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The goal of this paper is predicting the conformational distributions of ligand binding sites using the AlphaFold2 (AF2) protein structure prediction program with stochastic subsampling of the multiple sequence alignment (MSA). We explored the opening of cryptic ligand binding sites in 16 proteins, where the closed and open conformations define the expected extreme points of the conformational variation. Due to the many structures of these proteins in the Protein Data Bank (PDB), we were able to study whether the distribution of X-ray structures affects the distribution of AF2 models. We have found that AF2 generates both a cluster of open and a cluster of closed models for proteins that have comparable numbers of open and closed structures in the PDB and not too many other conformations. This was observed even with default MSA parameters, thus without further subsampling. In contrast, with the exception of a single protein, AF2 did not yield multiple clusters of conformations for proteins that had imbalanced numbers of open and closed structures in the PDB, or had substantial numbers of other structures. Subsampling improved the results only for a single protein, but very shallow MSA led to incorrect structures. The ability of generating both open and closed conformations for six out of the 16 proteins agrees with the success rates of similar studies reported in the literature. However, we showed that this partial success is due to AF2 "remembering" the conformational distributions in the PDB and that the approach fails to predict rarely seen conformations., Competing Interests: Competing interests statement:The authors declare no competing interest.

In an attempt to overcome silanophilic interactions like observed on popular reversed-phase butyl‑bonded silica stationary phases in protein HPLC, a mixed-mode stationary phase based on wide pore silica (3 µm, 300 Å) was prepared by co-immobilization of octyl and 2-pyridylethyl ligands. The surface modification was performed by a new approach using synthesized functional silatranes of the above ligands and prewetted silica. It allowed to generate a dense polymeric siloxane layer on the silica surface. Butyl-bonded silica and octyl/3-aminopropyl-bonded mixed-mode silica phases were prepared for comparison. The modified silicas were subsequently characterized by elemental analysis regarding ligand densities, by solid-state 29 Si and 13 C cross polarization/magic angle spinning nuclear magnetic resonance spectroscopy for confirming the surface-bonded structure, and by pH-dependent ζ-potential measurements via electrophoretic light scattering providing net surface charge information at distinct pH values. While the classical butyl‑bonded stationary phase revealed negative ζ-potential over the entire pH range investigated (pH 3.5-9.5) due to residual silanols and the mixed-mode octyl/3-aminopropyl-bonded silica positive ζ-potential over the entire pH range, pH-dependent charge reversal was observed at approximately pH 5.5 for the octyl/pyridyl-bonded stationary phase. Then, a test set of proteins differing in hydrophobicities and isoelectric points was employed to evaluate the retention characteristics of all three synthesized stationary phases over the pH range of 3 to 7.5 by acetonitrile-gradient elution reversed-phase HPLC. Under acidic conditions (pH 3) the mixed-mode phases octyl/pyridyl-silica and octyl/aminopropyl-silica showed reduced retention and improved peak shapes due to repulsive interactions preventing silanophilic interactions, while protein separations by their hydrophobicities were achieved (repulsive charge-assisted protein RPLC). Finally, the prepared novel mixed-mode octyl/pyridyl-bonded stationary phase was evaluated in hydrophobic charge induction chromatography mode for protein separation of the same test set. Instead of an organic modifier gradient, elution was enforced by a pH gradient from almost neutral to acidic pH at constant organic modifier content of 10 %. This chromatographic mode showed orthogonal retention characteristics and reversed elution order compared to above organic gradient RP-HPLC. In addition, significantly less organic solvent was used under these conditions, classifying it as a green protein LC technology., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. The author is an Editorial Board Member for Journal of Chromatogr A and was not involved in the editorial review or the decision to publish this article., (Copyright © 2024. Published by Elsevier B.V.)

This study presents an enhanced protein design algorithm that aims to emulate natural heterogeneity of protein sequences. Initial analysis revealed that natural proteins exhibit a permutation composition lower than the theoretical maximum, suggesting a selective utilization of the 20-letter amino acid alphabet. By not constraining the amino acid composition of the protein sequence but instead allowing random reshuffling of the composition, the resulting design algorithm generates sequences that maintain lower permutation compositions in equilibrium, aligning closely with natural proteins. Folding free energy computations demonstrated that the designed sequences refold to their native structures with high precision, except for proteins with large disordered regions. In addition, direct coupling analysis showed a strong correlation between predicted and actual protein contacts, with accuracy exceeding 82% for a large number of top pairs (>4L). The algorithm also resolved biases in previous designs, ensuring a more accurate representation of protein interactions. Overall, it not only mimics the natural heterogeneity of proteins but also ensures correct folding, marking a significant advancement in protein design and engineering., (© 2024 Author(s). Published under an exclusive license by AIP Publishing.)

Proteins are attractive building blocks for fabricating diverse and precise nanomaterials. However, the facile fabrication of multidimensional artificial assemblies is highly challenging. Here, inspired by the large-scale production technique of inorganic nanomaterials, we demonstrate the application of liquid phase exfoliation (LPE) on native protein ConA by the design of synthetic ligands. These ligands provide distinct in-plane and out-of-plane supramolecular interactions, allowing the generation of multidimensional architectures based on the same protein by dissociating a single interaction in solution, including 3D porous protein crystals, 2D sizable nanosheets, and 1D fibrils. Importantly, the exfoliated 2D sheets were dozens of times larger than the self-assembled nanosheets, resulting in a dramatic enhancement of the intrinsic bioactivity of the building blocks by receptor clustering and less endocytosis. These findings enable the successful application of LPE on biomacromolecules and open up an alternative avenue to generate advanced multidimensional nanomaterials, without the need for complex protein design and careful adjustment of self-assembly conditions.

Chemical shifts are a readily obtainable NMR observable that can be measured with high accuracy, and because they are sensitive to conformational averages and the local molecular environment, they yield detailed information about protein structure in solution. To predict chemical shifts of protein structures, we introduced the UCBShift method that uniquely fuses a transfer prediction module, which employs sequence and structure alignments to select reference chemical shifts from an experimental database, with a machine learning model that uses carefully curated and physics-inspired features derived from X-ray crystal structures to predict backbone chemical shifts for proteins. In this work, we extend the UCBShift 1.0 method to side chain chemical shift prediction to perform whole protein analysis, which, when validated against well-defined test data shows higher accuracy and better reliability compared to the popular SHIFTX2 method. With the greater abundance of cleaned protein shift-structure data and the modularity of the general UCBShift algorithms, users can gain insight into different features important for residue-specific stabilizing interactions for protein backbone and side chain chemical shift prediction. We suggest several backward and forward applications of UCBShift 2.0 that can help validate AlphaFold structures and probe protein dynamics.

Immunocapture liquid chromatography-mass spectrometry (IC-LC-MS) bioanalysis has become an indispensable technique across various scientific disciplines, ranging from drug discovery to clinical diagnostics. While traditional immunocapture techniques have proven to be effective, they often encounter limitations in sensitivity, specificity, and compatibility with MS analysis. Chemoenzymatic immunocapture and protein capture (IPC) offers a promising solution, combining the high specificity of antibodies or proteins with the versatility of enzymatic and chemical modifications. This Review explores the foundational principles of chemoenzymatic IPC and examines various modification strategies including bioorthogonal click-chemistry, enzymatic-tagging, and HaloTag/CLIP-tag. Recent advancements in chemoenzymatic IPC techniques have significantly expanded their applicability to a diverse range of biomolecules including small molecules, peptides, RNAs, and proteins. This Review focuses on improvements in analytical performance achieved through these innovative approaches. Moreover, we discuss the broad applications of chemoenzymatic immunocapture in drug discovery, clinical diagnostics, and environmental analysis and explore its potential for future advancements in bioanalysis. We propose a novel solid-phase chemoenzymatic IPC assay (SCEIA) that effectively utilizes bioorthogonal click chemistry and chemoenzymatic approaches for efficient IPC and target analyte release. In summary, chemoenzymatic IPC represents a transformative paradigm shift in IC-LC-MS bioanalysis. By overcoming the limitations of traditional IPC techniques, this approach paves the way for more robust, sensitive, and versatile analytical workflows.

Lysine residues on protein surfaces are abundant and often found in enzyme active sites, making them critical targets for studying undruggable proteins. However, the varied microenvironment surrounding lysine residues results in a wide range of p K a values, complicating site-specific covalent binding. In this study, we address the challenges posed by the diverse reactivity of amino side chains by modulating the amide reaction activity of heteroaromatic activated esters. By fine-tuning the type, position, and number of heteroatoms, we successfully rationalized the regulation of their amide reaction activity, leading to the design of probes for selective lysine labeling within the proteome for profiling purposes. Systematic optimization of these esters' reactivity and selectivity has yielded a series of effective probes suitable for both in vitro and cellular applications. These findings significantly enhance our understanding of protein functions and mechanisms, facilitated by the precise identification and analysis of protein labeling and profiling.

Markov State Models (MSMs) have been widely applied to understand protein folding mechanisms by predicting long time scale dynamics from ensembles of short molecular simulations. Most MSM estimators enforce detailed balance, assuming that trajectory data are sampled at an equilibrium. This is rarely the case for ab initio folding studies, however, and as a result, MSMs can severely underestimate protein folding stabilities from such data. To remedy this problem, we have developed an enhanced-sampling protocol in which (1) unbiased folding simulations are performed and sparse tICA is used to obtain features that best capture the slowest events in folding, (2) umbrella sampling along this reaction coordinate is performed to observe folding and unfolding transitions, and (3) the thermodynamics and kinetics of folding are estimated using multiensemble Markov models (MEMMs). Using this protocol, folding pathways, rates, and stabilities of a designed α-helical hairpin, Z34C, can be predicted in good agreement with experimental measurements. These results indicate that accurate simulation-based estimates of absolute folding stabilities are within reach, with implications for the computational design of folded miniproteins and peptidomimetics.

Vicinal dithiol proteins (VDPs) facilitate cellular redox homeostasis, modulate protein synthesis and participate in post-translational modifications through the dynamic equilibrium of dithiol and disulfide bonds. Herein, an activatable red emitting fluorescent probe, VDP-red, is developed for detecting VDPs. With the aid of this probe, we have discovered for the first time a reduction in the levels of reduced VDPs in a stroke mouse model. This work provides a fresh viewpoint for understanding stroke mechanisms.

19 F NMR spectroscopy has recently witnessed a resurgence as an attractive analytical tool for the study of the structure and dynamics of biomolecules in vitro and in cells, despite reports of its applications in biomolecular NMR since the 1970s. The high gyromagnetic ratio, large chemical shift dispersion, and complete absence of the spin 1/2 19 F nucleus from biomolecules results in background-free, high-resolution 19 F NMR spectra. The introduction of 19 F probes in a few selected locations in biomolecules reduces spectral crowding despite its increased line width in comparison to typical 1 H NMR line widths and allows rapid site-specific measurements from simple 1D spectra alone. The design and synthesis of novel 19 F probes with reduced line widths and increased chemical shift sensitivity to the surrounding environment, together with advances in labeling techniques, NMR methodology, and hardware, have overcome several drawbacks of 19 F NMR spectroscopy. The increased interest and widespread use of 19 F NMR spectroscopy of biomolecules is gradually establishing it as a sensitive and high-resolution probe of biomolecular structure and dynamics, supplementing traditional 13 C/ 15 N-based methods. This Review focuses on the advances in 19 F solution NMR spectroscopy of proteins in the past 5 years, with an emphasis on novel 19 F tags and labeling techniques, NMR experiments to probe protein structure and conformational dynamics in vitro, and in-cell NMR applications.

ConspectusProtein-protein interactions (PPIs) are essential in numerous biological processes and diseases, making them attractive yet challenging drug targets. While many advances have been made in traditional drug discovery, targeting PPIs has been difficult due to a lack of specialized chemical libraries designed to modulate these interactions. Current libraries mainly focus on conventional target proteins like enzymes or receptors as substrate analogs rather than small-molecule modulators targeting PPIs. These traditional drug targets behave differently from PPIs. Conventional druggable targets have relatively small surfaces and binding pockets that have allowed them to be targeted with current libraries, but PPIs behave differently than these traditional drug targets. As a result, there is an urgent need for an innovative approach to expand the druggable space.To address this, we developed a privileged substructure-based diversity-oriented synthesis (pDOS) strategy, aimed at creating maximal skeletal diversity to explore broader biochemical space. Pyrimidine serves as the privileged substructure in our approach, which employs several strategies: (i) silver-catalyzed or iodine-mediated tandem cyclizations to generate pyrimidine-embedded polyheterocycles; (ii) diverse pairing strategies to produce pyrimidodiazepine-containing polyheterocyclic skeletons with enhanced scaffold saturation; (iii) skeletal transformation to develop pyrimidine-fused medium-sized azacycles via chemoselective cleavages or migrations of N-N or C-N bond; (iv) design of small-molecule peptidomimetics that systematically mimic three pivotal protein secondary structures using pyrimidodiazepine-based scaffolds; and (v) identification of pyrimidodiazepine-based small-molecules that allosterically inhibits the interaction between human ACE2 and the receptor-binding domain (RBD) of the SARS-CoV-2 spike protein to block viral entry into host cells.Through these approaches, we generated 39 distinct pyrimidine-embedded frameworks, demonstrating significant molecular diversity validated by chemoinformatic analyses such as Tanimoto similarity and principal moment of inertia (PMI) analysis. This molecular diversity extends pyrimidine structures beyond traditional linear or bicyclic forms, creating polyheterocycles with enhanced 3D structural diversity. These novel frameworks overcome the limitation of simpler privileged scaffolds, offering promising tools for modulating PPIs.Our pDOS approach highlights how privileged structure-embedded polyheterocycles, particularly those based on pyrimidine, can effectively target previously undruggable PPIs. This strategy provides a new direction for drug discovery, allowing for the development of small molecules that operate beyond traditional drug-like rules. In addition to expanding the chemical space for PPI modulation, our pDOS strategy enables the creation of scaffolds that are particularly suited for targeting complex and dynamic protein interfaces. This innovation could significantly impact therapeutic development, offering solutions for previously intractable drug targets. By expanding the scope of pyrimidine-based scaffolds, we have opened up new possibilities for targeting PPIs and advancing chemical biology.This perspective demonstrates the potential outlines of our pDOS strategy in creating structurally diverse frameworks, offering a platform for the discovery of PPI modulators and facilitating the exploration of untapped biochemical spaces in drug development, potentially transforming the way we approach these complex biological interactions.

Despite extensive research on the sequence-determined self-assembly of both pathogenic and nonpathogenic proteins, the question of how the sequence identity would influence the coassembly or cross-seeding of diverse proteins without distinct sequence similarity remains largely unanswered. Here, we demonstrate that the rapid coaggregation of proteins with negligible sequence similarity is fundamentally governed by preferred heteromeric interactions between their partially unfolded states via the gain of additional charge complementarity and hydrophobic interactions. The partial loss of intramolecular interactions and concurrent gain of non-native intrinsically disordered regions with sticky groups become crucial for both aggressive heteromeric primary nucleation and secondary nucleation events. The results signify the direct relevance of sequence-independent conformational cross-talk between diverse proteins to the foundational events required for the growth of biological multiprotein amyloid deposits.

Weak protein interactions are associated with a broad array of biological functions and are often implicated in molecular dysfunction accompanying human disease. In addition, these interactions are a critical determinant in the effective manufacturing, stability, and administration of biotherapeutic proteins. Despite their prominence, much remains unknown about how molecular attributes influence the hydrodynamic and thermodynamic contributions to the overall interaction mechanism. To systematically probe these contributions, we have evaluated self-interaction in a diverse set of proteins that demonstrate a broad range of behaviors from attractive to repulsive. Analysis of the composite trending in the data provides a convenient interconversion among interaction parameters measured from the concentration dependence of the molecular weight, diffusion coefficient, and sedimentation coefficient, as well as insight into the relationship between thermodynamic and hydrodynamic interactions. We find relatively good agreement between our data and a model for interacting hard spheres in the range of weak self-association. In addition, we propose an empirically derived, general scaling relationship applicable across a broad range of self-association and repulsive behaviors., Competing Interests: Declaration of interests The authors were employees of Sanofi during the work described in this manuscript and may hold shares and/or stock options in the company. In response to reasonable requests, noncommercially available materials and experimental protocols, that Sanofi has the right to provide, will be made available to not-for-profit or academic requesters upon completion of a material transfer agreement. Requests may be made by contacting the corresponding author., (Copyright © 2024 Biophysical Society. Published by Elsevier Inc. All rights reserved.)

The native conformation of a protein plays a decisive role in ensuring its functionality. It is established that the spatial structure of proteins may exhibit a greater degree of conservation than the corresponding amino acid sequences. This study aims to clarify structural distinctions between homologous and non-homologous proteins with identical topology. The analysis focuses on secondary structures with special emphasis on their fraction, distribution along the polypeptide chain, and chirality. Three different groups of proteins with identical topology were considered according to the CATH database: a homologous group of Globins, a group of Phycocyanins, which is often considered as a potential relative of globins, and a diverse assembly of other globin-like proteins. Some structural patterns in the distribution of secondary structure have been identified within Globins. A similar profile was observed in Phycocyanins, in contrast to the third group. In addition, a distinguishable structural motif, including structures such as 3 10 -helix and irregular structure, has been found in both Globins and Phycocyanins, which can be proposed as an evolutionary imprint., Competing Interests: Declaration of competing interest Declarations of interest: None., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Many proteins have been recently shown to undergo a process of phase separation that leads to the formation of biomolecular condensates. Intriguingly, it has been observed that some of these proteins form dense droplets of sizeable dimensions already below the critical concentration, which is the concentration at which phase separation occurs. To understand this phenomenon, which is not readily compatible with classical nucleation theory, we investigated the properties of the droplet size distributions as a function of protein concentration. We found that these distributions can be described by a scale-invariant log-normal function with an average that increases progressively as the concentration approaches the critical concentration from below. The results of this scaling analysis suggest the existence of a universal behaviour independent of the sequences and structures of the proteins undergoing phase separation. While we refrain from proposing a theoretical model here, we suggest that any model of protein phase separation should predict the scaling exponents that we reported here from the fitting of experimental measurements of droplet size distributions. Furthermore, based on these observations, we show that it is possible to use the scale invariance to estimate the critical concentration for protein phase separation., Competing Interests: TA, SD, AL, MB, AT, MV, MF, AM No competing interests declared, (© 2024, Amico et al.)

Motional properties of proteins govern recognition, catalysis, and regulation. The dynamics of tightly interacting residues can form intramolecular dynamic networks, dependencies fine-tuned by evolution to optimize a plethora of functional aspects. The constructive interaction of residues from different proteins to assemble intermolecular dynamic networks is a similarly likely case but has escaped thorough experimental assessment due to interfering association/dissociation dynamics. Here, we use fast-MAS solid-state 15 N R 1ρ NMR relaxation dispersion aided by molecular-dynamics simulations to mechanistically assess the hierarchy of individual μs timescale motions arising from a crystal-crystal contact, in the absence of translational motion. In contrast to the monomer, where particular mutations entail isolated perturbations, specific intermolecular interactions couple the motional properties between distant residues in the same protein. The mechanistic insights obtained from this conceptual work may improve our understanding on how intramolecular allostery can be tuned by intermolecular interactions via assembly of dynamic networks from previously isolated elements., (© 2024 The Authors. Angewandte Chemie International Edition published by Wiley-VCH GmbH.)

Sulfur fluoride exchange (SuFEx) and phosphorus fluoride exchange (PFEx) click chemistries are advancing research across multiple disciplines. By genetically incorporating latent bioreactive unnatural amino acids (Uaas), these chemistries have been integrated into proteins, enabling precise covalent linkages with biological macromolecules and paving the way for new applications. However, their suboptimal reaction rates in proteins limit effectiveness, and traditional catalytic methods for small molecules are often incompatible with biological systems or in vivo applications. We demonstrated that introducing an arginine adjacent to the latent bioreactive Uaa significantly boosts SuFEx and PFEx reaction rates between proteins. This method is effective across various Uaas, target residues, and protein environments. Notably, it also enables efficient SuFEx reactions in acidic conditions, common in certain cellular compartments and tumor microenvironments, which typically hinder SuFEx reactions. Furthermore, we developed the first covalent cell engager that substantially enhances natural killer cell activation through improved covalent interaction facilitated by arginine. These findings provide mechanistic insights and offer a biocompatible strategy to harness these robust chemistries for advancing biological research and developing new biotherapeutics., (© 2024 The Authors. Angewandte Chemie International Edition published by Wiley-VCH GmbH.)

The success of AlphaFold in protein structure prediction highlights the power of data-driven approaches in scientific research. However, developing machine learning models to design and engineer proteins with desirable functions is hampered by limited access to high-quality data sets and experimental feedback. The Critical Assessment of Protein Engineering (CAPE) challenge addresses these issues through a student-focused competition, utilizing cloud computing and biofoundries to lower barriers to entry. CAPE serves as an open platform for community learning, where mutant data sets and design algorithms from past contestants help improve overall performance in subsequent rounds. Through two competition rounds, student participants collectively designed >1500 new mutant sequences, with the best-performing variants exhibiting catalytic activity up to 5-fold higher than the wild-type parent. We envision CAPE as a collaborative platform to engage young researchers and promote computational protein engineering.

Inspired by the properties of natural protein-based biomaterials, protein nanomaterials are increasingly designed with natural or engineered peptides or with protein building blocks. Few examples describe the design of functional protein-based materials for biotechnological applications that can be readily manufactured, are amenable to functionalization, and exhibit robust assembly properties for macroscale material formation. Here, we designed a protein-scaffolding system that self-assembles into robust, macroscale materials suitable for in vitro cell-free applications. By controlling the coexpression in Escherichia coli of self-assembling scaffold building blocks with and without modifications for covalent attachment of cross-linking cargo proteins, hybrid scaffolds with spatially organized conjugation sites are overproduced that can be readily isolated. Cargo proteins, including enzymes, are rapidly cross-linked onto scaffolds for the formation of functional materials. We show that these materials can be used for the in vitro operation of a coimmobilized two-enzyme reaction and that the protein material can be recovered and reused. We believe that this work will provide a versatile platform for the design and scalable production of functional materials with customizable properties and the robustness required for biotechnological applications.

Classification of protein domains based on homology and structural similarity serves as a fundamental tool to gain biological insights into protein function. Recent advancements in protein structure prediction, exemplified by AlphaFold, have revolutionized the availability of protein structural data. We focus on classifying about 9000 Pfam families into ECOD (Evolutionary Classification of Domains) by using predicted AlphaFold models and the DPAM (Domain Parser for AlphaFold Models) tool. Our results offer insights into their homologous relationships and domain boundaries. More than half of these Pfam families contain DPAM domains that can be confidently assigned to the ECOD hierarchy. Most assigned domains belong to highly populated folds such as Immunoglobulin-like (IgL), Armadillo (ARM), helix-turn-helix (HTH), and Src homology 3 (SH3). A large fraction of DPAM domains, however, cannot be confidently assigned to ECOD homologous groups. These unassigned domains exhibit statistically different characteristics, including shorter average length, fewer secondary structure elements, and more abundant transmembrane segments. They could potentially define novel families remotely related to domains with known structures or novel superfamilies and folds. Manual scrutiny of a subset of these domains revealed an abundance of internal duplications and recurring structural motifs. Exploring sequence and structural features such as disulfide bond patterns, metal-binding sites, and enzyme active sites helped uncover novel structural folds as well as remote evolutionary relationships. By bridging the gap between sequence-based Pfam and structure-based ECOD domain classifications, our study contributes to a more comprehensive understanding of the protein universe by providing structural and functional insights into previously uncharacterized proteins., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Rationale: Shiduqing Capsules, a well-known Chinese patent medicine, are widely used clinically for the treatment of pruritus. However, to date, there is a lack of research on its pharmacological substances and mechanisms of action., Methods: In the current study, the chemical components of Shiduqing Capsules were identified using UHPLC-QE-Orbitrap-MS technology. Molecular network analysis was employed to identify structurally similar compounds to the known chemical components. The potential molecular targets of the active ingredients were predicted using the SwissTargetPrediction website. The identified targets were further analyzed using gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis through the DAVID database. Molecular docking was used to validate the network pharmacology results., Results: Ultimately, A total of 51 chemical components of Shiduqing Capsules were identified. Molecular network analysis identified 21 flavonoids and 13 terpenoids. The core targets of these ingredients include TP53, AKT1, and STAT3. GO and KEGG enrichment analysis revealed 1,371 different biological functions and 177 signaling pathways. Molecular docking confirmed the high affinity between multiple core active ingredients of Shiduqing Capsules and pruritus targets., Conclusion: In conclusion, the effective ingredients of Shiduqing Capsules exert a multifaceted therapeutic effect on pruritus through multiple targets and pathways., (© 2024 John Wiley & Sons Ltd.)

Constructing a restoration strategy from bauxite residue to Technosols is a cost-effective and sustainable strategy for addressing the ecological and environmental issues caused by high alkalinity, salinity, and fine-grained bauxite residues. However, the quantitative contribution of restoration strategies on the upper bauxite residue-derived Technosols to the underlying untreated bauxite residue in the short term remains poorly understood. This study investigated the mediating mechanisms of vegetation and microbial metabolic effects on the alkalinity, nutrient content, and structure of the underlying bauxite residue (20-50 cm) through a simulated ecological reconstruction of the bauxite residue stockpile. Results indicated that implementing plant restoration strategies resulted in the content of polyphenolic compounds, lipids, tannins, and carbohydrates in bauxite residue dissolved organic matter (DOM) increased significantly from 52.5, 8.2, 3.3, and 2.0 % to 54.4, 10.4, 5.6, and 2.8 %, respectively, while the content of condensed aromatics, unsaturated hydrocarbons, and proteins/amino sugars decreased significantly from 15.5, 12.0, and 6.5 % to 12.1, 9.7, and 5.1 %, respectively. The newly produced molecules were concentrated in regions with low O/C and high H/C ratios, suggesting that short-term vegetation restoration strategies facilitate the transformation of substrate DOM towards easily decomposable and highly bioavailable substances. This led to the migration of the newly produced molecules to the underlying bauxite residue, and as a result, the protein and soluble microbial products of the underlying bauxite residue increased significantly, as well as the pH, exchangeable Na, and < 0.054 mm particles decreased from 10.2, 44.2 cmol kg -1 , and 28.1 % to 9.7, 27.1 cmol kg -1 , and 19.4 %, respectively, available nitrogen, urease, and 1-2 mm particles increased from 7.3 mg kg -1 , 0.2 U mg -1 , and 14.5 % to 7.6 mg kg -1 , 0.3 U kg -1 , and 21.7 %, respectively. Results of the structural equation model further confirmed that plant biomass, proteins/amino sugars, and condensed aromatics in the upper Technosol were the main factors controlling the aggregate formation of the underlying bauxite residue by mediating the protein-dominated biogenic organic matter produced by microbial metabolism., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Understanding protein function is crucial for deciphering biological systems and facilitating various biomedical applications. Computational methods for predicting Gene Ontology functions of proteins emerged in the 2000s to bridge the gap between the number of annotated proteins and the rapidly growing number of newly discovered amino acid sequences. Recently, there has been a surge in studies applying graph representation learning techniques to biological networks to enhance protein function prediction tools. In this review, we provide fundamental concepts in graph embedding algorithms. This study described graph representation learning methods for protein function prediction based on four principal data categories, namely PPI network, protein structure, Gene Ontology graph, and integrated graph. The commonly used approaches for each category were summarized and diagrammed, with the specific results of each method explained in detail. Finally, existing limitations and potential solutions were discussed, and directions for future research within the protein research community were suggested., Competing Interests: The authors declare that they have no competing interests., (© 2024 Vu et al.)

Dynamic conformational and structural changes in proteins and protein complexes play a central and ubiquitous role in the regulation of protein function, yet it is very challenging to study these changes, especially for large protein complexes, under physiological conditions. Here, we introduce a novel isobaric crosslinker, Qlinker, for studying conformational and structural changes in proteins and protein complexes using quantitative crosslinking mass spectrometry. Qlinkers are small and simple, amine-reactive molecules with an optimal extended distance of ~10 Å, which use MS2 reporter ions for relative quantification of Qlinker-modified peptides derived from different samples. We synthesized the 2-plex Q2linker and showed that the Q2linker can provide quantitative crosslinking data that pinpoints key conformational and structural changes in biosensors, binary and ternary complexes composed of the general transcription factors TBP, TFIIA, and TFIIB, and RNA polymerase II complexes., Competing Interests: JL, JR No competing interests declared, (© 2024, Luo and Ranish.)

Targeted protein degradation strategies leverage endogenous cellular degradation machinery to selectively eliminate a protein of interest. Emerging technologies are opening avenues in drug discovery and functional characterization of intracellular, membrane, and extracellular proteins. To view this SnapShot, open or download the PDF., Competing Interests: Declaration of interests B.L. and Y.D. are the authors of several patent applications on autophagy-based and proteosome-based TPD. B.L. and Y.D. are also shareholders of TPD-related biotech companies., (Copyright © 2024 Elsevier Inc. All rights reserved.)

The identification of individual protein-protein interactions (PPIs) began more than 40 years ago, using protein affinity chromatography and antibody co-immunoprecipitation. As new technologies emerged, analysis of PPIs increased to a genome-wide scale with the introduction of intracellular tagging methods, affinity purification (AP) followed by mass spectrometry (MS), and co-fractionation MS (CF-MS). Now, combining the resulting catalogs of interactions with complementary methods, including crosslinking MS (XL-MS) and cryogenic electron microscopy (cryo-EM), helps distinguish direct interactions from indirect ones within the same or between different protein complexes. These powerful approaches and the promise of artificial intelligence applications like AlphaFold herald a future where PPIs and protein complexes, including energy-driven protein machines, will be understood in exquisite detail, unlocking new insights in the contexts of both basic biology and disease., Competing Interests: Declaration of interests The Krogan Laboratory has received research support from Vir Biotechnology, F. Hoffmann-La Roche, Maze Therapeutics, and Rezo Therapeutics. N.J.K. is on the Board of Directors of Rezo Therapeutics, and he is a shareholder in Tenaya Therapeutics, Maze Therapeutics, Rezo Therapeutics, GEn1E Lifesciences, and Interline Therapeutics., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Traditional molecular dynamics simulation of biomolecules suffers from the conformational sampling problem. It is often difficult to produce enough valid data for post analysis such as free energy calculation and transition path construction. To improve the sampling, one practical solution is putting an adaptive bias potential on some predefined collective variables. The quality of collective variables strongly affects the sampling ability of a molecule in the simulation. In the past, collective variables were built with the sampling data at a constant temperature. This is insufficient because of the same sampling problem. In this work, we apply the standard weighted histogram analysis method to calculate the multi-ensemble averages of pairs of time-lagged features for the construction of both linear and nonlinear slow collective variables. Compared to previous single-ensemble methods, the presented method produces averages with much smaller statistical uncertainties. The generated collective variables help a peptide and a miniprotein fold to their near-native states in a short simulation time period. By using the method, enhanced sampling simulations could be more effective and productive., (© 2024 Author(s). Published under an exclusive license by AIP Publishing.)

Solid-state nuclear magnetic resonance (ssNMR) is indispensable for studying the structures, dynamics, and interactions of insoluble proteins in native or native-like environments. While ssNMR includes numerous nonselective techniques for general analysis, it also provides various selective methods that allow for the extraction of precise details about proteins. This perspective highlights three key aspects of selective methods: selective signals of protein segments, selective recoupling, and site-specific insights into proteins. These methods leverage protein topology, labeling strategies, and the tailored manipulation of spin interactions through radio frequency (RF) pulses, significantly promoting the field of protein ssNMR. With ongoing advancements in higher magnetic fields and faster magic angle spinning (MAS), there remains an ongoing need to enhance the selectivity and efficiency of selective ssNMR methods, facilitating deeper atomic-level insights into complex biological systems.

X-ray crystallography is an important technique to determine the positions of atoms in a protein crystal. However, because the native environment in which proteins function, is not a crystal, but a solution, it is not a priori clear if the crystal structure represents the functional form of the protein. Because the protein structure and function often depend critically on the pH, the question arises whether proton affinities are affected by crystallization. X-ray diffraction usually does not reveal protons, which makes it difficult to experimentally measure p K a shifts in crystals. Here, we investigate whether this challenge can be addressed by performing in silico titration with constant pH molecular dynamics (MD) simulations. We compare the computed p K a values of proteins between solution and crystal environment and analyze these differences in the context of molecular interactions. For the proteins considered in this work, p K a shifts were mostly found for residues at the crystal interfaces, where the environment is more apolar in the crystal than in water. Although convergence was an obstacle, our simulations suggest that in principle it is possible to apply constant pH MD to protein crystals routinely and assess the effect of crystallization on protein function more systematically than with standard MD simulations. We also highlight technical challenges that need to be addressed to make MD simulations of crystals more reliable.

Hydrogen-deuterium exchange (HDX) has become a pivotal method for investigating the structural and dynamic properties of proteins. The versatility and sensitivity of mass spectrometry (MS) made the technique the ideal companion for HDX, and today HDX-MS is addressing a growing number of applications in both academic research and industrial settings. The prolific generation of experimental data has spurred the concurrent development of numerous computational tools, designed to automate parts of the workflow while employing different strategies to achieve common objectives. Various computational methods are available to perform automated peptide searches and identification; different statistical tests have been implemented to quantify differences in the exchange pattern between two or more experimental conditions; alternative strategies have been developed to deconvolve and analyze peptides showing multimodal behavior; and different algorithms have been proposed to computationally increase the resolution of HDX-MS data, with the ultimate aim to provide information at the level of the single residue. This review delves into a comprehensive examination of the merits and drawbacks associated with the diverse strategies implemented by software tools for the analysis of HDX-MS data.

The genetic code is the foundation for all life. With few exceptions, the translation of nucleic acid messages into proteins follows conserved rules, which are defined by codons that specify each of the 20 proteinogenic amino acids. For decades, leading research groups have developed a catalogue of innovative approaches to extend nature's amino acid repertoire to include one or more noncanonical building blocks in a single protein. In this review, we summarize advances in the history of in vitro and in vivo genetic code expansion, and highlight recent innovations that increase the scope of biochemically accessible monomers and codons. We further summarize state-of-the-art knowledge in engineered cellular translation, as well as alterations to regulatory mechanisms that improve overall genetic code expansion. Finally, we distill existing limitations of these technologies into must-have improvements for the next generation of technologies, and speculate on future strategies that may be capable of overcoming current gaps in knowledge.

Protein lipidation dramatically affects protein structure, localization, and trafficking via remodeling protein-membrane and protein-protein interactions through hydrophobic lipid moieties. Understanding the biosynthesis of lipidated proteins, whether natural ones or mimetics, is crucial for reconstructing, validating, and studying the molecular mechanisms and biological functions of protein lipidation. In this Perspective, we first provide an overview of the natural enzymatic biosynthetic pathways of protein lipidation in mammalian cells, focusing on the enzymatic machineries and their chemical linkages. We then discuss strategies to biosynthesize protein lipidation in mammalian cells by engineering modification machineries and substrates. Additionally, we explore site-specific protein lipidation biosynthesis in vitro via enzyme-mediated ligations and in vivo primarily through genetic code expansion strategies. We also discuss the use of small molecule tools to modulate the process of protein lipidation biosynthesis. Finally, we provide concluding remarks and discuss future directions for the biosynthesis and applications of protein lipidation.

The chemical and physical properties of proteins are limited by the 20 canonical amino acids. Genetic code manipulation allows for the incorporation of noncanonical amino acids (ncAAs) that enhance or alter protein functionality. This review explores advances in the three main strategies for introducing ncAAs into biosynthesized proteins, focusing on the role of high throughput screening in these advancements. The first section discusses engineering aminoacyl-tRNA synthetases (aaRSs) and tRNAs, emphasizing how novel selection methods improve characteristics including ncAA incorporation efficiency and selectivity. The second section examines high-throughput techniques for improving protein translation machinery, enabling accommodation of alternative genetic codes. This includes opportunities to enhance ncAA incorporation through engineering cellular components unrelated to translation. The final section highlights various discovery platforms for high-throughput screening of ncAA-containing proteins, showcasing innovative binding ligands and enzymes that are challenging to create with only canonical amino acids. These advances have led to promising drug leads and biocatalysts. Overall, the ability to discover unexpected functionalities through high-throughput methods significantly influences ncAA incorporation and its applications. Future innovations in experimental techniques, along with advancements in computational protein design and machine learning, are poised to further elevate this field.

The MARTINI force field is one of the most used coarse-grained models for biomolecular simulations. Many limitations of the model including the protein-protein overaggregation have been improved in its latest version, MARTINI-3. In this study, we investigate the efficacy of the MARTINI-3 parameters for capturing the interactions of peripheral proteins with model plasma membranes. Particularly, we consider two classes of proteins, namely, annexin and epsin, which are known to generate negative and positive membrane curvatures, respectively. We find that current MARTINI-3 parameters are not able to correctly describe the protein-membrane interface and the protein-induced membrane curvatures for any of these proteins. The problem arises due to the lack of proper hydrophobic interactions between the protein residues and lipid tails. Making systematic adjustments, we show that a combination of reduction in the protein-water interactions and enhancement of protein-lipid hydrophobic interactions is essential for accurate prediction of the interfacial structure including the protein-induced membrane curvature. Next, we apply our model to a couple of other peripheral proteins, namely, Snf7, a core component of the ESCRT-III complex, and the PH domain of evectin-2. We find that our model captures the protein-membrane interfacial structure much more accurately than the MARTINI-3 model for all of the peripheral proteins considered in this study. However, the strategy described in this study may not be suitable for oligomeric transmembrane proteins where protein-protein hydrophobic interactions should be increased instead of protein-lipid hydrophobic interactions.

Biomaterials synthesized from cross-linked folded proteins have untapped potential for biocompatible, resilient, and responsive implementations, but face challenges due to costly molecular refinement and limited understanding of their mechanical response. Under a stress vector, these materials combine the gel-like response of cross-linked networks with the mechanical unfolding and extension of proteins from well-defined 3D structures to unstructured polypeptides. Yet the nanoscale dynamics governing their viscoelastic response remains poorly understood. This lack of understanding is further exacerbated by the fact that the mechanical stability of protein domains depends not only on their structure, but also on the direction of the force vector. To this end, here we propose a coarse-grained network model based on the physical characteristics of polyproteins and combine it with the mechanical unfolding response of protein domains, obtained from single molecule measurements and steered molecular dynamics simulations, to explain the macroscopic response of protein-based materials to a stress vector. We find that domains are about 10-fold more stable when force is applied along their end-to-end coordinate than along the other tethering geometries that are possible inside the biomaterial. As such, the macroscopic response of protein-based materials is mainly driven by the unfolding of the node-domains and rearrangement of these nodes inside the material. The predictions from our models are then confirmed experimentally using force-clamp rheometry. This model is a critical step toward developing protein-based materials with predictable response and that can enable applications for shape memory and energy storage and dissipation.

Achieving accurate characterization of protein structures in the gas phase continues to be a formidable challenge. To tackle this issue, the present study employs Molecular Dynamics (MD) simulations in tandem with enhanced sampling techniques (methods designed to efficiently explore protein conformations). The objective is to identify suitable structures of proteins by contrasting their calculated Collision Cross-Section (CCS) with those observed experimentally. Significant discrepancies were observed between the initial MD-simulated and experimentally measured CCS values through Ion Mobility-Mass Spectrometry (IMS-MS). To bridge this gap, we employed two distinct enhanced sampling methods, Harmonic Biasing Potential and Adaptive Biasing Force, which help the proteins overcome energy barriers to adopt more compact configurations. These techniques leverage the radius of gyration as a reaction coordinate (guiding parameter), guiding the system toward compressed states that potentially match experimental configurations more closely. The guiding forces are only employed to overcome existing barriers and are removed to allow the protein to naturally arrive at a potential gas phase configuration. The results demonstrated close alignment (within ∼4%) between simulated and experimental CCS values despite using different strengths and/or methods, validating their efficacy. This work lays the groundwork for future studies aimed at optimizing biasing methods and expanding the collective variables used for more accurate gas-phase structural predictions.

Recent advancements in deep learning and generative models have significantly expanded the applications of virtual screening for drug-like compounds. Here, we introduce a multitarget transformer model, PCMol , that leverages the latent protein embeddings derived from AlphaFold2 as a means of conditioning a de novo generative model on different targets. Incorporating rich protein representations allows the model to capture their structural relationships, enabling the chemical space interpolation of active compounds and target-side generalization to new proteins based on embedding similarities. In this work, we benchmark against other existing target-conditioned transformer models to illustrate the validity of using AlphaFold protein representations over raw amino acid sequences. We show that low-dimensional projections of these protein embeddings cluster appropriately based on target families and that model performance declines when these representations are intentionally corrupted. We also show that the PCMol model generates diverse, potentially active molecules for a wide array of proteins, including those with sparse ligand bioactivity data. The generated compounds display higher similarity known active ligands of held-out targets and have comparable molecular docking scores while maintaining novelty. Additionally, we demonstrate the important role of data augmentation in bolstering the performance of generative models in low-data regimes. Software package and AlphaFold protein embeddings are freely available at https://github.com/CDDLeiden/PCMol.

Cosolvent molecular dynamics (MDs) are an increasingly popular form of simulations where small molecule cosolvents are added to water-solvated protein systems. These simulations can perform diverse target characterization tasks, including cryptic and allosteric pocket identification and pharmacophore profiling and supplement suites of enhanced sampling methods to explore protein conformational landscapes. The behavior of these systems is tied to the cosolvents used, so the ability to define diverse and complex mixtures is critical in dictating the outcome of the simulations. However, existing methods for preparing cosolvent simulations only support a limited number of predefined cosolvents and concentrations. Here, we present CosolvKit, a tool for the preparation and analysis of systems composed of user-defined cosolvents and concentrations. This tool is modular, supporting the creation of files for multiple MD engines, as well as direct access to OpenMM simulations, and offering access to a variety of generalizable small-molecule force fields. To the best of our knowledge, CosolvKit represents the first generalized approach for the construction of these simulations.

DNA is a kind of flexible and versatile biomaterial for constructing nanostructures and nanodevices. Due to high biocompatibility and programmability and easy modification and fabrication, DNA nanotechnology has emerged as a powerful tool for application in intracellular targeted protein degradation. In this review, we summarize the recent advances in the design and mechanism of targeted protein degradation technologies such as protein hydrolysis targeted chimeras, lysosomal targeted chimeras, and autophagy based protein degradation. Subsequently, we introduce the DNA nanotechnologies of DNA cascade circuits, DNA nanostructures, and dynamic machines. Moreover, we present the latest developments in DNA nanotechnologies in targeted protein degradation. Finally, the vision and challenges are discussed.

Protein drugs are of great importance in maintaining the normal functioning of living organisms. Indeed, they have been instrumental in combating tumors and genetic diseases for decades. Among these pharmaceutical agents, those that target intracellular components necessitate the use of therapeutic proteins to exert their effects within the targeted cells. However, the use of protein drugs is limited by their short half-life and potential adverse effects in the physiological environment. The advent of nanoparticles offers a promising avenue for prolonging the half-life of protein drugs. This is achieved by encapsulating proteins, thereby safeguarding their biological activity and ensuring precise delivery into cells. This nanomaterial-based intracellular protein drug delivery system mitigates the rapid hydrolysis and unwarranted diffusion of proteins, thereby minimizing potential side effects and circumventing the limitations inherent in traditional techniques like electroporation. This review examines established protein drug delivery systems, including those based on polymers, liposomes, and protein nanoparticles. We delve into the operational principles and transport mechanisms of nanocarriers, discussing the various considerations essential for designing cutting-edge delivery platforms. Additionally, we investigate innovative designs and applications of traditional cytosolic protein delivery systems in medical research and clinical practice, particularly in areas like tumor treatment, gene editing and fluorescence imaging. This review sheds light on the current restrictions of protein delivery systems and anticipates future research avenues, aiming to foster the continued advancement in this field., Competing Interests: Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare no competing interests., (© 2024. The Author(s).)

Predicting the backbone torsion angles corresponding to each residue of a protein from its amino acid sequence alone is a challenging problem in computational biology. Existing torsion angle predictors mainly use profile features, which are generated by performing time-consuming multiple sequence alignments, for torsion angle prediction. Compared with traditional profile features, embedding features from pretrained protein language models have significant advantages in prediction performance and computational speed. However, embedding features usually have higher dimensions and different embedding features have significantly different dimensions. To this end, we design a novel parameter-efficient deep torsion angle predictor, PHAngle, specifically for embedding features. PHAngle is a parameterized hypercomplex convolutional network consisting of parameterized hypercomplex linear and convolutional layers whose weight parameters can be characterized as the sum of Kronecker products. Experimental results on six benchmark test sets including TEST2016, TEST2018, TEST2020&#95;HQ, CASP12, CASP13 and CASP-FM demonstrate that PHAngle achieves the state-of-the-art torsion angle performance with the fewest parameters compared to the nine existing methods. The source code and datasets are available at https://github.com/fengtuan/PHAngle ., (© 2024. The Author(s).)

Taylor Dispersion Analysis (TDA) allows diffusion coefficient (D) or hydrodynamic radius (R h ) determination on a wide range of size between angstroms and about 300 nm. However, solute adsorption phenomena can affect the repeatability and reproducibility of TDA. Several numerical studies addressed the theoretical impact of solute adsorption in TDA, but very few experimental studies focus on this topic and no experimental methodologies were proposed so far to reduce the impact of adsorption in TDA. In this work, an experimental protocol, called plug-in-front TDA, consisting of adding the solute in the eluent at a lower concentration compared to the injected sample, was proposed to strongly limit the impact of adsorption on the R h determination. This protocol was suggested based on the evidence that adsorption / desorption phenomena impacting narrow bore fused silica TDA in aqueous conditions are typically slow processes that can be counteracted by saturating the interaction sites during the experiments. Successful applications to proteins and mRNA lipid nanoparticles (LNP) in vaccine against Covid 19 and protein analysis were reported. TDA of proteins in conditions of strong interactions with the capillary surface was possible using the plug-in-front methodology. We anticipate that such experimental methodology will greatly help the experimentalist for implementing TDA in various applications., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Protein PEGylation represents a significant technological advancement in the development of protein-based therapeutics and is widely used to reduce immunogenicity, enhance pharmacokinetics, and/or improve stability. The improved pharmacokinetic profile of PEGylated proteins compared with the native protein results in sustained versus fluctuating plasma concentrations and carries the potential of less frequent administration. However, attachment of PEG to therapeutic proteins can alter their structural conformation, which exposes new epitopes to the immune system. The design of PEGylated proteins thus needs to balance the intended benefits with the potential risks associated with the immunogenicity of the PEG moiety itself or resulting from alterations in the conformation of the therapeutic protein. In recent years, advancements in protein PEGylation chemistry have offered the capability to target PEG attachment to specific amino acids to create more stable and bioactive therapies. The biophysical and biopharmaceutical features of PEGylated proteins can vary based on polymer size, shape, density, and conjugation site, and the immunogenicity of the conjugate can be further impacted by the properties of the therapeutic protein itself and the characteristics of the patient. It is important to note that not all patients will develop an immune response toward the PEG moiety, and not all immune responses are clinically meaningful. A comprehensive understanding of the factors that influence immunogenic responses to PEGylated proteins is important to optimize their therapeutic benefits. This article reviews the design and optimization of PEGylation strategies to enhance the clinical performance of protein-based therapeutics while minimizing immunogenic responses to the PEG moiety or PEGylated proteins., Competing Interests: J Gonçalves: Manuscript support – Chiesi; Grants or other contracts – AstraZeneca, Pfizer; Consulting fees – AstraZeneca, Celltrion, Chiesi, Samsung Biologics; Payment or honoraria – AstraZeneca, Celltrion, Chiesi, Pfizer, Samsung Biologics. P Caliceti: Manuscript support – Chiesi. The authors report no other conflicts of interest in this work., (© 2024 Gonçalves and Caliceti.)

The ability to predict how amino acid changes affect proteins has a wide range of applications including in disease variant classification and protein engineering. Many existing methods focus on learning from patterns found in either protein sequences or protein structures. Here, we present a method for integrating information from sequence and structure in a single model that we term SSEmb (Sequence Structure Embedding). SSEmb combines a graph representation for the protein structure with a transformer model for processing multiple sequence alignments. We show that by integrating both types of information we obtain a variant effect prediction model that is robust when sequence information is scarce. We also show that SSEmb learns embeddings of the sequence and structure that are useful for other downstream tasks such as to predict protein-protein binding sites. We envisage that SSEmb may be useful both for variant effect predictions and as a representation for learning to predict protein properties that depend on sequence and structure., (© 2024. The Author(s).)

Structure-based machine learning algorithms have been utilized to predict the properties of protein-protein interaction (PPI) complexes, such as binding affinity, which is critical for understanding biological mechanisms and disease treatments. While most existing algorithms represent PPI complex graph structures at the atom-scale or residue-scale, these representations can be computationally expensive or may not sufficiently integrate finer chemical-plausible interaction details for improving predictions. Here, we introduce MCGLPPI, a geometric representation learning framework that combines graph neural networks (GNNs) with MARTINI molecular coarse-grained (CG) models to predict PPI overall properties accurately and efficiently. Extensive experiments on three types of downstream PPI property prediction tasks demonstrate that at the CG-scale, MCGLPPI achieves competitive performance compared with the counterparts at the atom- and residue-scale, but with only a third of computational resource consumption. Furthermore, CG-scale pre-training on protein domain-domain interaction structures enhances its predictive capabilities for PPI tasks. MCGLPPI offers an effective and efficient solution for PPI overall property predictions, serving as a promising tool for the large-scale analysis of biomolecular interactions., (© 2024. The Author(s).)

The Lipari-Szabo generalized order parameter probes the picosecond to nanosecond time scale motions of a protein and is useful for rationalizing a multitude of biological processes such as protein recognition and ligand binding. Although these fast motions are an important and intrinsic property of proteins, it remains unclear what simulation conditions are most suitable to reproduce methyl symmetry axis side chain order parameter data ( S axis 2 ) from molecular dynamics simulations. In this study, we show that, while S axis 2 tends to converge within tens of nanoseconds, it is essential to run 10 to 20 replicas starting from configurations close to the experimental structure to obtain the best agreement with experimental S axis 2 values. Additionally, in a comparison of force fields, AMBER ff14SB outperforms CHARMM36m in accurately capturing these fast time scale motions, and we suggest that the origin of this performance gap is likely attributed to differences in side chain torsional parametrization and not due to differences in the global protein conformations sampled by the force fields. This study provides insight into obtaining accurate and reproducible S axis 2 values from molecular simulations and underscores the necessity of using replica simulations to compute equilibrium properties.

Recent breakthroughs in protein structure prediction have enhanced the precision and speed at which protein configurations can be determined. Additionally, molecular dynamics (MD) simulations serve as a crucial tool for capturing the conformational space of proteins, providing valuable insights into their structural fluctuations. However, the scope of MD simulations is often limited by the accessible timescales and the computational resources available, posing challenges to comprehensively exploring protein behaviors. Recently emerging approaches have focused on expanding the capability of AlphaFold2 (AF2) to predict conformational substates of protein. Here, we benchmark the performance of various workflows that have adapted AF2 for ensemble prediction and compare the obtained structures with ensembles obtained from MD simulations and NMR. We provide an overview of the levels of performance and accessible timescales that can currently be achieved with machine learning (ML) based ensemble generation. Significant minima of the free energy surfaces remain undetected., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2024. Published by Elsevier Inc.)

Homorepeats are motifs with reiterations of the same amino acid. They are prevalent in proteins associated with diverse physiological functions but also linked to several pathologies. Structural characterization of homorepeats has remained largely elusive, primarily because they generally occur in the disordered regions or proteins. Here, we address this subject by combining structures derived from machine learning with conformational sampling through physics-based simulations. We find that hydrophobic homorepeats have a tendency to fold into structured secondary conformations, while hydrophilic ones predominantly exist in unstructured states. Our data show that the flexibility rendered by disorder is a critical component besides the chemical feature that drives homorepeats composition toward hydrophilicity. The formation of regular secondary structures also influences their solubility, as pathologically relevant homorepeats display a direct correlation between repeat expansion, induction of helicity, and self-assembly. Our study provides critical insights into the conformational landscape of protein homorepeats and their structure-activity relationship., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

After the successful sequencing of nucleic acids, nanopore technology has now been applied to proteins. Recently, it has been demonstrated that an electro-osmotic flow can be used to induce the transport of unraveled polypeptides across nanopores. Polypeptide translocation, however, is too fast for accurate reading its amino acid compositions. Here, we show that the introduction of hydrophobic residues into the lumen of the nanopore reduces the protein translocation speed. Importantly, the introduction of a tyrosine at the entry of the nanopore and an isoleucine at the entry of the β-barrel of the nanopore reduced the speed of translocation to ∼10 amino acids/millisecond while keeping a relatively large ionic current, a crucial component for protein identification. These nanopores showed unique features within their current signatures, which may pave the way toward protein fingerprinting using nanopores.