HELM-GPT: de novo macrocyclic peptide design using generative pre-trained transformer.

MOTIVATION: Macrocyclic peptides hold great promise as therapeutics targeting intracellular proteins. This stems from their remarkable ability to bind flat protein surfaces with high affinity and specificity while potentially traversing the cell membrane. Research has already explored their use in developing inhibitors for intracellular proteins, such as KRAS, a well-known driver in various cancers. However, computational approaches for de novo macrocyclic peptide design remain largely unexplored. RESULTS: Here, we introduce HELM-GPT, a novel method that combines the strength of the hierarchical editing language for macromolecules (HELM) representation and generative pre-trained transformer (GPT) for de novo macrocyclic peptide design. Through reinforcement learning (RL), our experiments demonstrate that HELM-GPT has the ability to generate valid macrocyclic peptides and optimize their properties. Furthermore, we introduce a contrastive preference loss during the RL process, further enhanced the optimization performance. Finally, to co-optimize peptide permeability and KRAS binding affinity, we propose a step-by-step optimization strategy, demonstrating its effectiveness in generating molecules fulfilling both criteria. In conclusion, the HELM-GPT method can be used to identify novel macrocyclic peptides to target intracellular proteins. AVAILABILITY AND IMPLEMENTATION: The code and data of HELM-GPT are freely available on GitHub (https://github.com/charlesxu90/helm-gpt).

Quantifying functional impact of non-coding variants with multi-task Bayesian neural network.

MOTIVATION: Advances in high-throughput genotyping and sequencing technologies during recent years have revealed essential roles of non-coding regions in gene regulation. Genome-wide association studies (GWAS) suggested that a large proportion of risk variants are located in non-coding regions and remain unexplained by current expression quantitative trait loci catalogs. Interpreting the causal effects of these genetic modifications is crucial but difficult owing to our limited knowledge of how regulatory elements function. Although several computational methods have been designed to prioritize regulatory variants that substantially impact human phenotypes, few of them achieve consistently high performance even when large-scale multi-omic data are integrated. RESULTS: We propose a novel multi-task framework based on Bayesian deep neural networks, MtBNN, to quantify the deleterious impact of single nucleotide polymorphisms in non-coding genomic regions. With the high-efficiency provided by the multi-task Bayesian framework to integrate information from different sources, MtBNN is capable of extracting features from genomic sequences of large-scale chromatin-profiling data, such as chromatin accessibility and transcript factor binding affinities, and calculating the distribution of the probability that a non-coding variant disrupts regulatory activities. A series of comprehensive experiments show that MtBNN quantifies the functional impact of cis-regulatory variations with high accuracy, including expression quantitative trait locus, DNase I sensitivity quantitative trait locus and functional genetic variants located within ATAC-peaks that affect the accessibility of the corresponding peak and achieves significantly better performance than the existing methods. Moreover, MtBNN has applications in the discovery of potentially causal disease-associated single-nucleotide polymorphisms (SNPs), thus helping fine-map the GWAS SNPs. AVAILABILITY AND IMPLEMENTATION: Code can be downloaded from https://github.com/Zoesgithub/MtBNN. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Continuous Draw Spinning of Extra-Long Silver Submicron Fibers with Micrometer Patterning Capability.

Ultrathin metal fibers can serve as highly conducting and flexible current and heat transport channels, which are essential for numerous applications ranging from flexible electronics to energy conversion. Although industrial production of metal fibers with diameters of down to 2 µm is feasible, continuous production of high-quality and low-cost nanoscale metal wires is still challenging. Herein, we report the continuous draw spinning of highly conductive silver submicron fibers with the minimum diameter of ∼200 nm and length of more than kilometers. We obtained individual AgNO3/polymer fibers by continuous drawing from an aqueous solution at a speed of up to 8 m/s. With subsequent heat treatment, freestanding Ag submicron fibers with high mechanical flexibility and electric conductivity have been obtained. Woven mats of aligned Ag submicron fibers were used as transparent electrodes with high flexibility and high performance with sheet resistance of 7 Ω sq-1 at a transparency of 96%. Continuous draw spinning opened new avenues for scalable, flexible, and ultralow-cost fabrication of extra-long conductive ultrathin metal fibers.

Reference-informed prediction of alternative splicing and splicing-altering mutations from sequences.

Alternative splicing plays a crucial role in protein diversity and gene expression regulation in higher eukaryotes and mutations causing dysregulated splicing underlie a range of genetic diseases. Computational prediction of alternative splicing from genomic sequences not only provides insight into gene-regulatory mechanisms but also helps identify disease-causing mutations and drug targets. However, the current methods for the quantitative prediction of splice site usage still have limited accuracy. Here, we present DeltaSplice, a deep neural network model optimized to learn the impact of mutations on quantitative changes in alternative splicing from the comparative analysis of homologous genes. The model architecture enables DeltaSplice to perform "reference-informed prediction" by incorporating the known splice site usage of a reference gene sequence to improve its prediction on splicing-altering mutations. We benchmarked DeltaSplice and several other state-of-the-art methods on various prediction tasks, including evolutionary sequence divergence on lineage-specific splicing and splicing-altering mutations in human populations and neurodevelopmental disorders, and demonstrated that DeltaSplice outperformed consistently. DeltaSplice predicted ~15% of splicing quantitative trait loci (sQTLs) in the human brain as causal splicing-altering variants. It also predicted splicing-altering de novo mutations outside the splice sites in a subset of patients affected by autism and other neurodevelopmental disorders, including 19 genes with recurrent splicing-altering mutations. Among the new candidate disease risk genes, MFN1 is involved in mitochondria fusion, which is frequently disrupted in autism patients. Our work expanded the capacity of in silico splicing models with potential applications in genetic diagnosis and the development of splicing-based precision medicine.

PPML-Omics: A privacy-preserving federated machine learning method protects patients' privacy in omic data.

Modern machine learning models toward various tasks with omic data analysis give rise to threats of privacy leakage of patients involved in those datasets. Here, we proposed a secure and privacy-preserving machine learning method (PPML-Omics) by designing a decentralized differential private federated learning algorithm. We applied PPML-Omics to analyze data from three sequencing technologies and addressed the privacy concern in three major tasks of omic data under three representative deep learning models. We examined privacy breaches in depth through privacy attack experiments and demonstrated that PPML-Omics could protect patients' privacy. In each of these applications, PPML-Omics was able to outperform methods of comparison under the same level of privacy guarantee, demonstrating the versatility of the method in simultaneously balancing the privacy-preserving capability and utility in omic data analysis. Furthermore, we gave the theoretical proof of the privacy-preserving capability of PPML-Omics, suggesting the first mathematically guaranteed method with robust and generalizable empirical performance in protecting patients' privacy in omic data.

Ferroptosis Inducers Kill Mesenchymal Stem Cells Affected by Neuroblastoma.

Bone marrow (BM) is the most common site of neuroblastoma (NB) metastasis, and its involvement represents poor patient prognosis. In accordance with the "seed and soil" theory of tumor metastasis, BM provides a favorable environment for NB metastasis while bone marrow mesenchymal stem cells (BMSCs) have been recognized as a central part of tumor stroma formation. Yet, there is currently no effective method for intervening these BMSCs. We found that BMSCs affected by NB (NB-BMSCs) could significantly promote NB growth and migration. Additionally, tumor cell-endowed BMSCs showed stronger resistance to several chemotherapeutic agents. Surprisingly, NB-BMSCs were more sensitive to ferroptosis than normal BMSCs. NB-BMSCs had lower levels of intracellular free iron while synthesizing more iron-sulfur clusters and heme. Moreover, the Xc-/glutathione/glutathione peroxidase 4 (Xc-/GSH/GPX4) pathway of the anti-ferroptosis system was significantly downregulated. Accordingly, ferroptosis inducers erastin and RAS-selective lethal 3 (RSL3) could significantly kill NB-BMSCs with limited effects on normal BMSCs. BMSCs from NB patients with BM metastasis also showed poor anti-ferroptosis ability compared with those from NB patients without BM metastasis. In vivo studies suggested that co-injection of mice with BMSCs and NB cells could significantly promote the growth of tumor tissues compared with injecting NB cells alone. However, treatment with erastin or RSL3 resulted in the opposite effect to some extent. Our results revealed that NB-BMSCs were vulnerable to ferroptosis from downregulation of the Xc-/GSH/GPX4 pathway. Ferroptosis inducers could effectively kill NB-BMSCs, but not normal BMSCs. This study provides possible new ideas for the treatment of tumor-associated BMSCs in NB patients.

Eugenol-Preconditioned Mesenchymal Stem Cell-Derived Extracellular Vesicles Promote Antioxidant Capacity of Tendon Stem Cells In Vitro and In Vivo.

Tendon stem cells (TSCs) are often exposed to oxidative stress at tendon injury sites, which impairs their physiological effect as well as therapeutic application. Recently, extracellular vesicles (EVs) derived from bone marrow mesenchymal stem cells (BMSCs) were shown to mediate cell protection and survival under stress conditions. The function of BMSC-EVs may be affected by pretreatment with various factors such as eugenol (EUG)-a powerful antioxidant. In our previous study, we found that H2O2 significantly impaired TSC proliferation and tenogenic differentiation capabilities. Apoptosis and intracellular ROS accumulation in TSCs were induced by H2O2. However, such H2O2-induced damage was prevented by treatment with EUG-BMSC-EVs. Furthermore, EUG-BMSC-EVs activated the Nrf2/HO-1 pathway to counteract H2O2-induced damage in TSCs. In a rat patellar tendon injury model, the ROS level was significantly higher than that in the normal tendon and TSCs not pretreated showed a poor therapeutic effect. However, EUG-BMSC-EV-pretreated TSCs significantly improved tenogenesis and matrix regeneration during tendon healing. Additionally, the EUG-BMSC-EV group had a significantly improved fiber arrangement. Overall, EUG-BMSC-EVs protected TSCs against oxidative stress and enhanced their functions in tendon injury. These findings provide a basis for potential clinical use of EUG-BMSC-EVs as a new therapeutic vehicle to facilitate TSC therapies for tendon regeneration.

Unraveling the enhanced Oxygen Vacancy Formation in Complex Oxides during Annealing and Growth.

The reduction of oxides during annealing and growth in low pressure processes is a widely known problem. We hence investigate the influence of mere annealing and of growth in vacuum systems to shed light on the reasons behind the reduction of perovskites. When comparing the existing literature regarding the reduction of the perovskite model material SrTiO3 it is conspicuous that one finds different oxygen pressures required to achieve reduction for vacuum annealing and for chemically controlled reducing atmospheres. The unraveling of this discrepancy is of high interest for low pressure physical vapor depositions of thin films heterostructures to gain further understanding of the reduction of the SrTiO3. For thermal annealing, our results prove the attached measurement devices (mass spectrometer/ cold cathode gauge) to be primarily responsible for the reduction of SrTiO3 in the deposition chamber by shifting the thermodynamic equilibrium to a more reducing atmosphere. We investigated the impact of our findings on the pulsed laser deposition growth at low pressure for LaAlO3/SrTiO3. During deposition the reduction triggered by the presence of the laser plume dominates and the impact of the measurement devices plays a minor role. During post annealing a complete reoxidization of samples is inhibited by an insufficient supply of oxygen.

Disentanglement of growth dynamic and thermodynamic effects in LaAlO3/SrTiO3 heterostructures.

The influence of non-equilibrium and equilibrium processes during growth of LaAlO3/SrTiO3 (LAO/STO) heterostructures is analyzed. We investigate the electronic properties of LAO/STO heterostructures obtained at constant growth conditions after annealing in different oxygen atmospheres within the typical growth window (1 × 10(-4) mbar -1 × 10(-2) mbar). The variation of annealing conditions is found to cause a similar change of electronic properties as observed for samples grown in different oxygen pressure. The results indicate that equilibrium defect formation is the dominant process for establishing the properties of the two-dimensional electron gas (2DEG), while growth dynamics play a minor role in the typical LAO/STO growth regime. Furthermore, the effects of non-equilibrium processes occurring during growth are investigated in detail by quenching just-grown LAO/STO heterostructures directly after growth. We show that during growth the sample is pushed into a non-equilibrium state. After growth, the sample then relaxes towards equilibrium, while the relaxation rate strongly depends on the ambient pressure. The observed relaxation behavior is mainly associated with a reoxidation of the STO bulk, while the 2DEG is formed immediately after the growth.

Formation mechanism of Ruddlesden-Popper-type antiphase boundaries during the kinetically limited growth of Sr rich SrTiO3 thin films.

We elucidated the formation process for Ruddlesden-Popper-type defects during pulsed laser deposition of Sr rich SrTiO3 thin films by a combined analysis of in-situ atomic force microscopy, low energy electron diffraction and high resolution scanning transmission electron microscopy. At the early growth stage of 1.5 unit cells, the excess Sr results in the formation of SrO on the surface, resulting in a local termination change from TiO2 to SrO, thereby forming a Sr rich (2 × 2) surface reconstruction. With progressive SrTiO3 growth, islands with thermodynamically stable SrO rock-salt structure are formed, coexisting with TiO2 terminated islands. During the overgrowth of these thermodynamically stable islands, both lateral as well as vertical Ruddlesden-Popper-type anti-phase boundaries are formed, accommodating the Sr excess of the SrTiO3 film. We suggest the formation of thermodynamically stable SrO rock-salt structures as origin for the formation of Ruddlesden-Popper-type antiphase boundaries, which are as a result of kinetic limitations confined to certain regions on the surface.

The influence of the local oxygen vacancy concentration on the piezoresponse of strontium titanate thin films.

In this study, the influence of the local oxygen vacancy concentration on piezoresponse force microscopy (PFM) measurements was investigated. Ultra-thin single-crystalline SrTiO3 thin films were deposited on niobium doped SrTiO3 substrates and analyzed using a combined PFM and local conductive atomic force microscopy (LC-AFM) measurement setup. After applying different polarization voltages between ±2 V and ±5 V to the thin films, we simultaneously observed an anomalous contrast in the piezoresponse amplitude and phase signal as well as a changed local conductivity in the exact same region. Since classic ferroelectricity can be excluded as the reason for the observed contrast, an influence of the local oxygen vacancy concentration on the piezoresponse is considered. Additionally, the surface potential was measured using Kelvin probe force microscopy (KPFM) revealing a change in surface potential in the regions of the applied voltage. The observed relaxation of the surface potential over time was fitted to a local oxidation reaction of the previously reduced regions of the ultra-thin SrTiO3 film. We propose a model that relates the local oxygen vacancy concentration to the surface potential. The influence of the oxygen vacancy concentration on the PFM measurements is explained.

Surface Termination Conversion during SrTiO3 Thin Film Growth Revealed by X-ray Photoelectron Spectroscopy.

Emerging electrical and magnetic properties of oxide interfaces are often dominated by the termination and stoichiometry of substrates and thin films, which depend critically on the growth conditions. Currently, these quantities have to be measured separately with different sophisticated techniques. This report will demonstrate that the analysis of angle dependent X-ray photoelectron intensity ratios provides a unique tool to determine both termination and stoichiometry simultaneously in a straightforward experiment. Fitting the experimental angle dependence with a simple analytical model directly yields both values. The model is calibrated through the determination of the termination of SrTiO3 single crystals after systematic pulsed laser deposition of sub-monolayer thin films of SrO. We then use the model to demonstrate that during homoepitaxial SrTiO3 growth, excess Sr cations are consumed in a self-organized surface termination conversion before cation defects are incorporated into the film. We show that this termination conversion results in insulating properties of interfaces between polar perovskites and SrTiO3 thin films. These insights about oxide thin film growth can be utilized for interface engineering of oxide heterostructures. In particular, they suggest a recipe for obtaining two-dimensional electron gases at thin film interfaces: SrTiO3 should be deposited slightly Ti-rich to conserve the TiO2-termination.