AIMC Topic: Amino Acid Sequence

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LMProtein: a protein language model based framework for protein structural property prediction.

Physical chemistry chemical physics : PCCP
Recent advances in machine learning and self-supervised deep language modeling have made it possible to accurately predict protein structural properties. Most existing models and pretraining methods leverage evolutionary information in multiple seque...

Large Data Set Analysis Reveals Structural Origin of Peptide Collisional Cross Section Bimodal Behavior.

Journal of the American Society for Mass Spectrometry
Recent advances in ion mobility spectrometry have enabled the measurement of rotationally averaged collisional cross-sectional area (CCS) for millions of peptides as part of routine proteomic mass spectrometry workflows. One of the most striking find...

Deep Learning Exploration Expands the Natural Diversity of Metallothioneins in the Archaea Domain.

Journal of agricultural and food chemistry
The diversity and functions of metallothioneins (MTs) in Archaea remain poorly understood. This study identifies 180 archaeal MTs from 406 genomes, revealing distinct evolutionary lineages and structural diversity. Phylogenetic analysis suggests a no...

ProSECFPs: A Novel Fingerprint-Based Protein Representation Method for Missense Mutation Pathogenicity Prediction.

Journal of chemical information and modeling
Developing effective computational representations of protein sequences is crucial for advancing diverse areas of computational biology and bioinformatics. Ideal representations must be computationally efficient, scalable, informative, flexible acros...

Coevolutionary signals in multiple sequence alignments improve virulence factor prediction with an MSA Transformer.

Scientific reports
Identification of virulence factors (VFs) is critical for expanding our knowledge on bacterial pathogenesis and also for developing targeted strategies for the prevention and treatment of related infectious diseases. Understanding virulence factors r...

From Signal to Symphony: Exploring 2D Sequence Representations for Protein Function Prediction.

Journal of chemical information and modeling
Predicting protein function from its primary sequence is a fundamental challenge in computational biology. While deep learning has excelled, the optimal representation of sequence data remains an open question. This study explores protein sonificatio...

PS3N: leveraging protein sequence-structure similarity for novel drug-drug interaction discovery.

Scientific reports
Adverse drug events represent a key challenge in public health, especially concerning drug safety profiling and drug surveillance. Drug-drug interactions represent one of the most popular types of adverse drug events. Most computational approaches to...

DLFea4AMPGen de novo design of antimicrobial peptides by integrating features learned from deep learning models.

Nature communications
Deep learning models show promise in accelerating the design and optimization of antimicrobial peptides (AMPs), but current methods face challenges, such as low success rates, or large virtual library scales. In this study, we introduce DLFea4AMPGen,...

Temperature adaptation in structure and function in lactate dehydrogenase-A reflects convergent evolution in a few key protein regions.

Proceedings of the National Academy of Sciences of the United States of America
Adaptive differences in the thermal stabilities of enzyme structure and function play critical roles in establishing the thermal optima and limits of all organisms. Thus, understanding the mechanisms underlying these adaptations can yield insights in...

A Three-Module Machine Learning Framework for Protein Sequence- and Temperature-Dependent / Prediction in β-Glucosidases.

ACS synthetic biology
The catalytic activity of enzymes is intricately determined by their amino acid sequences and assay conditions, particularly temperature. Navigating the complex interplay among sequence, temperature, and catalytic function is crucial for unlocking a ...