AIMC Topic: Proteins

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Improvement of bioanalytical parameters through automation: suitability of a hand-like robotic system.

Analytical and bioanalytical chemistry
Commercial automation systems for small- and medium-sized laboratories, including research environments, are often complex to use. For liquid handling systems (LHS), development is required not only for the robot's movements but also for adapting the...

From Static to Dynamic Structures: Improving Binding Affinity Prediction with Graph-Based Deep Learning.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Accurate prediction of protein-ligand binding affinities is an essential challenge in structure-based drug design. Despite recent advances in data-driven methods for affinity prediction, their accuracy is still limited, partially because they only ta...

Predicting the Binding of Small Molecules to Proteins through Invariant Representation of the Molecular Structure.

Journal of chemical information and modeling
We present a computational scheme for predicting the ligands that bind to a pocket of a known structure. It is based on the generation of a general abstract representation of the molecules, which is invariant to rotations, translations, and permutati...

A Point Cloud Graph Neural Network for Protein-Ligand Binding Site Prediction.

International journal of molecular sciences
Predicting protein-ligand binding sites is an integral part of structural biology and drug design. A comprehensive understanding of these binding sites is essential for advancing drug innovation, elucidating mechanisms of biological function, and exp...

Rapid discrimination between deleterious and benign missense mutations in the CAGI 6 experiment.

Human genomics
We describe the machine learning tool that we applied in the CAGI 6 experiment to predict whether single residue mutations in proteins are deleterious or benign. This tool was trained using only single sequences, i.e., without multiple sequence align...

Optimizing protein sequence classification: integrating deep learning models with Bayesian optimization for enhanced biological analysis.

BMC medical informatics and decision making
Efforts to enhance the accuracy of protein sequence classification are of utmost importance in driving forward biological analyses and facilitating significant medical advancements. This study presents a cutting-edge model called ProtICNN-BiLSTM, whi...

An end-to-end framework for the prediction of protein structure and fitness from single sequence.

Nature communications
Significant research progress has been made in the field of protein structure and fitness prediction. Particularly, single-sequence-based structure prediction methods like ESMFold and OmegaFold achieve a balance between inference speed and prediction...

Protein Classes Predicted by Molecular Surface Chemical Features: Machine Learning-Assisted Classification of Cytosol and Secreted Proteins.

The journal of physical chemistry. B
Chemical structures of protein surfaces govern intermolecular interaction, and protein functions include specific molecular recognition, transport, self-assembly, etc. Therefore, the relationship between the chemical structure and protein functions p...

Holistic in silico developability assessment of novel classes of small proteins using publicly available sequence-based predictors.

Journal of computer-aided molecular design
The development of novel therapeutic proteins is a lengthy and costly process, with an average attrition rate of 91% (Thomas et al. Clinical Development Success Rates and Contributing Factors 2011-2020, 2021). To increase the probability of success a...

FitScore: a fast machine learning-based score for 3D virtual screening enrichment.

Journal of computer-aided molecular design
Enhancing virtual screening enrichment has become an urgent problem in computational chemistry, driven by increasingly large databases of commercially available compounds, without a commensurate drop in in vitro screening costs. Docking these large d...