AIMC Topic: Amino Acid Sequence

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SHARK: web server for alignment-free homology assessment for intrinsically disordered and unalignable protein regions.

Nucleic acids research Jul 7, 2025
Whereas alignment has been fundamental to sequence-based assessments of protein homology, it is ineffective for intrinsically disordered regions (IDRs) due to their lowered sequence conservation and unique sequence properties. Here, we present a web ...
Humans Machine Learning Amino Acid Sequence Intrinsically Disordered Proteins Sequence Homology, Amino Acid Sequence Analysis, Protein Sequence Alignment Animals Internet Software Algorithms
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StructMAn 2.0 Web: a web server for structural annotation of protein sequences and mutations.

Nucleic acids research Jul 7, 2025
StructMAn is a method for protein structural annotation. It describes each position of a protein sequence or specific variants in it in terms of their importance for the three-dimensional (3D) structure of the protein and its interactions with other ...
Proteins Models, Molecular Mutation Software User-Computer Interface Protein Conformation Molecular Sequence Annotation Internet Sequence Analysis, Protein Amino Acid Sequence
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Prediction of Specificity of α-Conotoxins to Subtypes of Human Nicotinic Acetylcholine Receptors with Semi-supervised Machine Learning.

ACS chemical neuroscience Jun 18, 2025
Conotoxins are a family of highly toxic neurotoxins composed of cysteine-rich peptides produced by marine cone snails. The most lethal cone snail species to humans is with fatality rates of up to ∼65% from a single sting, which is caused mostly by t...
Supervised Machine Learning Amino Acid Sequence Machine Learning Protein Binding Conus Snail Conotoxins Receptors, Nicotinic Humans
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PLPTP: A Motif-based Interpretable Deep Learning Framework Based on Protein Language Models for Peptide Toxicity Prediction.

Journal of molecular biology Jun 15, 2025
Peptide toxicity prediction holds significant importance in drug development and biotechnology, as accurately identifying toxic peptide sequences is crucial for designing safer peptide-based drugs. This study proposes a deep learning-based model for ...
Peptides Deep Learning Humans Neural Networks, Computer Computational Biology Amino Acid Sequence Amino Acid Motifs Proteins
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Discovery of unconventional and nonintuitive self-assembling peptide materials using experiment-driven machine learning.

Science advances Jun 13, 2025
Prediction of peptide secondary structure is challenging because of complex molecular interactions, sequence-specific behavior, and environmental factors. Traditional design strategies, based on hydrophobicity and structural propensity, can be biased...
Peptides Amino Acid Sequence Protein Conformation, beta-Strand Machine Learning Hydrophobic and Hydrophilic Interactions
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To Fly, or Not to Fly, That Is the Question: A Deep Learning Model for Peptide Detectability Prediction in Mass Spectrometry.

Journal of proteome research Jun 6, 2025
Identifying detectable peptides, known as flyers, is key in mass spectrometry-based proteomics. Peptide detectability is strongly related to peptide sequences and their resulting physicochemical properties. Moreover, the high variability in MS data c...
Amino Acid Sequence Proteomics Mass Spectrometry Software Animals Peptides Humans Deep Learning Peptide Library
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IFPTML Multi-Output Model for Anti-Retroviral Compounds Including the Drug Structure and Target Protein Sequence Information.

Journal of chemical information and modeling May 12, 2025
Retroviruses such as HIV cause significant diseases in humans and other organisms, making the discovery of antiretroviral (ARV) drugs a critical priority. While databases like ChEMBL contain valuable information, their complexity poses challenges. Th...
Amino Acid Sequence Viral Proteins Drug Discovery Humans Anti-Retroviral Agents Machine Learning
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COLOR: A Compositional Linear Operation-Based Representation of Protein Sequences for Identification of Monomer Contributions to Properties.

Journal of chemical information and modeling May 12, 2025
The properties of biological materials like proteins and nucleic acids are largely determined by their primary sequence. Certain segments in the sequence strongly influence specific functions, but identifying these segments, or so-called motifs, is c...
Proteins Amino Acid Sequence Deep Learning
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NNKcat: deep neural network to predict catalytic constants (Kcat) by integrating protein sequence and substrate structure with enhanced data imbalance handling.

Briefings in bioinformatics May 1, 2025
Catalytic constant (Kcat) is to describe the efficiency of catalyzing reactions. The Kcat value of an enzyme-substrate pair indicates the rate an enzyme converts saturated substrates into product during the catalytic process. However, it is challengi...
Algorithms Computational Biology Amino Acid Sequence Neural Networks, Computer Proteins Substrate Specificity Catalysis
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IAMPDB: A Knowledgebase of Manually Curated Insects-Derived Antimicrobial Peptides.

Journal of peptide science : an official publication of the European Peptide Society Mar 1, 2025
Insects, a majority of animal species, rely on innate immunity and antimicrobial peptides (AMPs), which are a part of their innate immunity, to combat diverse parasites and pathogens. These peptides have applications ranging from agriculture to antim...
Insect Proteins Animals Antimicrobial Peptides Knowledge Bases Databases, Protein Insecta Amino Acid Sequence
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