AIMC Topic: Proteins

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Deep_CNN_LSTM_GO: Protein function prediction from amino-acid sequences.

Computational biology and chemistry
Protein amino acid sequences can be used to determine the functions of the protein. However, determining the function of a single protein requires many resources and a tremendous amount of time. Computational Intelligence methods such as Deep learnin...

When homologous sequences meet structural decoys: Accurate contact prediction by tFold in CASP14-(tFold for CASP14 contact prediction).

Proteins
In this paper, we report our tFold framework's performance on the inter-residue contact prediction task in the 14th Critical Assessment of protein Structure Prediction (CASP14). Our tFold framework seamlessly combines both homologous sequences and st...

Testing machine learning techniques for general application by using protein secondary structure prediction. A brief survey with studies of pitfalls and benefits using a simple progressive learning approach.

Computers in biology and medicine
Many researchers have recently used the prediction of protein secondary structure (local conformational states of amino acid residues) to test advances in predictive and machine learning technology such as Neural Net Deep Learning. Protein secondary ...

FFENCODER-PL: Pair Wise Energy Descriptors for Protein-Ligand Pose Selection.

Journal of chemical theory and computation
Scoring functions are the essential component in molecular docking methods. An accurate scoring function is expected to distinguish the native ligand pose from decoy poses. Our previous experience (Pei et al. 2019, 59 (7), 3305-3315) proved that com...

SCONES: Self-Consistent Neural Network for Protein Stability Prediction Upon Mutation.

The journal of physical chemistry. B
Engineering proteins to have desired properties by mutating amino acids at specific sites is commonplace. Such engineered proteins must be stable to function. Experimental methods used to determine stability at throughputs required to scan the protei...

Structure-based protein design with deep learning.

Current opinion in chemical biology
Since the first revelation of proteins functioning as macromolecular machines through their three dimensional structures, researchers have been intrigued by the marvelous ways the biochemical processes are carried out by proteins. The aspiration to u...

Augmented sequence features and subcellular localization for functional characterization of unknown protein sequences.

Medical & biological engineering & computing
Advances in high-throughput techniques lead to evolving a large number of unknown protein sequences (UPS). Functional characterization of UPS is significant for the investigation of disease symptoms and drug repositioning. Protein subcellular localiz...

PARROT is a flexible recurrent neural network framework for analysis of large protein datasets.

eLife
The rise of high-throughput experiments has transformed how scientists approach biological questions. The ubiquity of large-scale assays that can test thousands of samples in a day has necessitated the development of new computational approaches to i...

Biomolecular simulation based machine learning models accurately predict sites of tolerability to the unnatural amino acid acridonylalanine.

Scientific reports
The incorporation of unnatural amino acids (Uaas) has provided an avenue for novel chemistries to be explored in biological systems. However, the successful application of Uaas is often hampered by site-specific impacts on protein yield and solubilit...

A deep-learning framework for multi-level peptide-protein interaction prediction.

Nature communications
Peptide-protein interactions are involved in various fundamental cellular functions and their identification is crucial for designing efficacious peptide therapeutics. Recently, a number of computational methods have been developed to predict peptide...