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The behavior of hydration water around familial Parkinson's disease linked mutants of α-synuclein may be linked to the early-onset of Parkinson's disease. For the first time, this study compares the local structure and dynamics of hydration water around different segments of some of the natural mutants of α-synuclein, i.e., E46K, G51D, A30P, and A53E, with that of the wild-type protein through explicit water MD simulations. The results show that the C-terminal segments of the fast aggregating mutants such as E46K and A30P are less exposed to water, while those of the slow aggregating ones such as A53E and G51D are more exposed to water relative to that of the wild-type protein. In addition, the water molecules are found to be more ordered around the C-terminal segment of the A53E and G51D mutants as compared to the wild-type protein. This is due to an increase in the overall charge of α-syn upon A53E and G51D mutations. The translational and rotational motions of water molecules in the hydration shell of the C-terminal segment of A53E and G51D mutants are found to be faster relative to that of the wild-type protein. This study validates the differential hydration environment around the C-terminal segment for the causative and protective mutants of α-synuclein. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate four decontamination strategies utilizing enzymatic agents available in most clinical settings to determine (1) the amount of biomaterial that can be removed in a group of previously used healing abutments (uHAs) and (2) the degree to which the decontaminated HAs are capable of inducing an inflammatory response in vitro compared to new HAs. Materials and Methods: In total, 50 HAs were collected following 2 to 4 weeks of intraoral use and distributed randomly into five test groups (groups A-E; n = 10 per group). Group A used enzymatic cleaner foam and an autoclave. Group B used an ultrasonic bath with enzymatic cleaner and an autoclave. Group C used a prophy jet, enzymatic cleaner foam, and an autoclave. Group D used a prophy jet, an ultrasonic bath with enzymatic cleaner, and an autoclave. Lastly, group E used a prophy jet and an autoclave. The control group consisted of 10 new and sterile HAs. Residual protein concentration was determined by a Micro BCA protein assay (Thermo Fisher Scientific), while HAs from each group were stained with Phloxine B and macroscopically examined for the presence of debris. To examine the inflammatory potential, human primary macrophages were exposed to HAs, and supernatant levels of nine cytokine and chemokine profiles were analyzed using a multiplex bead assay. Results: All test groups showed differences in the degree of visual decontamination compared to controls. Groups D and E displayed the most effective surface debris removal and reduced protein concentration, while group A was the least effective. However, compared to controls, all test groups showed high levels of inflammatory cytokine secretion via multiplex assay for up to 5 days. Conclusions: Our study found that decontamination of uHAs utilizing enzymatic cleaners failed to reestablish inert HA surfaces and prevent an inflammatory immune response in vitro. Clinicians should not reuse HAs even after attempts to decontaminate and sterilize HA surfaces. [ABSTRACT FROM AUTHOR]

Mass spectrometry-based methods can provide a global expression profile and structural readout of proteins in complex systems. Preserving the in vivo conformation of proteins in their innate state is challenging during proteomic experiments. Here, we introduce a whole animal in vivo protein footprinting method using perfusion of reagents to add dimethyl labels to exposed lysine residues on intact proteins which provides information about protein conformation. When this approach is used to measure dynamic structural changes during Alzheimers disease (AD) progression in a mouse model, we detect 433 proteins that undergo structural changes attributed to AD, independent of aging, across 7 tissues. We identify structural changes of co-expressed proteins and link the communities of these proteins to their biological functions. Our findings show that structural alterations of proteins precede changes in expression, thereby demonstrating the value of in vivo protein conformation measurement. Our method represents a strategy for untangling mechanisms of proteostasis dysfunction caused by protein misfolding. In vivo whole-animal footprinting should have broad applicability for discovering conformational changes in systemic diseases and for the design of therapeutic interventions.

Make-on-demand chemical libraries have drastically increased the reach of molecular docking, with the enumerated ready-to-dock ZINC-22 library approaching 6.4 billion molecules (July 2024). While ever-growing libraries result in better-scoring molecules, the computational resources required to dock all of ZINC-22 make this endeavor infeasible for most. Here, we organize and traverse chemical space with hierarchical navigable small-world graphs, a method we term retrieval augmented docking (RAD). RAD recovers most virtual actives, despite docking only a fraction of the library. Furthermore, RAD is protein-agnostic, supporting additional docking campaigns without additional computational overhead. In depth, we assess RAD on published large-scale docking campaigns against D4 and AmpC spanning 99.5 million and 138 million molecules, respectively. RAD recovers 95% of DOCK virtual actives for both targets after evaluating only 10% of the libraries. In breadth, RAD shows widespread applicability against 43 DUDE-Z proteins, evaluating 50.3 million associations. On average, RAD recovers 87% of virtual actives while docking 10% of the library without sacrificing chemical diversity.

We study, through molecular dynamics simulations, three aqueous solutions with one lysozyme protein and three different concentrations of trehalose and dimethyl sulfoxide (DMSO). We analyze the structural and dynamical properties of the protein hydration water upon cooling. We find that trehalose plays a major role in modifying the structure of the network of HBs between water molecules in the hydration layer of the protein. The dynamics of hydration water presents, in addition to the α-relaxation, typical of glass formers, a slower long-time relaxation process, which greatly slows down the dynamics of water, particularly in the systems with trehalose, where it becomes dominant at low temperatures. In all the solutions, we observe, from the behavior of the α-relaxation times, a shift of the Mode Coupling Theory crossover temperature and the fragile-to-strong crossover temperature toward higher values with respect to bulk water. We also observe a strong-to-strong crossover from the temperature behavior of the long-relaxation times. In the aqueous solution with only DMSO, the transition shifts to a lower temperature than in the case with only lysozyme reported in the literature. We observe that the addition of trehalose to the mixture has the opposite effect of restoring the original location of the strong-to-strong crossover. In all the solutions analyzed in this work, the observed temperature of the protein dynamical transition is slightly shifted at lower temperatures than that of the strong-to-strong crossover, but their relative order is the same, showing a correlation between the motion of the protein and that of the hydration water. [ABSTRACT FROM AUTHOR]

Cysteine is the most reactive naturally occurring amino acid due to the presence of a free thiol, presenting a tantalizing handle for covalent modification of peptides/proteins. Although many mass spectrometry experiments could benefit from site-specific modification of Cys, the utility of direct arylation has not been thoroughly explored. Recently, Spokoyny and co-workers reported a Au(III) organometallic reagent that robustly arylates Cys and tolerates a wide variety of solvents and conditions. Given the chromophoric nature of aryl groups and the known susceptibility of carbon-sulfur bonds to photodissociation, we set out to identify an aryl group that could efficiently cleave Cys carbon-sulfur bonds at 266 nm. A streamlined workflow was developed to facilitate rapid examination of a large number of aryls with minimal sample using a simple test peptide, RAAACGVLK. We were able to identify several aryl groups that yield abundant homolytic photodissociation of the adjacent Cys carbon-sulfur bonds with short activation times (

Protein tandem mass spectrometry (MS/MS) often generates sequence-informative fragments from backbone bond cleavages near the termini. This lack of fragmentation in the protein interior is particularly apparent in native top-down mass spectrometry (MS). Improved sequence coverage, critical for reliable annotation of posttranslational modifications and sequence variants, may be obtained from internal fragments generated by multiple backbone cleavage events. However, internal fragment assignments can be error prone due to isomeric/isobaric fragments from different parts of a protein sequence. Also, internal fragment generation propensity depends on the chosen MS/MS activation strategy. Here, we examine internal fragment formation in electron capture dissociation (ECD) and electron transfer dissociation (ETD) following native and denaturing MS, as well as LC/MS of several proteins. Experiments were undertaken on multiple instruments, including quadrupole time-of-flight, Orbitrap, and high-field Fourier-transform ion cyclotron resonance (FT-ICR) across four laboratories. ECD was performed at both ultrahigh vacuum and at similar pressure to ETD conditions. Two complementary software packages were used for data analysis. When feasible, ETD-higher energy collision dissociation MS3 was performed to validate/refute potential internal fragment assignments, including differentiating MS3 fragmentation behavior of radical versus even-electron primary fragments. We show that, under typical operating conditions, internal fragments cannot be confidently assigned in ECD or ETD. On the other hand, such fragments, along with some b-type terminal fragments (not typically observed in ECD/ETD spectra) appear at atypical ECD operating conditions, suggesting they originate from a separate ion-electron activation process. Furthermore, atypical fragment ion types, e.g., x ions, are observed at such conditions as well as upon EThcD, presumably due to vibrational activation of radical z-type ions.

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.

IHMCIF (github.com/ihmwg/IHMCIF) is a data information framework that supports archiving and disseminating macromolecular structures determined by integrative or hybrid modeling (IHM), and making them Findable, Accessible, Interoperable, and Reusable (FAIR). IHMCIF is an extension of the Protein Data Bank Exchange/macromolecular Crystallographic Information Framework (PDBx/mmCIF) that serves as the framework for the Protein Data Bank (PDB) to archive experimentally determined atomic structures of biological macromolecules and their complexes with one another and small molecule ligands (e.g., enzyme cofactors and drugs). IHMCIF serves as the foundational data standard for the PDB-Dev prototype system, developed for archiving and disseminating integrative structures. It utilizes a flexible data representation to describe integrative structures that span multiple spatiotemporal scales and structural states with definitions for restraints from a variety of experimental methods contributing to integrative structural biology. The IHMCIF extension was created with the benefit of considerable community input and recommendations gathered by the Worldwide Protein Data Bank (wwPDB) Task Force for Integrative or Hybrid Methods (wwpdb.org/task/hybrid). Herein, we describe the development of IHMCIF to support evolving methodologies and ongoing advancements in integrative structural biology. Ultimately, IHMCIF will facilitate the unification of PDB-Dev data and tools with the PDB archive so that integrative structures can be archived and disseminated through PDB.

Cryo-electron microscopy (cryo-EM) has been widely used to reveal the structures of proteins at atomic resolution. One key challenge is that almost all proteins are predominantly adsorbed to the air-water interface during standard cryo-EM specimen preparation. The interaction of proteins with air-water interface will significantly impede the success of reconstruction and achievable resolution. Here, we highlight the critical role of impenetrable surfactant monolayers in passivating the air-water interface problems, and develop a robust effective method for high-resolution cryo-EM analysis, by using the superstructure GSAMs which comprises surfactant self-assembled monolayers (SAMs) and graphene membrane. The GSAMs works well in enriching the orientations and improving particle utilization ratio of multiple proteins, facilitating the 3.3-Å resolution reconstruction of a 100-kDa protein complex (ACE2-RBD), which shows strong preferential orientation using traditional specimen preparation protocol. Additionally, we demonstrate that GSAMs enables the successful determinations of small proteins (

How did specific useful protein sequences arise from simpler molecules at the origin of life? This seemingly needle-in-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.

Protein-protein interactions (PPIs) are at the heart of the molecular landscape permeating life. Proteomics studies can explore this protein interaction landscape using mass spectrometry (MS). Thanks to their high sensitivity, mass spectrometers can easily identify thousands of proteins within a single sample, but that same sensitivity generates tangled spiderwebs of data that hide biologically relevant findings. So, what does a researcher do when she finds herself walking into spiderwebs? In a field focused on discovery, MS data require rigor in their analysis, experimental validation, or a combination of both. In this Review, we provide a brief primer on MS-based experimental methods to identify PPIs. We discuss approaches to analyze the resulting data and remove the proteomic background. We consider the advantages between comprehensive and targeted studies. We also discuss how scoring might be improved through AI-based protein structure information. Women have been essential to the development of proteomics, so we will specifically highlight work by women that has made this field thrive in recent years.

Computational protein design is advancing rapidly. Here we describe efficient routes starting from validated parallel and antiparallel peptide assemblies to design two families of α-helical barrel proteins with central channels that bind small molecules. Computational designs are seeded by the sequences and structures of defined de novo oligomeric barrel-forming peptides, and adjacent helices are connected by loop building. For targets with antiparallel helices, short loops are sufficient. However, targets with parallel helices require longer connectors; namely, an outer layer of helix-turn-helix-turn-helix motifs that are packed onto the barrels. Throughout these computational pipelines, residues that define open states of the barrels are maintained. This minimizes sequence sampling, accelerating the design process. For each of six targets, just two to six synthetic genes are made for expression in Escherichia coli. On average, 70% of these genes express to give soluble monomeric proteins that are fully characterized, including high-resolution structures for most targets that match the design models with high accuracy.

Protein-protein interactions (PPIs) are important for many biological processes, but predicting them from sequence data remains challenging. Existing deep learning models often cannot generalize to proteins not present in the training set and do not provide uncertainty estimates for their predictions. To address these limitations, we present TUnA, a Transformer-based uncertainty-aware model for PPI prediction. TUnA uses ESM-2 embeddings with Transformer encoders and incorporates a Spectral-normalized Neural Gaussian Process. TUnA achieves state-of-the-art performance and, importantly, evaluates uncertainty for unseen sequences. We demonstrate that TUnAs uncertainty estimates can effectively identify the most reliable predictions, significantly reducing false positives. This capability is crucial in bridging the gap between computational predictions and experimental validation.

Message passing neural networks (MPNNs) on molecular graphs generate continuous and differentiable encodings of small molecules with state-of-the-art performance on protein-ligand complex scoring tasks. Here, we describe the proximity graph network (PGN) package, an open-source toolkit that constructs ligand-receptor graphs based on atom proximity and allows users to rapidly apply and evaluate MPNN architectures for a broad range of tasks. We demonstrate the utility of PGN by introducing benchmarks for affinity and docking score prediction tasks. Graph networks generalize better than fingerprint-based models and perform strongly for the docking score prediction task. Overall, MPNNs with proximity graph data structures augment the prediction of ligand-receptor complex properties when ligand-receptor data are available.

Interactions among biomacromolecules, predominantly noncovalent, underpin biological processes. However, recent advancements in biospecific chemistry have enabled the creation of specific covalent bonds between biomolecules, both in vitro and in vivo. This Review traces the evolution of biospecific chemistry in proteins, emphasizing the role of genetically encoded latent bioreactive amino acids. These amino acids react selectively with adjacent natural groups through proximity-enabled bioreactivity, enabling targeted covalent linkages. We explore various latent bioreactive amino acids designed to target different protein residues, ribonucleic acids, and carbohydrates. We then discuss how these novel covalent linkages can drive challenging protein properties and capture transient protein-protein and protein-RNA interactions in vivo. Additionally, we examine the application of covalent peptides as potential therapeutic agents and site-specific conjugates for native antibodies, highlighting their capacity to form stable linkages with target molecules. A significant focus is placed on proximity-enabled reactive therapeutics (PERx), a pioneering technology in covalent protein therapeutics. We detail its wide-ranging applications in immunotherapy, viral neutralization, and targeted radionuclide therapy. Finally, we present a perspective on the existing challenges within biospecific chemistry and discuss the potential avenues for future exploration and advancement in this rapidly evolving field.

In drug discovery, the in silico prediction of binding affinity is one of the major means to prioritize compounds for synthesis. Alchemical relative binding free energy (RBFE) calculations based on molecular dynamics (MD) simulations are nowadays a popular approach for the accurate affinity ranking of compounds. MD simulations rely on empirical force field parameters, which strongly influence the accuracy of the predicted affinities. Here, we evaluate the ability of six different small-molecule force fields to predict experimental protein-ligand binding affinities in RBFE calculations on a set of 598 ligands and 22 protein targets. The public force fields OpenFF Parsley and Sage, GAFF, and CGenFF show comparable accuracy, while OPLS3e is significantly more accurate. However, a consensus approach using Sage, GAFF, and CGenFF leads to accuracy comparable to OPLS3e. While Parsley and Sage are performing comparably based on aggregated statistics across the whole dataset, there are differences in terms of outliers. Analysis of the force field reveals that improved parameters lead to significant improvement in the accuracy of affinity predictions on subsets of the dataset involving those parameters. Lower accuracy can not only be attributed to the force field parameters but is also dependent on input preparation and sampling convergence of the calculations. Especially large perturbations and nonconverged simulations lead to less accurate predictions. The input structures, Gromacs force field files, as well as the analysis Python notebooks are available on GitHub.

Protein stability plays a crucial role in a variety of applications, such as food processing, therapeutics, and the identification of pathogenic mutations. Engineering campaigns commonly seek to improve protein stability, and there is a strong interest in streamlining these processes to enable rapid optimization of highly stabilized proteins with fewer iterations. In this work, we explore utilizing a mega-scale dataset to develop a protein language model optimized for stability prediction. ESMtherm is trained on the folding stability of 528k natural and de novo sequences derived from 461 protein domains and can accommodate deletions, insertions, and multiple-point mutations. We show that a protein language model can be fine-tuned to predict folding stability. ESMtherm performs reasonably on small protein domains and generalizes to sequences distal from the training set. Lastly, we discuss our models limitations compared to other state-of-the-art methods in generalizing to larger protein scaffolds. Our results highlight the need for large-scale stability measurements on a diverse dataset that mirrors the distribution of sequence lengths commonly observed in nature.

In the folded state, biomolecules exchange between multiple conformational states crucial for their function. However, most structural models derived from experiments and computational predictions only encode a single state. To represent biomolecules accurately, we must move towards modeling and predicting structural ensembles. Information about structural ensembles exists within experimental data from X-ray crystallography and cryo-electron microscopy. Although new tools are available to detect conformational and compositional heterogeneity within these ensembles, the legacy PDB data structure does not robustly encapsulate this complexity. We propose modifications to the macromolecular crystallographic information file (mmCIF) to improve the representation and interrelation of conformational and compositional heterogeneity. These modifications will enable the capture of macromolecular ensembles in a human and machine-interpretable way, potentially catalyzing breakthroughs for ensemble-function predictions, analogous to the achievements of AlphaFold with single-structure prediction.

MOTIVATION: Sidechain rotamer libraries of the common amino acids of a protein are useful for folded protein structure determination and for generating ensembles of intrinsically disordered proteins (IDPs). However, much of protein function is modulated beyond the translated sequence through the introduction of post-translational modifications (PTMs). RESULTS: In this work, we have provided a curated set of side chain rotamers for the most common PTMs derived from the RCSB PDB database, including phosphorylated, methylated, and acetylated sidechains. Our rotamer libraries improve upon existing methods such as SIDEpro, Rosetta, and AlphaFold3 in predicting the experimental structures for PTMs in folded proteins. In addition, we showcase our PTM libraries in full use by generating ensembles with the Monte Carlo Side Chain Entropy (MCSCE) for folded proteins, and combining MCSCE with the Local Disordered Region Sampling algorithms within IDPConformerGenerator for proteins with intrinsically disordered regions. AVAILABILITY AND IMPLEMENTATION: The codes for dihedral angle computations and library creation are available at https://github.com/THGLab/ptm_sc.git.

Protein conformational changes play crucial roles in their biological functions. In recent years, the Markov State Model (MSM) constructed from extensive Molecular Dynamics (MD) simulations has emerged as a powerful tool for modeling complex protein conformational changes. In MSMs, dynamics are modeled as a sequence of Markovian transitions among metastable conformational states at discrete time intervals (called lag time). A major challenge for MSMs is that the lag time must be long enough to allow transitions among states to become memoryless (or Markovian). However, this lag time is constrained by the length of individual MD simulations available to track these transitions. To address this challenge, we have recently developed Generalized Master Equation (GME)-based approaches, encoding non-Markovian dynamics using a time-dependent memory kernel. In this Tutorial, we introduce the theory behind two recently developed GME-based non-Markovian dynamic models: the quasi-Markov State Model (qMSM) and the Integrative Generalized Master Equation (IGME). We subsequently outline the procedures for constructing these models and provide a step-by-step tutorial on applying qMSM and IGME to study two peptide systems: alanine dipeptide and villin headpiece. This Tutorial is available at https://github.com/xuhuihuang/GME%5ftutorials. The protocols detailed in this Tutorial aim to be accessible for non-experts interested in studying the biomolecular dynamics using these non-Markovian dynamic models. [ABSTRACT FROM AUTHOR]

Nuclear magnetic resonance (NMR) relaxation experiments shine light onto the dynamics of molecular systems in the picosecond to millisecond timescales. As these methods cannot provide an atomically resolved view of the motion of atoms, functional groups, or domains giving rise to such signals, relaxation techniques have been combined with molecular dynamics (MD) simulations to obtain mechanistic descriptions and gain insights into the functional role of side chain or domain motion. In this work, we present a comparison of five computational methods that permit the joint analysis of MD simulations and NMR relaxation experiments. We discuss their relative strengths and areas of applicability and demonstrate how they may be utilized to interpret the dynamics in MD simulations with the small protein ubiquitin as a test system. We focus on the aliphatic side chains given the rigidity of the backbone of this protein. We find encouraging agreement between experiment, Markov state models built in the χ1/χ2 rotamer space of isoleucine residues, explicit rotamer jump models, and a decomposition of the motion using ROMANCE. These methods allow us to ascribe the dynamics to specific rotamer jumps. Simulations with eight different combinations of force field and water model highlight how the different metrics may be employed to pinpoint force field deficiencies. Furthermore, the presented comparison offers a perspective on the utility of NMR relaxation to serve as validation data for the prediction of kinetics by state-of-the-art biomolecular force fields. [ABSTRACT FROM AUTHOR]

Differential scanning fluorimetry (DSF) is a method to determine the apparent melting temperature (Tma) of a purified protein. In DSF, the raw unfolding curves from which Tma is calculated vary widely in shape and complexity. However, the tools available for calculating Tma are only compatible with the simplest of DSF curves, hindering many otherwise straightforward applications of the technology. To overcome this limitation, we designed new mathematical models for Tma calculation that accommodate common forms of variation in DSF curves, including the number of transitions, the presence of high initial signal, and temperature-dependent signal decay. When tested these models against DSFbase, an open-source database of 6235 raw, real-life DSF curves, these models outperformed the existing standard approaches of sigmoid fitting and maximum of the first derivative. To make these models accessible, we created an open-source software and website, DSFworld (https://gestwickilab.shinyapps.io/dsfworld/). In addition to these improved fitting capabilities, DSFworld also includes features that overcome the practical limitations of many analysis workflows, including automatic reformatting of raw data exported from common qPCR instruments, labeling of data based on experimental variables, and flexible interactive plotting. We hope that DSFworld will enable more streamlined and accurate calculation of Tma values for DSF experiments.

Advances in machine learning have enabled sufficiently accurate predictions of protein structure to be used in macromolecular structure determination with crystallography and cryo-electron microscopy data. The Phenix software suite has AlphaFold predictions integrated into an automated pipeline that can start with an amino acid sequence and data, and automatically perform model-building and refinement to return a protein model fitted into the data. Due to the steep technical requirements of running AlphaFold efficiently, we have implemented a Phenix-AlphaFold webservice that enables all Phenix users to run AlphaFold predictions remotely from the Phenix GUI starting with the official 1.21 release. This webservice will be improved based on how it is used by the research community and the future research directions for Phenix.

The de novo design of small molecule-binding proteins has seen exciting recent progress; however, high-affinity binding and tunable specificity typically require laborious screening and optimization after computational design. We developed a computational procedure to design a protein that recognizes a common pharmacophore in a series of poly(ADP-ribose) polymerase-1 inhibitors. One of three designed proteins bound different inhibitors with affinities ranging from

The critical micelle concentration (CMC) is a crucial parameter in understanding the self-assembly behavior of surfactants. In this study, we combine simulation and experiment to demonstrate the predictive capability of molecularly informed field theories in estimating the CMC of biologically based protein surfactants. Our simulation approach combines the relative entropy coarse-graining of small-scale atomistic simulations with large-scale field-theoretic simulations, allowing us to efficiently compute the free energy of micelle formation necessary for the CMC calculation while preserving chemistry-specific information about the underlying surfactant building blocks. We apply this methodology to a unique intrinsically disordered protein platform capable of a wide variety of tailored sequences that enable tunable micelle self-assembly. The computational predictions of the CMC closely match experimental measurements, demonstrating the potential of molecularly informed field theories as a valuable tool to investigate self-assembly in bio-based macromolecules systematically. [ABSTRACT FROM AUTHOR]

Over the past two decades, the Khoury plate technique has been widely utilized with much success to facilitate bone augmentation in critically deficient bone areas. Simply, the technique includes harvesting autogenous bone plates and utilizing them with external screw fixation. The rigidity of the bone plates enclosing the bony defect on both the buccal and lingual surfaces allows for tension-free bone remodeling to occur, favoring an ideal bone-forming environment. Following, a 4- to 6-month healing period, a flap is raised, screws may be removed, and implants are placed. With advancements made in tissue engineering, a novel mineral-organic resorbable bone adhesive (MORBA) formulated from underwater biomimetic marine proteins has recently been utilized for immediate bone-to-bone or bone-to-metal fixation. MORBA is a synthetic, injectable, self-setting, and resorbable load-bearing adhesive biomaterial that exhibits over 300 pounds of fixation strength. The unique adhesive properties of MORBA enable it to immediately stabilize dental implants into host bone. The present article describes a novel modification of the Khoury plate technique that utilizes MORBA as a substitute for screws during bone plate fixation. A step-by-step protocol is described that utilizes this novel biomaterial, favoring a more biologic approach to this conventional technique. This novel modification is surgically easier to conduct and is a more biocompatible option that avoids screw fixation and removal. [ABSTRACT FROM AUTHOR]

Resistance training alongside proper protein intake is an important tool to mitigate the deleterious effects of the aging process on skeletal muscle tissue. However, it is not completely elucidated whether adding protein supplementation provides further improvements on body composition outcomes of older adults during resistance training programs. The present narrative review aimed to describe the most recent evidence regarding the effects of protein supplementation on both lean body mass and fat mass of older adults and to provide insight into how health professionals may use this information to properly design nutritional programs for this population. While individuals with previous training experience, not meeting daily protein requirement, or displaying frailty symptoms may experience some benefits, protein supplementation does not seem to provide further improvements in untrained and healthy subjects. Aspects such as the age of the individuals and the different characteristics of the supplementation protocols must be considered when making evidence-based decisions regarding this topic. [ABSTRACT FROM AUTHOR]

Vocal production learning (vocal learning) is a convergently evolved trait in vertebrates. To identify brain genomic elements associated with mammalian vocal learning, we integrated genomic, anatomical, and neurophysiological data from the Egyptian fruit bat (Rousettus aegyptiacus) with analyses of the genomes of 215 placental mammals. First, we identified a set of proteins evolving more slowly in vocal learners. Then, we discovered a vocal motor cortical region in the Egyptian fruit bat, an emergent vocal learner, and leveraged that knowledge to identify active cis-regulatory elements in the motor cortex of vocal learners. Machine learning methods applied to motor cortex open chromatin revealed 50 enhancers robustly associated with vocal learning whose activity tended to be lower in vocal learners. Our research implicates convergent losses of motor cortex regulatory elements in mammalian vocal learning evolution.

Enzymes populate ensembles of structures necessary for catalysis that are difficult to experimentally characterize. We use time-resolved mix-and-inject serial crystallography at an x-ray free electron laser to observe catalysis in a designed mutant isocyanide hydratase (ICH) enzyme that enhances sampling of important minor conformations. The active site exists in a mixture of conformations, and formation of the thioimidate intermediate selects for catalytically competent substates. The influence of cysteine ionization on the ICH ensemble is validated by determining structures of the enzyme at multiple pH values. Large molecular dynamics simulations in crystallo and time-resolved electron density maps show that Asp17 ionizes during catalysis and causes conformational changes that propagate across the dimer, permitting water to enter the active site for intermediate hydrolysis. ICH exhibits a tight coupling between ionization of active site residues and catalysis-activated protein motions, exemplifying a mechanism of electrostatic control of enzyme dynamics.

Network alignment aims to uncover topologically similar regions in the protein-protein interaction (PPI) networks of two or more species under the assumption that topologically similar regions tend to perform similar functions. Although there exist a plethora of both network alignment algorithms and measures of topological similarity, currently no gold standard exists for evaluating how well either is able to uncover functionally similar regions. Here we propose a formal, mathematically and statistically rigorous method for evaluating the statistical significance of shared GO terms in a global, 1-to-1 alignment between two PPI networks. Given an alignment in which k aligned protein pairs share a particular GO term g, we use a combinatorial argument to precisely quantify the p-value of that alignment with respect to g compared to a random alignment. The p-value of the alignment with respect to all GO terms, including their inter-relationships, is approximated using the Empirical Browns Method. We note that, just as with BLASTs p-values, this method is not designed to guide an alignment algorithm towards a solution; instead, just as with BLAST, an alignment is guided by a scoring matrix or function; the p-values herein are computed after the fact, providing independent feedback to the user on the biological quality of the alignment that was generated by optimizing the scoring function. Importantly, we demonstrate that among all GO-based measures of network alignments, ours is the only one that correlates with the precision of GO annotation predictions, paving the way for network alignment-based protein function prediction.

A wooden house frame consists of many different lumber pieces, but because of the regularity of these building blocks, the structure can be designed using straightforward geometrical principles. The design of multicomponent protein assemblies, in comparison, has been much more complex, largely owing to the irregular shapes of protein structures1. Here we describe extendable linear, curved and angled protein building blocks, as well as inter-block interactions, that conform to specified geometric standards; assemblies designed using these blocks inherit their extendability and regular interaction surfaces, enabling them to be expanded or contracted by varying the number of modules, and reinforced with secondary struts. Using X-ray crystallography and electron microscopy, we validate nanomaterial designs ranging from simple polygonal and circular oligomers that can be concentrically nested, up to large polyhedral nanocages and unbounded straight 'train track' assemblies with reconfigurable sizes and geometries that can be readily blueprinted. Because of the complexity of protein structures and sequence-structure relationships, it has not previously been possible to build up large protein assemblies by deliberate placement of protein backbones onto a blank three-dimensional canvas; the simplicity and geometric regularity of our design platform now enables construction of protein nanomaterials according to 'back of an envelope' architectural blueprints.

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.

Pinpointing functional, structural, and redox-sensitive cysteines is a central challenge of chemoproteomics. Here, we present a protocol comprising two dual-enrichment cysteine chemoproteomic techniques that enable capture of cysteines (Cys-LoC) and quantification of cysteine oxidation state (Cys-LOx) in a localization-specific manner. We describe steps for utilizing TurboID-mediated protein biotinylation for enrichment of compartment-specific proteins, followed by click-mediated biotinylation and enrichment of cysteine-containing peptides. Thus, changes to compartment-specific cysteine identification and redox state can be assessed in a variety of contexts. For complete details on the use and execution of this protocol, please refer to Yan et al. (2023).1.

Accurate parametrization of amino acids is pivotal for the development of reliable force fields for molecular modeling of biomolecules such as proteins. This study aims to assess amino acid electrostatic parametrizations with the polarizable Gaussian Multipole (pGM) model by evaluating the performance of the pGM-perm (with atomic permanent dipoles) and pGM-ind (without atomic permanent dipoles) variants compared to the traditional RESP model. The 100-conf-combterm fitting strategy on tetrapeptides was adopted, in which (1) all peptide bond atoms (-CO-NH-) share identical set of parameters and (2) the total charges of the two terminal N-acetyl (ACE) and N-methylamide (NME) groups were set to neutral. The accuracy and transferability of electrostatic parameters across peptides with varying lengths and real-world examples were examined. The results demonstrate the enhanced performance of the pGM-perm model in accurately representing the electrostatic properties of amino acids. This insight underscores the potential of the pGM-perm model and the 100-conf-combterm strategy for the future development of the pGM force field.

Cells run on initiation of protein-protein interactions, which are dynamically tuned spatially and temporally to modulate cellular events. This tuning can be physical, such as attaching the protein to a cargo or protein complex, thereby altering its diffusive properties, or modulating the distance between protein pairs, or chemical, by altering the proteins conformations (e.g., nucleotide binding state of an enzyme, post-translational modification of a protein, etc.). Because a dynamic and changing subset of proteins in the cell could be in any specific state, ensemble measurements are not ideal-to untangle which of the factors are important, and how, we need single-molecule measurements. Experimentally, until now we have not had good tools to precisely measure initiation of such protein-protein interactions at the single-molecule level. Here, we develop a new method to measure dynamics of initial protein-protein interactions, allowing measurement of how properties such as the distance between proteins, and their tethered length can modulate the rate of interactions. In addition to precise measurement distance dependent motor-MT rebinding dynamics, we demonstrate the use of a dithered optical trap to measure dynamic motor-MT interactions and further discuss the possibilities of this technique being applicable to other systems.

Macroautophagy/autophagy receptors target their substrates to phagophores for subsequent sequestration within autophagosomes. During phagophore membrane expansion in mammalian cells, autophagy receptors simultaneously interact with the ubiquitinated substrates and the LC3/GABARAP proteins on the expanding membrane. In this punctum, we summarize and discuss our recent research progress on synthetic autophagy receptors (AceTACs). The series of AceTACs were designed by engineering the essential interacting domains and motifs of SQSTM1/p62 (sequestosome 1), a major mammalian autophagy receptor. Particularly, we replaced the ubiquitin-associated domain of SQSTM1 with a target-specific antibody, redirecting the bifunctional interactions of wild-type SQSTM1 and directing the degradation target into the autophagy process. We successfully demonstrated the targeted degradation of aggregation-prone proteins using the AceTAC degraders. Moreover, we presented a model system with a guideline to induce targeted degradation of organelles through the autophagy machinery.

Recent developments in protein design rely on large neural networks with up to 100s of millions of parameters, yet it is unclear which residue dependencies are critical for determining protein function. Here, we show that amino acid preferences at individual residues-without accounting for mutation interactions-explain much and sometimes virtually all of the combinatorial mutation effects across 8 datasets (R2 ~ 78-98%). Hence, few observations (~100 times the number of mutated residues) enable accurate prediction of held-out variant effects (Pearson r > 0.80). We hypothesized that the local structural contexts around a residue could be sufficient to predict mutation preferences, and develop an unsupervised approach termed CoVES (Combinatorial Variant Effects from Structure). Our results suggest that CoVES outperforms not just model-free methods but also similarly to complex models for creating functional and diverse protein variants. CoVES offers an effective alternative to complicated models for identifying functional protein mutations.

Accumulating evidence has implicated impaired extracellular matrix (ECM) clearance as a key factor in fibrotic disease. Despite decades of research elucidating the effectors of ECM clearance, relatively little is understood regarding the upstream regulation of this process. Collagen is the most abundant constituent of normal and fibrotic ECM in mammalian tissues. Its catabolism occurs through extracellular proteolysis and cell-mediated uptake of collagen fragments for intracellular degradation. Given the paucity of information regarding the regulation of this latter process, here we execute unbiased genome-wide screens to understand the molecular underpinnings of cell-mediated collagen clearance. Using this approach, we discover a mechanism through which collagen biosynthesis is sensed by cells internally and directly regulates clearance of extracellular collagen. The sensing mechanism appears to be dependent on endoplasmic reticulum-resident protein SEL1L and occurs via a noncanonical function of this protein. This pathway functions as a homeostatic negative feedback loop that limits collagen accumulation in tissues. In human fibrotic lung disease, the induction of this collagen clearance pathway by collagen synthesis is impaired, thereby contributing to the pathological accumulation of collagen in lung tissue. Thus, we describe cell-autonomous, rheostatic collagen clearance as an important pathway of tissue homeostasis.

Obtaining accurate binding free energies from in silico screens has been a long-standing goal for the computational chemistry community. However, accuracy and computational cost are at odds with one another, limiting the utility of methods that perform this type of calculation. Many methods achieve massive scale by explicitly or implicitly assuming that the target protein adopts a single structure, or undergoes limited fluctuations around that structure, to minimize computational cost. Others simulate each protein-ligand complex of interest, accepting lower throughput in exchange for better predictions of binding affinities. Here, we present the PopShift framework for accounting for the ensemble of structures a protein adopts and their relative probabilities. Protein degrees of freedom are enumerated once, and then arbitrarily many molecules can be screened against this ensemble. Specifically, we use Markov state models (MSMs) as a compressed representation of a proteins thermodynamic ensemble. We start with a ligand-free MSM and then calculate how addition of a ligand shifts the populations of each protein conformational state based on the strength of the interaction between that protein conformation and the ligand. In this work we use docking to estimate the affinity between a given protein structure and ligand, but any estimator of binding affinities could be used in the PopShift framework. We test PopShift on the classic benchmark pocket T4 Lysozyme L99A. We find that PopShift is more accurate than common strategies, such as docking to a single structure and traditional ensemble docking─producing results that compare favorably with alchemical binding free energy calculations in terms of RMSE but not correlation─and may have a more favorable computational cost profile in some applications. In addition to predicting binding free energies and ligand poses, PopShift also provides insight into how the probability of different protein structures is shifted upon addition of various concentrations of ligand, providing a platform for predicting affinities and allosteric effects of ligand binding. Therefore, we expect PopShift will be valuable for hit finding and for providing insight into phenomena like allostery.

Molecular docking is a widely used technique for leveraging protein structure for ligand discovery, but it remains difficult to utilize due to limitations that have not been adequately addressed. Despite some progress toward automation, docking still requires expert guidance, hindering its adoption by a broader range of investigators. To make docking more accessible, we developed a new utility called DockOpt, which automates the creation, evaluation, and optimization of docking models prior to their deployment in large-scale prospective screens. DockOpt outperforms our previous automated pipeline across all 43 targets in the DUDE-Z benchmark data set, and the generated models for 84% of targets demonstrate sufficient enrichment to warrant their use in prospective screens, with normalized LogAUC values of at least 15%. DockOpt is available as part of the Python package Pydock3 included in the UCSF DOCK 3.8 distribution, which is available for free to academic researchers at https://dock.compbio.ucsf.edu and free for everyone upon registration at https://tldr.docking.org.

The proteasome degrades proteins, which is essential for cellular homeostasis. Ubiquitin independent proteolysis degrades highly disordered and misfolded proteins. A decline of proteasomal activity has been associated with multiple neurodegenerative diseases due to the accumulation of misfolded proteins. In this work, cyclic peptide proteasome stimulators (CyPPSs) that enhance the clearance of misfolded proteins were discovered. In the initial screen of predicted natural products (pNPs), several cyclic peptides were found to stimulate the 20S core particle (20S CP). Development of a robust structural activity relationship led to the identification of potent, cell permeable CyPPSs. In vitro assays revealed that CyPPSs stimulate degradation of highly disordered and misfolded proteins without affecting ordered proteins. Furthermore, using a novel flow-based assay for proteasome activity, several CyPPSs were found to stimulate the 20S CP in cellulo. Overall, this work describes the development of CyPPSs as chemical tools capable of stimulating the proteasome and provides strong support for proteasome stimulation as a therapeutic strategy for neurodegenerative diseases.

Chlorophyll c is a key photosynthetic pigment that has been used historically to classify eukaryotic algae. Despite its importance in global photosynthetic productivity, the pathway for its biosynthesis has remained elusive. Here we define the CHLOROPHYLL C SYNTHASE (CHLCS) discovered through investigation of a dinoflagellate mutant deficient in chlorophyll c. CHLCSs are proteins with chlorophyll a/b binding and 2-oxoglutarate-Fe(II) dioxygenase (2OGD) domains found in peridinin-containing dinoflagellates; other chlorophyll c-containing algae utilize enzymes with only the 2OGD domain or an unknown synthase to produce chlorophyll c. 2OGD-containing synthases across dinoflagellate, diatom, cryptophyte, and haptophyte lineages form a monophyletic group, 8 members of which were also shown to produce chlorophyll c. Chlorophyll c1 to c2 ratios in marine algae are dictated in part by chlorophyll c synthases. CHLCS heterologously expressed in planta results in the accumulation of chlorophyll c1 and c2, demonstrating a path to augment plant pigment composition with algal counterparts.

Meclizine (Antivert, Bonine) is a first-generation H1 antihistamine used in the treatment of motion sickness and vertigo. Despite its wide medical use for over 70 years, its crystal structure and the details of protein-drug interactions remained unknown. Single-crystal X-ray diffraction (SC-XRD) is previously unsuccessful for meclizine. Today, microcrystal electron diffraction (MicroED) enables the analysis of nano- or micro-sized crystals that are merely a billionth the size needed for SC-XRD directly from seemingly amorphous powder. In this study, MicroED to determine the 3D crystal structure of meclizine dihydrochloride is used. Two racemic enantiomers (R/S) are found in the unit cell, which is packed as repetitive double layers in the crystal lattice. The packing is made of multiple strong N-H-Cl- hydrogen bonding interactions and weak interactions like C-H-Cl- and pi-stacking. Molecular docking reveals the binding mechanism of meclizine to the histamine H1 receptor. A comparison of the docking complexes between histamine H1 receptor and meclizine or levocetirizine (a second-generation antihistamine) shows the conserved binding sites. This research illustrates the combined use of MicroED and molecular docking in unraveling elusive drug structures and protein-drug interactions for precision drug design and optimization.

Foldy is a cloud-based application that allows non-computational biologists to easily utilize advanced AI-based structural biology tools, including AlphaFold and DiffDock. With many deployment options, it can be employed by individuals, labs, universities, and companies in the cloud without requiring hardware resources, but it can also be configured to utilize locally available computers. Foldy enables scientists to predict the structure of proteins and complexes up to 6000 amino acids with AlphaFold, visualize Pfam annotations, and dock ligands with AutoDock Vina and DiffDock. In our manuscript, we detail Foldys interface design, deployment strategies, and optimization for various user scenarios. We demonstrate its application through case studies including rational enzyme design and analyzing proteins with domains of unknown function. Furthermore, we compare Foldys interface and management capabilities with other open and closed source tools in the field, illustrating its practicality in managing complex data and computation tasks. Our manuscript underlines the benefits of Foldy as a day-to-day tool for life science researchers, and shows how Foldy can make modern tools more accessible and efficient.

Many peptide hormones form an α-helix on binding their receptors1-4, and sensitive methods for their detection could contribute to better clinical management of disease5. De novo protein design can now generate binders with high affinity and specificity to structured proteins6,7. However, the design of interactions between proteins and short peptides with helical propensity is an unmet challenge. Here we describe parametric generation and deep learning-based methods for designing proteins to address this challenge. We show that by extending RFdiffusion8 to enable binder design to flexible targets, and to refining input structure models by successive noising and denoising (partial diffusion), picomolar-affinity binders can be generated to helical peptide targets by either refining designs generated with other methods, or completely de novo starting from random noise distributions without any subsequent experimental optimization. The RFdiffusion designs enable the enrichment and subsequent detection of parathyroid hormone and glucagon by mass spectrometry, and the construction of bioluminescence-based protein biosensors. The ability to design binders to conformationally variable targets, and to optimize by partial diffusion both natural and designed proteins, should be broadly useful.

Discovering ligands for amyloid fibrils, such as those formed by the tau protein, is an area of great current interest. In recent structures, ligands bind in stacks in the tau fibrils to reflect the rotational and translational symmetry of the fibril itself; in these structures, the ligands make few interactions with the protein but interact extensively with each other. To exploit this symmetry and stacking, we developed SymDOCK, a method to dock molecules that follow the proteins symmetry. For each prospective ligand pose, we apply the symmetry operation of the fibril to generate a self-interacting and fibril-interacting stack, checking that doing so will not cause a clash between the original molecule and its image. Absent a clash, we retain that pose and add the ligand-ligand van der Waals energy to the ligands docking score (here using DOCK3.8). We can check these geometries and energies using an implementation of ANI, a neural-network-based quantum-mechanical evaluation of the ligand stacking energies. In retrospective calculations, symmetry docking can reproduce the poses of three tau PET tracers whose structures have been determined. More convincingly, in a prospective study, SymDOCK predicted the structure of the PET tracer MK-6240 bound in a symmetrical stack to AD PHF tau before that structure was determined; the docked pose was used to determine how MK-6240 fit the cryo-EM density. In proof-of-concept studies, SymDOCK enriched known ligands over property-matched decoys in retrospective screens without sacrificing docking speed and can address large library screens that seek new symmetrical stackers. Future applications of this approach will be considered.

Biological interpretation of metabolomic datasets often ends at a pathway analysis step to find the over-represented metabolic pathways in the list of statistically significant metabolites. However, definitions of biochemical pathways and metabolite coverage vary among different curated databases, leading to missed interpretations. For the lists of genes, transcripts and proteins, Gene Ontology (GO) terms over-presentation analysis has become a standardized approach for biological interpretation. But, GO analysis has not been achieved for metabolomic datasets. We present a new knowledgebase (KB) and the online tool, Gene Ontology Analysis by the Integrated Data Science Laboratory for Metabolomics and Exposomics (IDSL.GOA) to conduct GO over-representation analysis for a metabolite list. The IDSL.GOA KB covers 2393 metabolic GO terms and associated 3144 genes, 1,492 EC annotations, and 2621 metabolites. IDSL.GOA analysis of a case study of older versus young female brain cortex metabolome highlighted 82 GO terms being significantly overrepresented (FDR