14 papers · ranked by Valyu relevance
Alejandro Martínez León, Lucas Andersen, Jochen S. Hub
User-Friendly Pipeline for Absolute Binding Free Energy Calculations Using Free Energy Perturbation or MM(PB/GB)SA Authors: Alejandro Martínez León, Lucas Andersen, Jochen S. Hub We present BindFlow, a Python-based software for automated absolute binding free energy (ABFE) calculations at the free energy perturbation…
Zhaohan Meng, Zhen Bai, Ke Yuan, Jiangning Song + 10 more
Protein-ligand binding governs enzymatic catalysis, metabolic homeostasis, and therapeutic modulation. Thus, the accurate prediction of these interactions underpins modern rational drug discovery. However, existing deep-learning frameworks largely operate as black-box predictors that fail to resolve the individual…
Aniruddh Goteti, Alexandra Vasilyeva, Çağlar Bozkurt
Predicting ligand binding sites is central to computational biology and drug discovery. Existing machine learning approaches either use protein sequence, structure, or both. While structure-based deep learning models typically outperform sequence-based methods, they often require high computational cost or…
Damiano Buratto, Wanding Wang, Xinyi Zhang, Qiujie Zhu + 4 more
The increasing availability of computational power is opening unprecedented opportunities in computational biology and drug design. Computer simulations based on physical models can now reproduce or replace critical biophysical experiments such as binding affinity evaluations in the drug screening process. Here we…
Keyu Xu, Zeyuan Di, Jianquan Zhao, Haicang Zhang + 1 more
Proteins are essential biological macromolecules that play a crucial role in living organisms. Protein-protein interactions, which govern various biological processes such as signal transduction, cell metabolism, and cell growth, are key aspect of protein function. The strength of these interactions, characterized by…
Yanli Wang, Frimpong Boadu, Jianlin Cheng, Lenore Cowen
MPBind was trained with true protein structures as input. To investigate how well it can predict binding sites from predicted protein structures, we randomly selected 20 protein chains for each of the five kinds of binding sites from Test2_data dataset (i.e. 100 protein chains in total) and applied AlphaFold 3 () to…
Mohammad Abdullah Aljasir, Sajjad Ahmad, Roberta Rocca, Alessia Bono + 1 more
Background/Objectives: GuaB, which is known as inosine 5′-phosphate dehydrogenase (IMPDH), is an enzymatic target involved in the de novo guanine biosynthetic pathway of the multidrug-resistant (MDR) Acinetobacter baumannii. GuaB has emerged as a potential therapeutic target to cope with increasing antibiotic…
Matthieu Vilain, Stéphane Aris-Brosou
The ever-growing amount of available biological data leads modern analysis to be performed on large datasets. Unfortunately, bioinformatics tools for preprocessing and analyzing data are not always designed to treat such large amounts of data efficiently. Notably, this is the case when encoding DNA and RNA sequences…
Ainur Abukaev, Constantin Völter, Mikhail Romodin, Sebastian Schwartzkopff + 6 more
A new Python package, pygid, enables fast batch processing of X-ray scattering data, with a particular focus on grazing-incidence geometries.
Zeyi Zhang, Carlos Mora Perez, Patrick Kwon, Martin Head‐Gordon + 1 more
PARSEC.py is a Python-based real-space Kohn-Sham density functional theory (real-space KS-DFT) framework designed to provide a user- and developer-friendly platform for first-principles electronic-structure simulations. Discretization on real-space grids eliminates basis-set approximations, while enabling systematic…
Dylan Silke, Julie Iskander, Junqi Pan, Andrew P. Thompson + 3 more
Leveraging artificial intelligence and deep learning to generate proteins de novo (a.k.a. ‘synthetic proteins’) has unlocked new frontiers of protein design. Deep learning models trained on protein structures can generate novel protein designs that explore structural landscapes unseen by evolution. This approach…
Tomasz Chady, Zuzanna Karolina Filutowska, Zhiyong Lu
The eccLib library provides a high-performance solution for parsing GTF and FASTA files in Python, thereby significantly enhancing project performance. eccLib was built with the Python/C API in mind, allowing for performance closely matching that of native C parsers, which would not be feasible if implemented directly…
Steven B. Wells, Hamna Shahnawaz, Joanne L. Jones
dreampy is a Python implementation of the R dreamlet framework for pseudobulk differential expression analysis of single-cell RNA-seq data. dreamlet combines voom precision-weighted linear mixed models with empirical Bayes moderation to handle batch effects, repeated measures, and other hierarchical structure in…
Pritam Kumar Panda
Protein–ligand interaction diagrams are a routine part of structural and medicinal chemistry, but the tools that produce them tend to force a choice: comprehensive detection with tabular output, publication-quality figures behind a licence, or a scripting environment that assumes expertise. PandaMap (Protein AND ligAnd…