Structures Of Protein Complexes Research Articles

AF2Complex predicts direct physical interactions in multimeric proteins with deep learning

Accurate descriptions of protein-protein interactions are essential for understanding biological systems. Remarkably accurate atomic structures have been recently computed for individual proteins by AlphaFold2 (AF2). Here, we demonstrate that the same neural network models from AF2 developed for single protein sequences can be adapted to predict the structures of multimeric protein complexes without retraining. In contrast to common approaches, our method, AF2Complex, does not require paired multiple sequence alignments. It achieves higher accuracy than some complex protein-protein docking strategies and provides a significant improvement over AF-Multimer, a development of AlphaFold for multimeric proteins. Moreover, we introduce metrics for predicting direct protein-protein interactions between arbitrary protein pairs and validate AF2Complex on some challenging benchmark sets and the E. coli proteome. Lastly, using the cytochrome c biogenesis system I as an example, we present high-confidence models of three sought-after assemblies formed by eight members of this system.

NMR Molecular Replacement Provides New Insights into Binding Modes to Bromodomains of BRD4 and TRIM24.

Structure-based drug discovery (SBDD) largely relies on structural information from X-ray crystallography because traditional NMR structure calculation methods are too time consuming to be aligned with typical drug discovery timelines. The recently developed NMR molecular replacement (NMR2) method dramatically reduces the time needed to generate ligand–protein complex structures using published structures (apo or holo) of the target protein and treating all observed NOEs as ambiguous restraints, bypassing the laborious process of obtaining sequence-specific resonance assignments for the protein target. We apply this method to two therapeutic targets, the bromodomain of TRIM24 and the second bromodomain of BRD4. We show that the NMR2 methodology can guide SBDD by rationalizing the observed SAR. We also demonstrate that new types of restraints and selective methyl labeling have the potential to dramatically reduce “time to structure” and extend the method to targets beyond the reach of traditional NMR structure elucidation.

The Function, Structure, and Origins of the ER Membrane Protein Complex.

The endoplasmic reticulum (ER) is the site of membrane protein insertion, folding, and assembly in eukaryotes. Over the past few years, a combination of genetic and biochemical studies have implicated an abundant factor termed the ER membrane protein complex (EMC) in several aspects of membrane protein biogenesis. This large nine-protein complex is built around a deeply conserved core formed by the EMC3-EMC6 subcomplex. EMC3 belongs to the universally conserved Oxa1 superfamily of membrane protein transporters, whereas EMC6 is an ancient, widely conserved obligate partner. EMC has an established role in the insertion of transmembrane domains (TMDs) and less understood roles during the later steps of membrane protein folding and assembly. Several recent structures suggest hypotheses about the mechanism(s) of TMD insertion by EMC, with various biochemical and proteomics studies beginning to reveal the range of EMC's membrane protein substrates.

Improved prediction of protein-protein interactions using AlphaFold2

Predicting the structure of interacting protein chains is a fundamental step towards understanding protein function. Unfortunately, no computational method can produce accurate structures of protein complexes. AlphaFold2, has shown unprecedented levels of accuracy in modelling single chain protein structures. Here, we apply AlphaFold2 for the prediction of heterodimeric protein complexes. We find that the AlphaFold2 protocol together with optimised multiple sequence alignments, generate models with acceptable quality (DockQ ≥ 0.23) for 63% of the dimers. From the predicted interfaces we create a simple function to predict the DockQ score which distinguishes acceptable from incorrect models as well as interacting from non-interacting proteins with state-of-art accuracy. We find that, using the predicted DockQ scores, we can identify 51% of all interacting pairs at 1% FPR.

A deep dilated convolutional residual network for predicting interchain contacts of protein homodimers.

MotivationDeep learning has revolutionized protein tertiary structure prediction recently. The cutting-edge deep learning methods such as AlphaFold can predict high-accuracy tertiary structures for most individual protein chains. However, the accuracy of predicting quaternary structures of protein complexes consisting of multiple chains is still relatively low due to lack of advanced deep learning methods in the field. Because interchain residue–residue contacts can be used as distance restraints to guide quaternary structure modeling, here we develop a deep dilated convolutional residual network method (DRCon) to predict interchain residue–residue contacts in homodimers from residue–residue co-evolutionary signals derived from multiple sequence alignments of monomers, intrachain residue–residue contacts of monomers extracted from true/predicted tertiary structures or predicted by deep learning, and other sequence and structural features.ResultsTested on three homodimer test datasets (Homo_std dataset, DeepHomo dataset and CASP-CAPRI dataset), the precision of DRCon for top L/5 interchain contact predictions (L: length of monomer in a homodimer) is 43.46%, 47.10% and 33.50% respectively at 6 Å contact threshold, which is substantially better than DeepHomo and DNCON2_inter and similar to Glinter. Moreover, our experiments demonstrate that using predicted tertiary structure or intrachain contacts of monomers in the unbound state as input, DRCon still performs well, even though its accuracy is lower than using true tertiary structures in the bound state are used as input. Finally, our case study shows that good interchain contact predictions can be used to build high-accuracy quaternary structure models of homodimers.Availability and implementationThe source code of DRCon is available at https://github.com/jianlin-cheng/DRCon. The datasets are available at https://zenodo.org/record/5998532#.YgF70vXMKsB.Supplementary information Supplementary data are available at Bioinformatics online.

Cryo-EM of membrane proteins: what have we learned in the last decade and what are the current challenges?

In the last decade the field of Structural Biology has made great advances in using electron microscopy to solve structures of protein complexes including membrane proteins to high resolution. Best practices of how to use Single Particle Cryo-EM and more importantly how to optimize a membrane protein sample such as an ion channel or a transporter will be discussed. Most membrane protein structures are currently resolved in a detergent micelle, but cryo-EM also makes it possible to look at membrane protein complexes in a lipid bilayer, such as a lipid nanodisc, liposomes, or even inside cells now.

Rigid-body assignment in integrative determination of protein complex structures

Integrative modeling of macromolecular protein complexes typically involve representing structurally available components of the complex generated using X-ray crystallography, cryo-EM or comparative modeling, as rigid bodies, and sampling their relative spatial arrangements to generate an ensemble of models that are maximally consistent with input information. While there exist some sparse guidelines in the literature for optimizing the resolution of flexible regions used to represent missing residues within and between the rigid components, the process of defining rigid regions is entirely ad-hoc, often leading to several tedious rounds of trial and error between the computational modeler and their experimental collaborator, alternating between guessing a rigid body definition and running a computationally expensive structural sampling using this definition, until the model achieves both a desirable precision and a quantifiably good fit to input data.

The structural coverage of the human proteome before and after AlphaFold.

The protein structure field is experiencing a revolution. From the increased throughput of techniques to determine experimental structures, to developments such as cryo-EM that allow us to find the structures of large protein complexes or, more recently, the development of artificial intelligence tools, such as AlphaFold, that can predict with high accuracy the folding of proteins for which the availability of homology templates is limited. Here we quantify the effect of the recently released AlphaFold database of protein structural models in our knowledge on human proteins. Our results indicate that our current baseline for structural coverage of 48%, considering experimentally-derived or template-based homology models, elevates up to 76% when including AlphaFold predictions. At the same time the fraction of dark proteome is reduced from 26% to just 10% when AlphaFold models are considered. Furthermore, although the coverage of disease-associated genes and mutations was near complete before AlphaFold release (69% of Clinvar pathogenic mutations and 88% of oncogenic mutations), AlphaFold models still provide an additional coverage of 3% to 13% of these critically important sets of biomedical genes and mutations. Finally, we show how the contribution of AlphaFold models to the structural coverage of non-human organisms, including important pathogenic bacteria, is significantly larger than that of the human proteome. Overall, our results show that the sequence-structure gap of human proteins has almost disappeared, an outstanding success of direct consequences for the knowledge on the human genome and the derived medical applications.

From systems to structure - using genetic data to model protein structures.

Understanding the effects of genetic variation is a fundamental problem in biology that requires methods to analyse both physical and functional consequences of sequence changes at systems-wide and mechanistic scales. To achieve a systems view, protein interaction networks map which proteins physically interact, while genetic interaction networks inform on the phenotypic consequences of perturbing these protein interactions. Until recently, understanding the molecular mechanisms that underlie these interactions often required biophysical methods to determine the structures of the proteins involved. The past decade has seen the emergence of new approaches based on coevolution, deep mutational scanning and genome-scale genetic or chemical–genetic interaction mapping that enable modelling of the structures of individual proteins or protein complexes. Here, we review the emerging use of large-scale genetic datasets and deep learning approaches to model protein structures and their interactions, and discuss the integration of structural data from different sources.

A solution NMR view of lipidic cubic phases: Structure, dynamics, and beyond

Nuclear magnetic resonance (NMR) spectroscopy is well-established nowadays for the elucidation of the 3D structures of proteins and protein complexes, the evaluation of biomolecular dynamics with atomistic resolution across a range of time scales, the screening of drug candidates with site specificity, and for the quantitation of molecular translational diffusion. Lyotropic lipidic cubic phases (LCPs) are lipid bilayer-based materials with a complex geometry, formed via the spontaneous self-assembly of certain lipids in an aqueous environment at specific temperature ranges. LCPs have been successfully applied to the in meso crystallization of membrane proteins for structural studies by X-ray crystallography, and have also shown promising potential for serving as matrices for drug and nutrient delivery/release in vivo. The characterization of the structural and dynamics properties of LCPs is of significant interest for the application of these materials. Here we present a systematic review detailing the characterization of LCPs by solution NMR. Using LCPs formed by monoolein (MO) as an example, various aspects of LCPs readily accessible by solution NMR are covered, including spectral perturbation in the presence of additives, quantification of hydration levels, 13C relaxation-based measurements for studying atom-specific dynamics along the MO hydrocarbon chain, PGSE NMR measurement of translational diffusion and its correlation with release profiles, and the encapsulation of soluble proteins in LCPs. A brief discussion of future perspectives for the characterization of LCPs by solution NMR is also presented.

In situ cryo-FIB/SEM Specimen Preparation Using the Waffle Method.

Cryo-focused ion beam (FIB) milling of vitrified specimens is emerging as a powerful method for in situ specimen preparation. It allows for the preservation of native and near-native conditions in cells, and can reveal the molecular structure of protein complexes when combined with cryo-electron tomography (cryo-ET) and sub-tomogram averaging. Cryo-FIB milling is often performed on plunge-frozen specimens of limited thickness. However, this approach may have several disadvantages, including low throughput for cells that are small, or at low concentration, or poorly distributed across accessible areas of the grid, as well as for samples that may adopt a preferred orientation. Here, we present a detailed description of the "Waffle Method" protocol for vitrifying thick specimens followed by a semi-automated milling procedure using the Thermo Fisher Scientific (TFS) Aquilos 2 cryo-FIB/scanning electron microscope (SEM) instrument and AutoTEM Cryo software to produce cryo-lamellae. With this protocol, cryo-lamellae may be generated from specimens, such as microsporidia spores, yeast, bacteria, and mammalian cells, as well as purified proteins and protein complexes. An experienced lab can perform the entire protocol presented here within an 8-hour working day, resulting in two to three cryo-lamellae with target thicknesses of 100-200 nm and dimensions of approximately 12 μm width and 15-20 μm length. For cryo-FIB/SEMs with particularly low-contamination chambers, the protocol can be extended to overnight milling, resulting in up to 16 cryo-lamellae in 24 h. Graphical abstract.

Protein Complex Structure Prediction Powered by Multiple Sequence Alignments of Interologs from Multiple Taxonomic Ranks and AlphaFold2

Protein Complex Structure Prediction Powered by Multiple Sequence Alignments of Interologs from Multiple Taxonomic Ranks and AlphaFold2

The Conformational Dynamics of Heterotrimeric G Proteins During GPCR-Mediated Activation.

Heterotrimeric G proteins (G proteins) are essential cellular signaling proteins that mediate extracellular signals to achieve various cellular functions. G-protein-coupled receptors (GPCRs) are the major guanine nucleotide exchange factors (GEFs) that induce G proteins to release guanosine diphosphate and rapidly bind to guanosine triphosphate, resulting in G protein activation. G proteins undergo dynamic conformational changes during the activation/inactivation process, and the precise structural mechanism of GPCR-mediated G protein activation is of great interest. Over the last decade, a number of GPCR-G protein complex structures have been identified, yet an understanding of the mechanisms underlying allosteric conformational changes during receptor-mediated G protein activation and GPCR-G protein coupling selectivity is only now emerging. This review discusses recent studies on the dynamic conformational changes of G proteins and provides insight into the structural mechanism of GPCR-mediated G protein activation.

Mutations on RBD of SARS-CoV-2 Omicron variant result in stronger binding to human ACE2 receptor

The COVID-19 pandemic caused by the SARS-CoV-2 virus has led to more than 270 million infections and 5.3 million of deaths worldwide. Several major variants of SARS-CoV-2 have emerged and posed challenges in controlling the pandemic. The recently occurred Omicron variant raised serious concerns about reducing the efficacy of vaccines and neutralization antibodies due to its vast mutations. We have modelled the complex structure of the human ACE2 protein and the receptor binding domain (RBD) of Omicron Spike protein (S-protein), and conducted atomistic molecular dynamics simulations to study the binding interactions. The analysis shows that the Omicron RBD binds more strongly to the human ACE2 protein than the original strain. The mutations at the ACE2-RBD interface enhance the tight binding by increasing hydrogen bonding interaction and enlarging buried solvent accessible surface area.

Enzymology and Dynamics by Cryogenic Electron Microscopy.

Cryogenic electron microscopy (cryo-EM) has revolutionized the field of structural biology, particularly in solving the structures of large protein complexes or cellular machineries that play important biological functions. This review focuses on the contribution and future potential of cryo-EM in related emerging applications-enzymatic mechanisms and dynamic processes. Work on these subjects can benefit greatly from the capability of cryo-EM to solve the structures of specific protein complexes in multiple conditions, including variations in the buffer condition, ligands, and temperature, and to capture multiple conformational states, conformational change intermediates, and reaction intermediates. These studies can expand the structural landscape of specific proteins or protein complexes in multiple dimensions and drive new advances in the fields of enzymology and dynamic processes. The advantages and complementarity of cryo-EM relative to X-ray crystallography and nuclear magnetic resonance with regard to these applications are also addressed.

Variable-Temperature Native Mass Spectrometry for Studies of Protein Folding, Stabilities, Assembly, and Molecular Interactions.

The structures and conformational dynamics of proteins, protein complexes, and their noncovalent interactions with other molecules are controlled specifically by the Gibbs free energy (entropy and enthalpy) of the system. For some organisms, temperature is highly regulated, but the majority of biophysical studies are carried out at room, nonphysiological temperature. In this review, we describe variable-temperature electrospray ionization (vT-ESI) mass spectrometry (MS)-based studies with unparalleled sensitivity, dynamic range, and selectivity for studies of both cold- and heat-induced chemical processes. Such studies provide direct determinations of stabilities, reactivities, and thermodynamic measurements for native and non-native structures of proteins and protein complexes and for protein-ligand interactions. Highlighted in this review are vT-ESI-MS studies that reveal 40 different conformers of chymotrypsin inhibitor 2, a classic two-state (native → unfolded) unfolder, and thermochemistry for a model membrane protein system binding lipid and its regulatory protein.

Structures of neurokinin 1 receptor in complex with Gq and Gs proteins reveal substance P binding mode and unique activation features.

The neurokinin 1 receptor (NK1R) is involved in inflammation and pain transmission. This pathophysiologically important G protein–coupled receptor is predominantly activated by its cognate agonist substance P (SP) but also by the closely related neurokinins A and B. Here, we report cryo–electron microscopy structures of SP-bound NK1R in complex with its primary downstream signal mediators, Gq and Gs. Our structures reveal how a polar network at the extracellular, solvent-exposed receptor surface shapes the orthosteric pocket and that NK1R adopts a noncanonical active-state conformation with an interface for G protein binding, which is distinct from previously reported structures. Detailed comparisons with antagonist-bound NK1R crystal structures reveal that insurmountable antagonists induce a distinct and long-lasting receptor conformation that sterically blocks SP binding. Together, our structures provide important structural insights into ligand and G protein promiscuity, the lack of basal signaling, and agonist- and antagonist-induced conformations in the neurokinin receptor family.

A Geometric Complementarity-Based Tool for Protein–Protein Docking

The computationally hard protein–protein complex structure prediction problem is continuously fascinating to the scientific community due to its biological impact. The field has witnessed the application of geometric algorithms, randomized algorithms, and evolutionary algorithms to name a few. These techniques improve either the searching or scoring phase. An effective searching strategy does not generate a large conformation space that perhaps demands computational power. Another determining factor is the parameter chosen for score calculation. The proposed method is an attempt to curtail the conformations by limiting the search procedure to probable regions. In this method, partial derivatives are calculated on the coarse-grained representation of the surface residues to identify the optimal points on the protein surface. Contrary to the existing geometric-based algorithms that align the convex and concave regions of both proteins, this method aligns the concave regions of the receptor with convex regions of the ligand only and thus reduces the size of conformation space. The method’s performance is evaluated using the 55 newly added targets in Protein–Protein Docking Benchmark v 5 and is found to be successful for around 47% of the targets.

How to build a ribosome from RNA fragments in Chlamydomonas mitochondria

Mitochondria are the powerhouse of eukaryotic cells. They possess their own gene expression machineries where highly divergent and specialized ribosomes, named hereafter mitoribosomes, translate the few essential messenger RNAs still encoded by mitochondrial genomes. Here, we present a biochemical and structural characterization of the mitoribosome in the model green alga Chlamydomonas reinhardtii, as well as a functional study of some of its specific components. Single particle cryo-electron microscopy resolves how the Chlamydomonas mitoribosome is assembled from 13 rRNA fragments encoded by separate non-contiguous gene pieces. Additional proteins, mainly OPR, PPR and mTERF helical repeat proteins, are found in Chlamydomonas mitoribosome, revealing the structure of an OPR protein in complex with its RNA binding partner. Targeted amiRNA silencing indicates that these ribosomal proteins are required for mitoribosome integrity. Finally, we use cryo-electron tomography to show that Chlamydomonas mitoribosomes are attached to the inner mitochondrial membrane via two contact points mediated by Chlamydomonas-specific proteins. Our study expands our understanding of mitoribosome diversity and the various strategies these specialized molecular machines adopt for membrane tethering.

DeepRank: a deep learning framework for data mining 3D protein-protein interfaces

Three-dimensional (3D) structures of protein complexes provide fundamental information to decipher biological processes at the molecular scale. The vast amount of experimentally and computationally resolved protein-protein interfaces (PPIs) offers the possibility of training deep learning models to aid the predictions of their biological relevance. We present here DeepRank, a general, configurable deep learning framework for data mining PPIs using 3D convolutional neural networks (CNNs). DeepRank maps features of PPIs onto 3D grids and trains a user-specified CNN on these 3D grids. DeepRank allows for efficient training of 3D CNNs with data sets containing millions of PPIs and supports both classification and regression. We demonstrate the performance of DeepRank on two distinct challenges: The classification of biological versus crystallographic PPIs, and the ranking of docking models. For both problems DeepRank is competitive with, or outperforms, state-of-the-art methods, demonstrating the versatility of the framework for research in structural biology.