Machine learning-guided protein engineering
Recent progress in engineering highly promising biocatalysts has increasingly involved
machine learning methods. These methods leverage existing experimental and simulation …
machine learning methods. These methods leverage existing experimental and simulation …
[HTML][HTML] Machine learning approaches in predicting allosteric sites
F Nerín-Fonz, Z Cournia - Current Opinion in Structural Biology, 2024 - Elsevier
Allosteric regulation is a fundamental biological mechanism that can control critical cellular
processes via allosteric modulator binding to protein distal functional sites. The advantages …
processes via allosteric modulator binding to protein distal functional sites. The advantages …
LMPhosSite: a deep learning-based approach for general protein phosphorylation site prediction using embeddings from the local window sequence and pretrained …
Phosphorylation is one of the most important post-translational modifications and plays a
pivotal role in various cellular processes. Although there exist several computational tools to …
pivotal role in various cellular processes. Although there exist several computational tools to …
Post-translational modification prediction via prompt-based fine-tuning of a GPT-2 model
Post-translational modifications (PTMs) are pivotal in modulating protein functions and
influencing cellular processes like signaling, localization, and degradation. The complexity …
influencing cellular processes like signaling, localization, and degradation. The complexity …
EMNGly: predicting N-linked glycosylation sites using the language models for feature extraction
Motivation N-linked glycosylation is a frequently occurring post-translational protein
modification that serves critical functions in protein folding, stability, trafficking, and …
modification that serves critical functions in protein folding, stability, trafficking, and …
PepCNN deep learning tool for predicting peptide binding residues in proteins using sequence, structural, and language model features
Protein–peptide interactions play a crucial role in various cellular processes and are
implicated in abnormal cellular behaviors leading to diseases such as cancer. Therefore …
implicated in abnormal cellular behaviors leading to diseases such as cancer. Therefore …
pLMSNOSite: an ensemble-based approach for predicting protein S-nitrosylation sites by integrating supervised word embedding and embedding from pre-trained …
Abstract Background Protein S-nitrosylation (SNO) plays a key role in transferring nitric
oxide-mediated signals in both animals and plants and has emerged as an important …
oxide-mediated signals in both animals and plants and has emerged as an important …
[HTML][HTML] Pathogen dynamics and discovery of novel viruses and enzymes by deep nucleic acid sequencing of wastewater
Wastewater contains an extensive reservoir of genetic information, yet largely unexplored.
Here, we analyzed by high-throughput sequencing total nucleic acids extracted from …
Here, we analyzed by high-throughput sequencing total nucleic acids extracted from …
Ensemble Learning with Supervised Methods Based on Large-Scale Protein Language Models for Protein Mutation Effects Prediction
Machine learning has been increasingly utilized in the field of protein engineering, and
research directed at predicting the effects of protein mutations has attracted increasing …
research directed at predicting the effects of protein mutations has attracted increasing …
Current computational tools for protein lysine acylation site prediction
Z Qin, H Ren, P Zhao, K Wang, H Liu… - Briefings in …, 2024 - academic.oup.com
As a main subtype of post-translational modification (PTM), protein lysine acylations (PLAs)
play crucial roles in regulating diverse functions of proteins. With recent advancements in …
play crucial roles in regulating diverse functions of proteins. With recent advancements in …