Understanding Polyproline's Unusual Thermoresponsive Properties Using a Polyproline-Based Double Hydrophilic Block Copolymer.

Polyproline is a unique thermoresponsive polymer characterized by large thermal and conformational hysteresis. This article employs polyproline-based double hydrophilic block copolymers (PNIPAM--PLP) to gain insight into polyproline's thermoresponsive mechanism. The amine-terminated poly(-isopropylacrylamide) (NH-PNIPAM) was used as the macroinitiator for ring-opening polymerization of proline-NCA monomers, resulting in various block copolymers (PNIPAM--PLP) with varying PLP block lengths.

Naphthalene Monoanhydride and Perylene Composites for Efficient Photocatalytic Hydrogen Evolution and Metal-Free Heterogeneous Oxidative Amidation.

This study explores the synthesis of two visible light active organic chromophore-based composites using naphthalene monoanhydride () and 1,7-dibromoperylene monoanhydride diester (). These chromophores feature favorable optical and electronic properties and polyaromatic skeletons with anhydride functionalities that facilitate π-π interactions between the chromophore and polymeric carbon nitride () or covalent connections of chromophores with NH groups of . Accordingly, heterogeneous chromophore- composite photocatalysts namely, and were prepared by adopting calcination (c) and composites and were prepared by physical adsorption (a) methods.

A surface reconstruction route for increasingly improved photocatalytic HO production using SrBiTaOCl.

Photocatalytic hydrogen peroxide (HO) generation is attractive for the chemical industry and energy production. However, photocatalysts generally deteriorate significantly during use to limit their application. Here we present highly active SrBiTaOCl single-crystal nanoplates for conversion of O to HO using ambient air with a production rate of ∼3 mmol h g (maximum 17.

Room Temperature Single-Component Organic Multiferroics with Large Magnetoelectric Coupling: Proficient Approach for Stray-Magnetic Field Harvesting.

Magnetoelectric materials are highly desirable for technological applications due to their ability to produce electricity under a magnetic field. Among the various types of magnetoelectric materials studied, their organic counterparts provide an opportunity to develop solution-processable, flexible, lightweight, and wearable electronic devices. However, there is a rare choice of solution-processable, flexible, lightweight magnetoelectric materials which has tremendous technological interest.

Waste Polyethylene-Derived Carbon Dots: Administration of Metal-Free Oxidizing Agents for Tunable Properties and Photocatalytic Hyperactivity.

The possibility of converting waste plastics into carbon dots (CDs) with 100% efficiencies using KMnO has emerged as a significant discovery in mitigating plastic pollution and upcycling. However, the lack of tunability of their properties, viz. aerial O harvesting, light-induced autophagy, and photoactivity using air as a free oxidant, has remained a bottleneck.

Mild chemistry synthesis of ultrathin BiOS nanosheets exhibiting 2D-ferroelectricity at room temperature.

Modern technology demands miniaturization of electronic components to build small, light, and portable devices. Hence, discovery and synthesis of new non-toxic, low cost, ultra-thin ferroelectric materials having potential applications in various electronic and optoelectronic devices are of paramount importance. However, achieving room-temperature ferroelectricity in two dimensional (2D) ultra-thin systems remains a major challenge as conventional three-dimensional ferroelectric materials lose their ferroelectricity when the thickness is brought down below a critical value owing to the depolarization field.

Enhancing alphafold-multimer-based protein complex structure prediction with MULTICOM in CASP15.

To enhance the AlphaFold-Multimer-based protein complex structure prediction, we developed a quaternary structure prediction system (MULTICOM) to improve the input fed to AlphaFold-Multimer and evaluate and refine its outputs. MULTICOM samples diverse multiple sequence alignments (MSAs) and templates for AlphaFold-Multimer to generate structural predictions by using both traditional sequence alignments and Foldseek-based structure alignments, ranks structural predictions through multiple complementary metrics, and refines the structural predictions via a Foldseek structure alignment-based refinement method. The MULTICOM system with different implementations was blindly tested in the assembly structure prediction in the 15 Critical Assessment of Techniques for Protein Structure Prediction (CASP15) in 2022 as both server and human predictors.

Impact of AlphaFold on structure prediction of protein complexes: The CASP15-CAPRI experiment.

We present the results for CAPRI Round 54, the 5th joint CASP-CAPRI protein assembly prediction challenge. The Round offered 37 targets, including 14 homodimers, 3 homo-trimers, 13 heterodimers including 3 antibody-antigen complexes, and 7 large assemblies. On average ~70 CASP and CAPRI predictor groups, including more than 20 automatics servers, submitted models for each target.

Improving AlphaFold2-based protein tertiary structure prediction with MULTICOM in CASP15.

Since the 14th Critical Assessment of Techniques for Protein Structure Prediction (CASP14), AlphaFold2 has become the standard method for protein tertiary structure prediction. One remaining challenge is to further improve its prediction. We developed a new version of the MULTICOM system to sample diverse multiple sequence alignments (MSAs) and structural templates to improve the input for AlphaFold2 to generate structural models.

Sodium-Glucose Cotransporter-2 (SGLT-2) Inhibitors in Heart Failure: An Umbrella Review.

Heart failure remains a leading cause of hospitalization and death, and presents a significant challenge for healthcare providers despite the advancements in its management. This umbrella review aimed to pool the results of meta-analyses on the use of sodium-glucose cotransporter-2 (SGLT-2) inhibitors in the treatment of heart failure patients. A literature search was done on five databases: PubMed, Cochrane Library, Scopus, Global Index Medicus, and Science Direct for articles with full texts available online.

Universal Piezo-Photocatalytic Wastewater Treatment on Realistic Pollutant Feedstocks by BiTaOCl: Origin of High Efficiency and Adjustable Synergy.

Clean water is a fundamental human right but millions struggle for it daily. Herein, we demonstrate a new piezo-photocatalyst with immense structural diversity for universal wastewater decontamination. Single-crystalline BiTaOCl nanoplates with exposed piezoelectric facets exhibit visible-light response, piezoelectric behavior with coercive voltages of ±5 V yielding 0.

Combining pairwise structural similarity and deep learning interface contact prediction to estimate protein complex model accuracy in CASP15.

Estimating the accuracy of quaternary structural models of protein complexes and assemblies (EMA) is important for predicting quaternary structures and applying them to studying protein function and interaction. The pairwise similarity between structural models is proven useful for estimating the quality of protein tertiary structural models, but it has been rarely applied to predicting the quality of quaternary structural models. Moreover, the pairwise similarity approach often fails when many structural models are of low quality and similar to each other.

Architectural Effect on Self-Assembly and Biorecognition of Randomly Grafted Linear and Branched Polymers at Liquid Crystal-Water Interfaces.

Because of simple synthetic strategies, randomly functionalized amphiphilic polymers have gained much attention. Recent studies have demonstrated that such polymers can be reorganized into different nanostructures, such as spheres, cylinders, vesicles, etc., similar to amphiphilic block copolymers.

Enhancing AlphaFold-Multimer-based Protein Complex Structure Prediction with MULTICOM in CASP15.

AlphaFold-Multimer has emerged as the state-of-the-art tool for predicting the quaternary structure of protein complexes (assemblies or multimers) since its release in 2021. To further enhance the AlphaFold-Multimer-based complex structure prediction, we developed a new quaternary structure prediction system (MULTICOM) to improve the input fed to AlphaFold-Multimer and evaluate and refine the outputs generated by AlphaFold2-Multimer. Specifically, MULTICOM samples diverse multiple sequence alignments (MSAs) and templates for AlphaFold-Multimer to generate structural models by using both traditional alignments and new Foldseek-based alignments, ranks structural models through multiple complementary metrics, and refines the structural models via a Foldseek structure alignment-based refinement method.

Combining pairwise structural similarity and deep learning interface contact prediction to estimate protein complex model accuracy in CASP15.

Estimating the accuracy of quaternary structural models of protein complexes and assemblies (EMA) is important for predicting quaternary structures and applying them to studying protein function and interaction. The pairwise similarity between structural models is proven useful for estimating the quality of protein tertiary structural models, but it has been rarely applied to predicting the quality of quaternary structural models. Moreover, the pairwise similarity approach often fails when many structural models are of low quality and similar to each other.

Deep learning for reconstructing protein structures from cryo-EM density maps: Recent advances and future directions.

Cryo-Electron Microscopy (cryo-EM) has emerged as a key technology to determine the structure of proteins, particularly large protein complexes and assemblies in recent years. A key challenge in cryo-EM data analysis is to automatically reconstruct accurate protein structures from cryo-EM density maps. In this review, we briefly overview various deep learning methods for building protein structures from cryo-EM density maps, analyze their impact, and discuss the challenges of preparing high-quality data sets for training deep learning models.

BiTaOCl as a New Class of Layered Perovskite Oxyhalide Materials for Piezopotential Driven Efficient Seawater Splitting.

Piezocatalytic water splitting is an emerging approach to generate "green hydrogen" that can address several drawbacks of photocatalytic and electrocatalytic approaches. However, existing piezocatalysts are few and with minimal structural flexibility for engineering properties. Moreover, the scope of utilizing unprocessed water is yet unknown and may widely differ from competing techniques due to the constantly varying nature of surface potential.

Confinement Matters: Stabilization of CdS Nanoparticles inside a Postmodified MOF toward Photocatalytic Hydrogen Evolution.

Insights into developing innovative routes for the stabilization of photogenerated charge-separated states by suppressing charge recombination in photocatalysts is a topic of immense importance. Herein, we report the synthesis of a metal-organic framework (MOF)-based composite where CdS nanoparticles (NPs) are confined inside the nanosized pores of Zr-based MOF-808, namely, CdS@MOF-808. Anchoring l-cysteine into the nanospace of MOF-808 via postsynthetic ligand exchange allows the capture of Cd ions from their aqueous solution, which are further utilized for in situ growth of CdS NPs inside the nanosized MOF pores.

Charge-Transfer Excitation within a Hybrid-(G)KS Framework through Cartesian Grid DFT.

Organic molecules that exhibit charge-transfer (CT) excited states are known to play an important role in processes linked to electron transfer properties and molecular conductance. In this article, we present a simple technique based on "Becke's excitation theorem" that offers an accurate picture of these electronic states. It expresses the correlated energy splitting between triplet and its corresponding singlet states by a two-electron integral, which is numerically evaluated by our recently developed strategy on Cartesian grid.

A deep dilated convolutional residual network for predicting interchain contacts of protein homodimers.

Motivation: Deep learning has revolutionized protein tertiary structure prediction recently. The cutting-edge deep learning methods such as AlphaFold can predict high-accuracy tertiary structures for most individual protein chains. However, the accuracy of predicting quaternary structures of protein complexes consisting of multiple chains is still relatively low due to lack of advanced deep learning methods in the field.

High-Performance Deep Learning Toolbox for Genome-Scale Prediction of Protein Structure and Function.

Computational biology is one of many scientific disciplines ripe for innovation and acceleration with the advent of high-performance computing (HPC). In recent years, the field of machine learning has also seen significant benefits from adopting HPC practices. In this work, we present a novel HPC pipeline that incorporates various machine-learning approaches for structure-based functional annotation of proteins on the scale of whole genomes.

Distance-based reconstruction of protein quaternary structures from inter-chain contacts.

Predicting the quaternary structure of protein complex is an important problem. Inter-chain residue-residue contact prediction can provide useful information to guide the ab initio reconstruction of quaternary structures. However, few methods have been developed to build quaternary structures from predicted inter-chain contacts.

Enzyme producing insect gut microbes: an unexplored biotechnological aspect.

To explore the unmapped biotechnologically important microbial platforms for human welfare, the insect gut system is such a promising arena. Insects, the inhabitant of all ecological niches, harbor a healthy diversified microbial population in their versatile gut environment. This deep-rooted symbiotic relationship between insects and gut microbes is the result of several indispensable microbial performances that include: enzyme production, detoxification of plant defense compounds and insecticides, maintenance of life cycle, host fertility, bioremediation, pest biocontrol, production of antimicrobial compounds, and in addition provide vitamins, amino acids, and lactic acids to their hosts.

DeepComplex: A Web Server of Predicting Protein Complex Structures by Deep Learning Inter-chain Contact Prediction and Distance-Based Modelling.

Proteins interact to form complexes. Predicting the quaternary structure of protein complexes is useful for protein function analysis, protein engineering, and drug design. However, few user-friendly tools leveraging the latest deep learning technology for inter-chain contact prediction and the distance-based modelling to predict protein quaternary structures are available.

Prediction of protein assemblies, the next frontier: The CASP14-CAPRI experiment.

We present the results for CAPRI Round 50, the fourth joint CASP-CAPRI protein assembly prediction challenge. The Round comprised a total of twelve targets, including six dimers, three trimers, and three higher-order oligomers. Four of these were easy targets, for which good structural templates were available either for the full assembly, or for the main interfaces (of the higher-order oligomers).