A detailed understanding of these interactions requires knowledge about protein–nucleic acid complex structures. Because the experimental determination of these complexes is time-consuming and perhaps futile in some instances, we have focused on computational docking methods starting from the separate structures. Docking methods are widely employed to study protein–protein interactions; however, only a few methods have been made available to model protein–nucleic acid complexes. Here, we describe NPDock (Nucleic acid–Protein Docking); a novel web server for predicting complexes of protein–nucleic acid structures which implements a computational workflow that includes docking, scoring of poses, clustering of the best-scored models and refinement of the most promising solutions. The NPDock server provides a user-friendly interface and 3D visualization of the results. The smallest set of input data consists of a protein structure and a DNA or RNA structure in PDB format. Advanced options are available to control specific details of the docking process and obtain intermediate results. The web server is available at  Proteins and nucleic acids are the two main types of biological macromolecules that often tend to function together in the cell. Protein–RNA and protein–DNA interactions play a fundamental role in a variety of biological processes, including DNA replication, RNA transcription, RNA splicing, degradation of nucleic acids and protein synthesis. These interactions are essential to cellular metabolism and the survival of all organisms. Defects in protein–nucleic acid interactions are implicated in a number of diseases, ranging from neurological disorders to cancer ,2. Our uTypically, these details are obtained from crystallizing a given protein–RNA or protein–DNA complex. However, experimental determination of most protein–nucleic acid complex structures by high-resolution methods is a tedious and difficult process .Computational techniques can complement experimental approaches in elucidating protein–nucleic acid interactions. In particular, docking methods aim at predicting the three-dimensional (3D) structures of macromolecular complexes, starting from the atomic coordinates of their components . AlthougThe methodology for prediction and modeling of proteins and protein–protein complexes is very well established ,6. NumerMost docking methods lack an intrinsic scoring function dedicated to assessing protein–RNA interactions. Such functions have been developed as standalone programs that allow for discriminating between well-docked and poorly docked complexes. For example, our group has developed statistical potentials QUASI-RNP and DARS-RNP that are deliberately coarse-grained to help take into account moderate conformational changes that may occur upon binding . Other mNPDock is implemented as a computational workflow that consists of the GRAMM program, which is a third-party method , and a sFirst, the GRAMM program is used to perform a rigid body global search and generate geometrically plausible protein–nucleic acid complex structures (decoys). Further analysis of decoys can be limited to those models that satisfy user-defined restraints; e.g., distance restraints between any set of residues defined by user. The decoys are scored and ranked using statistical potentials, developed for protein–RNA or protein–DNA complexes, respectively (see above). The best-scored decoys are then clustered using a procedure reported previously  and reprhttp://iimcb.genesilico.pl/NPDock/help). Any atom that should be taken into consideration during the docking process, including non-standard residues and ligands, must be relabeled as ‘ATOM’: atoms labeled ‘HETATM’ in the input files will be ignored and may appear as ‘holes’ in the output structures. Improperly named atoms will not interfere with the docking process and will appear in the output; however, they will be ignored while evaluating the interactions and, hence, probably lead to an improper score.The smallest set of input data consists of a protein structure file and a nucleic acid (DNA or RNA) structure file in PDB (both new and old), AMBER, CHARMM or mol2 formats. Mol2-formatted files are converted to the PDB format using OpenBabel . AMBER- In addition to the main input files, the user is able to modify parameters used in the docking process. In particular, the input can include a list of interface residues for both the receptor and the ligand, and the number of protein–nucleic acid residue pairs (from the above-mentioned list) that are required to be in contact; i.e. at a distance ≤10 Å from each other. If the interface residues are provided for one molecule only, then all residues from the other molecule are permitted to form contacts. In general, restricting the docking procedure to a well-defined interface is likely to improve the accuracy of the docking prediction.The user can also modify the parameters in the clustering procedure. By default, the 100 best-scored models are used for clustering. If no large clusters are identified, we suggest experimenting with a higher number of models (up to 1000). The default value for the RMSD threshold in the clustering procedure is set to 5 Å, which can be modified  for structures that are very large or generate a large number of different poses. In our experience, values between 5 and 10 Å led to the most reasonable results.The parameters of the rigid body refinement procedure can be also adjusted by the user. The number of simulation steps defines the length of the simulation. Long simulations allow the molecules to move out from a local minimum and may allow a more extensive sampling of the conformational landscape. However, long simulations are computationally costly; therefore, based on our experience, we recommend using values between 1000 and 10 000. The temperature of the first and last steps of the simulation defines the behavior of the Simulated Annealing algorithm. A high temperature allows for a larger freedom of movement; however, it should be combined with a longer simulation time to allow the system to cool down smoothly. We do not recommend changing the value of the last simulation step unless the user intends to perform very unorthodox sampling.For protein–RNA docking, we used DARS-RNP and QUARI-RNP potentials that were trained earlier . For proFor testing of protein–RNA docking, we used the same 12 protein–RNA complexes from the Varani and Fernandez benchmarks used in our earlier work on the DARS-RNP and QUARI-RNP potentials  to compaThe NPDock server is designed to automate the procedure of protein–nucleic acid complex structure modeling. Published examples of structural models developed with the computational pipeline corresponding to NPDock include Trm14 methyltransferase complexed with tRNA , and an We tested the NPDock server on 13 protein–RNA complexes used in our original work on RNP potentials   that comprises QUASI-DNP, the DFIRE potential  and a poet al. , on an iet al. .The results of testing the server obtained by the fully automated procedure were similar to the results obtained by the manual use of independent docking software and methods for scoring protein–DNA interactions (Supplementary Table S1). The benchmark is divided into easy, intermediate and difficult targets for docking. Therefore, we also analyzed the results by splitting the complexes into these three groups. For easy targets tested without restraints, 10/13 cases and 13/13 cases resulted in finding native-like structures for bound and unbound docking, respectively. For cases tested with a single defined residue restraint, 13/13 cases for the bound and 7/13 for the unbound found native-like structures. For intermediate targets, we obtained native-like decoys for 15/22 for the bound and 2/22 for the unbound cases. For intermediate targets with defined residues, we obtained 18/22 for the bound and 6/22 for unbound cases. For difficult targets, NPDock found 12/12 cases for bound docking with native-like structures and 3/12 for unbound docking without restraints and 4/12 with restraints. These results demonstrate that protein–DNA docking is also challenging, but generally easier than for protein–RNA docking, perhaps because of the more limited conformational changes in typical DNA structures. Further details can be found in the Supplementary results.A complete docking result consists of decoys and outputs from the docking process Figure . At the NPDock is a novel web server developed for protein–nucleic acid docking that uses specific protein–nucleic acid statistical potentials for scoring and selection of modeled complexes. NPDock implements a unique workflow based on a combination of computational methods that have been published and offers a user-friendly web interface to enter PDB structures and view the results. The automation of the entire process makes the protein–nucleic acid docking available to users who would otherwise become tripped up installing many complex programs locally and then carrying out many manual steps; each requiring a variety of manual format conversions that are highly prone to human error. Therefore, it can help users save even more than ten times the time required to run different methods separately and sequentially.Future plans are as follows. First, add additional potentials for protein–nucleic acid interactions (in particular for protein–RNA interactions), including potentials developed by third parties, as well as ones under development in our group. This extension may also encompass ‘meta’ scoring. Second, enable the docking of hybrid DNA–RNA molecules : we plan to implement a hybrid potential for simultaneous scoring of protein–RNA and protein–DNA interactions. Third, include a procedure of local flexible refinement of both protein and nucleic acid components, using methods such as REFINER  and SimRhttp://genesilico.pl/NPDock.The web server is available at This website is free and open to all users and there is no login requirement.Supplementary Data are available at NAR Online.


Sinorhizobium meliloti and its cognate electron acceptor SorU. Kinetic and thermodynamic analyses of the SorT/SorU interaction show the complex is dynamic in solution, and that the proteins interact with Kd = 13.5 ± 0.8 μM. The crystal structures of the oxidized SorT and SorU, both in isolation and in complex, reveal the interface to be remarkably electrostatic, with an unusually large number of direct hydrogen bonding interactions. The assembly of the complex is accompanied by an adjustment in the structure of SorU, and conformational sampling provides a mechanism for dissociation of the SorT/SorU assembly.Interprotein electron transfer underpins the essential processes of life and relies on the formation of specific, yet transient protein-protein interactions. In biological systems, the detoxification of sulfite is catalyzed by the sulfite-oxidizing enzymes (SOEs), which interact with an electron acceptor for catalytic turnover. Here, we report the structural and functional analyses of the SOE SorT from DOI:http://dx.doi.org/10.7554/eLife.09066.001 A key feature of many important chemical reactions in cells is the transfer of particles called electrons from one molecule to another. The sulfite oxidizing enzymes (or SOEs) are a group of enzymes that are found in many organisms. These enzymes convert sulfite, which is a very reactive compound that can damage cells, into another compound called sulfate. As part of this process the SOE transfers electrons from sulfite to other molecules, such as oxygen or a protein called cytochrome c. In the past, researchers have described the three-dimensional structure of three SOEs using a technique called X-ray crystallography. However, it has been difficult to study how SOEs pass electrons to other molecules because of the temporary nature of the interactions.Sinorhizobium meliloti. This included structures of the proteins on their own, and when they were bound to each other. These structures revealed that a subtle change in the shape of SorU occurs when the proteins interact, which enables an electron to be quickly transferred.McGrath et al. studied an SOE called SorT, which is found in bacteria. The SorT enzyme passes electrons from sulfite to another protein called SorU. McGrath used X-ray crystallography to determine the three-dimensional structures of versions of these proteins from a bacterium called McGrath et al. also found that the interface between the two proteins showed an unexpectedly high number of contact sites. These strengthen the interaction between the two proteins, which helps to make electron transfer more efficient. However, these contact sites do not prevent the two proteins from quickly moving apart after the electrons have been transferred. The next challenge is to find out whether these observations are common to SOEs from other forms of life.DOI:http://dx.doi.org/10.7554/eLife.09066.002 Although electron transfer reactions are key biochemical events, which underpin fundamental processes, such as respiration and photosynthesis, the study of the molecular details of the interprotein interactions at their core can be largely intractable. In particular, atomic resolution crystal structures of electron transfer complexes are rare, due to their fundamentally transient nature . ElectroInterprotein electron transfer processes are central to the redox conversions of cellular sulfur compounds, which are an evolutionarily ancient type of metabolism that has existed as long as cellular life . Sulfur-c  of 32 ± 5 μM and a kcat of 140 ± 11 s-1, confirming that SorU is the natural electron acceptor of SorT. Measurement of the thermodynamics of the SorT/SorU interaction by isothermal titration calorimetry (ITC) revealed a dissociation constant of Kd = 13.5 ± 0.8 μM with a determined stoichiometry of 0.8 ± 0.2. These values are in the range observed for other electron transfer complexes , both of which have much higher affinities for their respective electron accepting cytochromes c (both with Kc)M(Cyt  ca. 2 μM) (kcat = 47.5 ± 1.9 s-1), due to a mechanism requiring internal rearrangement, while turnover of SorAB with its natural electron acceptor (334 ± 11 s-1) is significantly faster than that for SorT (The ± 1.9 μM ), and tha. 2 μM) . The catfor SorT .In order to investigate whether there are any structural reasons for these differences, we solved the crystal structure of SorT by molecular replacement and refined it to 2.4 Å resolution. The structure shows two homodimeric assemblies per asymmetric unit , which iNevertheless, the fold of the SorT monomers is similar to those of other SOEs , comprisV form was achieved using a combination of Ti(III)citrate and a suite of organic redox mediators. The MoV EPR spectrum is similar to the so-called ‘high pH’ EPR signature of SOEs .The central assembly within the SorU/SorTer alone , Table 1er alone , Table 1er alone   mV and -18(±10) mV vs. NHE (pH 8)) were determined by an EPR-monitored redox titration where the initial EPR-silent MoVI form is reduced to the EPR-active MoV state  where the protein partners include mobile domains, and where conformational change is necessary for the creation of high affinity protein-protein interfaces . Howeverc that can act as electron acceptors to SOEs , the natural electron acceptor for vertebrate SOEs, compared to the high activity observed with SorU   . In its ; pI ~4)  show binomplexes .In addition to the electrostatic interactions that support the formation of the SorT/SorU complex, there are six hydrogen bonds found at the SorT/SorU protein-protein interface, as well as a salt bridge between the SorT active site residue Arg 78 and a propionate group of the SorU heme moiety . This isc subunit (SorB) and a Mo cofactor containing (SorA) catalytic subunit  relative to the structure of the SorT protomer  correlates with the fast measured turnover rates (kcat of 140 ± 11 s-1) and with the predominantly electrostatic nature of the protein-protein interface.The structure of the protein-protein interface in the SorT/SorU structure is particularly intriguing. The relative lack of bound water molecules, mirrors more the observations made of permanent heterodimeric complexes than transient interactions . Previou-1 and 47.5 ± 1.9 s-1, respectively), but significantly slower than that observed for the permanent SorAB complex (345 ± 11 s-1)  and CSO (25.0 ± 1.3 s 11 s-1) . ImportaorT/SorU . It shouorT/SorU , SorU isThe SorT/SorU complex described here thus represents an elegant compromise between the requirements for fast and efficient electron transfer and reaction specificity. It also illustrates new aspects for highly dynamic protein-protein interactions: (i) A relatively large number of hydrogen bonds and salt bridges may be required to form the initial stable protein complex, but this does not preclude a dynamic protein – protein interaction; (ii) relatively subtle structural adjustments in one redox partner (SorU) can facilitate electron transfer by ideally locating the redox active cofactors in close proximity; (iii) the comparatively complex binding interface in SorT/SorU can be counterbalanced by the conformational sampling of one protein relative to the other, which enables the rapid dissolution of the complex following electron exchange.It remains to be seen whether these principles apply to other SOE – external electron acceptor interactions. Future work should focus on investigating interaction interfaces in currently little studied SOEs where new types of interactions may be present as for many of these enzymes currently no external electron acceptor is known.-1) before crystallization via hanging-drop vapor diffusion with a reservoir solution containing 0.2 M sodium formate, 0.1 M Bis-Tris propane pH 7.5 and 20% PEG 3350. Crystals grew to a maximum size of ca. 150 x 100 x 20 μ in 4 days at 20°C and were flash-cooled in liquid nitrogen after brief soaking in mother liquor containing 30% glycerol. All diffraction data were collected on an ADSC Quantum 315r detector at the Australian Synchrotron on beamline MX2 at 100 K and were processed with HKL2000 R = 0.208-1, respectively. SAXS data were measured as described previously(I(q) vs q  using the program SAXSquant that includes corrections for sample absorbance, detector sensitivity, and the slit geometry of the instrument. Intensities were placed on an absolute scale using the known scattering from H2O. Protein scattering was obtained by subtraction of the scattering from the matched solvents . Molecular weight (rM) estimates for the proteins were made using the equation from Orthaber(AI(0)C△ρM2Mr=N where ANis Avogadro’s number, C is the protein concentration and △ρM=△ρυ△ρ is the protein contrast and υ the partial specific volume, both of which were determined using the program MULCh, generating two samples of total protein concentrations 2.75 and 6.25 mgmLreviously with the Orthaber: Mr=NAI(ram MULCh.The ATSAS program package was usedContinuous-wave X-band (ca. 9 GHz) (CW) electron paramagnetic resonance (EPR) spectra were recorded with a Bruker Elexsys E580 CW/pulsed EPR spectrometer fitted with a super high Q resonator; the microwave frequency and magnetic field were calibrated with a Bruker microwave frequency counter and a Bruker ER 036TM Teslameter, respectively. A microwave power of 20 mW was used and optimal spectral resolution was obtained by keeping the modulation amplitude to a 1/10 of the linewidth. A flow-through cryostat in conjunction with a Eurotherm (B-VT-2000) variable temperature controller provided temperatures of 127–133 K at the sample position in the cavity.Bruker’s Xepr (version 2.6b.45) software was used to control the data acquisition including, spectrometer tuning, signal averaging, temperature control and visualization of the spectra. Computer simulation of the EPR spectra were performed with the following spin Hamiltonian using the XSophe-Sophe-XeprView (version 1.1.4) computer simulation software suite on a perIV/V and MoV/VI redox potentials of SorT were determined by an EPR-monitored redox titration carried out in a Belle technology anaerobic box. The protein solution  also contained the following redox mediators at concentrations of ~50 µM: diaminodurol , dichlorophenolindophenol , 2,6-dimethylbenzoquinone , phenazine methosulfate , 2,5-dihydroxybenzoquinone  indigo trisulfonate , 2-hydroxy-1,4-naphthoquinone  and anthraquinone 2-sulfonate . The reductant was Ti(III) citrate and the oxidant was NaS2O8 (both ~100 mM). After addition of titrant and equilibration (15–30 min), the equilibrium potential was measured with a combination Pt wire/AgAgCl redox electrode attached to a Hanna 8417 meter calibrated against the quinhydrone redox couple (Eo′ (pH 7) = +284 mV vs NHE). A 100 μL aliquot of protein was withdrawn and transferred to an EPR tube (in the anaerobic box) which was then sealed and then carefully frozen in liquid nitrogen (outside the box). Potentials for all experiments were measured with a combination Pt wire-Ag/AgCl electrode attached to a Hanna 8417 meter. The intensity of the MoV signal (I) was recorded as a function of measured potential (E), The MoSpectroelectrochemistry of SorU in isolation and the SorT:SorU complex was performed with a Bioanalytical Systems BAS100B/W potentiostat connected to a Bioanalytical Systems thin layer spectroelectrochemical cell (0.5 or 1 mm pathlength) bearing a transparent Au mini-grid working electrode, a Pt wire counter and Ag/AgCl reference electrode. Redox mediators  used in the experiment were employed at concentrations of 50 μM .10.7554/None exhibit any significant absorption in the spectral range of interest at micromolar concentrations. The total solution volume was ca. 700 μL. The buffer was 20 mM Tris (pH 8) containing 200 mM NaCl as supporting electrolyte. The SorU concentration was ca. 50 μM, while experiments on the SorU:SorT complex used approximately equal concentrations of both proteins (50 μM). Spectra were acquired within a Belle Technology anaerobic box with an Ocean Optics USB2000 fibre optic spectrophotometer. Initially, the cell potential was poised at ca. -100 mV and the system was allowed to equilibrate until no further spectral changes were apparent (fully reduced SorU). The potentials were then increased in 50 mV increments and the spectrum was measured when no further changes were seen (5–10 min). When the protein was fully oxidized the potential was scanned in the reverse direction in 50 mV intervals to establish reversibility. Data were fitted using the program ReactLab Redox (Maeder and King).Affinity measurements were conducted using a Microcal ITC200 system  at 25°C using SorT and SorU in buffer  at final concentrations of 300 μM and 30 μM, respectively. SorT at a concentration of 300 μM was titrated with eighteen injections (2.0 μl each) of SorU. All affinity measurements were performed in triplicate and fitted using a single site mode. Protein concentrations were estimated using Bicinchoninic acid (BCA) protein assay kit .-1 cm-1 as determined by spectroelectrochemistry. Data fitting was carried out using Sigmaplot 12 (Systat).Sulfite dehydrogenase enzyme assays were carried out as described previously . The red In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.eLife. Your submission has been favorably evaluated by Michael Marletta (Senior editor). Michael also served as the Reviewing editor, and sought advice from two reviewers.Thank you for submitting your work entitled "Structural basis of interprotein electron transfer in bacterial sulfite oxidation" for peer review at The reviewers have discussed the reviews with one another and Michael Marletta has drafted this decision to help you prepare a revised submission.Summary:This manuscript by McGrath et al. reports the structure of a sulfite oxidizing enzyme (SorT) and its interaction with an electron acceptor SorU, a c-type cytochrome. The authors make the point of the importance of sulfite oxidizing enzyme (SOEs) in overall sulfur metabolism. They note this work is the first co-structure of an SOE with its native electron acceptor, and they further note this is likely the first such characterization for a Mo-containing enzyme. They compare and contrast their SorT structure to that of other SOE structures. In the co-structure they characterize the possible electron transfer pathway between the Mo of SorT and the heme of the SorU. Conformational changes of SorU that allow this interaction to shorten in the co-protein structure vs rigid docking of the individual monomers is described. The protein electrochemistry shows no significant change in the midpoint potential of SorU upon interacting with SorT. The authors discuss the electrostatic interactions between the two proteins, which they consider unusual for cytochrome based ET complexes, including the presence of salt bridges and 5 H-bonds. They then contrast to a relevant "permanent" heterodimer complex between SorB and SorA by which the SorU/SorT interactions are much lower, and the authors discuss specific differences in the protein/protein interactions of the two different pairs. The contrast is extended to looking at the relative temperature factors between proteins of the two pairs, with higher conformational flexibility in the SorU of the SorT/SorT complex. The high conformational flexibility is related by the authors to the thermodynamics of binding and the kinetics of the SorT reaction.Essential revisions:We feel that the focus of the paper is not on the protein-protein interface as we were led to believe. In fact there is no structure of the H bonding interactions (the R/propionate interaction is shown) and no discussion of water molecules at the interface. The authors need to either rewrite the paper to focus on the as advertised protein-protein interaction or refocus on a comparison of this sulfite dehydrogenase with others that have the heme domain tethered to the dehydrogenase domain.The authors have done a lot of work and there are not many examples of two proteins that are involved in electron transfer. The work is thus interesting and important. However, there is little discussion of the well characterized proteins with not only structure, but kinetics and where electrostatic interactions have been changed and the kinetic consequences have been evaluated. The work of Brian Hoffman in particular, but also that of Crane and Gray, is relevant. The authors need to define the focus of the paper. Most of the Discussion, for example, was focused on evolution. The Introduction was too general and did not contain enough background information as provided on sulfite dehydrogenases and what is known.Minor points:Kd determined for the protein-protein interactions and the details of the interface. What are the oxidation states, for example of the crystallized proteins etc? What is the reaction catalyzed by SorT?1) The Abstract is way too general and needs details on the system being investigated, the 2) The Introduction is also way too general, ex about sulfur metabolism, and should be focused on the systems that have been well studied crystallographically and kinetically and what has been learned.3) 4) 5) A new figure with the interface and the H bonding interactions, waters should be added in addition to Kd and turnover numbers, first rather than last.6) We think you should consider moving the details about the protein-protein interaction, such as 7) Is the catch phrase "caught in the act" necessary?eLife. Your revised article has been favorably evaluated by Michael Marletta (Senior editor and Reviewing editor) and two reviewers. The manuscript has been improved but there are some remaining issues that need to be addressed before acceptance, as outlined below:Thank you for resubmitting your work entitled "Structural basis of interprotein electron transfer in bacterial sulfite oxidation" for further consideration at In the subsection title “The crystal structure of the SorT homodimer reveals a unique subunit arrangement”, the “unique subunit arrangement” is with respect to what? Why not say a unique head to tail orientation or something alike?We found the structures in  Essential revisions:We feel that the focus of the paper is not on the protein-protein interface as we were led to believe. In fact there is no structure of the H bonding interactions (the R/propionate interaction is shown) and no discussion of water molecules at the interface. The authors need to either rewrite the paper to focus on the as advertised protein-protein interaction or refocus on a comparison of this sulfite dehydrogenase with others that have the heme domain tethered to the dehydrogenase domain. The authors have done a lot of work and there are not many examples of two proteins that are involved in electron transfer. The work is thus interesting and important. However, there is little discussion of the well characterized proteins with not only structure, but kinetics and where electrostatic interactions have been changed and the kinetic consequences have been evaluated. The work of Brian Hoffman in particular, but also that of Crane and Gray, is relevant. The authors need to define the focus of the paper. Most of the Discussion, for example, was focused on evolution. The Introduction was too general and did not contain enough background information as provided on sulfite dehydrogenases and what is known.We have included We have chosen to focus on the SorT/SorU protein-protein interaction as suggested and as described above included an additional figure and text to emphasise this part of the analysis.We have used the work of Hoffman and Gray to guide our analyses as suggested .We have refocused the Discussion away from the evolutionary aspects to a more detailed analysis of the structure in the context with the kinetics and thermodynamic measurements as suggested.We have re-written the Introduction to provide more detail on the background to sulfite dehydrogenases in particular.Minor points:1) The Abstract is way too general and needs details on the system being investigated, the Kddetermined for the protein-protein interactions and the details of the interface. What are the oxidation states, for example of the crystallized proteins etc.? What is the reaction catalyzed by SorT?The Abstract has been entirely rewritten to include the suggested points.2) The Introduction is also way too general, ex about sulfur metabolism, and should be focused on the systems that have been well studied crystallographically and kinetically and what has been learned.As mentioned in ‘Essential Revisions’ above, the Introduction has been rewritten to include responses to these suggestions.3) A new 4) We have removed 5) A new figure with the interface and the H bonding interactions, waters should be added in addition to We have prepared a new 6) We think you should consider moving the details about the protein-protein interaction, such as Kd and turnover numbers, first rather than last.These experiments are now detailed first in the Results section as suggested (subheading “The interactions between SorT and SorU are highly dynamic and efficient in solution”).7) Is the catch phrase "caught in the act" necessary?No; this has been removed.In the subsection title “The crystal structure of the SorT homodimer reveals a unique subunit arrangement”, the “unique subunit arrangement” is with respect to what? Why not say a unique head to tail orientation or something alike?We have changed the title of this section to the following as suggested: “The crystal structure of the SorT homodimer reveals a head to tail subunit arrangement”.We found the structures in We thank the editor for pointing this out – there was an overlooked error in


Drosophila Su(dx) was used to develop and validate the computational methodology, which was then used to determine the formulation hotspots for Fab A33. Commonly used excipients were examined and compared to the regions in Fab A33 prone to protein–protein interactions that could lead to aggregation. This approach could provide information on a molecular level about the protective interactions of excipients in protein formulations to aid the more rational development of future formulations.Protein formulation development relies on the selection of excipients that inhibit protein–protein interactions preventing aggregation. Empirical strategies involve screening many excipient and buffer combinations using force degradation studies. Such methods do not readily provide information on intermolecular interactions responsible for the protective effects of excipients. This study describes a molecular docking approach to screen and rank interactions allowing for the identification of protein–excipient hotspots to aid in the selection of excipients to be experimentally screened. Previously published work with  Therapeutic proteins are potent drug substances that are used and being widely developed to address unmet clinical needs in oncology, inflammation, and chronic pain. Proteins are dynamic structures susceptible to both aggregation and unfolding processes since the tertiary structure is primarily maintained by non-covalent interactions. Excipients are used in an effort to minimise deleterious processes leading to loss of biological activity. A key challenge is to formulate clinically used proteins so that they can be stored without loss of biological activity.Understanding protein–excipient interactions is important to optimise protein formulations. These interactions can change the surface properties of the proteins and may change their secondary and tertiary structure ,2, whichThe rational development of formulations derived from optimised interactions between proteins with specific excipients that effectively contribute to their stability and efficiency would be an important step-change in the field. Currently, there are attempts to use a combination of molecular docking and dynamics in selection of excipients to improve drug solubility ,4 or to Molecular docking has been extensively used in drug discovery both for the design of new drugs tailored to specific targets and to understand the interactions with known molecules that provide a scaffold for new, improved design. Still, the potential of molecular docking techniques for formulation design and optimisation has not been utilised to the same extent.In this study, we focus on the use of molecular docking techniques to develop a computational approach that will enable informed excipient selection by identifying hotspots for their interaction on the protein surface. The novelty of this approach lies in the attempt to identify interaction surfaces where an excipient may form favourable interactions with a protein. Excipients whose predicted interaction surfaces coincide with protein–protein interaction surfaces potentially responsible for protein aggregation may be good candidates for inclusion in protein formulation. The identification of protein–excipient interaction surfaces is carried out without prior knowledge of binding sites in contrast to well established docking methods used in structure-based drug design where the ligand binding sites are known. Our approach fully considers the three-dimensional structure of a protein and provides a time-efficient approach suited to be used in early formulation stages of protein therapeutics development regardless of protein class. The development of this approach has the potential to increase our ability to rationally design protein formulations during early preclinical development.Drosophila Su(dx) protein (WW34), was used to develop our computational approach to hotspot identification. Two different studies—one experimental and one computational—inspected the role of arginine (Arg) and glutamic acid (Glu) in this protein solubility. These studies were used as a reference for the development and validation of our approach.A well-studied protein, Drosophila Su(dx) protein (WW34) in the reduction of intermolecular protein–protein interactions has been studied experimentally by Golovanov et al. [L-amino acids Arg and Glu increased protein solubility and long-term stability without altering the ability to interact its target proteins or RNA. Furthermore, the simultaneous addition of these two amino acids to the buffer reduced aggregation of these proteins during concentration processes and protein degradation, resulting in increased formulation stability [Firstly, the effect of the combined addition of Arg and Glu to a solution of the WW domains 3 and 4 from v et al. . In thistability . These fet al. [23) and molecular dynamics. It was thought that interactions between the two amino acids (Arg and Glu) increase their presence on the protein surface, therefore reducing the number of protein–protein interactions. The synergistic effect of two amino acids used as excipients increased the number of Glu and Arg molecules close to the protein surface when compared to the individual addition of Arg or Glu only, and this synergistic effect was reported to be responsible for an increased solubility effect on WW34 [The synergistic effects for the combined use of arginine and glutamic acid to increase protein solubility was further described by Shukla et al.  using pr on WW34 . More re on WW34 , and thoWe report the development of a computational approach for excipient selection by identifying hotspots for their interaction on the protein surface. The hotspots, preferential binding surfaces for excipients, were identified using molecular docking. The use of this approach is further exemplified by prediction of Fab hotspots for commercially available excipients. The excipients were used as ligands and were selected from those used in commercial formulations of therapeutic proteins .in silico using molecular docking [Protein aggregation often involves non-covalent protein–protein interactions early in the process where nucleation, or the establishment of interacting multiple protein species, occurs. The process of aggregation occurs more readily in unfolded forms of a protein where there are more hydrophobic interactions possible than in the folded, active form of the protein. The tertiary structure of a folded protein is dynamic because it is reliant on non-covalent intramolecular interactions, so specific aggregation pathways are protein-dependent. Generally, the specific steps resulting in protein aggregation are poorly understood ,11, but  docking .Molecular docking is a methodology, which allows the study of the interactions between different molecules with many software packages available for this type of study. Knowledge of the molecular system is crucial for the decision about what type of software package to use. In general, it is possible to classify the software packages based on the molecular system that they are generally applied to. Often they can be either directed towards protein–protein (protein–DNA) interactions or small molecule–protein interactions. All docking software can perform either (i) rigid docking, where the conformation of the molecules is not altered during the docking process; or (ii) flexible docking, where the conformation of part or whole molecules change during the best-fit search.There are two stages in a molecular docking protocol: sampling and scoring . During G) into a sum of the weighted (ωi) free binding energies for each component  that contribute to the interaction (ΔGi) [Scoring functions of both protein–ligand and protein–protein docking software can be classified according to how the energy terms are derived . These con (ΔGi) . The weion (ΔGi) ,23. Finaon (ΔGi) . The eneon (ΔGi) .To demonstrate the potential of using docking to gain a better understanding of interactions involved in protein formulations, we applied protein–protein and protein–ligand docking on the WW34 protein with known interactions with two amino acids—Arg and Glu.et al. [et al. [23 < 0), the interaction with excipients is less favoured and the interaction between protein molecules is favoured to prevent excipient interactions with the protein surface. Increased protein–protein interaction and decreased protein–excipient interaction can often result in a decrease of protein solubility. Shukla et al. [et al. [23 = 8.8) and L-Glu (Γ23 = 3.6) was higher than for systems with only one of the excipients . This was due to the interactions between Arg and Glu, thus supporting that the synergistic effect of Arg and Glu enhanced the local concentration of the amino acids on the protein surface, leading to an increased solubility of WW34 [Shukla et al.  combinedet al.  with the [et al. . Brieflya et al.  performea et al. ,26. The  [et al. . The preIn this study, the interaction surfaces were determined using the molecular docking, and four different software packages were utilised for validation purposes. Two types of molecular docking were employed: the rigid docking to determine protein–protein interactions and flexible docking to evaluate protein–excipient interactions. For each category, two distinct software packages were used that generate ligand poses (excipient–protein docking) or different types of scoring functions (protein–protein docking) and evaluate the consistency of the observed poses. This provides complementary information that should ensure the identification of possible relevant interaction sites and aid the rational selection of excipients and the design of pharmaceutical formulations. The details of the scoring functions for each one of the software packages included in the study can be found in Most protein-small molecule docking software requires some information about the binding site for accurate results. Even though some packages allow for expansion of the area in the protein to be considered, usually by expanding a grid or box, this leads to a loss of definition. The larger the area, the less points within the box will be considered, thus potentially compromising the results. Software packages where the whole protein must be screened for potential sites of interaction with excipients would not suit the aim of this study. To develop the docking method, two different protein-small molecules software packages were initially used: GLUE  and iGemThe two software packages selected both allow whole protein docking without requiring information on the binding site. This is crucial in this approach to rationalise screenings of excipients of different classes that might be needed to stabilise a given protein. This means that there will likely be more than one hotspot (binding site) for different classes of excipients. For this study, the interactions of arginine with the target protein were evaluated and compared with previously published data .The results obtained with the GLUE software package identified two preferential sites for arginine interaction, while iGemDock solutions were all in the same site . NonetheThe residues that interact with arginine are the same as previously reported ,8. The oet al. showed that arginine molecules can form hydrogen bonds with residues ASP41 and GLU40 [Previous studies by Shuka nd GLU40 . The intTwo molecules of WW34 were docked against each other to identify the potential regions and residues that could be involved in interactions between two protein molecules and could be responsible for protein aggregation. This would allow us to evaluate if a protein–protein interface is the same as the arginine interaction site on the protein surface identified with protein–ligand docking . TherefoA number of different computational approaches have been developed to identify protein regions responsible for aggregation. However, most of these methods utilise available experimental data and bioinformatics tools to identify sequences of aggregation-prone regions (APR) and do not take protein tertiary structure into account . MoleculWe chose to utilise rigid protein–protein docking software, as it is fast and based on Fourier transform algorithms, minimising the computational time required for the identification of surfaces that could be responsible for aggregation. For validation purposes, two independent rigid docking software packages were used: Hex and GRAMMX. The main difference between these is how the surfaces of the receptor and ligand are defined and the approximations used for electrostatic contribution.et al. [These two software packages use different scoring functions to rank the results found; therefore, it would be expected that the orientation of the molecules given by HEX and GRAMMX would be different, which can be observed in the lowest energy solutions for the two different packages . Howeveret al. . The autMolecular docking software packages are designed to find and rank interactions between two molecules. This means that we will always obtain solutions. The energy values obtained are crucial to judge the reliability of the solutions obtained. For complexes of large molecules  or for detailed analysis of defined and known binding sites, differences in energy values can discriminate the docking interactions. However, when docking small molecules, the differences in energy values between solutions or even in its absolute value can be low and therefore not enough to confidently judge on an interaction. Molecular dynamics simulations can aid the understanding of protein–excipient interactions for systems that are well defined, as demonstrated with 50-ns simulations of bovine serum albumin in the presence of two excipients .To evaluate the dynamic behaviour of the identified interactions and to verify that the excipient will preserve interactions observed in binding poses identified by the docking protocol, further validation of the hotspots was conducted. The molecular dynamics simulation was performed using one of the solutions obtained from GLUE, WW24, and two arginine molecules as a starting point. The MD simulation was carried out on a fully solvated system using explicit solvent at 300 K and 1.03 bar under constant number of particles, pressure, and temperature (NPT) conditions. Although we did not evaluate the binding energy during the simulations, this allowed us to observe the interaction between arginine and the protein residues over the period of the simulation. From the trajectory analysis, it was possible to observe that whilst both WW34 and arginine change conformation, the interaction with the key identified residues, GLU40 and ASP41, is maintained throughout the simulation , namely The method was then applied in an effort to determine the hotspots for interactions between Fab A33 . Fabs caThe excipients selected have all been approved by FDA for parental use. As a starting point, we utilised those that are commonly used in commercial formulations. Most of these excipients have a stabilising role in protein formulations. Representative excipients from different classes were chosen: two amino acids, two saccharides, two sugar alcohol isomers, and two surfactants. For the docking experiments, the following excipients were used as ligands: arginine; glycine; sucrose; trehalose; manitol; sorbitol; polysorbate 20; polysorbate 80.GLUE software was used to establish the preferential spots of interaction between ligands and A33. The ligands and there interaction with A33 were firstly compared and analysed based on their structure and/or function similarity.Arginine is frequently used in parentally administered formulations as a viscosity reducer . HoweverArginine was found to interact in three different spots in the protein and five different solutions were retrieved . On the Trehalose and sucrose are non-reducing disaccharides that are commonly used for the freeze-drying of therapeutic proteins for parenteral administration, namely monoclonal antibodies ,42,43,44Mannitol and sorbitol are monosaccharide-derived polyols that are commonly used as pharmaceutical excipients . More relevant to this study, these are also used as carriers to improve cake homogeneity and the appearance of lyophilised products ,46,47. DPolysorbates are effective at preventing physical and chemical instability during processing and freeze-thaw cycles, even at very low concentrations, and they have relatively low toxicity .There were no differences between polysorbate 20 and 80 in term of number of solutions, interaction spots or binding affinity ; Table 4In addition to the above analysis where the number of docking solutions and spots of interactions were analysed for each excipient in the study, the three highest affinity solutions for each ligand were further inspected. The interaction framework of these molecules and the interaction spot on A33 was determined and plotted. This allowed for the determination of the A33 residues involved to evaluate the nature of the interactions in an effort to understand the differences observed in the binding energy values for each excipient. With all the information collected, it was possible to identify and define three hotspots for the interaction of A33 with the excipients ; Table 5Hex was used to perform protein–protein docking with Fab A33. This allowed further validation of the hotspots by evaluating if the residues that were involved in the interaction between two Fab A33 molecules were the same as the ones interacting with the excipients. The hotspots found for the protein–excipient interactions are located in regions prone to interactions between two A33 molecules. These interactions potentially lead to protein aggregation that can be prevented if the excipients are added to the protein.Tm) to further support the docking results. Protein Tm can be affected by a variety of factors and can be used to monitor unfolding and aggregation phenomena [Tm values [Thermal analysis was conducted with the Fab A33 in the presence and absence of excipients to quickly and qualitatively assess whether there was an effect of the presence of the excipients on the Fab melting temperature  were correlated with the melting temperatures . The corDrosophila Su(dx) (WW34) structure was taken from PDB 1TK7 [The structures of all excipients were drawn in Maestro 10.3  and a short minimisation protocol was performed using Macromodel 10.0 . The PDB 1TK7 . The 3D The docking of two protein molecules to determine interaction surfaces that could be responsible for the aggregation was performed using Hex; with its shape and electrostatic protocol, the scan steps were set to 0.75 with two substeps. The docking correlation order was set to 25 for steric scan and 25 for the fine search. The grid size was set to 1, and 100 solutions were recorded. Of these, the first structures of the first 10 clusters were saved as PDB files. The same experiment was performed using the web server GRAMM-X . The results were processed using Discovery Studio 4.0 . A 4-Å cutoff was allowed to identify residues involved in protein–protein interactions.α–Cα RMSD values [The Hex software package was used for the protein and nucleic acids interaction studies, though it also allows small protein–ligand interactions with a rigid ligand. Hex is a fast Fourier transform (FFT) docking correlation-based program; however, unlike previously developed programs, it uses soft polar Fourier correlations, minimising the computational time required to explore the Cartesian space ,52. The D values .GRAMM-X is also a fast Fourier transform (FFT)-based software that applies smoothed Lennard Jones potentials, refinement, and knowledge-based scoring. With this package, proteins are represented by a fine-grid softened by Lennard-Jones potential function calculated for a probe atom:otential . The minotential . To elimotential .Drosophila Su(dx) structure (1TK7) and homology model of A33 were set targets. The same structure was docked with iGemDock . The docking accuracy was set to the “Accurate Docking” setting by choosing a population size of 800 with 10 generations to produce 10 solutions. The docked ligand conformations were then re-ranked for the post-docking analysis. A 4-Å cutoff was allowed to identify residues involved in protein–protein interactions. A cutoff of 3.5 Å was used to build the interaction maps involved in the interaction with excipients.Molecular docking with GLUE was performed using GRID protocol. The three components of the GRID  protocol were used in these studies. GREATER  was used to generate the KOUT files for all molecules involved. GLUE  requires either KOUT or MOL2 files for the docking process. For this study, the PDB files were converted to KOUT files. For the docking process, with GLUE, all available probes were selected, and a maximum number of 100 binding sites were used with an energy cut off of −100 Kcal/mol. The maximum iteration value was set to 120. For ligand flexibility, 5 rotatable bonds were allowed, and electrostatics was included. Arginine and the selected commercially used excipients were used as ligands. The GRID is a calculation-based procedure that allows for the determination of energetically favourable binding sites on a molecule with a known structure. GRID has been used to study nucleic acids and individual molecules such as drugs or macromolecules (such as proteins) . GRID alEbind accounts for inter- and intra-molecular energies and an additional Epenal, and, in case the ligand is outside the search box, its fixed value is 10,000. The Epharma term is the sum of all hot-spot atoms of the interaction between ligand and protein:jw is the pharmacophore weight of the hot-spot atom j, i and j with the interaction type ijB forming by the pair-wise heavy atoms between ligands and proteins; ijB is either a hydrogen bond or a steric state; lig is the number of heavy atoms in the ligand; and hs is the number of hot-spot atoms in the receptor. The value of f is jw or 0. f is jw if the interaction type (ijB) equals the type of hotspots found on the target receptor. The ligand preferences include electrostatic  and hydrophilic characteristic . The Eligpre is a penalty value for a ligand, which is unable to satisfy the ligand preferences and is defined asWPelec and WPhb are the penalties for the electrostatic and hydrophilic preferences, respectively [The second docking package that was evaluated is iGemDock . This flectively . iGemDocectively . Its eneectively .Molecular dynamics simulations were performed with the top scores for 1TK7 and arginine from both GLUE and iGemDock. The simulations were performed with Desmond  within tMolecular dynamics methods calculate the forces between the atoms at each time-step, and from that these methods are designed to calculate the new positions and velocities of all of the atoms by applying classical laws of physics . Desmondw/v) for trehalose and sucrose, 2% (w/v) for glycine and arginine, 4% (w/v) for mannitol and sorbitol, and 0.4% (w/v) for Tween 20 and Tween 80. Avacta OPTIM 1000  was used to perform the stepped thermal unfolding study. The samples were ramped from 20 to 90 °C at 1 °C per step, with a temperature tolerance of 0.2 °C. Tm was determined as described by Chakroun et al. [Chemicals including trehalose, sucrose, glycine, arginine, mannitol, sorbitol, Tween 20, Tween 80, and sodium phosphate were purchased from Sigma-Aldrich . Excipients, sodium phosphate and purified Fab were prepared in stock solutions and mixed together to reach a final concentration of 1 mg/mL of Fab in 10 mM phosphate buffer at pH 7. The final concentration of excipient was variable: 5%  protein (WW34). The positive effect of the presence of excipients in the proximity of these hotspots was confirmed by molecular dynamics simulations, thereby validating this approach for predicting interaction interfaces. Furthermore, possible favourable interactions between Fab A33 and several excipients predicted by docking were confirmed qualitatively by Tm values from a stepped thermal study. The computational methods that provide information about the intermolecular interactions between excipients and proteins could play an important role in the design of therapeutic protein formulations during development. Molecular docking provides the means for the rapid identification of molecules that can form favourable interactions with protein surfaces, hence enabling a rational approach in the design of experimental formulation development and a more effective use of the small amounts of protein therapeutics that are usually available in early stage development.A combination of molecular docking methods has been successfully utilised to predict hotspots in protein excipient interfaces in a


While the genomic revolution has dramatically accelerated the discovery of disease-associated genes, the functional characterization of the corresponding proteins lags behind. Most proteins fulfill their tasks in complexes with other proteins, and analysis of protein–protein interactions (PPIs) can therefore provide insights into protein function. Several methods can be used to generate large-scale protein interaction networks. However, most of these approaches are not quantitative and therefore cannot reveal how perturbations affect the network. Here, we illustrate how a clever combination of quantitative mass spectrometry with different biochemical methods provides a rich toolkit to study different aspects of PPIs including topology, subunit stoichiometry, and dynamic behavior. Technologies like protein microarrays, phage display and the yeast two-hybrid system permit high-throughput screens for PPIs. However, these approaches rely on in vitro assays or heterologous biological systems. Therefore, it is not clear if PPIs detected by these methods occur in the relevant in vivo context.Proteins do not act in isolation but typically mediate their biological functions by interacting with other proteins . Owing tAffinity purification combined with mass spectrometry (AP-MS) has emerged as a particularly attractive method for PPI mapping . A majorWe argue that q-AP-MS is one of the most powerful technologies to map PPIs in health and disease. The aim of this Mini Review is to briefly explain the general principle of q-AP-MS and to emphasize the versatility of AP-MS to investigate various aspects of PPIs including quantities, topology, subunit stoichiometry, and dynamic behavior. We will begin with a brief introduction to quantitative shotgun proteomics.1). Second, in order to determine the amino acid sequence, the instruments selects individual peptides for fragmentation and records the resulting fragment spectra (MS2). Data generated in this manner is then compared to protein databases for peptide and protein identification  of proteins interacting with small molecules and peptides, it should be generally applicable to a range of ligands, including entire proteins, used as baits.Binding affinity is a particularly relevant quantity with regard to characterizing the interaction between two proteins. Typically, binding affinities are measured using methods such as ITC or SPR assays which require considerable quantities of purified proteins. q-AP-MS experiments can also be designed in a way to provide information about binding affinities : First, The examples described above show that a combination of quantitative shotgun proteomics with various biochemical methods can provide a rich toolkit to explore various aspects of PPIs. This can be employed to (i) identify binding partners with high specificity, (ii) assess the stoichiometry of complexes, (iii) provide information about interaction interfaces, (iv) analyze binding affinities, and (v) study dynamic changes of PPIs upon perturbation. Bearing in mind possible pitfalls , mass spThe 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.


Measuring protein structural similarity attempts to establish a relationship of equivalence between polymer structures based on their conformations. In several recent studies, researchers have explored protein-graph remodeling, instead of looking a minimum superimposition for pairwise proteins. When graphs are used to represent structured objects, the problem of measuring object similarity become one of computing the similarity between graphs. Graph theory provides an alternative perspective as well as efficiency. Once a protein graph has been created, its structural stability must be verified. Therefore, a criterion is needed to determine if a protein graph can be used for structural comparison. In this paper, we propose a measurement for protein graph remodeling based on graph entropy. We extend the concept of graph entropy to determine whether a graph is suitable for representing a protein. The experimental results suggest that when applied, graph entropy helps a conformational on protein graph modeling. Furthermore, it indirectly contributes to protein structural comparison if a protein graph is solid. Graph theory is now widely used in information theory, combinatorial optimization, structural biology, chemical molecule, and many other fields. Graph similarity measuring is a practical approach in various fields. When graphs are used to represent of structured objects, the problem of measuring similarities between objects becomes one of computing similarities between graphs . Proteinpolypeptide. By various synthesis, e.g., biosynthesis and chemical synthesis, a polypeptide is folded into a unique 3-dimensional structure. Usually, the structure of a protein determines its biological function performed in organisms. Knowledge of a protein structure can help us understand biological functions and evolution. Measuring protein similarities according to 3-dimensional structures of proteins provides a valuable tool for evaluating proteins with low sequence similarities when evolutionary relations among proteins cannot be detected by sequence alignment techniques. To perform a structural comparison of molecules, accurate information of two superimposed protein structures must be obtained. However, optimizing these two quantities simultaneously is difficult. Unlike the sequence alignment problem, the structural alignment problem has not even been classified as solvable.It is believed that proteins are important molecules for living organisms. In fact, they are essential parts of organisms and participate in almost every process within cells. A protein contains at least one linear chain of amino acid residues called a For decades, studies have attempted to define topological relations and notations on protein structures, a schematic description is essentially expected to describe its topology. Mathematical formulations of structural patterns can facilitate the composition in a polypeptide chain. A schematic description has the advantage of simplicity, making the implementation of graph theory as an alternative approach possible . By selee.g., helixes, sheets) and forces , folding into its specific characteristic and functional structure. With the help of graph transformation, folded polypeptide chains can be represented as a graph using several mapping rules. Proteins contain complex relationships in its polymer: residual reactions, covalent interactions, peptide bonding, and hydrophobic packing are essential parts in structural determination. The intention is to transform a protein structure into a graph. Formally, a graph transforming system consists of a set of graph rewrite rules: L → R, which L is called pattern graph and R is called replacement graph  and Y λ= . The similarity between X Gand Y Gcan be simply measured by the Euclidean distance of X λand Y λ. In this case, the similarity of X Gand Y Gis 1.414.Obviously, |1HDM, 1K5N, 2ENG, 1VCA, 1ZXQ, 1UXW, 1A2Y, 3ARD, 2Q3Z, and 2CRY. MHC, as an immune system in most vertebrates, encodes for a small complex cell surface protein. It is also known for HLA (Human Leukocyte Antigen), one of the most intensively studied genes in human [In this experiment, we validated the remodeling function of the P-graph by using extended graph entropy to verify the stability of a given P-graph. For the P-graph construction, please refer to . Thus, win human . Due to in human ; this isG =  be the P-graph after remodeling from the construction proposed by [V in G are created according to the DSSP. Under this metric, a protein secondary structure is represented by a single letter code, H-helix , T-hydrogen turn , or C-coiled (containing only C). For controlling one variable in this experiment, let the edge set E of G be changed from a specific range.Let posed by . VerticePID is the protein identification number in PDB [Domain. Furthermore, Dens means the density in the graph. It is defined as AVG indicates the average distance within DSSP vertices. If the distance of i vand j vis no greater than AVG, then there is an edge between them. In the table, +ke (−ke) means that we add (remove) the k shortest (longest) possible edges. For example, +1e means that we add the edge with the shortest length that is greater than AVG. In the table, NaC indicates that the resulting graph is not a connected graph.A comparison of MHC proteins is shown in Table r in PDB . Since ME| and I′(G) is as follows. First, when the density in G increases, the graph G appears to go from sparse to dense. However, its extended entropy does not increase completely with its density. It seems a little anomalous in this appearance. Second, the edge set in protein remodeling issue can be determined from its extended entropy. By definition, the P-graph G should be a connected graph. Once the G becomes a disconnected graph, we cannot decide its entropy. For example, 1A2Y is not a connected graph when the density is lower than 0.400. Third, E appears to be considerably related to V in graph entropy. Consider the P-graph 2CRY as another example. If a protein remodeling function adapts a specific value on the basis of its geometrical edge, then it might be an error to assume a fixed value as a criterion. This is an essential fact to stress. It is worth pointing out that the construction of a P-graph is limited by V .The relationship between |e.g., structure, topology, connectivity [Old shows a remodeling based on the specific value of edge length, and New indicates that the edges in G are adjusted by extended entropy. The value in each entry is the distance of the two spectra. If our method obtains a better result in the comparison, then we simply mark "+" to denote a better result; otherwise, it is marked "=" (not bad) or "−" (worse). Table To validate the previous assumptions, a method for protein structural comparison is adapted to measure its similarity. Graph spectra gives an alternative solution to graph matching. It is a set of relational parameters, consisting of a characteristic polynomial and eigenvectors of its adjacency matrix or Laplacian matrix. Graph spectra quantitatively provide graph information, ectivity . In TablThe procedure for computing the extended entropy for a P-graph was implemented and has been tested with the MHC PDB dataset. The environment was running under 2 Ghz PC with 512 MB of main memory with Linux-2.6.11-1.1369. The implementation was written using Bash-3.00.16(1) and Octave-3.0.0.In this paper, we proposed a measurement to determine graph stability for protein structure remodeling based on graph entropy. Our modified entropy validation shows a positive result for protein structural comparison. This graph-based approach offers a practical concept to support protein structural alignment.The authors declare that they have no competing interests.


The long-held principle that functionally important proteins evolve slowly has recently been challenged by studies in mice and yeast showing that the severity of a protein knockout only weakly predicts that protein’s rate of evolution. However, the relevance of these studies to evolutionary changes within proteins is unknown, because amino acid substitutions, unlike knockouts, often only slightly perturb protein activity. To quantify the phenotypic effect of small biochemical perturbations, we developed an approach to use computational systems biology models to measure the influence of individual reaction rate constants on network dynamics. We show that this dynamical influence is predictive of protein domain evolutionary rate within networks in vertebrates and yeast, even after controlling for expression level and breadth, network topology, and knockout effect. Thus, our results not only demonstrate the importance of protein domain function in determining evolutionary rate, but also the power of systems biology modeling to uncover unanticipated evolutionary forces. dynamical influence, which quantifies the importance of a protein or protein domain to the dynamics of the network of proteins in which it functions. Using 18 biochemically-detailed systems biology models, we compute dynamical influences for each protein domain in each model. We find that dynamical influence is indeed visible to natural selection and that within networks protein domains with higher dynamical influence evolve more slowly.Different proteins evolve at dramatically different rates. To understand this variation, it is necessary to determine which characteristics of proteins are visible to natural selection and how the strength of selection depends on those characteristics. One protein characteristic that is evidently visible to natural selection is expression level; more highly expressed proteins are subject to stronger purifying selection and evolve more slowly. Theory and intuition suggest another such characteristic should be some measure of functional importance, but studies of various measures of functional importance, such as knockout essentiality or knockout growth rate, have shown at best weak correlations with evolutionary rate. Here we develop a novel measure of functional importance,  Over evolutionary time, every protein accumulates amino acid changes at its own characteristic rate, which Zuckerkandl and Pauling likened to the ticking of a molecular clock . Remarkadynamical influence to protein domains for which amino acid substitutions were predicted to have respectively large or small effects on network dynamics. We hypothesized that network dynamics is a synthetic phenotype that is likely subject to natural selection. To test this hypothesis, we compared our predictions of dynamical influence in functionally and structurally conserved intracellular signaling and biosynthetic networks with genomic data on protein domain evolutionary rates in both vertebrates and yeast. We found that, within these networks, dynamical influence is as strongly correlated with evolutionary rate as many previously known correlates. Moreover, dynamical influence remains predictive when knockout phenotype, expression, and network topology are controlled for. Dynamical influence thus offers new insight into selective constraint within dynamical protein networks.We reasoned that knockouts do not mimic evolutionarily relevant mutations, which often have small or moderate effects . In partin silico experimentation [k [Biochemically-detailed systems biology models encapsulate vast amounts of molecular biology knowledge in a form that can be used for entation , 21. In ation [k . To asseD of a domain to be the geometric mean of the dynamical influences of the reaction rate constants for reactions in which it participates , and in 10 of 12 vertebrate networks and 6 of 6 yeast networks, we found a negative correlation. This is consistent with the expectation that most sequences and networks evolve primarily under purifying selection [Using this novel method, we determined protein domain dynamical influence and evolutionary rate for 18 conserved signaling and metabolic networks . We quanelection , in whicelection . Positivelection , 49, sugelection , 51, potThe strength of the correlation between dynamical influence and protein domain evolutionary rate varies considerably among networks  Tables. Because selection may act differently on networks with different functions, we considered a random-effects meta-analysis. Thus the sampled networks were assumed to represent a population of networks, among which the correlation between domain evolutionary rate and dynamical influence varies. The meta-analysis seeks to estimate the distribution of those correlations. We applied the random-effects meta-analysis method of Hunter and Schmidt , becauseAs a complementary approach to evaluating the relationship between domain evolutionary rate and dynamical influence, we also performed a permutation test for dependence between them. In this test, we compared the estimated mean correlation from the real data with a null distribution of mean correlations calculated from scrambled data . This teWe measured dynamical influence using hand-built systems biology models; what effect do uncertainties in these models have on our analysis? To be agnostic about what aspects of network dynamics are critical to fitness, in calculating dynamical influence we summed over the integrated sensitivities of all molecular species in the network. It is, however, often evident that the builders of each model had specific molecular species in which they were most interested. If we restricted our dynamical influence calculation to those species , we founk [Given a network model, substantial uncertainty can exist about the values of the rate constants k , becausek , 58. To k , 60, butk  that werk . This enk , 62 suggIn addition to rate constant uncertainty, different modelers may also make different assumptions when studying the same network regarding forms of interaction, which molecular players to include, or which conditions to consider. We assessed the effect of these assumptions using the models in our data which consider overlapping protein domains. The rank correlation between dynamical influences calculated for the same domains using different models varied considerably and was stronger for pairs of models with larger numbers of overlapping domains . WeightiWe defined dynamical influence in terms of differential perturbations to reaction rate constants , but mutDynamical influence captures the phenotypic effect within dynamical networks of small perturbations to protein domain activity, but how does it relate to factors previously linked to evolutionary rate? In many cases, previously known factors were discovered and validated using genome-wide analyses. The set of protein sequences for which dynamical influence can be calculated is smaller and potentially biased. First, dynamical influence considers effects on network dynamics after stimulus, so it is not applicable to proteins that do not respond to any stimuli. Second, we calculated dynamical influence from mathematical models, and such models exist for only some systems. Lastly, we calculated dynamical influence at the domain level, so we did not consider the evolution of linker sequences between domains. Because the previously known factors we consider are defined at the whole-protein level, they can never fully explain evolutionary rate at the domain level. Nevertheless, we used correlation analysis to understand how dynamical influence compares with previously known predictors of evolutionary rate, for the set of networks and protein domains represented in our study.In multicellular organisms, proteins that are expressed in more cell types  evolve more slowly , and thiExpanding from expression breadth, the strongest known correlate with protein evolutionary rate is expression level. Proteins with greater expression evolve more slowly in both yeast  and vertA significant advantage of our approach is that it captures how molecular inputs are integrated into functional phenotypic outcomes that may be selected upon. One aspect of network biology that has been previously considered in determining protein evolution is topology. Specifically, proteins with more interaction partners   or more Expression level and network topology are also represented in the models themselves, by the total abundance of the molecular species that represent each protein and by the reactions that connect them. The correlations of dynamical influence and evolutionary rate with these model-derived quantities  are simiThese assessments of dynamical influence relative to known contributors to protein evolution clearly indicate that our approach has uncovered previously unappreciated constraints on protein evolution. Is this new insight sufficient to explain the conundrums raised by knockout experiments? In our data, we found that the correlation between evolutionary rate and knockout measures of function was so weak as to be nonsignificant , consistBut, evolutionary rates are complex and likely integrate selection on multiple processes –5. To asThe existence of overlapping protein domains might inflate statistical significance in our analyses across models. To account for this, for all domains that appeared in more than one model, we randomly kept each domain in one of the models and deleted it from the others. We did this randomization one thousand times and repeated our correlation analysis each time, obtaining distributions of mean correlations and permutation p-values. There was a tail of large p-values for the fully controlled correlation between dynamical influence and evolutionary rate, corresponding to randomizations in which many domains happened to be retained in the few models with a positive correlation . The medDynamical systems biology models offer great promise for developing and testing evolutionary hypotheses , 75. Preκi of reaction rate constant ki byyc(t) is the time course of molecular species y in condition c, evaluated using the rate constant values k* from the original publication. The derivative dyc(t)/dki of the time course with respect to rate constant ki measures how sensitive that molecular species or metabolite is to changes in that rate constant. To make relative comparisons, we normalized these sensitivities by the value ki of the rate constant and the maximum ymax of molecular species y over all stimulation conditions. We normalized by ymax rather than using a control coefficient ki by squaring these normalized sensitivities, integrating over the time course of each stimulation condition, and summing over all molecular species and stimulation conditions.We defined the dynamical influence y(t)dlnk  because Dd of protein domain d to be the geometric mean of the influences κ of the Nd reaction rate constants for reactions in which that domain participates:We defined the dynamical influence agnitude .Stochastic noise plays an important role in many cellular networks , 85. In κ for each reaction constant, we reviewed the literature to determine the protein domain or domains at which the reaction occurs, and we assigned those influences to that domain or domains (We downloaded systems biology models in Systems Biology Markup Language (SBML) format  from the domains .UniProt protein ID’s were acquired from the BioModels annotation in the SBML file for each model and converted to NCBI Protein IDs for vertebrates or open reading frame (ORF) numbers for yeast. Some models specified more than one Uniprot ID for a single protein, in cases where there is more than one transcript identified and both appear to perform the same function . Where more than one Uniprot ID was specified, we reviewed the model publication and the protein network literature to select a single transcript. In the case of metabolic flux models that track metabolites rather than proteins, we used the names of the enzymes involved in the reactions to find the appropriate protein identifier.Vertebrate homologous protein alignments were downloaded from the NCBI Homologene database , and forSa. cerevisiae protein in yeast. Evolutionary rates were calculated using codeML from PAML Version 4.4b [Protein domain annotation was done manually using literature review, based on information for the human protein in vertebrate models or the ion 4.4b , with onion 4.4b . The Mgehttp://bioGPS.org/downloads. The data consist of microarray probes for a number of tissue types, with each probe’s name including the corresponding gene name, which we mapped to Ensembl gene IDs using Ensembl BioMart [Vertebrate gene expression and tissue specificity data was compiled from the mouse GNF1M dataset , downloa BioMart . We rest BioMart . To calchttp://younglab.wi.mit.edu/pub/data/orf_transcriptome.txt and used without modification.Yeast expression data  was obtaProtein abundance within each model was calculated as the sum of initial conditions for all molecular species corresponding to a given protein, including modified forms and complexes. None of the models we considered included transcription or translation, so total levels of all proteins were constant throughout the simulations.http://www.informatics.jax.org/phenotypes.shtml on July 11, 2011. We assembled phenotype information for homozygous knockouts and coded a gene as essential if it resulted in one of the following phenotypes: abnormal reproductive system physiology, prenatal lethality, perinatal lethality, postnatal lethality, premature death, abnormal reproductive system morphology, lethality at weaning, preweaning lethality, partial lethality, and all sub-phenotypes of these phenotypes. If homozygous knockout of a gene did not cause one or more of these phenotypes we coded it as non-essential. To validate our parsing of this data, we compared against the results of [We downloaded mouse knockout phenotype data from the Mouse Genome Informatics database  at http:sults of .Regression_Tc1_hom.txt downloaded from the Stanford YDPM database http://www-deletion.stanford.edu/YDPM/YDPM_index.html on March 13, 2013.Data for yeast knockout growth rate on YPD media were obtained from the file We downloaded protein-protein interaction data for both humans and yeast from the Interologous Interaction Database  on AprilDynamical influence and evolutionary rate are defined at the domain level, but all other factors in ρ0 in the population is estimated by the average of the observed correlations r of the sampled models, weighted by the sample size n of domains with relevant data in each model. So the estimated mean correlation isσρ of the population correlations sets the width of the distribution curves in σρ and substituting in the estimates K, which is 18 here . The vamated as σ^r2=∑ni. 3.5 in )σ^e2=∑n. 5.1 in ):SEClick here for additional data file.S1 Tableρ) and rank biserial (rb) correlation coefficients for variables evolutionary rate dN/dS (ω), dynamical influence (D), expression breadth (B), expression level (X), interaction degree (d), interaction betweenness centrality (C), and knock-out essentiality (E). Domains with missing values for any correlate were dropped prior to calculating correlations, and N represents the number of domains used in the analysis. For correlations, p-values were calculated via permutation of the data as described in the main text. For partial correlations, p-values were calculated by permutation of the residuals from the linear models [Spearman rank Click here for additional data file.S2 TableGr) data.As in (PDF)Click here for additional data file.S3 TableFor each pair of models with at least four overlapping domains, shown is the Spearman rank correlation and number of overlapping domains.(PDF)Click here for additional data file.S4 Tablek+ and k− being ±25% perturbations of each parameter. Tabulated are the rank correlations between domain influences calculated using finite and differential perturbations. The almost perfect correlations suggest that our dynamical influence analysis also applies to mutations of moderate effect.Dynamical influence was recalculated using finite perturbations ons dydk , with k+(PDF)Click here for additional data file.S5 Tableρ) correlation coefficients for variables evolutionary rate dN/dS (ω), dynamical influence (D), model-derived expression (MX), model-derived interaction degree (Md), model-derived interaction centrality (MC), expression breadth (B), knock-out essentiality (E), and knock-out growth rate (Gr).Meta-analysis confidence intervals and permutation p-values as in (PDF)Click here for additional data file.S6 TableMeta-analysis mean correlations and permutation p-values as in (PDF)Click here for additional data file.S1 FigShown are the full distributions that are summarized in the last row of (PDF)Click here for additional data file.S2 FigA: Vertebrates. B: Yeasts. In both, branch lengths represent amino acid divergence.(PDF)Click here for additional data file.S1 DatasetEach model corresponds to two sheets. The first sheet contains the reaction parameters, their dynamical influences, and the reactions they correspond to. The second sheet contains the protein and domain data, including assignment of reactions to domains and corresponding references (as PubMed IDs).(XLSX)Click here for additional data file.


The mammalian 20S proteasome is a heterodimeric cylindrical complex (α7β7β7α7), composed of four rings each composed of seven different α or β subunits with broad proteolytic activity. We review the mammalian proteins shown to directly interact with specific 20S proteasomal subunits and those subjected to ubiquitin-independent proteasomal degradation (UIPD). The published reports of proteins that interact with specific proteasomal subunits, and others found on interactome databases and those that are degraded by a UIPD mechanism, overlap by only a few protein members. Therefore, systematic studies of the specificity of the interactions, the elucidation of the protein regions implicated in the interactions (that may or may not be followed by degradation) and competition experiments between proteins known to interact with the same proteasomal subunit, are needed. Those studies should provide a coherent picture of the molecular mechanisms governing the interactions of cellular proteins with proteasomal subunits, and their relevance to cell proteostasis and cell functioning. The proteasome is a 2.5 MDa complex formed by a proteolytic core particle , and is a cylindrical shaped complex with a heterodimeric structure (α7β7β7α7 subunits). Attached to both bases of the cylinder is a regulatory particle 19S, RP) that consists of a horseshoe-like complex composed of a base and a lid. There are two copies of each of the three catalytic β subunits  in the CP and their active sites are located inside the catalytic chamber formed by the contiguous β rings [9S, RP thThe consensus mechanism of protein degradation by the 26S proteasome states that ubiquitin must be attached to the protein so that it can be tagged for degradation. The process starts with the recognition of the poly-ubiquitylated protein which is carried out by the base of the RP which leads to de-ubiquitylation (the lid of the RP), the unfolding of the protein (the base), and translocation into the catalytic chamber of the CP for proteolysis . HoweverOur aim is to provide a critical assessment of the research carried out in this area, by analyzing the specific interactions of mammalian cellular proteins with specific 20S (CP) proteasomal subunits. This will enable identification of the set of proteins that interact with the proteasome, and a comparison of these proteins with the set of proteins degraded by the UIPD mechanism [We performed several general  or specific (using the acronym of each subunit) searches into the published literature to identify proteins interacting with specific CP subunits. Although we endeavoured to carry out as comprehensive a survey as possible, it may be possible that some papers have been overlooked, which we recognize poses some limitations to this work. A succinct description of those protein interacting partners of specific CP subunits, and the consequences of those interactions is given below.The PSMA2 subunit of the 20S proteasome complex has been shown to directly interact with IκBα through its arm-repeats  likely mThe PSMA4 subunit interacts with amino acids 40 to 60 of Hepatitis C virus F protein and promotes its UIPD .The PSMA7 subunit has been reported to be one of the α-subunits that interacts with the REGα/β PA28 α/β) proteasomal activator as shown by yeast two-hybrid experiments, and the inhibition of proteasomal activation by the hepatitis B virus X protein-derived polypeptide, which binds directly to the PSMA7 subunit . PSMA7 C α/β protPSMA3 is known to form double ring heptameric structures (540 kDa) when expressed as a recombinant protein in bacteria . PSMA3 iin vitro by the proteasome [In vitro studies have also shown that PSMA3 interacts with splicing factors and other proteins involved in RNA metabolism [Egr-1  and aurooteasome . In vitrtabolism . Finallytabolism .PSMB6 has been shown to interact with Plasminogen Activator Inhibitor-2 (PAI-2) and this interaction may mediate its anti-apoptotic role . PSMB6 hThe intracellular domain of TrkA has been reported to interact with several proteins, including the PSMB1 proteasomal subunit, while TrkB and TrkC do not. This TRkA interaction results in the phosphorylation of PSMB1, although with unknown consequences in proteasomal function .HTLV-I Tax has been shown to interact with PSMB4 and may contribute to the targeting of either p105 or p65, and IκBα to the proteasome for processing or degradation, respectively . Smad1 iThe subunit specific interactions described above are schematically summarized in The list of cellular proteins whose degradation does not necessitate prior ubiquitylation is increasing. The proteins that have already been described ,37 incluFurther proteins have been added to the list of those being degraded by a UIPD mechanism since the last extensive revision . The newProteins implicated in DNA and chromatin structure degraded by UIPD include BAF57, a component of the mammalian SWI/SNF chromatin remodelling complex , and TopProteins implicated in cell cycle control and apoptosis that have been reported to be degraded by UIPD include: Aurora-A mediated by Az and regulated by binding to AURKAIP1 , Daxx whViral proteins degraded by UIPD include: HBX which probably regulates gene transcription , human cOther proteins with diverse cellular functions reported to be degraded by UIPD include: processing of the N-terminus of LC3 an ubiquitin-like protein that plays an essential role in autophagy , connexiin vitro degradation of tau [We have also added to this list those proteins that become substrates for UIPD via their interaction with proteasomal activators, mainly REGγ (PA28γ) and PA200/Blm10. We have already described that REGγ (PA28γ) can present p21, but it can also present other cell cycle regulators such as p16 (INK4A) and p19 (Arf) to proteasomes for degradation  and SRC-n of tau  which isn of tau . More ren of tau .The proteins shown to be degraded by a UIPD mechanism are summarized in Proteins shown to interact directly with the different mammalian 20S proteasomal subunits and which do not belong to the RP complex are summarized in Archea [Archea proteasome, formed by only one (or two) type(s) of α and β subunits, was protein degradation, this would suggest the existence of an archaic macromolecular complex with hidden catalytic sites which would offer two types of subunits for interactions with “thousands” of protein substrates for degradation or other functional consequences. What is the molecular basis of those interactions? Certainly, our knowledge is very limited at present. Duplication of the α and β genes, and diversification of their sequences has occurred during evolution [Proteasome-like structures are as old in evolutionary terms, as C-terminal diglycine ubiquitin or ubiquitin-like molecules, and are unevenly distributed in the different kingdoms including Archea . If the volution . In ordeTo validate a direct interaction between a specific proteasomal subunit and a specific cellular protein, most of the published research relies on two-hybrid studies and affinity-capture followed by mass spectrometry. Another experimental approach is the immunoblotting of pull-down experiments of crude cellular extracts, cell-free translation products or purified recombinant proteins using antibodies or recombinant proteins. Specificity, when analyzed, is defined by the use of proteasomal subunits that do not interact with the protein under study, or with a structural modification of the proteasomal subunit and/or the corresponding interacting protein partner. Those structure variants allow the determination of which regions of both partners are involved in the interaction. Alternatively, some groups have used cells expressing tagged proteasomal subunits to explore the binding of endogenous, or transfected tagged, cellular protein partners by co-immunoprecipitation or binding to affinity-resins.in vitro experiments reveals that the reported interactions that either cannot be reproduced using the entire proteasomal complex  or, where the interaction cannot be demonstrated in cell lysates, are irrelevant. This criticism would be correct for those interacting protein partners that are not directly degraded by the proteasome, but would not be sustainable for those that are degraded by the proteasome. In this case, the productive interaction ends in an enzymatic reaction. Accordingly, it may be difficult to isolate the CP-interacting protein intermediate of the reaction even at lower temperatures, or in the presence of proteasome inhibitors. The binding energy may contribute to lowering the energy barrier of peptide bond hydrolysis by an induced conformational change of the proteasome and/or the active sites. The alternative to validate the reported interaction would be through kinetic competition experiments, using specific inhibition of the degradation of the interacting protein by constructs of the specific proteasomal subunit in vitro or by transfection in cells, provided that it is demonstrated that the proteasome complex structure is not affected. Furthermore, competition experiments using proteins that are reported to bind to the same  proteasomal subunits would be very helpful to understand the relative kinetic constants and the strength of the interactions between the different proteins.A critical analysis of the in vivo of a specific interaction found in vitro would be to demonstrate the competition between two protein partners that bind to the same proteasomal subunit.The same experimental approach could also be applied to cell studies albeit with some obvious limitations. One clear limitation to the analysis of the interactions with proteasome subunits in the cell would be that the amount of free and unassembled 20S proteasomal subunits is likely to be very low, with the possible exception of tumor cells where they are overproduced and degraded ,73. CompThe number of proteins which participate in many cellular functions , describin toto  or in part  of its sequence, a so-called unstructured region [There have been some attempts to determine the minimal requirements of a protein substrate to be degraded by a UIPD mechanism ,79,80, bd region ,82. EnerFinally, another criticism commonly made in the reports of the UIPD of a protein substrate is that the 19S complex or proteasomal activators have to participate in this process. This is because the α-ring channel of the CP is too narrow and has to be opened to allow the transit of the extended protein into the catalytic chamber where it will be degraded .in vitro and in vivo. The degradation of cellular proteins by a UIPD mechanism implies the binding, either directly or indirectly (via another protein interacting with the proteasome) of those proteins to proteasomal subunits. It is also time to determine the proteasomal subunits responsible for this UIPD mechanism. Again, specificity, the protein regions implicated, and competition experiments will contribute to a better understanding of the mechanism of their proteasomal degradation. Both approaches could combine to form a coherent picture of the relevance of the cellular protein interactions with the proteasome and proteasomal function (including the UIDP mechanism) in cell proteostasis and cell function. Further investigations of the molecular basis of the UIDP and UDPD mechanism of the same proteins, where applicable, and those that are only degraded by a UDPD mechanism, would provide a relational and hierarchical view of the proteasome pathway, as the main proteostatic mechanism of protein degradation in the cell.Proteasome subunits have been shown to interact with many cellular proteins; we have only described those, reported in specific published research papers, which interact with mammalian CP subunits. It is time to start to qualify the specificity, the regions of both partners implicated in the process, and to quantify, using competition experiments, the relative strength of the interactions of those proteins, both