Here, we present the crystal structure of an N-terminal fragment of Saccharomyces cerevisiae Hsp104 with the N domain of one molecule bound to the C-terminal helix of the neighboring D1 domain. Consistent with mimicking substrate interaction, mutating the putative substrate-binding site in a constitutively active Hsp104 variant impairs the recovery of functional protein from aggregates. We find that the observed substrate-binding defect can be rescued by Hsp70/40 chaperones, providing a molecular explanation as to why the N domain is dispensable for protein disaggregation when Hsp70/40 is present, yet essential for the dissolution of Hsp104-specific substrates, such as yeast prions, which likely depends on a direct N domain interaction.Hsp104 is a ring-forming protein disaggregase that rescues stress-damaged proteins from an aggregated state. To facilitate protein disaggregation, Hsp104 cooperates with Hsp70 and Hsp40 chaperones (Hsp70/40) to form a bi-chaperone system. How Hsp104 recognizes its substrates, particularly the importance of the N domain, remains poorly understood and multiple, seemingly conflicting mechanisms have been proposed. Although the N domain is dispensable for protein disaggregation, it is sensitive to point mutations that abolish the function of the bacterial Hsp104 homolog  However, the majority of stress-inducible molecular chaperones does not recognize or remodel protein aggregates. Instead, cells have evolved powerful ATP-driven protein disaggregases that have the remarkable ability to rescue stress-damaged proteins from an aggregated state4.The capacity of cells to tolerate a variety of stress conditions is essential for organismal health. To protect cells from damage, molecular chaperones assist in the folding of other proteins, thereby providing the first line of defense against protein misfolding and aggregation5. Hsp104 chaperones are multi-domain proteins consisting of an N domain, an M domain, and two AAA+ type nucleotide-binding domains, termed D1 and D2, which can be subdivided into a larger α/β domain (D1- and D2-large) and a smaller α-helical domain  , plants (Hsp101), and eubacteria (ClpB), but are absent in animal cellsll) Fig. . The M d16, and extracts polypeptides that are then threaded through the Hsp104 hexamer by pore loops in the D1 and D2 domains18, thereby coupling the energy of ATP binding and hydrolysis to drive protein unfolding. Although Hsp104 is a functional ATPase, efficient protein disaggregation depends on the functional cooperation and physical interaction with the Hsp70 system (Hsp70/40), which directs the Hsp104 hexamer to amorphous aggregates and yeast prions in vivo19, activates the Hsp104 motor in a species-specific manner21, and assists in the recovery of Hsp104 substrates that depend on Hsp70/40 for folding22. Although the Hsp104:Hsp70/40 bi-chaperone system represents the functionally active form in vivo, the Hsp104 protein-remodeling activity can also be elicited in vitro in the absence of Hsp70/40 by mutation or under chemically defined nucleotide conditions that asymmetrically decelerate the ATPase activity of Hsp10423. Additionally, modification of the M domain by point mutation25 or by insertion of T4 lysozyme within the M domain helix 2 (Hsp104T4L)11 renders Hsp104 constitutively active for protein disaggregation and independent of Hsp70/40 chaperones in a substrate-dependent manner.It is now widely accepted that Hsp104 recognizes amorphous and ordered protein aggregates27 by mediating substrate interaction31, it was later shown that the N domain is dispensable for protein disaggregation in vitro33 and thermotolerance development in vivo34. Although the N domain appears to be dispensable for protein disaggregation, it was shown more recently that the N domain orientation can affect protein disaggregation35 and abolishes ClpB-dependent protein disaggregation when mutated36. Consistent with an important but non-essential role in eubacteria, the N domain of Saccharomyces cerevisiae Hsp104 is dispensable for thermotolerance development and yeast prion replication37, as well as for protein binding and recovery in vitro and in vivo38. However, the Hsp104 N domain enhances protein disaggregation in vitro7, mediates the interaction with the prion form of Sup35p15, and is essential for yeast prion dissolution16 and curing by Hsp104 overexpression37.Despite major advances in our mechanistic understanding of Hsp104 function, it remains poorly understood how Hsp104 recognizes and binds protein substrates of diverse sequence, shape, and size. In this context, the importance of the N domain to Hsp104 function is perhaps most controversial. Initially proposed to be essential for ClpB functionS. cerevisiae Hsp104 N-terminal fragment (residues 1–360) consisting of the N domain and an extended D1-large domain. We find that the N domain is bound to a C-terminal helical segment of the neighboring D1 domain, mimicking substrate interaction. We show by structure-guided mutagenesis that the pseudo two-fold symmetrical N domain features a bipartite peptide-binding interface, involving the first helix of each N domain repeat (helix A1 and A5). Hsp104 variants featuring point mutations in either interface are defective in protein disaggregation. While helix A5 mutants are impaired in Hsp70 interaction, mutations in helix A1 are defective in substrate binding and can be rescued by Hsp70/40 chaperones. We propose that the N domain plays an important role in protein disaggregation by facilitating both substrate binding and Hsp70 interaction to accommodate a variety of Hsp104 substrates.Delineating the functional role of the non-essential N domain is difficult because of the close physical and synergistic interaction between Hsp104 and the cognate Hsp70 chaperone system. Like Hsp104, Hsp70 is a functional ATPase that chaperones misfolded and aggregation-prone proteins. Because Hsp70 binding to the M domain is essential to unleash the protein disaggregating activity of Hsp104, Hsp104 mutants that abolish protein disaggregation could be impaired in substrate interaction, in Hsp70 cooperation, or both. Here, we present the crystal structure of an ND1) comprising the N domain and an extended D1-large domain featuring the first α helix of the D1-small domain, termed the C1 helix (residues 343–351)39 39 as a search model, even when performing the analysis with the individual N and D1 domains. To solve the phase problem, we crystallized an engineered Hsp104ND1 construct featuring three methionine substitutions at Leu36, Phe118, and Leu248 (Hsp104MMM), and determined the crystal structure of this variant by seleno-methionine (SeMet) MAD phasing 39 Fig. . Despite)39 Fig. , no strung Table . No unacS. cerevisiae Hsp104 N domain9 and Chaetomium thermophilum Hsp104 D1-large domain10 that were recently reported, and superimpose pair-wise with an RMSD of only 0.60 ± 0.09Å2 (N domain) and 1.59 ± 0.12 Å2 (D1-large domain). The N domain can be divided into two repeats related by a pseudo two-fold symmetry. However, unlike bacterial ClpA/B proteins, the N domain of Hsp104 features a four amino acid insertion that extends the length of an intervening loop region (residues 40–49) conferring asymmetry to the yeast N domain structure , helix A5 , and additional hydrophobic residues (Phe7 and Pro88)  Fig. . NotablyV45 Fig. , but oveD of 14.0 ± 1.7 μM.To confirm that this Hsp104 N domain-peptide interaction is also observed in solution, we synthesized a peptide with its sequence derived from the C1 helix (GEVAEPSVRQTVAILRGLQ) and measured the binding interaction using biolayer interferometry Fig. . Our resin vivo37, an Hsp104 variant lacking the N domain (Hsp104ΔN) is functional and cooperates with Hsp70/40 in protein disaggregation in vitro , and Leu92, Leu96, and Phe110 (Set-2), mutated them to alanine either alone or in combination, and determined their effect on Hsp104 ATPase activity  or their double (Hsp104F7A/L15A) and triple combination (Set-1) are defective in recovering FFL activity in the presence of Hsp70/40, Hsp104 variants carrying one or more Set-2 mutation  are substantially impaired  and 43.5% reduced activity (Set-2T4L), respectively, in recovering heat-aggregated β-gal activity  and its Set-1N and Set-2N mutants, alone and in combination with I116A, then determined their protein stability by dye binding using differential scanning fluorimetry. The melting temperatures (Tm) of the quadruple Set-1N/I116A and Set-2N/I116A mutants are severely left shifted compared to Hsp104N, decreasing the apparent Tm by 37.1 °C and 28.6 °C, respectively  were generated by QuikChange site-directed mutagenesis. Corresponding Hsp104T4L mutants were generated by cassette mutagenesis. To generate AN-Hsp104, the Hsp104 N domain (M1-L164) was replaced with the equivalent N domain of E. coli ClpA (M1-E166) and cloned into pProEX HTb. Isolated N domain constructs consisting of the first 165 residues (Hsp104N) were PCR amplified and cloned into pProEX HTb.E. coli BL21-CodonPlus (DE3)-RIL cells . Protein expression was induced with 0.5 or 1 mM IPTG for 4 h hours at 37 °C (Hsp104ND1), for 2 h at 37 °C (ClpA), for 4 h at 30 °C (His6-Hsp104N and His6-Ydj1), 6 h at 25 °C , or for 16 h at 16 °C . SeMet-labeled Hsp104MMM was prepared by transforming an E. coli methionine auxotroph strain, B834 (DE3) pLysS. Cells were grown in defined medium supplemented with 50 mg/L seleno-DL-methionine. Preparation of SeMet-labeled Hsp104MMM was otherwise identical to Hsp104ND149. Cells were lysed using a microfluidizer in buffer A  containing 30 mM imidazole followed by ultracentrifugation at 45,000 rpm for 1 h at 4 °C. Proteins were purified from cleared lysates by Ni-NTA agarose affinity chromatography. Proteins were eluted in buffer A containing 300 mM imidazole. The N-terminal His6-tag was cleaved by incubating the protein with His6-TEV protease at 4 °C overnight. The cleaved protein was diluted 1:10 with buffer A and reapplied to a Ni-NTA agarose column to remove the liberated His6-tag and His6-tagged TEV. Protein concentration was measured by a colorimetric assay using the Protein Assay Dye Reagent . Hsp104ND1/Hsp104MMM was further purified in negative binding mode to a DEAE sepharose column followed by cation exchange chromatography. Hsp104N and its variants were further purified by anion exchange chromatography. AN-Hsp104 and Hsp104T4L variants were purified similarly to Hsp104 in buffer A containing 10% glycerol using Ni-NTA agarose followed by size-exclusion chromatography on a Superdex 200 10/300 GL column . His6-Ydj1 and His6-Hsp70 were purified by Ni-NTA affinity chromatography as described21. His6-Ydj1 was further purified on a TOYOPEARL Butyl-650S column  followed by anion exchange chromatography. His6-hHsp70 was further purified by anion exchange chromatography.Protein constructs were overexpressed in 49. Crystals were harvested into reservoir solution and flash frozen in liquid nitrogen without additional cryo-protectant. Crystals belonged to space group P6522 with unit cell dimensions of a = b = 179.1 Å, c = 69.7 Å, and one molecule per asymmetric unit. Crystals of SeMet-labeled Hsp104MMM protein were grown at 4 °C by the hanging-drop vapor diffusion method by mixing 2 µl of protein (20 mg/ml) in 50 mM Tris-HCl, pH 7.6, 100 mM NaCl, 10 mM MgCl2 and in the presence or absence of 5 mM nucleotide with 2 µl of reservoir solution consisting of 19% PEG4000 (w/v), 100 mM MES, pH 6.5, and 20 mM sodium formate. Crystals were harvested into reservoir solution containing 25% (v/v) glycerol and flash frozen in liquid nitrogen. A three wavelength MAD experiment was performed by collecting data near the absorption peak and the edge of selenium, 0.9789 and 0.9792 Å, corresponding to the maximum f″ and the minimum f′, respectively, and at a low energy remote of 1.0024 Å. Data sets were obtained from a single crystal and were processed using the HKL2000 software package50  with 1 µl of reservoir solution containing 33% PEG 400 (v/v), 50 mM HEPES-HCl pH 7.2, and 150 mM sodium formate9 mg/ml w52 Table . Atomic N concentrations or BSA control in a 96-well black plate under rocking. Curve fitting and analysis were performed using the GraphPad Prism software .Biolayer interferometry was used to examine N domain interaction with peptide, and was performed using an Octet RED96 instrument . Biotinylated C1 peptide (Biotin-Ahx-GEVAEPSVRQTVAILRGLQ) was synthesized commercially  and immobilized onto streptavidin-coated biosensors at a concentration of 0.5 µM in binding buffer , and 0.1 mg/ml BSA). Biosensors were immersed in a 200 µl protein solution for 10 min to allow for association and subsequently transferred into 200 µl binding buffer to follow dissociation for 10 min. Binding curves were obtained at 25 °C at different Hsp104To prepare aggregates of model substrates, firefly luciferase  was denatured in 7 M urea in refolding buffer  for 30 min at 22 °C. β-galactosidase  was thermally aggregated in refolding buffer for 40 min at 59 °C. Glucose-6-phosphate dehydrogenase  was thermally aggregated in denaturation buffer  for 5 min at 47 °C, diluted 10-fold in refolding buffer, and treated for 1 min at 47 °C.40. G6PDH activity was measured at 30 °C in 50 mM Tris-HCl pH 8.0, 5 mM MgCl2, 0.5 mM NAD, and 2 mM glucose-6-phosphate by monitoring the formation of NADH at 340 nm using a UV-1601 spectrophotometer .Chemically denatured FFL was diluted 125-fold in refolding buffer containing the bi-chaperone system , 5 mM ATP, and an ATP regenerating system consisting of 25 mM phosphoenolpyruvate and 2 μM pyruvate kinase. Heat-aggregated G6PDH (2.5 µM) and β-gal (0.2 µM) were mixed with the bi-chaperone system (1 µM each) together with 4 mM ATP, 20 mM phosphoenolpyruvate, and 2 μM pyruvate kinase. The activities of recovered model substrates were measured as describedT4L and variants (0.2 µM monomer) were incubated with 2 mM ATP at 22 °C for 15 min in the absence or presence of 0.2 mg/ml κ-casein. The amount of released inorganic phosphate was measured using the malachite green assay53.Hsp104 and variants (0.5 µM monomer) or Hsp104N, Set-1N, Set-2N, Set-1N/I116A, and Set-2N/I116A were diluted in buffer consisting of 25 mM HEPES-KOH pH 7.5, 150 mM potassium acetate, and 10 mM DTT. To measure protein stability, 4 mg/ml protein was used per well together with 1x SYPRO Orange . PCR plates were sealed with optical seal, shaken, and centrifuged at 4000 rpm for 2 min after adding the protein. Thermal scanning (20 °C to 95 °C at 0.06 °C/sec) was performed using a Roche Lightcycler 480 . Fluorescence intensity was measured every 1 sec at an excitation wavelength of 465 nm and an emission wavelength of 580 nm. Curve fitting and melting temperature calculations were performed according to manufacturer’s instruction.Thermal shift assays were performed in 396-well thin-wall PCR plates. Purified Hsp104Supplementary Figures


Lately, we see that Internet of things (IoT) is introduced in medical services for global connection among patients, sensors, and all nearby things. The principal purpose of this global connection is to provide context awareness for the purpose of bringing convenience to a patient’s life and more effectively implementing clinical processes. In health care, monitoring of biosignals of a patient has to be continuously performed while the patient moves inside and outside the hospital. Also, to monitor the accurate location and biosignals of the patient, appropriate mobility management is necessary to maintain connection between the patient and the hospital network. In this paper, a binding update scheme on PMIPv6, which reduces signal traffic during location updates by Virtual LMA (VLMA) on the top original Local Mobility Anchor (LMA) Domain, is proposed to reduce the total cost. If a Mobile Node (MN) moves to a Mobile Access Gateway (MAG)-located boundary of an adjacent LMA domain, the MN changes itself into a virtual mode, and this movement will be assumed to be a part of the VLMA domain. In the proposed scheme, MAGs eliminate global binding updates for MNs between LMA domains and significantly reduce the packet loss and latency by eliminating the handoff between LMAs. In conclusion, the performance analysis results show that the proposed scheme improves performance significantly versus PMIPv6 and HMIPv6 in terms of the binding update rate per user and average handoff latency. Lately, we see that Internet of things (IoT) is introduced in medical services for global connection among patients, sensors, and all nearby things. Healthcare applications utilizing body sensor networks generate a vast amount of data. Cloud computing among with the Internet of Things (IoT) concept is a new trend for efficient managing and processing of sensor data online. Cloud computing is becoming increasingly popular. A large number of data are outsourced to the cloud by data owners , 2.The principal purpose of this global connection is to provide context awareness for the purpose of bringing convenience to a patient’s life and more effectively implementing clinical processes. As IoT is simply M2M (machine to machine) communications, sensor node is reportedly the most appropriate for this new technology. Besides, IPv6 over low power wireless personal area network (6LoWPAN), which can be used in IoT for communicating with sensors, is lately drawing a lot of attention . Hence, In this paper, we propose and analyze a user-specific, region-specific registration technique for all future IP-based wireless networks to achieve minimal network signal and packet transmission costs to support integrated mobility. When we are given a set of parameters that characterize the operation and operating conditions of Mobile Nodes (MN) for network communication costs, it can be seen that there is an optimal zone that minimizes costs to provide efficient service management operation and mobility of the MN. In this paper, to meet the next generation of wireless/mobile network characteristics mentioned above, PMIPv6  is appliHierarchical Mobile IPv6 (HMIPv6) is a protocol for reducing the handover delay of a network Mobile Node (MN) in Mobile IP, as proposed by IETF. HMIPv6 was introduced into a hierarchy in binding management  to reducPMIPv6 is a mobility management scheme developed by IETF for managing the mobility of the MN in the network instead of MN when moving the MN in the Access Network. PMIPv6 is a network-based mobility management protocol that allows MN to change its location without any signal being created. The Mobility Access Gateway (MAG) that supplies the newly introduced mobile services and Local Mobility Anchor (LMA) in PMIPv6 are responsible for managing the movement of the MN in the PMIPv6 domain. MAG is operated in the routers attached to the link and is responsible for processes relating to the movement of the MN. LMA is in operation for a UE to the HA in the Proxy Mobile IP domain  to maintThe initial connection procedure for the PMIPv6 domain of the MN is shown in The packets are sent to the MAG via the IP tunnel between MAG-LMA, and the LMA is received and forwarded to the MN after the address set is sent to the MN in the PMIPv6 domain outside the domain. After the transmitting packet from MN is passed to the LMA using an IP tunnel from the MAG, it is transmitted again on the LMA as a destination . The MN The previous MAG (pMAG) is detected via the MN Detach; the MN is not present on the access link. PBU to the LMA will be notified of the departure of the MN through a message. The LMA performs an operation for deleting the Binding Cache Entry (BCE) related to the MN and sends a PBA. When the MN is connected to the new MAG (nMAG), nMAG performs the initial access procedure of the MN, and the MN sends the HNP information to the MN received during the initial connection via the assigned RS/RA message exchange. Thus, the MN may use a Home of Address (HoA) allocated first. However, packets that are exchanged between the MN and the CN are transmitted via the technique through the PMIPv6 MAG and LMA, as well as the MAG and LMA in the CN of the MN. The transmission scheme through the following alternative routes requires long transfer delays and significant processing costs. Various performance-enhancing techniques have been proposed to address this problem.The Internet of Things (IoT) can be implemented by highly constrained devices such as Smart City, Smart Health Care, or Smart Car . These dRecently, efficient use of the IP mobility management protocols from the network, given the explosive growth of consumer mobile service, has been required. Wireless mobile services consumes significantly more bandwidth and cost compared with existing services in the current network. IP mobility management is still one of the most difficult research topic despite the development of various protocols by the Internet Engineering Task Force (IETF). Mobile IPv6 (MIPv6)  was devew value is actually the number of lines of overlapping cells and represents the degree of overlap in The concept of micro-cells was adopted to improve the system capacity in a place with a high user density. However, the smaller cell size is to increase the BU frequency of the MN, and the signaling traffic associated with mobility management increases as a result. Each domain consists of a group of cells, and each cell of the LMA service area belongs to exactly one domain LMA. The location of the MN in a mobile network is confirmed by the LMA in that it belongs to the domain and the MAG to which the MN belongs in the LMA domain, as informed periodically. Additionally, it detects the change in the MN from the LMA domain and sends a BU message to the network. However, the overlap scheme is to prevent the short-term changes due to the MN moving along a boundary of two LMA domains where BU traffic increases. That is, LMA overlapped domain traffic may be reduced . The w vThis scheme is for the MAG in the boundary zone of the LMA. It is configured to receive the LMA option from all adjacent LMAs and perform the change, not the handoff between the LMA, when the MN is connected with the MAG of the border areas of overlap with the LMA domain. The LMA changes because the MN disconnects after it is moved to another LMA while maintaining the connection to the current LMA. Thus, it is the same procedure as the handoff between LMA; additional packet loss and handoff delay will not occur. If the MN moves to the LMA domain boundary of the adjacent MAG, it changes to virtual mode, and the motion is considered to be within the VLMA domain. Because the MN performs the LMA change within the MAG boundary region, packet loss and additional handoff delay are not generated. The location management scheme proposed in this paper introduces the concept of a virtual layer. As shown in For example, LMA_5,1 consists of LMA_2,2, LMA_4,2, and part of LMA_5,2. The LMA domain of Layer-1 is connected to three VLMA domains, representing the LMA of Layer-2. LMA_5 is connected with VLMA_2, 4, and 5. The proposed structure is effective to prevent the occurrence of the ping-pong effect if the MN is moved along the boundary of two adjacent LMA domains or the location updates signal traffic through the cell using a virtual layer dispersion.Each MN monitors the broadcast message of the MAG. If the current LMA domain is different from the previously registered LMA domain, MN starts the location update to inform the system of the new LMA domain. When incoming session requests from the MN arrive, the system starts responding to register it. The proposed scheme can be implemented by a unique ID assigned to each LMA domain of Layer-1 and -2, covering the service area by homologous LMA domains. Although the original LMA domain partially overlaps with the virtual layer domain of VLMA, each cell is managed by the other two, but the MN is registered in the domain of only one LMA domain. The initial selection is determined depending on the distance to the center of the cell from the cells residing in the two LMA domains. A shorter distance governs the LMA domain selection, whereas it is selected at random when the distance is the same. The MN of cell A in P is included in the BU message to register a PBU message in the LMA -encoding mobility options. Other types of defined options for encoding are described in 6.2 of P is inclI and V, and the extended flag has the following properties. The I flag indicates the classification of the layer , and the V flag indicates that the MAG signaled a physical connection with VLMA. The MAG is in this case will be considered in the Layer-1 LMA and will be ignored for any option that LMA cannot understand. Network operators may place several LMA at different distances from the MAG within the respective domain. The LMA domain is used to select PMIPv6 in V flag that determines the VLMA and physical connections. MAG will send a return value passed because the search process, again with LMA and preference values corresponding to the MAG position information of the PBU message in PMIPv6, is registered with the PBU message. The search procedure is described in detail as follows. Depending on the position of the MAG where the MN approaches the boundary, the value of preference is also reduced by one and transmitted to the MN through an RA message.In this proposed scheme, modifying some of the Handoff Indicator options in the message comprises new extension flags V flag) between LMA1 and VLMA1 and then execute the LMA change. As shown in I flag is 0 from LMA1 and from VLMA1 is 1, so VLMA1 is selected. After the current link to VLMA1 is changed, the physical connection is established, and then the V flag values that are physically connected to the current LMA domain are checked. If there is a physical connection, perform the LMA change. If two LMA domains are overlapping and MN is in the area, such as an overlapping region of MAG5, MAG6, and MAG7, MN belongs to the MAG and will receive transmission information via the search process using the MN Policy Profile from LMA1 and VLMA1. Preference values are used to hide the location information of MAG of the LMA domain; MAGs are sent to the LMA through the PBU message after Preference is reduced to 1. For example, when the MN is in MAG7, the preference values are 1 through the search process from LMA1 of Layer-1; after VLMA1 of Layer-2 receives the preference value of 3 through the procedure, a PBU updates to LMA1 and VLMA1. When the MN moves from MAG6 to MAG7, handoff occurs between LMAs and packets are received from the CN via LMA1 and MAG7 (passage 1). MAG7 is located on the border MAG in the overlap region from the MN changes to VLMA1 from LMA1. The data packets pass through MAG7 and VLMA1 (passage 2) and are transmitted to the MN. MAG7 is located at the domain inside, so MAG7 does not incur handoff between LMAs. In the proposed scheme, when MAG is located on the border, the value of preference is always maintained at 1 when the MN is connected to the network; the MAG is aware that the MN has arrived at the boundary of the LMA domain. MAG causes MN to check the physical connection  message size, 64.LCoTI: Co-test initialization (CoTI) message size, 64.LHoT: Home Test (HoT) message size, 74.LCoT: Co-test (CoT) message size, 74.LRtSol Pr: Router solicitation for proxy advertisement (RtSolPr) message size, 52.L RtAdvPr: The proxy router advertisement message size, 80.LHI: The handover initiation (HI) message size, 52.LHAck: Handover confirmation (HAck) message size, 52.The byte sizes of the messages used in HMIPv6, PMIPv6 , 22, andA cost analysis model was developed for evaluating the performance of the proposed IP mobility management protocol. Signal cost messages , 23. CPDd CPD(·) .α and β are weighting factors for a wired link and a wireless link, respectively. They are used to emphasize the link stability. Packet delivery cost for λS is the average session arrival rate at the MN’s wireless interface, E(S) is the average session length in packets, One of the MNs in HMIPv6 is managed precisely by the MAP. Because MN is used to set up a local CoA in the new access network of a given MAP domain, MN is updated only if there is new location information for MAP. This is because the MAP functions as a local HA for the MN, so it registers the movement of the MN to the HA and the CN for a given transmission MAP domain. Movement of the MN in the HMIPv6 access network is managed by the MAP, which is limited from the inside domain handover. Thus, signal charges for HMIPv6 The total cost for HMIPv6 As with HMIPv6, PMIPv6 precisely manages the movement of the MN. However, unlike HMIPv6, which allows the connection point of the entity to be changed, the general MN PMIPv6 domain provides mobility services, such as LMA and MAG. Some mobility signaling messages sent from the MN do not exist in PMIPv6. Considering this, the signal is equal to the cost of The cost of the signal is reduced compared with the formula for In the formula above, the The packet tunneling cost The total cost for the PMIPv6 Our proposed method is to minimize the occurrence of the MN handoff when moving by adding a virtual layer on PMIPv6. That is, in the case of the PMIPv6 techniques, when the handoff occurs when the MAG serves the MN to connect through the VLMA Preference according to the value of the MAG sends a PBU to the LMA change procedure changes the previous LMA, the LMA receives an ACK from the handoff receiving a PBU to VLMA already known, without the need to complete and is transmitted to the PBA. Thus, the signal cost The signal cost from the above equation is reduced, compared with The packet tunneling cost In the formula above, The total cost of the proposed μcThis section shows the cost analysis of the mobility management protocol. Assuming that the coverage of the MAG circle exceeds the boundary of the MN, which is equal to the ratio ν is the average velocity of the MN, and R refers to the coverage radius of the MAG.In the formula above, hC−H = 4. The average value of the number of hops between the CN and the gate is hC−G = 6. The average number of hops between the HA and the gate is hH−G = 4. The average value of the number of hops between the gate and the AR is hG−A = 4. The average number of hops between the AR and MN is hA−M = 1. the average session length in packets is E(S) = 10. Inter-LMA handoff probability is α = 1. Intra-LMA handoff probability is β = 1.5. the ratio of data packets going through HA is ω = . the IPv6 tunneling overhead is We use the default value of the variable as follows: The average value of the number of hops between the CN and HA is m, and ν is varied from 0 to 30m/s. This protocol provides local mobility services, such as HMIPv6, and PMIPv6 in ν increases, the signal cost also increases. However, when HMIPv6, PMIPv6, and the proposed scheme are compared, the proposed scheme incurs much less signal cost. Mobility services for the MN are managed locally by the MAP in HMIPv6. While the MN changes the connection point in a given HMIPv6 domain, the only exchange of messages is between the MN and the LBU and LBAck MAP.Figs ν is fixed at 20m/s, and R is in the range of 400-800m. As shown in In addition to the MN in the PMIPv6 mobility services and PBAck, only a PBU message is exchanged between the MAG and the LMA. The proposed scheme shows better performance than PMIPv6. Next, ω and E(S) are fixed at 0.2 and 10 and, compared with λs, show the variation in packet transmission cost. As λs increases, the cost of any mobility management protocol packet transfer is also increased, exponentially. However, in MIPv6, the analysis shows better performance than the others because the packets are sent to the transmission path routed almost directly from CN. Next, λs and E(S) are fixed at 1 and 10, respectively, and ω is set from 0.1 to 1. In ω, whereas MIPv6 and FMIPv6 have a tremendous impact on ω. ω in MIPv6 and FMIPv6 for packet transmission costs increases dramatically. This is because the quantity of data packets going through the indirect path also increases. Additionally, the packets will be tunneled overhead for FMIPv6 consumption more than the others.ω and E(S) are fixed at 0.2 and 10, respectively, compared with λs showing changes in packet tunneling costs. The result is similar to that of the packet transfer cost. Thus, even if applying a local mobility management structure for managing the movement of the MN in the PMIPv6 and the proposed method in the HMIPv6 domain, there is an increase in packet tunneling overhead. For example, data packets from the MN in HMIPv6 must go through to the MN in the MAP. RO in MIPv6 is impossible in FMIPv6; that is, when ω = 1, HMIPv6, PMIPv6, and the proposed method showed good performance.Figs Sφ is the mobility ratio, calculated by λs/μc(SMR), similar to the Call-To-Mobility ratio commonly used in performance evaluation studies of a wireless communication network. ω = 0.2, λs = 0.2, and v = m/s and is represented by R = 500m. With Sφ at a higher value, MIPv6, Predictive FMIPv6, and Reactive FMIPv6 all show good performance. This is because the high value of Sφ indicates a low mobility rate in the fixed session. This phenomenon becomes greater as Sφ is increased. HMIPv6, PMIPv6, and the proposed method reduce the cost of mobility signals in a high mobility rate environment. However, additional tunneling overhead is generated for the data packet delivery service for the MN. Next, ω is fixed at 1, and we again analyze the change in the total cost in Sφ. When the results shown in Figs In this section, we analyze the total cost for each mobility management protocol. In this paper, we analyzed and compared existing IP mobility management protocols, including the virtual layer, based on PMIPv6 in terms of signal costs, packet transfer costs, packet tunneling costs, and total costs. In local mobility management, HMIPv6, PMIPv6, and the proposed scheme are regional approaches that manage the movement of the MN in a given mobility domain to reduce the number of mobility signaling messages for MN. That is, the movement of the MN is passed through the HA and CN. Thus, the signal costs will be significantly reduced in PMIPv6, the proposed scheme and HMIPv6 Figs  and 15. S1 File(PDF)Click here for additional data file.


Many entries in the protein data bank (PDB) are annotated to show their component protein domains according to the Pfam classification, as well as their biological function through the enzyme commission (EC) numbering scheme. However, despite the fact that the biological activity of many proteins often arises from specific domain-domain and domain-ligand interactions, current on-line resources rarely provide a direct mapping from structure to function at the domain level. Since the PDB now contains many tens of thousands of protein chains, and since protein sequence databases can dwarf such numbers by orders of magnitude, there is a pressing need to develop automatic structure-function annotation tools which can operate at the domain level.This article presents ECDomainMiner, a novel content-based filtering approach to automatically infer associations between EC numbers and Pfam domains. ECDomainMiner finds a total of 20,728 non-redundant EC-Pfam associations with a F-measure of 0.95 with respect to a “Gold Standard” test set extracted from InterPro. Compared to the 1515 manually curated EC-Pfam associations in InterPro, ECDomainMiner infers a 13-fold increase in the number of EC-Pfam associations.http://ecdm.loria.fr/.These EC-Pfam associations could be used to annotate some 58,722 protein chains in the PDB which currently lack any EC annotation. The ECDomainMiner database is publicly available at  Proteins perform many essential biological functions such as catalysing metabolic reactions and mediating signals between cells. These functions are often carried out by distinct “domains”, which may be identified as highly conserved regions within a multiple alignment of a group of similar protein sequences, as in the Pfam classification . It is wAs well as sequence-based and structure-based classifications, proteins may also be classified according to their function. For example, the Enzyme Commission  uses a hFurthermore, there are several ways in which a protein might provide one or more enzymatic functions, as illustrated in Fig. While all of the above mentioned approaches can provide associations between PDB protein chains and enzyme EC numbers, to our knowledge, very few approaches have been published for automatically assigning EC numbers to structural domains. SCOPEC  uses seqIn contrast, the dcGO ontology database for protein domains produced in 2012 is still available online and provides several ontological annotations  for more than 2000 SCOP domain families .The dcGO approach follows the principle that if a GO term tends to be attached to proteins in UniProtKB that contain a certain domain, then that term should be associated with that domain. The statistical significance of an association is assessed against a random chance association using a hypergeometric distribution followed by multiple hypotheses testing in terms of false discovery rate. The dcGO approach addresses the issues of hierarchical structure of most biological ontologies and the nature of domain composition for multi-domain proteins. However, a mapping onto Pfam domains is proposed only for GO terms and not for EC numbers.Here, we describe a recommender-based approach call “ECDomainMiner” for associating Pfam domains with EC numbers, which builds on our previously described statistical approach . Recommedoes not attempt to identify catalytic sites or catalytic residues. Rather, we aim to detect frequent co-occurrences of Pfam domains and EC numbers in order to deconvolute the often complex EC-Pfam relationships within multi-domain and multi-function protein chains. We assess the performance of our approach against a “Gold Standard” dataset derived from InterPro [Here, we use content-based filtering to associate EC numbers with Pfam domains from existing EC-chain and Pfam-chain associations from SIFTS, and from EC-sequence and Pfam-sequence associations from SwissProt and TrEMBL, where protein chains and sequences serve as the common attributes through which EC-Pfam associations are made. Note that our approach InterPro , and we Our data sources are SIFTS for EC number and Pfam domain annotations of PDB chains, and Uniprot for EC number and Pfam domain annotations of protein sequences. UniProt is divided into three parts: (i) the non-redundant, high quality, manually curated SwissProt part, (ii) the TrEMBL data that are annotated using Unified Rules , called In addition, in order to parameterise and evaluate ECDomainMiner, we use the InterPro database  which coTo avoid bias due to duplicate structures or sequences in the four source datasets, all PDB chains and Uniprot sequences were grouped into clusters having 100% sequence identity using the Uniref non-redundant cluster annotations , and eacFor the reference data, we extracted from InterPro a total of 1515 EC-Pfam associations in which each EC number had all four digits and each Pfam accession number referred either to a Pfam domain or a Pfam family (i.e. Pfam motifs and repeats were excluded). These associations were considered to be “positive examples”, and were randomly divided into two equal “training” and “test” subsets. However, for training purposes, we also needed some “negative examples”. We therefore created a set of “false” EC-Pfam associations by first shuffling the CID-EC and CID-Pfam associations from SIFTS dataset, and by then randomly collecting 1515 wrong EC-Pfam associations from the shuffled datasets. In the rest of this article, we will refer to the combined set of 758 randomly chosen positive examples from InterPro and 758 randomly chosen negative examples as our “training dataset” and the remaining 1513 positive and negative examples as our “test dataset”.The main idea underlying the discovery of hidden EC-Pfam associations is to assign a feature vector to each EC number and each Pfam domain, where the length of the vector is given by the total number of PDB and UniProt CIDs, and where each vector element marks the existence (1) or absence (0) of an EC number or Pfam domain annotation for a particular CID. Each possible EC-Pfam association is then scored using the cosine similarity between the corresponding pair of EC and Pfam feature vectors.The various steps of our content-based filter approach for finding associations between 4-digit EC numbers and Pfam domains are illustrated in Fig. S, of the product matrix represents a raw association score between an EC number, ec, and a Pfam domain, d.After this join operation, all EC-CID relations are encoded in a binary matrix, where a 1 represents the presence of an association and a 0 represents no association. This matrix is then row-normalised such that each row has unit magnitude when considered as a vector. Similarly, all PDB CID-Pfam relations are encoded in a second binary matrix which is column-normalised. Consequently, the product of the two normalised matrices corresponds to a matrix of cosine similarity scores between the rows of the first matrix and the columns of the second matrix. Thus, each element, CSec,d: Similarly, raw EC-Pfam association scores are calculated from EC-CID and Pfam-CID relations extracted from SwissProt, TrEMBL and Unirule. Then, because we wish to draw upon the relations from all four datasets, we combine the four raw scores as a weighted average to give a single normalized confidence score, i∈{SIFTS,Swissprot,TrEMBL,UniRule} enumerates the four datasets, wi are weight factors, to be determined, and where an individual association score, Si, is set to zero whenever there is no data for the  pair in dataset i.where In order to find the best values for the four weight factors, receiver-operator-characteristic (ROC) curves  were calEach weight was varied from 0.0 to 1.0 in steps of 0.1, and for each combination of weights a ROC curve of the ranked association scores was calculated. The combination of weights that gave the largest area under the curve (AUC) of the ROC curve was selected.R, precision, P, and their harmonic mean in order to obtain a “F-measure” using: Having determined the best weight for each data source, we next wished to determine an overall threshold for the confidence score. To do this in an objective way, we scored and ranked the members of the training dataset, and labeled them true or false according to a threshold value that was varied from 0.0 to 1.0 in steps of 0.01. For each threshold value, we counted the number of positive examples above the threshold (TPs), negative examples above the threshold (FPs), negative examples below the threshold (TNs), and positive examples below the threshold (FNs). We then calculated the recall, The score threshold that gave the best F-measure was checked on the test subset and selected as the best threshold to use for accepting inferred associations.The above approach has focused on finding explicit co-occurrences between Pfam domains and 4-digit EC numbers. However, it is possible to find more associations by relaxing the criteria for co-occurrences of EC-Pfam annotations by looking for matches only at the 3-digit EC level. Indeed, we have observed several cases where true associations according to the InterPro training dataset were assigned confidence scores below the threshold value because they had too few (4-digit EC number) instances to provide sufficient support. Therefore, the above procedure was repeated using 3-digit EC numbers to give a 3-digit scoring scheme (with different weight factors and a different score threshold). Then, any 4-digit EC-Pfam association below the 4-digit threshold, but consistent with a 3-digit EC-Pfam association above the 3-digit threshold, was added to the final list of accepted 4-digit EC-Pfam associations. It should be clarified that “consistent” means here that the 4-digit EC number is a descendant of the 3-digit EC number and that the Pfam domains are the same.p-value”, that an EC number and a Pfam domain might be found to be associated simply by chance. For example, it is natural to suppose such associations can be predicted at random if ec or d are highly represented in the structure/sequence CIDs. In principle, in order to estimate the probability of getting our EC-Pfam associations by chance, one could generate random datasets by shuffling the relations between EC numbers and CIDs on the one hand, and between Pfam domains and CIDs on the other hand. However, this is quite impractical given the very large numbers of CIDs, EC numbers, and Pfam domains, and the complexity of the filtering procedure that would have to be repeated for each shuffled version of the dataset. Therefore, as in [ec and d follows a hypergeometric distribution.While the above procedure provides a systematic way to infer EC-Pfam associations, we wished to estimate the statistical significance, and thus the degree of confidence, that might be attached to those predictions. More specifically, we wished to calculate the probability, or “e, as in , we assuN denote the total number of CIDs, Nd the number of CIDs related to the Pfam domain d, and Nec the number of CIDs related to the EC number ec, the hypergeometric probability distribution is given by Letting Xec,d equal to or greater than the observed number Kec,d of CIDs associated with both d and ec. Traditionally, a p-value of less than 0.05 is taken to be statistically significant. However, because this test is applied to a large number of EC-Pfam associations, we apply a Bonferoni correction which takes into account the so-called family-wise error rate (FWER) [p-value less than 0.05/T as denoting a statistically significant inferred EC-Pfam association in a dataset, with T the total number of tested EC-Pfam associations for this dataset, In order to distinguish EC-Pfam associations using both confidence scores and p-values, we classify them into three classes, “Gold”, “Silver”, and “Bronze”. An association is assigned to the Gold class if both its EC-Pfam score is greater than the determined threshold and all its p-values  are statistically significant. An association is labeled Silver if its score is above the threshold but one or more of its p-values is not statistically significant, or if its score is below the threshold  . We therwSIFTS=0.1, wSwissProt=1.0, wTrEMBL=0.1, and wUniRule=0.6. These weights clearly give greater importance to the candidate associations in SwissProt and UniRule, respectively, compared to those in SIFTS and TrEMBL.After clustering identical structures and sequences, and calculating raw association scores Fig. , our merThe optimal score threshold was determined according to the F-measure training procedure using our training dataset “” sectionThe results of the ECDomainMiner approach are summarized in Table After applying the 0.04 score threshold, the number of EC-Pfam associations falls to 8,256 with an overlap of about 96% of InterPro reference associations. Using the relaxed 3-digit association approach “” sectionTable p-values as described in “p-values in all source datasets), 11,426 Silver associations , and 4201 Bronze associations.The greatest ECDomainMiner scale-up factor occurs for associations involving the oxydoreductases (EC branch 1). The smaller scale-up factor observed for Pfam domains (2.8 versus 6.4 for EC numbers) can be explained by the fact that not all Pfam domains display an enzymatic activity. Thus there is a natural limit in the coverage of Pfam database by our EC-Pfam associations, whereas there is no such limit for the coverage of EC numbers. Combining the confidence scores with the calculated http://supfam.org/SUPERFAMILY/dcGO). The Domain2EC file includes 7249 associations with 4-digit EC numbers, of which 3774 are related to SCOP “Families” and 3475 to SCOP “SuperFamilies”. Because InterPro only tabulates SCOP family domains, we limited our comparison to the set of 3774 SCOP2EC family associations. The SCOP families were mapped to Pfam families according to InterPro mapping files in order to generate a set of 2500 “Pfam2EC” associations (i.e. EC-Pfam associations which may be deduced directly from the SCOP2EC data). This set  of low-scoring associations from the merged source dataset. This suggests that most of the eliminated associations involve Pfam domains that are not catalytically active. Indeed, if a Pfam domain is not regularly associated with protein chains or sequences having an enzymatic activity, the ECDomainMiner score for that domain is very low, and hence no EC number is assigned to that domain. This applies in particular to accessory domains that can co-occur with various catalytic domains in multi-domain proteins. A good example of such an accessory domain is PF00188 (the CAP protein family) which is a part of 216 different architectures. Among these architectures, there are 3 and 5 different architectures, which additionally contain PF00112 (Peptidase C1 domain) and PF00069 (Protein kinase domain), respectively. According to Pfam website, PF00188 is catalytically inactive but PF00112 and PF00069 are active. In fact, ECDomainMiner assigns PF00112 to 26 different EC numbers with a majority of EC 3.4.22 (Cysteine endopeptidases), and PF00069 to 28 different EC numbers that all start with 2.7 (Transferring phosphorus-containing groups). However, ECDomainMiner does not assign PF00188 to any EC number. This is because a large number of protein chains and sequences containing either PF00112 or PF00069 and associated with the above-mentioned EC activities, do not contain PF00188. In other words the catalytic activities of PF00112 and PF00069 are not strictly dependent on the presence of PF00188. Moreover, the SIFTS and UniProt databases indicate that PF00188 is associated with 43 different PDB chains and 5197 different protein sequences. However, none of those PDB chains are associated with an EC number in SIFTS and only 31 protein sequences (24 in TrEMBL and 7 in UniRule) are associated with at least one 4-digit EC number. Consequently, the association score of PF00188 with any EC number is zero for both the SIFTS and SwissProt datasets and is quite low (less than 0.02) for both the TrEMBL and UniRule datasets. Thus, the confidence scores of all of the associations involving PF00188 in ECDomainMiner are lower than our threshold of 0.04, and so these candidate associations are filtered out. This mechanism explains how an accessory domain is not assigned to an EC number by ECDomainMiner, and suggests that most of the retained associations are proper candidates for domain functional annotation.Exploring the ECDomainMiner results readily reveals that a given EC number or Pfam domain can be involved in one or more distinct EC-Pfam associations. Figure Although this represents rather high proportions of the total number of EC numbers and Pfam domains in ECDomainMiner , the intersection of the concerned EC-Pfam single associations yields a list of only 97 one-to-one EC-Pfam associations, of which 62, 34, and 1 are Gold, Silver, and Bronze associations, respectively. Comparison with the InterPro reference dataset reveals that two thirds (65) of these one-to-one associations are novel compared to InterPro. Interestingly, we confirmed in our source datasets that all of these associations involve single-domain proteins. Thus, these unambiguous associations constitute the most reliable novel associations calculated by ECDomainMiner.The complete list of one-to-one EC-Pfam associations found by ECDomainMiner may be downloaded from the ECDomainMiner web site. Interestingly 14 of these associations  concern “DUF” (domain of unknown function) or “UPF” (uncharacterised protein family) Pfam entries. These are listed in part (A) of Table p-values provide a level of support for each association that would be very difficult to obtain from text mining alone.These examples demonstrate that ECDomainMiner can be used to enrich domain annotation. Visual inspection of the one-to-one EC-Pfam associations indicates that about one quarter of them 23) could have been retrieved simply by comparing the names associated with the EC number and the Pfam identifier, which are nearly identical , associated with the same EC number, ec, (i) we reject those pairs for which at least one ec-annotated CID (in any source dataset) occurs in relation with d1 and not d2 or with d2 and not d1, (ii) for all other pairs we calculate for each source dataset the ratio of the number of ec-annotated CIDs related to d1 and d2, to the total number of ec-annotated CIDs. A support ratio of 1 means that all CIDs annotated with ec in a dataset are also related to d1 and d2. A similar algorithm was used for triplets and quadruples of Pfam domains. For a support ratio of 1 in at least one source dataset, we found 907, 191 and 47 obligate associations between an EC number and a pair, a triplet or a quadruplet of Pfam domains. These associations are available from the ECDomainMiner website. Two examples are given in part (C) of Table The multi-partner associations calculated by ECDomainMiner provide many more complex EC-Pfam associations. As a first analysis of such multiple associations, we looked for obligate pairs or tuples of Pfam domains that are always associated with a given EC number. Briefly, for any pair of Pfam domains, d1, d, associaInterestingly, filtering the names of the Pfam domains with the expressions “N-terminal” and “C-terminal” yielded 58 obligate pairs containing both a N-terminal and a C-terminal domain of the same function. This indicates that our approach is finding enzymes in which the catalytic function is provided by the interface between two consecutive Pfam domains. Only 4 of these obligate pair associations are currently documented in InterPro.Our analysis of the December 2015 release of the SIFTS database reveals that about 45% of PDB entries lack an EC number annotation. Of course, such an annotation is not expected to be present in all PDB entries because not all proteins have enzymatic activity. Nonetheless, it is interesting to use ECDomainMiner to analyse the number of PDB entries that contain Pfam domains which are present in EC-Pfam associations. Table Overall, we calculated that these chains map to a total of 24,995 PDB entries that could benefit from the additional annotations inferred by ECDomainMiner. For those chains lacking EC annotations, ECDomainMiner finds Gold, Silver, and Bronze EC-Pfam associations for 41,246, 44,406 and 34,820 PDB chains, respectively. In particular, 1334 PDB chains could benefit from our dataset of 97 non ambiguous one-to-one EC-Pfam associations.streptococcus pneumoniae tigr4, which is consistent with the enzymatic activity found by ECDomainMiner, and which could not be deduced from the Pfam domain name (UPF0052).The ECDomainMiner web server may be queried by EC number or Pfam domain. Thus, if one wishes to search for associations for a protein chain that currently lacks any EC annotation in the PDB (e.g. chain 2q7xA), one first needs to retrieve from the PDB the Pfam domain(s) that it contains . Then, querying the ECDomainMiner server with each Pfam domain identifier will show the associated EC numbers , along with the associated filtering scores and quality classes. In this example, ECDomainMiner finds a Gold quality association between PF01933, present in PDB chain 2q7xA, and EC number 2.7.8.28 (2-phospho-L-lactate transferase) which consequently can be associated with PDB entry 2q7x. Interestingly, PDB entry 2q7x is described as a putative phospho transferase from p-values, we have assigned an intuitive quality rating  to each EC-Pfam association found. These calculated associations are publicly available on the ECDomainMiner web site. We anticipate that our content-based filtering approach may be applied to other annotation vocabularies or ontologies, and we are currently working to extend our approach to discover new GO-Pfam annotations.We have presented a content-based filtering approach for associating EC numbers with Pfam domains. This approach has been shown to be able to infer a total of 20,728 non-redundant EC-Pfam associations, which corresponds to over 13 times as many EC-Pfam associations as currently exist in InterPro. Furthermore, thanks to our calculated We believe that enriching protein chain annotations will facilitate a better understanding and exploitation of structure-function relationships at the domain level. While many of the associations calculated by ECDomainMiner are consistent with those recently made available by the domain-centric dcGO approach for finding EC-SCOP associations, the ECDomainMiner results set contains many more associations than dcGO. Indeed, the ECDomainMiner result set contains 18,836 EC-Pfam which are not available in dcGO. Our analysis of the simple one-to-one associations found by ECDomainMiner shows that several DUF or UPF entries in Pfam may be assigned functions from the EC classification, and that obvious inconsistencies in the annotation texts may easily be corrected or unified. However, only a relatively small number (less than 0.5%) of EC-Pfam associations in our result set are simple one-to-one associations, indicating that there exist a large number of many-to-many relations between EC numbers and Pfam domains. Further analyses of these complex associations using graph database and machine-learning techniques could reveal many more hidden protein structure-function relationships.


The incursion of influenza A(H1N1)pdm09 virus was detected by Norway's active serosurveillance of its pig population in 2009. Since then, surveillance data from 2010 to 2014 revealed that 54% of 5643 herd tests involving 1567 pig herds and 28% of 23 036 blood samples screened positive for antibodies against influenza A virus. Positive herds were confirmed to have influenza A(H1N1)pdm09 virus infection by haemagglutination inhibition test. In 50% of positive herd tests, ⩾60% of the sampled pigs in each herd had antibodies against influenza A(H1N1)pdm09 virus. This within-herd animal seroprevalence did not vary for type of production, herd size or year of test. The overall running mean of national herd seroprevalence, and annual herd incidence risks fluctuated narrowly around the means of 45% and 32%, respectively, with the highest levels recorded in the three densest pig-producing counties. The probability of a herd being seropositive varied in the five production classes, which were sow pools, multiplier herds, conventional sow herds, nucleus herds, and fattening herds in descending order of likelihood. Large herds were more likely to be seropositive. Seropositive herds were highly likely to be seropositive the following year. The study shows that influenza A(H1N1)pdm09 virus is established in the Norwegian pig population with recurrent and new herd infections every year with the national herd seroprevalence in 2014 hovering at around 43% . Influenza A viruses (IAVs) are ubiquitous in both humans and animals, and are endemic in most pig populations worldwide –6. Severad hoc basis, usually undertaken when funding was available. While prominent organizations like the Centers for Disease Control and Prevention (CDC), the European Influenza Surveillance Network (EISN) and the World Health Organization (WHO) have well-developed and continuous human influenza surveillance systems , H1N2 [A/Sw/Gent/7623/99(H1N2)] and H3N2 [A/Sw/Flanders/1/98(H3N2)]. CDC identified and described the first antigen  is the vector of for the three predictors in our two models: (1) year of test, (2) herd size, and (3) production class for the ith observation of the jth herd and kth county; ujk is a vector of random intercepts unique to each herd, where ujk ~ N , and vk is a vector of random intercepts unique to each county, where vk ~ N ; and εijk is the vector of error terms where εijk ~ N.The mixed random-intercept logistic regression models were formulated as follows:The likelihood ratio test aided model selection. To decide on the significance of additional predictors for the two models, a difference of <2 of the log likelihood score was regarded as non-significant and the most parsimonious model was chosen . We testThere were 1816 herd tests where herds were tested consecutively for at least 2 years. To examine recurrent herd infection rates, univariable logistic regressions stratified on production class estimated the probability that a seropositive herd would be seropositive again the following year. Similarly, we investigated new herd infection rates by estimating the probability that a seronegative herd would test seropositive the following year by using mixed logistic regression.We also plotted the incidence risks stratified by the four production classes of fattening, nucleus, multiplier and conventional sow herds. We excluded the sow pools from this analysis given their small numbers of only 14 herds and also that there was only one or no uninfected sow pool herds to calculate incidence risk for the following year.We investigated the within-herd animal seroprevalence by observing the proportion of pigs testing positive in 1028 positive herd tests that had at least five pigs tested. A cumulative probability on animal prevalence plot of these positive herds revealed the infectiveness of the disease in pigs kept in close proximity, typical of pig production. Factors causing variations to animal prevalence were investigated with scatter plots for the three categorical fixed effects of interest, i.e. year of test, production class and herd size. Graphical analyses were followed up with multivariable regression using general linear regression for categorical variables to investigate whether within-herd prevalence varied with the same three fixed effects.Confidence intervals (CIs) using Stata for binomial outcomes gave inferential statistics on binomial probabilities of prevalence and incidence .Surveillance data of 5643 herd tests on 23 039 samples from 2010 to 2014 showed that 6513 (28%) of the samples screened ELISA positive for antibodies against IAV in 2470 herd tests. Of these blood samples positive for antibodies against IAV, 5857 were confirmed by the HI test to be antibodies against H1N1pdm09 with 23·6% showing reactions to sole antigen H1N1pdm09. Seventy-six per cent of the samples with reactions to multiple antigens in addition to H1N1pdm09 were all deemed cross-reactions by our criteria for herd diagnosis.n = 41; multiplier, n = 51; conventional, n = 188; sow pools, n = 13) that were tested for multiple 4 or 5 years. The proportion of these herds by production class that were repeatedly seropositive for 4 or 5 years, were 11/41 (27%) for nucleus herds, 20/51 (40%) for multiplier herds, 54/188 (29%) for conventional sow herds and 11/13 (85%) for sow pools. Conversely, the proportion of herds that tested negative for all the years they were tested were 11/41 (27%) for nucleus herds, 7/51 (14%) for multiplier herds, 23/188 (12%) for conventional sow herds and 0/13 (0%) for sow pools.Looking at herds that were consecutively tested, there were 293 sow herds . The 10th percentile was 20%, 25th percentile, 30%, 50th percentile or median, 60%, 75th percentile 81%, 90th percentile 100%. We did not find any variations in animal prevalence in the five production classes or in the three quantiles of herd sizes using graphical comparisons (not shown) or multivariable regression analysis (not shown).Given the possible variations in animal prevalence as shown in Mixed logistic regression analysis  shows thn = 1327) that were negative and tested again the following year, n = 14) and that they were either all positive and therefore had predicted the following year perfectly or there was one negative sow pool herd that was predicted perfectly to be negative next year.For herds , positive sow herds that were still positive after 2 years were the result of recurrent infections. Recurrent infections in positive herds occur frequently in nucleus, multiplier and conventional sow herds but with highest probability in the smallest group, the sow pools. This is not surprising because of this group's unique pattern of frequent contacts between multiple satellite herds.Spatial analysis revealed that the counties with the three densest pig populations also correspondingly had the highest proportion of positive herds . This isad hoc surveillance studies conducted to investigate the persistence and transmission dynamics of influenza viruses circulating in some European pig herds  [In Norway, the persistence of H1N1pdm09 in the pig population can be attributed to several production factors that favour the infection dynamics of the influenza virus. Although our study has not ascertained the sources of the virus in these recurrent and new infections, the varying production class-specific probabilities revealed in our regression analysis and graphical plots suggest transmission patterns are related to their production operations. Elsewhere, since the outbreak of H1N1pdm09 in pigs, there have been studies on pigs such as those by the EISN, investigating the dominant swIAV subtypes circulating in European pig populations , and als, Spain) , 39. ThePrevious studies have shown that people working with pig herds may have transmitted H1N1pdm09 to pigs , 20, 28.n = 14) had the highest levels of seroprevalence because they had the highest rates of recurrent infections. This was expected given their special operating mode that allows mixing of sows from various satellite pig herds (n = 10–20), thereby increasing the risk of horizontal spread between herds. Human–pig contact frequency and the accumulated duration with different people are also higher in the sow pool system thus increasing the sources and risk of reverse zoonosis.The small group of sow pool herds  of testing seropositive compared to fattening herds. Nucleus herds, closed to pigs from other sources, had a lower probability of being positive . Seroprevalence and incidence risks of conventional sow herds  were unexpectedly similar to nucleus herds. The anomaly is accounted for by the lower sensitivity in herd tests for conventional herds because fewer pigs are sampled per herd test in these herds. As shown in With regard to the closed nucleus herds with highest biosecurity, the recurrent infections in positive nucleus herds were likely caused by circulation of the virus within the herd or caused by continual spillovers from human infections as mentioned earlier. Nucleus herds generally have higher gilt replacement rates (60–70% are first-parity sows) in overlapping batches. The shorter cycle and higher rates of replacements also means that more newly susceptible hosts become available faster for new infections and the propagation of the virus.As to animal prevalence in positive herds, ~60% of positive herds with at least 5 pigs sampled had ⩾50% of the animals testing seropositive . The higThe temporal trends of seroprevalence, recurrent and new herd infection rates observed in our surveillance data from 2010 to 2014 suggest that H1N1pdm09 will remain in the Norwegian pig population for as long as humans or pigs, or both, act as reservoirs and continue to transmit the virus to susceptible pigs. The persistence of the virus in the pig population has economic consequences even though the infection in the naive Norwegian pigs was mostly subclinical , 27. A lH1N1pdm09 in pigs is not only widespread across Western Europe, reassortant subtypes have already appeared, probably due to co-infections of H1N1pdm09 with established swIAVs . It follIn conclusion, although sampling for herd tests had varied across the pig herds and therefore made direct prevalence inferences somewhat challenging, we think that Norway's active surveillance gives a fairly representative picture of the natural infection dynamics of the virus in the Norwegian pig population given the absence of any intervention measures.Five years after the incursion of the new influenza virus, H1N1pdm09, the prevalence of seropositive herds has not fallen below 40%. This strongly indicates that the virus has adapted well and established itself in the Norwegian pig population. To mount measures against the spread of influenza virus to new pig herds, and to break the chain of infection in infected herds, further research on the transmission dynamics of the H1N1pdm09 virus in the Norwegian pig population and economic analyses would give farmers and the food safety authorities guidance on feasible approaches.


UBF), pigeon pea (PPF), and sweetpotato (SPF) flour blends. Simplex centroid mixture design was used to obtain 17 blends from the flours. The nutrient composition, color, and functional properties of the blends were evaluated using standard methods. Data were subjected to analysis of variance and treatment means were compared using Duncan's multiple range test at 5% probability level. There were significant (p < .05) differences in the nutrient composition, and functional and pasting properties of the blends. The crude protein, crude fiber, ash, foaming capacity, emulsion capacity, and least gelation capacity of the blends increased as the PPF level increased. The blends had Na/K ratio of <1.0. The dispersibility, bulk density, water, and oil absorption capacities of the blends increased as SPF and UBF increased. The peak, setback, and final viscosities increased as UBF and SPF inclusion increased,whereas pasting temperature and time increased as the PPF level increased. The L*, a*, and b* values of the flour blends which were significantly (p < .05) different ranged from 79.58 to 102.71, −0.15 to 2.79, and 13.82 to 23.69, respectively. Cooking banana‐pigeon pea‐sweetpotato flour blends are desirable for alleviating malnutrition in Nigeria and developing new food formulations.This study investigated some quality attributes of unripe cooking banana ( It was found to possess good agronomic characteristics and is less seasonal in production than dessert banana and plantain , vitamins , polyphenols, resistant starch, and antioxidants  is an important underutilized legume in south‐west Nigeria , a sweet‐tasting tuberous root, is ranked fourth in terms of consumption in the world after wheat, maize, and rice  with colour index No. 1 . Composite blends of UBF, PPF and SPF were mixed according to the different ratios generated from using the simplex centroid mixture design (Table The flours were thoroughly blended using a Kenwood mixer (Model HM 430) and packaged in different polyethylene bags for analyses.2.62.6.1The composite flour blends were analyzed for their moisture, crude protein, crude fat, crude fiber, and ash contents according to the method described by AOAC . Carbohy2.6.2The titration method of AOAC  was used2.6.3The total carotenoid content was determined by the method described by Chan and Cavaletto  using UV2.6.4The mineral content of the samples were determined using the wet method as described by Onwuka . Calcium2.6.5Tannin was determined according to the Folis—Denis colorimetric method as described by Jaffe . The met2.72.7.1This was determined by the method described by Kulkarni, Kulkarni, and Ingle . Ten gra2.7.2The bulk density (BD) of the sample was determined using the method described by Onwuka . About 12.7.3The method described by Onwuka  was used2.7.4.The method of Onwuka  was used2.7.5Foaming capacity (FC) was determined according to the method described by Onwuka . Two gra2.7.6Emulsification capacity (EC) was determined using the method of Kaushal et al. . Two gra2.7.7Least gelation concentration (LGC) of flours was determined by the method of Sathe, Desphande, and Salunkhe . Sample 2.8This was determined using the Rapid Visco Analyser  as described by Newport Scientific . The sam2.9The color intensity of the composite flour blends were measured using a Konica Minolta Colour Measuring System  as described by Ahmed and Hussein .2.10p < .05).The data collected from the proximate, functional, antinutritional analysis, and pasting properties were presented as means of three determinations. The data were subjected to one‐way analysis of variance using SPSS statistical software version 21.0. The mean were separated by applying Duncan multiple Range test at 95% confidence level (33.1The Proximate composition and energy content of the blends is presented in Table 3.2p < .05) in the mineral content of the composite flour blends with respect to magnesium, sodium, calcium, and potassium (Table There was a significant difference (um Table . It rangum Table .3.3p < .05) difference existed for tannin, oxalate, phytate, and tryspin inhibitor contents of the composite flour blends as shown in Table Significant  differences were seen in some functional properties of the composite flour blends  is used to evaluate the flour heaviness, handling requirement and the type of packaging materials suitable for storage and transportation of food materials  of foods  measures the ability of food material to absorb oil. OAC varied from 92.93% to 154.03% showing the flour blend has high oil absorption capacity as a result of the hydrophobic character of protein in the flour. The presence of protein exposes more non‐polar amino acids to the fat and enhances hydrophobicity as a result of which the flour absorbs more oil  is used to determine the ability of the flour to foam which is dependent on the presence of the flexible protein molecules which decrease the surface tension of water  measures the minimum amount of flour needed to form a gel in a measured volume of water. It varies from flour to flour depending on the relative ratios of their structural constituents like protein, carbohydrates, and lipids  different. Peak viscosity which is the maximum viscosity developed during or soon after the heating aspect of the test  measures the ability of the starch to form starch and viscous paste or gel after cooking and cooling  gives an idea about retrogradation tendency of starch in flour sample. The SV which ranged from 2.08 to 111.38 RVU increased as the percentage inclusion of PPF reduced. This indicates reduction in the textural characteristics of the samples since setback has been correlated with texture  different  of the different composite blends were significantly (nt Table . The L* 4The nutrient composition, functional, and pasting properties of unripe cooking banana, pigeon pea, and sweetpotato flour blends was studied. The crude protein, crude fiber, ash, foaming capacity, emulsion capacity, and least gelation capacity of the blends increased as the PPF level increased. The dispersibility, bulk density, water, and oil absorption capacities of the blends increased as SPF and UBF increased. The blends were rich in magnesium and had Na/K ratio of <1.0. The peak, final, setback, and final viscosities increased as UBF and SPF inclusion increased while pasting temperature and time of the composite blends increased as the percentage PPF level increased. The color of the blends was light and showed a predominance of yellow color. Furthermore, the other quality attributes of the blends showed that they could be used in the preparation of complementary foods and as substitute raw materials for wheat in the production of pastas, puddings and biscuits. The study paves way for enhanced utilization of cooking banana, pigeon pea, and sweetpotato in the country. This paper revealed that cooking banana, pigeon pea, and sweet potato flour blends are good sources of protein, fiber, and carotenoids and are desirable to improve the nutritional wellbeing of Nigerians.None declared.


The overarching goal of delineating molecular principles underlying differentiation of protein kinase clients and chaperone-based modulation of kinase activity is fundamental to understanding activity of many oncogenic kinases that require chaperoning of Hsp70 and Hsp90 systems to attain a functionally competent active form. Despite structural similarities and common activation mechanisms shared by cyclin-dependent kinase (CDK) proteins, members of this family can exhibit vastly different chaperone preferences. The molecular determinants underlying chaperone dependencies of protein kinases are not fully understood as structurally similar kinases may often elicit distinct regulatory responses to the chaperone. The regulatory divergences observed for members of CDK family are of particular interest as functional diversification among these kinases may be related to variations in chaperone dependencies and can be exploited in drug discovery of personalized therapeutic agents. In this work, we report the results of a computational investigation of several members of CDK family  that represented a broad repertoire of chaperone dependencies—from nonclient CDK5, to weak client CDK6, and strong client CDK9. By using molecular simulations of multiple crystal structures we characterized conformational ensembles and collective dynamics of CDK proteins. We found that the elevated dynamics of CDK9 can trigger imbalances in cooperative collective motions and reduce stability of the active fold, thus creating a cascade of favorable conditions for chaperone intervention. The ensemble-based modeling of residue interaction networks and community analysis determined how differences in modularity of allosteric networks and topography of communication pathways can be linked with the client status of CDK proteins. This analysis unveiled depleted modularity of the allosteric network in CDK9 that alters distribution of communication pathways and leads to impaired signaling in the client kinase. According to our results, these network features may uniquely define chaperone dependencies of CDK clients. The perturbation response scanning and rigidity decomposition approaches identified regulatory hotspots that mediate differences in stability and cooperativity of allosteric interaction networks in the CDK structures. By combining these synergistic approaches, our study revealed dynamic and network signatures that can differentiate kinase clients and rationalize subtle divergences in the activation mechanisms of CDK family members. The therapeutic implications of these results are illustrated by identifying structural hotspots of pathogenic mutations that preferentially target regions of the increased flexibility to enable modulation of activation changes. Our study offers a network-based perspective on dynamic kinase mechanisms and drug design by unravelling relationships between protein kinase dynamics, allosteric communications and chaperone dependencies. Protein kinases govern functional processes in cellular networks by acting as dynamic molecular switches that fluctuate between ensembles of the inactive and active forms –7. StrucStructural studies of protein kinases have shown that the inactive kinase conformations may fall into a number of classes in which certain key features of the inactivation mechanism are conserved –18. A coCDK proteins is a group of serine/threonine kinases with multiple isoforms that are quintessential of kinase-targeted drug discovery with more than 20 inhibitors in clinical trials, and the first FDA-approved drug palbociclib –35. ThisAmong notable divergences in the regulatory mechanisms of CDK proteins are radically different dependencies on the Hsp90 chaperone machinery that is required for some members of CDK family for fold maturation and stabilization of the active conformation. The 90 kDa heat-shock proteins Hsp90s manage late stages of conformational development, maturation and folding for a wide array of protein client substrates, including protein kinases –61. The Structure-functional characterization of the kinase clientele also indicated that the Hsp90-Cdc37 machinery often favors kinases that assume a Cdk/Src-like inactive state and engage in activation-promoting interactions with binding partners to achieve their functional form  72]. Th. Th72]. Computational studies have elucidated many aspects of CDK structure, dynamics and binding. Molecular dynamics (MD) simulations and coarse-grained elastic models , 79 haveIn this work, we report the results of a computational investigation of several members of CDK family  that represented a broad repertoire of chaperone dependencies—from nonclient CDK5, to weak client CDK6, and strong client CDK9. The principal hypotheses and conceptual framework of our study are based on the premise that mechanisms of CDK regulation and activation by binding partners are ultimately determined by the dynamic conformational landscapes and organization of the allosteric interaction networks in CDK structures. In this formulation, binding of activating partners and phosphorylation could promote thermodynamic shifts between allosteric states by modulating structure and stability of the interaction networks. These phenomena can be conveniently described using an ensemble-based model of allosteric interactions –93 and gMultiple crystal structures of CDK5-p25 , CDK6/V-Using DMD approach, we simulated multiple crystal structures of CDK5, CDK6 and CDK9 proteins and explored conformational landscapes of the unbound and bound CDK states . The obtWe also monitored conformational mobility of critical residues involved in the formation of active conformations in studied CDK structures. CDK5 does not require phosphorylation despite presence of S159 site at a position which is equivalent to the phosphorylation site in CDK2. In the CDK5-p25 complex, the conserved arginine residues  remain stable and come close together to form an interaction cluster with S159 in the T-loop, contributing to stabilization of the active conformation . In the To highlight the interplay between evolution and stability, we probed a relationship between conformational dynamics and sequence conservation in the CDK family. Using mutual information (MI) and coevolutionary analysis in the framework of MISTIC approach ,107 we eTo characterize differences in functional motions and collective dynamics of CDK proteins, we explored two complementary approaches: Principal Component Analysis (PCA) ,109 and To complement PCA results, we also explored ENM analysis of collective movements by using crystal structures of the studied CDK proteins . Using GStructural mapping of the GNM-derived essential mobility profiles highlighted positions of the hinge sites and their overlap with the binding interface residues . An exteTo determine differences in the energetics of CDK5 (nonclient) and CDK9 (client) structures and identify energetic hotspots, we conducted alanine scanning along with a mutational sensitivity analysis of protein residues in these structures. In the alanine scanning, we employed the FoldX force field method  implemenAmong energetic hotspots of CDK5 were also several other important residues including R125 (HRD motif), R149 and Y179 (T-loop). In CDK5, the conserved and stable arginine residues (R125 and R149) form an interaction cluster with Y179 and S159 in the T-loop . The intWe also performed a mutational sensitivity analysis of CDK5 by computing ΔΔG changes obtained from systematic substitutions of each protein residue . The resA more significant disparity in the stability of the kinase lobes was seen in the CDK9 structures as mutations of the N-lobe residues resulted in small changes . In partUsing a graph-based representation of protein structures –96, we c for each pair of residues using the average values over conformational ensembles. This network parameter is defined as the number of shortest paths in the total ensemble that proceed through a given edge. The edges of high centrality values represent the inter-modular bridges that direct most of the communication traffic in the system. Accordingly, the removal or alteration of these bridges may affect allosteric communications between many pairs of residue nodes by changing the shortest inter-residue routes. Computation of the ensemble of short paths between any pair of residues was based on the community decomposition by the Girvan-Newmann algorithm –133. ThiWe argue that this divergence of global network characteristics can be associated with underlying differences in chaperone dependencies of CDK5 and CDK9 proteins.Structural mapping of high centrality edges exposed probable routes of allosteric communications and highlighted key differences between ensembles of short paths in the CDK structures . We obseIn network terms, our results imply that mutations of key mediating centers from the HRD and DFG motifs may severely impair the efficiency of allosteric communications in CDK5 and CDK9 proteins. At the same time, the adverse effects of mutations in other positions could be potentially mitigated in CDK5 because of a large allosteric network and presence of alternative routes between the kinase lobes. To substantiate these arguments, we performed DMD simulations analyzing conformational ensembles and residue interaction networks for several representative CDK5 mutants F145A (DFG motif) and S159A. According to our hypothesis, the known severe effect of the F145A mutant on kinase stability and activity should manifest in global alterations of the residue interaction network and irreparable damage to allosteric communications in CDK5 structures. At the same time, we proposed that a detrimental effect of S159A mutant on the allosteric interaction network could be less dramatic and weaken allosteric signaling in CDK5 rather than completely abolishing activity. The central finding of this analysis was that CDK5-F145A mutation can induce significant global changes on the dynamics of the catalytic domain by increasing flexibility of the core regions that manifested in the drastically lowered centrality of the αC-helix (residues 43–55), catalytic HRD motif (residues 124–126), DFG motif (residues 144–146) and the T-loop as compared to the CDK5-WT protein . DynamicStructural mapping of high centrality edges in the CDK5-F145A structure highlighted the observed changes in the distribution of major communication pathways. The network of inter-residue bridges becomes sparser and more fragmented, consisting of small isolated clusters . AccordiΔR(i) = H-1F(i) that is then translated into N×N PRS matrix, SPRS. In this matrix, the ijth element evaluates the sensitivity of mode i to perturbation at position j. By using this approach as implemented in i, of u(k) describes the displacement of residue i along the kth mode and ([u(k)]i)2 as a function of residue number i defines the mobility profiles Mik) [KLConsScore was also calculated using MSA profile of the protein kinase family with the aid of MISTIC server [A graph-based model of protein structure considers residues as network nodes while inter-residue edges represent residue interactions. The details of graph construction using residue interaction cut-off strength (Imin) , 96 wereImin) –166. TheImin) –100 and Imin) –128. ForImin) –169. TheC server ,127. SeqP(i) is the frequency of amino acid i in that position and Q(i) is the background frequency of the amino acid in nature calculated using an amino acids background frequency distribution obtained from the UniProt database.Here, wij is defined by the generalized correlation coefficient rMI  measuring both dynamic and coevolutionary coupling between residue pairs [In the network model of protein structures, weight ue pairs :wij=−lowij that connects nodes i and j is calculated using the generalized correlation coefficients rMI  associated with the dynamic correlation and mutual information shared by each pair of residues [The length (i.e. weight) of the edge residues ,171. Theresidues  that comCβ, that is followed by 1,000 steps of steepest decent and Newton—Raphson minimizations to remove steric clashes and close contacts before calculating the energy terms and measuring the effect of alanine mutations on protein stability. Residues for which alanine mutations result in a significant destabilization effect  can be regarded as potential energetic hot spots that are important for thermodynamic stability of the protein structures. Statistically significant average ΔΔG values were obtained from 100 independent samples of conformational ensembles for each studied structure [A systematic alanine scanning of protein residues in the CDK5 and CDK6 structures was performed using FoldX approach . For thitructure . Each ofi is defined to be the sum of the fraction of shortest paths between all pairs of residues that pass through residue i:gjk denotes the number of shortest geodesics paths connecting j and k, and gjk(i) is the number of shortest paths between residues j and k passing through the node ni. Residues with high occurrence in the shortest paths connecting all residue pairs have a higher betweenness values. The residue betweenness values and the matrix of shortest communication pathways between residue pairs provide a measure of signaling flow passing through edges of the network that is used for network partition into local communities. Protein structure networks were initially analyzed for detection of k-cliques and k-clique communities using Clique Percolation algorithm [k-cliques that can be reached from each other through a series of adjacent k-cliques. We employed a community definition according to which in a k-clique community two k-cliques share k-1 or k-2 nodes. Computation of the network parameters was performed using the Clique Percolation Method as implemented in the CFinder program [Using the constructed protein structure networks, we computed the residue-based betweenness parameter. The betweenness of residue lgorithm  in which program . The com program –133 was Ehb is calculated using a geometry-based empirical function [Ecut,hb = -1.0 kcal/mol are included in the network. Hydrophobic contacts are considered between all carbon and sulfur atoms separated by a distance less than the sum of their van der Waals radii (1.7 Å for C and 1.8 Å for S) plus a temperature-independent Dcut,hp = 0.25 Å [α atoms are part of the giant cluster before the transition, and leave the giant cluster after transition are identified as locally weak spots in the constraint network. A residue is considered flexible if its Cα atom is either in a flexible region or part of a small rigid cluster of less than four atoms. In our application of the FIRST approach, we considered conformational ensembles of multiple crystal structures of CDK proteins. The identification of weak spots is carried out for 1,000 representative conformations of the ensemble for each of the crystal structure. The frequencies of residues to become weak spots are computed and averaged over conformational ensembles of multiple crystal structures for each studied CDK protein. The frequency of all residues being predicted as a weak spot throughout the ensemble is counted and, finally, all weak spots are assigned a rank according to the decreasing order of their frequency.We utilized FIRST (Floppy Inclusion and Rigid Substructure Topography) approach –146 and function  and only= 0.25 Å . In the = 0.25 Å ,148. In S1 FigKinome mapping of Hsp90-Cdc37 clients extracted from experimental studies –74 is de(TIF)Click here for additional data file.S2 FigThe Kullback-Leibler (KL) conservation score is mapped onto respective kinase residues in the crystal structures of CDK5 (A), CDK6 (B) and CDK9 proteins (C). The KL profiles are shown in red bars for CDK5 (A), green bars for CDK6 (B) and blue bars for CDK9 residues (C). Sequence conservation of critical functional regions HRD and DFG is highlighted by filled marron diamonds. Sequence mapping onto crystal structures residues is undertaken to facilitate direct comparison with conformational dynamics and structural stability of CDK proteins.(TIF)Click here for additional data file.S3 FigResidue-based centrality distributions of the CDK5-F145A mutant (A) and CDK5-S159A mutant (B). The network profile of the WT CDK5-p25 (pdb id 3O0G) is shown in (A) and (B) in filled brown bars as a reference for comparison with the centrality profiles of the mutants. The centrality distributions for CDK5-F145A mutant and CDK5-S159A mutant are shown in marron bars. The distributions are derived by averaging computations of network parameters over the conformational ensembles obtained from DMD simulations of CDK5 mutants. Structural mapping of high centrality edges in the CDK5-F145A mutant complex (C) and in the CDK5-S159A complex (D). The kinase domains are shown in green ribbons and P25 protein is shown in cyan ribbons. The residues forming high centrality edges are shown in red spheres.(TIF)Click here for additional data file.