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One of the challenges and a significant part of a protein structure’s prediction in three-dimensional space is a side chain prediction/packing. This area of research has a large importance, due to its various applications in protein design. In recent years, many methodologies and techniques have been crafted for side chain prediction such as DLPacker, FASPR, SCWRL4 and OPUS-Rota4. In this research, we address the problem from a different perspective. We employed a machine learning model to predict the side chain packing of protein molecules given only the Cα trace. We analyzed 32,000 protein molecules to extract important geometrical features that can distinguish between different orientations of side chain rotamers. We designed and implemented a Random Forest model to tackle this problem. Given the accuracy of existing state-of-the-art approaches, our model represents an improvement from among other models. The results of our experiment show that Random Forest is highly effective, achieving a total average accuracy of 73.7% for proteins and 73.3% for individual amino acids. 

method for structure determination. Despite the substantial growth in deposited cryo-EM maps driven by advances in microscopy and image processing, accurately constructing models from these maps remains challenging. Extracting secondary structure information from EM maps is valuable for cryo-EM modeling. In this context, we introduce a novel deep learning secondary structure annotation framework specifically designed for intermediate-resolution cryo-EM maps, employing a three-dimensional Inception architecture. Testing it on diverse datasets, including maps with authentic intermediate resolutions, demonstrates its accuracy and robustness in identifying secondary structures in cryo-EM maps. We conducted a comparative analysis of our results against frameworks that exist in the state-of-the-art, and our framework demonstrated superior performance across nearly all secondary structure elements. We employed the F1 accuracy metric, yielding an average F1 score of 0.657 for helix, 0.712 for coil, and 0.596 for sheet predictions. Notably, certain helix and sheet predictions achieved an impressive F1 score of 0.881. 

Single-atom catalysts have the advantage of high chemical efficiency, which requires atomic-scale control during catalyst formation. In order to address this challenge, this work explores the synthesis of single-atom platinum (SA-Pt) catalysts using atomic-layer deposition (ALD) on vertical graphene (VG), in which a large number of graphene edges serve as energetically favorable nucleation sites for SA-Pt, as predicted by density functional theory calculations. Interestingly, SA-Pt has been achieved on VGs at low ALD cycle numbers of up to 60. With a further increase in the number of ALD cycles, an increasing number of Pt clusters with diameters <2 nm and Pt nanoparticles (NPs) with diameters >2 nm become dominant (nano-Pt @VG). This is in contrast to the observation of predominantly nano-Pt on other carbon nanostructures, such as carbon nanotubes and monolayer graphene, under the same ALD growth conditions, indicating that the edge states on VG indeed play a critical role in facilitating the formation of SA-Pt. Profound differences are revealed in a comparative study on H2 sensing. SA-Pt exhibits both a higher sensitivity and faster response than its nano-Pt counterpart by more than an order of magnitude, illustrating the high catalytic efficiency of SAPt and its potential for gas sensing and a variety of other catalytic applications. 

Abstract Nanohybrids based on van der Waals (vdW) heterostructures of two dimensional (2D) atomic materials have recently emerged as a unique scheme for designing high‐performance quantum sensors. This work explores vdW nanohybrids for photodetection, which consist of graphene decorated with intermingled transition‐metal dichalcogenide (TMDC) nanodiscs (TMDC‐NDs) obtained using wafer‐size, layer‐by‐layer growth. The obtained TMDC‐NDs/graphene nanohybrids take advantage of strong quantum confinement in graphene for high charge mobility and hence high photoconductive gain, and localized surface plasmonic resonance (LSPR) enabled on the TMDC‐NDs for enhanced light absorption. Since the LSPR depends on the nanostructure's size and density, intermingled TMDC‐NDs of different kinds of TMDCs, such as WS₂(W) and MoS₂(M), have been found to allow small‐size, high‐concentration TMDC‐NDs to be achieved for high photoresponse. Remarkably, high photoresponsivity up to 31 A/W (550 nm wavelength and 20 µW cm⁻²light intensity) has been obtained on the WMW‐NDs/graphene nanohybrids photodetectors made using three consecutive coatings of WS₂(1st and 3rd coating) and MoS₂(2nd coating), which is considerably higher by a factor of ≈4 than that of the counterparts MoS₂‐ND/graphene or WS₂‐NDs/graphene devices. This result provides a facile approach to control the size and concentration of the TMDC‐NDs for high‐performance, low‐cost optoelectronic device applications. 

Abstract In the carbon nanotubes film/graphene heterostructure decorated with catalytic Pt nanoparticles using atomic layer deposition (Pt-NPs/CNTs/Gr) H 2 sensors, the CNT film determines the effective sensing area and the signal transport to Gr channel. The former requires a large CNT aspect ratio for a higher sensing area while the latter demands high electric conductivity for efficient charge transport. Considering the CNT’s aspect ratio decreases, while its conductivity increases ( i.e. , bandgap decreases), with the CNT diameter, it is important to understand how quantitatively these effects impact the performance of the Pt-NPs/CNTs/Gr nanohybrids sensors. Motivated by this, this work presents a systematic study of the Pt-NPs/CNTs/Gr H 2 sensor performance with the CNT films made from different constituent CNTs of diameters ranging from 1 nm for single-wall CNTs, to 2 nm for double-wall CNTs, and to 10–30 nm for multi-wall CNTs (MWCNTs). By measuring the morphology and electric conductivity of SWCNT, DWCNT and MWCNT films, this work aims to reveal the quantitative correlation between the sensor performance and relevant CNT properties. Interestingly, the best performance is obtained on Pt-NPs/MWCNTs/Gr H 2 sensors, which can be attributed to the compromise of the effective sensing area and electric conductivity on MWCNT films and illustrates the importance of optimizing sensor design.