Utilizing machine learning-based QSAR model to overcome standalone consensus docking limitation in beta-lactamase inhibitors screening: a proof-of-concept study.

In virtual drug screening, consensus docking is a standard in-silico approach consisting of a combined result from optimized docking experiments, a minimum of two results combination. Therefore, consensus docking is subjected to a lower success rate than the best docking method due to its mathematical nature, an unavoidable limitation. This study aims to overcome this drawback via random forest, an ensemble machine learning model.

RevGraphVAMP: A protein molecular simulation analysis model combining graph convolutional neural networks and physical constraints.

Molecular dynamics simulation is a crucial research domain within the life sciences, focusing on comprehending the mechanisms of biomolecular interactions at atomic scales. Protein simulation, as a critical subfield, often utilizes MD for implementation, with trajectory data play a pivotal role in drug discovery. The advancement of high-performance computing and deep learning technology becomes popular and critical to predict protein properties from vast trajectory data, posing challenges regarding data features extraction from the complicated simulation data and dimensionality reduction.

A Weakly Supervised Learning Method for Cell Detection and Tracking Using Incomplete Initial Annotations.

The automatic detection of cells in microscopy image sequences is a significant task in biomedical research. However, routine microscopy images with cells, which are taken during the process whereby constant division and differentiation occur, are notoriously difficult to detect due to changes in their appearance and number. Recently, convolutional neural network (CNN)-based methods have made significant progress in cell detection and tracking.

JCcirc: circRNA full-length sequence assembly through integrated junction contigs.

Recent studies have shed light on the potential of circular RNA (circRNA) as a biomarker for disease diagnosis and as a nucleic acid vaccine. The exploration of these functionalities requires correct circRNA full-length sequences; however, existing assembly tools can only correctly assemble some circRNAs, and their performance can be further improved. Here, we introduce a novel feature known as the junction contig (JC), which is an extension of the back-splice junction (BSJ).

Substrate-Controlled Catalysis in the Ether Cross-Link-Forming Radical SAM Enzymes.

Darobactin is a heptapeptide antibiotic featuring an ether cross-link and a C-C cross-link, and both cross-links are installed by a radical S-adenosylmethionine (rSAM) enzyme DarE. How a single DarE enzyme affords the two chemically distinct cross-links remains largely obscure. Herein, by mapping the biosynthetic landscape for darobactin-like RiPP (daropeptide), we identified and characterized two novel daropeptides that lack the C-C cross-link present in darobactin and instead are solely composed of ether cross-links.

The relationship between lifestyle, driving anger and dangerous driving behaviours-An explorative study in a Chinese sample.

Drivers who exhibit dangerous driving behaviours, such as aggressive, risky, and negative emotion cognition driving, are more likely to be involved in road crashes. A key motivator behind unsafe driving behaviours is driving anger. However, it is unclear whether lifestyle, driving anger, and dangerous driving behaviours are related.

Identification of Autism spectrum disorder based on a novel feature selection method and Variational Autoencoder.

The development of noninvasive brain imaging such as resting-state functional magnetic resonance imaging (rs-fMRI) and its combination with AI algorithm provides a promising solution for the early diagnosis of Autism spectrum disorder (ASD). However, the performance of the current ASD classification based on rs-fMRI still needs to be improved. This paper introduces a classification framework to aid ASD diagnosis based on rs-fMRI.

Inter-Residue Distance Prediction From Duet Deep Learning Models.

Residue distance prediction from the sequence is critical for many biological applications such as protein structure reconstruction, protein-protein interaction prediction, and protein design. However, prediction of fine-grained distances between residues with long sequence separations still remains challenging. In this study, we propose DuetDis, a method based on duet feature sets and deep residual network with squeeze-and-excitation (SE), for protein inter-residue distance prediction.

Bioinformatics Screening of Potential Biomarkers from mRNA Expression Profiles to Discover Drug Targets and Agents for Cervical Cancer.

Bioinformatics analysis has been playing a vital role in identifying potential genomic biomarkers more accurately from an enormous number of candidates by reducing time and cost compared to the wet-lab-based experimental procedures for disease diagnosis, prognosis, and therapies. Cervical cancer (CC) is one of the most malignant diseases seen in women worldwide. This study aimed at identifying potential key genes (KGs), highlighting their functions, signaling pathways, and candidate drugs for CC diagnosis and targeting therapies.

Evaluation of residue-residue contact prediction methods: From retrospective to prospective.

Sequence-based residue contact prediction plays a crucial role in protein structure reconstruction. In recent years, the combination of evolutionary coupling analysis (ECA) and deep learning (DL) techniques has made tremendous progress for residue contact prediction, thus a comprehensive assessment of current methods based on a large-scale benchmark data set is very needed. In this study, we evaluate 18 contact predictors on 610 non-redundant proteins and 32 CASP13 targets according to a wide range of perspectives.

On the Conformational Dynamics of β-Amyloid Forming Peptides: A Computational Perspective.

Understanding the conformational dynamics of proteins and peptides involved in important functions is still a difficult task in computational structural biology. Because such conformational transitions in β-amyloid (Aβ) forming peptides play a crucial role in many neurological disorders, researchers from different scientific fields have been trying to address issues related to the folding of Aβ forming peptides together. Many theoretical models have been proposed in the recent years for studying Aβ peptides using mathematical, physicochemical, and molecular dynamics simulation, and machine learning approaches.

Stability of Aβ-fibril fragments in the presence of fatty acids.

We consider the effect of lauric acid on the stability of various fibril-like assemblies of Aβ peptides. For this purpose, we have performed molecular dynamics simulations of these assemblies either in complex with lauric acid or without presence of the ligand. While we do not observe a stabilizing effect on Aβ -fibrils, we find that addition of lauric acid strengthens the stability of fibrils built from the triple-stranded S-shaped Aβ -peptides considered to be more toxic.

The effect of retro-inverse D-amino acid Aβ-peptides on Aβ-fibril formation.

Peptides build from D-amino acids resist enzymatic degradation. The resulting extended time of biological activity makes them prime candidates for the development of pharmaceuticals. Of special interest are D-retro-inverso (DRI) peptides where a reversed sequence of D-amino acids leads to molecules with almost the same structure, stability, and bioactivity as the parent L-peptides but increased resistance to proteolytic degradation.

Large fatty acid-derived Aβ42 oligomers form ring-like assemblies.

As the primary toxic species in the etiology of Alzheimer disease (AD) are low molecular weight oligomers of Aβ, it is crucial to understand the structure of Aβ oligomers for gaining molecular insights into AD pathology. We have earlier demonstrated that in the presence of fatty acids, Aβ42 peptides assemble as 12-24mer oligomers. These Large Fatty Acid-derived Oligomers (LFAOs) exist predominantly as 12mers at low and as 24mers at high concentrations.

Stability of the N-Terminal Helix and Its Role in Amyloid Formation of Serum Amyloid A.

Colonic amyloidosis is the result of overexpression of the serum amyloid A (SAA) protein in inflammatory bowel disease or colon cancer. Crucial for amyloid formation are the first ten N-terminal residues, which in the crystal structure are a part of a 27-residue long helix. Here, we study this 27-residue N-terminal region of SAA by a multiexchange variant of replica exchange molecular dynamics.

Conversion between parallel and antiparallel β-sheets in wild-type and Iowa mutant Aβ fibrils.

Using a variant of Hamilton-replica-exchange, we study for wild type and Iowa mutant Aβ the conversion between fibrils with antiparallel β-sheets and such with parallel β-sheets. We show that wild type and mutant form distinct salt bridges that in turn stabilize different fibril organizations. The conversion between the two fibril forms leads to the release of small aggregates that in the Iowa mutant may shift the equilibrium from fibrils to more toxic oligomers.

Out-of-Register Aβ Assemblies as Models for Neurotoxic Oligomers and Fibrils.

We propose a variant of the recently found S-shaped Aβ-motif that is characterized by out-of-register C-terminal β-strands. We show that chains with this structure can form not only fibrils that are compatible with the NMR signals but also barrel-shaped oligomers that resemble the ones formed by the much smaller cylindrin peptides. By running long all-atom molecular dynamics simulations at physiological temperatures with an explicit solvent, we study the stability of these constructs and show that they are plausible models for neurotoxic oligomers.

Ring-like N-fold Models of Aβ fibrils.

When assembling as fibrils Aβ peptides can only assume U-shaped conformations while Aβ can also arrange as S-shaped three-stranded chains. We show that this allows Aβ peptides to assemble pore-like structures that may explain their higher toxicity. For this purpose, we develop a scalable model of ring-like assemblies of S-shaped Aβ chains and study the stability and structural properties of these assemblies through atomistic molecular dynamics simulations.

Fibril-Barrel Transitions in Cylindrin Amyloids.

We introduce Replica-Exchange-with-Tunneling (RET) simulations as a tool for studies of the conversion between polymorphic amyloids. For the 11-residue amyloid-forming cylindrin peptide we show that this technique allows for a more efficient sampling of the formation and interconversion between fibril-like and barrel-like assemblies. We describe a protocol for optimized analysis of RET simulations that allows us to propose a mechanism for formation and interconversion between various cylindrin assemblies.

Simulating Protein Fold Switching by Replica Exchange with Tunneling.

Recent experiments suggest that an amino acid sequence encodes not only the native fold of a protein but also other forms that are essential for its function or are important during folding or association. These various forms populate a multifunnel folding and association landscape where mutations, changes in environment, or interaction with other molecules switch between the encoded folds. We introduce replica exchange with tunneling as a way to efficiently simulate switching between distinct folds of proteins and protein aggregates.

Stability of a Recently Found Triple-β-Stranded Aβ1-42 Fibril Motif.

Amyloid-β peptides form polymorphous amyloid fibrils are correlated with the pathogenesis of Alzheimer's disease. Recently, a new ssNMR high-resolution structure has been reported for wild-type Aβ1-42 fibrils that is characterized by a strand-turn-strand-turn-strand motif instead of the U-shape form seen in previously known wild-type Aβ-fibril structures. Analyzing molecular dynamics simulations we comment on the relative weight of the new fibril structure and present evidence that its stability depends on hydrophobic contacts involving the C-terminal residues I41 and A42, but not on the salt bridge K28-A42.

Synergistic Inhibitory Effect of Peptide-Organic Coassemblies on Amyloid Aggregation.

Inhibition of amyloid aggregation is important for developing potential therapeutic strategies of amyloid-related diseases. Herein, we report that the inhibition effect of a pristine peptide motif (KLVFF) can be significantly improved by introducing a terminal regulatory moiety (terpyridine). The molecular-level observations by using scanning tunneling microscopy reveal stoichiometry-dependent polymorphism of the coassembly structures, which originates from the terminal interactions of peptide with organic modulator moieties and can be attributed to the secondary structures of peptides and conformations of the organic molecules.

Protein Ensembles: How Does Nature Harness Thermodynamic Fluctuations for Life? The Diverse Functional Roles of Conformational Ensembles in the Cell.

All soluble proteins populate conformational ensembles that together constitute the native state. Their fluctuations in water are intrinsic thermodynamic phenomena, and the distributions of the states on the energy landscape are determined by statistical thermodynamics; however, they are optimized to perform their biological functions. In this review we briefly describe advances in free energy landscape studies of protein conformational ensembles.

Tunable assembly of amyloid-forming peptides into nanosheets as a retrovirus carrier.

Using and engineering amyloid as nanomaterials are blossoming trends in bionanotechnology. Here, we show our discovery of an amyloid structure, termed "amyloid-like nanosheet," formed by a key amyloid-forming segment of Alzheimer's Aβ. Combining multiple biophysical and computational approaches, we proposed a structural model for the nanosheet that is formed by stacking the amyloid fibril spines perpendicular to the fibril axis.