AIMC Topic: Protein Conformation

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A scalable equivariant graph network framework for precise protein function prediction.

Genome biology
BACKGROUND: Protein function research helps in understanding the complex biological processes that occur within cells. However, the intricate nature of protein structures and functions, along with the rapid growth of protein sequence data, presents a...

Rapid and Accurate Protein Structure Database Search Using Inverse Folding Model and Contrastive Learning.

Journal of chemical information and modeling
Protein structure database search has become increasingly challenging due to the growing number of experimental and computational structures. We introduce mTM-align2, a novel two-step approach for rapid and accurate protein structure database search....

A machine learning protocol for predicting structural distributions of amyloid-forming proteins from 2D IR spectra.

Proceedings of the National Academy of Sciences of the United States of America
Protein misfolding plays a central role in diseases such as Alzheimer's disease, Parkinson's disease, type 2 diabetes, and transthyretin amyloidosis (ATTR), often driven by specific aggregation-prone segments such as A and A of amyloid-42 (A42), -Syn...

High-accuracy protein complex structure modeling based on sequence-derived structure complementarity.

Nature communications
In living organisms, proteins perform key functions required for life activities by interacting to form complexes. Determining the protein complex structure is crucial for understanding and mastering biological functions. Although AlphaFold2 makes a ...

A multi-geometric graph fusion network for protein-ligand affinity prediction.

Physical chemistry chemical physics : PCCP
Protein-ligand binding affinity prediction plays a crucial role in drug discovery. While recent works use two-dimensional graph neural networks to improve affinity prediction, we find that the three-dimensional geometric information of proteins and l...

A Practical Guide to Transition State Analysis in Biomolecular Simulations with TS-DAR.

The journal of physical chemistry. B
Conformational changes essential for protein function involve transitions through multiple short-lived, high-energy states within the complex free energy landscape. While existing methods, such as Markov State Models and non-Markovian approaches buil...

Structure-Based Classification of CRISPR/Cas9 Proteins: A Machine Learning Approach to Elucidating Cas9 Allostery.

Journal of molecular biology
The CRISPR/Cas9 system is a powerful gene-editing tool. Its specificity and stability rely on complex allosteric regulation. Understanding these allosteric regulations is essential for developing high-fidelity Cas9 variants with reduced off-target ef...

Efficient Generation of Protein and Protein-Protein Complex Dynamics via SE(3)-Parameterized Diffusion Models.

Journal of chemical information and modeling
Protein and protein-protein complex conformations play a critical role in biological functions, while exploring these via traditional molecular dynamics (MD) simulation is computationally expensive. Enhanced sampling methods offer improvements but re...

Modeling protein-small molecule conformational ensembles with PLACER.

Proceedings of the National Academy of Sciences of the United States of America
Modeling the conformational heterogeneity of protein-small molecule interactions is important for understanding natural systems and evaluating designed systems but remains an outstanding challenge. We reasoned that while residue-level descriptions of...

Predicting protein-protein interactions in the human proteome.

Science (New York, N.Y.)
Protein-protein interactions (PPIs) are essential for biological function. Coevolutionary analysis and deep-learning (DL)-based protein structure prediction have enabled comprehensive PPI identification in bacteria and yeast, but these approaches hav...