A new two-dimensional (2D) non-MXene transition metal carbide, Mo3C2, was found using the USPEX code. Comprehensive first-principles calculations show that the Mo3C2 monolayer exhibits thermal, dynamic, and mechanical stability, which can ensure excellent durability in practical applications. The optimized structures of Lix@(3×3)-Mo3C2 (x = 1-36) and Nax@(3×3)-Mo3C2 (x = 1-32) were identified as prospective anode materials. The metallic Mo3C2 sheet exhibits low diffusion barriers of 0.190 eV for Li and 0.118 eV for Na and low average open circuit voltages of 0.31-0.55 V for Li and 0.18-0.48 V for Na. When adsorbing two layers of adatoms, the theoretical energy capacities are 344 and 306 mA h g-1 for Li and Na, respectively, which are comparable to that of commercial graphite. Moreover, the Mo3C2 substrate can maintain structural integrity during the lithiation or sodiation process at high temperature. Considering these features, our proposed Mo3C2 slab is a potential candidate as an anode material for future Li- and Na-ion batteries.

In this chapter, we predict the structure of the Arabidopsis receptor-homology-transmembrane-RING-H2 isoform 1 (RMR1) in complex with the C-terminal sorting determinant of cruciferin (CRU1) by AlphaFold2 using the ColabFold web interface and to perform molecular dynamics simulation to probe the dynamics of the predicted structures. Our results predict that the C-terminal carboxylate group of ctVSD of CRU1 is recognized by the conserved Arg89 of the cargo-binding loop of RMR1 and Arg468 of CRU1 by negative charge residues in the cargo-binding pocket of RMR1. The procedures described here are useful for modeling of other protein complexes.

Secreted signaling peptides are central regulators of growth, development, and stress responses, but specific steps in the evolution of these peptides and their receptors are not well understood. Also, the molecular mechanisms of peptide-receptor binding are only known for a few examples, primarily owing to the limited availability of protein structural determination capabilities to few laboratories worldwide. Plants have evolved a multitude of secreted signaling peptides and corresponding transmembrane receptors. Stress-responsive SERINE RICH ENDOGENOUS PEPTIDES (SCOOPs) were recently identified. Bioactive SCOOPs are proteolytically processed by subtilases and are perceived by the leucine-rich repeat receptor kinase MALE DISCOVERER 1-INTERACTING RECEPTOR-LIKE KINASE 2 (MIK2) in the model plant Arabidopsis thaliana. How SCOOPs and MIK2 have (co)evolved, and how SCOOPs bind to MIK2 are unknown. Using in silico analysis of 350 plant genomes and subsequent functional testing, we revealed the conservation of MIK2 as SCOOP receptor within the plant order Brassicales. We then leveraged AI-based structural modeling and comparative genomics to identify two conserved putative SCOOP-MIK2 binding pockets across Brassicales MIK2 homologues predicted to interact with the \"SxS\" motif of otherwise sequence-divergent SCOOPs. Mutagenesis of both predicted binding pockets compromised SCOOP binding to MIK2, SCOOP-induced complex formation between MIK2 and its coreceptor BRASSINOSTEROID INSENSITIVE 1-ASSOCIATED KINASE 1, and SCOOP-induced reactive oxygen species production, thus, confirming our in silico predictions. Collectively, in addition to revealing the elusive SCOOP-MIK2 binding mechanism, our analytic pipeline combining phylogenomics, AI-based structural predictions, and experimental biochemical and physiological validation provides a blueprint for the elucidation of peptide ligand-receptor perception mechanisms.

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Studying the relationship between sequences and their corresponding three-dimensional structure assists structural biologists in solving the protein-folding problem. Despite several experimental and in-silico approaches, still understanding or decoding the three-dimensional structures from the sequence remains a mystery. In such cases, the accuracy of the structure prediction plays an indispensable role. To address this issue, an updated web server (CSSP-2.0) has been created to improve the accuracy of our previous version of CSSP by deploying the existing algorithms. It uses input as probabilities and predicts the consensus for the secondary structure as a highly accurate three-state Q3 (helix, strand, and coil). This prediction is achieved using six recent top-performing methods: MUFOLD-SS, RaptorX, PSSpred v4, PSIPRED, JPred v4, and Porter 5.0. CSSP-2.0 validation includes datasets involving various protein classes from the PDB, CullPDB, and AlphaFold databases. Our results indicate a significant improvement in the accuracy of the consensus Q3 prediction. Using CSSP-2.0, crystallographers can sort out the stable regular secondary structures from the entire complex structure, which would aid in inferring the functional annotation of hypothetical proteins. The web server is freely available at https://bioserver3.physics.iisc.ac.in/cgi-bin/cssp-2/.

Understanding how proteins evolve under selective pressure is a longstanding challenge. The immensity of the search space has limited efforts to systematically evaluate the impact of multiple simultaneous mutations, so mutations have typically been assessed individually. However, epistasis, or the way in which mutations interact, prevents accurate prediction of combinatorial mutations based on measurements of individual mutations. Here, we use artificial intelligence to define the entire functional sequence landscape of a protein binding site in silico, and we call this approach Complete Combinatorial Mutational Enumeration (CCME). By leveraging CCME, we are able to construct a comprehensive map of the evolutionary connectivity within this functional sequence landscape. As a proof of concept, we applied CCME to the ACE2 binding site of the SARS-CoV-2 spike protein receptor binding domain. We selected representative variants from across the functional sequence landscape for testing in the laboratory. We identified variants that retained functionality to bind ACE2 despite changing over 40% of evaluated residue positions, and the variants now escape binding and neutralization by monoclonal antibodies. This work represents a crucial initial stride toward achieving precise predictions of pathogen evolution, opening avenues for proactive mitigation.

Molecular docking is used to anticipate the optimal orientation of a particular molecule to a target to form a stable complex. It makes predictions about the 3D structure of any complex based on the binding characteristics of the ligand and the target receptor usually a protein. It is an exceptionally useful tool, which is used as a model to study how ligands attach to proteins. Docking can also be used for studying the interaction of ligands and proteins to analyze inhibitory efficacy. The ligand may also be a protein, making it possible to study interactions between two different proteins using the numerous docking tools available for basic research on protein interactions. The protein-protein docking is a crucial approach to understanding the protein interactions and predicting the structure of protein complexes that have not yet been experimentally determined. Moreover, the protein-protein interactions can predict the function of target proteins and the drug-like properties of molecules. Therefore, protein docking assists in uncovering insights into protein interactions and also aids in a better understanding of molecular pathways/mechanisms. This chapter comprehends the various tools for protein-protein docking (pairwise and multiple), including their methodologies and analysis of output as results.

Artificial intelligence has revolutionized the field of protein structure prediction. However, with more powerful and complex software being developed, it is accessibility and ease of use rather than capability that is quickly becoming a limiting factor to end users. LazyAF is a Google Colaboratory-based pipeline which integrates the existing ColabFold BATCH software to streamline the process of medium-scale protein-protein interaction prediction. LazyAF was used to predict the interactome of the 76 proteins encoded on the broad-host-range multi-drug resistance plasmid RK2, demonstrating the ease and accessibility the pipeline provides.

Spectroscopic data, particularly diffraction data, are essential for materials characterization due to their comprehensive crystallographic information. The current crystallographic phase identification, however, is very time consuming. To address this challenge, we have developed a real-time crystallographic phase identifier based on a convolutional self-attention neural network (CPICANN). Trained on 692 190 simulated powder X-ray diffraction (XRD) patterns from 23 073 distinct inorganic crystallographic information files, CPICANN demonstrates superior phase-identification power. Single-phase identification on simulated XRD patterns yields 98.5 and 87.5% accuracies with and without elemental information, respectively, outperforming JADE software (68.2 and 38.7%, respectively). Bi-phase identification on simulated XRD patterns achieves 84.2 and 51.5% accuracies, respectively. In experimental settings, CPICANN achieves an 80% identification accuracy, surpassing JADE software (61%). Integration of CPICANN into XRD refinement software will significantly advance the cutting-edge technology in XRD materials characterization.

From its conception, X-ray crystallography has provided a unique understanding of the structure, bonding and electronic state of materials, which, in turn, unlocks a means of examining the properties and function of crystalline systems. Using state-of-the-art single-crystal X-ray diffraction, along with UV-Vis spectroscopy and DFT calculations, Zwolenik et al. [(2024). IUCrJ, 11, 519-527] have provided a comprehensive study of the structure-optical property relationship of 1,3-diacetylpyrene with methodologies that are increasingly accessible to non-specialist laboratories.