AIMC Topic: Peptides

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Discovery of unconventional and nonintuitive self-assembling peptide materials using experiment-driven machine learning.

Science advances
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...

Using Machine Learning to Fast-Track Peptide Nanomaterial Discovery.

ACS nano
Peptides can serve as building blocks for supramolecular materials because of their unique ability to self-assemble, offering potential applications in drug delivery, tissue engineering, and nanotechnology. In this review, we describe peptide self-as...

Investigating the Nature of PRM:SH3 Interactions Using Artificial Intelligence and Molecular Dynamics.

Journal of chemical information and modeling
Understanding the binding interactions within protein-peptide complexes is crucial for elucidating key physicochemical phenomena in biological systems. Among the outcomes of these interactions, biomolecular condensates have recently emerged as vital ...

ABP-Xplorer: A Machine Learning Approach for Prediction of Antibacterial Peptides Targeting -tRNA-Methyltransferase (TrmD).

Journal of chemical information and modeling
(MAB) infections pose a significant treatment challenge due to their intrinsic resistance to antibiotics, requiring prolonged multidrug regimens with limited success and frequent relapses. tRNA (m1G37) methyltransferase (TrmD), an enzyme essential f...

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
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...

A hybrid protocol for peptide development: integrating deep generative models and physics simulations for biomolecular design targeting IL23R/IL23.

International journal of biological macromolecules
Recent advances in machine learning have revolutionized molecular design; however, a gap remains in integrating generative models with physics-based simulations to develop functional modulators, such as stable peptides, for challenging targets like t...

GRU4ACE: Enhancing ACE inhibitory peptide prediction by integrating gated recurrent unit with multi-source feature embeddings.

Protein science : a publication of the Protein Society
Accurate identification of angiotensin-I-converting enzyme (ACE) inhibitory peptides is essential for understanding the primary factor regulating the renin-angiotensin system and guiding the development of new drug candidates. Given the inherent chal...

AI-Assisted Protein-Peptide Complex Prediction in a Practical Setting.

Journal of computational chemistry
Accurate prediction of protein-peptide complex structures plays a critical role in structure-based drug design, including antibody design. Most peptide-docking benchmark studies were conducted using crystal structures of protein-peptide complexes; as...

Biomolecular Actuators for Soft Robots.

Chemical reviews
Biomolecules present promising stimuli-responsive mechanisms to revolutionize soft actuators. Proteins, peptides, and nucleic acids foster specific intermolecular interactions, and their boundless sequence design spaces encode precise actuation capab...

Artificial Intelligence-Guided Cancer Engineering for Tumor Normalization Executed by Tumor Lysosomal-Triggered Supramolecular Chiral Peptide.

ACS nano
Cancer engineering for tumor normalization offers a promising therapeutic strategy to reverse malignant cells and their supportive tumor microenvironment into a more benign state. Herein, an artificial intelligence (AI) approach was developed using m...