Target Information
| Target General Information | Top | |||||
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| Target ID |
T52447
(Former ID: TTDR00907)
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| Target Name |
Rotamase D (PPID)
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| Synonyms |
Peptidylprolyl isomerase D; Peptidyl-prolyl cis-trans isomerase D; PPIase D; Cyclophilin-related protein; Cyclophilin-40; CYP40; CYP-40; 40 kDa peptidyl-prolyl cis-trans isomerase
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| Gene Name |
PPID
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| Target Type |
Patented-recorded target
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| Function |
Proposed to act as a co-chaperone in HSP90 complexes such as in unligated steroid receptors heterocomplexes. Different co-chaperones seem to compete for association with HSP90 thus establishing distinct HSP90-co-chaperone-receptor complexes with the potential to exert tissue-specific receptor activity control. May have a preference for estrogen receptor complexes and is not found in glucocorticoid receptor complexes. May be involved in cytoplasmic dynein-dependent movement of the receptor from the cytoplasm to the nucleus. May regulate MYB by inhibiting its DNA-binding activity. Involved in regulation of AHR signaling by promoting the formation of the AHR:ARNT dimer; the function is independent of HSP90 but requires the chaperone activity. Involved in regulation of UV radiation-induced apoptosis. Promotes cell viability in anaplastic lymphoma kinase-positive anaplastic large-cell lymphoma (ALK+ ALCL) cell lines. PPIase that catalyzes the cis-trans isomerization of proline imidic peptide bonds in oligopeptides and may therefore assist protein folding.
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| BioChemical Class |
Cis-trans-isomerase
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| UniProt ID | ||||||
| EC Number |
EC 5.2.1.8
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| Sequence |
MSHPSPQAKPSNPSNPRVFFDVDIGGERVGRIVLELFADIVPKTAENFRALCTGEKGIGH
TTGKPLHFKGCPFHRIIKKFMIQGGDFSNQNGTGGESIYGEKFEDENFHYKHDREGLLSM ANAGRNTNGSQFFITTVPTPHLDGKHVVFGQVIKGIGVARILENVEVKGEKPAKLCVIAE CGELKEGDDGGIFPKDGSGDSHPDFPEDADIDLKDVDKILLITEDLKNIGNTFFKSQNWE MAIKKYAEVLRYVDSSKAVIETADRAKLQPIALSCVLNIGACKLKMSNWQGAIDSCLEAL ELDPSNTKALYRRAQGWQGLKEYDQALADLKKAQGIAPEDKAIQAELLKVKQKIKAQKDK EKAVYAKMFA Click to Show/Hide
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| 3D Structure | Click to Show 3D Structure of This Target | AlphaFold | ||||
| HIT2.0 ID | T85CC2 | |||||
| Cell-based Target Expression Variations | Top | |||||
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| Cell-based Target Expression Variations | ||||||
| Different Human System Profiles of Target | Top |
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Human Similarity Proteins
of target is determined by comparing the sequence similarity of all human proteins with the target based on BLAST. The similarity proteins for a target are defined as the proteins with E-value < 0.005 and outside the protein families of the target.
A target that has fewer human similarity proteins outside its family is commonly regarded to possess a greater capacity to avoid undesired interactions and thus increase the possibility of finding successful drugs
(Brief Bioinform, 21: 649-662, 2020).
Human Tissue Distribution
of target is determined from a proteomics study that quantified more than 12,000 genes across 32 normal human tissues. Tissue Specificity (TS) score was used to define the enrichment of target across tissues.
The distribution of targets among different tissues or organs need to be taken into consideration when assessing the target druggability, as it is generally accepted that the wider the target distribution, the greater the concern over potential adverse effects
(Nat Rev Drug Discov, 20: 64-81, 2021).
Human Pathway Affiliation
of target is determined by the life-essential pathways provided on KEGG database. The target-affiliated pathways were defined based on the following two criteria (a) the pathways of the studied target should be life-essential for both healthy individuals and patients, and (b) the studied target should occupy an upstream position in the pathways and therefore had the ability to regulate biological function.
Targets involved in a fewer pathways have greater likelihood to be successfully developed, while those associated with more human pathways increase the chance of undesirable interferences with other human processes
(Pharmacol Rev, 58: 259-279, 2006).
Biological Network Descriptors
of target is determined based on a human protein-protein interactions (PPI) network consisting of 9,309 proteins and 52,713 PPIs, which were with a high confidence score of ≥ 0.95 collected from STRING database.
The network properties of targets based on protein-protein interactions (PPIs) have been widely adopted for the assessment of target’s druggability. Proteins with high node degree tend to have a high impact on network function through multiple interactions, while proteins with high betweenness centrality are regarded to be central for communication in interaction networks and regulate the flow of signaling information
(Front Pharmacol, 9, 1245, 2018;
Curr Opin Struct Biol. 44:134-142, 2017).
Human Similarity Proteins
Human Tissue Distribution
Human Pathway Affiliation
Biological Network Descriptors
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Note:
If a protein has TS (tissue specficity) scores at least in one tissue >= 2.5, this protein is called tissue-enriched (including tissue-enriched-but-not-specific and tissue-specific). In the plots, the vertical lines are at thresholds 2.5 and 4.
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| KEGG Pathway | Pathway ID | Affiliated Target | Pathway Map |
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| Necroptosis | hsa04217 | Affiliated Target |
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| Class: Cellular Processes => Cell growth and death | Pathway Hierarchy | ||
| Cellular senescence | hsa04218 | Affiliated Target |
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| Class: Cellular Processes => Cell growth and death | Pathway Hierarchy | ||
| Degree | 6 | Degree centrality | 6.45E-04 | Betweenness centrality | 1.12E-04 |
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| Closeness centrality | 2.26E-01 | Radiality | 1.40E+01 | Clustering coefficient | 4.00E-01 |
| Neighborhood connectivity | 4.03E+01 | Topological coefficient | 2.33E-01 | Eccentricity | 11 |
| Download | Click to Download the Full PPI Network of This Target | ||||
| Chemical Structure based Activity Landscape of Target | Top |
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| Drug Property Profile of Target | Top | |
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| (1) Molecular Weight (mw) based Drug Clustering | (2) Octanol/Water Partition Coefficient (xlogp) based Drug Clustering | |
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| (3) Hydrogen Bond Donor Count (hbonddonor) based Drug Clustering | (4) Hydrogen Bond Acceptor Count (hbondacc) based Drug Clustering | |
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| (5) Rotatable Bond Count (rotbonds) based Drug Clustering | (6) Topological Polar Surface Area (polararea) based Drug Clustering | |
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| "RO5" indicates the cutoff set by lipinski's rule of five; "D123AB" colored in GREEN denotes the no violation of any cutoff in lipinski's rule of five; "D123AB" colored in PURPLE refers to the violation of only one cutoff in lipinski's rule of five; "D123AB" colored in BLACK represents the violation of more than one cutoffs in lipinski's rule of five | ||
| Target Poor or Non Binders | Top | |||||
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| Target Poor or Non Binders | ||||||
| Target Regulators | Top | |||||
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| Target-interacting Proteins | ||||||
| References | Top | |||||
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| REF 1 | Cyclophilin D as a drug target. Curr Med Chem. 2003 Aug;10(16):1485-506. | |||||
| REF 2 | Inhibitors of cyclophilins and uses thereof. US8901295. | |||||
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