Temporarily revert mention of TL-MHC/TLBind/TLImm

Author: susannasiebert

docs/index.rst

@@ -84,8 +84,6 @@ ____________
   - MixMHCpred (class I binding score and percentile)
   - MixMHC2pred (class II binding score and percentile)
   - PRIME (class I immunogenicity score and percentile)
-  - TLBind (class I binding score and class I presentation score)
-  - TLImm (class I immunogenicity score)
   - ImmuScope (class II immunogenicity score)
 
 - In order to support a more comprehensive evaluation of candidates, aggregate binding, presentation,

docs/install.rst

@@ -386,32 +386,6 @@ On certain system you may need to recompile the PRIME executable:
    cd /opt/PRIME/lib
    g++ -O3 PRIME.cc -o PRIME.x
 
-Installing TL-MHC
------------------
-
-If you wish to run the TLBind and TLImm prediction algorithms, you will need to
-install TL-MHC on your system. This package not a direct dependency of
->>>>>>> c61d5908 (Add installation instructions for MixMHCpred, MixMHC2pred, PRIME, and TL-MHC)
-
-.. code-block:: none
-
-   pip install git+https://github.com/griffithlab/TL-MHC.git#egg=TL-MHC
-
-.. note::
-
-   TL-MHC needs to be installed in the same python 3
-   environment as the ``pvactools`` package.
-
-You can check that TL-MHC was installed successfully by running:
-
-.. code-block:: none
-
-   pip show TL-MHC
-
-This should show information about the TL-MHC installation.
-
-.. _blast:
-
 Installing BLAST
 ----------------
 

docs/releases/7_0.rst

@@ -26,8 +26,6 @@ ____________
   - MixMHCpred (class I binding score and percentile)
   - MixMHC2pred (class II binding score and percentile)
   - PRIME (class I immunogenicity score and percentile)
-  - TLBind (class I binding score and class I presentation score)
-  - TLImm (class I immunogenicity score)
   - ImmuScope (class II immunogenicity score)
 
 - In order to support a more comprehensive evaluation of candidates, aggregate binding, presentation,

docs/tools.rst

@@ -134,20 +134,6 @@ _____
 
 - License: `Academic License <https://github.com/GfellerLab/PRIME/blob/master/PRIME_license.pdf>`_
 
-TL-MHC
-______
-
-- Website: https://www.kavrakilab.org/publications/fasoulis2024-transfer.html
-- GitHub: https://github.com/KavrakiLab/TL-MHC
-- Citation: Fasoulis R, Rigo MM, Antunes DA, Paliouras G, Kavraki LE. Transfer learning improves pMHC
-  kinetic stability and immunogenicity predictions. Immunoinformatics (Amst). 2024 Mar;13:100030.
-
-  doi: http://dx.doi.org/10.1016/j.immuno.2023.100030
-
-  PubMed PMID: `38577265 <https://pubmed.ncbi.nlm.nih.gov/38577265/>`_
-
-- License: None provided
-
 ImmuScope
 _________