Chalcogen bonds arethe specific interactions involving group 16elements as electrophilic sites. The role of chalcogen atoms as stickysites in biomolecules is underappreciated, and the few available studieshave mostly focused on S. Here, we carried out a statistical analysisover 3562 protein structures in the Protein Data Bank (PDB) containing18 266 selenomethionines and found that Se···Ochalcogen bonds are commonplace. These findings may help the futuredesign of functional peptides and contribute to understanding therole of Se in nature. As such,thePDB offers the unique opportunity to analyze protein structures, derivingmeaningful statistics about the occurrence of chemical interactions.Protein–ligand and protein–protein binding, as wellas interactions governing protein folding, can be analyzed, contributing,e.g., to an understanding of the role of chemical interactions, andpotentially unveil yet unknown contributions.2 Alongside conventional interactions controlling protein folding,i.e., hydrogen bonding, electrostatic forces, and hydrophobic interactions,more recently, important additional contributions from less commoninteractions have been identified, including halogen bonding,3 chalcogen bonding,4 and interactions involving aromatic rings,5 e.g., π···π stacking in amyloids,6 among others. A full understanding of the roleof these noncanonical interactions in protein folding may have importantimplications, such as in advanced drug design, enhanced drug-targetprotein affinity,7 force fields for improvedmolecular simulations,8 and new insightsin biomolecular recognition processes.9 The focus of this Letter is on one of these noncanonical interactions,specifically the impact of a chalcogen bond involving selenium (Se)atoms on protein structures.The Protein Data Bank (PDB)is a 50-year old worldwide repository of 3D structures of biologicalmacromolecules, including proteins and nucleic acids.10 Although the earliest examples of chalcogen-bonded complexescan be traced back to 1843,11 recently,there has been an upsurge in the number of papers reporting successfulexamples of the use of this interaction in several fields, such ascrystal engineering12 and organic catalysis,13 among others.14 Asfar as the implications of chalcogen bonding in the biomolecular fieldare concerned, only in the early 2000s were ChBs found to play a rolein protein structures.16 Sulfur–oxygen ChBs havealso been found to occur between the sulfur cation of S-adenosylmethionine and oxygen atoms in methyltransferase activesites mediating recognition and catalysis.17 Furthermore, some of us have recently demonstrated the importanceof this interaction in the inhibition mechanism of maltase glucoamylaseby salacinol and katalanol inhibitors, through the ChB between thesulfonium ion center and the catalytic nucleophile Asp443 residue.18 It is also known that the biologically importantreaction of ebselen, a glutathione peroxidase mimic, with biologicalcysteine thiol groups is favored by the ability of selenium to actas a ChB donor.19 Finally, the PDB hasrecently been surveyed in order to systematically explore the stabilizingpotential of chalcogen bonding in protein–ligand complexes,indicating that chalcogen bonding does indeed play a dominant rolein stabilizing some of the interaction motifs studied.20 This is relevant in drug design; in fact, arecent survey has demonstrated that this interaction is often isostericwith analogous hydrogen bonding.7The term chalcogen bond (ChB) isused to designate the specificsubset of inter- and intramolecular interactions formed by chalcogenatoms wherein the group 16 element is the electrophilic site 1.10 Alth21 either on the protein22 or on the ligand.23 Se atoms have only occasionally been considered, and, to the bestof our knowledge, only from the ligand point of view.20 Selenium is seldom present in native proteins,24 where it is shown to impart permanent oxidationresistance.25 However, Se-containing proteinsare well represented in the PDB (nearly 10 000 structures),because incorporation of selenomethionine instead of natural methionineby genetic engineering methods is aiding protein structure phasing,thanks to the anomalous diffraction signal provided by Se.26 This gives the unique opportunity to statisticallyanalyze Se-based ChBs that stabilize proteins, since it can be foreseenthat Se is a better ChB donor than S, due to its higher polarizability.ChB analyses in protein structures have mostlyfocused on sulfuras the donor atom,27 (i) only X-ray crystal structuresrefined at a resolution better than 2 Å; (ii) only structures determined in the 90–110 K temperature range;(iii) sequence redundancy was reduced to 40% pairwisesequence identity with CD-HIT.28 This resultedin a nonredundant, high-quality, and homogeneous ensemble of 3562protein chains containing 18 266 selenomethionines.With this hypothesis in mind, we extracted from the PDB (March2021) an ensemble of protein 3D structures containing selenomethionineswith the following, commonly accepted, selection criteria:30 The azimuthal angle φ between thebisector of the Cγ–Seδ−Cε angle andthe vector from Seδ and the projection of the O atom on theplane defined by Cγ, Seδ, and Cε was constrainedin the 0–180° range because of the local, intrinsic Cv2 symmetry of the Cγ–Seδ−Cεmoiety. For the same reason, the polar angle θ between the normalto the plane defined by Cγ, Seδ, and Cε and theSeδ−O vector was constrained in the 0–90°range. The position of the oxygen atom relative to the selenium atomwas also monitored by the angle α, defined as the supplementaryof the angle C–Se–O .This angle provides a piece of very direct information; i.e., theformation of a chalcogen bond is expected to be associated with smallα values, close to 0°.Inthe ChBs observed in this ensemble, by far the most representednucleophilic atoms are oxygen atoms either in the protein backboneor in the amino acid side-chains. Only a few other nucleophilic atomsare present, unless post-translational modifications occur. An oxygenatom was considered to be in contact with the selenium atom if 2 ≤Se···O distance ≤ 3.5 Å . A total of 1173 contacts were observed.The position of the oxygen atom relative to the selenium atom hasbeen described through the spherical coordinates shown in When the O atom approachingthe Se atom belonged to a C=Ogroup, the data were divided into five groups. The first with theamido group of the Asn side chains ; the second with the amido group of theGln side chain ;the third with the carboxylic group of the Glu side chain ; the fourth with the carboxylicgroup of the Asp side-chain ; and the fifth and last one with any backbone carbonyl.The region around the selenium atom was dividedinto 180 sectorsby dividing the φ range into 18 intervals of 10° each (from0° to 180°) and by dividing the θ range into 10 equalintervals (from sin θ = 0 to sin θ = 1). In this way,each sector has the same surface, and 0.6% of the oxygen atoms shouldbe observed in each sector if their distribution is continuous anduniform.Supporting Information Figure S1). This observationis confirmed by the relationship between φ and θ shownin Supporting Information Figure S2).Supporting Information Figure S3). This suggests that theobserved selenium–oxygen contacts are, indeed, influenced bychalcogen bonds.The superposition of all the oxygen atoms on the same C–Se–Cframework shows a clear clustering of the oxygen atoms in the regionsopposite that of the C–Se bonds 2B, and ad scatterplot is quitedispersed . This is notsurprising since the side chains of Asp and Glu are usually solvent-exposedwhile methionines are usually buried in the protein core. The depletionof charged, carboxylate oxygen atoms is compensated by a modest increasein the percentage of amide and hydroxylic oxygen atoms.Whena chalcogen bond is detected (α ≤ 20°),it is more often opposed to the Se–Cε bond (54%) thanto the Se–Cγ bond (46%). This might imply a differentelectronic density in the two directions. However, it might be a simpleconsequence of steric hindrance: While the Cε carbon atom isterminal, the Cγ one is connected to the rest of the selenomethionineside chain.3 hybridizedoxygen, like in the hydroxyl groups of Ser, Thr, and Tyr, it is necessaryto consider the angle C–O–Se (referred thereinafterto as β). If, on the contrary, it is a sp2 hybridizedoxygen, like in the carboxylate or amide side chains of Asp, Glu,Asn, and Gln or like in all backbone carbonyls, it is necessary toconsider not only the angle β but also the torsion C–C–O–Se(referred thereinafter τ). The distributions of the βand τ values are shown in A comprehensive analysis of the geometric featuresof the studiedChBs requires a detailed analysis of the angular distribution of seleniumatoms around oxygen. If this is a tetrahedral spXylellafastidiosa .31 The selenomethionine 324 interacts with the side-chain oxygen atomof serine 328, and it can be hypothesized that this interaction, whichinvolves two solvent accessible residues that are just five positionsapart along the protein sequence, stabilizes the N-terminal moietyof the helix. Another selenium–oxygen contact is observed inthe crystal structure of aminopeptidase N from Neisseria meningitides .32 This interaction involvestwo residues that are buried in the protein core and are very distantalong the protein sequence (seleniomethione 259 and glutamate 117).The third example is observed in the crystal structure of a putativeracemase from Pseudovibrio sp. JE062 .33 Two residues close in the protein sequence,i.e., selenomethionine 104 and glycine 98, are involved in the interaction,which might stabilize the helix, and, contrary to the previous twoexamples, involves a backbone oxygen atom instead of a side-chainoxygen atom.Three relevant examples of selenium–oxygencontacts, alldue to structural genomics consortia, are shown in 10- to α-helical transition is sequencedependent, occurring at O(2) for the peptide incorporating selenomethionine,and shifted by a residue to O(3) for peptides having methionine andS-benzyl cysteine at the same residue.34 The observed ChB is within the geometrical parameters observed in Interestingly, we also found a Se···O ChB in theCSD deposited structure of a heptapeptide, where it was demonstratedthat the 335 On the other hand, Se···OChBs can be exploited to manipulate structures and functions of peptidesand proteins in synthetic biology. In this respect, Se-functionalizedself-assembling peptides are under current investigation.In conclusion, by a statisticalanalysis on a set of PDB crystalstructures of proteins containing selenomethionines, we have demonstratedthat Se···O chalcogen bonds are commonplace. The interactionsexplored in this work must be true also for methionine-containingproteins since a SeMet residue does not occur naturally in the structuresexamined in the PDB, but SeMet is, instead, a replacement for Metin heavy atom replacement experiments to solve structures. However,since the Se atom is more polarizable, one might expect the noncovalentO···Se interactions to be stronger than the correspondingO···S interaction. The results reported in this Letterare relevant for a 2-fold reason. On one hand, this might help provideinsights into the chemical basis of selenium-over-sulfur discriminationin nature.


Progress in technology and algorithms throughout the past decade has transformed the field of protein design and engineering. Computational approaches have become well-engrained in the processes of tailoring proteins for various biotechnological applications. Many tools and methods are developed and upgraded each year to satisfy the increasing demands and challenges of protein engineering. To help protein engineers and bioinformaticians navigate this emerging wave of dedicated software, we have critically evaluated recent additions to the toolbox regarding their application for semi-rational and rational protein engineering. These newly developed tools identify and prioritize hotspots and analyze the effects of mutations for a variety of properties, comprising ligand binding, protein–protein and protein–nucleic acid interactions, and electrostatic potential. We also discuss notable progress to target elusive protein dynamics and associated properties like ligand-transport processes and allosteric communication. Finally, we discuss several challenges these tools face and provide our perspectives on the further development of readily applicable methods to guide protein engineering efforts. Proteins have been studied intensively already for several decades to reap immense benefits through their applications in green industry, biomedicine, sustainable agriculture and other areas . PromineThe recent trend is to use both these approaches in unison, which is termed semi-rational engineering or focused directed evolution, to overcome the primary restrictions of the two approaches. In this strategy, rational components and computer predictions are used to prioritize the most promising protein sites for mutagenesis and frequently also implement restrictions on the diversity of introduced mutations to the most viable ones , resultiin silico mutagenesis to evaluate the effects of mutations and prioritize promising variants  mutations disrupting anti-HEL antibody binding at levels of 27% and 59% of the WT, (ii) the most disruptive mutation of HIV-1-CD4 complex and (iii) five mutations disrupting RFP oligomerization while preserving stable monomers [When searching for interface hotspots, the objective is typically to improve the binding affinity or avoid mutating those residues rather than destabilizing the protein complex. However, in some cases, disruption is desirable, which is the purpose of the DisruPPI software . To obtamonomers .Residues governing the efficient exchange of cognate ligands between binding sites buried in protein cores and the bulk solvent have recently been recognized as potent hotspots for the engineering of a wide range of properties like activity, selectivity or stability . HoweverCaverDock software was developed as an alternative to expensive analyses of ligand transport using MD simulations ,59. It rOnce hotspots have been selected, the next step is to assess the effects of particular mutations at the site on the target properties of the protein. With this in mind, we have grouped tools based on the type of evaluated feature, starting with protein interactions, followed by flexibility and electrostatics. In this section, we present 10 tools that can be useful for prioritizing particular mutations for inclusion into a smart library based on their predicted effects. MutaBind , iSEE 5 and mCSMProteins interact with each other, nucleic acids or small chemical compounds. As such, they are involved in all essential processes in any living cell. Recognizing the importance of protein–protein interactions, numerous tools have been developed to predict the impact of mutations in residues forming these interactions .−1 [−1 RMSE) on a subset of their training dataset (862 mutations on 16 complexes) [de novo designed influenza inhibitors complexed with hemagglutinin used as targets (T55 and T56) in the 26th round of the CAPRI prediction experiment [−1 for the T55 and T56 targets, respectively [de novo proteins forming an interface that is not commonly present in the training dataset of native proteins, the lack of respective crystal structures and the use of enrichment value as a measurement of binding affinity changes. Also, we would like to highlight that due to the limited number of highly stabilizing mutations present in the SKEMPI and CAPRI datasets, the prediction accuracy for such mutations cannot be adequately estimated and is presumably less reliable. MutaBind web-server can process up to 16 single-point mutations per run, delivering the binding affinity change, confidence level and structure model of each mutant.To expand a set of publicly available methods capable of quantitatively predicting the effects of single-point mutations on binding energy, the MutaBind web-server was developed . This to−1 . Additiomplexes) . Two de periment  with aboectively , MutaBinectively , FoldX [ectively  and moleectively ), showin−1 during 10-fold cross-validation [−1, which is comparable to the performance of the other three state-of-the-art tools evaluated by the authors [A similar approach has been adopted by the iSEE method , utilizilidation . Unfortulidation , iSEE ex authors , i.e. Fo authors  and Bind authors . The dro authors . Nonethe−1 and showed an RMSE of 2.55 and 4.06 kcal.mol−1 for mutations of T55 and T56 targets from the 26th round of CAPRI competition [The most recent addition to the toolbox for evaluation of the effects of mutations on protein–protein interaction is the mCSM-PPI2 web-server . A charapetition . Based opetition . The mCSpetition . In the While there is no single study benchmarking all three reviewed tools for predicting the effect of mutations on protein–protein interactions, the results on the independent datasets from the CAPRI competition  indicateSimilarly to protein–protein interactions, interactions of proteins with nucleic acids are the basis of many crucial cellular processes such as replication, repair, recombination, transcription, translation and gene expression regulation. However, the development of rapid predictive methods has had much less success given a notably different physicochemical nature of these interactions, i.e. the prevalence of polar interactions that are often much harder to model precisely , and theBenefiting from the recent release of high-quality data for protein–nucleic acid interactions in the second version of ProNIT database, the mCSM-NA web-server was developed. The tool uses the previously described graph-based approach, adopting additional descriptors for the atoms of nucleic acids, which are divided into three categories: a phosphate group, sugar and nitrogenous base . From th−1. The authors evaluated the performance of PremPDI, SAMPDI and mCSM-NA tools on subsets of training datasets overlapping among the pairs of tools, showing that on these datasets, PremPDI performs similarly to mCSM-NA and markedly outperforms SAMPDI. The PremPDI server provides an interactive calculation setup, enabling up to 16 single-point mutations to be specified, for which the prediction of the effect of the mutation and mutant 3D structure is generated.An alternative approach to assess the effect of mutations on protein–DNA interactions was adopted by the PremPDI web-server , relyingAgain, the two reviewed tools exhibited similar predictive performance for the effect of mutations on interactions of proteins with DNA. The main difference comes from the user experience and server applicability. Firstly, mCSM-NA can evaluate complexes containing RNA unlike PremPDI. The machine-learning predictions by mCSM-NA are several-fold faster than the demanding physics-based predictions implemented in PremPDI, especially for larger biomolecular systems  [To facilitate the use of NMA for evaluating effects of mutation on protein dynamics and stability, DynaMut web-server  employs l.mol−1) . With twl.mol−1)  and SDM l.mol−1) . Here wel.mol−1) .The electrostatic potential of proteins can affect protein adsorption, ligand binding or thermodynamic stability of a protein complex . Differewww.pymol.org/) or VMD [To overcome this deficiency, the Mutantelec web-server was developed to enable evaluation of the effects that mutations have on electrostatic potential . Additio) or VMD . The p53) or VMD .Similar intentions have prompted a reimplementation of the AESOP framework  into a mEngineering efforts do not inevitably end with characterizing properties of the altered protein since information about the mutants can be stored and further reused. This way, an additional cycle of design can be performed iteratively, combining experimental and computational stages. For more convenient analyses, data coming from different sources can be mapped onto the 3D structure of proteins, and there are web-services capable of mapping conservation , coevoluHence, the BioStructMap tool was developed , allowinThe typical engineering workflow often covers a range of structural and evolutionary analyses, predictions of the effects of mutation on protein function and stability, and their visualization. Execution of such analysis requires bioengineers to search for suitable computational tools and transfer data from one tool to another, which is frequently complicated by incompatibilities among these tools. To relieve bioengineers of such hurdles, integrative services gather different methodologies for hotspot identification and mutation analyses and streamline the flow of data into automated workflows.The HotSpot Wizard web-server is a platform developed with the primary purpose of finding protein engineering hotspots by combining structural and sequential analyses . The secThe reviewed computational tools can provide valuable guidance for protein engineering toward tailoring a wide range of target properties. Notably, the applicability of the protein engineering toolbox has been expanded from targeting traditionally engineered regions of proteins, such as binding sites and transport-pathways, to addressing even more elusive targets including protein dynamics and allostery. Here, most tools resorted to approximating the dynamics by rapid methods based on NMA to minimize overall execution time and required computing resources. However, we expect that other approaches including the geometric-constraints-based method, tCONCOORD , or the There is a clear trend of protein engineering software becoming more user-friendly, commonly featuring an attractive web-server interface. However, we have noted only a handful of integrative efforts to provide meta-servers where the user can employ different approaches to protein design. Even the current one-stop-shop platforms like HotSpot Wizard do not yet offer comprehensive approaches. In particular, they do not allow analysis of possible interactions of the target protein with other molecules, leaving such burdensome tasks to their users. Therefore, an essential step toward complete workflows is to integrate tools for the prediction of macromolecular binding sites and evaluation of actual protein interactions with cognate binding partners, ultimately creating complete pipelines which will be even more attractive for protein engineers.Since a significant fraction of the reviewed tools employs various methods of supervised machine learning, the quality of the available datasets for development and testing is of utmost importance. We have seen an example of the role that a more extensive dataset can play for the accuracy of the predictive methods for the effect of a mutation on protein–protein interactions. In that case, the release of the SKEMPI 2.0 database, which more than doubled the number of annotated mutations and almost tripled the diversity in protein–protein complexes, enabled the derivation of more accurate predictive models adopted by the mCSA-PPI2 tool. However, even this database is notably biased by the strategies and research objectives traditionally applied in the protein engineering community , for exaAnother aspect related to the experimental datasets is their sparsity. For most proteins, information on the effects of mutations is available for a few sites only, and even for those that were mutated, often not all variants were tested. Such situations markedly complicate the performance evaluation of tools for hotspot identification and mutation prioritization. The data sparsity also makes any rigorous comparison among different methods almost impossible since experimental data rarely cover the top hits proposed by individual tools. As a remedy, we suggest turning to datasets from systematic mutagenesis projects like the dataset of 13 massively mutated proteins compiled from the literature and patents  and, morComputationally supported rational and semi-rational protein engineering constitute widely accepted and efficient approaches. These approaches become more accessible to the broad scientific community due to the development of user-friendly tools, 18 of which are reviewed here.These novel tools enable identification of engineering hotspots promising for the modification of protein function based on allosteric communication, interactions with other proteins or ligands.Mutations can be evaluated for their impact on protein interactions with other macromolecules, protein dynamics or an electrostatic potential that can be estimated by user-friendly software covered in this review.Identification of hotspot residues and prediction of the effect of their mutation can be easily performed via integrative platforms like HotSpot Wizard. This platform was successfully applied in multiple design experiments leading to the identification of enhanced variants showcased here.Supplementary_Table1_bbaa150Click here for additional data file.Supplementary_Table2_bbaa150Click here for additional data file.Supplementary_Table3_bbaa150Click here for additional data file.


Autophagy summarizes evolutionarily conserved, intracellular degradation processes targeting cytoplasmic material for lysosomal degradation. These encompass constitutive processes as well as stress responses, which are often found dysregulated in diseases. Autophagy pathways help in the clearance of damaged organelles, protein aggregates and macromolecules, mediating their recycling and maintaining cellular homeostasis. Protein-protein interaction networks contribute to autophagosome biogenesis, substrate loading, vesicular trafficking and fusion, protein translocations across membranes and degradation in lysosomes. Hypothesis-free proteomic approaches tremendously helped in the functional characterization of protein-protein interactions to uncover molecular mechanisms regulating autophagy. In this review, we elaborate on the importance of understanding protein-protein-interactions of varying affinities and on the strengths of mass spectrometry-based proteomic approaches to study these, generating new mechanistic insights into autophagy regulation. We discuss in detail affinity purification approaches and recent developments in proximity labeling coupled to mass spectrometry, which uncovered molecular principles of autophagy mechanisms.Abbreviations: AMPK: AMP-activated protein kinase; AP-MS: affinity purification-mass spectrometry; APEX2: ascorbate peroxidase-2; ATG: autophagy related; BioID: proximity-dependent biotin identification; ER: endoplasmic reticulum; GFP: green fluorescent protein; iTRAQ: isobaric tag for relative and absolute quantification; MS: mass spectrometry; PCA: protein-fragment complementation assay; PL-MS: proximity labeling-mass spectrometry; PtdIns3P: phosphatidylinositol-3-phosphate; PTM: posttranslational modification; PUP-IT: pupylation-based interaction tagging; RFP: red fluorescent protein; SILAC: stable isotope labeling by amino acids in cell culture; TAP: tandem affinity purification; TMT: tandem mass tag. The majority of proteins are degraded via two pathways: the ubiquitin proteasome system and autophagy. In comparison to the specific degradation of the ubiquitin proteasome system, autophagy is thought to degrade substrates nonselectively and selectively due to cargo receptors . Thus, aATG) genes/proteins have been reported in yeast. Most of these are conserved between yeast and mammals and have crucial roles in the progress of autophagy ), which polymerizes to form dynamic microtubules. These help in autophagosome formation, motility and fusion with lysosomes [Homo-oligomers generally form highly stable, permanent structures and are symmetric in nature. They also form scaffolds for stable interactions with other macromolecules. For example, yeast Atg7 is an E1-like enzyme that forms a functionally-active homo-dimeric complex  [d = 3.2 µM) to form an active hetero-oligomeric PtdIns3K class-III complex [d = 160 µM). CDH1 forms also hetero-oligomers with CDH2/N-cadherin, which interact stronger, mediating contacts of different cell types. Autophagy ensures optimal cell growth via regulating the abundance of CDH1, a protein involved in CTNNB1 (catenin beta 1)-WNT signaling [In contrast, transient complexes associate and dissociate based on physiological conditions. Proteins can function either independently or by forming a complex ,48,49. T complex . Weak trignaling  , co-fractionation coupled to MS (CoFrac-MS) and PL-MS methods. In vivo methods are techniques carried out in a living organism and commonly address binary interactions [Due to the importance of PPIs, numerous cell biological and biochemical methods exist to study them .g., see  for moreractions . Exampleractions .MS-based proteomic studies are widely used to understand mechanisms in biological pathways by analyzing PPIs. Technological advancements in terms of MS-instrumentation, liquid chromatography (LC), sample preparation methods, techniques used to enrich interactomes, high throughput analyses, and bioinformatics data interpretation have greatly increased the usage of MS-based approaches. The ultimate goal of proteomic studies is to comprehensively characterize the proteome, protein expression levels, PTMs, and build functionally relevant protein networks. Based on the nature of protein identifications and characterizations, two approaches are discriminated: top-down and bottom-up proteomics .In top-down proteomics, intact proteins are ionized, followed by fragmentation and measurement in a high-resolution mass spectrometer. This approach provides a complete characterization of proteoforms including PTMs. The bottom-up proteomic approach involves chemical or enzymatic digestion of proteins into peptides prior to ionization and MS measurement. This method is easily automatable and generally helps in the identification of large numbers of proteins compared to top-down approaches, which require a more sophisticated front-end biochemistry to generate samples for MS analyses .Different strategies in MS sample acquisition exist: data-dependent acquisition (DDA), data-independent acquisition (DIA), both unbiased discovery methods, and directed/targeted proteomics approaches. These strategies differ in the depth or coverage of protein identifications. DDA is based on a selection and identification of the most abundant ions in the sample. In DIA, a relatively new method, ions are not selected. Entire groups of ions are measured, generating a “digital map” of the entire sample ,64–70. WThe aforementioned strategies have advantages and dis-advantages, leading to trade-offs in sensitivity, specificity, reproducibility, accuracy, and dynamic range . As signal intensities recorded by MS depend on ionization properties of respective biomolecules, MS is not a truly quantitative analytical approach. Therefore, quantitative proteomics strategies have been developed that allow a systematic quantification of samples revealing changes between measured proteomes. Quantitative information is key to characterize molecular pathways both at protein and PTM level. Quantification can be performed in two ways, either absolute or relative. Absolute quantification is possible by comparing signal intensities of biomolecules with signals of known amounts of respective synthetic/purified standard substances. Relative quantification is performed by comparing ion intensities between different samples. Quantification of proteins or peptides can be performed label-free or via stable isotope labeling strategies, the latter being more accurate as samples can be analyzed in single MS experiments. Labeling approaches add tags that differ in mass but do not interfere with ionization properties. Tags can be incorporated at protein or peptide level, enzymatically, chemically, or metabolically .in vivo incorporation of 13C and 15N isotope-labeled metabolites such as amino acids. In one of the most common approaches, stable isotope labeling by amino acids in cell culture (SILAC), labeled lysine (K) and arginine (R) variants are used. Commonly three different SILAC labels are used to perform relative quantifications between three biological conditions [18O-labeled water allows the incorporation of 18O to neo-C-termini generated by proteolytic digestion [13C and 15N isotopes, and can be highly multiplexed [Metabolic labeling summarizes strategies that commonly lead to the nditions ,73. SILAigestion ,75. Chemtiplexed ,77. Currtiplexed ,79. Due tiplexed ,80. The tiplexed ,81,82.c parts of antibodies, peptides and lipids [AP-MS is one of the widely used methods to identify and characterize PPIs. The principle of this method is to isolate a protein of interest (referred to as bait protein) and to identify/quantify its interacting proteins by MS. For AP, either an antibody recognizing the endogenous protein or the expression of a tagged-protein variant in combination with a tag-recognizing structure are commonly used. Affinity tags or antibodies are captured by other macromolecules covalently attached to a solid matrix such as agarose beads. Macromolecules can be oligonucleotides (DNA/RNA), proteins, such as proteins A and G binding the Fd lipids . In geneWe will briefly explain the two most common AP approaches: (i) antibody-based immunoprecipitation of endogenous proteins and (ii) affinity tags, with critical examples related to mammalian autophagy. In antibody-based purifications, the endogenous bait along with its binding partners can be enriched in their native environment. For instance, autophagy induction by the tumor suppressor protein CDKN2A/ARF, which is a growth suppressor localizing to the nucleolus under growth conditions and to mitochondria under autophagy-inducing conditions, was discovered by an AP-MS approach. Endogenous CDKN2A and its binding partners were enriched using an anti-CDKN2A antibody prior to MS measurements. Under autophagy conditions, CDKN2A interacted with BCL2L1/Bcl-XL at mitochondria, interfering with the BCL2L1-BECN1 interaction and freeing BECN1 to bind to the PIK3C3 complex to promote autophagosome biogenesis . In geneYears of research and tremendous improvements in protein engineering helped in developing an array of affinity tags differing in size, length, and affinity to study interactomes. These affinity tags can be classified as protein- and peptide-based tags. There are plenty of single-peptide affinity tags: HA (hemagglutinin), FLAG, MYC/c-myc, 6x/8x His, 2x SBP (streptavidin-binding peptide), and CBP , to name a few. In addition, there are protein tags e.g., GFP (green fluorescent protein), MBP , and GST (glutathione-S-transferase) . Next toAforementioned tags were used in many studies to uncover PPIs modulating sequential events in autophagosome biogenesis. Here, we focus on studies performed in mammalian cells and refer the reader to some excellent articles explaining in detail the studies that have so far been carried out in yeast to understand autophagy . A list Moreover, AP-MS approaches were used to analyze organellar compositions, representing a valuable alternative approach for the characterization of cellular stress-response pathways. SILAC-labeled MCF-7 cells expressing eGFP-LC3 were utilized to compare autophagosome proteome changes upon different stress conditions. Vesicle fractionations in combination with anti-GFP-based enrichments, were coupled to MS analyses to reveal stimulus-dependent autophagosome proteomes . AffinitPost-translational modifications such as phosphorylation, ubiquitination, and sumoylation modulate interactions between autophagy proteins at a particular spatial and temporal resolution . For exaAP methods coupled to either western blot or MS allow analyses of protein complexes in all organelles and compartments without the requirement of prefractionation. The aforementioned examples highlight the robustness of these approaches in identifying PPIs and PPI-modulating PTMs to shed light on the molecular mechanisms in autophagy pathways. However, these approaches also have shortcomings. In general, co-purifying contaminants or nonspecific proteins, either binding to the solid matrix or the antibody, are also enriched, making it sometimes hard to distinguish false and true interactors ,105. UsaCritically, AP-MS data are commonly binary and do, therefore, neither yield insights into the structure (and stoichiometry) of complexes nor differentiate between direct and indirect interaction partners. Importantly, AP can isolate only stable or strong bait interactors with nanomolar or higher affinities; thus, dynamic, weak, and transient interactions are often missed .Escherichia coli that has been further engineered to BirAR118G to improve its catalytic activity . Cells expressing BioID-fused bait proteins are incubated with biotin for 16–24 h to reach maximum labeling efficiency. This relatively long incubation time is required to generate sufficient proximally biotinylated proteins, which can then be subsequently enriched using classical high-affinity streptavidin or neutravidin beads prior to LC-MS/MS sample preparation and measurements , was engineered without DNA-binding domain. This ligase was used to identify novel RNA-binding and proximal proteins; the respective approach was termed RNA-protein interaction detection (RaPID) [in vitro or in vivo in 3 h using lower concentrations of biotin than in the original approach [Introducing further mutations to BioID improved its labeling efficiency several-fold and led to two other commonly used biotin ligases called TurboID and miniTurbo . Compareg domain . Overall (RaPID) . For thi (RaPID) . One of approach .Table 2.PTM ligases are enzymes that commonly add tags onto lysine residues of bait-proximal proteins. Pupylation-based interaction tagging (PUP-IT) is a method that was developed to identify PPIs at membranes. This approach involves the expression of a bait protein fused to the bacterial Pup ligase PafA and co-expression of a Pup (prokaryotic ubiquitin-like protein) variant with a C-terminal Gly-Gly-Glu sequence. The Pup ligase catalyzes the phosphorylation of the C-terminal Glu residue, leading to its activation and conjugation to lysine residues of proteins that are in proximity to the bait. Due to the low diffusible nature of the activated Pup tag a smaller labeling radius is achieved, thus reducing background. The major shortcoming of this method is the large size of ~54 kDa of the ligase and its low catalytic activity, which render it inadequate to capture snapshots of PPIs . Neddyla2O2)  and HRP (horseradish peroxidase) enzymes, which catalyze the conversion of biotin-phenol to biotin-phenoxyl radical in the presence of hydrogen peroxide (H2O2) . This meNotably, tremendous improvements have been made in protein fragment complementation assays (PCA) coupled to PL approaches. Split variants of labeling enzymes were generated using inactive N- and C-terminal fragments of BioID or APEX2 . The resUp to date, only a few studies utilized PL coupled to MS-based quantitative proteomics to uncover the roles of proteins involved in autophagy . TBC1D14APEX2 labeling of human ATG8 homologs coupled to MS-based quantitative proteomics was used to analyze autophagosome content. APEX2-LC3C labeling identified a reproducible interaction of LC3C with a protein called MTX1 (metaxin 1). MTX1 binds to SAMM50 located on the outer mitochondrial membrane. Together with MTX2 bound to the cytosolic face of MTX1, these proteins form the sorting and assembly machinery (SAM) complex. This complex, together with the mitochondrial contact site and cristae junction organizing system (MICOS), maintain cristae structure, mitochondrial morphology, and homeostasis. Colocalization studies and functional biochemical analyses of MTX1 revealed its role in autophagic clearance of parts of damaged mitochondria in a piecemeal fashion via LC3C and SQSTM1 . RecentlD474N a ubiquitin-binding defective mutant, and TAX1BP1 in HeLa cells coupled to TMT (8/9 plex)-based quantitative MS analysis was performed comparing antimycin A/oligomycin A (inducing depolarization of mitochondria) treatments for 1 and 3 h with non-treated cells to identify proteins proximal to the receptors during mitophagy. In combination with a CRISPR-based genetic screen coupled to mitophagy flux assays, HK2 (hexokinase-2) was characterized as a scaffold, forming a 700-kDa complex consisting of PINK1-PRKN and other ubiquitinated proteins, essential for the clearance of damaged mitochondria [APEX2 labeling of the mitophagy receptors OPTN, OPTNchondria .The role of galectins in maintenance, repair, removal, and biogenesis of lysosomes upon injury has been extensively characterized using APEX2 labeling coupled to quantitative MS. Galectins are beta-galactoside binding proteins with an intrinsic carbohydrate recognition domain (CRD). This property helps in sensing membrane damage due to exposure of membrane glycans. LGALS8  was shown to induce autophagy upon endomembrane damage by regulating MTORC1 activity via changes in the activation state of RRAG GTPases . APEX2-LThus, the aforementioned examples show the impact of PL-based MS in understanding autophagy-relevant mechanisms. PL provides information about the proximal proteome change around the bait of interest at a given spatial and temporal resolution. However, PL methods reveal many proximal neighbors and potentially interacting proteins leading to an inherently high background. Therefore, the controls used to distinguish true versus false-positive interactors are critical. Commonly, five different types of controls are employed: (i) cells without any PL enzyme treated with biotin/biotin-phenol, (ii) the enzyme fused to an unrelated protein such as GFP or RFP, (iii) the enzyme fused to an inactive or mutant version of the respective bait, (iv) the enzyme fused to a compartment-specific, unrelated protein, which localizes similarly to the bait, and (v) cells with the free PL enzyme ,145. GivOver two decades, there has been a significant increase in the understanding of mechanisms regulating autophagy. Technological improvements contributed to the expanding list of autophagy-related and -associated proteins. MS-based proteomics studies helped tremendously in identifying and characterizing relevant proteins. Overall, various factors influence the identification of PPIs, like the range of affinities and composition of complexes, next to intrinsic methodological shortcomings of the used analytical approach. Thus, in-depth knowledge of potential methods and understanding of influential experimental factors that might modulate PPIs is essential for choosing appropriate approaches to characterize any PPI. Here, we introduced various MS-based methods available to study PPIs, explained in detail principles and applications of AP-MS- and PL-MS-based approaches, and listed examples of autophagy relevant studies. There are still various unanswered, mechanistic questions like: which membrane sources are employed under which conditions for autophagosome biogenesis? Which factors influence the localization of autophagy initiation complexes at ER sites? Which triggering factors regulate the balance between selective and nonselective autophagy, and how do proteins modulate autophagosome size and shape under these conditions? Extensive characterizations of PTMs modulating PPIs are essential for understanding the above questions. In the future, it will be essential to combine the analyses of PTMs and PPIs. The increasing scanning speed and sensitivity of mass spectrometers will help in generating more detailed views of the regulation of PTMs. Both DIA and DDA methods will likely contribute to generating truly comprehensive datasets that will also be useful for systems biology-based approaches. However, the limited dynamic range of mass spectrometers still poses a challenge to be addressed. Modified peptides still have to be enriched, but enrichment approaches often differ depending on which PTMs are analyzed, impeding the study of PTM crosstalk. Top-down proteomics approaches might partially address this issue. Finally, the growing field of MS-based lipidomics will be essential to fully understand the membrane dynamics underlying autophagy. Thus, we believe that cutting-edge MS approaches will continue to help to address autophagy-related questions and lead to a comprehensive understanding of autophagy-related processes.


To address the high false positive/negative rates of PPI networks and detect protein complexes with multiple topological structures, we developed a novel improved memetic algorithm (IMA). IMA first combines the topological and biological properties to obtain a weighted PPI network with reduced noise. Next, it integrates various clustering results to construct the initial populations. Furthermore, a fitness function is designed based on the five topological properties of the protein complexes. Finally, we describe the rest of our IMA method, which primarily consists of four steps: selection operator, recombination operator, local optimization strategy, and updating the population operator. In particular, IMA is a combination of genetic algorithm and a local optimization strategy, which has a strong global search ability, and searches for local optimal solutions effectively. The experimental results demonstrate that IMA performs much better than the base methods and existing state-of-the-art techniques. The source code and datasets of the IMA can be found at  Many complex systems in the real world are often modeled with complex networks, such as computer networks, social networks, and biological networks. The detection of community structure is an essential property of complex networks, and it helps us understand the structure and functionally of complex networks. Protein complexes mined from the PPI network are representative of detecting community structure in complex networks. In proteomics, proteins rarely act alone, and often organize together to form protein complexes to perform specific biological functions cooperatively . TherefoTo date, a variety of computational methods for detecting protein complexes in PPI networks have been proposed. Based on our work, we provide a summary of the related works by classifying the existing methods into three types: methods based on reducing noise, methods based on different topological structures, and methods based on evolutionary algorithms.Because the PPI networks are derived from high-throughput experiments, the high false positive and false negative rates in the PPI networks are high , which sIn recent years, various computational methods based on different topological structures have been developed to detect protein complexes in PPI networks. Among these methods, different types of topological structures are commonly assumed to be protein complexes. Some methods partition proteins into many non-overlapping clusters by using partition functions or principles. For example, Markov Clustering (MCL)  is a simIntensive studies on evolutionary algorithms have also been conducted. In recent years, some researchers have provided new ideas for solving protein complex identification problems by using optimized algorithms, by employing the characteristics of highly adaptive and good optimization abilities. Some successful methods have been applied to tackle the problems of identifying protein complexes and efficiency. In 2015, To solve these issues, we present a novel algorithm, named IMA, which uses an improved memetic algorithm we designed to detect protein complexes from the PPI network. First, we constructed a weighted PPI network by using the common neighbor, gene expression data, GO-slim data, and subcellular location data to reduce the impact of noise on our IMA. Second, many high-quality initial individuals, including protein complexes with different topological structures, are generated using EWCA , SE-DMTGIn the materials and methods section, we introduce the datasets and standard protein complexes used in the evaluation of IMA and define all phases of IMA separately. In the experiments and results section, we evaluated the proposed method and compare it with the state-of-the-art methods. The case study and discussion section shows some examples of protein complexes detected by IMA and we conclude this paper in conclusion section.In this study, we used four PPI networks including Krogan , DIP Xe, combinehttps://github.com/RongquanWang/IMA/Additional file 3.Furthermore, two sets of standard protein complexes from the literature  were usehttps://downloads.yeastgenome.org/curation/literature/go_slim_mapping.tab. The gene expression data were obtained from https://www.ncbi.nlm.nih.gov/sites/GDSbrowser. Additionally, the subcellular localization data set for yeast proteins was obtained from https://compartments.jensenlab.org/Downloads.The GO-slim data can explain the biological function of proteins, and it can be downloaded from Since PPI networks are defined using graph-theoretic concepts, we first provide some of the terminologies used in our paper and then describe the IMA method in detail.G, where V is the set of vertices  and E is the set of edges (protein interactions) between the vertices. The neighbors of v in G, denoted by N(v), are the set of vertices adjacent to v.A PPI network can be represented as an undirected graph C =  be a subgraph of G. The neighbors of C are defined by P = .Biologically, protein complexes are groups of proteins that interact with each other at the same time and place, forming a single multi-molecular machine. However, due to the inherent topological structures of protein complexes  in PPI nMany studies  have shov and u. The HCN is defined by NCN = N(v) ∩ N(u) is the number of common neighbors between proteins v and u. N(v) and N(u) represent the number of neighbors that proteins v and u are connected with, respectively. HCN can further balance the comprehensive connectivity of the two interacting proteins, which may consist of the same protein complex.The larger the number of common neighbors between the two proteins, the stronger the interaction of the two proteins, and they are more likely to participate in the same protein complex. Some common neighbor similarity measures  were usev and u in a PPI network, their gene expression profiles are v = {v1, v2, …, vn} and u = {u1, u2, …, un}, respectively. Here, we use the person correlation coefficient , as defined in v and u in n time points, respectively. PCC indicates the co-expression of the vector representation between two interacting proteins. As the value of PCC ranges from −1 to 1, we set PCC in . The higher the value of PCC, the larger the probability of co-expression of proteins v and u and formation of a protein complex.Next, the gene expression data describes proteins under various conditions in a biological process . The genfficient  to calcuO ∈ RN×M, where N denotes the number of proteins in the PPI network and M denotes the number of GO slim attributes. Based on matrix O, we constructed a protein attribute affinity matrix, S ∈ RN×N. Each entry FS reflects the GO slim attribute similarity between proteins v and u, as defined in From the perspective of protein function, we used GO-slim data to reflect the functional similarity of proteins. Moreover, we generated an attribute matrix SL, and is defined in SL(v)| and |SL(u)| denote the number of subcellular localizations of proteins v and u, respectively. |SL(v) ∩ SL(u)| represents the number of common subcellular localization attributes between proteins v and u.Generally, if two interacting proteins have the same subcellular location, the interaction between the proteins is more reliable. Proteins in the protein complex should be localized in the same inner cellular compartment. Here, we defined the subcellular location similarity W are expressed by The edges whose weight is 0 are seen as noise and are removed from the PPI network, and the rest of edges whose weight A memetic algorithm  is typicFSfitness . Finally, a roulette wheel selection strategy is used to update the population . The individual with the highest fitness function . In the following subsections, we provide more details on the parts of the IMA algorithm.IMA begins with initial populations  and then repeats an iterative procedure many times (generations) (lines 3–18). Two parent individuals in the current population are selected using a tournament selection strategy for each generation . Two new children individuals were generated based on two selected parents using the recombination operator . Then, we select the children with the highest fitness function  The individuals in the initial population should be high quality. It means that the protein complex detection method should produce the high accuracy initial population; 2) Individuals in the initial population need to be diverse. Obtaining initial population method should identify the protein complexes with different topological structures. Based on the above two points, we choose EWCA  threshold and ranges from 0.3 to 0.62 with 0.04 increment. The second approach is the SE-DMTG algorithm, which generates o individuals by randomly constructing seed queues, and then uses its seed-extended algorithm to generate individuals. The last method is the MPC-C algorithm, which is used to construct o individuals by setting different values of the filter Score(C) cutoff (from 0.1 to 0.8 with a step size of 0.1). Finally, we obtain Pop = 3 × o individuals and combine them to create the initial population, where the parameter o is 8.The first approach is the EWCA, which generates Algorithm 1The pseudo-code of general framework of IMA.cohesiveness(C), density(C), AIEW(C), ABEW(C), and AWM(C).We first define the fitness function of the IMA method; this fitness function should reflect the topological properties of protein complexes in PPI networks. Generally, a high-quality protein complex is a group of proteins that are densely interconnected but only sparsely connected with the rest of the PPI network , 2020. MC = , where VC is the set of proteins in cluster C, EC is the set of interactions in cluster C, and WC is the set of weights between the pair of proteins. According to previous studies (Win(C) is the sum of the weights of all edges among C, and Wout(C) is the sum of the weights of the edges connecting nodes in C to other nodes in the rest of the PPI network. studies , the cohC is defined by Win(C) is the sum of the weights of the edges between them, and VC is the number of nodes in C. hypothesC being a protein complex. First, it is the average inner edge weight (AIEW), and it can estimate the reliability of the internal edges of the cluster C. This is defined in Win(C) is the sum of the weights of the edges among them, and |EC| is the number of edges in cluster C. AIEW(C) is the average weight of the inner edges in cluster C.In this paper, we propose three measures to estimate the likelihood of a local cluster C. This is defined in BEC| = {|u ∈ C, v∉C} is the number of border edges that connect the cluster C with the rest of the PPI network, and Wout(C) is the sum of the weights of the edges connecting nodes in C to the neighbor of the cluster C. ABEW(C) is the average weight of the border edges in cluster C.Second, it is the average border edge weight (ABEW), and it can measure the reliability of the border edges of cluster C is highly average weight connected among them and has a low average weight interaction with the rest of the network. This is defined in C being a protein complex. This fitness function is denoted by density(C) and AIEW(C) seek a dense intra-connection topological structure, whereas ABEW(C) identifies the sparse topological structure inter-connecting with the rest of the PPI network. cohesiveness(C) and AWM(C) are used to identify the topological structures with densely interconnected nodes that are sparsely connected to the rest of the PPI network.Third, it is the average weighted modularity (AWM), which indicates that the cluster FF(Ci) is used to identify protein complexes with various topological structures in the individual FS. Finally, the fitness function (FSfitness) of the individual is the sum of FF(Ci), which is defined in Ci represents the ith cluster in the individual FS. FF(Ci) is a multi-objective function that is designed to capture the community structure of protein complexes . k is the total number of protein complexes found in an individual FS.As a result, FSfitness. Generally, the higher the FSfitness of an individual FS, and the better the quality of the individual. Therefore, the protein complex detection problem can be regarded as an optimization problem by maximizing the value of FSfitness in a PPI network.In this study, the goal of our IMA method is to find the individual with the maximum value of The selection operator is an essential operation used in the memetic algorithm. The main idea of the selection strategy is that the better an individual, the higher is its chance of being a parent. In the IMA algorithm, the recombination operator and updating population operator need individual selection strategies.In the parent selection operator, a selection strategy is required to select two parent individuals from the current population to generate individual children. The fundamental principle of this operation is that individuals with a higher fitness function are more likely to be selected as parent individuals. Here, we use a binary tournament selection strategy to select the parent individuals. Meanwhile, the recombination operator also uses a binary tournament selection strategy to select protein complexes from composite parent individuals to create individual children.Lastly, the population updating strategy needs to update the population according to the fitness of individuals in current population and generate children individuals. In this updating process, we use the roulette wheel selection strategy to update the current population to balance the relatively good individuals and avoid precocity.The recombination operator is the critical diversification mechanism of memetic algorithm. An effective recombination operator should generate not only diversified solutions but also transfer significant components from parents to children . The recAlgorithm 2Recombination operator.parent1 and parent2 represent the parents, and let len(parent1) and len(parent2) be the number of protein complexes in each parent. Parents parent1 and parent2 are merged into a composite parent. Note that if there are redundant protein complexes, and we only leave one. As a result, we obtain a composite parent individual compositeparent in line 2. Based on the length of parent1 (len(parent1)) and the length of parent2 (len(parent2)), we determine the length of the new children (childrenlen) by randomly generating a number between lenlow and lenup in lines 3–5. The multi-objective function  that belong to the Childrensi and connect at least one protein in the rest of the PPI networks in line 9. Then, we find the inner_max by improving the multi-objective function , which is the set of proteins that connect at least one inner protein of the current Childrensi in line 15. Then, we detect the boundarymax by increasing the multi-objective function  or iteration > 20 in lines 19–22, it is an identified protein complex Opcsi in line 24. Next, we use the local optimization strategy to optimize the rest of the protein complexes in Childrensmax in line 2–25. If the fitness function of the optimized individual Opcs is not larger than that of the Childrensmax, for the individual Childrensmax, its local optimization strategy is deemed invalid in lines 26–29. Finally, we output this optimal child, Opcs, in line 30.In this process, for each protein complex tion see  maximum tion see  maximum Algorithm 3The local optimization strategy.Population diversity is also a vital issue for memetic algorithms to effectively avoid prematurity. When a new child individual is produced with the recombination operator and local optimization strategy, the fitness function  of the nIn the experiment, our operational environment was a windows 10 operating system with an Intel(R) Core(TM) i7-9700 CPU with a physical memory of 16 GB, and a speed of the processor was 3.60 GHz. The IMA was run on PyCharm Community Edition 2017.2.2. The IMA was implemented using the Python 3.There are several statistical matching-based metrics that estimate the quality of the detected protein complexes based on different protein complex detection methods. Meanwhile, biological relevance-based metrics, which are supplementary to statistical matching-based metrics, are used to evaluate the biological significance of identified protein complexes.ipc and a known protein complex kpc contain common proteins each other, their overlapping score ) is calculated using ipc and kpc are the protein set of ipc and the protein set of kpc, respectively. If OS ≥ λ, ipc is matched with kpc, where λ is a threshold.If a detected protein complex OS is larger than 0.2  was used to measure the number of proteins in the standard protein complexes that could be covered by the identified protein complexes . This iseference .Sn indicates that the identified protein complexes cover the proteins in the standard protein complexes better. In contrast, a higher PPV indicates that the identified protein complexes are more likely to be actual protein complexes. Accuracy (ACC) is the geometric average of PPV and Sn, which is denoted by Generally, a higher io (MMR)  can measJaccard    is denotCL) . For alldatasets .IMA includes two parameters which need to be tuned:Generations(MaxIter) and Population size (Pop). Pop controls the number of initial population by EWCA, SE-DMTG, MPC-C. MaxIter determines the number of iterations of population optimization. Effect of Pop. Effect of MaxIter. https://github.com/RongquanWang/IMA/Additional file 1.rar.According to To demonstrate the performance of the IMA, we compared it with 12 state-of-the-art methods. These include MCODE , MCL Va, IPCA L, COACH , ClusterThe performance of the methods was compared on four PPI networks based on two standard protein complexes. The experimental results of our IMA and other methods on these PPI networks are listed in https://github.com/RongquanWang/IMA/Additional file 2.First, we compared them with the standard protein complexes 1. As shown in https://github.com/RongquanWang/IMA/Additional file 4. From the experimental results, we can see that the performance of IMA algorithm on CYC2008 protein complex dataset and MIPS protein complex dataset is basically consistent with the performance on two datasets (standard protein complexes 1 and standard protein complexes 2). This experimental results show that IMA algorithm has strong adaptability and stability to different standard protein complexes.The comparison results obtained using standard protein complexes 2 in p-value of the identified protein complexes is closely related to the size of the identified protein complexes (p-values from 1E-2 to 1E-20 were used to analyze their functional enrichment. We used a p-value test to analyze the protein complexes discovered by the IMA, MCODE, MCL, IPCA, COACH, ClusterONE, PEWCC, ProRank+, WPNCA, WEC, EWCA, SE-DMTG, and MPC-C. The results of the p-values of these methods are shown in We needed to conduct a multi-angle analysis for this statistic, because the omplexes . For thip-value from E-2 to E-20 in As shown in According to the definition of colocalization score, it is based on the average colocalization of all detected protein complexes. It should be noted that the lower the number of detected protein complexes, the higher the colocalization score. Here, we used the ProCope tool  to calcuhttps://github.com/RongquanWang/IMA/Examples.IMA algorithm can detect protein complexes with multiple topological structures. th protein complex in standard protein complexes 1 in the WI-PHI dataset so as to display the detection result more clearly. From In this paper, we present a novel IMA method for identifying protein complexes in PPI network. The key idea of IMA is enabled us to design an improved memetic algorithm to optimize a fitness function for identifying protein complexes in PPI networks based on existing contending methods and a weighted PPI network. Here, an improved memetic algorithm is the cooperation of a genetic algorithm with a local optimization strategy. A genetic algorithm is used to improve the diversity of the population, and the local optimization strategy helps to locate better solutions more quickly. Furthermore, we designed a fitness function to overcome the limitations of a single objective function in estimating an individual’s fitness. The experimental results show that IMA significantly outperforms the existing outstanding algorithms in various metrics. We will use graph neural networks  and othe


This topical review is focused on the development of radiopharmaceuticals containing the radioisotopes of rhenium and technetium, and examines the occurrence of these transition-metal complexes in protein structures in the Worldwide Protein Data Bank. In summing up and looking forward, the authors ask what is the best way for this field to progress. 188/186Re or 99mTc involve a d-block transition-metal radioactive isotope and therefore bring additional factors such as metal oxidation states, isotope purity and half life into play. This topical review is focused on the development of radiopharmaceuticals containing the radioisotopes of rhenium and technetium and, therefore, on the occurrence of these organometallic complexes in protein structures in the Worldwide Protein Data Bank (wwPDB). The purpose of incorporating the group 7 transition metals of rhenium/technetium in the protein and the reasons for study by protein crystallography are described, as certain PDB studies were not aimed at drug development. Technetium is used as a medical diagnostic agent and involves the 99mTc isotope which decays to release gamma radiation, thereby employed for its use in gamma imaging. Due to the periodic relationship among group 7 transition metals, the coordination chemistry of rhenium is similar  to that of technetium. The types of reactions the potential model radiopharmaceutical would prefer to partake in, and by extension knowing which proteins and biomolecules the compound would react with in vivo, are needed. Crystallography studies, both small molecule and macromolecular, are a key aspect in understanding chemical coordination. Analyses of bonding modes, coordination to particular residues and crystallization conditions are presented. In our Forward look as a concluding summary of this topical review, the question we ask is: what is the best way for this field to progress?Radiopharmaceutical development has similar overall characteristics to any biomedical drug development requiring a compound’s stability, aqueous solubility and selectivity to a specific disease site. However, organometallic complexes containing  It is important to confirm how a chosen target is involved in the disease and whether it can interact and be affected by a drug molecule. The search for a promising molecule or ‘lead compound’ can now be undertaken with the hope that the disease can be arrested. Lead compounds are assessed early on for safety according to their pharmokinetics or absorption, distribution, metabolism, excretion and toxicity (ADME/Tox) properties, whereby the function and performance of hundreds of different compounds are tested in vitro  imaging. It is widely utilized for diagnostic nuclear medicine with 80% of current radiopharmaceuticals administered clinically containing this radioisotope , and thus permits detailed chemical and reactivity analysis without the hazards of using the radioactive isotopes 186/188Re, 99mTc and 99Tc. There are two rhenium radionuclides utilized in therapeutic nuclear medicine, namely 186Re and 188Re, which function by means of β-irradiation. Rhenium complexes have been developed for bone metastasis   the chemical basis of this transition-metal family in its interactions with biological molecules;(b) the effects that a non-natural metal may have within an organic macromolecular model;(c) whether any possible chemical trends can be identified from the biological structural data; and(d) the likely stability or even strict relevance of the measured structural data, such as that involving the specific crystallization conditions used and tabulated in Table 1et al.  interactions may too play a role. Each of these mechanisms will have a direct effect on the viability of the complex as a radiopharmaceutical and on the design of the next iteration of potential model radiopharmaceuticals. Theoretical calculations describing the reactivity binding of rhenium complexes to biomolecules . However, the protein structures currently available in the PDB only include rhenium, not technetium.The purposes of the protein studies listed in the wwPDB that contain rhenium metal centres show a variety of applications. These have included (i) multi- or single-wavelength anomalous dispersion (MAD or SAD) phasing using relatively simple rhenium compounds, (ii) electron transfer and/or electron tunnelling or (iii) for medical applications. We describe all three aspects and highlight key observations made either by the authors or through our examination of the data. Many studies have included the investigation of both rhenium and technetium with their respective biological activity and confirmed the presence of technetium via alternative methods of characterization 3]+  was not considered a research priority of these particular studies. Variation in protein–metal coordination is found across this group, such as for 1hnu where rhenium is bound to the active site of the enoyl-CoA isomerase. Structure 5k1j has rhenium coordinated at His88, as well as being present at multiple sites in varying low occupancy. The study 3lya altogether shows 16 bound [ReCl6]2− ions to residues including histidine, tryptophan, aspartic and glutamic acid. The study 6f9p, also utilizing [ReCl6]2−, found that the rhenium retains only one chloride and its ligands are replaced by amino-acid interactions, notably two histidines, i.e. a special case for this particular protein  that involved rhenium for phasing used relatively simple rhenium compounds: , in a mutant Pseudomonas aeruginosa azurin to examine the electron-transfer capability between distant metal redox centres within the protein. In 2i7o the rhenium complex [ReI(CO)3(dmp)]  was attached to the histidine-124 residue 3]+ moiety in the sixth ligand position. Additional studies  core to the protein via the histidine imidazole moiety.Electron tunnelling is a quantum mechanical phenomenon that occurs when electrons move through a barrier that classically should not be possible to traverse. In proteins, electron tunnelling can move electrons between donor and acceptor sites separated by distances ranging from 10 to 30 Å on a millisecond or even femtosecond time scale  ideal radiodiagnostic properties    core (Ceretec)  used for imaging of bone metastasis has a +4 oxidation state and is thought to coordinate in an octahedral fashion. Furthermore, 99mTc-tetrofosmin (Myoview)  core. Moreover, 99mTc-NOET, a neutrally charged myocardial imaging agent, consists of a 99mTc(V)N2+ core  has a +1 oxidation state and has octahedral coordination surrounded by isonitrile ligands. The water-soluble and readily synthesized fac-[Tc99m(CO)3]+ core from the IsoLink Kit  similarly has a +1 oxidation state with octahedral coordination . Due to technetium’s  and 62.6% 187Re , it is therefore considered stable for standard laboratory use.2.3.1.et al. (1998V oxo core. The NMR study investigated the coordination of a [ReOCl3(Me2S)(OPPh3)] complex in solution. The authors’ goal was to design 188Re- or 99mTc-radiolabelled α-melanocyte stimulating hormone (α-MSH) analogues in which metal coordination was an integral part of the molecule’s structure (OPPh3)] organometallic complex. The α-MSH analogues, cyclized through site-specific rhenium and technetium metal coordination, were structurally characterized and analysed for their ability to bind to α-MSH receptors present on melanoma cells and in tumour-bearing mice. Crystal structure analysis of the Re–peptide complex showed that the disulfide bond of the original peptide was replaced by thiolate–metal–thiolate cyclization. When the metal binding site was redesigned, a second-generation Re–peptide complex (ReCCMSH) formed, which displayed a receptor binding affinity of 2.9 nM, 25-fold higher than the initial ReMSH analogue.Both the Tc and Re oxo complexes in 2.3.2.et al. (20123axf) who presented a strategy to engineer proteins that may selectively recognize the perrhenate (ReO4−) ion so as to develop a new method to label proteins. The ReO4− anion is tetrahedral in shape and contains the rhenium atom in the +7 oxidation state with a d 0 configuration. It is similar in size and shape to perchlorate and the valence is isoelectronic to permanganate. It is also stable over a broad pH range  binding protein (ModA) from Escherichia coli can bind perrhenate with high affinity and were able to solve the crystal structure of ModA with a bound ReO4− (3r26). The authors also synthesized a mutant protein containing a disulfide linkage, which exhibited increased affinity for the perrhenate (3axf). These protein structures both indicate that the ReO4− ion occupies the MoO42− binding site using the same amino-acid residues that are involved in molybdate binding. The overall protein structure of the perrhenate-bound ModA is unchanged compared with that of the molybdate-bound form  wet al., 2021et al., 2014et al., 2011N,N′; N,O′; O,O′ or tridentate coordinated complexes under physiological conditions are still being explored  therefore make a valuable contribution to understanding molybdate protein interactions, particularly if it can be generalized so that more perrhenate-bound proteins can selectively be stabilized with the presence of disulfide linkages. The authors indicate that the binding protein originates from a bacterium as the molybdate transporter in Homo sapiens has yet to be discovered. This could be applied for targeted delivery to an organ of concern, if other molybdate/perrhenate-labelled proteins could be identified. Secondly, the question arises if it would be possible to substitute the perrhenate oxo core (ReO4−) with an alternative core, such as the fac-[Re(CO)3]+ core that we have shown in our studies, summarized in Section 2.3.3The affinity of most proteins for the radionuclides of rhenium and technetium is not known. The effect of the bifunctional chelator on the metal reactivity (Jacobs 2.3.3.fac-[MI(CO)3] (M = Re or Tc), are widely utilized due to their water solubility, as well as their relatively simple synthetic procedure which is conducted under mild conditions in aqueous solutions. The aqua complex fac-[MI(CO)3(H2O)3] is coordinated by three tightly bound CO ligands and three labilizable water ligands. It is a highly attractive possibility for radiopharmaceutical design due to the high stability of the fac-[M(CO)3]+ core in water and the potential of exchanging the labile solvent ligands to allow coordination with many different types of ligands M(CO)3] complex  Re(CO)3] complex coordinates to the Zn atom in the active site, thus forming a protein–ligand bond. The [(Cp)Re(CO)3] complex has no further interactions with either the protein or water molecules  3] complexes to proteins, specifically to understand the protein–metal coordination for radiopharmaceutical development, have been described by Binkley et al. (20103kam), Binkley et al.  and Santoro et al. (2012fac-[ReI(CO)3] core has consistencies, such as the metal core showing binding preference to a histidine imidazole . The exception to the histidine imidazole sole preference was our study 3] core ,  al. 2011, Zobi &  al. 2011 3]+ and fac-[Re4(μ3-OH)4(CO)12]} were observed in the protein–rhenium crystal structures studied. We deem this expanded group of rhenium complexes seen bound to a protein as a breakthrough in the whole field, particularly as it is synthetically possible to substitute one rhenium atom with either technetium-99m or technetium-99 to form a mixed rhenium and technetium version where more than one metal centre is present and with possible further theranostic applications 3(bipy)]+  complexes as inhibitors of the SARS-CoV-2 main protease 3CLpro (3-chymotrypsin-like protease) 3] complex to the 3CLpro preferentially via the Cys145 amino acid. The authors suggest that rhenium(I) tricarbonyl complexes can serve as a starting scaffold for the development of potent selective SARS-CoV-2 inhibitors.Of additional medical interest is the recent report of the synthesis and biophysical evaluation of a series of 3.We have stated the need to identify any possible trends that may be occurring in this field as they could provide key information on whether there is any binding preference occurring between the group 7 transition-metal series and proteins. The themes of other research labs have been described and particularly the reasons as to why rhenium was chosen. It is also important to note the variety of proteins used, the wide range of organometallic complexes and the crystallization conditions Table 1.aIn our research, we have firstly identified that there are more amino-acid types binding to rhenium organometallics than previously seen. Secondly, via our most recent research, we have expanded the available repertoire of rhenium compounds to include multi-metal-centre complexes  show direct coordination of the metal to a histidine moiety via the imidazole group. This is a marked preference for one particular amino-acid residue, particularly when considering that crystallization conditions were markedly variable and involved various organometallic cores and oxidation states. Furthermore, 11 different proteins were analysed containing basically a full range of amino-acid residue types on their protein surfaces.The challenge for the chemist is how to localize the organometallic binding to the cancerous cells but not the normal tissue. Specific area injection is an obvious answer to this challenge, such as the use of heterogeneous So, how might this whole field progress? Fundamentally there are some significant technical obstacles from a crystallographic aspect that must be addressed in future research.etc. We also wish to emphasize the need of FAIR data principles  in the field of macromolecular and chemical crystallography. The purpose of our review is not to criticize the authors of past articles we have referred to who did not have the tools available today. But we ask the question how can published data and tools currently made available to macromolecular crystallographers be interoperably utilized by scientists in disciplines other than the original purpose  become more prominent? Also, could structure studies of proteins at variable temperatures increase our understanding of dynamic movements by examining the flexibility of side chains, or by the loss of water molecules . This is quite challenging because the co-crystallization of the organometallic of interest with a protein should also be carried out at 37°C, not only the X-ray diffraction data collection. The crystallization conditions at room temperature (∼20 to 25°C) may not be the same at 37°C. Organometallic reaction-rate constants generally increase by a factor of two or three for each 10°C rise in temperature . Thus, the import of a particular PDB entry into Coot . However, organometallic complexes are problematic to describe because their bonding scheme cannot fully be explained by valence-bond theory (David We hope that this topical review survey and descriptions of possible improvements to the methods will stimulate this important field for further, even enhanced, medical application.


Proteins and peptides are amongst the most widely used research reagents but often their quality is inadequate and can result in poor data reproducibility. Here we propose a simple set of guidelines that, when correctly applied to protein reagents should provide more reliable experimental data. Other reviews have attempted to quantify the economic cost dimension represented by data irreproducibility3, focusing on specific reagents widely used by the scientific research community such as antibodies4. These reports make uncomfortable reading for researchers, who by training are indeed aware that reproducibility is a critical issue that needs to be tackled5. The problem is openly acknowledged by both funding bodies6 and journals8. Thus far, however, the issue appears to have been addressed on a field-by-field basis rather than through a community-wide effort.There have been several publications over the last decade highlighting the problems of irreproducibility in preclinical research over a wide range of scientific disciplines (see ref. 12) describing the effects of poor protein quality on the validity and reproducibility of experimental data, to date there has been little visible response to this specific problem from the research community.Although purified proteins are used in numerous fields of research, no clear standard for the quality control (QC) of protein reagents currently exist and those that do exist are vastly under-utilized. These controls however should be deemed essential from a scientific point of view, to allow the identification of poor quality or artefactual research as early as possible to limit snowball effects; whereby a published paper can rapidly spawn a huge number of secondary papers and citations even when the original data are not reproducible. Although there have been many reports  at fifty percent, equating to a staggering economic cost of $28 billion per annum in the US alone, of which thirtysix percent ($10.4 billion worth of research) was directly attributed to poor quality ‘biological reagents and reference materials’. At present we are aware of only very few journals where there is a requirement for authors to include QC data for the proteins used as ‘reagents’ in their studies. This situation appears to be in direct contrast to e.g., the high standards of statistical analyses and declarations of statistical compliance required in articles submitted to high-end journals when presenting genomic, proteomic and structural data13. With the aim of addressing this obvious imbalance, and in response to the problem of data reproducibility when protein reagents are involved, a working group comprised of members of both the ARBRE-MOBIEU and the P4EU networks produced a list of recommended tests . These guidelines were developed with reference to the available literature14 and the extensive professional experience of the working group members, to aid in the validation of protein samples used in biological research. They have been embraced by a wide community of specialists (a full list of these researchers can be found on ARBRE-MOBIEU and P4EU website) and comprise three parts: (1) minimal information, (2) minimal QC tests, and (3) extended QC tests. We propose a list of minimal QC tests that are based on simple experimental methods that are widely available , Reversed Phase Liquid Chromatography (RPLC). Mass Spectrometry (MS) and RPLC help to detect the presence of contaminating proteins, sample proteolysis and minor truncations.Homogeneity/dispersity refers here to the size distribution of the protein sample, which can generally be correlated with oligomeric state  or the presence of aggregates. Whereas poly-dispersity is not per se an indication of instability, preparations showing the presence of ‘incorrect’ oligomeric states or higher order ‘aggregates’ suggest that the protein may not be in an optimal/functional state. This can have a dramatic effect on the results of experiments to determine e.g. enzyme kinetics and protein-ligand interactions, essentially as a result of an overestimation of the concentration of active protein. Protein homogeneity/dispersity may be assessed by Dynamic Light Scattering (DLS), size exclusion chromatography (SEC) or, preferably, by SEC coupled to multi-angle light scattering.The identity of a sample can be confirmed using either ‘bottom-up’ MS (mass fingerprinting or tryptic digests) or ‘top-down’ MS (by measuring intact protein mass). The former will confirm that the correct protein is being used and not e.g. a host protein of similar mass that has been purified in error. The latter will confirm the identity of the protein and will also indicate whether it has suffered any proteolysis during purification (intactness/micro-heterogeneity).Escherichia coli that are destined for use in experiments with cultured cells should be tested for the presence of lipopolysaccharides/endotoxins and UV spectrophotometry is mandatory for DNA/RNA binding proteins.In addition to this short list of minimal/essential controls, other techniques are recommended to further characterize protein samples and their suitability as experimental reagents, for instance the folding state of proteins and the specific activity of enzymes. Proteins produced in 15.Examples in which protein quality assessment resulted in improvements of sample quality with critical impact on downstream experimental results are presented in supplementary information  integration of these essential sections to their former status to allow other researchers to reproduce the data therein without resorting to making contact with the authors. Although this effect has been partly mitigated by the current availability of Supplementary Data sections in many on-line journals, the presented data often falls short of a full description of the experimental conditions used and often lacks any form of QC data relating to protein quality. The present interest of Editors for the systematic storage of (raw) data [https://www.springernature.com/gp/open-research/open-data/practical-challenges-white-paper] should consider also the inclusion of this methodological data.This condition, which should ideally be the norm, is in reality challenged by several factors as reported in a recent surveyWe suggest that implementation of guidelines for protein quality evaluation should be considered an entry point towards the development of improved and ideally compulsory reporting practices of data obtained with protein reagents. It is our contention that ‘Supplementary Data’ sections should also contain details of the QC tests performed on any protein/peptide reagents used in a study, independent of the source of the protein reagent , in order to give referees and readers an indication of the quality of the materials being used to derive any given data set. To this effect, we suggest the development—in co-operation with journal editors—of a standardized form for QC reporting and annotation for authors to complete during the submission process. A model of such a checklist is illustrated in Supplementary Table Supplementary Information