AIMC Topic: Proteins

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Protein model quality assessment using rotation-equivariant transformations on point clouds.

Proteins
Machine learning research concerning protein structure has seen a surge in popularity over the last years with promising advances for basic science and drug discovery. Working with macromolecular structure in a machine learning context requires an ad...

Improving de novo protein binder design with deep learning.

Nature communications
Recently it has become possible to de novo design high affinity protein binding proteins from target structural information alone. There is, however, considerable room for improvement as the overall design success rate is low. Here, we explore the au...

Basis for Accurate Protein p Prediction with Machine Learning.

Journal of chemical information and modeling
pH regulates protein structures and the associated functions in many biological processes via protonation and deprotonation of ionizable side chains where the titration equilibria are determined by p's. To accelerate pH-dependent molecular mechanism ...

A deep learning model for predicting optimal distance range in crosslinking mass spectrometry data.

Proteomics
Macromolecular assemblies play an important role in all cellular processes. While there has recently been significant progress in protein structure prediction based on deep learning, large protein complexes cannot be predicted with these approaches. ...

De novo design of protein interactions with learned surface fingerprints.

Nature
Physical interactions between proteins are essential for most biological processes governing life. However, the molecular determinants of such interactions have been challenging to understand, even as genomic, proteomic and structural data increase. ...

MutBLESS: A tool to identify disease-prone sites in cancer using deep learning.

Biochimica et biophysica acta. Molecular basis of disease
Understanding the molecular basis and impact of mutations at different stages of cancer are long-standing challenges in cancer biology. Identification of driver mutations from experiments is expensive and time intensive. In the present study, we coll...

Do "Newly Born" orphan proteins resemble "Never Born" proteins? A study using three deep learning algorithms.

Proteins
"Newly Born" proteins, devoid of detectable homology to any other proteins, known as orphan proteins, occur in a single species or within a taxonomically restricted gene family. They are generated by the expression of novel open reading frames, and a...

Measuring cryo-TEM sample thickness using reflected light microscopy and machine learning.

Journal of structural biology
In cryo-transmission electron microscopy (cryo-TEM), sample thickness is one of the most important parameters that governs image quality. When combining cryo-TEM with other imaging methods, such as light microscopy, measuring and controlling the samp...

Exploiting conformational dynamics to modulate the function of designed proteins.

Proceedings of the National Academy of Sciences of the United States of America
With the recent success in calculating protein structures from amino acid sequences using artificial intelligence-based algorithms, an important next step is to decipher how dynamics is encoded by the primary protein sequence so as to better predict ...

From Deep Mutational Mapping of Allosteric Protein Landscapes to Deep Learning of Allostery and Hidden Allosteric Sites: Zooming in on "Allosteric Intersection" of Biochemical and Big Data Approaches.

International journal of molecular sciences
The recent advances in artificial intelligence (AI) and machine learning have driven the design of new expert systems and automated workflows that are able to model complex chemical and biological phenomena. In recent years, machine learning approach...