Computational Methods for Protein Structures

Computational methods employing machine learning algorithms are powerful tools that can be used to predict the effect of mutations on protein structure. This is important in neurodegenerative disorders, where some mutations can cause the formation of toxic protein aggregations. Uncovering the relationships between mutation and protein structure can be used to better understand disease. Discover the latest research here.

July 17, 2020
Open Access

Modeling the dielectric constants of crystals using machine learning

The Journal of Chemical Physics
Kazuki MoritaAron Walsh
May 10, 2020
Preprint
Open Access

Multiplexed measurement of variant abundance and activity reveals VKOR topology, active site and human variant impact

BioRxiv : the Preprint Server for Biology
M. A. ChiassonDouglas M Fowler
May 18, 2020
Open Access

Biased Gene Conversion Constrains Adaptation in Arabidopsis thaliana

Genetics
Tuomas Hämälä, Peter Tiffin
May 8, 2020
Open Access

Learning physical properties of liquid crystals with deep convolutional neural networks

Scientific Reports
Higor Y D SigakiHaroldo V Ribeiro
June 9, 2020
Open Access

Mining of effective local order parameters for classifying crystal structures: A machine learning study

The Journal of Chemical Physics
Hideo DoiTakeshi Aoyagi
April 16, 2020

Thermodynamic and Evolutionary Coupling between the Native and Amyloid State of Globular Proteins

Cell Reports
Tobias LangenbergJoost Schymkowitz
June 5, 2020

Predicting the stability of mutant proteins by computational approaches: an overview

Briefings in Bioinformatics
Anna MarabottiAngelo Facchiano
July 21, 2020
Open Access

Machine learning through cryptographic glasses: combating adversarial attacks by key-based diversified aggregation

EURASIP Journal on Information Security
Olga TaranSlava Voloshynovskiy
June 25, 2020

Development of a new polarized hyperspectral imaging microscope

Proceedings of SPIE
Ximing ZhouBaowei Fei
April 11, 2020
Open Access

Identifying Antifreeze Proteins Based on Key Evolutionary Information

Frontiers in Bioengineering and Biotechnology
Shanwen SunShuguang Han
June 17, 2020

Predicting Crystallization Tendency of Polymers Using Multifidelity Information Fusion and Machine Learning

The Journal of Physical Chemistry. B
Shruti VenkatramRampi Ramprasad
June 28, 2020
Open Access

Uncovering the effects of interface-induced ordering of liquid on crystal growth using machine learning

Nature Communications
Rodrigo Freitas, Evan J Reed
May 31, 2020

A two-stage approach towards protein secondary structure classification

Medical & Biological Engineering & Computing
Kushal Kanti GhoshUjjwal Maulik

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