Cyclic peptides as drugs for intracellular targets: the next frontier in peptide therapeutic development

LK Buckton, MN Rahimi… - Chemistry–A European …, 2021 - Wiley Online Library
Developing macrocyclic peptides that can reach intracellular targets is a significant
challenge. This review discusses the most recent strategies used to develop cell permeable …

Artificial intelligence: the milestone in modern biomedical research

K Athanasopoulou, GN Daneva, PG Adamopoulos… - …, 2022 - mdpi.com
In recent years, the advent of new experimental methodologies for studying the high
complexity of the human genome and proteome has led to the generation of an increasing …

Structural biology in the clouds: the WeNMR-EOSC ecosystem

RV Honorato, PI Koukos, B Jiménez-García… - Frontiers in molecular …, 2021 - frontiersin.org
Structural biology aims at characterizing the structural and dynamic properties of biological
macromolecules at atomic details. Gaining insight into three dimensional structures of …

SAAFEC-SEQ: a sequence-based method for predicting the effect of single point mutations on protein thermodynamic stability

G Li, SK Panday, E Alexov - International journal of molecular sciences, 2021 - mdpi.com
Modeling the effect of mutations on protein thermodynamics stability is useful for protein
engineering and understanding molecular mechanisms of disease-causing variants. Here …

Big data and artificial intelligence (AI) methodologies for computer-aided drug design (CADD)

JW Lee, MA Maria-Solano, TNL Vu… - Biochemical Society …, 2022 - portlandpress.com
There have been numerous advances in the development of computational and statistical
methods and applications of big data and artificial intelligence (AI) techniques for computer …

Machine learning techniques for protein function prediction

R Bonetta, G Valentino - Proteins: Structure, Function, and …, 2020 - Wiley Online Library
Proteins play important roles in living organisms, and their function is directly linked with
their structure. Due to the growing gap between the number of proteins being discovered …

Enhanced prediction of hot spots at protein-protein interfaces using extreme gradient boosting

H Wang, C Liu, L Deng - Scientific reports, 2018 - nature.com
Identification of hot spots, a small portion of protein-protein interface residues that contribute
the majority of the binding free energy, can provide crucial information for understanding the …

Machine learning approaches for protein–protein interaction hot spot prediction: Progress and comparative assessment

S Liu, C Liu, L Deng - Molecules, 2018 - mdpi.com
Hot spots are the subset of interface residues that account for most of the binding free
energy, and they play essential roles in the stability of protein binding. Effectively identifying …

iSEE: Interface structure, evolution, and energy‐based machine learning predictor of binding affinity changes upon mutations

C Geng, A Vangone, GE Folkers… - Proteins: Structure …, 2019 - Wiley Online Library
Quantitative evaluation of binding affinity changes upon mutations is crucial for protein
engineering and drug design. Machine learning‐based methods are gaining increasing …

PPI-hotspotID: A Method for Detecting Protein-Protein Interaction Hot Spots from the Free Protein Structure

YC Chen, K Sargsyan, JD Wright, YH Chen, YS Huang… - eLife, 2024 - elifesciences.org
Experimental detection of residues critical for protein-protein interactions (PPI) is a
timeconsuming, costly, and labor-intensive process. Hence, high-throughput PPI-hot spot …