Ultra-precision piezoelectric inchworm motor (PIM) is widely used in the optical equipment, microelectronics semiconductor industry and precision manufacturing for motion and positioning, but the multi-physics field simulation model for estimating PIM performance and assisting motor design is rarely studied. The simulation model in this paper aimed to provide researchers with direct and convenient PIM performance evaluation to assist the motor design and development. According to the existing advanced inchworm motor products, a multi-physics field coupling model involving solid mechanics and electrostatics using the finite element method (FEM) was established. The motion gesture and performance (driving force and travel) of the PIM were analyzed, respectively. The simulation results showed that the motion gesture of the inchworm motor was well consistent with that of the actual motor product. The driving force from the simulation was close to that of the actual product, and the maximum error was 2.8%. As for the PIM travel, there was a maximum travel error of 0.6 μm between the simulation and official data. The performance parameters of the piezoelectric materials under certain specifications can be simulated by the multi-physics field coupling model. Therefore, the multi-physics field coupling simulation model is suitable for PIM performance evaluation and assisting motor development. A piezoelectric motor is common in ultra-precision motion, positioning, and micro-assembly with its characteristics of high resolution, instant response, and long travel ,2,3,4. Id33, d15, and d31 exist. For the d33 mode, the deformation direction is consistent with that of the electric field and polarization as shown in d33 strain mode. For the d15 mode, if the electric field direction is on the x-axis of the global coordinate system and the polarization direction is perpendicular to it, the volume deformation (shear deformation) is the vector sum of the PM’s deformation on the x-axis and the z-axis as presented in x-axis and z-axis but not on the y-axis. For the d31 mode, illustrated in z-axis, the deformation direction is on the x-axis. The motion gesture of the inchworm motor in this paper is composed of the d33 and d15 modes [d33 strain mode is responsible for the extension and contraction of the actuation legs in the vertical direction, while the d15 strain mode is responsible for generating shear deformation in the horizontal direction, which makes the motor shaft move in the same direction. This direct contact method without a flexible mechanism avoids the mechanical coupling process, so there is no reduction in accuracy and reliability [The working principle of a piezoelectric motor is an inverse piezoelectric effect. That is, when the voltage is applied to an unconstrained piezoelectric material (PM), the PM will produce geometric deformation . The def15 modes . The d33iability .The key technique of the ultra-precision piezoelectric motor has been widely studied. Simulation modeling is a powerful tool for PIM design in many studies. Li et al.  simulateIn this paper, according to the advanced inchworm motor, which is developed by PI Motion, the multi-physics coupling simulation was built for PIM performance estimation. During movement, contact friction will occur between the actuation foot and the motor shaft. The contact friction is a highly nonlinear process which undoubtedly increases the complexity of the model and makes the model very difficult to solve. Therefore, the two studies were carried out as follows: (1) focusing on the analysis of the gesture motion of the four-foot PIM, a coupling model without a drive shaft was established; (2) a model with a driving shaft was established to study the motor’s performance.CE, the strain gradually decreases. The strain increases with the increasing electric field when the electric field strength exceeds the CE. If the positive electric field decreases to critical negative electric field C−E, the strain increases gradually. When the electric field is less than C−E, the strain increases with the decreasing electric field. So PIC255 is applied to the actuator’s application in dynamic operations . In addition, the Curie temperature of the PIC255 is 350 °C; it ensures that the PIC255 has extraordinary stability at normal temperature [The core piezoelectric material of the PIM is PIC255 which is classification in accordance with EN 50324-1. The PIC255 belongs to “soft” ferroelectric ceramic based on lead zirconate titanate (PZT) and barium titanate. The tetragonal unit cell of “soft” piezo ceramic is shown in perature .According to PICA Shear actuation and PiezoWalker Motors of PI Motion in References ,22, the The whole physical model was the coupling of solid mechanics and electrostatics; the model with the driving shaft also involved the highly nonlinear contact friction between the four actuation legs and the driving shaft. Nonlinearity will sharply increase the difficulty of model solving, so the model needs to reduce the number of contact surfaces as much as possible. The minimum number of contact surfaces of the model with a drive shaft is four as presented in The whole model including domain 1, domain 2.1 and domain 2.2, and domain 3 is described by solid mechanics. The two types of piezoelectric constitutive equations in solid mechanics were used to analyze the inverse piezoelectric effect which reflected the coupling between the structural (strain and stress) and electrical domains (the electric field). The strain and stress of the material can be obtained from the piezoelectric constitutive equations. The strain and stress of the volume elements on the contact surfaces produce normal and tangential force which is needed in the Coulomb friction model. Finally, the piezoelectric constitutive equation must be applied to all PM regions (domains 2.1 and 2.2).S is the 6 × 1 strain matrix, reflecting the deformation degree of PM. ES is the sixth order flexibility coefficient square matrix, T is the 6 × 1 stress matrix, d is the 3 × 6 piezoelectric strain constant matrix, E is the 3 × 1 electric field strength matrix in the X, Y, and Z direction of the orthogonal global Cartesian coordinate, D is the electric displacement, ε0 is the vacuum dielectric constant, rTε is the relative dielectric constant at constant stress, and t is the transposition symbol. The above coefficient matrix forms are determined by the PIC255 material characteristics. The PIC255 belongs to a tetragonal crystal system, so the flexibility coefficient matrix ES is the symmetrical matrix; it only contains six independent elements as shown in Equation (2):The strain–charge constitutive equation reflects the coupling relationship between material stress and relative permittivity at constant stress which is shown in Equation (1):d33, d15, and d31 are left after polarization of PM, and its piezoelectric strain coefficient matrix becomes:Generally, only three piezoelectric strain constants of the rTε33) and perpendicular (rTε11) to the polarization direction. The matrix form of the relative permittivity rTε is as follows:After PM polarization, the relative permittivity mainly exists in the directions that are both parallel  is as below:Ec is the same as that of the flexibility coefficient matrix ES, which is also determined by the PIC 255 crystal system type as presented in Equation (6):The form of the stiffness coefficient matrix e strictly follows the form of strain coefficient matrix d; it is listed below:Similarly, the form of piezoelectric stress coefficient matrix rSε matrix form is consistent with that of the rTε matrix form as presented in Equation (8):This rTε and rSε is shown in Equation (9):The relationship between d can better explain the deformation principle of the PM’s stack which is composed of multiple basic PM units. The advantages of the piezoelectric stack are long stroke and high displacement accuracy. The basic movement of the actuation leg is realized by d33 mode deformation in domain 2.1 and d15 mode deformation in domain 2.2.Introducing the strain coefficient matrix n basic PMs, and the electric field direction is the same as the polarization direction which is shown in d33 mode longitudinal deformation in domain 2.1 is expressed as:When the piezoelectric stack consists of d15 mode shear deformation in domain 2.2 is expressed as:If the electric field direction is perpendicular to the polarization direction which is seen in n pieces of PM that are loaded with V volts is equivalent to that of a block of PM that is loaded with nV volts.Assuming that the deformation degree of a basic PM unit is large enough, according to Equations (10) and (11), the deformation effect of X, Y, Z), in general, the polarization direction of the PM defaults to the z-axis, so the d33 mode deformation in domain 2.1 is realized by applying an electric field in the z-axis, as presented in d15 mode deformation in domain 2.2, a rotation coordinate system  is configured to domain 2.2, making the polarization direction perpendicular to the electric field direction. The definition of the Euler angles α, β, γ are shown in Supposing that the PIM simulation model is established in a global coordinate system , which needs to be determined in the solution process. In addition, the contact condition is a unilateral inequality constraint.The contact friction among the four actuation feet and the driving shaft is analyzed by a Coulomb model. Consequently, the tangential traction is obtained from Equation (14), namely, PIM driving force.The increase of nonlinear components makes the convergence of the model very difficult. It is possible to solve the convergence problem and determine contact traction and friction force by setting the maximum number of iterations in the model solver.V is the input voltage signal, P is the polarization vector field, ρ is the space charge density, and rε is the relative permittivity matrix; it could be rTε or rSε, depending on the form of the piezoelectric constitutive equation used. Both E and D can be solved by Equation (15a) and (15b):The electrostatic physical field is applied to domains 2.1 and 2.2. The entire physical process can be described by Equation (15):In this section, the material properties of domain 1, domains 2.1 and 2.2, and domain 3 are introduced, respectively. Next, the input excitation is set to achieve the PIM movement. Finally, the model mesh is described.ES, d, rTε in the strain–charge equation and Ec, e, rSε in the stress–charge equation.Based on the above theory analysis, the coefficient characteristics matrices of PIC255 in domain 2.1 and domain 2.2 are the key information that must be determined, for example, ES, rTd, ε in the strain–charge are shown in The coefficient matrices, Ec, e, rSε in the stress–charge equation are shown in The coefficient matrices, As for the driving shaft in the domain 1 and fixed axle in the domain 3, C1045 is the driving shaft material according to Reference . Steel AThe movement state of the non-adjacent actuation legs is the same;For the adjacent actuation legs, when one leg has completed all movements in one cycle, the other leg starts to follow same movements.The laws of motion of the PIM can be obtained from Reference  and summm0 and m1 represent the magnification factor of the longitudinal and shear deformation respectively, depending on number of layers of piezoelectric stack. There, these two magnification factors correspond to n in Equations (10) and (11). The m0 and m1 of P-123.01, P-143.01, P-153.01 are presented in U is the initial voltage, corresponding to V in the Equation (15). T is the time that requires to complete all movement gesture of one actuation leg. The excitation function needs to be smooth to prevent the model from non-convergence when the magnification factors or initial voltage is set large. If the smooth effect is not good, the smooth transition area needs to be enlarged. According to the official recommended voltage of PIC255, U is set to 250 volts. Moreover, T is one second, the solver time step should be set depending on the model convergence, and the recommended step size is 1 × 10−2 to 1 × 10−5. The four types of excitation functions are listed below:In response to coupling and nonlinear problems, a fine mesh was utilized to divide the model, but it can bring about a sharp increase in the degree of solution freedom. The computer memory and solution time were greatly increased which seriously reduced the efficiency of model debugging. Therefore, the principle of mesh generation is to minimize the number of meshes without affecting model solution. d33 mode and d15 mode), driving force, and travel of the PIM were the key factors in the PIM design. All of the above were closely related to the material properties and size  of the PIC255.The realization of the PIM motion gesture determined the feasibility of the multi-physics field simulation model of PIM. The motion gesture , the piezoelectric stack will be broken in the shear direction. The maximum driving force was equal to the maximum shear load.Equation (17) is the absolute error formula. T) as illustrated in The travel is the displacement of the driving shaft in the shear direction during the action completed period (In this paper, a multi-physics coupling model of an inchworm motor was established based on PI Motion’s advanced inchworm motor products. The motion gesture and performance parameters of the inchworm motor were the research priority for the motor design and performance estimation. Three different sizes of products, including P123.01, P143.01, P153.01, were simulated with 250 V initial voltage in one second. The key piezoelectric material was PIC255. Two conclusions are as follows:d33 and d15 mode deformations of PIC255 were perfectly realized by simulation. That is, the inchworm’s gesture characteristic was perfectly realized, and it is highly consistent with PI Motion’s products. The realization of motion gesture proves that the multi physical field coupling simulation is feasible for assisting the PIM design.The The maximum driving force error of P123.01 between the simulation data and the official data was 2.8%, the errors for P143.01 and P153.01 were 1.50% and 1.65%, respectively. The driving forces of the three types of products obtained from the simulation were close to those provided by the actual official data. In addition, the driving force increases with the increasing piezoelectric material size. As for the PIM travel, there was a maximum travel error of 0.6 μm between the simulation and the official standard for the P123.01, the errors for P143.01 and P153.01 were 0.1 μm and 0.02 μm. The travel data from the simulation and the official standard was well matched. So, the performance of the PIM can be directly obtained by multi-physics field coupling simulation.In conclusion, the multi-physics field coupling simulation model involving solid mechanics and electrostatics is suitable for PIM performance evaluation and assisting motor design.


The three protocols listed here describe how to perform a CD‐Search (Basic Protocol 1), a Batch CD‐Search (Basic Protocol 2), and a Standalone RPS‐BLAST and rpsbproc (Basic Protocol 3). © 2019 The Authors.The Conserved Domain Database (CDD) is a freely available resource for the annotation of sequences with the locations of conserved protein domain footprints, as well as functional sites and motifs inferred from these footprints. It includes protein domain and protein family models curated in house by CDD staff, as well as imported from a variety of other sources. The latest CDD release  contains more than 57,000 domain models, of which almost 15,000 were curated by CDD staff. The CDD curation effort increases coverage and provides finer‐grained classifications of common and widely distributed protein domain families, for which a wealth of functional and structural data have become available. The CDD maintains both live search capabilities and an archive of pre‐computed domain annotations for a selected subset of sequences tracked by the NCBI's Entrez protein database. These can be retrieved or computed for a single sequence using CD‐Search or in bulk using Batch CD‐Search, or computed via standalone RPS‐BLAST plus the rpsbproc software package. The CDD can be accessed via Basic Protocol 1: CD‐searchBasic Protocol 2: Batch CD‐searchBasic Protocol 3: Standalone RPS‐BLAST and rpsbproc The Conserved Domain Database (CDD) of the National Center for Biotechnology Information (NCBI) is a collection of protein family and protein domain models. A domain is defined as a compact, discrete unit of 3D structure, typically in the range of 50 to 200 amino acids in size, and as a unit of molecular evolution that can be utilized to establish evolutionary classifications; a domain is usually associated with discrete aspects of protein function, such as enzyme activity, membrane transport, or nucleic‐acid binding, to name a few. Domain models in the CDD include many fine‐grained hierarchical classifications for selected domain families established with the help of phylogenetic analyses and manually curated by CDD staff, as well as sets of domain models imported from external high‐quality and comprehensive resources, collected as annotated multiple sequence alignments and converted into position‐specific score matrices. The current CDD collection (version 3.17) contains 57,242 total models: 14,908 models from the CDD curation effort, 35 NCBIfams  domain and site annotations for the majority of protein sequences in the NCBI's Entrez system. The CDD has been integrated with several resources at the NCBI, including BLAST, Protein, and Gene, and with external collections such as InterPro , in order to provide a comprehensive workflow that will fit most user's needs.The conserved domain summary pages give access to a wealth of data associated with each domain family, including hierarchical classifications, taxonomic information, sequence alignments, structural interaction data, domain architectures, functional site annotations, and literature. Figure https://www.ncbi.nlm.nih.gov/cdd/) to access the CDD's domain information in the CDD resource. In Basic Protocols You can access the CDD resource by using CD‐Search for a single nucleotide or protein sequence query, Batch CD‐Search for up to 4000 queries at a time, or standalone RPS‐BLAST plus rpsbproc running searches on your local infrastructure. You can also query Entrez  number1https://www.ncbi.nlm.nih.gov/Structure/cdd/wrpsb.cgi  in the List of domain hits.Scroll over the cartoon of the CD domain to reveal a pop‐up panel showing the An example of this type of result for a CD domain is shown in Figure 8List of domain hits to see how your query is aligned with the domain model.Click on the plus [+] in the An example of an expanded domain hit for a CD domain is shown in Figure 9To launch and view the CD summary page on your domain of interest, click on the CD link in the List of Domain Hits, and click on the cartoon “bubble” of the CD of interest or on the symbols (triangles) indicating the location of feature annotations. Invoking the CD summary pages via links from the Graphical Summary will result in your query imbedded into the sequence alignment on the CD summary page.An example of a CD domain summary page is shown in Figure Graphic Summary tab. If conserved domain hits are detected on the query sequence, you will see the message “Putative conserved domains have been detected”; clicking on the image below this message will take you to the familiar CD‐Search results page for your query.CD‐Search results can also be accessed through the protein BLAST results pages, because CD‐Searches are also run during protein BLAST searches. In the recently revised BLAST results pages, adopted in August 2019, CD‐Search results appear under the An example of a BLAST search of gi 223460687 (mouse myosin VIIB protein) showing the CD‐Search result is shown in Figure Basic Protocol 2https://www.ncbi.nlm.nih.gov/Structure/bwrpsb/bwrpsb.cgi) to compute and retrieve domain annotations for a batch of protein queries. Basic Protocol Use Batch CD‐Search , accession number, or gi (GeneInfo) number, and separated by line breaks; different query types can be mixed in a single Batch CD‐Search  text box, so that you will be notified when the job is complete.Input your email address(s) in the If you enter multiple email addresses, separate these with commas.4Submit” button or hitting Enter.Run Batch CD‐Search by pressing the “Batch CD‐Search will run with the default settings.5Sample data.View the Preliminary Results. If the search has been successful, a preliminary web page will be returned displaying the message “Search completed successfully” and with The sample data include an indication of whether the domain hit is a specific or a superfamily hit, the PSSM‐ID, the from‐to domain intervals, the E‐value, BitScore, the domain accession, the domain short names, and the CDD Superfamily cluster. An example of preliminary web page output obtained using the myosin motor domain test set provided is shown in Figure 6Search‐ID: Save the complete Search ID string found at the top of the Statistics box to access the complete results (master data structure) for up to 2 days after the search is first run.Save the 7Browse the complete results (master data structure).Browse results and/or Download data.You have the option to 8Browse results button on the Preliminary Results web page.Press the This launches a page similar to the one shown in Figure 9Navigate Results panel, select multiple query sequences by holding down the keyboard Ctrl key and using the keyboard arrow keys to scroll through the query list. Then press the Show selected queries button to display your selections.Browse and compare multiple results. In the The default redundancy is to view Concise results and show functional sites. You can customize both redundancy  and whether to show functional sites, either on an individual protein basis or for the complete set. An example of this for the myosin motor domain test set is shown in Figure 10Navigate Results panel, check the Compact Mode box, and then press the Show selected queries button.To view results in Compact mode, in the An example of Compact‐view output for the myosin motor domain test set is shown in Figure In this mode you can compare, at a glance, the domain architectures and different domains of your selected sequences.11Navigate Results panel, select a query sequence, and then press the Search for similar architectures button.To search for similar architectures, in the Search for similar architecture  program, which looks for domain architectures  that are similar at the domain superfamily level to that of your selected protein. Each hit represents a unique DA that contains one or more domains of the selected protein. CDART groups together proteins that share the same DA. Clicking on the (+) beside a CDART hit opens a list of representative, nonredundant (nr) sequences in that group.12Browse results page, you can download three categories of Target Data: Domain hits, Align details, and Features.From the You can also do this from the Preliminary web page Figure .13Browse results page, select the Download data panel, with the default setting (Target data: Domain Hits and Data mode: Concise), and press the Download button.To download Domain Hit Data, on the An example Domain Hits output for the myosin motor domain test set is shown in Figure defline and include the domain defline boxes, as well as defining which level of DataMode/Redundancy you prefer: Concise, Standard, or Full. The Domain Hits output includes the Query , the Hit Type (specific hits or superfamily hit), the domain from‐to interval, the E‐value, the BitScore (defined at https://www.ncbi.nlm.nih.gov/books/NBK21106/def‐item/app8/) for comparing alignment scores between different CD‐Searches, the domain's CDD accession number, and the short name.The Domain hits output is a tab‐delimited table that returns CDD models that have statistically significant hits to the protein sequences in your query list. Additional options are available when downloading these data. For example, to check the Superfamily only, include the query 14Browse results page, in the Download data panel, with appropriate settings , press the Download button.To download Alignment details data, from the An example Align details output for the myosin motor domain test set is shown in Figure Align format as ASN.1 (Abstract Syntax Notation One) text, XML (Extensible Markup Language), or JSON (JavaScript Object Notation).The Align details output is in a familiar BLAST format, with pairwise alignment details between the query sequence and the consensus sequence from each domain model or superfamily that has a hit. Exact matches are marked by a pipe symbol “I” between the query and consensus sequence. In addition to BLAST Text, you have the option to download Download data panel in the Browse results page, with appropriate settings , and press the Download button.To download Features Data, open the An example of Features output for the myosin motor domain test set is shown in Figure The Features output is a tab‐delimited table that lists conserved features annotated on the domain models as mapped to the query; it includes the Query number, the Type (categorization as specific or nonspecific hit), the Title of the feature, the Coordinates (single‐letter amino acid codes and their positions on the query sequence), the complete size , the mapped size (the number of residues in the query protein that match residues in the feature that was annotated on the domain model), and the source domain (the PSSM ID of the domain model on which the feature was annotated).Basic Protocol 3https://ftp.ncbi.nih.gov/pub/mmdb/cdd/rpsbproc/e) to compute and retrieve domain annotation programmatically. Basic Protocol Use Standalone RPS‐BLAST and rpsbproc , Windows Command Processor (Windows), and Terminal (Mac), respectivelyThe gzip utility, required to decompress files: For the Linux and Mac platforms, commonly a built‐in utility by default; for the Windows platform, the specified software, including 7‐Zip, WinZip, and others, can be usedhttps://curl.haxx.se/download.html and installed manuallyThe curl utility, for downloading files from FTP site : For the Linux platform, commonly installed by default; for Windows and Mac platforms, can be downloaded from Specific FTP software, for downloading files from FTP site more efficiently : e.g., FileZillaInput queries in FASTA format: i.e., protein or nucleotide sequenceshttps://ftp.ncbi.nih.gov/pub/mmdb/cdd/rpsbproc/README).Detailed instructions on how to retrieve the RPS‐BLAST executable and rpsbproc utility and run them locally can be found in the rpsbproc README file at the CDD FTP site . Run the command rpsblast with argument “‐help” to check the usage information  is available at the NCBI BLAST FTP site and as part of the NCBI C++ toolkit distribution. Detailed documentation for BLAST at NCBI, including RPS‐BLAST, can be found in BLASTn Figure .E‐value threshold (by default set to 10), sorted by E‐value. For each hit, information such as the conserved domain's PSSMID, a set of scores , and the sequence alignment between the conserved domain and the query sequence can be returned. In order to run the rpsbproc utility, the output file generated by RPS‐BLAST executables needs to be stored in ASN.1 format, using “.asn” as the filename extension.For each query sequence, standalone RPS‐BLAST lists the conserved domain models that scored below a certain ‐help” to check the usage information . rpsbproc reads the output of rpsblast/rpstblastn and fills in domain superfamily and functional site information, as well as structural motifs, for each region of the sequence. It then re‐sorts the hits and calculates a set of nonredundant representative hits. The result is presented in a tab‐delimited flat file and can be looked at either programmatically or manually. Run rpsbproc command with argument “n Figure .To run RPS‐BLAST locally and use rpsbproc to process the output, you must first collect the applications needed. You can download the pre‐built rpsblast, rpstblastn, and rpsbproc binaries from the NCBI FTP site, which are directly executable on Windows and Linux platforms, with no complex installation required. For those who need (or desire) to build these utilities locally, you can download the source code tarballs from the NCBI FTP site. Please note that these programs are NCBI C++ toolkit applications and require the NCBI C++ toolkit to build. Please follow the README file to build these utilities locally. For Linux and Mac users, please refer to the rpsbproc README file for detailed instruction to run standalone RPS‐BLAST and rpsbproc utility. Below are step‐by‐step instructions for running these executables on a Windows platform.1https://ftp.ncbi.nih.gov/pub/mmdb/cdd/rpsbproc/README) to your project folder for reference in the following steps.Download the rpsbproc README file  to the project folder from the NCBI BLAST FTP site (https://ftp.ncbi.nih.gov/blast/executables/LATEST/). Then, open the Windows Command Processor (cmd.exe) and navigate to the project folder to run the command below to uncompress the downloaded file, which creates a folder named ncbi‐blast‐2.9.0+ in the project folder.tar ‐zxf "ncbi‐blast‐2.9.0+‐x64‐win64.tar.gz"Retrieve the RPS‐BLAST executable by downloading the RPS‐BLAST executable  to the project folder from the NCBI CDD FTP site (https://ftp.ncbi.nih.gov/pub/mmdb/cdd/rpsbproc/). In the Windows Command Processor, navigate to the project folder and run the command below to uncompress the zip‐file downloaded.tar ‐zxf rpsbproc‐0.5.0‐x64‐win.zipRetrieve the rpsbproc executable by downloading the executable (Now you have rpsbproc.exe and rpsbproc.exe.manifest files in the project folder.4https://ftp.ncbi.nih.gov/pub/mmdb/cdd/little_endian/) to the folder named db under the project folder. Uncompress the files separately in the current db directory using the commands below:tar ‐zxf Cdd_LE.tar.gztar ‐zxf Cdd_NCBI_LE.tar.gztar ‐zxf Cog_LE.tar.gztar ‐zxf Kog_LE.tar.gztar ‐zxf Pfam_LE.tar.gztar ‐zxf Prk_LE.tar.gztar ‐zxf Smart_LE.tar.gztar ‐zxf Tigr_LE.tar.gzCreate the search database for RPS‐BLAST by downloading the preformatted search database (files) from the CDD FTP site (5https://ftp.ncbi.nih.gov/pub/mmdb/cdd/) to the data folder.bitscore_specific.txtcddannot.dat.gzcddannot_generic.dat.gzcddid.tbl.gzcdtrack.txtfamily_superfamily_linksCreate the data folder by downloading the domain‐annotation files (listed below) from the CDD FTP site . Navigate to the project folder and run RPS‐BLAST using the command below. Backslashes are used because this command is run on a Windows command processor.rpsblast.exe ‐query sequence.fasta ‐db .\db\Cdd ‐evalue 0.01 ‐outfmt 11 ‐out sequence.asnRun RPS‐BLAST by opening the Windows Command Processor .The resulting CD‐Search results display contains three sections: Protein classification, Graphical summary, and List of domain hits for the query. At the top, above the protein classification section, it shows the query as well as the view that is currently being used . The CD Summary page can be launched from either the Graphical summary or the List of domain hits and contains detailed information about your domain of interest.The Protein classification section displays a suggested name for the query protein, a label that may specify a suggested function, and a link to the SPARCLE . The full display format shows all matching domain models identified by RPS‐BLAST for each region of the query sequence and can be very redundant. The display can be customized to hide the display of site annotation features by selecting the show extra options and deselecting the Show site features, as well as magnifying the display using the Horizontal zoom and the Zoom to residue level selections.The Graphical summary shows the domain hits and annotated features. Feature annotations are denoted by triangles colored the same as the domains they correspond to. The Hovering over the triangles in the site features triggers a pop‐up window with information on the number of feature residues that map to the query sequence. Clicking on the triangle takes you to the CD summary page, where your query is embedded into the CD alignment with the residues involved in the site features highlighted and marked with hash marks (#).List of domain hits panel. Clicking on the domain graphic also takes you to the CDD page with your query embedded into the CD alignment.Hovering over the domains triggers a pop‐up window with a description of the domain and highlights the corresponding row in the List of domain hits lists the conserved domains identified on the query sequence. For each conserved domain identified, it displays its short name, its accession number, a description of the domain, the interval on the query that is covered by the domain footprint, and the E‐value. Clicking on the (+) next to each name reveals the full description of the domain and shows the alignment of the query sequence to a representative (consensus) sequence of the domain model, together with the numerical domain model identifier (PSSM‐ID) and the alignment bit score. Click on the domain model's accession number to view the multiple sequence alignments of the proteins used to develop the corresponding domain model. Note that your query sequence is not embedded in this version of the CD summary page.The BLAST Request ID (RID) is shown at the bottom of the Standard and Full displays and allows you to retrieve the search results using the RID anytime within the 36 hr following the search, without having to re‐execute it.If a live search was performed, the Refine Search button (which will retain your query) or select New Search from the selection bar immediately below the logo at the top of the page. Go to the OPTIONS panel. Use the Search against database option pulldown to select a specific database. Change the E‐value to stricter or more permissive by changing the value in the Expect Value threshold option. If you would like to mask out compositionally biased regions, check Apply low‐complexity filter . Composition based statistics adjustment, which is selected by default, abolishes the need to mask out compositionally biased regions in query sequences, for the most part. Keep both the Composition based statistics adjustment and the Apply low‐complexity filter options on at the same time to filter out some false positives that may still slip through the cracks of the composition‐correction, or click both of them off to find more distant relatives for compositionally biased queries. To perform a live search, check the Force live search box (it will be checked if you choose settings different from the CD‐Search default). You can also Rescue borderline hits and Suppress weak overlapping hits by selecting the appropriate boxes  of the annotated site(s), evidence of various types , and a tab for PubMed References that lists relevant articles about the specific domain or protein family and more generic reviews of the wider superfamily. Annotation selections are highlighted in the Sequence Alignment panel and noted by hash marks at the very bottom of the page that show how the query sequence is aligned with respect to the CD sequences.At the top of the CD summary page, you will see the CD accession number and a description of the CD. Below this you may see a box with a tab for Sequence Cluster tree of the CD that matched your query. If the domain is part of a hierarchical classification, you will also see a tree‐like representation of that hierarchy, with the CD that matched your query highlighted with a dark blue background, as shown in Figure Below the Conserved Features/Sites and PubMed References panel, there is a Interactive Display with the CDTree button after selecting to download the selected CD or the entire hierarchy for viewing and further analysis with the CDTree software package .Click on the Links , Statistics , and Structure information, where you can download Cn3d, a molecular structure and multiple sequence alignment viewer (https://www.ncbi.nlm.nih.gov/Structure/CN3D/cn3d.shtml) to visualize and manipulate the sequence and structure alignment for your query in the context of its CD hit.The right hand side of the CD summary page contains information blocks titled Interactive View after setting the number of aligned rows that you would like displayed in Cn3D. Upon launching, it displays three panels: a CDD Descriptive Items panel that shows some of the information found on the CD summary page , a visualization window that shows the model's 3D structures if present, and a multiple sequence alignment window containing the query sequence embedded in the CD alignment.Select the E‐values and scores, domain model names and accessions, and the positions of functional sites such as catalytic residues, binding sites, and motifs. A wealth of information on your protein collection is returned in a single search.Basic Protocol Browsing results, please find help for interpreting the graphical results for each individual protein in Basic Protocol When Basic Protocol automatic search, which either runs a live RPS‐BLAST search or retrieves precalculated results for each single item on the list depending on its sequence format. For most query sequences specified via sequence identifiers, precalculated RPS‐BLAST results are available and will be retrieved; if no results are available, a live search will be executed. For queries entered as FASTA or raw sequence, live searches will be run. You may opt for a Live search only mode, which runs a live RPS‐BLAST search for every item on the query list, or a Pre‐computed only mode, which only retrieves a precalculated RPS‐BLAST result where available but will ignore other queries.The default search mode described is the automatic search) runs against the complete CDD database  at an E‐value threshold of 0.01. The Search against database pull‐down menu provides the option to limit your search to only the NCBI in‐house‐curated subset, or to any one of the other databases included in the CDD. The current version number for each database can be found at https://www.ncbi.nlm.nih.gov/Structure/cdd/docs/cdd_news.html. You also have the option to enter a different E‐value threshold.The default mode . The data section always starts with a DATA token and ends with an ENDDATA token. In between, there can be several sessions, each of which start with a SESSION token and end with an ENDSESSION token. Each session is given an ordinal and unique number, which is known as the session ID. Each session is composed of queries, which are unit blocks of sessions. Every single query block contains three optional sections, namely domains, sites, and motifs. The full structure of the data section is illustrated in Figure E‐value; bit score, accession; short name; and whether the alignment is incomplete on the N terminus, C terminus, or both; and superfamily PSSM ID  can give you (the user) a wealth of information about your protein of interest. In the cases of unclassified or novel proteins, this domain information provides vital clues to protein function, and often domain annotation is the only available hint toward molecular and cellular function for novel uncharacterized proteins.In addition to results from the in‐house curation effort, the CDD contains domain models from external sources such as Pfam. Agreement between annotations from two or more resources provides users confidence about the domains identified, whereas disagreements between them—which may be as trivial as different domain boundary definitions, or more serious in the case where different functional domains are identified for the same region of a query—may indicate that results should be interpreted with caution.E‐values and scores, and the locations of functional sites and interactions—can for larger numbers of queries be returned in a tabular form suitable for further processing.The three CD‐Search protocols described in this paper outline methods for users to submit queries of a single protein or in batches of very large numbers of proteins. The results from these searches—such as domain model identification and accessions, domain footprints  on the query, The CDD was first described in the literature in 2002  on August 5, 2019. The rspbproc utility available at the CDD FTP site was the version released June 29, 2015. The searches were carried out in August 2019 against CDD database version 3.17, released April 3, 2019. Please note that using updated versions of the CDD database, RPS‐BLAST, and rpsbproc utility may result in slightly different results.To demonstrate the various CD‐Searches for Basic Protocols https://www.ncbi.nlm.nih.gov/sparcle). For example, you could search in SPARCLE/advanced search builder with "Myosin" in the name field. Detailed SPARCLE help is available by clicking the question mark box on the SPARCLE results page.The CDD predicts domains on your protein(s) of interest and provides important clues about its function. To pursue options for further analysis, readers are encouraged to launch SPARCLE (the Subfamily Protein Architecture Labeling Engine; see Guidelines for Understanding Results section on Basic Protocol https://www.ncbi.nlm.nih.gov/Structure/cdd/cdd_help.shtml#BatchRPSBWebAPIThe three CD‐Search protocols in this paper describe querying a single protein and large numbers of proteins, interacting with the CDD though its web interfaces or programmatically. You may also want to try Batch CD‐Search as an interface for scripted data retrieval. A query can be submitted as either an HTTP GET or an HTTP POST request. An HTTP GET request is submitted as a URL. The program performs the search, collects all the data into a master data structure, and extracts the subset of information you have requested for the final output. The Base URL, valid parameters, and examples of URLs for HTTP GET requests, as well as sample PERL scripts for HTTP POST operations, can be found at: Note that unlike running CD‐Search and Batch CD‐Search, running RPSBLAST is time consuming. It takes 2 s on average to process one protein or nucleotide sequence; thus, for instance, if you have 10,000 sequences in your FASTA file, it may take 5 to 6 hr to finish. However, the rpsbproc processing is fairly quick: it takes only >30 s to process the RPS‐BLAST output of 10,000 protein sequences.Help documentation is provided in Table     Click here for additional data file.


As a part of the innate immune system, humans encode proteins that inhibit viruses such as HIV-1. These broadly acting antiviral proteins do not protect humans from viral infections because viruses encode proteins that antagonize the host antiviral proteins to evade the innate immune system. One such example of a host antiviral protein is APOBEC3C (A3C), which weakly inhibits HIV-1. Here, we show that we can improve the antiviral activity of A3C by duplicating the DNA sequence to create a synthetic tandem domain and, furthermore, that the proteins thus generated are relatively resistant to the viral antagonist Vif. Together, these data give insights about how nature has evolved a defense against viral pathogens such as HIV. apolipoprotein B mRNA-editing enzyme catalytic polypeptide-like 3 , act by deaminating cytidines to uridines during the reverse transcription reaction of HIV-1. The A3 locus encodes seven genes, named A3A to A3H. These genes have either one or two cytidine deaminase domains, and several of these A3s potently restrict HIV-1. A3C, which has only a single cytidine deaminase domain, however, inhibits HIV-1 only very weakly. We tested novel double domain protein combinations by genetically linking two A3C genes to make a synthetic tandem domain protein. This protein created a “super restriction factor” that had more potent antiviral activity than the native A3C protein, which correlated with increased packaging into virions. Furthermore, disabling one of the active sites of the synthetic tandem domain protein resulted in an even greater increase in the antiviral activity—recapitulating a similar evolution seen in A3F and A3G . These A3C tandem domain proteins do not have an increase in mutational activity but instead inhibit formation of reverse transcription products, which correlates with their ability to form large higher-order complexes in cells. Finally, the A3C-A3C super restriction factor largely escaped antagonism by the HIV-1 viral protein Vif.Humans encode proteins, called restriction factors, that inhibit replication of viruses such as HIV-1. The members of one family of antiviral proteins,  The evoS188) and the more active variant of A3C (A3CI188). Remarkably, we found that all A3C tandem domain variants had greater antiviral activity than their single domain counterparts. We found that the tandem deaminase A3C variants are packaged into virions at higher levels than their single-domain counterparts, likely explaining the majority of the increase in antiviral activity. In the natural double domain APOBEC3 proteins, only the C-terminal cytidine deaminase active sites are used for hypermutation  that is naturally found between the N- and C-terminal domains of A3D and A3F, as well as the natural deletion in the N terminus of the second domain of A3C such that the C-terminal domain of A3C-A3C begins at the fifth amino acid of A3C, a methionine  amino acid sequence that links the two deaminase domains is highlighted in yellow. The end of the first domain and the beginning of the second domain of A3C-A3C are delineated with a red arrow. The isoleucine human polymorphism in each of the domains is shown in blue text. The conserved A3 cytidine deaminase motif, His-X-Glu-XCopyright © 2020 McDonnell et al.2020McDonnell et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the I188-A3CI188 protein. However, the tandem domain A3C proteins were better packaged into virions than the natural single domain A3 proteins. In virions, we found that there was about a 1.6-fold increase in packaging for A3CS188-A3CS188 compared to A3CS188 and about a 2.9-fold increase in packaging of A3CI188-A3CI188 relative to A3CS188 , the protein completely lost its relatively weak antiviral activity , the antiviral activity did not change significantly  compared to the wild-type A3CI188-A3CI188. Similarly to A3CS188-A3CS188, the N-terminal active-site mutant had antiviral activity that was not statistically significant from that seen with the wild-type A3CI188-A3CI188  Single-cycle infectivity assay measuring percent infectivity of each A3C variant and active-site mutant against HIV-1ΔEnvΔVif. Results from each experiment were normalized to those from a no-A3 control. The bar graph shows means results of three biological replicates, each performed with triplicate infections. Error bars represent the SEM. Statistical differences were determined by unpaired t tests and are indicated as follows: *, P ≤ 0.05; **, P ≤ 0.01; ns, not significant. Download FIG S2, PDF file, 0.1 MB.(A) Western blot analysis of intracellular expression levels of active-site point mutation in A3CCopyright © 2020 McDonnell et al.2020McDonnell et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the pol. The number of unique sequencing reads for each condition ranged from 90,833 to 905,916. Only 0.2% of the reads from the plasmid-only control had any G-to-A mutations  reduced relative to that made in the presence of A3CS188  soluble control  or a primary-isolate Vif (patient identifier [ID] 1203) that has been previously shown to degrade A3H and A3C variants , and A3CI188-A3CI188 inhibited infection to 10% with both HIV-1 Vifs . We found that the A3C-A3C tandem domain proteins did not show an increase in mutation frequency compared to their single domain counterparts but rather that the dominant mechanism of restriction operated through inhibition of reverse transcription. Additionally, all the A3C tandem domain variants were found to be resistant to HIV-1 Vif degradation. Together, these data point toward a selective advantage of double domain versus single domain A3 proteins against a viral target and support the idea that additional potency and escape from viral antagonism can be derived from combinations of APOBEC3 domains that have not been sampled in nature.Although human A3C can weakly inhibit HIV-1 replication, here we show that a super restriction factor can be created by linking two A3C sequences. These tandem domain proteins both increase anti-HIV-1 activity and yield a restriction factor that is partially resistant to antagonism by HIV-1 Vif. We have shown that this increase in antiviral activity is mostly explained by increased packaging of tandem domain A3s into budding virions. Moreover, we found that A3CIn order for the A3 proteins to be antiviral, they must be packaged into virions to function during the reverse transcription process that occurs in the target cell. We found that the increases in the antiviral activity of the A3C tandem domain proteins relative to their single domain counterparts closely paralleled the increased activity of A3 packaged into budding virions  and 2. H35–S188-A3CS188 double-inactive-site mutant (A3CS188-A3CS188 E68A E254A) still retained antiviral activity that, in fact, was indistinguishable from the activity seen with wild-type A3CS188-A3CS188  and was codon optimized to distinguish the N terminus from the C terminus. The codon-optimized N terminus was linked to the single domain human A3C at the C terminus using a short amino acid sequence that is found in A3F and A3D, Arg-Asn-Pro (RNP), and that is naturally found in the A3C single domain. The C-terminal domain begins at amino acid 6 (15-nucleotide deletion) to include this RNP sequence and extends to include the remainder of A3C followed by a flexible linker and a triple FLAG (3XFLAG) tag. The A3C-A3C tandem domain was constructed using overlapping extension PCR as described previously  and was 2 incubator. SUPT1 cells, acquired from ATCC (CRL-1942), were maintained similarly but in RPMI medium , 10% HyClone fetal bovine serum , 1% penicillin-streptomycin , and 10 mM HEPES. HEK293T cells were plated in 6-well dishes for transfections at a density of 1.5 × 105 cells/ml.HEK293T cells  were cultured in Dulbecco’s modified Eagle’s medium (DMEM)  with 10% HyClone fetal bovine serum  and 1% penicillin-streptomycin  at 37°C in a humidified COIntracellular expression of APOBEC3 proteins during virion production was determined by lysis of the virion-producing 293T cells with NP-40 buffer with protease inhibitor . Lysate samples were resolved on a 4% to 12% SDS-PAGE gel using morpholineethanesulfonic acid (MES) buffer and transferred to a nitrocellulose membrane for Western blotting. Anti-FLAG , anti-tubulin , and anti-p24gag  , 40 antiThe amount of A3 packaged into virions was evaluated by cotransfecting 600 ng pcDNA4/TO.A3.3XFLAG and 1,000 ng HIV-1ΔVifΔEnvLuc2 (unless otherwise denoted) in a 6-well plate. At 3 days posttransfection, cell lysates were harvested as described above and 1.5 ml of the supernatant was collected, filtered through a 0.2-μm-pore-size filter, and spun down using a tabletop microcentrifuge for 1 h at maximum speed at 4°C to pellet the virions. The supernatant was aspirated off, and 25 μl of NuPAGE 4× loading dye  was added to each sample. Samples were boiled for 10 min at 95°C and loaded on an SDS-PAGE gel.5 cells/ml in a 6-well plate. The following day, cells were transfected with 600 ng provirus , 100 ng l-VSV-G (vesicular stomatitis virus G), and 400 ng pcDNA4/TO.A3.3XFLAG or pcDNA4/TOPO empty vector unless otherwise indicated. At 72 h later, virus was harvested and normalized for virion production using an RT-qPCR assay, as described previously  and luciferase expression was measured on a luminometer . Infectivity of each virus was normalized to 100% based on a no-A3 control. All HIV-1 constructs are based on the LAI strain. A clade B patient-derived Vif (PID: 1203) that is able to antagonize A3H haplotype II was previously described , 100 ng l-VSV-G, and 400 ng pcDNA4/TO.A3.3XFLAG or pcDNA4/TO empty vector. Three days later, virions were harvested and quantified similarly to the single-cycle infectivity assay mentioned above. A total of 2 × 106 SUPT1 cells were infected with 500 μl of each Benzonase-treated virus with and without a 10 mM nevirapine control and subjected to spinoculation at 1,100 × g for 30 min at 30°C. Twelve hours later, unintegrated viral cDNA was isolated using a Qiagen miniprep kit .To analyze A3-mediated mutations, 293T cells were seeded at a density of 1.5 × 10https://jbloomlab.github.io/dms_tools2/bcsubamp.html). This approach attaches unique molecular identifiers (8*N) to each DNA molecule, which enables increased correction of PCR and sequencing errors by determining molecule-specific consensus sequences. A region of pol  of a substitution, underwent a substitution (site 0), or was present immediately downstream of a substitution (site +1). We then calculated the frequency for each nucleotide at each of these three sites in each sample. Data for technical replicates were combined by averaging the frequencies of each nucleotide at each site across the two replicates for each sample. Next, we calculated background-corrected frequencies for each nucleotide at each site using the frequencies in the plasmid control sample as our background. We based this frequency correction on the “type 2” logos from Gorodkin et al. .ikq is the frequency of base ikp is that frequency in the background, then the background corrected frequency is as follows:If We then calculated the information content of each site as follows:A is the set of nucleotides  and N is the number of elements in A . HIV cDNA was amplified with TaqMan gene expression master mix , J1 FWD (late RT F)—ACAAGCTAGTACCAGTTGAGCCAGATAAG, J2 REV (late RT R) GCCGTGCGCGCTTCAGCAAGC, and LRT-P (late RT probe)—6-carboxyfluorescein (FAM)-CAGTGGCGCCCGAACAGGGA-6-carboxytetramethylrhodamine (TAMRA)  , 49. Dat2, 0.002 EDTA [pH 8.0], 3.5% Triton X-100) (g) for 37 min at 4°C, as described previously  using a low-salt-and-low-EDTA buffer   with proeviously . GradienPRJNA605864 and run accession numbers SRR11059567 to SRR11059589. The computational pipeline used to analyze the sequencing data and generate https://github.com/jbloomlab/SuperRestrictionFactor_Hypermutation).The sequencing reads were uploaded to the NCBI SRA with BioProject accession number


Rho GTPase signaling promotes proliferation, invasion, and metastasis in a broad spectrum of cancers. Rho GTPase activity is regulated by the deleted in liver cancer (DLC) family of bona fide tumor suppressors which directly inactivate Rho GTPases by stimulating GTP hydrolysis. In addition to a RhoGAP domain, DLC proteins contain a StAR-related lipid transfer (START) domain. START domains in other organisms bind hydrophobic small molecules and can regulate interacting partners or co-occurring domains through a variety of mechanisms. In the case of DLC proteins, their START domain appears to contribute to tumor suppressive activity. However, the nature of this START-directed mechanism, as well as the identities of relevant functional residues, remain virtually unknown. Using the Catalogue of Somatic Mutations in Cancer (COSMIC) dataset and evolutionary and structure-function analyses, we identify several conserved residues likely to be required for START-directed regulation of DLC-1 and DLC-2 tumor-suppressive capabilities. This pan-cancer analysis shows that conserved residues of both START domains are highly overrepresented in cancer cells from a wide range tissues. Interestingly, in DLC-1 and DLC-2, three of these residues form multiple interactions at the tertiary structural level. Furthermore, mutation of any of these residues is predicted to disrupt interactions and thus destabilize the START domain. As such, these mutations would not have emerged from traditional hotspot scans of COSMIC. We propose that evolutionary and structure-function analyses are an underutilized strategy which could be used to unmask cancer-relevant mutations within COSMIC. Our data also suggest DLC-1 and DLC-2 as high-priority candidates for development of novel therapeutics that target their START domain. Rho GTPases are a subfamily of G proteins involved in signal transduction. These proteins regulate multiple pathways, including the actin cytoskeleton, cell polarity, cell cycle progression, and microtubule dynamics . SignaliRho GTPases have a globular structure with a limited druggable surface, making them particularly challenging drug targets ,6,7. EffAdjacent to the DLC RhoGAP domain is a StAR-related lipid transfer (START) domain. START domains adopt a helix-grip fold structure, forming deep hydrophobic binding pockets that undergo conformational changes upon binding of lipophilic ligands ,26,27. SThe presence of a START domain suggests DLC proteins may be regulated by a lipophilic ligand, rendering them more easily targetable by natural or synthetic small molecules. However, while there is considerable in vivo and in vitro evidence for them as bona fide tumor suppressors, the role of their START domain remains poorly understood. In fact, the residues mediating START function remain virtually unknown. This is a key bottleneck for targeted modulation of DLC tumor suppressive activity. Here, we use an evolutionary approach to identify conserved residues in DLC START domains that are also mutated in the Catalogue of Somatic Mutations in Cancer (COSMIC) dataset. We find that conserved residues of DLC-1 and DLC-2 START domains are highly overrepresented in tumor samples from a broad spectrum of cancers. Moreover, using comparative structural modeling, we identify a set of three residues, present in both DLC-1 and DLC-2 START domains, that cluster and interact at the tertiary level. Mutations identified in COSMIC are predicted to break these non-covalent interactions and disrupt START tertiary structure and function. Our results support the notion that the START domain is involved in DLC-mediated tumor suppression and that DLC proteins may, thus, be particularly tractable therapeutic targets. Importantly, these residues would not have emerged from traditional scans for mutational hotspots. As such, we propose that tandem evolutionary and structural analyses could help unmask hidden cancer-relevant mutations within the COSMIC dataset.COSMIC contains numerous missense, nonsense, and frameshift mutations in the START domains of DLC-1, DLC-2, and DLC-3 . We focup-value = 0.006) and DLC-2  START domains . These findings are consistent with the frequent downregulation of DLC-1 and DLC-2, but not DLC-3, in many cancers [To identify functional residues, we performed a multiple sequence alignment (MSA), as evolutionary conservation is a strong indicator of critical residues within protein domains . START d domains . By cont cancers . Taken tConservation of residues implies critical roles in function . For exaInformed by the crystal structure of truncated DLC-2 , we genMutations in COSMIC falling within conserved residues were then mapped to these homology models . StrikinThe close proximity of these residues in three-dimensional space presents the possibility that they may interact at the tertiary level. We examined this idea using CooT  and idenTheir conservation, proximity, and extensive intermolecular interactions suggest that missense mutants in the arginine, serine, or phenylalanine residues would disrupt START structure and function. To test this, we separately replaced each residue with one of the missense mutations identified in COSMIC . We thenIn DLC-1, the R947C mutation abolishes its cation–π interaction with F1078 and hydrogen bond with Q1081 but leaves the S1077–K1073 hydrogen bond intact D. S1077FIn the case of DLC-2, the R969C mutation similarly abolishes its cation–π interaction with F1101, as well as its hydrogen bond to S1100 D. S1100FThe arginine-to-cysteine mutations in both DLC-1 R947C) and DLC-2 (R969C) disrupt cation–π interactions, as well as multiple hydrogen bonds. Thus, we predict this substitution to be the most destabilizing in both START domains. This is also in line with global analyses of the COSMIC dataset, which found that arginine-to-glutamine and arginine-to-cysteine substitutions occur with high frequency in driver mutations C and DLC. These sFunctional residues mediating the tumor suppressive activity of the DLC START domain remain virtually unknown. Here, we undertook a pan-cancer analysis and identified residues within DLC-1 and DLC-2 START domains likely to affect the function of these genes. We also show that COSMIC missense mutations in DLC-1 and DLC-2 START domains are significantly overrepresented in evolutionarily-conserved residues predicted to be critical for proper folding, interaction, and/or activity ,41. ImpoThree of these substitutions, occurring in both DLC-1 and DLC-2, disrupt multiple tertiary-level interactions and are likely to impact START structure. The remaining overrepresented COSMIC missense mutations may similarly impact START activity or affect other properties, such as ligand binding or conformational change. A potential DLC START regulatory mechanism is suggested by studies of both minimal and multidomain START-containing proteins, demonstrating that START domains can stimulate regulatory outputs in an allosteric manner ,35. If tThese data also suggest that mutation hotspots might not capture the full spectrum of cancer-relevant mutations in COSMIC. For instance, the conserved arginine, serine, and phenylalanine residues in DLC-1 and DLC-2 appear to work together to stabilize START structure . Single DLC-1 and DLC-2 are frequently down-regulated, as opposed to mutated, in cancer ,48. For https://cancer.sanger.ac.uk/cosmic) for the dominant isoforms of DLC-1 (DLC1_ENST00000358919.6), DLC-2 (ENST00000336934.9), and DLC-3 (ENST00000374599.7). DLC START domains were identified using HHpred [https://www.ensembl.org/Tools/VEP) [Mutation data were obtained from the COSMIC database  . Missenshttps://www.megasoftware.net/) [To build an MSA and identify conserved residues in DLC START domains, orthologs of DLC-1, DLC-2, and DLC-3 were identified using NCBI’s Blastp program against the reference proteins (refseq_protein) database . In totare.net/)  and trimre.net/) . A strinre.net/) . ResidueThe expected number of mutations in conserved residues assuming a random distribution was calculated by using the percentage of identical residues multiplied by the total number of mutations in the START domain of each paralog.The expected number of mutations in non-conserved residues was determined by subtracting the expected number of mutations in conserved residues from the total number of mutations. Chi-square values were calculated using the formula (Equations (1) and (2)):These summed chi-square values were then calculated using IBM SPSS Statistics 26.https://toolkit.tuebingen.mpg.de/) [https://www2.mrc-lmb.cam.ac.uk/personal/pemsley/coot/) to identify predicted interactions formed with the conserved residues using the Environmental Distances tool with a cut-off of 4.0 Å [Homology modeling of each START domain was performed using the online Max Planck Institute Bioinformatics Toolkit (mpg.de/) . START dmpg.de/)  to prediof 4.0 Å . Mutated


Allostatic load reflects the cumulative strain on organic functions that may gradually evolve into overt disease. Our aim was to evaluate the allostatic parameters in the context of aging, and identify the parameters that may be suitable for an allostatic load index for elderly people (>60 years). From previously published studies, 11 allostatic (bio)markers could be identified that sustain sufficient variability with aging to capture meaningful changes in health status. Based on reported statistics , seven of these could be adopted in a Bayesian Belief Network (BBN), providing the probability of “disturbed” allostasis in any given elder. Additional statistical analyses showed that changes in IL-6 and BMI contributed the most to a “disturbed” allostasis, indicating their prognostic potential in relation to deteriorating health in otherwise generally healthy elderly. In this way, and despite the natural decline in variance that irrevocably alters the prognostic relevance of most allostatic (bio)markers with aging, it appeared possible to outline an allostatic load index specifically for the elderly. The allostatic parameters here identified might consequently be considered a useful basis for future quantitative modelling in the context of  aging. Healthy aging is conceptualized by one’s ability to adapt and efficiently respond to endogenous and/or exogenous stressors that occur throughout one’s lifespan  ,2,3. TheIn their landmark paper, Juster et al.  publisheThe process of aging typically implies a funnel of steadily declining (neuro)endocrine, physiological, and psychophysiological variance components: with time, the natural variance in independent (bio)markers decreases significantly, and consequently, the cumulative overall variance , reflecting a steadily decrease of the adaptive capability of an individual. This process will inevitably lead to critical transitions in health status (towards (chronic) disease and/or mortality), that might likely, though not yet proven, follow specific mathematical trajectories of decay , and is Consequently, previously published studies do not necessarily incorporate all identified—allostatic—parameters  in repoE). Finally, in the elderly, major life events  might have a significant impact on their already relatively ‘frail’ health status, with full ‘allostatic’ recovery becoming ever less likely when aging progresses. Consequently, accurately monitoring even subtle changes in health status, for example anticipating and/or in response to major life events, specifically in populations with a natural decline in adaptive capability, might be considered highly relevant, as illustrated by Thomas and colleagues . To tackE) should be able to capture meaningful changes in the health status of specifically elderly, and/or adequately quantify inter-individual differences in health prognosis. Consequently, we hypothesize that only those allostatic markers with sufficient natural variance in aging populations provide a significant contribution to such a coefficient and should be adopted. For these reasons, the present paper aimed to outline a model quantifying the likelihood of allostatic state being “disturbed”, or an allostatic load being built up, in any given elder. This implies that the resulting coefficient . This means that due to methodological (Bayesian) restrictions, only a small subset of the initial library could be incorporated .For every included study, the following criteria were used to establish eligibility for our modeling efforts: studies should include a (sub)sample of subjects >60 years of age; should incorporate at least one of the 26 allostatic parameters as published by Juster ; and shoExclusion criteria were: the defined allostatic (bio)marker significantly declines with aging, and consequently the expected proportional variance factor in the model rapidly decreases in the elderly  ; outcomeA Bayesian belief network (BBN) is a probabilistic graphical model that represents statistical relationships among variables ,30. To eSuch a model can be constructed node by node using the component marginal and conditional probabilities by utilizing the generalized form of Bayes Theorem:GUI) see .https://www.bayesfusion.com/), which through a Graphical User Interface (GUI) allows for the creation of the network structure (nodes and arcs) as well as populating those nodes with conditional probabilities between nodes represented by prevalence and conditional distributions. Due to the sparse amount of published data on the biomarker components of allostatic load in elderly, a stepped model approach was chosen:The approach used to build the BBN can be seen as an additive or “bottom up” in that, instead of building a Bayesian network that fits collected data, a network was built one node at a time from published data, including prevalence and distributions of both biomarkers and related medical outcomes. This was made possible through the use of the GeNIe Version 2.3 , where proper statistics were available, along with well-defined cutoffs for biomarkers.Model 2, an extended version of model 1: considering any published data relating the relevant allostatic markers to each other, but specifically in the context of aged populations. This implies the exclusion of particular parameters that either decline or increase to an extent that their natural variance does not contribute (any more) to the effective monitoring of meaningful changes in health status in elderly. For example over time or circumstances . Overall, this resulted in a shortlist of allostatic biomarkers that should be valid and applicable, e.g., in more conservative formats of Allostatic Load Indices, in the elderly.Our search strategy resulted in 38 studies covering elderly populations, of which 11 met the Bayesian requirements of published prevalence of deviated values of the relevant  (bio)markers directly related to well defined outcome states  see . TypicalThe 11 published studies covering any type of allostatic markers in the elderly, providing statistics to contribute to the proposed Bayesian model, are represented in A sensitivity analysis was performed using built-in functionality in GeNIe Version 2.3 to determine the influence of the individual nodes in our BBN. Sensitivity analysis allowed us to determine the influence of observing the states of specific nodes, which implies the proportional importance of the node in relation to the overall outcome that was defined as allostatic load.In Our effort to develop a statistically sound (Bayesian) probability model to quantify the probability of disturbed allostasis in elderly (aged >60 years) provided a well-founded framework , the model will automatically provide a personalized probability for a potentially present allostatic load. The study’s aim and methodology provided two significant challenges. Firstly, the adopted methodology prescribed strict statistical/data requirements, implying that only a few allostatic markers as defined by Juster  could beSecondly, the natural rate of degradation in variance of many allostatic (bio)markers may actually diminish their relevance in elderly populations. This finding may very well be closely associated with the abovementioned methodological challenge of notably few studies published on specific allostatic (bio)markers in the elderly. This may be due to an either gradual or abrupt, though progressive and significant, reduction in the proportional contribution of quite some biomarkers on changing health status in aging ,33,35. Aldosterone, for example, appears to slowly decrease with aging , althougIt seems valid, however, to conclude that with aging, the contribution of most allostatic (bio)markers on health irrevocably changes and in some cases might even diminish, as measurable concentrations may progressively decline and become insignificant, a process that might be a very fundamental component of aging in itself. Not necessarily surprisingly, the present study provides ample evidence that their potential as health-related parameters, either independently or incorporated in any kind of allostatic index, seems to gradually decline. This means that from a normative perspective, the allostatic load will steadily increase with age, whilst the standardized variance over this cumulative load is likely to substantially decrease. This is a process with significant inter-individual differences in progression, which are likely directly associated with exogeneous factors like exposure to infections, lifestyle, medical procedures, major life events, and/or psychological strain. It seems a valid conclusion that studies unraveling such allostatic dynamics associated with generally good or slowly declining health in elderly remain relevant.Based on the present findings, in the elderly, allostatic markers seem to be categorized in three classes: (1) markers that gradually decline with aging, with insufficient variance left in elderly to adequately capture changes in health status over time ; (2) allostatic markers that maintain significant variance to show an adaptive response to repetitive or severe stress , likely associated with allostatic processes and overall health status; (3) markers with a yet highly significant variability and reactivity in response to immediate stressors , being more or less representative for the sustained systemic flexibility and variability , and in Importantly, the present results are supported by a recent study of Freire et al. who used a selection of allostatic (bio)markers in the elderly that showed a remarkable resemblance to the results presented here  generally healthy elderly people. Secondly, a BBN model can only go as far as to provide the instantaneous probability of an individual suffering from a disturbed allostatic load based on the biomarker data collected in references to the overall (population based) statistics. However, it does not provide predictive power regarding causation, and only when frequently assessed might provide some information on relevant changes over time. To capture “allostatic dynamics” on an individual level, more advanced types of mathematical/data driven approaches seem essential. For example machine learning methods using dynamic (time-series) data based on repeated measures designs, with relatively high sampling frequencies of the relevant (bio)markers. Such exercises might be of significant importance to progress in the personalized medicine of aging, although they are not found in the literature so far. In this perspective, it is worthwhile to note that only few previous efforts have been made to use Bayesian modeling in the context of allostasis, with the study of Stephan  a notablThe identified categories of variance components in relation to a developing allostatic load in elderly  underline another tangible matter: disparate phase patterns in response to exogenous stressors in the elderly. Whilst some parameters rapidly respond and recover (within seconds to minutes: “responsive”), others might become disturbed more extensively with considerably longer recovery trajectories over time. Consequently, when allostatic load is considered as a more continuous scale, it may be concluded that the overall “allostatic” variance in any given individual contains quite different variance ranges, both in time and extent, originating from the heterogenous combination of incorporated parameters. Patterns of multilevel frequencies of variance may very well follow general mathematical outlines for early warning signals for upcoming critical transitions, as addressed above . In the Finally, of course other frameworks and/or parameters could be considered to capture  health status in elderly. Al Saedi , for exaE). Together with the 11 allostatic parameters identified here, this “proof of concept” may therefore be considered as a useful basis for future quantitative modeling in healthy aging, not least because allostasis and allostatic load are often referred to in studies of the elderly, but primarily as a theoretical concept. Consequently, future efforts may focus on further developing probabilistic and/or prognostic models in the elderly based on the concept of allostasis, and in this way refine their applicability in the context of personalized medicine. Apart from other mathematical approaches to model and quantify Allostatic Load , the pre


Seagrass form the basis for critically important marine ecosystems. Previously, we implemented a deep convolutional neural network (CNN) model to detect seagrass in multispectral satellite images of three coastal habitats in northern Florida.However, a deep CNN model trained at one location usually does not generalize to other locations due to data distribution shifts. In this paper, we developed a semi-supervised domain adaptation method to generalize a trained deep CNN model to other locations for seagrass detection. First, we utilized a generative adversarial network loss to align marginal data distribution between source domain and target domain using unlabeled data from both data domains. Second, we used a few labelled samples from the target domain to align class specific data distributions between the two domains, based on the contrastive semantic alignment loss. We achieved the best results in 28 out of 36 scenarios as compared to other state-of-the-art domain adaptation methods. Seagrasses create critically important marine ecosystems that provide food to marine animals and humans, stabilize the sea bottom, and absorb carbon dioxide from the environment. Seagrass can be found in coastal areas all over the world . PreviouDeep CNN models usually require a large number of labelled training data to achieve competitive results. For seagrass quantification, these labelled data are obtained by in situ observations that are time consuming and labor intensive. Consequently, it can be difficult to collect enough labeled data to train a separate model for each location. However, a well-trained deep CNN model at one location may fail at another location if seagrass density distribution shifts from source domain to target domain. This happens due to the change of appearance/distribution of seagrass from one location to another. Our previous models degraded if directly applied to different locations for seagrass detection , 5.For seagrass detection, we usually have a large amount of unlabeled data for a given new location and it is possible to obtain limited labeled data by domain experts. In this study, we propose a novel domain adaptation approach that uses both unlabeled data and a few labeled samples to learn an effective classifier for new locations. First, we utilized an unsupervised adversarial domain adaptation approach to adapt target domain representation to mimic source domain representation so that the classifier trained in source domain may work in target domain. In the unsupervised domain adaption step, we do not use any labeled samples from the target domain to solve the domain adaptation problem. Second, we utilized a supervised approach with the contrastive semantic alignment loss to learn domain invariant representations between source and target domains. The first step aligns marginal distribution between domains and the second step aligns class specific distributions using a few labeled samples from target domain. The contrastive semantic alignment loss consists of semantic alignment and separation losses. Here, the semantic alignment loss keeps the same class samples from different domains as close as possible. The class separation loss tries to put different class samples from different domains as far as possible. The proposed domain adaptation approach optimizes target domain embedding function to create a simple classifier that can work effectively in the target domain.A novel approach that uses both unlabeled and a few labeled samples in the target domain to learn a domain invariant embedding for domain adaptation. It can utilize a large amount of unlabeled data for efficient training.To the best of our knowledge, this is the first attempt and successful system that can generalize deep CNN models for seagrass detection from one location to another.The paper is structured as follows: Section Contributions of our proposed approach are:Automated systems to map seagrass distribution in multispectral satellite images have been developed. Traganos et al. proposed a support vector machine (SVM) approach to map the Mediterranean seagrass distribution in Greece utilizing Sentinel-2 satellite imagery , 7. LionDeep learning models are a subset of machine learning methods which were inspired to mimic mammal’s vision system. A typical deep learning model consists of multiple layers of feature extraction processing units named as “neurons”. During training, these neurons learn to extract useful features from data to perform classification or regression. Deep learning has been successfully applied in image classification , 18, imaet. al proposed an unsupervised domain adaptation method that used the adversarial loss to match source and target domain distributions [et. al proposed a semi-supervised approach for domain adaptation which used the Siamese architecture for domain adaptation [Domain adaptation techniques can be applied if there are not enough labeled data available to train a deep learning model from scratch in a new domain. In domain adaptation, a model in source domain is first trained using available large training dataset. A domain adaptation method is then applied to adapt the trained model to a new domain (named as target domain) w/o a few labeled samples from the target domain. Tzeng ibutions . Motiianaptation . This moThe diagram of the proposed domain adaptation method for seagrass detection is shown in Fig. Figure entclass1pt{minimaentclass1pt{minimaentclass1pt{minimaE denotes the expectation function, and l denotes any related loss functions.Let entclass1pt{minimaf, can be modeled as two functions as G is the embedding function from the input image X to embedding space and C is the function for predicting the class label from the embedding space. So A classifier, D in Fig. D is the discriminator used in the GAN model [D, distinguishes whether a sample belongs to source domain or target domain. The target embedding function D. During training, we keep By following the idea in Tzeng et al. , we utilentclass1pt{minimaAN model  and workIf there is a distribution shift between source and target domains, the source deep CNN model will not perform well in the target domain. We utilize a few labeled samples in the target domain and some labeled samples in the source domain to jointly adapt n et al.  as shownentclass1pt{minimaWe define the classification loss asd is a distance metric between the distribution of k is a similarity matrix which adds a penalty when the distribution of To align class specific embedding between source and target domains, we use the CSA loss to jointly adapt entclass1pt{minimaDuring training, the semantic alignment loss (orange arrows) keeps the same class samples from different domains as close as possible. The class separation loss (red dashed line) tries to put different class samples from different domains as far as possible. The classification loss (blue solid line) ensures high classification accuracy in the embedding space. During testing, we use the trained target mapping function to put the unseen target samples into domain invariant space. The overall classification and contrastive semantic alignment loss becomesquations –8) are  are 8\dod in Eq.  in Eq.  in Eq.  is Eucli) in Eq.  is a sima and b denote class labels and d, is defined as Euclidean distance in the embedded space,k, is calculated asm is the margin that specifies the separability in the embedding space. The combination of The semantic alignment loss and class separation loss are defined as distance or similarity between distributions. It is not easy to estimate conditional distribution for each class given just a few labelled samples in target domain. Following the method described in , we compfined in . Note thWe validated the proposed model on three multispectral images captured by the WorldView-2 satellite at three locations in Florida coastal area: Saint Joseph Bay (SJB), Keeton Beach (KB) and Saint George Sound (SGS). Each image has eight bands  with spatial resolution of 2 meters. An experienced domain expert (co-author of this paper) labelled some regions for five classes in each image: seagrass, sea, sand, land, and inter tidal as shown as green, blue, cyan, yellow and magenta in Fig. With these measurements, we calculated the following attributes:z was the difference in depth between the sensors placed at 0.65 m and 0.21 m.Spectral upwelling diffuse attenuation coefficient, Upwelling radiance just beneath the air–water interface eers Law .Remote sensing reflectance We then reduced the spectral resolution of the field measurements to match the spectral bands of the WorldView-2 image based on the published spectral response functions . Finally, we performed a linear regression between the 22 in situ measurements to their corresponding WorldView-2 spectra at the same location and created the gain and offset for each band to effectively remove atmospheric signals from the image.We performed atmospheric correction in the three WorldView-2 satellite multispectral images by matching the images with in situ measurements collected at 22 stations across the images on the same day by a survey boat. At each station, the following measurements were obtained by two spectroradiometer systems in tandem:t-distributed stochastic neighbor embedding (t-SNE) algorithm [We compared the spectral signatures of each class in the multispectral WorldView-2 images taken at different locations. To better visualize the high-dimensional spectral information, we utilized the lgorithm  to comprk-fold CV, we split data into k parts and kept one part for testing and the remaining parts for training. We repeated this experiment k times such that each part was tested once.At each of the three locations, we performed cross-validation for seagrass detection in the labeled regions. The experimental results gave us performance upper limits for domain adaptation. In In the domain adaptation experiments, each image was used as source image to train a deep CNN model and it was then adapted to other two locations guided by a few labeled samples from the new locations.The source-only model used source domain samples to train a deep CNN model and the model was then directly applied to new locations for seagrass detection.Adversarial discriminative domain adaptation (ADDA)  adapts tWe trained a deep CNN model in the source domain and used a few labeled data samples from the target domain to fine-tune the model. This is a baseline model for transfer learning.This model used the contrastive semantic alignment loss and classification loss to learn the embedding function and classification layers . We usedWe first used the GAN loss to adapt the embedding function trained in the source domain. Then the CCSA loss together with a few labeled samples from target domain was utilized to further adapt the model to new locations as detailed in Sect. Figure t-SNE representations for samples from all the three locations. We use green, blue, cyan, yellow and magenta to represent seagrass, sea, sand, land and intertidal classes. We use three shades to represent three different locations: the most bright shade, most dark shade and shade between this two to represent SJB, SGS and KB samples, respectively . One “shot” means one labeled sample per class in target domain is used to adapt the model. Each scenario was performed three times with randomly selected labelled samples from target domain, and means and standard deviations are shown in Table Source+Target (f.t.) that achieved the best results in 4 out of 36 scenarios in Table We conducted six domain adaptation experiments for the three WorldView-2 satellite images as KB t-SNE algorithm with the following procedure: t-SNE algorithm (before adaptation),Compress the original samples from source and target domains (200 = 5*5*8 dimensions) to 2 dimensions using the Feed original samples from source and target domains to the embedding functions, t-SNE algorithm (after adaptation),Compress the new representations to 2 dimensions using the Plot the compressed data samples on 2D plane using different colors for different classes. Use blue, cyan, green, yellow and magenta colors to represent sea, sand, seagrass, land and intertidal class. Utilize two different shades of same color to denote target and source samplest-SNE results are shown in Fig. We demonstrate how the proposed model maps samples from different domains to the embedding space by utilizing the Our proposed model contained two loss functions: semantic contrastive alignment loss and GAN loss. If we remove the GAN loss from the proposed model and just use semantic contrastive alignment loss for domain adaptation, the model would be equivalent to the CCSA model. If we remove the contrastive semantic alignment loss from the proposed model, then it will be equivalent to the unsupervised ADDA model. Our proposed model also used joint optimization for the source embedding function, The classification maps produced by our proposed model, CCSA approach, and baseline model are shown in Fig. Source+Target (f.t.) (84.78%). For KB Source+Target (f.t.) (63.39%). Similar trends can also be found in SGS Our proposed approach produced the best results for 28 out of 36 domain adaptation experimental scenarios as shown in Table As we utilizing more labeled samples from the target domain, the proposed method can still provide better domain adaptation, winning four or five out of the six experimental scenarios with 2-shot up to 10-shot cases. On average, however, the winning margin decreased as more labeled samples were used for adaptation. For the 10-shot scenario, CCSA and the proposed method achieved similar results and the results were close to the threefold CV results, indicating that adding more labeled samples from target domain did not provide more benefits.For most of the scenarios, standard deviations of the proposed method were much smaller than these of other methods. Our method first utilized a large number of unlabeled samples in both domains to perform domain adaptation. We then used a few labeled samples from target domain to semantically align class specific distribution in the embedding space. The first step of the method aligned marginal distribution based upon a large number of unlabeled data and worked as a regularizer for the subsequent semantic alignment. Therefore, the proposed method can provide more stable performances.t-SNE plots for data samples or embeddings in source and target domains before and after domain adaptation. Before adaptation, we can see that data distributions in source domain and target domain are not aligned. ADDA aligned distributions between the source and target domains, but there is no guarantee that the same class samples from different domains will be mapped closer in the embedding space. With the guidance of labelled samples, CCSA and the proposed model can do a better semantic alignment: same class samples from different domains can be mapped closer, and the proposed method can do a better job as compared to CCSA.Figure We only performed the KB As compared to the physics model classification maps in Fig. Automatic seagrass detection systems in multispectral images are important tools for seagrass monitoring. Labelling atmospherically corrected multispectral images is labor intensive and time consuming. We developed a semi-supervised domain adaptation method for deep CNN models for seagrass detection. The proposed model first used unlabelled samples in both domains to adapt source domain model to target domain based on the GAN loss. Then it utilized contrastive semantic loss with a few labelled samples from target domain to further adapt the model. In addition, the source model and target model were jointly optimized in the second step. We evaluated the proposed model in three atmospherically corrected WorldView-2 multispectral images taken in Florida and achieved the best results among 28 out of 36 experimental scenarios. Future work will evaluate the proposed model with other image detection methods across broader regional areas such as the southeastern USA.