On TCR binding predictors failing to generalize to unseen peptides

Several recent studies investigate TCR-peptide/-pMHC binding prediction using machine learning or deep learning approaches. Many of these methods achieve impressive results on test sets, which include peptide sequences that are also included in the training set. In this work, we investigate how state-of-the-art deep learning models for TCR-peptide/-pMHC binding prediction generalize to unseen peptides. We create a dataset including positive samples from IEDB, VDJdb, McPAS-TCR, and the MIRA set, as well as negative samples from both randomization and 10X Genomics assays. We name this collection of samples TChard. We propose the hard split, a simple heuristic for training/test split, which ensures that test samples exclusively present peptides that do not belong to the training set. We investigate the effect of different training/test splitting techniques on the models’ test performance, as well as the effect of training and testing the models using mismatched negative samples generated randomly, in addition to the negative samples derived from assays. Our results show that modern deep learning methods fail to generalize to unseen peptides. We provide an explanation why this happens and verify our hypothesis on the TChard dataset. We then conclude that robust prediction of TCR recognition is still far for being solved

Online Protocol Annotation: A Method to Enhance Undergraduate Laboratory Research Skills

A well-constructed, step-by-step protocol is a critical starting point for teaching undergraduates new techniques, an important record of a lab's standard procedures, and a useful mechanism for sharing techniques between labs. Many research labs use websites to archive and share their protocols for these purposes. Here we describe our experiences developing and using a protocol website for the additional purpose of enhancing undergraduate research training. We created our lab's protocol website in a message board format that allows undergraduates to post comments on protocols describing the lessons they learned, questions that arose, and/or insights they gained while learning to execute specific research protocols. Encouraging and expecting students to comment on the protocols they are learning to execute is beneficial for both the student and for the lab in which they are training. For the student, annotations encourage active reflection on their execution of techniques and emphasize the important message that attending to and understanding details of a protocol is a critical factor in producing reliable data. For the lab, annotations capture valuable insights for future generations of researchers by describing missing details, hints, and common hurdles for newcomers

(137)Cs and (40)K activity concentration measurements and elemental analysis in lichen samples collected from the Giresun province of northeastern Turkey

WOS: 000258806900006PubMed: 18763187About 21 years after the Chernobyl accident, (137)Cs and (40)K activity concentration measurements using gamma-ray spectroscopy and elemental analysis using energy dispersive X-ray spectroscopy were performed in five different lichen species collected from the Giresun province of northeastern Turkey. Being a symbiosis of algae and fungi, lichens are mostly used for environmental measurements since the fungal partner is responsible for the uptake of necessary nutrients or harmful substances, such as heavy metals of radionuclides. The gamma activity results showed that (137)Cs, an artificial radionuclide released from the Chernobyl power plant accident, is still eminent in the environment of the province. The mean activity concentrations of (137)Cs and (40)K ranged from 24 to 254 with the mean value of 102 Bq kg(-1) and from 345 to 2103 with the mean value of 1143 Bq kg(-1) in dry weight. The results of the elemental analyses showed potassium, calcium, titanium, iron, tin, and barium in different concentrations.Karadeniz Technical UniversityKaradeniz Teknik University [2005.111.001.1]This work was supported by Karadeniz Technical University under the project number 2005.111.001.1