Integrating Cross-Linking Experiments with Ab Initio Protein-Protein Docking

Modeling Flexible Protein Structure With AlphaFold2 and Crosslinking Mass Spectrometry

We propose a pipeline that combines AlphaFold2 (AF2) and crosslinking mass spectrometry (XL-MS) to model the structure of proteins with multiple conformations. The pipeline consists of two main steps: ensemble generation using AF2 and conformer selection using XL-MS data. For conformer selection, we developed two scores-the monolink probability score (MP) and the crosslink probability score (XLP)-both of which are based on residue depth from the protein surface. We benchmarked MP and XLP on a large dataset of decoy protein structures and showed that our scores outperform previously developed scores. We then tested our methodology on three proteins having an open and closed conformation in the Protein Data Bank: Complement component 3 (C3), luciferase, and glutamine-binding periplasmic protein, first generating ensembles using AF2, which were then screened for the open and closed conformations using experimental XL-MS data. In five out of six cases, the most accurate model within the AF2 ensembles-or a conformation within 1 Å of this model-was identified using crosslinks, as assessed through the XLP score. In the remaining case, only the monolinks (assessed through the MP score) successfully identified the open conformation of glutamine-binding periplasmic protein, and these results were further improved by including the "occupancy" of the monolinks. This serves as a compelling proof-of-concept for the effectiveness of monolinks. In contrast, the AF2 assessment score was only able to identify the most accurate conformation in two out of six cases. Our results highlight the complementarity of AF2 with experimental methods like XL-MS, with the MP and XLP scores providing reliable metrics to assess the quality of the predicted models. The MP and XLP scoring functions mentioned above are available at https://gitlab.com/topf-lab/xlms-tools.

Assessment of Two Restraint Potentials for Coarse-Grained Chemical-Cross-Link-Assisted Modeling of Protein Structures

The influence of distance restraints from chemical cross-link mass spectroscopy (XL-MS) on the quality of protein structures modeled with the coarse-grained UNRES force field was assessed by using a protocol based on multiplexed replica exchange molecular dynamics, in which both simulated and experimental cross-link restraints were employed, for 23 small proteins. Six cross-links with upper distance boundaries from 4 Å to 12 Å (azido benzoic acid succinimide (ABAS), triazidotriazine (TATA), succinimidyldiazirine (SDA), disuccinimidyl adipate (DSA), disuccinimidyl glutarate (DSG), and disuccinimidyl suberate (BS3)) and two types of restraining potentials ((i) simple flat-bottom Lorentz-like potentials dependent on side chain distance (all cross-links) and (ii) distance- and orientation-dependent potentials determined based on molecular dynamics simulations of model systems (DSA, DSG, BS3, and SDA)) were considered. The Lorentz-like potentials with properly set parameters were found to produce a greater number of higher-quality models compared to unrestrained simulations than the MD-based potentials, because the latter can force too long distances between side chains. Therefore, the flat-bottom Lorentz-like potentials are recommended to represent cross-link restraints. It was also found that significant improvement of model quality upon the introduction of cross-link restraints is obtained when the sum of differences of indices of cross-linked residues exceeds 150.

Hybrid structural modeling of alloantibody binding to human leukocyte antigen with rapid and reproducible cross-linking mass spectrometry

Alloantibody recognition of donor human leukocyte antigen (HLA) is associated with poor clinical transplantation outcomes. However, the molecular and structural basis for the alloantibody-HLA interaction is not well understood. Here, we used a hybrid structural modeling approach on a previously studied alloantibody-HLA interacting pair with inputs from ab initio, in silico, and in vitro data. Highly reproducible cross-linking mass spectrometry data were obtained with both discovery- and targeted mass spectrometry-based approaches approaches. The cross-link information was then used together with predicted antibody Fv structure, predicted antibody paratope, and in silico-predicted interacting surface to model the antibody-HLA interaction. This hybrid structural modeling approach closely recapitulates the key interacting residues from a previously solved crystal structure of an alloantibody-HLA-A∗11:01 pair. These results suggest that a predictive-based hybrid structural modeling approach supplemented with cross-linking mass spectrometry data can provide functionally relevant structural models to understand the structural basis of antibody-HLA mismatch in transplantation.

Critical Assessment of Methods for Predicting the 3D Structure of Proteins and Protein Complexes

Advances in a scientific discipline are often measured by small, incremental steps. In this review, we report on two intertwined disciplines in the protein structure prediction field, modeling of single chains and modeling of complexes, that have over decades emulated this pattern, as monitored by the community-wide blind prediction experiments CASP and CAPRI. However, over the past few years, dramatic advances were observed for the accurate prediction of single protein chains, driven by a surge of deep learning methodologies entering the prediction field. We review the mainscientific developments that enabled these recent breakthroughs and feature the important role of blind prediction experiments in building up and nurturing the structure prediction field. We discuss how the new wave of artificial intelligence-based methods is impacting the fields of computational and experimental structural biology and highlight areas in which deep learning methods are likely to lead to future developments, provided that major challenges are overcome.

me53 encoded by Autographa californica multiple nucleopolyhedrovirus: from mechanism to function

me53, a highly conserved immediate early gene in all Lepidoptera baculoviruses, has been of great interest in recent years. Autographa californica multiple nucleopolyhedrovirus (AcMNPV) is in the family Baculoviridae, genus Alphabaculovirus. The me53 gene of AcMNPV has been sequenced, and it was transcribed late after infection. The structure of ME53 protein and its roles in the infection of host cells were summarized and discussed, including that (1) the production of Budding Virus (BV); (2) nucleocapsid formation in the host nuclei; (3) ME53 forms a lesion on the cell membrane of AcMNPV-infected cells and co-locates with GP64 and the primary capsid protein VP39; (4) the nuclear translocation signal sequence of ME53 is essential for optimal baculovirus production. In this review, we focus on the emerging roles of ME53 by discussing novel mechanisms identified to mediate or interact by ME53, which provides an important reference for the effective transformation, utilization and improvement of the anti-insect activity of AcMNPV.

Cross-linking and mass spectrometry as a tool for studying the structural biology of ribonucleoproteins

Cross-linking and mass spectrometry (XL-MS) workflows represent an increasingly popular technique for low-resolution structural studies of macromolecular complexes. Cross-linking reactions take place in the solution state, capturing contact sites between components of a complex that represent the native, functionally relevant structure. Protein-protein XL-MS protocols are widely adopted, providing precise localization of cross-linking sites to single amino acid positions within a pair of cross-linked peptides. In contrast, protein-RNA XL-MS workflows are evolving rapidly and differ in their ability to localize interaction regions within the RNA sequence. Here, we review protein-protein and protein-RNA XL-MS workflows, and discuss their applications in studies of protein-RNA complexes. The examples highlight the complementary value of XL-MS in structural studies of protein-RNA complexes, where more established high-resolution techniques might be unable to produce conclusive data.

Modeling and Structure Determination of Homo-Oligomeric Proteins: An Overview of Challenges and Current Approaches

Protein homo-oligomerization is a very common phenomenon, and approximately half of proteins form homo-oligomeric assemblies composed of identical subunits. The vast majority of such assemblies possess internal symmetry which can be either exploited to help or poses challenges during structure determination. Moreover, aspects of symmetry are critical in the modeling of protein homo-oligomers either by docking or by homology-based approaches. Here, we first provide a brief overview of the nature of protein homo-oligomerization. Next, we describe how the symmetry of homo-oligomers is addressed by crystallographic and non-crystallographic symmetry operations, and how biologically relevant intermolecular interactions can be deciphered from the ordered array of molecules within protein crystals. Additionally, we describe the most important aspects of protein homo-oligomerization in structure determination by NMR. Finally, we give an overview of approaches aimed at modeling homo-oligomers using computational methods that specifically address their internal symmetry and allow the incorporation of other experimental data as spatial restraints to achieve higher model reliability.

Pseudopotentials for coarse-grained cross-link-assisted modeling of protein structures

Pseudopotentials for the chemical cross-links comprising the glutamic- and aspartic-acid side chains bridged with adipic- (ADH) or pimelic-acid hydrazide (PDH), and the lysine side chains bridged with glutaric (BS² G) or suberic acid (BS³ ) for coarse-grained cross-link-assisted simulations were determined by canonical molecular dynamics with the Amber14sb force field. The potentials depend on the distance between side-chain ends and on side-chain orientation, this preventing from making cross-link contacts across the globule in simulations. The potentials were implemented in the UNRES coarse-grained force field and their effect on the quality of models was assessed with 11 monomeric and 1 dimeric proteins, using synthetic or experimental cross-link data. Simulations with the new potentials resulted in improvement of the generated models compared to unrestrained simulations in more instances compared to those with the statistical potentials.

Extracellular crosslinking mass spectrometry reveals HLA class I - HLA class II interactions on the cell surface

Human Leukocyte Antigen (HLA) complexes are critical cell-surface protein assemblies that facilitate T-cell surveillance of almost all cell types in the body. While T-cell receptor binding to HLA class I and class II complexes is well-described with detailed structural information, the nature of cis HLA interactions within the plasma membrane of the surveyed cells remains to be better characterized, as protein-protein interactions in the membrane environment are technically challenging to profile. Here we performed extracellular chemical crosslinking on intact antigen presenting cells to specifically elucidate protein-protein interactions present in the external plasma membrane. We found that the crosslink dataset was dominated by inter- and intra-protein crosslinks involving HLA molecules, which enabled not only the construction of an HLA-centric plasma membrane protein interaction map, but also revealed multiple modes of HLA class I - HLA class II interactions with further structural modeling based on crosslinker distance restraints. Collectively, our data demonstrate that HLA molecules colocalize and can be densely packed on the plasma membrane.

Advances to tackle backbone flexibility in protein docking

Computational docking methods can provide structural models of protein-protein complexes, but protein backbone flexibility upon association often thwarts accurate predictions. In recent blind challenges, medium or high accuracy models were submitted in less than 20% of the 'difficult' targets (with significant backbone change or uncertainty). Here, we describe recent developments in protein-protein docking and highlight advances that tackle backbone flexibility. In molecular dynamics and Monte Carlo approaches, enhanced sampling techniques have reduced time-scale limitations. Internal coordinate formulations can now capture realistic motions of monomers and complexes using harmonic dynamics. And machine learning approaches adaptively guide docking trajectories or generate novel binding site predictions from deep neural networks trained on protein interfaces. These tools poise the field to break through the longstanding challenge of correctly predicting complex structures with significant conformational change.

Docking approaches for modeling multi-molecular assemblies

Computational docking approaches aim to overcome the limited availability of experimental structural data on protein-protein interactions, which are key in biology. The field is rapidly moving from the traditional docking methodologies for modeling of binary complexes to more integrative approaches using template-based, data-driven modeling of multi-molecular assemblies. We will review here the predictive capabilities of current docking methods in blind conditions, based on the results from the most recent community-wide blind experiments. Integration of template-based and ab initio docking approaches is emerging as the optimal strategy for modeling protein complexes and multimolecular assemblies. We will also review the new methodological advances on ab initio docking and integrative modeling.

High-density chemical cross-linking for modeling protein interactions

Detailed mechanistic understanding of protein complex function is greatly enhanced by insights from its 3-dimensional structure. Traditional methods of protein structure elucidation remain expensive and labor-intensive and require highly purified starting material. Chemical cross-linking coupled with mass spectrometry offers an alternative that has seen increased use, especially in combination with other experimental approaches like cryo-electron microscopy. Here we report advances in method development, combining several orthogonal cross-linking chemistries as well as improvements in search algorithms, statistical analysis, and computational cost to achieve coverage of 1 unique cross-linked position pair for every 7 amino acids at a 1% false discovery rate. This is accomplished without any peptide-level fractionation or enrichment. We apply our methods to model the complex between a carbonic anhydrase (CA) and its protein inhibitor, showing that the cross-links are self-consistent and define the interaction interface at high resolution. The resulting model suggests a scaffold for development of a class of protein-based inhibitors of the CA family of enzymes. We next cross-link the yeast proteasome, identifying 3,893 unique cross-linked peptides in 3 mass spectrometry runs. The dataset includes 1,704 unique cross-linked position pairs for the proteasome subunits, more than half of them intersubunit. Using multiple recently solved cryo-EM structures, we show that observed cross-links reflect the conformational dynamics and disorder of some proteasome subunits. We further demonstrate that this level of cross-linking density is sufficient to model the architecture of the 19-subunit regulatory particle de novo.

Proper evaluation of chemical cross-linking-based spatial restraints improves the precision of modeling homo-oligomeric protein complexes

Background

The function of oligomeric proteins is inherently linked to their quaternary structure. In the absence of high-resolution data, low-resolution information in the form of spatial restraints can significantly contribute to the precision and accuracy of structural models obtained using computational approaches. To obtain such restraints, chemical cross-linking coupled with mass spectrometry (XL-MS) is commonly used. However, the use of XL-MS in the modeling of protein complexes comprised of identical subunits (homo-oligomers) is often hindered by the inherent ambiguity of intra- and inter-subunit connection assignment.

Results

We present a comprehensive evaluation of (1) different methods for inter-residue distance calculations, and (2) different approaches for the scoring of spatial restraints. Our results show that using Solvent Accessible Surface distances (SASDs) instead of Euclidean distances (EUCs) greatly reduces the assignation ambiguity and delivers better modeling precision. Furthermore, ambiguous connections should be considered as inter-subunit only when the intra-subunit alternative exceeds the distance threshold. Modeling performance can also be improved if symmetry, characteristic for most homo-oligomers, is explicitly defined in the scoring function.

Conclusions

Our findings provide guidelines for proper evaluation of chemical cross-linking-based spatial restraints in modeling homo-oligomeric protein complexes, which could facilitate structural characterization of this important group of proteins.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-3032-x) contains supplementary material, which is available to authorized users.

Performance and enhancement of the LZerD protein assembly pipeline in CAPRI 38-46

We report the performance of the protein docking prediction pipeline of our group and the results for Critical Assessment of Prediction of Interactions (CAPRI) rounds 38-46. The pipeline integrates programs developed in our group as well as other existing scoring functions. The core of the pipeline is the LZerD protein-protein docking algorithm. If templates of the target complex are not found in PDB, the first step of our docking prediction pipeline is to run LZerD for a query protein pair. Meanwhile, in the case of human group prediction, we survey the literature to find information that can guide the modeling, such as protein-protein interface information. In addition to any literature information and binding residue prediction, generated docking decoys were selected by a rank aggregation of statistical scoring functions. The top 10 decoys were relaxed by a short molecular dynamics simulation before submission to remove atom clashes and improve side-chain conformations. In these CAPRI rounds, our group, particularly the LZerD server, showed robust performance. On the other hand, there are failed cases where some other groups were successful. To understand weaknesses of our pipeline, we analyzed sources of errors for failed targets. Since we noted that structure refinement is a step that needs improvement, we newly performed a comparative study of several refinement approaches. Finally, we show several examples that illustrate successful and unsuccessful cases by our group.

What method to use for protein-protein docking?

A number of well-established servers perform 'free' docking of proteins of known structures. In contrast, template-based docking can start from sequences if structures are available for complexes that are homologous to the target. On the basis of the results of the CAPRI-CASP structure prediction experiments, template-based methods yield more accurate predictions if good templates can be found, but generally fail without such templates. However, free global docking, or focused docking around even poor quality template-based models, can still generate acceptable docked structures in these cases. In accordance with the analysis of a benchmark set, free docking of heterodimers yields acceptable or better predictions in the top 10 models for around 40% of structures. However, it is likely that a combination of template-based and free docking methods can perform better for targets that have template structures available. Another way of improving the reliability of predictions is adding experimental information as restraints, an option built into several docking servers.

Protein-protein interactions of human glyoxalase II: findings of a reliable docking protocol

Glyoxalase II (GlxII) is an antioxidant glutathione-dependent enzyme, which catalyzes the hydrolysis of S-d-lactoylglutathione to form d-lactic acid and glutathione (GSH). The last product is the most important thiol reducing agent present in all eukaryotic cells that have mitochondria and chloroplasts. It is generally known that GSH plays a crucial role not only in the cellular redox state but also in various cellular processes. One of them is protein S-glutathionylation, a process that can occur through an oxidation reaction of proteins' thiol groups by GSH. Changes in protein S-glutathionylation have been associated with a range of human diseases such as diabetes, cardiovascular and pulmonary diseases, neurodegenerative diseases and cancer. Within a major project aimed at elucidating the role of GlxII in the mechanism of S-glutathionylation, a reliable computational protocol consisting of a protein-protein docking approach followed by atomistic Molecular Dynamics (MD) simulations was developed and it was applied to the prediction of molecular associations between human GlxII (in the presence and absence of GSH) and some proteins that are known to be S-glutathionylated in vitro, such as actin, malate dehydrogenase (MDH) and glyceraldehyde-3-phosphate dehydrogenase (GAPDH). The computational results show a high propensity of GlxII to interact with actin and MDH through its active site and a high stability of the GlxII-protein systems when GSH is present. Moreover, close proximities of GSH with actin and MDH cysteine residues have been found, suggesting that GlxII could be able to perform protein S-glutathionylation by using the GSH molecule present in its catalytic site.

Chemical cross-linking with mass spectrometry: a tool for systems structural biology

Biological processes supporting life are orchestrated by a highly dynamic array of protein structures and interactions comprising the interactome. Defining the interactome, visualizing how structures and interactions change and function to support life is essential to improved understanding of fundamental molecular processes, but represents a challenge unmet by any single analytical technique. Chemical cross-linking with mass spectrometry provides identification of proximal amino acid residues within proteins and protein complexes, yielding low resolution structural information. This approach has predominantly been employed to provide structural insight on isolated protein complexes, and has been particularly useful for molecules that are recalcitrant to conventional structural biology studies. Here we discuss recent developments in cross-linking and mass spectrometry technologies that are providing large-scale or systems-level interactome data with successful applications to isolated organelles, cell lysates, virus particles, intact bacterial and mammalian cultured cells and tissue samples.