune was used to perform rule-based pathway modeling, simulation, and training. LPS stimulation altered proteins known to be affected by TLR4 pathway activation (e.g., phosphorylated ERK1 increased from 30,000 to 250,000 copies per cell). Hundreds of protein-protein association rates were predicted. The (phospho)protein absolute abundance values and protein-protein association rates are being used as parameters for TLR4 pathway models. The parameter space is being explored to identify parameter sets that accurately reproduce the experimental data. Experimental and computational techniques are being integrated to generate a strongly data driven model of the TLR pathway. This work was supported by the Intramural Research Program of NIAID, NIH. This work was supported by the Intramural Research Program of NIAID, NIH.

DGCddG: Deep Graph Convolution for Predicting Protein-Protein Binding Affinity Changes Upon Mutations.

Effectively and accurately predicting the effects of interactions between proteins after amino acid mutations is a key issue for understanding the mechanism of protein function and drug design. In this study, we present a deep graph convolution (DGC) network-based framework, DGCddG, to predict the changes of protein-protein binding affinity after mutation. DGCddG incorporates multi-layer graph convolution to extract a deep, contextualized representation for each residue of the protein complex structure. The mined channels of the mutation sites by DGC is then fitted to the binding affinity with a multi-layer perceptron. Experiments with results on multiple datasets show that our model can achieve relatively good performance for both single and multi-point mutations. For blind tests on datasets related to angiotensin-converting enzyme 2 binding with the SARS-CoV-2 virus, our method shows better results in predicting ACE2 changes, may help in finding favorable antibodies. Code and data availability: https://github.com/lennylv/DGCddG.

An Evolutionary Algorithm with Gene Ontology-Aware Crossover Operator for Protein Complex Detection

Evolutionary algorithms (EAs), as global search methods, are proved to be more robust than their counterpart local heuristics for detecting protein complexes in protein-protein interaction (PPI) networks. Typically, the source of robustness of these EAs comes from their components and parameters. These components are solution representation, selection, crossover, and mutation. Unfortunately, almost all EA based complex detection methods suggested in the literature were designed with only canonical or traditional components. Further, topological structure of the protein network is the main information that is used in the design of almost all such components. The main contribution of this paper is to formulate a more robust EA with more biological consistency. For this purpose, a new crossover operator is suggested where biological information in terms of both gene semantic similarity and protein functional similarity is fed into its design. To reflect the heuristic roles of both semantic and functional similarities, this paper introduces two gene ontology (GO) aware crossover operators. These are direct annotation-aware and inherited annotation-aware crossover operators. The first strategy is handled with the direct gene ontology annotation of the proteins, while the second strategy is handled with the directed acyclic graph (DAG) of each gene ontology term in the gene product. To conduct our experiments, the proposed EAs with GO-aware crossover operators are compared against the state-of-the-art heuristic, canonical EAs with the traditional crossover operator, and GO-based EAs. Simulation results are evaluated in terms of recall, precision, and F measure at both complex level and protein level. The results prove that the new EA design encourages a more reliable treatment of exploration and exploitation and, thus, improves the detection ability for more accurate protein complex structures.

Structures of Vac8-containing protein complexes reveal the underlying mechanism by which Vac8 regulates multiple cellular processes

Vac8, a yeast vacuolar protein with armadillo repeats, mediates various cellular processes by changing its binding partners; however, the mechanism by which Vac8 differentially regulates these processes remains poorly understood. Vac8 interacts with Nvj1 to form the nuclear-vacuole junction (NVJ) and with Atg13 to mediate cytoplasm-to-vacuole targeting (Cvt), a selective autophagy-like pathway that delivers cytoplasmic aminopeptidase I directly to the vacuole. In addition, Vac8 associates with Myo2, a yeast class V myosin, through its interaction with Vac17 for vacuolar inheritance from the mother cell to the emerging daughter cell during cell divisions. Here, we determined the X-ray crystal structure of the Vac8-Vac17 complex and found that its interaction interfaces are bipartite, unlike those of the Vac8-Nvj1 and Vac8-Atg13 complexes. When the key amino acids present in the interface between Vac8 and Vac17 were mutated, vacuole inheritance was severely impaired invivo. Furthermore, binding of Vac17 to Vac8 prevented dimerization of Vac8, which is required for its interactions with Nvj1 and Atg13, by clamping the H1 helix to the ARM1 domain of Vac8 and thereby preventing exposure of the binding interface for Vac8 dimerization. Consistently, the binding affinity of Vac17-bound Vac8 for Nvj1 or Atg13 was markedly lower than that of free Vac8. Likewise, free Vac17 had no affinity for the Vac8-Nvj1 and Vac8-Atg13 complexes. These results provide insights into how vacuole inheritance and other Vac8-mediated processes, such as NVJ formation and Cvt, occur independently of one another.

Cross-linking mass spectrometry discovers, evaluates, and corroborates structures and protein–protein interactions in the human cell

Significant recent advances in structural biology, particularly in the field of cryoelectron microscopy, have dramatically expanded our ability to create structural models of proteins and protein complexes. However, many proteins remain refractory to these approaches because of their low abundance, low stability, or-in the case of complexes-simply not having yet been analyzed. Here, we demonstrate the power of using cross-linking mass spectrometry (XL-MS) for the high-throughput experimental assessment of the structures of proteins and protein complexes. This included those produced by high-resolution but invitro experimental data, as well as in silico predictions based on amino acid sequence alone. We present the largest XL-MS dataset to date, describing 28,910 unique residue pairs captured across 4,084 unique human proteins and 2,110 unique protein-protein interactions. We show that models of proteins and their complexes predicted by AlphaFold2, and inspired and corroborated by the XL-MS data, offer opportunities to deeply mine the structural proteome and interactome and reveal mechanisms underlying protein structure and function.

Structure of the heterotrimeric membrane protein complex FtsB-FtsL-FtsQ of the bacterial divisome

The synthesis of the cell-wall peptidoglycan during bacterial cell division is mediated by a multiprotein machine, called the divisome. The essential membrane protein complex of FtsB, FtsL and FtsQ (FtsBLQ) is at the heart of the divisome assembly cascade in Escherichia coli. This complex regulates the transglycosylation and transpeptidation activities of the FtsW-FtsI complex and PBP1b via coordination with FtsN, the trigger for the onset of constriction. Yet the underlying mechanism of FtsBLQ-mediated regulation is largely unknown. Here, we report the full-length structure of the heterotrimeric FtsBLQ complex, which reveals a V-shaped architecture in a tilted orientation. Such a conformation could be strengthened by the transmembrane and the coiled-coil domains of the FtsBL heterodimer, as well as an extended β-sheet of the C-terminal interaction site involving all three proteins. This trimeric structure may also facilitate interactions with other divisome proteins in an allosteric manner. These results lead us to propose a structure-based model that delineates the mechanism of the regulation of peptidoglycan synthases by the FtsBLQ complex.