A conditional protein diffusion model generates artificial programmable endonuclease sequences with enhanced activity.

Deep learning-based methods for generating functional proteins address the growing need for novel biocatalysts, allowing for precise tailoring of functionalities to meet specific requirements. This advancement leads to the development of highly efficient and specialized proteins with diverse applications across scientific, technological, and biomedical fields. This study establishes a pipeline for protein sequence generation with a conditional protein diffusion model, namely CPDiffusion, to create diverse sequences of proteins with enhanced functions. CPDiffusion accommodates protein-specific conditions, such as secondary structures and highly conserved amino acids. Without relying on extensive training data, CPDiffusion effectively captures highly conserved residues and sequence features for specific protein families. We applied CPDiffusion to generate artificial sequences of Argonaute (Ago) proteins based on the backbone structures of wild-type (WT) Kurthia massiliensis Ago (KmAgo) and Pyrococcus furiosus Ago (PfAgo), which are complex multi-domain programmable endonucleases. The generated sequences deviate by up to nearly 400 amino acids from their WT templates. Experimental tests demonstrated that the majority of the generated proteins for both KmAgo and PfAgo show unambiguous activity in DNA cleavage, with many of them exhibiting superior activity as compared to the WT. These findings underscore CPDiffusion's remarkable success rate in generating novel sequences for proteins with complex structures and functions in a single step, leading to enhanced activity. This approach facilitates the design of enzymes with multi-domain molecular structures and intricate functions through in silico generation and screening, all accomplished without the need for supervision from labeled data.

Decoupling of the onset of anharmonicity between a protein and its surface water around 200 K.

The protein dynamical transition at ~200 K, where the biomolecule transforms from a harmonic, non-functional form to an anharmonic, functional state, has been thought to be slaved to the thermal activation of dynamics in its surface hydration water. Here, by selectively probing the dynamics of protein and hydration water using elastic neutron scattering and isotopic labeling, we found that the onset of anharmonicity in the two components around 200 K is decoupled. The one in protein is an intrinsic transition, whose characteristic temperature is independent of the instrumental resolution time, but varies with the biomolecular structure and the amount of hydration, while the one of water is merely a resolution effect.

Enhancing efficiency of protein language models with minimal wet-lab data through few-shot learning.

Accurately modeling the protein fitness landscapes holds great importance for protein engineering. Pre-trained protein language models have achieved state-of-the-art performance in predicting protein fitness without wet-lab experimental data, but their accuracy and interpretability remain limited. On the other hand, traditional supervised deep learning models require abundant labeled training examples for performance improvements, posing a practical barrier. In this work, we introduce FSFP, a training strategy that can effectively optimize protein language models under extreme data scarcity for fitness prediction. By combining meta-transfer learning, learning to rank, and parameter-efficient fine-tuning, FSFP can significantly boost the performance of various protein language models using merely tens of labeled single-site mutants from the target protein. In silico benchmarks across 87 deep mutational scanning datasets demonstrate FSFP's superiority over both unsupervised and supervised baselines. Furthermore, we successfully apply FSFP to engineer the Phi29 DNA polymerase through wet-lab experiments, achieving a 25% increase in the positive rate. These results underscore the potential of our approach in aiding AI-guided protein engineering.

Mn2+-induced structural flexibility enhances the entire catalytic cycle and the cleavage of mismatches in prokaryotic argonaute proteins.

Prokaryotic Argonaute (pAgo) proteins, a class of DNA/RNA-guided programmable endonucleases, have been extensively utilized in nucleic acid-based biosensors. The specific binding and cleavage of nucleic acids by pAgo proteins, which are crucial processes for their applications, are dependent on the presence of Mn2+ bound in the pockets, as verified through X-ray crystallography. However, a comprehensive understanding of how dissociated Mn2+ in the solvent affects the catalytic cycle, and its underlying regulatory role in this structure-function relationship, remains underdetermined. By combining experimental and computational methods, this study reveals that unbound Mn2+ in solution enhances the flexibility of diverse pAgo proteins. This increase in flexibility through decreasing the number of hydrogen bonds, induced by Mn2+, leads to higher affinity for substrates, thus facilitating cleavage. More importantly, Mn2+-induced structural flexibility increases the mismatch tolerance between guide-target pairs by increasing the conformational states, thereby enhancing the cleavage of mismatches. Further simulations indicate that the enhanced flexibility in linkers triggers conformational changes in the PAZ domain for recognizing various lengths of nucleic acids. Additionally, Mn2+-induced dynamic alterations of the protein cause a conformational shift in the N domain and catalytic sites towards their functional form, resulting in a decreased energy penalty for target release and cleavage. These findings demonstrate that the dynamic conformations of pAgo proteins, resulting from the presence of the unbound Mn2+ in solution, significantly promote the catalytic cycle of endonucleases and the tolerance of cleavage to mismatches. This flexibility enhancement mechanism serves as a general strategy employed by Ago proteins from diverse prokaryotes to accomplish their catalytic functions and provide useful information for Ago-based precise molecular diagnostics.

Nonionic polymer and amino acid-assisted synthesis of ZSM-5 nanocrystals and their catalytic application in the alkylation of 2-methylnaphthalene.

The synthesis of nanosized ZSM-5 zeolites with high crystallinity and suitable acidity is very significant for their great potential in various catalytic applications. Herein, a series of zeolite ZSM-5 crystals with different particle sizes and SiO2/Al2O3 ratios (10-30) were synthesized by a temperature-varying two-step crystallization method in a concentrated gel system containing L-lysine and/or polyvinylpyrrolidone (PVP) additives. By optimizing the addition amounts of the two additives, the crystal size of the ZSM-5 zeolite could be reduced to less than 100 nm. Meanwhile, relatively high crystallinity and framework Al incorporation rates could be achieved, resulting in the generation of high-quality ZSM-5 nanocrystals. The nanosized H-form ZSM-5 zeolite with a SiO2/Al2O3 ratio of 20 showed enhanced catalytic efficiency and stability for the alkylation of 2-methylnaphthalene (2-MN) with methanol to produce an important intermediate, 2,6-dimethylnaphthalene (2,6-DMN). A relatively high and steady yield of 2,6-DMN (above 7.2%) could be achieved during 20 h time-on-stream at 400 °C. The smaller crystal size, higher crystallinity and framework Al content could provide more accessible Brønsted acid sites in the 10-membered ring channel of the ZSM-5 nanocrystals, which are the main active sites responsible for the shape-selectivity of the targeted product of 2,6-DMN. As a result, the formation of other side products like 1-MN and poly-MN could be effectively inhibited, thus leading to an improved 2,6-DMN yield and coke resistance over the nanosized ZSM-5 catalyst.

Protein Engineering with Lightweight Graph Denoising Neural Networks.

Protein engineering faces challenges in finding optimal mutants from a massive pool of candidate mutants. In this study, we introduce a deep-learning-based data-efficient fitness prediction tool to steer protein engineering. Our methodology establishes a lightweight graph neural network scheme for protein structures, which efficiently analyzes the microenvironment of amino acids in wild-type proteins and reconstructs the distribution of the amino acid sequences that are more likely to pass natural selection. This distribution serves as a general guidance for scoring proteins toward arbitrary properties on any order of mutations. Our proposed solution undergoes extensive wet-lab experimental validation spanning diverse physicochemical properties of various proteins, including fluorescence intensity, antigen-antibody affinity, thermostability, and DNA cleavage activity. More than 40% of ProtLGN-designed single-site mutants outperform their wild-type counterparts across all studied proteins and targeted properties. More importantly, our model can bypass the negative epistatic effect to combine single mutation sites and form deep mutants with up to seven mutation sites in a single round, whose physicochemical properties are significantly improved. This observation provides compelling evidence of the structure-based model's potential to guide deep mutations in protein engineering. Overall, our approach emerges as a versatile tool for protein engineering, benefiting both the computational and bioengineering communities.

Piezo-phototronic effect regulated broadband photoresponse of a-Ga2O3/ZnO heterojunction.

Amorphous Ga2O3 (a-Ga2O3) films have attracted considerable attention in the field of photodetectors due to their excellent optical absorption response and photoelectric properties. However, there are few studies that have utilized the piezo-phototronic effect to regulate the broadband photoresponse of Ga2O3-based photodetectors. Here, a flexible a-Ga2O3/ZnO heterojunction was constructed, which demonstrated a broadband response range from deep ultraviolet (265 nm) to the near-infrared (1060 nm) and realized a bidirectional adjustable photocurrent response via the piezo-phototronic effect. Under 265 nm illumination and 0.5 V bias, the responsivity and detectivity of the a-Ga2O3/ZnO heterojunction reached up to 12.19 A W-1 and 4.71 × 1011 Jones under 0.164% compressive strain, corresponding to enhancements of 67.7% and 66.8% compared to those under a strain-free state, respectively. Moreover, the broadband photoresponse of the a-Ga2O3/ZnO heterojunction beyond the bandgap limit was tunable under bidirectional strain. The working mechanism of photoresponse performance for the a-Ga2O3/ZnO heterojunction at different wavelengths was elucidated in detail. Oxygen vacancy-assisted carrier generation was found to be influenced by the wavelength of incident light, which mainly determined the broadband photoresponse of the heterojunction. The modulation of the a-Ga2O3/ZnO heterojunction photodetector was interpreted in light of the strain-induced regulation of the barrier height. This work represents an important step toward the development of adjustable broadband photodetectors based on a-Ga2O3 films.

Crystallographic Deciphering of Spontaneous Self-Assembly of Achiral Calciphores to Chiral Complexes.

Thiapyricins (TPC-A/B, 1 and 2), which are new metallophore scaffolds exhibiting selective divalent cation binding property, were produced in response to metal-deprived conditions by Saccharothrix sp. TRM_47004 isolated from the Lop Nor Salt Lake. TPCs represent a thiazolyl-pyridine skeleton of a calcium-binding natural product, calciphore, owing to the selectivity to calcium ions among diverse metal ions. The thiapyricins exhibited notable co-crystalline characteristics of the apo- and holo-forms with racemic enantiomers comprising a pair of space isomers in a Δ/Λ-form. Therefore, we postulated a mechanism for the four-hierarchical self-assembly of achiral natural products into chiral complexes. Furthermore, their metal-chelating trait aided the adaptation of the host during metal starvation by increasing the production of TPCs. This study presents a structural paradigm of a new calciphore, provides insight into the mechanism of natural product assembly, and highlights the causality between the production of the metallophore and metallic habitats.