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Amino Acid Sequence

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Highly accurate protein structure prediction with AlphaFold.

Nature
Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort, the structures of around 100,000 unique proteins have been determined, but this r...

PyPEF-An Integrated Framework for Data-Driven Protein Engineering.

Journal of chemical information and modeling
Data-driven strategies are gaining increased attention in protein engineering due to recent advances in access to large experimental databanks of proteins, next-generation sequencing (NGS), high-throughput screening (HTS) methods, and the development...

SidechainNet: An all-atom protein structure dataset for machine learning.

Proteins
Despite recent advancements in deep learning methods for protein structure prediction and representation, little focus has been directed at the simultaneous inclusion and prediction of protein backbone and sidechain structure information. We present ...

Predicting HIV drug resistance using weighted machine learning method at target protein sequence-level.

Molecular diversity
Acquired immune deficiency syndrome (AIDS) is a fatal disease caused by human immunodeficiency virus (HIV). Although 23 different drugs have been available, the treatment of AIDS remains challenging because the virus mutates very quickly which can le...

Prediction of Protein Solubility Based on Sequence Feature Fusion and DDcCNN.

Interdisciplinary sciences, computational life sciences
BACKGROUND: Prediction of protein solubility is an indispensable prerequisite for pharmaceutical research and production. The general and specific objective of this work is to design a new model for predicting protein solubility by using protein sequ...

Identification of subtypes of anticancer peptides based on sequential features and physicochemical properties.

Scientific reports
Anticancer peptides (ACPs) are a kind of bioactive peptides which could be used as a novel type of anticancer drug that has several advantages over chemistry-based drug, including high specificity, strong tumor penetration capacity, and low toxicity ...

ActTRANS: Functional classification in active transport proteins based on transfer learning and contextual representations.

Computational biology and chemistry
MOTIVATION: Primary and secondary active transport are two types of active transport that involve using energy to move the substances. Active transport mechanisms do use proteins to assist in transport and play essential roles to regulate the traffic...

MULTICOM2 open-source protein structure prediction system powered by deep learning and distance prediction.

Scientific reports
Protein structure prediction is an important problem in bioinformatics and has been studied for decades. However, there are still few open-source comprehensive protein structure prediction packages publicly available in the field. In this paper, we p...

Predicting Drug-Target Interactions with Deep-Embedding Learning of Graphs and Sequences.

The journal of physical chemistry. A
Computational approaches for predicting drug-target interactions (DTIs) play an important role in drug discovery since conventional screening experiments are time-consuming and expensive. In this study, we proposed end-to-end representation learning ...

In-Pero: Exploiting Deep Learning Embeddings of Protein Sequences to Predict the Localisation of Peroxisomal Proteins.

International journal of molecular sciences
Peroxisomes are ubiquitous membrane-bound organelles, and aberrant localisation of peroxisomal proteins contributes to the pathogenesis of several disorders. Many computational methods focus on assigning protein sequences to subcellular compartments,...