A natural adhesive-based nanomedicine initiates photothermal-directed in situ immunotherapy with durability and maintenance.

Tumor immunotherapies have emerged as a promising frontier in the realm of cancer treatment. However, challenges persist in achieving localized, durable immunostimulation while counteracting the tumor's immunosuppressive environment. Here, we develop a natural mussel foot protein-based nanomedicine with spatiotemporal control for tumor immunotherapy. In this nanomedicine, an immunoadjuvant prodrug and a photosensitizer are integrated, which is driven by their dynamic bonding and non-covalent assembling with the protein carrier. Harnessing the protein carrier's bioadhesion, this nanomedicine achieves a drug co-delivery with spatiotemporal precision, by which it not only promotes tumor photothermal ablation but also broadens tumor antigen repertoire, facilitating in situ immunotherapy with durability and maintenance. This nanomedicine also modulates the tumor microenvironment to overcome immunosuppression, thereby amplifying antitumor responses against tumor progression. Our strategy underscores a mussel foot protein-derived design philosophy of drug delivery aimed at refining combinatorial immunotherapy, offering insights into leveraging natural proteins for cancer treatment.

Molecular mechanics studies of factors affecting overall rate in cascade reactions: Multi-enzyme colocalization and environment.

Millions of years of evolution have optimized many biosynthetic pathways by use of multi-step catalysis. In addition, multi-step metabolic pathways are commonly found in and on membrane-bound organelles in eukaryotic biochemistry. The fundamental mechanisms that facilitate these reaction processes provide strategies to bioengineer metabolic pathways in synthetic chemistry. Using Brownian dynamics simulations, here we modeled intermediate substrate transportation of colocalized yeast-ester biosynthesis enzymes on the membrane. The substrate acetate ion traveled from the pocket of aldehyde dehydrogenase to its target enzyme acetyl-CoA synthetase, then the substrate acetyl CoA diffused from Acs1 to the active site of the next enzyme, alcohol-O-acetyltransferase. Arranging two enzymes with the smallest inter-enzyme distance of 60 Å had the fastest average substrate association time as compared with anchoring enzymes with larger inter-enzyme distances. When the off-target side reactions were turned on, most substrates were lost, which suggests that native localization is necessary for efficient final product synthesis. We also evaluated the effects of intermolecular interactions, local substrate concentrations, and membrane environment to bring mechanistic insights into the colocalization pathways. The computation work demonstrates that creating spatially organized multi-enzymes on membranes can be an effective strategy to increase final product synthesis in bioengineering systems.

BracketMaker: Visualization and optimization of chemical protein synthesis.

Chemical protein synthesis (CPS), in which custom peptide segments of ~20-60 aa are produced by solid-phase peptide synthesis and then stitched together through sequential ligation reactions, is an increasingly popular technique. The workflow of CPS is often depicted with a "bracket" style diagram detailing the starting segments and the order of all ligation, desulfurization, and/or deprotection steps to obtain the product protein. Brackets are invaluable tools for comparing multiple possible synthetic approaches and serve as blueprints throughout a synthesis. Drawing CPS brackets by hand or in standard graphics software, however, is a painstaking and error-prone process. Furthermore, the CPS field lacks a standard bracket format, making side-by-side comparisons difficult. To address these problems, we developed BracketMaker, an open-source Python program with built-in graphic user interface (GUI) for the rapid creation and analysis of CPS brackets. BracketMaker contains a custom graphics engine which converts a text string (a protein sequence annotated with reaction steps, introduced herein as a standardized format for brackets) into a high-quality vector or PNG image. To aid with new syntheses, BracketMaker's "AutoBracket" tool automatically performs retrosynthetic analysis on a set of segments to draft and rank all possible ligation orders using standard native chemical ligation, protection, and desulfurization techniques. AutoBracket, in conjunction with an improved version of our previously reported Automated Ligator (Aligator) program, provides a pipeline to rapidly develop synthesis plans for a given protein sequence. We demonstrate the application of both programs to develop a blueprint for 65 proteins of the minimal Escherichia coli ribosome.

Low-dose cryo-electron ptychography of proteins at sub-nanometer resolution.

Cryo-transmission electron microscopy (cryo-EM) of frozen hydrated specimens is an efficient method for the structural analysis of purified biological molecules. However, cryo-EM and cryo-electron tomography are limited by the low signal-to-noise ratio (SNR) of recorded images, making detection of smaller particles challenging. For dose-resilient samples often studied in the physical sciences, electron ptychography - a coherent diffractive imaging technique using 4D scanning transmission electron microscopy (4D-STEM) - has recently demonstrated excellent SNR and resolution down to tens of picometers for thin specimens imaged at room temperature. Here we apply 4D-STEM and ptychographic data analysis to frozen hydrated proteins, reaching sub-nanometer resolution 3D reconstructions. We employ low-dose cryo-EM with an aberration-corrected, convergent electron beam to collect 4D-STEM data for our reconstructions. The high frame rate of the electron detector allows us to record large datasets of electron diffraction patterns with substantial overlaps between the interaction volumes of adjacent scan positions, from which the scattering potentials of the samples are iteratively reconstructed. The reconstructed micrographs show strong SNR enabling the reconstruction of the structure of apoferritin protein at up to 5.8 Å resolution. We also show structural analysis of the Phi92 capsid and sheath, tobacco mosaic virus, and bacteriorhodopsin at slightly lower resolutions.

Quantitative Characterization of the Impact of Protein-Protein Interactions on Ligand-Protein Binding: A Multi-Chain Dynamics Perturbation Analysis Method.

Many protein-protein interactions (PPIs) affect the ways in which small molecules bind to their constituent proteins, which can impact drug efficacy and regulatory mechanisms. While recent advances have improved our ability to independently predict both PPIs and ligand-protein interactions (LPIs), a comprehensive understanding of how PPIs affect LPIs is still lacking. Here, we examined 63 pairs of ligand-protein complexes in a benchmark dataset for protein-protein docking studies and quantified six typical effects of PPIs on LPIs. A multi-chain dynamics perturbation analysis method, called mcDPA, was developed to model these effects and used to predict small-molecule binding regions in protein-protein complexes. Our results illustrated that the mcDPA can capture the impact of PPI on LPI to varying degrees, with six similar changes in its predicted ligand-binding region. The calculations showed that 52% of the examined complexes had prediction accuracy at or above 50%, and 55% of the predictions had a recall of not less than 50%. When applied to 33 FDA-approved protein-protein-complex-targeting drugs, these numbers improved to 60% and 57% for the same accuracy and recall rates, respectively. The method developed in this study may help to design drug-target interactions in complex environments, such as in the case of protein-protein interactions.

A Point Cloud Graph Neural Network for Protein-Ligand Binding Site Prediction.

Predicting protein-ligand binding sites is an integral part of structural biology and drug design. A comprehensive understanding of these binding sites is essential for advancing drug innovation, elucidating mechanisms of biological function, and exploring the nature of disease. However, accurately identifying protein-ligand binding sites remains a challenging task. To address this, we propose PGpocket, a geometric deep learning-based framework to improve protein-ligand binding site prediction. Initially, the protein surface is converted into a point cloud, and then the geometric and chemical properties of each point are calculated. Subsequently, the point cloud graph is constructed based on the inter-point distances, and the point cloud graph neural network (GNN) is applied to extract and analyze the protein surface information to predict potential binding sites. PGpocket is trained on the scPDB dataset, and its performance is verified on two independent test sets, Coach420 and HOLO4K. The results show that PGpocket achieves a 58% success rate on the Coach420 dataset and a 56% success rate on the HOLO4K dataset. These results surpass competing algorithms, demonstrating PGpocket's advancement and practicality for protein-ligand binding site prediction.

Maximal Genetic Code Symmetry Is a Physicochemical Purine-Pyrimidine Symmetry Language for Transcription and Translation in the Flow of Genetic Information from DNA to Proteins.

Until now, research has not taken into consideration the physicochemical purine-pyrimidine symmetries of the genetic code in the transcription and translation processes of proteinogenesis. Our Supersymmetry Genetic Code table, developed in 2022, is common and unique for all RNA and DNA living species. Its basic structure is a purine-pyrimidine symmetry net with double mirror symmetry. Accordingly, the symmetry of the genetic code directly shows its organisation based on the principle of nucleotide Watson-Crick and codon-anticodon pairing. The maximal purine-pyrimidine symmetries of codons show that each codon has a strictly defined and unchangeable position within the genetic code. We discovered that the physicochemical symmetries of the genetic code play a fundamental role in recognising and differentiating codons from mRNA and the anticodon tRNA and aminoacyl-tRNA synthetases in the transcription and translation processes. These symmetries also support the wobble hypothesis with non-Watson-Crick pairing interactions between the translation process from mRNA to tRNA. The Supersymmetry Genetic Code table shows a specific arrangement of the second base of codons, according to which it is possible that an anticodon from tRNA recognises whether a codon from mRNA belongs to an amino acid with two or four codons, which is very important in the purposeful use of the wobble pairing process. Therefore, we show that canonical and wobble pairings essentially do not lead to misreading and errors during translation, and we point out the role of physicochemical purine-pyrimidine symmetries in decreasing disorder according to error minimisation and preserving the integrity of biological processes during proteinogenesis.

Utilizing Molecular Dynamics Simulations, Machine Learning, Cryo-EM, and NMR Spectroscopy to Predict and Validate Protein Dynamics.

Protein dynamics play a crucial role in biological function, encompassing motions ranging from atomic vibrations to large-scale conformational changes. Recent advancements in experimental techniques, computational methods, and artificial intelligence have revolutionized our understanding of protein dynamics. Nuclear magnetic resonance spectroscopy provides atomic-resolution insights, while molecular dynamics simulations offer detailed trajectories of protein motions. Computational methods applied to X-ray crystallography and cryo-electron microscopy (cryo-EM) have enabled the exploration of protein dynamics, capturing conformational ensembles that were previously unattainable. The integration of machine learning, exemplified by AlphaFold2, has accelerated structure prediction and dynamics analysis. These approaches have revealed the importance of protein dynamics in allosteric regulation, enzyme catalysis, and intrinsically disordered proteins. The shift towards ensemble representations of protein structures and the application of single-molecule techniques have further enhanced our ability to capture the dynamic nature of proteins. Understanding protein dynamics is essential for elucidating biological mechanisms, designing drugs, and developing novel biocatalysts, marking a significant paradigm shift in structural biology and drug discovery.

The Impact of ARMS2 (rs10490924), VEGFA (rs3024997), TNFRSF1B (rs1061622), TNFRSF1A (rs4149576), and IL1B1 (rs1143623) Polymorphisms and Serum Levels on Age-Related Macular Degeneration Development and Therapeutic Responses.

Age-related macular degeneration (AMD) is a major global health problem as it is the leading cause of irreversible loss of central vision in the aging population. Anti-vascular endothelial growth factor (anti-VEGF) therapies are effective but do not respond optimally in all patients. This study investigates the genetic factors associated with susceptibility to AMD and response to treatment, focusing on key polymorphisms in the ARMS2 (rs10490924), IL1B1 (rs1143623), TNFRSF1B (rs1061622), TNFRSF1A (rs4149576), VEGFA (rs3024997), ARMS2, IL1B1, TNFRSF1B, TNFRSF1A, and VEGFA serum levels in AMD development and treatment efficacy. This study examined the associations of specific genetic polymorphisms and serum protein levels with exudative and early AMD and the response to anti-VEGF treatment. The AA genotype of VEGFA (rs3024997) was significantly associated with a 20-fold reduction in the odds of exudative AMD compared to the GG + GA genotypes. Conversely, the TT genotype of ARMS2 (rs10490924) was linked to a 4.2-fold increase in the odds of exudative AMD compared to GG + GT genotypes. In females, each T allele of ARMS2 increased the odds by 2.3-fold, while in males, the TT genotype was associated with a 5-fold increase. Lower serum IL1B levels were observed in the exudative AMD group compared to the controls. Early AMD patients had higher serum TNFRSF1B levels than controls, particularly those with the GG genotype of TNFRSF1B rs1061622. Exudative AMD patients with the CC genotype of TNFRSF1A rs4149576 had lower serum TNFRSF1A levels compared to the controls. Visual acuity (VA) analysis showed that non-responders had better baseline VA than responders but experienced decreased VA after treatment, whereas responders showed improvement. Central retinal thickness (CRT) reduced significantly in responders after treatment and was lower in responders compared to non-responders after treatment. The T allele of TNFRSF1B rs1061622 was associated with a better response to anti-VEGF treatment under both dominant and additive genetic models. These findings highlight significant genetic and biochemical markers associated with AMD and treatment response. This study found that the VEGFA rs3024997 AA genotype reduces the odds of exudative AMD, while the ARMS2 rs10490924 TT genotype increases it. Lower serum IL1B levels and variations in TNFRSF1B and TNFRSF1A levels were linked to AMD. The TNFRSF1B rs1061622 T allele was associated with better anti-VEGF treatment response. These markers could potentially guide risk assessment and personalized treatment for AMD.

AI-Predicted Protein Deformation Encodes Energy Landscape Perturbation.

AI algorithms have proven to be excellent predictors of protein structure, but whether and how much these algorithms can capture the underlying physics remains an open question. Here, we aim to test this question using the Alphafold2 (AF) algorithm: We use AF to predict the subtle structural deformation induced by single mutations, quantified by strain, and compare with experimental datasets of corresponding perturbations in folding free energy ΔΔG. Unexpectedly, we find that physical strain alone-without any additional data or computation-correlates almost as well with ΔΔG as state-of-the-art energy-based and machine-learning predictors. This indicates that the AF-predicted structures alone encode fine details about the energy landscape. In particular, the structures encode significant information on stability, enough to estimate (de-)stabilizing effects of mutations, thus paving the way for the development of novel, structure-based stability predictors for protein design and evolution.

Unraveling the complexity: Advanced methods in analyzing DNA, RNA, and protein interactions.

Exploring the intricate interplay within and between nucleic acids, as well as their interactions with proteins, holds pivotal significance in unraveling the molecular complexities steering cancer initiation and progression. To investigate these interactions, a diverse array of highly specific and sensitive molecular techniques has been developed. The selection of a particular technique depends on the specific nature of the interactions. Typically, researchers employ an amalgamation of these different techniques to obtain a comprehensive and holistic understanding of inter- and intramolecular interactions involving DNA-DNA, RNA-RNA, DNA-RNA, or protein-DNA/RNA. Examining nucleic acid conformation reveals alternative secondary structures beyond conventional ones that have implications for cancer pathways. Mutational hotspots in cancer often lie within sequences prone to adopting these alternative structures, highlighting the importance of investigating intra-genomic and intra-transcriptomic interactions, especially in the context of mutations, to deepen our understanding of oncology. Beyond these intramolecular interactions, the interplay between DNA and RNA leads to formations like DNA:RNA hybrids (known as R-loops) or even DNA:DNA:RNA triplex structures, both influencing biological processes that ultimately impact cancer. Protein-nucleic acid interactions are intrinsic cellular phenomena crucial in both normal and pathological conditions. In particular, genetic mutations or single amino acid variations can alter a protein's structure, function, and binding affinity, thus influencing cancer progression. It is thus, imperative to understand the differences between wild-type (WT) and mutated (MT) genes, transcripts, and proteins. The review aims to summarize the frequently employed methods and techniques for investigating interactions involving nucleic acids and proteins, highlighting recent advancements and diverse adaptations of each technique.

Aromatic Residues in Proteins: Re-Evaluating the Geometry and Energetics of π-π, Cation-π, and CH-π Interactions.

Aromatic residues can participate in various biomolecular interactions, such as π-π, cation-π, and CH-π interactions, which are essential for protein structure and function. Here, we re-evaluate the geometry and energetics of these interactions using quantum mechanical (QM) calculations, focusing on pairwise interactions involving the aromatic amino acids Phe, Tyr, and Trp and the cationic amino acids Arg and Lys. Our findings reveal that π-π interactions, while energetically favorable, are less abundant in structured proteins than commonly assumed and are often overshadowed by previously underappreciated, yet prevalent, CH-π interactions. Cation-π interactions, particularly those involving Arg, show strong binding energies and a specific geometric preference toward stacked conformations, despite the global QM minimum, suggesting that a rather perpendicular T-shape conformation should be more favorable. Our results support a more nuanced understanding of protein stabilization via interactions involving aromatic residues. On the one hand, our results challenge the traditional emphasis on π-π interactions in structured proteins by showing that CH-π and cation-π interactions contribute significantly to their structure. On the other hand, π-π interactions appear to be key stabilizers in solvated regions and thus may be particularly important to the stabilization of intrinsically disordered proteins.

An expanded molecular and systems toolbox for imaging, mapping, and controlling local translation.

Localized protein translation occurs through trafficking of mRNAs and protein translation machineries to different compartments of the cell, leading to rapid on-site synthesis of proteins in response to signaling cues. The spatiotemporally precise nature of the local translation process necessitates continual developments of technologies reviewed herein to visualize and map biomolecular components and the translation process with better spatial and temporal resolution and with fewer artifacts. We also discuss approaches to control local translation, which can serve as a design paradigm for subcellular genetic devices for eukaryotic synthetic biology.

Blobs form during the single-file transport of proteins across nanopores.

The transport of biopolymers across nanopores is an important biological process currently under investigation for the rapid analysis of DNA and proteins. While the transport of DNA is generally understood, methods to induce unfolded protein translocation have only recently been discovered (Yu et al., 2023, Sauciuc et al., 2023). Here, we found that during electroosmotically driven translocation of polypeptides, blob-like structures typically form inside nanopores, often obstructing their transport and preventing addressing individual amino acids. This is in contrast with the electrophoretic transport of DNA, where the formation of such structures has not been reported. Comparisons between different nanopore sizes and shapes and modifications by different surface chemistries allowed formulating a mechanism for blob formation. We also show that single-file transport can be achieved by using 1) nanopores that have an entry and an internal diameter smaller than the persistence length of the polymer, 2) nanopores with a nonsticky (i.e., nonaromatic) inner surface, and 3) moderate translocation velocities. These experiments provide a basis for understanding polypeptide transport under confinement and for improving the design and engineering of nanopores for protein analysis.

N-degron pathways.

An N-degron is a degradation signal whose main determinant is a "destabilizing" N-terminal residue of a protein. Specific N-degrons, discovered in 1986, were the first identified degradation signals in short-lived intracellular proteins. These N-degrons are recognized by a ubiquitin-dependent proteolytic system called the Arg/N-degron pathway. Although bacteria lack the ubiquitin system, they also have N-degron pathways. Studies after 1986 have shown that all 20 amino acids of the genetic code can act, in specific sequence contexts, as destabilizing N-terminal residues. Eukaryotic proteins are targeted for the conditional or constitutive degradation by at least five N-degron systems that differ both functionally and mechanistically: the Arg/N-degron pathway, the Ac/N-degron pathway, the Pro/N-degron pathway, the fMet/N-degron pathway, and the newly named, in this perspective, GASTC/N-degron pathway (GASTC = Gly, Ala, Ser, Thr, Cys). I discuss these systems and the expanded terminology that now encompasses the entire gamut of known N-degron pathways.

Multi-modal contrastive learning of subcellular organization using DICE.

The data deluge in biology calls for computational approaches that can integrate multiple datasets of different types to build a holistic view of biological processes or structures of interest. An emerging paradigm in this domain is the unsupervised learning of data embeddings that can be used for downstream clustering and classification tasks. While such approaches for integrating data of similar types are becoming common, there is scarcer work on consolidating different data modalities such as network and image information. Here, we introduce DICE (Data Integration through Contrastive Embedding), a contrastive learning model for multi-modal data integration. We apply this model to study the subcellular organization of proteins by integrating protein-protein interaction data and protein image data measured in HEK293 cells. We demonstrate the advantage of data integration over any single modality and show that our framework outperforms previous integration approaches. Availability: https://github.com/raminass/protein-contrastive Contact: raminass@gmail.com.

learnMSA2: deep protein multiple alignments with large language and hidden Markov models.

MOTIVATION: For the alignment of large numbers of protein sequences, tools are predominant that decide to align two residues using only simple prior knowledge, e.g. amino acid substitution matrices, and using only part of the available data. The accuracy of state-of-the-art programs declines with decreasing sequence identity and when increasingly large numbers of sequences are aligned. Recently, transformer-based deep-learning models started to harness the vast amount of protein sequence data, resulting in powerful pretrained language models with the main purpose of generating high-dimensional numerical representations, embeddings, for individual sites that agglomerate evolutionary, structural, and biophysical information. RESULTS: We extend the traditional profile hidden Markov model so that it takes as inputs unaligned protein sequences and the corresponding embeddings. We fit the model with gradient descent using our existing differentiable hidden Markov layer. All sequences and their embeddings are jointly aligned to a model of the protein family. We report that our upgraded HMM-based aligner, learnMSA2, combined with the ProtT5-XL protein language model aligns on average almost 6% points more columns correctly than the best amino acid-based competitor and scales well with sequence number. The relative advantage of learnMSA2 over other programs tends to be greater when the sequence identity is lower and when the number of sequences is larger. Our results strengthen the evidence on the rich information contained in protein language models' embeddings and their potential downstream impact on the field of bioinformatics. Availability and implementation:  https://github.com/Gaius-Augustus/learnMSA, PyPI and Bioconda, evaluation: https://github.com/felbecker/snakeMSA.

GORetriever: reranking protein-description-based GO candidates by literature-driven deep information retrieval for protein function annotation.

SUMMARY: The vast majority of proteins still lack experimentally validated functional annotations, which highlights the importance of developing high-performance automated protein function prediction/annotation (AFP) methods. While existing approaches focus on protein sequences, networks, and structural data, textual information related to proteins has been overlooked. However, roughly 82% of SwissProt proteins already possess literature information that experts have annotated. To efficiently and effectively use literature information, we present GORetriever, a two-stage deep information retrieval-based method for AFP. Given a target protein, in the first stage, candidate Gene Ontology (GO) terms are retrieved by using annotated proteins with similar descriptions. In the second stage, the GO terms are reranked based on semantic matching between the GO definitions and textual information (literature and protein description) of the target protein. Extensive experiments over benchmark datasets demonstrate the remarkable effectiveness of GORetriever in enhancing the AFP performance. Note that GORetriever is the key component of GOCurator, which has achieved first place in the latest critical assessment of protein function annotation (CAFA5: over 1600 teams participated), held in 2023-2024. AVAILABILITY AND IMPLEMENTATION: GORetriever is publicly available at https://github.com/ZhuLab-Fudan/GORetriever.

Biophysical characterization of hydrogen sulfide: A fundamental exploration in understanding significance in cell signaling.

Hydrogen sulfide (H2S) has emerged as a significant signaling molecule involved in various physiological processes, including vasodilation, neurotransmission, and cytoprotection. Its interactions with biomolecules are critical to understand its roles in health and disease. Recent advances in biophysical characterization techniques have shed light on the complex interactions of H2S with proteins, nucleic acids, and lipids. Proteins are primary targets for H2S, which can modify cysteine residues through S-sulfhydration, impacting protein function and signaling pathways. Advanced spectroscopic techniques, such as mass spectrometry and NMR, have enabled the identification of specific sulfhydrated sites and provided insights into the structural and functional consequences of these modifications. Nucleic acids also interact with H2S, although this area is less explored compared to proteins. Recent studies have demonstrated that H2S can induce modifications in nucleic acids, affecting gene expression and stability. Techniques like gel electrophoresis and fluorescence spectroscopy have been utilized to investigate these interactions, revealing that H2S can protect DNA from oxidative damage and modulate RNA stability and function. Lipids, being integral components of cell membranes, interact with H2S, influencing membrane fluidity and signaling. Biophysical techniques such as electron paramagnetic resonance (EPR) and fluorescence microscopy have elucidated the effects of H2S on lipid membranes. These studies have shown that H2S can alter lipid packing and dynamics, which may impact membrane-associated signaling pathways and cellular responses to stress. In the current work we have integrated this with key scientific explainations to provide a comprehensive review.

TPPU_DSF: A Web Application to Calculate Thermodynamic Parameters Using DSF Data.

Here we present TPPU_DSF (https://maciasnmr.net/tppu_dsf/). This is a free and open-source web application that opens, converts, fits, and calculates the thermodynamic parameters of protein unfolding from standard differential scanning fluorimetry (DSF) data in an automated manner. The software has several applications. In the context of screening compound libraries for protein binders, obtaining thermodynamic parameters provides a more robust approach to detecting hits than the changes in the melting temperature (Tm) alone, thereby helping to increase the number of positive hits in screening campaigns. Moreover, changes in ΔGuo indicate protein response to binding at lower compound concentrations than those in the Tm, thereby reducing the costs associated with the amounts of protein and compounds required for the assays. Also, by adding thermodynamic information to the Tm comparison, the software can contribute to the optimization of protein constructs and buffer conditions, a common practice before structural and functional projects.