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We introduce a data-driven epistatic model of protein evolution, capable of generating evolutionary trajectories spanning very different time scales reaching from individual mutations to diverged homologs. Our in silico evolution encompasses random nucleotide mutations, insertions and deletions, and models selection using a fitness landscape, which is inferred via a generative probabilistic model for protein families. We show that the proposed framework accurately reproduces the sequence statistics of both short-time (experimental) and long-time (natural) protein evolution, suggesting applicability also to relatively data-poor intermediate evolutionary time scales, which are currently inaccessible to evolution experiments. Our model uncovers a highly collective nature of epistasis, gradually changing the fitness effect of mutations in a diverging sequence context, rather than acting via strong interactions between individual mutations. This collective nature triggers the emergence of a long evolutionary time scale, separating fast mutational processes inside a given sequence context, from the slow evolution of the context itself. The model quantitatively reproduces epistatic phenomena such as contingency and entrenchment, as well as the loss of predictability in protein evolution observed in deep mutational scanning experiments of distant homologs. It thereby deepens our understanding of the interplay between mutation and selection in shaping protein diversity and functions, allows one to statistically forecast evolution, and challenges the prevailing independent-site models of protein evolution, which are unable to capture the fundamental importance of epistasis. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Oral delivery of proteins faces challenges due to the harsh conditions of the gastrointestinal (GI) tract, including gastric acid and intestinal enzyme degradation. Permeation enhancers are limited in their ability to deliver proteins with high molecular weight and can potentially cause toxicity by opening tight junctions. To overcome these challenges, we propose the use of montmorillonite (MMT) as an adjuvant that possesses both inflammation-oriented abilities and the ability to regulate gut microbiota. This adjuvant can be used as a universal protein oral delivery technology by fusing with advantageous binding amino acid sequences. We demonstrated that anti-TNF-α nanobody (VII) can be intercalated into the MMT interlayer space. The carboxylate groups (-COOH) of aspartic acid (D) and glutamic acid (E) interact with the MMT surface through electrostatic interactions with sodium ions (Na+). The amino groups (NH2) of asparagine (N) and glutamine (Q) are primarily attracted to the MMT layers through hydrogen bonding with oxygen atoms on the surface. This binding mechanism protects VII from degradation and ensures its release in the intestinal tract, as well as retaining biological activity, leading to significantly enhanced therapeutic effects on colitis. Furthermore, VII@MMT increases the abundance of short-chain fatty acids (SCFAs)-producing strains, including Clostridia, Prevotellaceae, Alloprevotella, Oscillospiraceae, Clostridia_vadinBB60_group, and Ruminococcaceae, therefore enhance the production of SCFAs and butyrate, inducing regulatory T cells (Tregs) production to modulate local and systemic immune homeostasis. Overall, the MMT adjuvant provides a promising universal strategy for protein oral delivery by rational designed protein. [ABSTRACT FROM AUTHOR]

TORC1 (target of rapamycin complex 1) is a highly conserved protein kinase that plays a central role in regulating cell growth. Given the role of mammalian TORC1 (mTORC1) in metabolism and disease, understanding mTORC1 downstream signaling and feedback loops is important. mTORC1 recognizes some of its substrates via a five amino acid binding sequence called the TOR signaling (TOS) motif. mTORC1 binding to a TOS motif facilitates phosphorylation of a distinct, distal site. Here, we show that LST2, also known as ZFYVE28, contains a TOS motif (amino acids 401 to 405) and is directly phosphorylated by mTORC1 at serine 670 (S670). mTORC1-mediated S670 phosphorylation promotes LST2 monoubiquitination on lysine 87 (K87). Monoubiquitinated LST2 is stable and displays a broad reticular distribution. When mTORC1 is inactive, unphosphorylated LST2 is degraded by the proteasome. The absence of LST2 enhances EGFR (epidermal growth factor receptor) signaling. We propose that mTORC1 negatively feeds back on its upstream receptor EGFR via LST2. [ABSTRACT FROM AUTHOR]

The process of protein phase separation into liquid condensates has been implicated in the formation of membraneless organelles (MLOs), which selectively concentrate biomolecules to perform essential cellular functions. Although the importance of this process in health and disease is increasingly recognized, the experimental identification of proteins forming MLOs remains a complex challenge. In this study, we addressed this problem by harnessing the power of AlphaFold2 to perform computational predictions of the conformational properties of proteins from their amino acid sequences. We thus developed the CoDropleT (co-condensation into droplet transformer) method of predicting the propensity of co-condensation of protein pairs. The method was trained by combining experimental datasets of co-condensing proteins from the CD-CODE database with curated negative datasets of non-co-condensing proteins. To illustrate the performance of the method, we applied it to estimate the propensity of proteins to co-condense into MLOs. Our results suggest that CoDropleT could facilitate functional and therapeutic studies on protein condensation by predicting the composition of protein condensates. [ABSTRACT FROM AUTHOR]

How did specific useful protein sequences arise from simpler molecules at the origin of life? This seemingly needlein-a-haystack problem has remarkably close resemblance to the old Protein Folding Problem, for which the solution is now known from statistical physics. Based on the logic that Origins must have come only after there was an operative evolution mechanism--which selects on phenotype, not genotype--we give a perspective that proteins and their folding processes are likely to have been the primary driver of the early stages of the origin of life. [ABSTRACT FROM AUTHOR]

Proteins play a key role in biological electron transport, but the structure-function relationships governing the electronic properties of peptides are not fully understood. Despite recent progress, understanding the link between peptide conformational flexibility, hierarchical structures, and electron transport pathways has been challenging. Here, we use single-molecule experiments, molecular dynamics (MD) simulations, nonequilibrium Green's function-density functional theory (NEGF-DFT), and unsupervised machine learning to understand the role of secondary structure on electron transport in peptides. Our results reveal a two-state molecular conductance behavior for peptides across several different amino acid sequences. MD simulations and Gaussian mixture modeling are used to show that this two-state molecular conductance behavior arises due to the conformational flexibility of peptide backbones, with a high-conductance state arising due to a more defined secondary structure (beta turn or 310 helices) and a low-conductance state occurring for extended peptide structures. These results highlight the importance of helical conformations on electron transport in peptides. Conformer selection for the peptide structures is rationalized using principal component analysis of intramolecular hydrogen bonding distances along peptide backbones. Molecular conformations from MD simulations are used to model charge transport in NEGF-DFT calculations, and the results are in reasonable qualitative agreement with experiments. Projected density of states calculations and molecular orbital visualizations are further used to understand the role of amino acid side chains on transport. Overall, our results show that secondary structure plays a key role in electron transport in peptides, which provides broad avenues for understanding the electronic properties of proteins. [ABSTRACT FROM AUTHOR]

Reptilian skin coloration is spectacular and diverse, yet little is known about the ontogenetic processes that govern its establishment and the molecular signaling pathways that determine it. Here, we focus on the development of the banded pattern of leopard gecko hatchlings and the transition to black spots in the adult. With our histological analyses, we show that iridophores are present in the white and yellow bands of the hatchling and they gradually perish in the adult skin. Furthermore, we demonstrate that melanophores can autonomously form spots in the absence of the other chromatophores both on the regenerated skin of the tail and on the dorsal skin of the Mack Super Snow (MSS) leopard geckos. This color morph is characterized by uniform black coloration in hatchlings and black spots in adulthood; we establish that their skin is devoid of xanthophores and iridophores at both stages. Our genetic analyses identified a 13-nucleotide deletion in the PAX7 transcription factor of MSS geckos, affecting its protein coding sequence. With our single-cell transcriptomics analysis of embryonic skin, we confirm that PAX7 is expressed in iridophores and xanthophores, suggesting that it plays a key role in the differentiation of both chromatophores. Our in situ hybridizations on whole-mount embryos document the dynamics of the skin pattern formation and how it is impacted in the PAX7 mutants. We hypothesize that the melanophores-iridophores interactions give rise to the banded pattern of the hatchlings and black spot formation is an intrinsic capacity of melanophores in the postembryonic skin. [ABSTRACT FROM AUTHOR]

Protein folding and evolution are intimately linked phenomena. Here, we revisit the concept of exons as potential protein folding modules across a set of 38 abundant and conserved protein families. Taking advantage of genomic exon-intron organization and extensive protein sequence data, we explore exon boundary conservation and assess the foldon-like behavior of exons using energy landscape theoretic measurements. We found deviations in the exon size distribution from exponential decay indicating selection in evolution. We show that when taken together there is a pronounced tendency to independent foldability for segments corresponding to the more conserved exons, supporting the idea of exon-foldon correspondence. While 45% of the families follow this general trend when analyzed individually, there are some families for which other stronger functional determinants, such as preserving frustrated active sites, may be acting. We further develop a systematic partitioning of protein domains using exon boundary hotspots, showing that minimal common exons correspond with uninterrupted alpha and/or beta elements for the majority of the families but not for all of them. [ABSTRACT FROM AUTHOR]

The prediction of protein 3D structure from amino acid sequence is a computational grand challenge in biophysics and plays a key role in robust protein structure prediction algorithms, from drug discovery to genome interpretation. The advent of AI models, such as AlphaFold, is revolutionizing applications that depend on robust protein structure prediction algorithms. To maximize the impact, and ease the usability, of these AI tools we introduce APACE, AlphaFold2 and advanced computing as a service, a computational framework that effectively handles this AI model and its TB-size database to conduct accelerated protein structure prediction analyses in modern supercomputing environments. We deployed APACE in the Delta and Polaris supercomputers and quantified its performance for accurate protein structure predictions using four exemplar proteins: 6AWO, 6OAN, 7MEZ, and 6D6U. Using up to 300 ensembles, distributed across 200 NVIDIA A100 GPUs, we found that APACE is up to two orders of magnitude faster than off-the-self AlphaFold2 implementations, reducing time-to-solution from weeks to minutes. This computational approach may be readily linked with robotics laboratories to automate and accelerate scientific discovery. [ABSTRACT FROM AUTHOR]

Predicting which proteins interact together from amino acid sequences is an important task. We develop a method to pair interacting protein sequences which leverages the power of protein language models trained on multiple sequence alignments (MSAs), such as MSA Transformer and the EvoFormer module of AlphaFold. We formulate the problem of pairing interacting partners among the paralogs of two protein families in a differentiable way. We introduce a method called Differentiable Pairing using Alignment-based Language Models (DiffPALM) that solves it by exploiting the ability of MSA Transformer to fill in masked amino acids in multiple sequence alignments using the surrounding context. MSA Transformer encodes coevolution between functionally or structurally coupled amino acids within protein chains. It also captures inter-chain coevolution, despite being trained on single-chain data. Relying on MSA Transformer without fine-tuning, DiffPALM outperforms existing coevolutionbased pairing methods on difficult benchmarks of shallow multiple sequence alignments extracted from ubiquitous prokaryotic protein datasets. It also outperforms an alternative method based on a state-of-the-art protein language model trained on single sequences. Paired alignments of interacting protein sequences are a crucial ingredient of supervised deep learning methods to predict the three-dimensional structure of protein complexes. Starting from sequences paired by DiffPALM substantially improves the structure prediction of some eukaryotic protein complexes by AlphaFold-Multimer. It also achieves competitive performance with using orthology-based pairing. [ABSTRACT FROM AUTHOR]

Proteins mediate their functions through chemical interactions; modeling these interactions, which are typically through sidechains, is an important need in protein design. However, constructing an all-atom generative model requires an appropriate scheme for managing the jointly continuous and discrete nature of proteins encoded in the structure and sequence. We describe an all-atom diffusion model of protein structure, Protpardelle, which represents all sidechain states at once as a "superposition" state; superpositions defining a protein are collapsed into individual residue types and conformations during sample generation. When combined with sequence design methods, our model is able to codesign all-atom protein structure and sequence. Generated proteins are of good quality under the typical quality, diversity, and novelty metrics, and sidechains reproduce the chemical features and behavior of natural proteins. Finally, we explore the potential of our model to conduct all-atom protein design and scaffold functional motifs in a backbone- and rotamer-free way. [ABSTRACT FROM AUTHOR]

Predicting the effects of one or more mutations to the in vivo or in vitro properties of a wild-type protein is a major computational challenge, due to the presence of epistasis, that is, of interactions between amino acids in the sequence. We introduce a computationally efficient procedure to build minimal epistatic models to predict mutational effects by combining evolutionary (homologous sequence) and few mutational-scan data. Mutagenesis measurements guide the selection of links in a sparse graphical model, while the parameters on the nodes and the edges are inferred from sequence data. We show, on 10 mutational scans, that our pipeline exhibits performances comparable to state-of-the-art deep networks trained on many more data, while requiring much less parameters and being hence more interpretable. In particular, the identified interactions adapt to the wild-type protein and to the fitness or biochemical property experimentally measured, mostly focus on key functional sites, and are not necessarily related to structural contacts. Therefore, our method is able to extract information relevant for one mutational experiment from homologous sequence data reflecting the multitude of structural and functional constraints acting on proteins throughout evolution. [ABSTRACT FROM AUTHOR]

Protein evolution is guided by structural, functional, and dynamical constraints ensuring organismal viability. Pseudogenes are genomic sequences identified in many eukaryotes that lack translational activity due to sequence degradation and thus over time have undergone "devolution." Previously pseudogenized genes sometimes regain their protein-coding function, suggesting they may still encode robust folding energy landscapes despite multiple mutations. We study both the physical folding landscapes of protein sequences corresponding to human pseudogenes using the Associative Memory, Water Mediated, Structure and Energy Model, and the evolutionary energy landscapes obtained using direct coupling analysis (DCA) on their parent protein families. We found that generally mutations that have occurred in pseudogene sequences have disrupted their native global network of stabilizing residue interactions, making it harder for them to fold if they were translated. In some cases, however, energetic frustration has apparently decreased when the functional constraints were removed. We analyzed this unexpected situation for Cyclophilin A, Profilin-1, and Small Ubiquitin-like Modifier 2 Protein. Our analysis reveals that when such mutations in the pseudogene ultimately stabilize folding, at the same time, they likely alter the pseudogenes' former biological activity, as estimated by DCA. We localize most of these stabilizing mutations generally to normally frustrated regions required for binding to other partners. [ABSTRACT FROM AUTHOR]

Protein capsids are a widespread form of compartmentalization in nature. Icosahedral symmetry is ubiquitous in capsids derived from spherical viruses, as this geometry maximizes the internal volume that can be enclosed within. Despite the strong preference for icosahedral symmetry, we show that simple point mutations in a virus-like capsid can drive the assembly of unique symmetry-reduced structures. Starting with the encapsulin from Myxococcusxanthus, a 180-mer bacterial capsid that adopts the well-studied viral HK97 fold, we use mass photometry and native charge detection mass spectrometry to identify a triple histidine point mutant that forms smaller dimorphic assemblies. Using cryoelectron microscopy, we determine the structures of a precedented 60-mer icosahedral assembly and an unexpected 36-mer tetrahedron that features significant geometric rearrangements around a new interaction surface between capsid protomers. We subsequently find that the tetrahedral assembly can be generated by triple-point mutation to various amino acids and that even a single histidine point mutation is sufficient to form tetrahedra. These findings represent a unique example of tetrahedral geometry when surveying all characterized encapsulins, HK97-like capsids, or indeed any virus-derived capsids reported in the Protein Data Bank, revealing the surprising plasticity of capsid self-assembly that can be accessed through minimal changes in the protein sequence. [ABSTRACT FROM AUTHOR]

Proteins carrying a signal peptide and/or a transmembrane domain enter the intracellular secretory pathway at the endoplasmic reticulum (ER) and are transported to the Golgi apparatus via COPII vesicles or tubules. SAR1 initiates COPII coat assembly by recruiting other coat proteins to the ER membrane. Mammalian genomes encode two SAR1 paralogs, SAR1A and SAR1B. While these paralogs exhibit ~90% amino acid sequence identity, it is unknown whether they perform distinct or overlapping functions in vivo. We now report that genetic inactivation of Sar1a in mice results in lethality during midembryogenesis. We also confirm previous reports that complete deficiency of murine Sar1b results in perinatal lethality. In contrast, we demonstrate that deletion of Sar1b restricted to hepatocytes is compatible with survival, though resulting in hypocholesterolemia that can be rescued by adenovirus-mediated overexpression of either SAR1A or SAR1B. To further examine the in vivo function of these two paralogs, we genetically engineered mice with the Sar1a coding sequence replacing that of Sar1b at the endogenous Sar1b locus. Mice homozygous for this allele survive to adulthood and are phenotypically normal, demonstrating complete or near-complete overlap in function between the two SAR1 protein paralogs in mice. These data also suggest upregulation of SAR1A gene expression as a potential approach for the treatment of SAR1B deficiency (chylomicron retention disease) in humans. [ABSTRACT FROM AUTHOR]

The design of protein-protein interfaces using physics-based design methods such as Rosetta requires substantial computational resources and manual refinement by expert structural biologists. Deep learning methods promise to simplify protein--protein interface design and enable its application to a wide variety of problems by researchers from various scientific disciplines. Here, we test the ability of a deep learning method for protein sequence design, ProteinMPNN, to design two-component tetrahedral protein nanomaterials and benchmark its performance against Rosetta. ProteinMPNN had a similar success rate to Rosetta, yielding 13 new experimentally confirmed assemblies, but required orders of magnitude less computation and no manual refinement. The interfaces designed by ProteinMPNN were substantially more polar than those designed by Rosetta, which facilitated in vitro assembly of the designed nanomaterials from independently purified components. Crystal structures of several of the assemblies confirmed the accuracy of the design method at high resolution. Our results showcase the potential of deep learning--based methods to unlock the widespread application of designed protein--protein interfaces and self-assembling protein nanomaterials in biotechnology. [ABSTRACT FROM AUTHOR]

Sponges (Porifera) contain many peptide-specialized metabolites with potent biological activities and significant roles in shaping marine ecology. It is well established that symbiotic bacteria produce bioactive "sponge" peptides, both on the ribosome (RiPPs) and nonribosomally. Here, we demonstrate that sponges themselves also produce many bioactive macrocyclic peptides, such as phakellistatins and related proline-rich macrocyclic peptides (PRMPs). Using the Stylissa carteri sponge transcriptome, methods were developed to find sequences encoding 46 distinct RiPP-type core peptides, of which ten encoded previously identified PRMP sequences. With this basis set, the genome and transcriptome of the sponge Axinella corrugata was interrogated to find 35 PRMP precursor peptides encoding 31 unique core peptide sequences. At least 11 of these produced cyclic peptides that were present in the sponge and could be characterized by mass spectrometry, including stylissamides A-D and seven previously undescribed compounds. Precursor peptides were encoded in the A. corrugata genome, confirm-ing their animal origin. The peptides contained signal peptide sequences and highly repetitive recognition sequence-core peptide elements with up to 25 PRMP copies in a single precursor. In comparison to sponges without PRMPs, PRMP sponges are incredibly enriched in potentially secreted polypeptides, with >23,000 individual signal peptide encoding genes found in a single transcriptome. The similarities between PRMP biosynthetic genes and neuropeptides in terms of their biosynthetic logic suggest a fundamental biology linked to circular peptides, possibly indicating a widespread and underappreciated diversity of signaling peptide post-translational modifications across the animal kingdom. [ABSTRACT FROM AUTHOR]

This article explores the topic of protein folding, which is essential for understanding biological processes in cells. Proteins need to fold into specific three-dimensional structures to function properly, and misfolded proteins can lead to diseases like Alzheimer's and Parkinson's. Recent advancements in AI-based methods, such as DeepMind's AlphaFold, have allowed for accurate predictions of protein structures. The article discusses the impact of these advancements and the collaboration between experimental and computational approaches in protein folding research. It introduces a new method called CoDropleT, which uses deep learning to extract protein properties from their amino acid sequences. The article also presents experimental validation of a predicted protein complex structure and discusses other studies on protein aggregation, external forces on protein stability, and the role of evolution in protein unfolding. The collection of articles highlights the interdisciplinary nature of protein folding research and the promising future of protein science. [Extracted from the article]

Amino acid mutations that lower a protein's thermodynamic stability are implicated in numerous diseases, and engineered proteins with enhanced stability can be important in research and medicine. Computational methods for predicting how mutations perturb protein stability are, therefore, of great interest. Despite recent advancements in protein design using deep learning, in silico prediction of stability changes has remained challenging, in part due to a lack of large, high-quality training datasets for model development. Here, we describe ThermoMPNN, a deep neural network trained to predict stability changes for protein point mutations given an initial structure. In doing so, we demonstrate the utility of a recently released megascale stability dataset for training a robust stability model. We also employ transfer learning to leverage a second, larger dataset by using learned features extracted from ProteinMPNN, a deep neural network trained to predict a protein's amino acid sequence given its three-dimensional structure. We show that our method achieves state-of-the-art performance on established benchmark datasets using a lightweight model architecture that allows for rapid, scalable predictions. Finally, we make ThermoMPNN readily available as a tool for stability prediction and design. [ABSTRACT FROM AUTHOR]