AI Biology & Docking

Deep-learning-based single-domain and multidomain protein structure prediction with D-I-TASSER - Nature Biotechnology Protein structures are predicted by integrating deep learning potentials with iterative threading fragment assembly simulations.

Going Head-to-Head with AlphaFold3 ...

29.05.2025 13:42 — 👍 0    🔁 0    💬 0    📌 0

AI system predicts protein fragments that can bind to or inhibit a target FragFold, developed by MIT Biology researchers, is a computational method with potential for impact on biological research and therapeutic applications.

AI system FragFold predicts protein fragments that can bind to or inhibit a target, offering potential therapeutic applications and advancing biological research. Developed by MIT researchers, it leverages AlphaFold to predict fragment inhibitors with high accuracy. #ai (8)

22.02.2025 16:30 — 👍 5    🔁 2    💬 0    📌 0

AlphaFold prediction of structural ensembles of disordered proteins - Nature Communications Here, the authors introduce AlphaFold-Metainference, which uses inter-residue distances predicted by AlphaFold to generate structural ensembles of disordered proteins.

Alphafold contraints molecular dynamics simulations to give structural ensemble of [disordered proteins](www.nature.com/articles/s41...)

19.02.2025 16:53 — 👍 3    🔁 1    💬 0    📌 0

TorchANI FF

Gromacs 2025.0 provides basic support for running simulations with Neural Network Potential (NNP) [models](manual.gromacs.org/current/refe...
[TorchAny FF](raw.githubusercontent.com/aiqm/torchan...)

17.02.2025 17:01 — 👍 0    🔁 0    💬 0    📌 0

overview of results for PLAID!

1/🧬 Excited to share PLAID, our new approach for co-generating sequence and all-atom protein structures by sampling from the latent space of ESMFold. This requires only sequences during training, which unlocks more data and annotations:

bit.ly/plaid-proteins
🧵

06.12.2024 17:44 — 👍 121    🔁 37    💬 1    📌 3

Computational design of serine hydrolases The design of enzymes with complex active sites that mediate multistep reactions remains an outstanding challenge. With serine hydrolases as a model system, we combined the generative capabilities of ...

Computational design of serine hydrolases | Science www.science.org/doi/10.1126/...
AI ensemble generation and "diffusion" achieve a 96,000-fold increase in engineered enzyme efficiency.

16.02.2025 09:59 — 👍 0    🔁 0    💬 0    📌 0

Conformational ensembles reveal the origins of serine protease catalysis Enzymes exist in ensembles of states that encode the energetics underlying their catalysis. Conformational ensembles built from 1231 structures of 17 serine proteases revealed atomic-level changes acr...

Conformational ensembles reveal the origins of serine protease catalysis | Science www.science.org/doi/10.1126/...
How molecular dynamics accounts for enzymatic efficiency: a meta analyses of Xray structures.

16.02.2025 09:40 — 👍 0    🔁 0    💬 0    📌 0

For years, my role teaching this advanced structural biology course involved chronicling the gradual progress in computational biology...

23.11.2024 14:14 — 👍 0    🔁 0    💬 0    📌 0