AIMC Topic: Proteins

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Enhancing Drug-Target Interaction Prediction through Transfer Learning from Activity Cliff Prediction Tasks.

Journal of chemical information and modeling
Recently, machine learning (ML) has gained popularity in the early stages of drug discovery. This trend is unsurprising given the increasing volume of relevant experimental data and the continuous improvement of ML algorithms. However, conventional m...

CAML: Commutative Algebra Machine Learning─A Case Study on Protein-Ligand Binding Affinity Prediction.

Journal of chemical information and modeling
Recently, Suwayyid and Wei introduced commutative algebra as an emerging paradigm for machine learning and data science. In this work, we propose commutative algebra machine learning (CAML) for the prediction of protein-ligand binding affinities. Spe...

Computer-Aided Drug Discovery for Undruggable Targets.

Chemical reviews
Undruggable targets are those of therapeutical significance but challenging for conventional drug design approaches. Such targets often exhibit unique features, including highly dynamic structures, a lack of well-defined ligand-binding pockets, the p...

PrankWeb 4: a modular web server for protein-ligand binding site prediction and downstream analysis.

Nucleic acids research
Knowledge of protein-ligand binding sites (LBSs) is crucial for advancing our understanding of biology and developing practical applications in fields such as medicine or biotechnology. PrankWeb is a web server that allows users to predict LBSs from ...

DEMO-EMol: modeling protein-nucleic acid complex structures from cryo-EM maps by coupling chain assembly with map segmentation.

Nucleic acids research
Atomic structure modeling is a crucial step in determining the structures of protein complexes using cryo-electron microscopy (cryo-EM). This work introduces DEMO-EMol, an improved server that integrates deep learning-based map segmentation and chain...

InDeepNet: a web platform for predicting functional binding sites in proteins using InDeep.

Nucleic acids research
Predicting functional binding sites in proteins is crucial for understanding protein-protein interactions (PPIs) and identifying drug targets. While various computational approaches exist, many fail to assess PPI ligandability, which often involves c...

HawkDock version 2: an updated web server to predict and analyze the structures of protein-protein complexes.

Nucleic acids research
Protein-protein interactions (PPIs) are fundamental to cellular functions, yet predicting and analyzing their 3D structures remains a critical and computationally demanding challenge. To address this, the HawkDock web server was developed as an integ...

StructMAn 2.0 Web: a web server for structural annotation of protein sequences and mutations.

Nucleic acids research
StructMAn is a method for protein structural annotation. It describes each position of a protein sequence or specific variants in it in terms of their importance for the three-dimensional (3D) structure of the protein and its interactions with other ...

FoldScript: a web server for the efficient analysis of AI-generated 3D protein models.

Nucleic acids research
Artificial intelligence (AI)-based 3D protein modelling software have revolutionized structural biology, often predicting protein structures with unprecedented confidence. However, to get the most out of AI, it is advisable to consider the informatio...

GOBeacon: An ensemble model for protein function prediction enhanced by contrastive learning.

Protein science : a publication of the Protein Society
Accurate prediction of protein function is fundamental to understanding biological processes, with computational methods becoming increasingly essential as experimental methods struggle to keep pace with the rate of newly discovered proteins. Despite...