018 激酶是什么？

Aim of this talktorial

In this talktorial, we will talk about kinases: why are they important in life and drug design, what do they look like, and what data resources are available? Finally, we select a set of kinases which will be analyzed in the forthcoming talktorials T024-T028 with respect to their similarity, the goal being to gain insight into potential off-target effects.

Contents in Theory

• Kinases in a nutshell
  • The human kinome
  • Kinase structures and important motifs
• Kinase resources
  • Kinase structures and related information
  • Bioactivity data
• Kinase similarity: Off-targets and promiscuous binding
• Kinase dataset compilation

Contents in Practical

• Define the kinases of interest

References

• Kinases as drug targets: Nat. Rev. Drug Discov. (2021), 20(7), 551-569
• Sequence-based kinase clustering: Manning et al. Science (2002), 298(5600), 1912-1934
• KinMap:
  • Paper: BMC Bioinformatics (2017), 18, 16
  • Website: http://www.kinhub.org/kinmap/
• KLIFS
  • KLIFS URL: https://klifs.net/
  • KLIFS database: Nucleic Acid Res. (2020), 49(D1), D562-D569
  • KLIFS binding site definition: J. Med. Chem. (2014), 57(2), 249-277
• Bioactivity data
  • Karaman et al. dataset: Nature Biotechnology (2008), 26, 127-132
  • Davis et al. dataset: Nature Biotechnology (2011), 29, 1046-1051
  • KIBA dataset: J. Chem. Inf. Model. (2014), 54(3), 753-743
  • PKIS dataset: PLOS ONE (2017), 12, 1-20
• Kinase selection: Molecules (2021), 26(3), 629

Theory

Kinases in a nutshell

Kinases are established drug targets to combat cancer and inflammatory diseases (Nat. Rev. Drug Discov. (2021), 20(7), 551-569). They are involved in most aspects of cell life by phosphorylating, and thereby activating, either themselves or other proteins. They are among the most frequently mutated proteins in tumors.

As of September 2021, $5782$ X-ray structures of human kinases have been resolved (see the KLIFS database) and $67$ FDA-approved small molecule protein kinase inhibitors are on the market (see the list compiled by the Blue Ridge Institute for Medical Research). Most of the approved drugs bind in the ATP-binding pocket and intermediate surroundings.

Despite of decades of kinase research, there are still many open challenges:

• A large fraction of the kinome is un-/underexplored.
• Many kinase inhibitors are promiscuous binders causing off-target effects or enabling polypharmacology.
• There are occurrences of drug resistance due to mutations.

The human kinome

The human kinome consists of over $500$ protein kinases, but this number may vary depending on the data resource (see overview on Kinodata).

As reported in Science (2002), 298(5600), 1912-1934, Manning et al. clustered the human protein kinases based on their sequence similarity into eight major groups (AGC, CAMK, CK1, CMGC, RGC, STE, TK, TKL) and one "Other" group for unassigned kinases, as well as atypical kinases. The kinase clustering is visualized as the Manning kinome tree. The kinase resource KinMap enables mapping of kinase data onto that tree, e.g. the number of X-ray structures per kinase as shown in Figure 1.

Figure 1: Number of PDB structures per kinase mapped onto the Manning kinome tree using KinMap. Check the appendix on how to generate this KinMap tree.

Kinase structures and important motifs

Kinase sequences and structures are highly conserved. Important regions in the kinase pocket include (see Figure 2):

• Hinge region: Forms key hydrogen bonds to ligands.
• DFG motif: Flips between phenylalanine (F) and aspartate (D), driving the active and inactive state.
• αC-helix: Forms in the αC-in conformation a salt bridge between highly conserved lysine (K) and glutamine (Q).
• Glycine-rich (G-rich) loop: Stabilizes ATP binding.

Figure 2: Kinase structure with important key motifs: Hinge region, DFG motif, αC-helix, and G-rich loop. The example structure shown here represents CDK2, PDB ID: 1FIN. Check the appendix on how to generate this visualization with opencadd.

Kinase resources

The focus on this protein family has led to a plethora of freely available data on compounds, bioactivity, and structures that are being used for computational drug development (Annual Reports in Medicinal Chemistry 50 (2017): 197-236).

Kinase structures and related information: KLIFS

The KLIFS database (Nucleic Acid Res. (2020), 49(D1), D562-D569, J. Med. Chem. (2014), 57(2), 249-277) fetches all kinase structures deposited in the structural database PDB (Acta Cryst. (2002), D58, 899-907, Structure (2012), 20(3), 391-396) and processes them as follows: All multi-chain structures in the PDB are split into monomers and aligned to each other with a special focus on a pre-defined binding site of $85$ residues (Figure 3). For example, this means that the conserved gatekeeper (GK) residue at KLIFS position $45$ can be easily queried for any of the over $10,000$ monomeric kinase structures in KLIFS.

Figure 3: Kinase binding site residues as defined by KLIFS. Figure and description taken from: J. Med. Chem. (2014), 57(2), 249-277.

Each structure, kinase, and ligand in KLIFS is associated with an identifier (we will use those at times in the downstream talktorials):

• Structure KLIFS ID
• Kinase KLIFS ID
• Ligand KLIFS ID

KLIFS contains not only kinase structures and their pocket definitions (used in Talktorials T024 and T025) but also a lot of structure-, kinase-, and/or ligand-associated data:

• Interaction fingerprints (used in Talktorial T026)
• Structure conformations
• Bioactivity data from ChEMBL
• Approved drugs
• Subpocket coverage by co-crystallized ligands

Bioactivity data

ChEMBL is a well-known bioactivity database, which releases updated versions every now and then. In September 2021, there are over two million compounds and $14,000$ targets that are stored. In ChEMBL29, there are over $160,000$ measurements on kinases (see Figure 4).

• kinodata GitHub repository: https://github.com/openkinome/kinodata
• kinodata ChEMBL29 release: https://github.com/openkinome/kinodata/releases/tag/v0.3 (activities-chembl29_v0.3.zip)

As with other data types, the coverage of bioactivity data is highly unbalanced among the human kinases, depending on how much research is spent on certain kinases.

Figure 4: Number of ChEMBL29 bioactivities per kinase mapped onto the Manning kinome tree using KinMap. Check the appendix on how to generate this KinMap tree.

However, ChEMBL is not the only available bioactivity database. Below is an non-exhaustive list of available kinase profiling data sets.

• Karaman et al. dataset
  • Paper: Nature Biotechnology (2008), 26, 127-132
  • Data: KinMap data (JSON)
• Davis et al. dataset
  • Paper: Nature Biotechnology (2011), 29, 1046-1051
  • Data: KinMap data (JSON)
• KIBA dataset
  • Paper: J. Chem. Inf. Model. (2014), 54(3), 753-743
  • Data: SI data (XLSX)
• PKIS dataset
  • Paper: PLOS ONE (2017), 12, 1-20
  • Data: SI data (XLSX)

Kinase similarity: Off-targets and promiscuous binding

As described before, kinases are highly conserved, especially in their binding site. This high similarity is a challenge in drug design because ligands may form similar binding modes not only with their designated target (on-target) but also with other targets (off-targets). Such promiscuous binding can cause mild to severe side effects.

Predicting these side effects is non-trivial since some off-targets are not obvious. For example, the EGFR inhibitor Erlotinib shows affinities to other kinases in the highly sequentially-similar TK kinase group. However, it also strongly affects the off-targets GAK, LOK, and SLK, which are in more remote kinase groups (Figure 5).

Figure 5: Profiling data for EGFR inhibitor Erlobinib from the Karaman et al. dataset (Nature Biotechnology (2008), 26, 127-132) mapped onto the Manning kinome tree using KinMap. Check the appendix of this notebook on how to generate this figure.

In the following four talktorials, namely Talktorials T024-027, we will assess kinase similarity from different perspectives, which we compare with each other in Talktorial T028:

• Talktorial T024: Kinase similarity based on KLIFS pocket sequence
• Talktorial T025: Kinase similarity based on KiSSim pocket structure
• Talktorial T026: Kinase similarity based on KLIFS interaction fingerprint
• Talktorial T027: Kinase similarity based on ligand promiscuity (ChEMBL bioactivity data)
• Talktorial T028: Compare kinase similarity measures from Talktorials T024-T027

Kinase dataset compilation

In the course of the kinase similarity talktorials (Talktorials T024-T028), we will use nine kinases from a study published in Molecules (2021), 26(3), 629, which were selected for the following reasons:

• Profile 1 combined EGFR and ErbB2 as targets and BRAF as a (general) anti-target.
• Out of similar considerations, Profile 2 consisted of EGFR and PI3K as targets and BRAF as anti-target. This profile is expected to be more challenging as PI3K is an atypical kinase and thus less similar to EGFR than for example ErbB2 used in Profile 1.
• Profile 3, comprised of EGFR and VEGFR2 as targets and BRAF as anti-target, was contrasted with the hit rate that we found with a standard docking against the single target VEGFR2 (Profile 4).
• To broaden the comparison and obtain an estimate for the promiscuity of each compound, the kinases CDK2, LCK, MET and p38α were included in the experimental assay panel and the structure-based bioinformatics comparison as commonly used anti-targets.

Practical

Define the kinases of interest

We have collected information about these nine kinases in the CSV file T023_what_is_a_kinase/data/kinase_selection.csv:

• kinase: Kinase name as used in Molecules (2021), 26(3), 629
• kinase_klifs: Kinase name as used in the KLIFS database
• uniprot_id: Kinase UniProt ID
• group: Kinase group as defined by Manning et al. Science (2002), 298(5600), 1912-1934
• full_kinase_name: Full kinase name as used in Molecules (2021), 26(3), 629

We will load this dataset in all downstream talktorials to assess kinase similarity from different perspectives.

Note: You can run the kinase similarity Talktorials T024-T028 with your own set of kinases. To do so, please update the following files:

• Update the T023_what_is_a_kinase/data/kinase_selection.csv file with your kinases; the only mandatory columns are kinase_klifs and uniprot_id.
• Update the T023_what_is_a_kinase/data/pipeline_configs.csv file with your configurations:
  • Set "DEMO" to 0.
  • Choose the number of structures per kinases to be used in T025 (KiSSim) and T026 (IFP). If "N_STRUCTURES_PER_KINASE" is set to -1, all structures are used; if set to a number (X), the best X structures are being used for the encoding and comparison (w.r.t. resolution and KLIFS quality score). The latter makes sense for a test run of your data (running the T025 on all structures is time-consuming).
  • If you run the notebooks on all structures (see "N_STRUCTURES_PER_KINASE"), we recommend to increase the number of cores to be used in T025 (KiSSim) by redefining "N_CORES".

Let's take a look at the currently set configurations:

Appendix

KinMap data

There are some KinMap trees shown in this notebook. The code below generates the KinMap CSV files to be uploaded to KinMap: http://www.kinhub.org/kinmap.

Note:

1. PNG downloads do not seem to work anymore, thus download as SVG and convert to PNG in your terminal (Linux) via convert -density 25 my_kinmap_figure.svg my_kinmap_figure.png (SVG cannot be included in Jupyter notebooks out-of-the-box).
2. If SVG download doesn't render the figure properly, open your favorite text editor and copy paste this into the SVG file: xmlns:xlink="http://www.w3.org/1999/xlink", resulting in something similar to this in the first few lines:

<svg id="svgCopy" viewBox="0 0 1591 1959" preserveAspectRatio="xMinYMin meet" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" style=""><desc>Created with Snap</desc><defs></defs><g

Number of PDB structures per kinase

Generate the number of structures per kinase in the KinMap format to be mapped onto the kinome tree.

Identify the kinase which has the most structures.

Number of ChEMBL bioactivities per kinase

Generate the number of ChEMBL bioactivities per kinase in the KinMap format to be mapped onto the kinome tree.

Note: The cell below takes a few seconds to execute.

Erlotinib profiling data from Karaman et al. dataset

• Go to http://www.kinhub.org/kinmap/
• Select "Data Source": Profiling
• Select "Data type": Karaman et al., 2018
• Select "Karaman et al., 2018": Erlotinib
• Click "Add source"
• In settings, select "RoyalBlue" in Fill
• Click "Apply"
• Click on the speech bubble on the top right of the kinome tree to disable annotations.

Note: the name of the on/off-targets (EGFR, GAK, LOK, SLK) have been added manually.

Kinase structure visualization with opencadd

We are using as an example the ATP-bound CDK2 structure with the KLIFS ID 4367.

Reference

https://github.com/volkamerlab/teachopencadd

Reprint statement

Original title: What is a kinase?

Authors:

• Dominique Sydow, 2021, Volkamer lab, Charité
• Talia B. Kimber, 2021, Volkamer lab, Charité
• Andrea Volkamer, 2021, Volkamer lab, Charité