13 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…
Yanli Wang, Frimpong Boadu, Jianlin Cheng, Lenore Cowen
The graph representations of all the protein chains in the training_data dataset were used to train MPBind. The binary cross-entropy loss function was employed to measure the difference between the predicted scores and binding site labels. During training, the loss function of MPBind was minimized using the Adam…
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…
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…
Qiuyu Li, Zeyu Xu, Yanhao Zhu, Wanyun Zhou + 5 more
Accurate prediction of drug-target molecular recognition is essential for early-stage drug discovery, spanning binding occurrence, binding site localization, and binding affinity estimation. However, current approaches frequently treat these tasks independently, thereby overlooking the shared mechanistic principles…
Tomasz Chady, Zuzanna Karolina Filutowska, Zhiyong Lu
GTF/GFF version 2 and FASTA file formats are widely used in genomics owing to their simplicity and ease of use. Consequently, parsing these files is often the first step in many projects, and can become a bottleneck. Indeed, this was the experience of the authors, who found a Python-based parser to be too slow and…
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…
Stephen R Piccolo, Harlan P Stevens
Using OpenAI’s Chat Completions API, we evaluated the ability to translate the example solutions and test code for the Python exercises to other programming languages: C++, Rust, Julia, and JavaScript. When invoking the API, we used version “gpt-4-0314” of the model and the default temperature setting of 0.7. For each…
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…
Alexander Alsalihi, Robert M. Flight, Hunter N. B. Moseley
The recount3 online resource provides tens of thousands of uniformly processed RNA-seq samples across human and mouse from major sequencing repositories like the Sequence Read Archive. While access to these datasets has traditionally been centered in the R/Bioconductor ecosystem, the growing prominence of Python in…
Shiqing Zhao, Yanhao Zhu, Ruizhou Li, Zeyu Xu + 7 more
Nanobody-antigen molecular recognition underpins nanobody discovery and development, necessitating accurate determination of binding occurrence, interface residues, and affinity. Current predictors are architecturally designed for the massive, heterogeneous spectrum of general protein-protein interactions, diluting the…
Gianluca Quargnali, Pablo Rivera-Fuentes
Deep learning methods for protein structure generation, sequence design, and structure and property prediction have created unprecedented opportunities for protein engineering and drug discovery. However, using these tools often requires navigating incompatible software environments, diverse input/output formats, and…