Sequence, post-translational modifications, ligand/metal binding, protein folding, and complex stoichiometry can thus all be probed directly. Here, we review recent developments in this new and exciting field of research. While this work is written primarily from a mass spectrometry perspective, it is targeted to all bioanalytical scientists who are interested in applying these methods to their own biochemistry and chemical biology research.In biology, it can be argued that if the genome contains the script for a cell's life cycle, then the proteome constitutes an ensemble cast of actors that brings these instructions to life. Their interactions with each other, co-factors, ligands, substrates, and so on, are key to understanding nearly any biological process. Mass spectrometry is well established as the method of choice to determine protein primary structure and location of post-translational modifications. In recent years, top-down fragmentation of intact proteins has been increasingly combined with ionisation of noncovalent assemblies under non-denaturing conditions,  Top-down mass spectrometry techniques break up native proteins and complexes to reveal all levels of structural information. In the early 2000s, the human genome was sequenced, leading to the identification of ∼20 000 genes, which might seem like a relatively low number when considering our biological complexity.2 During the process of transcription, about 93% of human genes undergo alternative splicing, resulting in variations at the mRNA level.2 Further diversity can be introduced after mRNA is translated, with various post-translational modifications (PTMs) occurring to the proteins.2 PTMs are not directly encoded in the genome, and many of them are dynamically regulated in response to environmental stress. Combined, DNA, mRNA, and protein-level variations give rise to a diverse set of molecular forms that derive from a single gene. In 2013, a single term, ‘proteoform’,1,3 was adopted to clarify the nomenclature surrounding protein complexity and to promote basic and clinical research efforts towards developing technologies for proteoform characterisation.1 The total number of human proteoforms has been estimated to be in the hundreds of thousands or even millions.2 Mass spectrometry (MS) has emerged as the most versatile and comprehensive method for proteoform characterisation.4 Proteins form various noncovalent complexes to perform their biological functions, further complicating the proteome landscape beyond what has been traditionally understood under the proteoform concept. Although MS has mainly been used to obtain information regarding protein sequence, it has been increasingly utilized to understand higher-order structure.5At the DNA level, mutations and single-nucleotide polymorphisms represent a substantial source of variation.6 and matrix-assisted laser desorption/ionisation (MALDI)7,8 developed in the late 1980s. By the year 2000, these innovations, ESI-MS in particular, were used to analyse biomolecules with molecular weights up to 1 MDa. Today, nanoESI9,10 – in which the flow rate and droplet size are drastically reduced, resulting in far less sample consumption – as well as many other ionisation techniques, are coupled to many different MS instrument platforms for a broad range of applications.5,11Characterisation of large biomolecules by MS was made possible by soft ionisation techniques such as electrospray ionisation (ESI)1.1via ESI, and analysed by MS, allowing identification, quantification, and PTM characterisation for many thousands of proteins. Information regarding protein isoforms, PTM stoichiometry, and combinatorial PTMs is, however, lost when using peptides as protein surrogates.12,13 The bottom-up approach has also been applied for higher-order structural characterisation using methods such as limited proteolysis,14 chemical crosslinking,15 and protein footprinting.16In the conventional bottom-up protein analysis approach, proteins are extracted, chemically or enzymatically digested, separated by liquid chromatography (LC), ionised 13 While some technical challenges remain, developments in top-down protein analysis over the past few years have progressed its capability to unambiguously identify, characterise, and quantify thousands of proteoforms with high throughput. Recent developments in the growth, development, and applications in biomedical research of top-down protein MS are covered in several recent reviews.5,12,13,17 Simultaneously, another MS-based technology that has enabled key new biological insights is known as native MS. Native MS aims to preserve the solution structure of proteins and protein complexes during the transfer to the gas phase. Some of the key background terminology, abbreviations, and techniques relevant to the rapidly evolving fields of native and top-down MS are listed in ‘Top-down’ mass spectrometry, which forgoes the digestion step, has proven to be the premier MS-based technology for unambiguous proteoform characterisation, enabling in-depth sequencing, the discovery of novel proteoforms, and quantification of disease-associated PTMs.1.25,18 At its most basic level, native MS can return information on the makeup of protein complexes by providing molecular weights more accurately than conventional biophysical methods  or analytical ultracentrifugation) especially for heterogeneous samples. In relatively simple cases, this can suffice to indicate the number of monomers in a complex and determine differences in complex makeup.19–22 In addition to mass alone, the 3D shape of proteins and complexes can be simultaneously investigated by ion mobility-mass spectrometry (IM-MS).23 Previous studies have shown that IM-MS correlates with known protein structure,24 and that binding of ligands, cofactors, or metal ions can affect the observed structure, with important implications for e.g., drug discovery.25 In addition, collision-induced unfolding (CIU), i.e., increasing the internal energy of an ion prior to IM-MS analysis, allows concomitant study of conformational stability, providing more details than IM alone.26 While it is now commonly accepted that the stoichiometry of noncovalent protein complexes can be determined using native MS, the extent to which protein solution folding is retained, i.e., the degree of ‘nativeness’ of the gas-phase ions, is more controversial. Ion mobility experiments show that the overall 3D shape of proteins – especially in the lower charge states naturally generated by ESI from non-denaturing solutions – is usually consistent with structures obtained from X-ray diffraction (XRD) and nuclear magnetic resonance (NMR) .24 Additional compelling evidence has been provided by ‘soft-landing’ experiments, in which gas-phase ions of large protein complexes are mass-selected and then gently decelerated and collected on a grid. Subsequent electron microscopy (EM) imaging then demonstrated preservation of native-like structures throughout the process of ionisation, dehydration, and soft-landing.27,28For nearly thirty years, ongoing efforts have endeavoured to use MS to understand noncovalent protein complexes by transferring them into the gas phase without loss of higher-order structure.29 Conversely, using electron transfer dissociation (ETD), Vachet and co-workers found that the gas-phase salt bridge pattern of small proteins was more consistent with the pattern present in the known native structure than any non-native alternatives,30,31 and gas-phase infrared spectroscopy carried out by von Helden and co-workers showed results consistent with preservation of alpha-helices and beta-sheets in native MS of myoglobin and beta-lactoglobulin, respectively.32 All of this supports the idea that, while significant expertise and care are needed, gas-phase structure may in fact reflect important aspects of native solution structure.Other analyses have further indicated that the overall structure of proteins is generally conserved in the gas phase. Using electron capture dissociation (ECD), McLafferty and co-workers have argued for refolding of small proteins in the gas phase to a non-native secondary and tertiary structure.m/z ions. Today, this is possible using commercially available time-of-flight,33 Orbitrap,34 and Fourier transform ion cyclotron resonance (FTICR)35 instruments. These instruments can transmit large protein complexes without providing excessive activation that could compromise protein complex structure. In recent years, important progress in native purification methods such as native gel-eluted liquid fraction entrapment electrophoresis (GeLFrEE) separation,36 native gel electrophoresis,37 ion exchange chromatography (IEX),38 hydrophobic interaction chromatography (HIC),39,40 and online buffer exchange, has made these methods more applicable for complex mixtures and for characterisation of endogenous ligands.36,41As ions formed under non-denaturing ESI conditions typically have low charge states, native MS instruments must be able to transmit and detect high-18,42 intrinsically disordered proteins,43 highly dynamic or heterogeneous complexes,19 or very large systems such as intact virus capsids44 can all be investigated using native MS, as well as their associated proteoforms.45 Many of these are highly challenging to study by other analytical techniques such as NMR or XRD, and therefore the insights from MS can be vital to understanding these proteins and complexes. It is worth noting here that, while lower-resolution, MS-based methods are not necessarily less native than conventional methods – the crystalline state is far removed indeed from the native protein environment. These classical methods are also more prone than MS to sampling a single low-energy state or an ensemble average, and a combination of biophysical approaches can be needed to capture the dynamic nature of protein conformation. One aspect of protein structure to which native MS can be expected to be extended in the coming years is the study of protein quinary structure, which is defined by specific interactions in the crowded cellular environment that are weaker and more transient than those responsible for quaternary structure, and has recently been successfully investigated with non-native MS methods such as chemical crosslinking.46,47 Continued advances in sample processing and instrument development are expected to make these new experiments more routine for protein complex analysis in the near future. As will be discussed in the rest of this review, native ionisation has in recent years been increasingly combined with top-down protein fragmentation, allowing probing of different structural levels and relating sequence information to higher-order structure and complex formation.By using appropriate sample preparation, ionisation conditions, and instrumentation, many challenging analytes such as membrane proteins,2.2.12.1.117 Native MS without breaking up the complexes only provides limited information regarding quaternary structure and is largely blind to subunit connectivity and location of ligand binding within the complexes. For unknown complexes, intact mass alone is not enough for determining stoichiometry and composition. In addition, gentle tuning conditions used to maintain structural integrity of noncovalent complexes can result in insufficient desolvation, peak broadening, and increased uncertainty in mass determination. For fragile complexes, the result of this is that the achievable mass resolution may be too low for precisely defining the binding of small ligands. Recent publications by the Kelleher48 and Heck49 laboratories have demonstrated the use of charge detection MS of noncovalent complexes on Orbitrap instruments. In these experiments, small numbers of ions were allowed in the trap, allowing highly repeatable mass measurement of individual ions. A histogram of the single-particle centroid masses constructed after thousands of these measurements provides significantly (approximately an order of magnitude) higher resolving power than conventional Orbitrap MS. Still, heterocomplexes that have multiple subunits with very similar masses are difficult to characterise just from the intact mass, especially when there is high uncertainty in mass measurement.50 Another limitation of mass measurement alone of intact proteins and complexes is that their (average) masses might shift slightly due to natural variations in isotopic abundance, an effect which is able to cause mass shifts greater than the accuracy of modern high-end mass spectrometers.51The complex-up strategy aims at subunit dissociation of noncovalent complexes without cleaving covalent bonds.via addition of chemical denaturants has been used to partially dissociate native protein complexes into subcomplexes, including successful applications to RNA polymerase and exosomes.52,53 This technique enables a simple way to access the subunit connectivity without changing the downstream native MS detection method. Because the dissociation occurs in solution, this method may not be easily applicable to highly heterogenous samples, as released subcomplexes cannot be tracked to their originating precursors. Furthermore, the protocol for partial denaturation requires optimisation for each complex and can fail for proteins that are resistant to mild denaturants or precipitate easily upon denaturation. Other solution-phase methods exist to study higher-order protein structure by subsequent MS analysis, for example chemical crosslinking,54 protein footprinting methods including fast photochemical oxidation of proteins (FPOP),16 and hydrogen–deuterium exchange;55–58 however, these are beyond the scope of this perspective. Integration of information from different native and non-native techniques can provide valuable structural insights.59,60Solution disruption 2.1.217 however, early examples of subunit release via activation of protein complexes in the gas phase were reported in 1994 by Smith and co-workers.61 Under harsh source conditions, several model tetrameric complexes were dissociated into monomers and trimers. This was surprising because the trimers were not known to be physiologically relevant. This dissociation pattern of monomer stripping appeared to be ubiquitous in several early studies using gas collision to activate the complexes , or higher-energy collisional dissociation, HCD in some instruments), and was also seen for blackbody infrared radiative dissociation (BIRD).62 Essentially, a monomer in the complex was stripped from the complex, leaving behind the stripped (n − 1)-mer (n is the number of subunits in the precursor complex).61,63,64 The stripped monomers carry away a disproportionate fraction of the total charge relative to their mass. This seemingly odd pattern was described as ‘asymmetric’ dissociation and was studied in detail by several follow-up reports.62,65 Accumulating experimental and computational studies have suggested that charge plays an important role in gas-phase protein unfolding and dissociation.62,65–67 The mechanism of charge migration to the ejected monomer is not fully understood, but mobile charge62,68,69 and salt bridge rearrangement theories70,71 have been proposed.The term ‘complex-up’ was coined in 2019;72 As larger oligomers carry more charge in native MS, the signals for all these oligomers end up as an overlapping, unresolvable cluster around m/z 10 000. The released (n − 1)-mers, (n − 2)-mers, and (n − 3)-mers from sequential monomer stripping could, however, be mass resolved and from these, the stoichiometry of the intact complexes was inferred.72 Although CID can be used to identify the composition of unknown complexes, the dissociation may be incomplete and insufficient to release all subunits of multimeric hetero-complexes.50,73 Typically, subunits at the periphery are preferentially ejected in CID.74–76 Because of the significant unfolding and the ubiquitous monomer stripping dissociation pattern, extracting subunit connectivity and architecture from CID data is usually not straightforward.CID has been used to release subunits from protein complexes for confirming complex composition. For example, the ubiquitous monomer-stripping pattern was used to activate αB-crystallin complexes with polydisperse stoichiometry (primarily 24–33 mer).2.1.377 SID is more efficient than CID in converting kinetic energy of an ion to internal energy because of the larger mass of a surface target compared to neutral gas molecules. Refractory protein complexes  are difficult to dissect by CID but can be dissociated by SID.78,79 In addition, the activation in SID occurs on a much shorter time scale than (low-energy) CID, in which protein ions undergo many lower-energy collisions , in which proteins collide with a surface target, can produce folded subunits with minimal structural rearrangement for a number of model protein complexes.llisions . This ra82 Weaker interfaces will therefore be cleaved at lower collision energy than stronger interfaces in SID. The structurally informative dissociation by SID enables quaternary structure characterisation of unknown proteins that are recalcitrant to classical structural biology techniques. Toyocamycin nitrile hydratase (TNH) and bacterial biominerialisation enzyme Mnx are two heterocomplexes that resist crystallization. Their mass  also puts these complexes in a range that is too large to be easily studied by NMR, but too small for cryo-EM. SID experiments of these complexes were quick, with data acquisition typically on the order of minutes to hours, and provided critical information to define their quaternary structures.50,73,88,91Systematic examination of model complexes showed that the SID collision energy required to cleave a given interface is correlated with the interface strength calculated from the (known) structure of the complex.3F3 hexamer and MnxG at low collision energy , providing limited information for mapping the assembly  and others are expected to follow in the near future.Recently, SID was also applied to characterise the subunit arrangement of a plant pseudoenzyme-enzyme hetero-complex between PDX1.2 and PDX1.3 in 2.1.494 electron-based activation generally does not cause significant disruption of higher-order structure35 and is thus ineffective for complex-up. Intramolecular energy redistribution after conversion of photon energy to vibrational modes is thought to be responsible for breaking of noncovalent interactions in UVPD.95 IRMPD of protein complexes, on the other hand, has produced similar results to CID, likely because of the low energy of infrared photons, of which dozens or even hundreds are absorbed to cause dissociation.96 In contrast, 193 nm UVPD of several model protein complexes showed CID-like asymmetric dissociation at low pulse energy, but changed to more symmetric dissociation (SID-like) at higher pulse energy.97,98 This change of dissociation behaviour as a function of input energy in UVPD is reminiscent of the mechanistic difference between the slow heating in CID and rapid heating in SID. Even though SID has been shown to induce dissociation of complexes with minimal unfolding, the monomer stripping pathway indicative of unfolding can also be observed in SID, especially for large protein complexes. Previously, a ‘shattering’ mechanism  was proposed for SID of peptides,99 but dissociation may occur at a much slower rate after activation for larger molecules with high degrees of freedom and significant intramolecular energy redistribution/relaxation. SID of large protein complexes could also result in multiple, inelastic collisions like those seen for cluster ions.100 The energy deposition could be affected depending on how the protein ions interact with the surface.101Other than CID and SID, photo-activation by ultraviolet (UVPD) and infrared multiphoton photodissociation (IRMPD) have also been used in complex-up experiments. While exceptions have been reported,66,67,98,102 Charge reduction through solution additives and gas-phase reactions can supress protein unfolding, and generally help SID by increasing the percentage of structurally informative products.66 In contrast, subunit dissociation is suppressed in CID for charge-reduced precursors. Instead, CID tends to benefit from supercharging, allowing more SID-like dissociation.67 Computational and theoretical studies have indicated that charge can move upon activation, as further discussed in Section 2.3. The energy landscape of protein complexes in the gas phase is thus strongly affected by charge, resulting in different behaviours with different activation methods. When the goal is to study quaternary structure with a complex-up strategy, conditions should therefore be optimised to minimize unfolding and structural rearrangement.In addition to the differences in activation techniques, the charge of protein complexes is another important factor in complex-up experiments.2.22.2.1n (n = 3 or more) analysis of complexes ionised under non-denaturing conditions. This type of experiment has been referred to as ‘complex-down’17 MS or recently as ‘nativeomics’.103 The extra layer of information afforded by fragmentation of covalent bonds compared to the methods described in Section 2.1 can facilitate a greater understanding of protein complex function.While mass measurement of monomers and noncovalent (sub)complexes is informative, oftentimes complementary sequence analysis is desired to identify the component proteoforms. There are multiple ways to obtain this information from intact proteins. Top-down MS under denaturing conditions yields important information on sequence and PTMs, but information regarding noncovalent interactions or protein folding is lost. Efficient sequencing can be combined with obtaining information on noncovalent complex stoichiometry through MS103 Next, the internal  energy of the complex is increased sufficiently to induce ejection of monomers or non-covalent ligands without breaking covalent bonds. Normally, activation is provided by techniques such as CID or SID. CID can be implemented either after a specific precursor m/z is isolated in the gas phase, or through elevated acceleration voltage of all ions in the source. In the latter case, the technique is often referred to as in-source dissociation (ISD), which generally requires highly purified samples or online separation so that the products can be traced to the precursor unambiguously. As described in Section 2.1, the charge density of the ejected subunits is usually higher than that of the original complex, making them amenable to sequencing by several commonly-used ion activation methods. The ejected ion – monomer or ligand – is subsequently (re-)isolated and fragmented in a (pseudo-)MS3 or MS4 workflow, providing in-depth information on e.g., phosphorylation sites,104 metal ion binding,36 or sequencing of peptide ligands .106 It was found that GroEL monomers could be ejected from the complex in the source and further fragmented with HCD. Sequence-informative fragments could be readily observed in the low-m/z region of the mass spectrum with this workflow. More recently, Heck and co-workers obtained sequence information on the Aquifex aeolicus lumazine synthase (AaLS) virus-like nanocontainer (>1 MDa) with UVPD fragmentation, resulting in a mix of monomer ejection and backbone fragmentation.107 It was found that optimal collisional cooling and trapping before UVPD fragmentation allowed for efficient detection of intact virus nanocontainers, monomers, and sequence fragments. Combining knowledge of the intact mass of protein complexes from native MS with accurate mass measurement of the ejected, desolvated, highly-charged monomers (MS2), and sequence information (MS3) in this manner, allows for the determination of the exact proteoform composition of the complex.The first step of these experiments is to transfer a noncovalent complex from the solution phase to the gas phase without excessive disruption of its higher-order structure.ands see .105 In 22.2.222 Notably, they found N-terminal acetylation and removal of the last two amino acids on the C-terminus, complementing data that was absent from cryo-EM, which did not have enough resolution to discern these features. In another example of how complex-down analysis has been shown to aid in the identification of protein complex mutations by providing deep sequence information on ejected monomers, Compton and co-workers demonstrated how complex-down analysis of proteins with regulatory post-translational modifications leads to more accurate structural and functional characterisation of those complexes.36 Sequence analysis of the triosephosphate isomerase complex indicated the presence of a proteoform that was not modified, a proteoform that was phosphorylated at serine 20, and a proteoform that was N-terminally acetylated. The phosphorylated and acetylated proteoforms did not dimerise with themselves or each other, indicating that phosphorylation at serine 20 and N-terminal acetylation act to inhibit the dimerisation of the complex.36In addition to identifying the primary sequence of subunits, complex-down analysis can elucidate sequence abnormalities such as deletions and mutations in native protein complexes. These data can help characterise the structure and function of protein complexes. Sharon and co-workers found novel sequence information on the alpha subunit of the rat 20S proteasome with complex-down analysis.104 It was found that the level of phosphorylation of the fructose-1,6-bisphosphatase 1 (FBP1) complex differed depending on whether the cells were grown on carbon-starved media, glucose media, or were heat-shocked. Complex-down analysis of the monomers indicated that phosphorylation on Ser12 or Thr13 was highly expressed in cells that were grown on glucose media. Phosphorylation at Ser12 is known to deactivate this complex, indicating that the complex is deactivated under these conditions and the cells readily switch from performing gluconeogenesis to glycolysis. In all these examples, complex-down analysis efficiently located key modifications and/or sequence variants on native protein complexes, illuminating key structural and functional characteristics of those complexes.Sharon and co-workers have used complex-down analysis to determine how yeast cells use phosphorylation to regulate certain cellular pathways under different growth conditions.2.2.3108 Increasing the level of this activation leads to the ejection of protein monomers, allowing complex-down sequencing . In some cases, complex-down analysis has been shown to map important regions of native proteins. Sobott and co-workers recently studied three membrane proteins, i.e., the pentameric mechanosensitive ion channel of large conductance (MscL), the tetrameric Kirbac potassium channel, and the hexameric hepatitis C p7 viroporin. Performing CID-based complex-down MS, they found that b and y fragment ions mainly stemmed from dissociation in the membrane-spanning regions of the monomers.110 This was consistent with earlier work by Kelleher and co-workers, who performed top-down LC-MS of denatured integral membrane protein monomers, and found that transmembrane domains were more likely to fragment in collisional than electron-based dissociation.111Brodbelt and co-workers used UVPD for effective sequencing of aquaporin Z monomers ejected from the native tetramer.2.2.436 HIC,39,40 SEC,112 capillary zone electrophoresis (CZE),112 and IEX38 have efficiently aided in the analysis of heterogeneous protein mixtures. Kelleher and co-workers demonstrated the use of native GELFrEE coupled to MS for the characterisation of protein complexes from cobra venom.113 Complex-down analysis enabled the characterisation of protein complexes, and in some cases allowed identification of glycosylated and metal-bound proteoforms. In a different example, Sun and co-workers showed that SEC and CZE can separate multiple proteins and protein complexes before analysis with complex-down mass spectrometry on a lysate of E. coli, resulting in the identification of 672 proteoforms and 23 protein complexes.112 These examples illustrate how, by preserving the native structure of complexes, these high-throughput techniques can yield critical information regarding complex formation and ligand binding that is not easily accessible by conventional proteomics techniques. New developments in MS instrumentation,113 spectrum deconvolution software,112 and fast peak identification software113 are expected to facilitate high-throughput complex-down studies becoming more routine in the future.Recently, high-throughput native separation techniques including GELFrEE,m/z to intact mass. Spectral interpretation of native MS has largely relied on manual analysis, although deconvolution software such as UniDec and iFAMS has been developed in recent years.60,114 Likewise, analysis of top-down MS data has largely involved manual analysis or manual validation due to the complexity of the data. Data interpretation for complex-down spectra is similar to TDMS of denatured proteins; therefore, many existing software packages for TDMS can be retrofitted for complex-down data, including ProSight PTM 2.0,115 ProSight Lite,116 MASH Suite,117–119 ProteinGoggle,120 TopPIC,121 Informed-Proteomics,122 Masstodon,123 and others. Even then, some unique challenges unique to complex-down remain, including the need for multi-stage activation, poorly resolved precursor complexes, and low intensity peaks, and so manual analysis or validation still plays an important role in practice. An up-to-date overview of software packages for analysis of top-down data can be found on the webpages of the Consortium for Top-Down Proteomics (https://www.topdownproteomics.org) and an excellent review was recently published.12Data analysis has so far been a particular bottleneck in these approaches, as native MS spectra often do not have isotopic resolution, and so common software for small molecule and peptide analysis cannot be used to convert 2.32.3.1etc.) that characterise higher-order structure can protect parts of the protein from fragmentation and/or prevent the release of fragments. Due to the second factor, the lack of fragments in certain regions of the protein can inform on higher-order protein structure.35The methods discussed in Section 2.2 rely on gas-phase fragmentation of the protein backbone, but only after the higher-order structure of a protein complex has been largely annihilated. The ‘native top-down’ strategy also refers to fragmentation of the protein backbone, but fragmentation methods are directly applied to native proteins or complexes instead of unfolded proteins. Native TD generally results in less extensive fragmentation than denatured TD because (1) the lower charge states of native species in ESI have a negative impact on the efficiency of most fragmentation methods, and (2) the noncovalent interactions  is an electron-based fragmentation method that uses higher-energy electrons to fragment proteins generating a/x and b/y ions in addition to c/z˙ ions.125 EID offers more extensive fragmentation than ECD and, as electron energy can be tuned through user-accessible instrument parameters, both experiments can be performed using the same instrumentation. In addition, it was recently reported that EID of proteins results in more internal fragments than ECD which could help with protein sequence coverage.126 ETD is mechanistically similar to ECD, but uses a radical anion rather than a cathode to provide electrons.127 As in ECD, the transfer of the low-energy electron typically results in c/z˙ fragment ions.Electron-based dissociation (ExD) is useful for probing protein structure because it generates backbone fragments without annihilating the higher-order structure of the protein. In ECD, protein ions capture low energy (1–3 eV) electrons, leading to the formation of a/b/c/x/y/z ions from the mixed mode of both vibrational and electronic excitation.95 In comparison, UVPD with 157 nm photons (F2 excimer laser) appears to be more effective at generating a/x ions and has specificity towards disulphide bond cleavage.128,129 UVPD at wavelengths of 213 nm 130 and 266 nm 131 has also been reported for native TD of small proteins. In the following paragraphs, we will consider how different aspects of higher-order structure can be probed using native TD fragmentation.Native TD can be performed using UVPD, and light sources – usually lasers – with different wavelengths have been reported for protein characterisation. Most of the reported native TD work has used 193 nm UVPD (ArF excimer laser), which yields 2.3.229 and cytochrome c132 after being transferred into the gas phase. It was found that these proteins show more fragmentation with an increase in source temperature and pre-ECD infrared activation. This indicated that these proteins start to unfold in the gas phase when their internal energy is increased by these ‘slow’ activation methods. These experiments on small monomeric proteins clearly demonstrated the feasibility of accessing secondary and tertiary structures using MS techniques and spurred research on larger proteins and protein complexes.Fourier transform ion cyclotron resonance instruments can readily be used to perform ECD because low-energy electrons can be efficiently trapped by the static electromagnetic field in the ICR cell. Early experiments used ECD to probe structural changes in ubiquitin30 and disulphide bonds133 can hold fragments together, preventing their release and detection in MS. This phenomenon is known as ‘electron capture/transfer with no dissociation’ . This principle can be used to probe secondary and tertiary structure. Barran and co-workers have shown that the stability of certain secondary structure elements of proteins can be probed by fragmenting different charges states.134 It has also been found that the location of disulphide bonds can be mapped with ExD fragmentation.133,135 ExD techniques have also been shown to probe tertiary structure on protein complexes such as alcohol dehydrogenase (ADH). ECD136 and ETD137 of native tetrameric yeast ADH both yielded primarily N-terminal fragments, while virtually no fragmentation in the C-terminal region was observed. For the C-terminus, this behaviour can be easily rationalised, as it is buried in the interior of the complex. To rationalise relative fragment abundances from the N-terminal region, explanations have been proposed based on local backbone flexibility and surface exposure  can be detected  and the smaller holo fragments (short stretches with the ligand) can together define the site (or region) of the bound ligand.136,140,151 Using this analysis, it is possible to localise binding sites on proteins and complexes, providing insight into the function of ligands.Both ExD and UVPD can identify ligand binding sites in native TD through monitoring backbone fragments that retain the noncovalent ligand. Since these ligands are generally weakly bound to proteins and protein complexes, activation through CID tends to disrupt the ligand binding, causing information about the binding site to be lost. In contrast, ExD and UVPD can dissociate the peptide backbone while preserving the ligand. In the fragmentation spectrum, the larger 151 Soon after that, Sadler and co-workers showed that binding sites of the drug cisplatin can be localised on peptides with ETD fragmentation.152 Since then, ECD has been used to pinpoint NAD+ on alcohol dehydrogenase153 and small aggregation-inhibiting compounds on amyloid proteins.154,155 Correlation of these binding sites with structural changes can suggest possible mechanisms of noncovalent ligand binding.155 Several of these examples also illustrate the capability of native MS-based methods to probe intrinsically disordered proteins and their complexes. More recently, UVPD has been used to localise noncovalent ligands on protein complexes, such as haem on myoglobin,143 NADPH and methotrexate on dihydrofolate reductase,142 and GTP on eIF4E.140 These examples clearly show that ExD and UVPD preserve noncovalent ligands when cleaving the peptide backbone and can readily localise these ligands on proteins and protein complexes.Loo and co-workers demonstrated for the first time in 2006 that ECD could be used to localise noncovalently bound spermine on the amyloidogenic protein α-synuclein.156,157 In order to preserve the metal/peptide complex, low-energy CID was used in these studies; however, it remains a concern that the increase in internal energy could mobilise the metal cation sufficiently to migrate across the protein/peptide in the gas phase prior to backbone fragmentation. In some cases, weakly bound metal ions might be lost in CID, as discussed in Section 2.1.3. Since the development of ExD and photodissociation techniques, metal ions can be more reliably pinpointed on peptides and proteins. Metal ion binding sites have been identified on native peptide and protein monomers such as amyloid β,158 α-synuclein,159,160 and carbonic anhydrase161 with ECD, as well as native protein complexes such as alcohol dehydrogenase.153,162 Similarly, UVPD is also effective in determining metal binding sites as shown in model metalloproteins including staphylococcal nuclease, azurin, and calmodulin.145 Brodbelt and co-workers have also used this method to localise zinc ions within the insulin pentamer.140 Interestingly, in some of these studies it was shown that some CID fragments also retained the metal cation, and the pattern of apo and holo CID fragments was consistent with that from ECD – this was likely due to the 80-fold lower dielectric permittivity of vacuum compared to water strengthening the electrostatic protein–metal interactions. For the same reason, metal binding can sometimes survive monomer ejection, and a complex-down approach (see Section 2.2) has also been successfully applied to identify binding sites of endogenous metal cofactors of both soluble36 and membrane163 proteins.Fragmenting protein/metal ion complexes can aid in the localisation of metal ions. Early fragmentation of peptide/metal complexes used CID to dissociate the peptide backbone.164 Other pioneering work was performed by Langridge-Smith and co-workers in 2011, who used ECD to study the binding interface between the anterior gradient-2 protein and its hexapeptide ligand PTTIYY, concluding that binding involves the C-terminal part of the protein.165 Schneeberger and Breuker have used CID to determine the binding site of RNA on proteins, again taking advantage of the strengthening in the gas phase of electrostatic interactions to the point where they are occasionally able to survive backbone fragmentation.166 Recently, O'Connor and co-workers have shown that ECD fragmentation of oligomers of the amyloidogenic peptide amylin (implicated in type 2 diabetes) can provide information on the binding interface between monomers , or the 29 N-terminal residues (c29 fragment), led the authors to propose a model in which dimerisation occurs between Ser29 of the first, and Asn35 of the second monomer, in a staggered fashion.Taking this a step further, native TD can provide information on binding interfaces between biological macromolecules. In this way, native TDMS has proven useful for understanding the quaternary structure of protein complexes as well as ‘lower’ levels of higher-order structure as discussed in Section 2.3.2. In 2009, Woods and co-workers used both ECD and ETD to investigate the residues involved in binding between small (<10 residues) acidic and basic peptides.mers see .167 Spec2.3.4141,168 Although some side chain rearrangements are expected from removal of solvent – for example, positively charged lysine side chains will rapidly form interactions with backbone carbonyl groups, as this intramolecular solvation is energetically highly favourable – the overall fold of protein complexes is generally believed to be kinetically trapped on the time scale of the MS analysis.5 However, inadvertent excessive gas-phase pre-activation of protein complexes – beyond what is needed for efficient desolvation – can lead to significant structural rearrangements, which can be manifested by changes in native TD spectra. Therefore, it is necessary to optimise MS tuning to reduce the amount of activation applied to a protein or protein complex. With little activation it seems that overall protein structure is preserved in the gas phase and can be readily probed for structural characteristics.As discussed in Section 1.2, there is an ongoing debate on how closely gas-phase structure reflects that in solution. Native TD has offered critical experimental data for understanding the evolving protein structures in the gas phase.136 The experimental data suggested that ADH unfolds through a ‘peeling an onion’ mechanism in which the N-terminus gradually unravels. Conceptually similar experiments reporting ECD and ETD of partially-unfolded haemoglobin were carried out in 2015 by the Gross,169 Loo,170 and Sobott139 groups, and recently ECD and ion mobility were performed on the same instrument, providing two orthogonal methods to probe unfolding of this tetramer.171 Recently, the unfolding of ADH was re-examined by native TD using both ECD and 193 nm UVPD on the same instrument, further indicating that the N-terminus unravels with increasing collision energy  refolding.Gross and co-workers demonstrated that native TD ECD fragments from the ADH tetramer reached deeper into the complex (starting from N-terminus into the core) as it unfolded with increasing activation in the ion source.ergy see .141 Larg32,173 Circular dichroism was recently combined with MS for oligonucleotides.174 With ongoing development of advanced light sources coupled to native MS,44,175 we expect the possibility of performing spectroscopy analysis of native proteins and complexes for deep structural characterisation to emerge in the near future. The ability to mass-isolate species by MS-based methods could potentially transform structural biology research by offering complementary methods to study non-homogenous samples .The examples discussed here show the tremendous experimental detail that native TD offers for improving our understanding of gas-phase protein structure. Previous computational and theoretical studies have largely relied on complex-up experiments at the intact protein/subunit level, which do not provide information at the amino acid level. We anticipate that the integration of native TD and computational modelling will greatly enhance our understanding of critical factors that modulate gas-phase protein fragmentation and dissociation. The ability to perform gas-phase spectroscopy on intact proteins and complexes in MS is particularly exciting and allows a new level of structural information to be accessed. Action ion spectroscopy coupled to MS using free electron lasers with variable wavelengths is well-suited for structure analysis as demonstrated by work on small molecules, peptides, and oligonucleotides. This concept is based on the varying fragmentation efficiency of precursor ions in response to enhanced absorption at resonant photon energies. Infrared ion spectroscopy has already been demonstrated on small monomeric proteins.3.The biology of proteins must be considered through the lens of their ability to interact with one another, other biomolecules, and various cofactors and substrates. For this reason, complete knowledge of the primary structure, including post-translational modifications, is a necessary but insufficient condition to understand protein function. Recent methodological developments have enabled direct probing of the higher-order structure of native proteins and complexes, including membrane proteins, by mass spectrometry. At the lowest level of information, the mass and overall size of a complex can be measured using ion mobility-mass spectrometry; however, by combining native ionisation with top-down fragmentation, a range of workflows become accessible. These allow probing of subunit connectivity (complex-up), efficient ligand identification and monomer sequencing (complex-down), and elucidation of secondary and tertiary structure (native TD). The most commonly used ion activation methods in these experiments are summarised in There are no conflicts to declare.


The PDB database provides more than 150,000 entries for biological macromolecular structures. The vast majority of the entries comprise proteins. Thus, we can resort to a large dataset that encodes information about sequence–structure–function relationships. Many bioinformatics approaches take advantage of this information and utilize it for a wealth of basic biological, biochemical, and biophysical problems. A well-established key approach is the statistical analysis of experimentally resolved structures for the subsequent derivation of statistical scoring functions . Such SSFs are employed in numerous bioinformatics methods, e.g., for the assessment of experimentally determined structures or in the prediction of 3D protein structures, protein–protein interactions, protein–ligand interactions, protein stability, etc.The original purpose of what is now termed “statistical scoring function” was the description of molecular interactions. Different authors established energy-like functions depending on the distance between two atoms, dihedral angles, density of neighboring atoms (contacts), or orientation and distance. Meanwhile, SSFs are viewed in more general ways by certain authors, which is also reflected in distinct contributions to this Special Issue.Georgoulia and Bjelic investigated the prediction of protein–protein binding interactions in dimeric coiled coils. For this purpose, they applied Rosetta, a versatile set of computational methods for molecular modeling and analysis developed at David Baker’s lab. The heart of Rosetta are molecular scoring functions that combine physics-based terms such as Van der Waals interactions with statistics-based terms such as a distance-dependent atom pair potential.Laimer and Lackner contributed a method named MHCII3D that concentrates on the structure-based prediction of peptides that bind to MHC II molecules. Biologically, such an interaction is part of natural antigen recognition and triggers immune responses. This process plays also a key role in diseases such as allergies and cancer. MHCII3D employs comparative modeling and a distance-dependent SSF for the prediction of potential binding peptides. In contrast to competitor methods, it does not employ machine learning and is therefore not constrained by learning data from wet lab binding experiments.Poot Velez et al. describe a new way of representing protein structure and apply it to the prediction of protein–protein interactions (PPIs). They map the atomic three-dimensional structures of proteins participating in protein–protein interactions to compact representations based on 26 residue cluster classes (RCCs). Variants of combining the individual RCCs of binding partners are then analyzed with respect to their ability to correctly classify PPIs with machine learning methods.Alshehri et al. present the first large-scale characterization of intrinsically disordered proteins in the proteome of the genus Camelus. Here, SSFs are used (in addition to other methods) to predict disordered proteins/protein regions in Camelus species and Homo sapiens. Those proteins are then functionally annotated and systematically compared with respect to their degree of disorder and their biological roles. The authors highlight several functional categories that are overrepresented among Camelus disordered proteins when compared to the human proteome.The applicability of SSFs derived from known protein structures is naturally challenged by so-called xenoproteins, which are built from amino acids not part of life’s standard alphabet. Mayer-Bacon et al. review empirical work on the incorporation of non-canonical amino acids into proteins and juxtapose these findings with more theoretical considerations about the properties of amino acids from an evolutionary perspective. The review stimulates rethinking of many of the assumptions underlying current prediction models.


Chondroitin sulfate (CS) and dermatan sulfate (DS) are linear anionic polysaccharides that are widely present on the cell surface and in the cell matrix and connective tissue. CS and DS chains are usually attached to core proteins and are present in the form of proteoglycans (PGs). They not only are important structural substances but also bind to a variety of cytokines, growth factors, cell surface receptors, adhesion molecules, enzymes and fibrillary glycoproteins to execute series of important biological functions. CS and DS exhibit variable sulfation patterns and different sequence arrangements, and their molecular weights also vary within a large range, increasing the structural complexity and diversity of CS/DS. The structure-function relationship of CS/DS PGs directly and indirectly involves them in a variety of physiological and pathological processes. Accumulating evidence suggests that CS/DS serves as an important cofactor for many cell behaviors. Understanding the molecular basis of these interactions helps to elucidate the occurrence and development of various diseases and the development of new therapeutic approaches. The present article reviews the physiological and pathological processes in which CS and DS participate through their interactions with different proteins. Moreover, classic and emerging glycosaminoglycan (GAG)-protein interaction analysis tools and their applications in CS/DS-protein characterization are also discussed. Chondroitin sulfate (CS) and dermatan sulfate (DS) are acidic linear anionic polysaccharides in the glycosaminoglycan (GAG) family. CS is composed of repeated disaccharide units [→4GlcAβ1→3 GalNAcβ1→], where GlcA and GalNAc refer to D-glucuronic acid and N-acetylgalactosamine . The difLike most GAGs, CS and DS are located in the animal extracellular matrix (ECM), on the cell surface or associated with the plasma membrane . TherefoThe charge density and the position of sulfation on the CS/DS chains are the main factors that affect their ability to interact with target proteins, while a minimal chain length is usually required for binding . It was Characterization of the CS/DS-protein complex is of great significance for the design of more specifically targeted protein/GAG analogs and the subsequent development of more effective therapeutic drugs with fewer side effects. However, the structural motifs and interaction mechanisms between CS/DS and proteins are not fully understood due to the heterogeneity and complexity of CS/DS structures. The rapid development of analytical methods, including affinity approaches, mass spectrometry (MS), nuclear magnetic resonance (NMR), and computational approaches, makes it possible to study the GAG-protein complex with higher resolution, which will help in understanding the interaction mechanisms between CS/DS and various protein ligands and in the development of new treatment methods.This review focuses on the interactions between CS/DS and proteins and their roles in human diseases. In addition, some classic and emerging GAG-protein interaction analysis tools and their applications in CS/DS-protein characterization are also discussed.In the human body, CS/DS plays an important role in the physiological and pathological processes by interacting with a large number of proteins. The development of certain diseases is usually related to the imbalance of CS/DSPGs biosynthesis and changes in CS/DS chain structure. The major diseases that are related to the interaction between CS/DS and proteins are presented in Tumor-associated glycans and PGs play an important role in promoting the invasion and metastasis of malignant cells, participating in cell-cell and cell-ECM interactions, and promoting the adhesion and migration of tumor cells. In many tumor tissues, changes in the production level of PGs and the structure of GAG have been observed. Among PGs, the important role of HSPG in tumor adhesion, invasion, migration, proliferation and angiogenesis has been recognized for a long time . In receO-sulfation of CS exhibited the dual role in the development, progression and metastasis of cancer. The monosulfated substitution provided interaction surface for CS and influenced its binding to various cytokines and growth factors, cell surface receptors and other important proteins, which resulted in the complex role of CS/DS in tumor , as CSPG accounts for about 20% of the total ECM of the CNS . Notablyin vitro studies have proven the specificity of the sulfation mode for some of the above processes. In vitro experiments have shown that CS-E and CS-D, which are rich in highly sulfated modifications, are very important for regulating the functions of CSPGs. They can interact with growth factors and inhibitory cell surface receptors  is a chronic, dynamic and evolving process that involves changes in the morphology and structure of arterial vessels, leading to the formation of atherosclerotic plaques, which ultimately leads to myocardial infarction or stroke . PGs areThe length of the CS chain and the sulfation modification mode are the main factors that affect the interaction between LDL and CSPG. The interaction between chondroitin-6-sulfate and LDL plays a vital role in atherosclerotic diseases. The increase in chondroitin-6-sulfate in early lesions of atherosclerosis leads to the accumulation, oxidation and hydrolysis of LDL and promotes the development of atherosclerosis .In addition to the abovementioned physiological and pathological processes, CS/DS also plays a key regulatory role in inflammation, autoimmune diseases, genetic diseases, kidney diseases and other diseases. Studies have shown that CS/DSPGs promote the inflammatory response by binding immune receptors such as Toll-like receptors, selectin, CD44 and β1 integrin, which helps drive the development of traumatic brain/spinal cord injury and multiple sclerosis . The oveAs mentioned above, CS/DS plays an important regulatory role in many physiological and pathological processes. An accurate understanding of the CS/DS mechanism in these processes will help in developing CS/DS-based polysaccharide drugs, along with new therapies and applications. The rapid progress of analytical methods will promote the decryption of the molecular basis of CS/DS-protein interactions. The following section will introduce the analytical methods used for characterizing GAG-protein interactions in recent years and their applications in CS/DS-proteins.At present, many analytical methods for revealing the molecular mechanism and binding motifs of GAG-protein complexes have been developed, including affinity methods, MS, NMR, and computational approaches. The following reviews the progress and applications of various analytical methods in the analysis of CS/DS-protein complexes. Modern analytical tools that can be used to characterize the binding between CS/DS and proteins are categorized into different groups based on their purposes and summarized in Many affinity-based analytical techniques and tools are used to explore the role of biomolecular interactions in physiological and pathological phenomena, including affinity chromatography, gel mobility shift assays (GMSA), isothermal titration calorimetry (ITC), biosensor-based surface plasmon resonance (SPR), biolayer interferometry (BLI), and quartz crystal microbalance (QCM) techniques. In research in recent decades, the above methods have not only been used to prove interactions between GAGs and proteins but have also been used to screen the interacting sequences of GAGs and proteins.Affinity chromatography is a method used to separate interesting GAGs or interesting proteins from a mixture based on the highly specific macromolecular binding between GAG and the target protein. This method generally involves coupling interesting proteins or interesting GAGs to agarose and coating them onto affinity columns . BindingGMSA can show different electrophoretic mobility rates according to the size of the analyte molecule and the nature and size of the charge. It has been successfully applied to the interaction between GAG and proteins. Generally, AMAC is used to label the reducing end of GAG, and the offset of the GAG-protein complex band and the free GAG band is displayed by fluorescence irradiation to determine the binding affinity . GMSA caPlasmodium falciparum, ITC can directly obtain the enthalpy, entropy, free energy (binding constant), and stoichiometry in a single analysis. The binding constant can reflect the binding ability of two molecules, so ITC is also used for GAG-protein interaction analysis. To verify the structure of the interaction between CS-A and the VAR2CSA protein, which is a member of the erythrocyte membrane protein-1 family of N-sulfo and 6-O-sulfo groups play a key role in the interaction. Of the other GAGs, only CS-E and CS-B exhibit strong combination with TIMP-3 -interacting host molecule and the Lmp1 region that may participate in this interaction, SPR can directly and quantitatively analyze unlabeled molecular interactions in real time and has been successfully used for the biophysical characterization of glycosaminoglycan (GAG) protein interactions. BLI is an SPR alternative technology developed later with a higher throughput and flexibility. As an example of a study detecting the binding motif of GAG, h TIMP-3 . In the ka) and dissociation (kd) rate constants] and affinity between the ligand and the analyte can be evaluated. Therefore, QCM has also been successfully used to characterize the interaction between GAGs and proteins. Different from SPR and BLI, QCM is an acoustic biosensor that uses the piezoelectric effect. The number of molecules bound on the surface determines the frequency of the QCM crystal, so the kinetic rate constant [association  MS, and the recently emerged native MS. Among them, surface-labeling MS, which is more mature, is used to characterize the CS/DS-protein case. The principle of surface labeling is that chemical probes preferentially modify the parts of biomolecules exposed to the solvent, and the amino acids buried in the folded protein core or interacting proteins are not labeled. Then, proteolysis and LC/MS/MS are used to monitor the labeling site and degree of the protein . Accordi15N-edited heteronuclear single quantum coherence (15N-HSQC) spectra of 15N-labeled proteins titrated with different GAG oligosaccharide concentrations , saturation transfer difference (STD), and the transferred nuclear Overhauser effect (trNOE) . The esstrations . Based oresidues . By compresidues , the STDf FGF-10 . The trNf FGF-10 . Additiof FGF-10 . NMR is The complexity and heterogeneity of natural GAGs limit the in-depth study of the structure of GAG-protein interactions using various analytical tools. The latest developments in computational tools and technologies have made significant progress in the field of GAG modeling, and as an alternative strategy, they have promoted the study of GAG-protein interactions. Of course, various calculation methods and techniques have been discussed . RaghuraSimilar to other GAGs, CS/DS is involved in a large number of biological processes. In the body, CS/DS directly or indirectly participates in a variety of physiological and pathological processes by interacting with a variety of protein ligands, such as growth factors, cell surface receptors, and adhesion molecules. The characterization of a wide range of CS/DS-protein interactions is essential for mapping the biological functions of CS/DS and finding new therapies that target specific CS/DS-protein interactions. Due to the complexity and heterogeneity of the CS/DS structure, the molecular basis of most CS/DS-protein interactions is still unclear. The rapid development of multiple analytical tools and analytical methods will facilitate uncovering more of the mystery behind CS/DS-protein interactions and provide a template for the development of novel therapeutics based on CS/DS-protein interactions.Both authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Many proteins have been found to operate in a complex with various biomolecules such as proteins, nucleic acids, carbohydrates, or lipids. Protein complexes can be transient, stable or dynamic and their association is controlled under variable cellular conditions. Complexome profiling is a recently developed mass spectrometry-based method that combines mild separation techniques, native gel electrophoresis, and density gradient centrifugation with quantitative mass spectrometry to generate inventories of protein assemblies within a cell or subcellular fraction. This review summarizes applications of complexome profiling with respect to assembly ranging from single subunits to large macromolecular complexes, as well as their stability, and remodeling in health and disease.  The function of many proteins often requires stable or dynamic associations with other biomolecules, e.g., proteins, nucleic acids, carbohydrates or lipids in order to form large macromolecular assemblies. Protein complexes can be transient or stable and frequently need additional factors for the coordinated assembly of individual subunits into mature and functional macromolecular entities ,2. ConseComplexome profiling (CP) can overcome these limitations as this untargeted strategy collects information of the entire interactome within a biological sample without enrichment by specific antibodies or tags ,17,18. BComplexome profiling ,18, protIn general, any biochemical native separation technique in combination with mass spectrometry is able to generate comprehensive protein-interaction maps. The initial strategy of protein correlation profiling (PCP) used sucrose density gradient fractionation to identify new components of human centrosomes . DensityAlthough very useful for studies on large assemblies of biomolecules, density gradients have inherent low resolution, require a large amount of sample, and it is difficult to differentiate between co-purification in a fraction or an actual physical protein–protein interaction. Depending on the separation resin, size exclusion chromatography (SEC) isolates protein complexes up to several MDa and is suitable to analyze a broad range of cellular protein complexes ,30,31. IBNE became a very robust and reproducible, micro-scale high resolution separation method to examine composition of protein complexes in a broad range of samples from bacterial membranes, soluble subcellular components, and membrane fractions from eukaryotic cells to tissue specimens from patients . StandarThe aim of each complexome profiling experiment is to gain information of native protein complexes and to compare the appearance and abundance of assemblies under different conditions or in disease states. Biological samples for CP range from bacteria ,40, yeasIn recent years, further developments on CP  includedAssembly of protein complexes  or even TMEM126B was the first assembly factor identified by complexome profiling . Rat heaIn recent years, CP has become a standard tool to study assembly defects in patients with mitochondrial disorders. Profiles from patients mainly show typical accumulation of assembly intermediates that can be used for the interpretation of the significance of a subunit, an assembly factor in the assembly pathway or a ribosomal protein ,50,64,71Most initial CP approaches used label free quantification. As all fragments from each gel lane or density gradients have to be measured in single MS runs, such approaches need extensive machine time and the data from several separation gradients have to be merged. The introduction of metabolic labels in SILAC-based CP allows direct comparison of protein migration and the abundance of two or three different samples in one native gel lane ,65. AlthSILAC-based CP is limited to samples that can be metabolically labeled either in cell culture or with appropriate isotope containing diet . In addiViscum album) [Shifts in availability of substrates, oxygen, and various stresses require cellular adaptation to new conditions. This includes a fast response mechanism on the level of proteins and complexes such as in signaling pathways and metabolic enzymes or a long term response on the level of gene expression. CP was recently used to study the molecular consequences on respiratory complex I during chronic hypoxia in the human leukemia monocytic cell line THP-1 . This stm album) . This obm album) .Protein complex assembly has been frequently studied in cell culture from fast dividing cell lines or patient fibroblasts ,73. All Introducing SILAC as a pulse  for seveAnother component functioning as a service factor is DNAJC30. Patients with defects in DNAJC30 develop recessive Leber’s hereditary optic neuropathy . ComplexChemical crosslinking MS (XL-MS) enabled structural investigations on various protein complexes. Crosslinkers used in MS are mainly homo-bifunctional reagents with two reactive sites to covalently bridge two lysine residues in a close neighborhood to capture a dynamic interaction and conformation . VariousMost of the CP applications are protein-only approaches. Introduced RNA sequencing (RNA-seq) in a complexome analysis expand the technique . GradienAfter mass spectrometry, all slices are analyzed with standard proteomics software tools such as: MaxQuant, Proteome discoverer, PEAKS ,82,83. IOnce a protein list is created, quantification values of each protein within the fractions are used to compare migration behaviors in native gels or sucrose density gradients. That can be accomblished by using several tools to analyze complexome profiling data . Co-sepaFurthermore, ComPrAn stands out as an additional tool to analyze complexomes and was initially developed for qDGMS data . This frIf profiles from serveral sets of experiments need to be compared, the COmplexome Profiling ALignment (COPAL) tool can be used to merge several profiles from different gel runs. Using COPAL, it was possible to detect remodeling of mitochondrial complexes in Barth syndrome .Recently, the complexome profiling Data Resource (CEDAR) repository was installed. CEDAR includes a storage and information sharing platform to support the reuse of complexome profiling data . Many reCP has become a very useful tool to study protein complexes, to investigate dynamic processes of complex assembly, for remodeling, and protein turnover. The implementation of SILAC and TMT for multiplexing CP enhances speed of analysis and comparison of multiple samples. Data from in-gel crosslinking MS will give important insights into the molecular dynamics of protein complexes and conformations in future. Pulse SILAC and closer studies on posttranslational modifications generate “profiles within profiles” to better understand protein complex remodeling and maintenance in intact cellular physiology. CP has been already used to understand the assembly defects in mitochondrial disorders and will be very helpful to explore cellular protein networks and the impact of protein complexes on disease development, progression, and the benefit of treatments.


Drug discovery is a cost and time-intensive process that is often assisted by computational methods, such as virtual screening, to speed up and guide the design of new compounds. For many years, machine learning methods have been successfully applied in the context of computer-aided drug discovery. Recently, thanks to the rise of novel technologies as well as the increasing amount of available chemical and bioactivity data, deep learning has gained a tremendous impact in rational active compound discovery. Herein, recent applications and developments of machine learning, with a focus on deep learning, in virtual screening for active compound design are reviewed. This includes introducing different compound and protein encodings, deep learning techniques as well as frequently used bioactivity and benchmark data sets for model training and testing. Finally, the present state-of-the-art, including the current challenges and emerging problems, are examined and discussed. Drug discovery remains a key challenge in the field of bio-medicine. Traditionally, the discovery of drugs begins with the identification of targets for a disease of interest. It is followed by high-throughput screening (HTS) experiments to determine hits within the synthesized compound library, i.e., compounds showing promising bioactivity. Then, the hit compounds are optimized to lead compounds to increase potency and other desired properties, such as solubility, or vanishing toxic and off-target effects. After these pre-clinical studies, potential drug candidates have to pass a series of clinical trials to become approved drugs. On average, more than 2 billion US dollars and about 10–15 years are spent for developing a single drug [To rationalize and speed up drug development, computational methods have been widely incorporated in the design process in the past three decades. One prominent method is virtual screening (VS), which is used to prioritize compounds from (ultra) large compound libraries which have a high potential to bind to a target of interest . VS methNowadays, VS has become an integral part of drug discovery. It is usually implemented in the form of a hierarchical workflow, combining different methods  as filters to prioritize potentially active compounds ,8. VS meStructure-based methods (1) require 3D structural information of both ligand and protein as a complex or at least of the protein with some knowledge about the binding site. The most commonly used technique is molecular docking, which predicts one or several binding pose(s) of a query ligand in the receptor structure and estimates their binding affinity . While pLigand-based methods (2), including QSAR (quantitative structure-activity relationship) modeling, molecular similarity search and ligand-based pharmacophores, are relatively mature technologies . Unlike Machine learning (ML) aims at learning a task from data by optimizing a performance measure. There exist three main approaches : 1. Unsu“Classification and regression trees“. In the 90s, Cortes and Vapnik [Traditional supervised ML methods follow the idea that given some data, a predictive model is constructed by optimizing the difference between a given labeled output and the output predicted by the model. Some of these methods date back to the last century. For example, neural networks were first developed in the 60s by Rosenblatt . Later, d Vapnik  introducd Vapnik  proposedNevertheless, over the last few years, ML methods have gained a lot of popularity. This may be explained by three major aspects: 1. Data availability: thanks to automation and digitalization, as well as memory capacities, the amount of stored data has never been greater. 2. Computing power, such as graphics processing units (GPUs) and parallelization, has significantly allowed expensive model training. Cloud computing, for instance, Google Colaboratory , allows Deep learning (DL)  is a subAs mentioned above, automation and storage have had a major impact on the amount of data existing nowadays. Recently, Google has published an image data set of over 9 million data points called “Open Images Dataset V4”  as well In the biomedical field, the size of the data sets is starting to reach similar scales. The amount of publicly available bioactivity data keeps increasing every year. To date, the well-known ChEMBL database  has 17,2Given the increasing amount of available structural and bioactivity data as well as the recent progress in machine—especially deep—learning, it is no wonder that virtual screening strategies could benefit from this synergy.While ML methods have been applied in the field for over two decades already ,44,45, DIn this section, the main encodings of ligand, protein and complex, the different deep learning models as well as the most used (benchmark) data sets are introduced.The interactions between protein and ligands are complex, and encoding the most informative bits in a computer-readable format is one of the main challenges in both cheminformatics and bioinformatics. In the following sections, the encodings for ligands in virtual screening are described, followed by protein and complex encodings. The details are reported for those used in the studies discussed in The starting point of several ligand encodings is the molecular graph, where nodes and edges represent the molecular atoms and bonds, respectively see .GraphX, gives a per atom description, where the type of information stored in each node is decided a priori. Common per atom examples are atomic type and degree [X is N is the number of nodes, i.e., atoms, in the graph and D the number of pre-defined features. The second matrix, called connectivity matrix, describes the structure of the molecule. Its purpose is to illustrate how the nodes are connected in the graph, i.e., via bonds. Two frequent formats store this information: 1. the adjacency matrix A of dimension i is connected to node j and 0 otherwise.2. The coordinate (COO) format of dimension E represents the number of edges in the graph. Thus, the first and second rows represent the index of the source and target nodes, respectively. Using both the feature matrix and the connectivity matrix, the molecular graph encoding can further be used to apply machine learning algorithms.The molecular graph can be encoded using two matrices: the first one, called the feature matrix d degree . The dimSMILESAn efficient way of storing information from the molecular graph using string characters is the simplified molecular input line entry system (SMILES) developed by Weininger . The maiLabel and One-Hot EncodingIn this section, the concepts of label and one-hot encoding are explained in the context of SMILES, but the idea can be translated to broader applications, such as natural language processing (NLP) . MathemaIn label encoding, the characters in the dictionary are enumerated. This enumeration using integer numbers is also sometimes referred to as integer encoding. Using the integer labels, a SMILES can be transformed into an integer vector by associating each character with its integer label see a. The adThe one-hot encoding resolves this issue by assigning a binary vector to each character in the dictionary. A SMILES can then be constructed by concatenating the binary vectors as they appear in the SMILES see b. The maCircular FingerprintCircular fingerprints are, once folded, fixed-length binary vectors that determine the presence (encoded by 1) of a substructure or the absence of it (encoded by 0). The recursive algorithm behind extended-connectivity fingerprints (ECFP)  starts wOther EncodingsLigands are evidently not restricted to these encodings . For exaProteins are macromolecules that are involved in many biochemical reactions. They are composed of distinct amino acid sequences, which result in folding in specific 3D protein structures .Protein IdentifierA simple way to discriminate models from ligand information only is to include the identifier (ID) of the protein. Such a descriptor adds no information whatsoever about the physicochemical properties of the protein, the amino acid composition, nor the 3D conformation. It is merely a way for a machine learning model to be able to differentiate several proteins. For example, the one-hot encoding of the protein ID can be used, as in the study by Sorgenfrei et al. .Protein SequenceThe (full) sequence of a protein, often referred to as the primary structure, is the enumeration of the amino acids as they appear from the beginning (N-terminus) to end (C-terminus) of the protein, in which each of the 20 standard amino acids can be encoded as a single letter. The length of a protein can vary greatly, some of them containing thousands of amino acid residues. Although the sequence is a compact way of storing information about the primary structure, it does not give any information about the 3D structure of the protein. In opposition to the full sequence, it is possible to only consider the sequence from the binding site, reducing greatly the number of residues.Z-ScalesThe z-scale descriptors published in the late 80s by Hellberg et al.  are consDomains and MotifsA domain is a structural unit within a protein that is conserved and the overall role of a protein is often governed by its domain function. PROSITE  and PfamStructural Property SequenceIn the study by Karimi et al. , the proDescribing the protein-ligand complex involves descriptions that capture the interactions between the two binding partners. Herein, we group them into interaction fingerprints, 3D grids, graphs and other, see Interaction FingerprintInteraction fingerprints (IFPs) describe—as the name implies—the interactions between a protein and a ligand based on a defined set of rules ,76. TypiIFPs encoding interaction types: The structural interaction fingerprint (SIFt) [n binding site residues as an t (SIFt)  describe(PyPLIF)  and the (PyPLIF)  encode e(PyPLIF)  uses the(PyPLIF)  scoring The aforementioned IFPs vary in size and are sensitive to the order of the residues, which limits their application for ML purposes. Thus, SILRID , a bindiIFPs including distance bits: To describe the interactions more explicitly, distances with respect to interaction pairs or triples were introduced. APIF [ed. APIF , an atomPharm-IF , while uPharm-IF . The finIFPs including circular fingerprint idea: To become more independent of pre-defined interaction types, circular fingerprint inspired IFPs were introduced by encoding all possible interaction types  for atoms from protein and ligand are used. (SPLIF)  is const (SPLIF) , where n3D GridAnother type of encoding are 3D grids, in which the protein is embedded into a three-dimensional Cartesian grid centered on the binding site. Similar to pixel representation in images, each grid point holds one  values that describe the physicochemical properties of the complex at this specific position in 3D space. Such grids can, for example, be unfolded to a 1D floating point array  or transGraphi belongs to the ligand, atom j to the protein, and they live in a neighborhood of 5 Å, theni and j, and i and j both belong to either the ligand or the protein, then Although the description of a small molecule as a graph seems natural, the idea can be adapted to a molecular complex. As in the ligand case see , two maiN is the number of atoms and j is in the neighborhood of atom i and if k is the bond type between them. If not, that same entry is 0. This scheme numerically encodes the spatial graph as well as the bonds through edge type.The other graph form of protein-ligand developed by Feinberg et al.  consistsOther EncodingsMoreover, there are also other encoding methods to describe a complex, which will only be shortly introduced here. Topology-based methods, as reported by Cang and Wei , describAlso, simply the protein-ligand atom pairs together with their distances can be used as input. In the work by Zhu et al. , all atoAs mentioned in the introduction, machine learning can be split into supervised, unsupervised and reinforcement learning. In this section, we focus on supervised learning which is a framework that is used when the data is constituted of some input and an associated label and the aim is to predict the outcome corresponding to a given input. Subsequently, typical evaluation strategies of machine learning models will shortly be introduced.In the supervised framework, two subclasses are usually considered: the first one, called classification, deals with discrete outputs. In the binary case, this reduces to outputs that can take either 0 or 1 values. In the context of virtual screening, a simple example would be the activity determination of a compound against a protein: active Common machine learning algorithms include tree-based methods such as random forests (RFs), tree boosting, and support vector machines (SVMs). However, over the last decades, deep learning has gained a lot of momentum and the rest of this section will be dedicated to the idea behind the deep learning models described in Neural networks (NNs) , also soA simple example of a neural network connects the input to the output with one single hidden layer and is sometimes called a “vanilla network” or a “single layer perceptron” , in oppoConvolutional neural networks (CNNs)  , ChapterD. RNNs aGraph neural networks (GNNs)  are modeA subtlety that can be added to GNNs is a gated recurrent unit (GRU), which is a way to moderate the flow of information coming from other nodes and from previous computation steps . These pGraph attention neural networks (GANNs)  are grapMore details on graph neural networks can be found in the review by Zhou et al. . In the To assess the performance of any machine learning method, the data is commonly split into training and test sets. The model is trained on the training set and evaluated by comparing the predicted labels to the given labels on the hold out (test) set. Here, the metrics used in R, the Spearman’s correlation coefficient For regression tasks, the metrics reported are the mean squared error (MSE) and the root mean squared error (RMSE). For classification tasks, the area under the ROC—receiver operating characteristic—curve (AUC), the accuracy or the enrichment factor (EF) are used. For both regression and classification, the Pearson correlation coefficient Cross-validation (CV) is very often used to estimate the prediction error and usually performed using five or ten folds, and the results are reported as mean performance (±standard deviation). Additionally, CV can be used for hyper-parameter tuning. Please refer to the work by Hastie et al.  for a fuThe quality and quantity of data sets in the biomedical field have increased largely over the last years, boosting the usage of ML and DL models in drug discovery. The main source of freely available 3D structural information of proteins as well as protein-ligand complexes is the well-known Protein Data Bank (PDB) , holdingThe PDBbind  databaseBindingDB  is a pubBindingMOAD (Mother of All Databases) ,115 is aPubChem  is the wChEMBL ,117 is aSince some protein families sparked a special interest in pharmaceutical sciences due to their central therapeutic role, target-family specific data sets have been composed. Kinases, for example, play a major role in many diseases and have been extensively studied, also computationally , for drug et al.  derived The above introduces data sets commonly used for training ML models in the context of VS. Nevertheless, defined benchmarking data sets are needed for a standardized comparison among different methods and studies . Here, fR [The comparative assessment of scoring functions (CASF) benchmark  is develR . 2. The R [ρ ,125. 3. R [ρ  to analyR [ρ , showingNote that the CASF team has evaluated scoring functions from well-known docking programs, such as AutoDock vina , Gold 8, and GliThe directory of useful decoys (DUD)  is a virThe maximum unbiased validation (MUV) data set  is basedNote that several collections of data sets for the purpose of benchmarking molecular ML models, with focus on model architectures and/or encodings, have recently been made freely available. These include, but are not limited to: 1. MoleculeNet  to benchIn this section, recent developments in virtual screening (VS) are described and specifically how deep learning (DL) helps to improve drug-target binding, i.e., activity/potency prediction. Our review focuses on methods using protein and ligand information see , either In this section, recent methods that require complex structure information, usually explicitly or implicitly described by the interactions between the protein and the ligand, will be discussed see . The varInteraction Fingerprint-Based StudiesInteraction fingerprints, which are often used for binding site comparison ,142, havIn 2009, Sato et al.  combinedg et al.  used ensIn 2019, Li et al.  introduc3D Grid-Based StudiesMany methods using a 3D grid representation of a protein-ligand complex—comparable to pixels in 3D images—for affinity prediction, have evolved over the last years ,89,92,93One of the first published models, AtomNet  uses a CSimilarly, BindScope  voxelizeR of 0.807) over five runs. In this case, DeepAtom outperformed RF-Score [R value increased to 0.831 for the PDBbind core set.DeepAtom  uses a 3RF-Score , a classRF-Score   and X-Score (R of 0.643)   .R between 0.5 and 0.6. With these values, DeepBindGP performs slightly better than AutoDock vina, while being in a similar range as Pafnucy [DeepBindRG  and Deep Pafnucy . DeepVS,In this section, pair-based/PCM models from the literature are presented see . As discLigand as SMILESIn 2018, Öztürk et al.  proposedely , a wave of deep learning methods and applications to boost virtual screening, i.e., affinity—but also other properties, such as ADMETox—prediction, has emerged. This development is coupled to the availability of more and more compounds, structures and mapped bioactivity data, together with novel encoding techniques and deep learning technologies. These include not only model architectures, that seem to fit well the nature of biological objects such as ligands and proteins, but also open-source software and computer hardware evolution.Around thirty papers related to deep learning-based virtual screening are described in details in this review see . Most ofNevertheless, several challenges still remain open and new ones have also emerged, including: 1. precision of chemical encoding, 2. generalization of chemical space, 3. lack of  data, 4. comparability of models, and 5. interpretability. All of which will be discussed in the following.Precision of chemical encoding: The better performance of structure-based methods using ML-based vs. classical SFs is often attributed to the avoidance of a pre-determined functional form of the protein-ligand complexes, meaning that the precision of the chemical description does not necessarily lead to more accurate binding affinity prediction [1. ediction . ContribGeneralization of chemical space: As mentioned in the work by Lim et al. [2. m et al. , althougm et al. .Lack of  data: Deep learning is very data greedy and usually, the bigger the training set is, the better the results. Goodfellow et al. [3. w et al.  suggest w et al. , which ew et al. , which sw et al.  performsw et al.  translatw et al. ,61. NoteComparability of models, benchmark data, open-source: Reviewing a multitude of studies and wanting to compare and rank them is understandable, but also unreasonable for several reasons, starting with the data and the splits. Models that have been trained on different data or even different tasks should hardly be compared; a regression task or a classification task, even when using similar performance metrics, are not analogous. Assuming that the models do use the same data, if the splits are different, then the evaluation can no longer be directly compared. Assuming now that the splits are identical, then if the metrics used are different, again no fair comparison can be made, as pointed out by Feinberg et al. [4. g et al. . This meHaving benchmark data sets freely available—together with a code basis—such as MoleculeNet , TDC 13 or work Moreover, while several data sets are available to benchmark the performance of different approaches in VS, Sieg et al.  recentlyInterpretability: With the rise of deep learning, the complexity of the architectures and the depth of the models comes the issue of interpretability. Such models are often considered as black boxes and understanding the mechanism in the hidden layers is a challenge. However, research undertaken in this direction aims at deciphering what the algorithm has learned [5.  learned ,174. Thi learned .For further considerations on the type, quality and quantity of the data as well as the challenges of DL models built thereof to impact different areas of drug discovery, the reader is kindly referred to two recent reviews by Bender and Cortés-Ciriano ,176.In this work, the recent progress in DL-based VS methods has been reviewed, exemplifying the boost in development and application over the last few years. While some challenges due to, for example data coverage and unbiased evaluation sets, molecular encoding and modeling the respective biological protein-ligand binding event still remain, the reported results show the unprecedented advances in the field.


When subunit structures are unavailable, they are predicted by template- or distance-prediction-based modelling methods. Heterodimer complex structures can be predicted by both template-based and ab initio docking, depending on the template's availability. Structural templates are detected from the protein structure database based on both the sequence and structure similarities. The templates for heterodimers may be selected from monomer and homo-oligomer structures, as well as from hetero-oligomers, owing to the evolutionary relationships of heterodimers with domains of monomers or subunits of homo-oligomers. In addition, the server employs one of the best ab initio docking methods when heterodimer templates are unavailable. The multiple heterodimer structure models and the associated scores, which are provided by the web server, may be further examined by user to test or develop functional hypotheses or to design new functional molecules.Protein–protein interactions play crucial roles in diverse biological processes, including various disease progressions. Atomistic structural details of protein–protein interactions may provide important information that can facilitate the design of therapeutic agents. GalaxyHeteromer is a freely available automatic web server (  Protein–protein interactions (PPIs) play key roles in a wide range of biological processes, ranging from development and ageing to various disease progressions . Therefoab initio docking , in terms of the CASP measure GDT-TS  as Seok-server. This method selects a model through a random forest classifier from the models that were predicted by template-based structure prediction method, GalaxyTBM , and froe GDT-TS   of different residues using a deep residual convolutional neural network, which is based on MSA-based features, including sequence profile and raw coevolutionary coupling features from CCMPred , followiSequence-based template search is performed on the database of monomer and homo-oligomer proteins, named DB-Mo/Ho, as shown in Figure Structure-based template search is performed on the database of heterodimers, named DB-Het, as shown in Figure https://predictioncenter.org/casp14/doc/presentations/). The complex structure prediction method shares the same components for both hetero- and homo-oligomer structure prediction in terms of subunit structure prediction, template-based docking, ab initio docking, and complex structure refinement.The protein–protein complex structure prediction method, which contains GalaxyHeteromer as a new component in addition to GalaxyHomomer , particiab initio docking method GalaxyTongDock_A  success rates  are 33.3%, 53.7%, 55.6% and 68.5%, respectively, for GalaxyHeteromer, whereas those for HDOCK are 38.9%, 40.7%, 44.4% and 59.3%, respectively.Next, the performance of GalaxyHeteromer is compared to that of HDOCK , which iGalaxyHeteromer puts more emphasis on providing multiple alternative solutions for possible complex structures by exploring multiple templates when compared to HDOCK. The provided multiple models may be combined with separate experimental information to select more feasible complex structures.ab initio docking and complex refinement, are implemented in the GALAXY programme package written in Fortran90. The web application uses the Python programming language and MySQL database. The JavaScript Protein Viewer (http://biasmv.github.io/pv/) is used for the visualization of models.The server runs on a cluster of 25 Linux servers of 2.40 GHz Intel Xeon E5-2620 v3 12-core processors. The overall GalaxyHeteromer pipeline is implemented using Python. Several components of the pipeline, such as Amino acid sequences in FASTA format or 3D structures in PDB format for two subunit proteins are the required input. The number of residues in each subunit is restricted as <1000 for computational efficiency. Average run time is 4 h, when structures of both the subunits are provided, and 16 h, when only sequences are provided. It usually takes longer for larger proteins.ab initio docking. User can also download up to 50 models and information associated with them. An example output page is shown in Figure On the output page, 10 models are visualized and the following information associated with the models is provided in a table: template type ; template PDB ID and template score for models generated by template-based docking; and GalaxyTongDock_A score and cluster size for models generated by ab initio docking for maximum performance, depending upon the availability of templates in the structure database. In template-based docking, evolutionary relationships of a target protein complex with the domains/subunits of monomer/homo-oligomer proteins, as well as with the subunits of hetero-oligomers, are detected. The provided multiple complex structures may be combined with the available experimental data to select more feasible models for explaining biological functions or designing molecules regulating the functions.Herein, a newly developed web server, GalaxyHeteromer, is presented for prediction of heterodimer protein complex structure. The server predicts heterodimer structure from sequences or structures of composing subunits. If subunit structures are unavailable, the server automatically predicts them by up-to-date template- and distance-prediction-based structure prediction methods. GalaxyHeteromer performs both template-based and https://galaxy.seoklab.org/heteromer. The databases, DB-Mo, DB-Ho, and DB-Het, and the model structures generated for the benchmark sets and per-model accuracy metrics can be downloaded at http://galaxy.seoklab.org/suppl/heteromer.html.The GalaxyHeteromer web server is available at