Single Disulfide Bond in Host Defense Thanatin Analog Peptides: Antimicrobial Activity, Atomic-Resolution Structures and Target Interactions.

Host defense antimicrobial peptides (AMPs) are promising lead molecules with which to develop antibiotics against drug-resistant bacterial pathogens. Thanatin, an inducible antimicrobial peptide involved in the host defense of insects, is gaining considerable attention in the generation of novel classes of antibiotics. Thanatin or thanatin-based analog peptides are extremely potent in killing bacterial pathogens in the Enterobacteriaceae family, including drug-resistant strains of and .

Systematic benchmarking of deep-learning methods for tertiary RNA structure prediction.

The 3D structure of RNA critically influences its functionality, and understanding this structure is vital for deciphering RNA biology. Experimental methods for determining RNA structures are labour-intensive, expensive, and time-consuming. Computational approaches have emerged as valuable tools, leveraging physics-based-principles and machine learning to predict RNA structures rapidly.

Evaluations of the Perturbation Resistance of the Deep-Learning-Based Ligand Conformation Optimization Algorithm.

In recent years, the deep learning (DL) technique has rapidly developed and shown great success in scoring the protein-ligand binding affinities. The protein-ligand conformation optimization based on DL-derived scoring functions holds broad application prospects, for instance, drug design and enzyme engineering. In this study, we evaluated the robustness of a DL-based ligand conformation optimization protocol (DeepRMSD+Vina) for optimizing structures with input perturbations by examining the predicted ligand binding poses and scoring.

Uncovering the Differed Susceptibility of ( and ) to Fungicide Phenamacril: From Computational and Experimental Perspectives.

and are two subtypes of (Fo), a pathogenic filamentous fungus. Phenamacril (PHA), a -specific fungicide that targets myosin I, exhibits significant hyphal growth inhibition in but shows weak resistance in , despite only two amino acid differences in the PHA-binding pocket of myosin I. In this study, we aim to elucidate the molecular basis for the differential sensitivity of myosin I variants (FoMyoI and FoMyoI) to phenamacril through computational methods and biochemical validation.

Engineered Reactive Oxygen Species (ROS)-Responsive Artificial H/Cl Ion Channels for Targeted Cancer Treatment.

Reactive oxygen species (ROS)-responsive ion channels regulate the ion flow across the membranes in response to alterations in the cellular redox state, playing a crucial role in cellular adaptation to oxidative stress. Despite their significance, replicating ROS-responsive functionality in artificial ion channels remains elusive. In this study, we introduce a novel class of artificial H/Cl ion channels activatable by elevated ROS levels in cancer cells.

ProAffinity-GNN: A Novel Approach to Structure-Based Protein-Protein Binding Affinity Prediction via a Curated Data Set and Graph Neural Networks.

Protein-protein interactions (PPIs) are crucial for understanding biological processes and disease mechanisms, contributing significantly to advances in protein engineering and drug discovery. The accurate determination of binding affinities, essential for decoding PPIs, faces challenges due to the substantial time and financial costs involved in experimental and theoretical methods. This situation underscores the urgent need for more effective and precise methodologies for predicting binding affinity.

Unraveling the molecular mechanism of FgGcn5 inhibition by phenazine-1-carboxamide: combined in silico and in vitro studies.

Background: Fusarium head blight (FHB), mainly caused by Fusarium graminearum (F. graminearum), remains a devastating disease worldwide. The histone acetyltransferase Gcn5 plays a crucial role in epigenetic regulation.

OpenDock: a pytorch-based open-source framework for protein-ligand docking and modelling.

Motivation: Molecular docking is an invaluable computational tool with broad applications in computer-aided drug design and enzyme engineering. However, current molecular docking tools are typically implemented in languages such as C++ for calculation speed, which lack flexibility and user-friendliness for further development. Moreover, validating the effectiveness of external scoring functions for molecular docking and screening within these frameworks is challenging, and implementing more efficient sampling strategies is not straightforward.

Protein language models are performant in structure-free virtual screening.

Hitherto virtual screening (VS) has been typically performed using a structure-based drug design paradigm. Such methods typically require the use of molecular docking on high-resolution three-dimensional structures of a target protein-a computationally-intensive and time-consuming exercise. This work demonstrates that by employing protein language models and molecular graphs as inputs to a novel graph-to-transformer cross-attention mechanism, a screening power comparable to state-of-the-art structure-based models can be achieved.

Benchmarking reverse docking through AlphaFold2 human proteome.

Predicting the binding of ligands to the human proteome via reverse-docking methods enables the understanding of ligand's interactions with potential protein targets in the human body, thereby facilitating drug repositioning and the evaluation of potential off-target effects or toxic side effects of drugs. In this study, we constructed 11 reverse docking pipelines by integrating site prediction tools (PointSite and SiteMap), docking programs (Glide and AutoDock Vina), and scoring functions (Glide, Autodock Vina, RTMScore, DeepRMSD, and OnionNet-SFCT), and then thoroughly benchmarked their predictive capabilities. The results show that the Glide_SFCT (PS) pipeline exhibited the best target prediction performance based on the atomic structure models in AlphaFold2 human proteome.

Advancing Bioactivity Prediction through Molecular Docking and Self-Attention.

Bioactivity refers to the ability of a substance to induce biological effects within living systems, often describing the influence of molecules, drugs, or chemicals on organisms. In drug discovery, predicting bioactivity streamlines early-stage candidate screening by swiftly identifying potential active molecules. The popular deep learning methods in bioactivity prediction primarily model the ligand structure-bioactivity relationship under the premise of Quantitative Structure-Activity Relationship (QSAR).

RmsdXNA: RMSD prediction of nucleic acid-ligand docking poses using machine-learning method.

Small molecule drugs can be used to target nucleic acids (NA) to regulate biological processes. Computational modeling methods, such as molecular docking or scoring functions, are commonly employed to facilitate drug design. However, the accuracy of the scoring function in predicting the closest-to-native docking pose is often suboptimal.

An allosteric mechanism for potent inhibition of SARS-CoV-2 main proteinase.

The main proteinase (M) of SARS-CoV-2 plays a critical role in cleaving viral polyproteins into functional proteins required for viral replication and assembly, making it a prime drug target for COVID-19. It is well known that noncompetitive inhibition offers potential therapeutic options for treating COVID-19, which can effectively reduce the likelihood of cross-reactivity with other proteins and increase the selectivity of the drug. Therefore, the discovery of allosteric sites of M has both scientific and practical significance.

Structures, Interactions and Activity of the N-Terminal Truncated Variants of Antimicrobial Peptide Thanatin.

Gram-negative bacteria are intrinsically more resistant to many frontline antibiotics, which is attributed to the permeability barrier of the outer membrane, drug efflux pumps and porins. Consequently, discovery of new small molecules antibiotics to kill drug-resistant Gram-negative bacteria presents a significant challenge. Thanatin, a 21-residue insect-derived antimicrobial peptide, is known for its potent activity against Enterobacter Gram-negative bacteria, including drug-resistant strains.

zPoseScore model for accurate and robust protein-ligand docking pose scoring in CASP15.

We introduce a deep learning-based ligand pose scoring model called zPoseScore for predicting protein-ligand complexes in the 15th Critical Assessment of Protein Structure Prediction (CASP15). Our contributions are threefold: first, we generate six training and evaluation data sets by employing advanced data augmentation and sampling methods. Second, we redesign the "zFormer" module, inspired by AlphaFold2's Evoformer, to efficiently describe protein-ligand interactions.

Membrane Potential-Dependent Uptake of Cationic Oligoimidazolium Mediates Bacterial DNA Damage and Death.

The treatment of bacterial infections is becoming increasingly challenging with the emergence of antimicrobial resistance. Thus, the development of antimicrobials with novel mechanisms of action is much needed. Previously, we designed several cationic main-chain imidazolium compounds and identified the polyimidazolium PIM1 as a potent antibacterial against a wide panel of multidrug-resistant nosocomial pathogens, and it had relatively low toxicity against mammalian epithelial cells.

Release of frustration drives corneal amyloid disaggregation by brain chaperone.

TGFBI-related corneal dystrophy (CD) is characterized by the accumulation of insoluble protein deposits in the corneal tissues, eventually leading to progressive corneal opacity. Here we show that ATP-independent amyloid-β chaperone L-PGDS can effectively disaggregate corneal amyloids in surgically excised human cornea of TGFBI-CD patients and release trapped amyloid hallmark proteins. Since the mechanism of amyloid disassembly by ATP-independent chaperones is unknown, we reconstructed atomic models of the amyloids self-assembled from TGFBIp-derived peptides and their complex with L-PGDS using cryo-EM and NMR.

Gradient Nanoconfinement Facilitates Binding of Transcriptional Factor NF-κB to Histone- and Protamine-DNA Complexes.

Mechanically induced chromosome reorganization plays important roles in transcriptional regulation. However, the interplay between chromosome reorganization and transcription activities is complicated, such that it is difficult to decipher the regulatory effects of intranuclear geometrical cues. Here, we simplify the system by introducing DNA, packaging proteins (i.

Modulation of Mechanical Properties of Short Bioinspired Peptide Materials by Single Amino-Acid Mutations.

The occurrence of modular peptide repeats in load-bearing (structural) proteins is common in nature, with distinctive peptide sequences that often remain conserved across different phylogenetic lineages. These highly conserved peptide sequences endow specific mechanical properties to the material, such as toughness or elasticity. Here, using bioinformatic tools and phylogenetic analysis, we have identified the GX8 peptide with the sequence GLYGGYGX (where X can be any residue) in a wide range of organisms.

Revealing the biotoxicity of phosphorene oxide nanosheets based on the villin headpiece.

Phosphorene, a novel member of the two-dimensional nanomaterial family, has demonstrated great potential in biomedical applications, such as photothermal therapy, drug delivery and antibacterial. However, phosphorene is unstable and easily oxidized in an aerobic environment. In this paper, using larger-scale molecular dynamics simulations, we investigated the disruption of phosphorene oxide (PO) to the structure of a model protein, villin headpiece subdomain (HP35).

Hypoxia deactivates epigenetic feedbacks via enzyme-derived clicking proteolysis-targeting chimeras.

Epigenetic mediation through bromodomain and extraterminal (BET) proteins have progressively translated protein imbalance into effective cancer treatment. Perturbation of druggable BET proteins through proteolysis-targeting chimeras (PROTACs) has recently contributed to the discovery of effective therapeutics. Unfortunately, precise and microenvironment-activatable BET protein degradation content with promising tumor selectivity and pharmacological suitability remains elusive.