DNA shape features improve prediction of CRISPR/Cas9 activity.

The CRISPR/Cas9 genome editing technology has transformed basic and translational research in biology and medicine. However, the advances are hindered by off-target effects and a paucity in the knowledge of the mechanism of the Cas9 protein. Machine learning models have been proposed for the prediction of Cas9 activity at unintended sites, yet feature engineering plays a major role in the outcome of the predictors.

TCR-ESM: Employing protein language embeddings to predict TCR-peptide-MHC binding.

Cognate target identification for T-cell receptors (TCRs) is a significant barrier in T-cell therapy development, which may be overcome by accurately predicting TCR interaction with peptide-bound major histocompatibility complex (pMHC). In this study, we have employed peptide embeddings learned from a large protein language model- Evolutionary Scale Modeling (ESM), to predict TCR-pMHC binding. The TCR-ESM model presented outperforms existing predictors.

Improved Machine Learning-Based Model for the Classification of Off-Targets in the CRISPR/Cpf1 System.

Targeted nucleases are widely used for altering the specific location of the genome with precision. The endonucleases facilitate efficient genome editing via designing a guide RNA (gRNA) consisting of a 20-nucleotide target sequence. gRNA preferably binds to the target location, but the on- and off-target activities of gRNAs vary widely.

Hybrid Multitask Learning Reveals Sequence Features Driving Specificity in the CRISPR/Cas9 System.

CRISPR/Cas9 technology is capable of precisely editing genomes and is at the heart of various scientific and medical advances in recent times. The advances in biomedical research are hindered because of the inadvertent burden on the genome when genome editors are employed-the off-target effects. Although experimental screens to detect off-targets have allowed understanding the activity of Cas9, that knowledge remains incomplete as the rules do not extrapolate well to new target sequences.

Computational Methods and Deep Learning for Elucidating Protein Interaction Networks.

Protein interactions play a critical role in all biological processes, but experimental identification of protein interactions is a time- and resource-intensive process. The advances in next-generation sequencing and multi-omics technologies have greatly benefited large-scale predictions of protein interactions using machine learning methods. A wide range of tools have been developed to predict protein-protein, protein-nucleic acid, and protein-drug interactions.

A Machine Learning Approach to Identify the Importance of Novel Features for CRISPR/Cas9 Activity Prediction.

The reprogrammable CRISPR/Cas9 genome editing tool's growing popularity is hindered by unwanted off-target effects. Efforts have been directed toward designing efficient guide RNAs as well as identifying potential off-target threats, yet factors that determine efficiency and off-target activity remain obscure. Based on sequence features, previous machine learning models performed poorly on new datasets, thus there is a need for the incorporation of novel features.

Implementing accelerated dynamics to unravel the effects of high-fidelity Cas9 mutants on target DNA and guide RNA hybrid stability.

The clustered regularly interspersed short palindromic repeats (CRISPR) and its associated nuclease (Cas9) offers a unique and easily reprogrammable system for editing eukaryotic genomes. Cas9 is guided to the target by an RNA strand, and precise edits are created by introducing double-stranded breaks. However, nuclease activity of Cas9 is also triggered at other sites other than the target sit, which is a major limitation for various applications.