Ins and outs of AlphaFold2 transmembrane protein structure predictions

Phylogenetic and Structural Analyses of Vesicular Glutamate Transporters

Vesicular glutamate transporters are members of the solute carrier 17 (SLC17) family, and mammals express three closely related isoforms: vGluT1-3. While vGluT genes have been identified across various species in the Animalia kingdom, the evolutionary relationships and the natural history of vGluT members remain poorly understood. This study aimed to address these gaps by presenting a phylogenetic analysis of vGluTs across the animal kingdom. The study also included a detailed sequence analysis and structural modeling of vGluT isoforms among species. The phylogenetic tree revealed distinct clusters corresponding to the vGluts isoform 1, 2, and 3, with functional amino acid residues highly conserved among them. Invertebrate vGluTs emerged as the most divergent proteins, serving as the root of the tree. Sequence analysis confirmed the high conservation of vGluTs transmembrane core regions but identified high variations in the N and C-terminal ones. Structural analysis revealed that AlphaFold2-predicted models demonstrated high confidence quality in the transmembrane domains, but exhibited limited local similarity in the N-terminal, C-terminal, and loop regions. On the other hand, the expected topology of these helices was accurately captured and positioned in the Swiss-Model-generated structures, with the functionally relevant residues precisely positioned in three-dimensional space. In conclusion, we expect that our findings will contribute to a deeper understanding of vesicular glutamate transporter structure and function, as well as their roles across distinct species and biological contexts.

A key residue of the extracellular gate provides quality control contributing to ABCG substrate specificity

For G-type ATP-binding cassette (ABC) transporters, a hydrophobic "di-leucine motif" as part of a hydrophobic extracellular gate has been described to separate a large substrate-binding cavity from a smaller upper cavity and proposed to act as a valve controlling drug extrusion. Here, we show that an L704F mutation in the hydrophobic extracellular gate of Arabidopsis ABCG36/PDR8/PEN3 uncouples the export of the auxin precursor indole-3-butyric acid (IBA) from that of the defense compound camalexin (CLX). Molecular dynamics simulations reveal increased free energy for CLX translocation in ABCG36L704F and reduced CLX contacts within the binding pocket proximal to the extracellular gate region. Mutation L704Y enables export of structurally related non-ABCG36 substrates, IAA, and indole, indicating allosteric communication between the extracellular gate and distant transport pathway regions. An evolutionary analysis identifies L704 as a Brassicaceae family-specific key residue of the extracellular gate that controls the identity of chemically similar substrates. In summary, our work supports the conclusion that L704 is a key residue of the extracellular gate that provides a final quality control contributing to ABCG substrate specificity, allowing for balance of growth-defense trade-offs.

De novo design of transmembrane fluorescence-activating proteins

The recognition of ligands by transmembrane proteins is essential for the exchange of materials, energy and information across biological membranes. Progress has been made in the de novo design of transmembrane proteins1-6, as well as in designing water-soluble proteins to bind small molecules7-12, but de novo design of transmembrane proteins that tightly and specifically bind to small molecules remains an outstanding challenge13. Here we present the accurate design of ligand-binding transmembrane proteins by integrating deep learning and energy-based methods. We designed pre-organized ligand-binding pockets in high-quality four-helix backbones for a fluorogenic ligand, and generated a transmembrane span using gradient-guided hallucination. The designer transmembrane proteins specifically activated fluorescence of the target fluorophore with mid-nanomolar affinity, exhibiting higher brightness and quantum yield compared to those of enhanced green fluorescent protein. These proteins were highly active in the membrane fraction of live bacterial and eukaryotic cells following expression. The crystal and cryogenic electron microscopy structures of the designer protein-ligand complexes were very close to the structures of the design models. We showed that the interactions between ligands and transmembrane proteins within the membrane can be accurately designed. Our work paves the way for the creation of new functional transmembrane proteins, with a wide range of applications including imaging, ligand sensing and membrane transport.

Learning transition path and membrane topological signatures in the folding pathway of bacteriorhodopsin (BR) fragment with artificial intelligence

Membrane protein folding in the viscous microenvironment of a lipid bilayer is an inherently slow process that challenges experiments and computational efforts alike. The folding kinetics is moreover associated with topological modulations of the biological milieu. Studying such structural changes in membrane-embedded proteins and understanding the associated topological signatures in membrane leaflets, therefore, remain relatively unexplored. Herein, we first aim to estimate the free energy barrier and the minimum free energy path (MFEP) connecting the membrane-embedded fully and partially inserted states of the bacteriorhodopsin fragment. To achieve this, we have considered independent sets of simulations from membrane-mimicking and membrane-embedded environments, respectively. An autoencoder model is used to elicit state-distinguishable collective variables for the system utilizing membrane-mimicking simulations. Our in-house Expectation Maximized Molecular Dynamics algorithm is initially used to deduce the barrier height between the two membrane-embedded states. Next, we develop the Geometry Optimized Local Direction search as a post-processing algorithm to identify the MFEP and the corresponding peptide conformations from the autoencoder-projected trajectories. Finally, we apply a graph attention neural network (GAT) model to learn the membrane surface topology as a function of the associated peptide structure, supervised by the membrane-embedded simulations. The resultant GAT model is then utilized to predict the membrane leaflet topology for the peptide structures along MFEP, obtained from membrane-mimicking simulations. The combined framework is expected to be useful in capturing key phenomena accompanying folding transitions in membranes. We discuss opportunities and avenues for further development.

Conformationally adaptive therapeutic peptides for diseases caused by intrinsically disordered proteins (IDPs). New paradigm for drug discovery: Target the target, not the arrow

The traditional model of protein structure determined by the amino acid sequence is today seriously challenged by the fact that approximately half of the human proteome is made up of proteins that do not have a stable 3D structure, either partially or in totality. These proteins, called intrinsically disordered proteins (IDPs), are involved in numerous physiological functions and are associated with severe pathologies, e.g. Alzheimer, Parkinson, Creutzfeldt-Jakob, amyotrophic lateral sclerosis (ALS), and type 2 diabetes. Targeting these proteins is challenging for two reasons: i) we need to preserve their physiological functions, and ii) drug design by molecular docking is not possible due to the lack of reliable starting conditions. Faced with this challenge, the solutions proposed by artificial intelligence (AI) such as AlphaFold are clearly unsuitable. Instead, we suggest an innovative approach consisting of mimicking, in short synthetic peptides, the conformational flexibility of IDPs. These peptides, which we call adaptive peptides, are derived from the domains of IDPs that become structured after interacting with a ligand. Adaptive peptides are designed with the aim of selectively antagonizing the harmful effects of IDPs, without targeting them directly but through selected ligands, without affecting their physiological properties. This "target the target, not the arrow" strategy is promised to open a new route to drug discovery for currently undruggable proteins.

TMVisDB: Annotation and 3D-visualization of Transmembrane Proteins

Since the rise of cellular life, transmembrane proteins (TMPs) have been crucial to various cellular processes through their central role as gates and gatekeepers. Despite their importance, experimental high-resolution structures for TMPs remain underrepresented due to experimental challenges. Given its performance leap, structure predictions have begun to close the gap. However, identifying the membrane regions and topology in three-dimensional structure files on a large scale still requires additional in silico predictions. Here, we introduce TMVisDB to sieve through millions of predicted structures for TMPs. This resource enables both browsing through 46 million predicted TMPs and visualizing them along with their topological annotations without having to tap into costly predictions of the AlphaFold3-style. TMVisDB joins AlphaFoldDB structure predictions and transmembrane topology predictions from the protein language model (pLM) based method TMbed. We showcase the utility of TMVisDB for the analysis of proteins through two use cases, namely the B-lymphocyte antigen CD20 (Homo sapiens) and the cellulose synthase (Novosphingobium sp. P6W). We demonstrate the value of TMVisDB for large-scale analyses through findings pertaining to all TMPs predicted for the human proteome. TMVisDB is freely available at https://tmvisdb.rostlab.org.

AlphaFold and what is next: bridging functional, systems and structural biology

INTRODUCTION

The DeepMind's AlphaFold (AF) has revolutionized biomedical and biocience research by providing both experts and non-experts with an invaluable tool for predicting protein structures. However, while AF is highly effective for predicting structures of rigid and globular proteins, it is not able to fully capture the dynamics, conformational variability, and interactions of proteins with ligands and other biomacromolecules.

AREAS COVERED

In this review, we present a comprehensive overview of the latest advancements in 3D model predictions for biomacromolecules using AF. We also provide a detailed analysis its of strengths and limitations, and explore more recent iterations, modifications, and practical applications of this strategy. Moreover, we map the path forward for expanding the landscape of AF toward predicting structures of every protein and peptide, and their interactions in the proteome in the most physiologically relevant form. This discussion is based on an extensive literature search performed using PubMed and Google Scholar.

EXPERT OPINION

While significant progress has been made to enhance AF's modeling capabilities, we argue that a combined approach integrating both various in silico and in vitro methods will be most beneficial for the future of structural biology, bridging the gaps between static and dynamic features of proteins and their functions.

An in silico framework to visualize how cancer-associated mutations influence structural plasticity of the chemokine receptor CCR3

G protein Coupled Receptors (GPCRs) are the largest family of cell surface receptors in humans. Somatic mutations in GPCRs are implicated in cancer progression and metastasis, but mechanisms are poorly understood. Emerging evidence implicates perturbation of intra-receptor activation pathway motifs whereby extracellular signals are transmitted intracellularly. Recently, sufficiently sensitive methodology was described to calculate structural strain as a function of missense mutations in AlphaFold-predicted model structures, which was extensively validated on experimental and predicted structural datasets. When paired with Molecular Dynamics (MD) simulations, these tools provide a facile approach to screen mutations in silico. We applied this framework to calculate the structural and dynamic effects of cancer-associated mutations in the chemokine receptor CCR3, a Class A GPCR involved in cancer and autoimmune disorders. Residue-residue contact scoring refined effective strain results, highlighting significant remodeling of inter- and intra-motif contacts along the highly conserved GPCR activation pathway network. We then integrated AlphaFold-derived predicted Local Distance Difference Test scores with per-residue Root Mean Square Fluctuations and activation pathway Contact Analysis (CONAN) from coarse grain MD simulations to identify statistically significant changes in receptor dynamics upon mutation. Finally, analysis of negative control mutants suggests false positive results in AlphaFold pipelines should be considered but can be mitigated with stricter control of statistical analysis. Our results indicate selected mutants influence structural plasticity of CCR3 related to ligand interaction, activation, and G protein coupling, using a framework that could be applicable to a wide range of biochemically relevant protein targets following further validation.

Computational approach based on freely accessible tools for antimicrobial drug design

Antimicrobial drug development is crucial for public health, especially with the emergence of pandemics and drug resistance that prompts the search for new therapeutic resources. In this context, in silico assays consist of a valuable approach in the rational drug design because they enable a faster and more cost-effective identification of drug candidates compared to in vitro screening. However, once a potential drug is identified, in vitro and in vivo assays are essential to verify the expected activity of the compound and advance it through the subsequent stages of drug development. This work aims to outline an in silico protocol that utilizes only freely available computational tools for identifying new potential antimicrobial agents, which is also suitable in the broad spectrum of drug design. Additionally, this paper reviews relevant computational methods in this context and provides a summary of information concerning the protein-ligand interaction.

Prediction of conformational states in a coronavirus channel using Alphafold-2 and DeepMSA2: Strengths and limitations

The envelope (E) protein is present in all coronavirus genera. This protein can form pentameric oligomers with ion channel activity which have been proposed as a possible therapeutic target. However, high resolution structures of E channels are limited to those of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), responsible for the recent COVID-19 pandemic. In the present work, we used Alphafold-2 (AF2), in ColabFold without templates, to predict the transmembrane domain (TMD) structure of six E-channels representative of genera alpha-, beta- and gamma-coronaviruses in the Coronaviridae family. High-confidence models were produced in all cases when combining multiple sequence alignments (MSAs) obtained from DeepMSA2. Overall, AF2 predicted at least two possible orientations of the α-helices in E-TMD channels: one where a conserved polar residue (Asn-15 in the SARS sequence) is oriented towards the center of the channel, 'polar-in', and one where this residue is in an interhelical orientation 'polar-inter'. For the SARS models, the comparison with the two experimental models 'closed' (PDB: 7K3G) and 'open' (PDB: 8SUZ) is described, and suggests a ∼60˚ α-helix rotation mechanism involving either the full TMD or only its N-terminal half, to allow the passage of ions. While the results obtained are not identical to the two high resolution models available, they suggest various conformational states with striking similarities to those models. We believe these results can be further optimized by means of MSA subsampling, and guide future high resolution structural studies in these and other viral channels.

Structural Rearrangement of the AT1 Receptor Modulated by Membrane Thickness and Tension

Membrane-embedded mechanosensitive (MS) proteins, including ion channels and G-protein coupled receptors (GPCRs), are essential for the transduction of external mechanical stimuli into biological signals. The angiotensin II type 1 (AT1) receptor plays many important roles in cardiovascular regulation and is associated with diseases such as hypertension and congestive heart failure. The membrane-mediated activation of the AT1 receptor is not well understood, despite this being one of the most widely studied GPCRs within the context of biased agonism. Here, we use extensive molecular dynamics (MD) simulations to characterize the effect of the local membrane environment on the activation of the AT1 receptor. We show that membrane thickness plays an important role in the stability of active and inactive states of the receptor, as well as the dynamic interchange between states. Furthermore, our simulation results show that membrane tension is effective in driving large-scale structural changes in the inactive state such as the outward movement of transmembrane helix 6 to stabilize intermediate active-like conformations. We conclude by comparing our simulation observations with AlphaFold 2 predictions, as a proxy to experimental structures, to provide a framework for how membrane mediated stimuli can facilitate activation of the AT1 receptor through the β-arrestin signaling pathway.

SARS-CoV-2 envelope protein alters calcium signaling via SERCA interactions

The clinical management of severe COVID-19 cases is not yet well resolved. Therefore, it is important to identify and characterize cell signaling pathways involved in virus pathogenesis that can be targeted therapeutically. Envelope (E) protein is a structural protein of the virus, which is known to be highly expressed in the infected host cell and is a key virulence factor; however, its role is poorly characterized. The E protein is a single-pass transmembrane protein that can assemble into a pentamer forming a viroporin, perturbing Ca2+ homeostasis. Because it is structurally similar to regulins such as, for example, phospholamban, that regulate the sarco/endoplasmic reticulum calcium ATPases (SERCA), we investigated whether the SARS-CoV-2 E protein affects the SERCA system as an exoregulin. Using FRET experiments we demonstrate that E protein can form oligomers with regulins, and thus can alter the monomer/multimer regulin ratio and consequently influence their interactions with SERCAs. We also confirm that a direct interaction between E protein and SERCA2b results in a decrease in SERCA-mediated ER Ca2+ reload. Structural modeling of the complexes indicates an overlapping interaction site for E protein and endogenous regulins. Our results reveal novel links in the host-virus interaction network that play an important role in viral pathogenesis and may provide a new therapeutic target for managing severe inflammatory responses induced by SARS-CoV-2.

SPOT: A machine learning model that predicts specific substrates for transport proteins

Transport proteins play a crucial role in cellular metabolism and are central to many aspects of molecular biology and medicine. Determining the function of transport proteins experimentally is challenging, as they become unstable when isolated from cell membranes. Machine learning-based predictions could provide an efficient alternative. However, existing methods are limited to predicting a small number of specific substrates or broad transporter classes. These limitations stem partly from using small data sets for model training and a choice of input features that lack sufficient information about the prediction problem. Here, we present SPOT, the first general machine learning model that can successfully predict specific substrates for arbitrary transport proteins, achieving an accuracy above 92% on independent and diverse test data covering widely different transporters and a broad range of metabolites. SPOT uses Transformer Networks to represent transporters and substrates numerically. To overcome the problem of missing negative data for training, it augments a large data set of known transporter-substrate pairs with carefully sampled random molecules as non-substrates. SPOT not only predicts specific transporter-substrate pairs, but also outperforms previously published models designed to predict broad substrate classes for individual transport proteins. We provide a web server and Python function that allows users to explore the substrate scope of arbitrary transporters.

Caught between a ROCK and a hard place: current challenges in structure-based drug design

The discipline of structure-based drug design (SBDD) is several decades old and it is tempting to think that the proliferation of experimental structures for many drug targets might make computer-aided drug design (CADD) straightforward. However, this is far from true. In this review, we illustrate some of the challenges that CADD scientists face every day in their work, even now. We use Rho-associated protein kinase (ROCK), and public domain structures and data, as an example to illustrate some of the challenges we have experienced during our project targeting this protein. We hope that this will help to prevent unrealistic expectations of what CADD can accomplish and to educate non-CADD scientists regarding the challenges still facing their CADD colleagues.

The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins

Artificial intelligence-driven advances in protein structure prediction in recent years have raised the question: has the protein structure-prediction problem been solved? Here, with a focus on nonglobular proteins, we highlight the many strengths and potential weaknesses of DeepMind's AlphaFold2 in the context of its biological and therapeutic applications. We summarize the subtleties associated with evaluation of AlphaFold2 model quality and reliability using the predicted local distance difference test (pLDDT) and predicted aligned error (PAE) values. We highlight various classes of proteins that AlphaFold2 can be applied to and the caveats involved. Concrete examples of how AlphaFold2 models can be integrated with experimental data in the form of small-angle X-ray scattering (SAXS), solution NMR, cryo-electron microscopy (cryo-EM) and X-ray diffraction are discussed. Finally, we highlight the need to move beyond structure prediction of rigid, static structural snapshots toward conformational ensembles and alternate biologically relevant states. The overarching theme is that careful consideration is due when using AlphaFold2-generated models to generate testable hypotheses and structural models, rather than treating predicted models as de facto ground truth structures.

AlphaFold2 in biomedical research: facilitating the development of diagnostic strategies for disease

Proteins, as the primary executors of physiological activity, serve as a key factor in disease diagnosis and treatment. Research into their structures, functions, and interactions is essential to better understand disease mechanisms and potential therapies. DeepMind's AlphaFold2, a deep-learning protein structure prediction model, has proven to be remarkably accurate, and it is widely employed in various aspects of diagnostic research, such as the study of disease biomarkers, microorganism pathogenicity, antigen-antibody structures, and missense mutations. Thus, AlphaFold2 serves as an exceptional tool to bridge fundamental protein research with breakthroughs in disease diagnosis, developments in diagnostic strategies, and the design of novel therapeutic approaches and enhancements in precision medicine. This review outlines the architecture, highlights, and limitations of AlphaFold2, placing particular emphasis on its applications within diagnostic research grounded in disciplines such as immunology, biochemistry, molecular biology, and microbiology.

PtdIns4P is required for the autophagosomal recruitment of STX17 (syntaxin 17) to promote lysosomal fusion

The autophagosomal SNARE STX17 (syntaxin 17) promotes lysosomal fusion and degradation, but its autophagosomal recruitment is incompletely understood. Notably, PtdIns4P is generated on autophagosomes and promotes fusion through an unknown mechanism. Here we show that soluble recombinant STX17 is spontaneously recruited to negatively charged liposomes and adding PtdIns4P to liposomes containing neutral lipids is sufficient for its recruitment. Consistently, STX17 colocalizes with PtdIns4P-positive autophagosomes in cells, and specific inhibition of PtdIns4P synthesis on autophagosomes prevents its loading. Molecular dynamics simulations indicate that C-terminal positively charged amino acids establish contact with membrane bilayers containing negatively charged PtdIns4P. Accordingly, Ala substitution of Lys and Arg residues in the C terminus of STX17 abolishes membrane binding and impairs its autophagosomal recruitment. Finally, only wild type but not Ala substituted STX17 expression rescues the autophagosome-lysosome fusion defect of STX17 loss-of-function cells. We thus identify a key step of autophagosome maturation that promotes lysosomal fusion.Abbreviations: Cardiolipin: 1',3'-bis[1-palmitoyl-2-oleoyl-sn-glycero-3-phospho]-glycerol; DMSO: dimethyl sulfoxide; GST: glutathione S-transferase; GUV: giant unilamellar vesicles; LAMP1: lysosomal associated membrane protein 1; MAP1LC3/LC3: microtubule associated protein 1 light chain 3; PA: 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphate; PC/POPC: 1-palmitoyl-2-oleoyl-glycero-3-phosphocholine; PG: 1-palmitoyl-2-linoleoyl-sn-glycero-3-phospho-(1'-rac-glycerol); PI: L-α-phosphatidylinositol; PI4K2A: phosphatidylinositol 4-kinase type 2 alpha; PIK3C3/VPS34: phosphatidylinositol 3-kinase catalytic subunit type 3; POPE/PE: 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphoethanolamine; PS: 1-stearoyl-2-linoleoyl-sn-glycero-3-phospho-L-serine; PtdIns(3,5)P2: 1,2-dioleoyl-sn-glycero-3-phospho-(1"-myo-inositol-3',5'-bisphosphate); PtdIns3P: 1,2- dioleoyl-sn-glycero-3-phospho-(1'-myo-inositol-3'-phosphate); PtdIns4P: 1,2-dioleoyl-sn-glycero-3-phospho-(1"-myo-inositol-4'-phosphate); SDS-PAGE: sodium dodecyl sulfate-polyacrylamide gel electrophoresis; STX17: syntaxin 17.

Experimental and computational approaches for membrane protein insertion and topology determination

Membrane proteins play pivotal roles in a wide array of cellular processes and constitute approximately a quarter of the protein-coding genes across all organisms. Despite their ubiquity and biological significance, our understanding of these proteins remains notably less comprehensive compared to their soluble counterparts. This disparity in knowledge can be attributed, in part, to the inherent challenges associated with employing specialized techniques for the investigation of membrane protein insertion and topology. This review will center on a discussion of molecular biology methodologies and computational prediction tools designed to elucidate the insertion and topology of helical membrane proteins.

The S-component fold: a link between bacterial transporters and receptors

The processes of nutrient uptake and signal sensing are crucial for microbial survival and adaptation. Membrane-embedded proteins involved in these functions (transporters and receptors) are commonly regarded as unrelated in terms of sequence, structure, mechanism of action and evolutionary history. Here, we analyze the protein structural universe using recently developed artificial intelligence-based structure prediction tools, and find an unexpected link between prominent groups of microbial transporters and receptors. The so-called S-components of Energy-Coupling Factor (ECF) transporters, and the membrane domains of sensor histidine kinases of the 5TMR cluster share a structural fold. The discovery of their relatedness manifests a widespread case of prokaryotic "transceptors" (related proteins with transport or receptor function), showcases how artificial intelligence-based structure predictions reveal unchartered evolutionary connections between proteins, and provides new avenues for engineering transport and signaling functions in bacteria.

The application of large language models in medicine: A scoping review

This study systematically reviewed the application of large language models (LLMs) in medicine, analyzing 550 selected studies from a vast literature search. LLMs like ChatGPT transformed healthcare by enhancing diagnostics, medical writing, education, and project management. They assisted in drafting medical documents, creating training simulations, and streamlining research processes. Despite their growing utility in assisted diagnosis and improving doctor-patient communication, challenges persisted, including limitations in contextual understanding and the risk of over-reliance. The surge in LLM-related research indicated a focus on medical writing, diagnostics, and patient communication, but highlighted the need for careful integration, considering validation, ethical concerns, and the balance with traditional medical practice. Future research directions suggested a focus on multimodal LLMs, deeper algorithmic understanding, and ensuring responsible, effective use in healthcare.

Analysis of AlphaMissense data in different protein groups and structural context

Single amino acid substitutions can profoundly affect protein folding, dynamics, and function. The ability to discern between benign and pathogenic substitutions is pivotal for therapeutic interventions and research directions. Given the limitations in experimental examination of these variants, AlphaMissense has emerged as a promising predictor of the pathogenicity of missense variants. Since heterogenous performance on different types of proteins can be expected, we assessed the efficacy of AlphaMissense across several protein groups (e.g. soluble, transmembrane, and mitochondrial proteins) and regions (e.g. intramembrane, membrane interacting, and high confidence AlphaFold segments) using ClinVar data for validation. Our comprehensive evaluation showed that AlphaMissense delivers outstanding performance, with MCC scores predominantly between 0.6 and 0.74. We observed low performance on disordered datasets and ClinVar data related to the CFTR ABC protein. However, a superior performance was shown when benchmarked against the high quality CFTR2 database. Our results with CFTR emphasizes AlphaMissense's potential in pinpointing functional hot spots, with its performance likely surpassing benchmarks calculated from ClinVar and ProteinGym datasets.

A Perspective on Protein Structure Prediction Using Quantum Computers

Despite the recent advancements by deep learning methods such as AlphaFold2, in silico protein structure prediction remains a challenging problem in biomedical research. With the rapid evolution of quantum computing, it is natural to ask whether quantum computers can offer some meaningful benefits for approaching this problem. Yet, identifying specific problem instances amenable to quantum advantage and estimating the quantum resources required are equally challenging tasks. Here, we share our perspective on how to create a framework for systematically selecting protein structure prediction problems that are amenable for quantum advantage, and estimate quantum resources for such problems on a utility-scale quantum computer. As a proof-of-concept, we validate our problem selection framework by accurately predicting the structure of a catalytic loop of the Zika Virus NS3 Helicase, on quantum hardware.

Screening androgen receptor agonists of fish species using machine learning and molecular model in NORMAN water-relevant list

Androgen receptor (AR) agonists have strong endocrine disrupting effects in fish. Most studies mainly investigate AR binding capacity using human AR in vitro. However, there is still few methods to rapidly predict AR agonists in aquatic organisms. This study aimed to screen AR agonists of fish species using machine learning and molecular models in water-relevant list from NORMAN, a network of reference laboratories for monitoring contaminants of emerging concern in the environment. In this study, machine learning approaches (e.g., Deep Forest (DF)), Random Forests and artificial neural networks) were applied to predict AR agonists. Zebrafish, fathead minnow, mosquitofish, medaka fish and grass carp are all important aquatic model organisms widely used to evaluate the toxicity of new pollutants, and the molecular models of ARs from these five fish species were constructed to further screen AR agonists using AlphaFold2. The DF method showed the best performances with 0.99 accuracy, 0.97 sensitivity and 1 precision. The Asn705, Gln711, Arg752, and Thr877 residues in human AR and the corresponding sites in ARs from the five fish species were responsible for agonist binding. Overall, 245 substances were predicted as suspect AR agonists in the five fish species, including, certain glucocorticoids, cholesterol metabolites, and cardiovascular drugs in the NORMAN list. Using machine learning and molecular modeling hybrid methods rapidly and accurately screened AR agonists in fish species, and helping evaluate their ecological risk in fish populations.

Computational drug development for membrane protein targets

The application of computational biology in drug development for membrane protein targets has experienced a boost from recent developments in deep learning-driven structure prediction, increased speed and resolution of structure elucidation, machine learning structure-based design and the evaluation of big data. Recent protein structure predictions based on machine learning tools have delivered surprisingly reliable results for water-soluble and membrane proteins but have limitations for development of drugs that target membrane proteins. Structural transitions of membrane proteins have a central role during transmembrane signaling and are often influenced by therapeutic compounds. Resolving the structural and functional basis of dynamic transmembrane signaling networks, especially within the native membrane or cellular environment, remains a central challenge for drug development. Tackling this challenge will require an interplay between experimental and computational tools, such as super-resolution optical microscopy for quantification of the molecular interactions of cellular signaling networks and their modulation by potential drugs, cryo-electron microscopy for determination of the structural transitions of proteins in native cell membranes and entire cells, and computational tools for data analysis and prediction of the structure and function of cellular signaling networks, as well as generation of promising drug candidates.

Tld1 is a regulator of triglyceride lipolysis that demarcates a lipid droplet subpopulation

Cells store lipids in the form of triglyceride (TG) and sterol ester (SE) in lipid droplets (LDs). Distinct pools of LDs exist, but a pervasive question is how proteins localize to and convey functions to LD subsets. Here, we show that the yeast protein YDR275W/Tld1 (for TG-associated LD protein 1) localizes to a subset of TG-containing LDs and reveal it negatively regulates lipolysis. Mechanistically, Tld1 LD targeting requires TG, and it is mediated by two distinct hydrophobic regions (HRs). Molecular dynamics simulations reveal that Tld1's HRs interact with TG on LDs and adopt specific conformations on TG-rich LDs versus SE-rich LDs in yeast and human cells. Tld1-deficient yeast display no defect in LD biogenesis but exhibit elevated TG lipolysis dependent on lipase Tgl3. Remarkably, overexpression of Tld1, but not LD protein Pln1/Pet10, promotes TG accumulation without altering SE pools. Finally, we find that Tld1-deficient cells display altered LD mobilization during extended yeast starvation. We propose that Tld1 senses TG-rich LDs and regulates lipolysis on LD subpopulations.

B-Cell Epitope Prediction for Antipeptide Paratopes with the HAPTIC2/HEPTAD User Toolkit (HUT)

B-cell epitope prediction is key to developing peptide-based vaccines and immunodiagnostics along with antibodies for prophylactic, therapeutic and/or diagnostic use. This entails estimating paratope binding affinity for variable-length peptidic sequences subject to constraints on both paratope accessibility and antigen conformational flexibility, as described herein for the HAPTIC2/HEPTAD User Toolkit (HUT). HUT comprises the Heuristic Affinity Prediction Tool for Immune Complexes 2 (HAPTIC2), the HAPTIC2-like Epitope Prediction Tool for Antigen with Disulfide (HEPTAD) and the HAPTIC2/HEPTAD Input Preprocessor (HIP). HIP enables tagging of residues (e.g., in hydrophobic blobs, ordered regions and glycosylation motifs) for exclusion from downstream analyses by HAPTIC2 and HEPTAD. HAPTIC2 estimates paratope binding affinity for disulfide-free disordered peptidic antigens (by analogy between flexible-ligand docking and protein folding), from terms attributed to compaction (in view of sequence length, charge and temperature-dependent polyproline-II helical propensity), collapse (disfavored by residue bulkiness) and contact (with glycine and proline regarded as polar residues that hydrogen bond with paratopes). HEPTAD analyzes antigen sequences that each contain two cysteine residues for which the impact of disulfide pairing is estimated as a correction to the free-energy penalty of compaction. All of HUT is freely accessible online ( https://freeshell.de/~badong/hut.htm ).

Outer membrane β-barrel structure prediction through the lens of AlphaFold2

Most proteins found in the outer membrane of gram-negative bacteria share a common domain: the transmembrane β-barrel. These outer membrane β-barrels (OMBBs) occur in multiple sizes and different families with a wide range of functions evolved independently by amplification from a pool of homologous ancestral ββ-hairpins. This is part of the reason why predicting their three-dimensional (3D) structure, especially by homology modeling, is a major challenge. Recently, DeepMind's AlphaFold v2 (AF2) became the first structure prediction method to reach close-to-experimental atomic accuracy in CASP even for difficult targets. However, membrane proteins, especially OMBBs, were not abundant during their training, raising the question of how accurate the predictions are for these families. In this study, we assessed the performance of AF2 in the prediction of OMBBs and OMBB-like folds of various topologies using an in-house-developed tool for the analysis of OMBB 3D structures, and barrOs. In agreement with previous studies on other membrane protein classes, our results indicate that AF2 predicts transmembrane β-barrel structures at high accuracy independently of the use of templates, even for novel topologies absent from the training set. These results provide confidence on the models generated by AF2 and open the door to the structural elucidation of novel transmembrane β-barrel topologies identified in high-throughput OMBB annotation studies or designed de novo.

Channel Formation in Cry Toxins: An Alphafold-2 Perspective

Bacillus thuringiensis (Bt) strains produce pore-forming toxins (PFTs) that attack insect pests. Information for pre-pore and pore structures of some of these Bt toxins is available. However, for the three-domain (I-III) crystal (Cry) toxins, the most used Bt toxins in pest control, this crucial information is still missing. In these Cry toxins, biochemical data have shown that 7-helix domain I is involved in insertion in membranes, oligomerization and formation of a channel lined mainly by helix α4, whereas helices α1 to α3 seem to have a dynamic role during insertion. In the case of Cry1Aa, toxic against Manduca sexta larvae, a tetrameric oligomer seems to precede membrane insertion. Given the experimental difficulty in the elucidation of the membrane insertion steps, we used Alphafold-2 (AF2) to shed light on possible oligomeric structural intermediates in the membrane insertion of this toxin. AF2 very accurately (<1 Å RMSD) predicted the crystal monomeric and trimeric structures of Cry1Aa and Cry4Ba. The prediction of a tetramer of Cry1Aa, but not Cry4Ba, produced an 'extended model' where domain I helices α3 and α2b form a continuous helix and where hydrophobic helices α1 and α2 cluster at the tip of the bundle. We hypothesize that this represents an intermediate that binds the membrane and precedes α4/α5 hairpin insertion, together with helices α6 and α7. Another Cry1Aa tetrameric model was predicted after deleting helices α1 to α3, where domain I produced a central cavity consistent with an ion channel, lined by polar and charged residues in helix α4. We propose that this second model corresponds to the 'membrane-inserted' structure. AF2 also predicted larger α4/α5 hairpin n-mers (14 ≤n ≤ 17) with high confidence, which formed even larger (~5 nm) pores. The plausibility of these models is discussed in the context of available experimental data and current paradigms.

Selective Activation of a TRPC6 Ion Channel Over TRPC3 by Metalated Type-B Polycyclic Polyprenylated Acylphloroglucinols

Selective modulation of TRPC6 ion channels is a promising therapeutic approach for neurodegenerative diseases and depression. A significant advancement showcases the selective activation of TRPC6 through metalated type-B PPAP, termed PPAP53. This success stems from PPAP53's 1,3-diketone motif facilitating metal coordination. PPAP53 is water-soluble and as potent as hyperforin, the gold standard in this field. In contrast to type-A, type-B PPAPs offer advantages such as gram-scale synthesis, easy derivatization, and long-term stability. Our investigations reveal PPAP53 selectively binding to the C-terminus of TRPC6. Although cryoelectron microscopy has resolved the majority of the TRPC6 structure, the binding site in the C-terminus remained unresolved. To address this issue, we employed state-of-the-art artificial-intelligence-based protein structure prediction algorithms to predict the missing region. Our computational results, validated against experimental data, indicate that PPAP53 binds to the 777LLKL780-region of the C-terminus, thus providing critical insights into the binding mechanism of PPAP53.

How AlphaFold2 shaped the structural coverage of the human transmembrane proteome

AlphaFold2 (AF2) provides a 3D structure for every known or predicted protein, opening up new prospects for virtually every field in structural biology. However, working with transmembrane protein molecules pose a notorious challenge for scientists, resulting in a limited number of experimentally determined structures. Consequently, algorithms trained on this finite training set also face difficulties. To address this issue, we recently launched the TmAlphaFold database, where predicted AlphaFold2 structures are embedded into the membrane plane and a quality assessment (plausibility of the membrane-embedded structure) is provided for each prediction using geometrical evaluation. In this paper, we analyze how AF2 has improved the structural coverage of membrane proteins compared to earlier years when only experimental structures were available, and high-throughput structure prediction was greatly limited. We also evaluate how AF2 can be used to search for (distant) homologs in highly diverse protein families. By combining quality assessment and homology search, we can pinpoint protein families where AF2 accuracy is still limited, and experimental structure determination would be desirable.

Unique Properties of Nutrient Channels on Plasmodium-Infected Erythrocytes

Intracellular malaria parasites activate an ion and organic solute channel on their host erythrocyte membrane to acquire a broad range of essential nutrients. This plasmodial surface anion channel (PSAC) facilitates the uptake of sugars, amino acids, purines, some vitamins, and organic cations, but remarkably, it must exclude the small Na+ ion to preserve infected erythrocyte osmotic stability in plasma. Although molecular, biochemical, and structural studies have provided fundamental mechanistic insights about PSAC and advanced potent inhibitors as exciting antimalarial leads, important questions remain about how nutrients and ions are transported. Here, I review PSAC's unusual selectivity and conductance properties, which should guide future research into this important microbial ion channel.

Fungal carboxylate transporters: recent manipulations and applications

Carboxylic acids containing acidic groups with additional keto/hydroxyl-groups or unsaturated bond have displayed great applicability in the food, agricultural, cosmetic, textile, and pharmaceutical industries. The traditional approach for carboxylate production through chemical synthesis is based on petroleum derivatives, resulting in concerns for the environmental complication and energy crisis, and increasing attention has been attracted to the eco-friendly and renewable bio-based synthesis for carboxylate production. The efficient and specific export of target carboxylic acids through the microbial membrane is essential for high productivity, yield, and titer of bio-based carboxylates. Therefore, understanding the characteristics, regulations, and efflux mechanisms of carboxylate transporters will efficiently increase industrial biotechnological production of carboxylic acids. Several transporters from fungi have been reported and used for improved synthesis of target products. The transport activity and substrate specificity are two key issues that need further improvement in the application of carboxylate transporters. This review presents developments in the structural and functional diversity of carboxylate transporters, focusing on the modification and regulation of carboxylate transporters to alter the transport activity and substrate specificity, providing new strategy for transporter engineering in constructing microbial cell factory for carboxylate production. KEY POINTS: • Structures of multiple carboxylate transporters have been predicted. • Carboxylate transporters can efficiently improve production. • Modification engineering of carboxylate transporters will be more popular in the future.

Structural Modeling of Cytokine-Receptor-JAK2 Signaling Complexes Using AlphaFold Multimer

Homodimeric class 1 cytokine receptors include the erythropoietin (EPOR), thrombopoietin (TPOR), granulocyte colony-stimulating factor 3 (CSF3R), growth hormone (GHR), and prolactin receptors (PRLR). These cell-surface single-pass transmembrane (TM) glycoproteins regulate cell growth, proliferation, and differentiation and induce oncogenesis. An active TM signaling complex consists of a receptor homodimer, one or two ligands bound to the receptor extracellular domains, and two molecules of Janus Kinase 2 (JAK2) constitutively associated with the receptor intracellular domains. Although crystal structures of soluble extracellular domains with ligands have been obtained for all of the receptors except TPOR, little is known about the structure and dynamics of the complete TM complexes that activate the downstream JAK-STAT signaling pathway. Three-dimensional models of five human receptor complexes with cytokines and JAK2 were generated here by using AlphaFold Multimer. Given the large size of the complexes (from 3220 to 4074 residues), the modeling required a stepwise assembly from smaller parts, with selection and validation of the models through comparisons with published experimental data. The modeling of active and inactive complexes supports a general activation mechanism that involves ligand binding to a monomeric receptor followed by receptor dimerization and rotational movement of the receptor TM α-helices, causing proximity, dimerization, and activation of associated JAK2 subunits. The binding mode of two eltrombopag molecules to the TM α-helices of the active TPOR dimer was proposed. The models also help elucidate the molecular basis of oncogenic mutations that may involve a noncanonical activation route. Models equilibrated in explicit lipids of the plasma membrane are publicly available.

TMKit: a Python interface for computational analysis of transmembrane proteins

Transmembrane proteins are receptors, enzymes, transporters and ion channels that are instrumental in regulating a variety of cellular activities, such as signal transduction and cell communication. Despite tremendous progress in computational capacities to support protein research, there is still a significant gap in the availability of specialized computational analysis toolkits for transmembrane protein research. Here, we introduce TMKit, an open-source Python programming interface that is modular, scalable and specifically designed for processing transmembrane protein data. TMKit is a one-stop computational analysis tool for transmembrane proteins, enabling users to perform database wrangling, engineer features at the mutational, domain and topological levels, and visualize protein-protein interaction interfaces. In addition, TMKit includes seqNetRR, a high-performance computing library that allows customized construction of a large number of residue connections. This library is particularly well suited for assigning correlation matrix-based features at a fast speed. TMKit should serve as a useful tool for researchers in assisting the study of transmembrane protein sequences and structures. TMKit is publicly available through https://github.com/2003100127/tmkit and https://tmkit-guide.herokuapp.com/doc/overview.

Investigating the conformational landscape of AlphaFold2-predicted protein kinase structures

Summary

Protein kinases are a family of signaling proteins, crucial for maintaining cellular homeostasis. When dysregulated, kinases drive the pathogenesis of several diseases, and are thus one of the largest target categories for drug discovery. Kinase activity is tightly controlled by switching through several active and inactive conformations in their catalytic domain. Kinase inhibitors have been designed to engage kinases in specific conformational states, where each conformation presents a unique physico-chemical environment for therapeutic intervention. Thus, modeling kinases across conformations can enable the design of novel and optimally selective kinase drugs. Due to the recent success of AlphaFold2 in accurately predicting the 3D structure of proteins based on sequence, we investigated the conformational landscape of protein kinases as modeled by AlphaFold2. We observed that AlphaFold2 is able to model several kinase conformations across the kinome, however, certain conformations are only observed in specific kinase families. Furthermore, we show that the per residue predicted local distance difference test can capture information describing structural flexibility of kinases. Finally, we evaluated the docking performance of AlphaFold2 kinase structures for enriching known ligands. Taken together, we see an opportunity to leverage AlphaFold2 models for structure-based drug discovery against kinases across several pharmacologically relevant conformational states.

Availability and implementation

All code used in the analysis is freely available at https://github.com/Harmonic-Discovery/AF2-kinase-conformational-landscape.

Tld1 is a novel regulator of triglyceride lipolysis that demarcates a lipid droplet subpopulation

Cells store lipids in the form of triglyceride (TG) and sterol-ester (SE) in lipid droplets (LDs). Distinct pools of LDs exist, but a pervasive question is how proteins localize to and convey functions to LD subsets. Here, we show the yeast protein YDR275W/Tld1 (for TG-associated LD protein 1) localizes to a subset of TG-containing LDs, and reveal it negatively regulates lipolysis. Mechanistically, Tld1 LD targeting requires TG, and is mediated by two distinct hydrophobic regions (HRs). Molecular dynamics simulations reveal Tld1 HRs interact with TG on LDs and adopt specific conformations on TG-rich LDs versus SE-rich LDs in yeast and human cells. Tld1-deficient yeast display no defect in LD biogenesis, but exhibit elevated TG lipolysis dependent on lipase Tgl3. Remarkably, over-expression of Tld1, but not LD protein Pln1/Pet10, promotes TG accumulation without altering SE pools. Finally, we find Tld1-deficient cells display altered LD mobilization during extended yeast starvation. We propose Tld1 senses TG-rich LDs and regulates lipolysis on LD subpopulations.

Viroporins of Mpox Virus

Mpox or monkeypox virus (MPXV) belongs to the subclass of Poxviridae and has emerged recently as a global threat. With a limited number of anti-viral drugs available for this new virus species, it is challenging to thwart the illness it begets. Therefore, characterizing new drug targets in the virus may prove advantageous to curbing the disease. Since channels as a family are excellent drug targets, we have sought to identify viral ion channels for this virus, which are instrumental in formulating channel-blocking anti-viral drugs. Bioinformatics analyses yielded eight transmembranous proteins smaller or equal to 100 amino acids in length. Subsequently, three independent bacteria-based assays have pointed to five of the eight proteins that exhibit ion channel activity. Finally, we propose a tentative structure of four ion channels from their primary amino acid sequences, employing AlphaFold2 and molecular dynamic simulation methods. These results may represent the first steps in characterizing MPXV viroporins en route to developing blockers that inhibit their function.

Spf1 and Ste24: quality controllers of transmembrane protein topology in the eukaryotic cell

DNA replication, transcription, and translation in eukaryotic cells occur with decreasing but still high fidelity. In contrast, for the estimated 33% of the human proteome that is inserted as transmembrane (TM) proteins, insertion with a non-functional inverted topology is frequent. Correct topology is essential for function and trafficking to appropriate cellular compartments and is controlled principally by responses to charged residues within 15 residues of the inserted TM domain (TMD); the flank with the higher positive charge remains in the cytosol (inside), following the positive inside rule (PIR). Yeast (Saccharomyces cerevisiae) mutants that increase insertion contrary to the PIR were selected. Mutants with strong phenotypes were found only in SPF1 and STE24 (human cell orthologs are ATP13A1 and ZMPSte24) with, at the time, no known relevant functions. Spf1/Atp13A1 is now known to dislocate to the cytosol TM proteins inserted contrary to the PIR, allowing energy-conserving reinsertion. We hypothesize that Spf1 and Ste24 both recognize the short, positively charged ER luminal peptides of TM proteins inserted contrary to the PIR, accepting these peptides into their large membrane-spanning, water-filled cavities through interaction with their many interior surface negative charges. While entry was demonstrated for Spf1, no published evidence directly demonstrates substrate entry to the Ste24 cavity, internal access to its zinc metalloprotease (ZMP) site, or active withdrawal of fragments, which may be essential for function. Spf1 and Ste24 comprise a PIR quality control system that is conserved in all eukaryotes and presumably evolved in prokaryotic progenitors as they gained differentiated membrane functions. About 75% of the PIR is imposed by this quality control system, which joins the UPR, ERAD, and autophagy (ER-phagy) in coordinated, overlapping quality control of ER protein function.

AlphaCutter: Efficient removal of non-globular regions from predicted protein structures

A huge number of high-quality predicted protein structures are now publicly available. However, many of these structures contain non-globular regions, which diminish the performance of downstream structural bioinformatic applications. In this study, we develop AlphaCutter for the removal of non-globular regions from predicted protein structures. A large-scale cleaning of 542,380 predicted SwissProt structures highlights that AlphaCutter is able to (1) remove non-globular regions that are undetectable using pLDDT scores and (2) preserve high integrity of the cleaned domain regions. As useful applications, AlphaCutter improved the folding energy scores and sequence recovery rates in the re-design of domain regions. On average, AlphaCutter takes less than 3 s to clean a protein structure, enabling efficient cleaning of the exploding number of predicted protein structures. AlphaCutter is available at https://github.com/johnnytam100/AlphaCutter. AlphaCutter-cleaned SwissProt structures are available for download at https://doi.org/10.5281/zenodo.7944483.

Impact of E484Q and L452R Mutations on Structure and Binding Behavior of SARS-CoV-2 B.1.617.1 Using Deep Learning AlphaFold2, Molecular Docking and Dynamics Simulation

During the outbreak of COVID-19, many SARS-CoV-2 variants presented key amino acid mutations that influenced their binding abilities with angiotensin-converting enzyme 2 (hACE2) and neutralizing antibodies. For the B.1.617 lineage, there had been fears that two key mutations, i.e., L452R and E484Q, would have additive effects on the evasion of neutralizing antibodies. In this paper, we systematically investigated the impact of the L452R and E484Q mutations on the structure and binding behavior of B.1.617.1 using deep learning AlphaFold2, molecular docking and dynamics simulation. We firstly predicted and verified the structure of the S protein containing L452R and E484Q mutations via the AlphaFold2-calculated pLDDT value and compared it with the experimental structure. Next, a molecular simulation was performed to reveal the structural and interaction stabilities of the S protein of the double mutant variant with hACE2. We found that the double mutations, L452R and E484Q, could lead to a decrease in hydrogen bonds and higher interaction energy between the S protein and hACE2, demonstrating the lower structural stability and the worse binding affinity in the long dynamic evolutional process, even though the molecular docking showed the lower binding energy score of the S1 RBD of the double mutant variant with hACE2 than that of the wild type (WT) with hACE2. In addition, docking to three approved neutralizing monoclonal antibodies (mAbs) showed a reduced binding affinity of the double mutant variant, suggesting a lower neutralization ability of the mAbs against the double mutant variant. Our study helps lay the foundation for further SARS-CoV-2 studies and provides bioinformatics and computational insights into how the double mutations lead to immune evasion, which could offer guidance for subsequent biomedical studies.

The intrinsically disordered protein glue of the myelin major dense line: Linking AlphaFold2 predictions to experimental data

Numerous human proteins are classified as intrinsically disordered proteins (IDPs). Due to their physicochemical properties, high-resolution structural information about IDPs is generally lacking. On the other hand, IDPs are known to adopt local ordered structures upon interactions with e.g. other proteins or lipid membrane surfaces. While recent developments in protein structure prediction have been revolutionary, their impact on IDP research at high resolution remains limited. We took a specific example of two myelin-specific IDPs, the myelin basic protein (MBP) and the cytoplasmic domain of myelin protein zero (P0ct). Both of these IDPs are crucial for normal nervous system development and function, and while they are disordered in solution, upon membrane binding, they partially fold into helices, being embedded into the lipid membrane. We carried out AlphaFold2 predictions of both proteins and analysed the models in light of experimental data related to protein structure and molecular interactions. We observe that the predicted models have helical segments that closely correspond to the membrane-binding sites on both proteins. We furthermore analyse the fits of the models to synchrotron-based X-ray scattering and circular dichroism data from the same IDPs. The models are likely to represent the membrane-bound state of both MBP and P0ct, rather than the conformation in solution. Artificial intelligence-based models of IDPs appear to provide information on the ligand-bound state of these proteins, instead of the conformers dominating free in solution. We further discuss the implications of the predictions for mammalian nervous system myelination and their relevance to understanding disease aspects of these IDPs.

Mammalian lipid droplets: structural, pathological, immunological and anti-toxicological roles

Mammalian lipid droplets (LDs) are specialized cytosolic organelles consisting of a neutral lipid core surrounded by a membrane made up of a phospholipid monolayer and a specific population of proteins that varies according to the location and function of each LD. Over the past decade, there have been significant advances in the understanding of LD biogenesis and functions. LDs are now recognized as dynamic organelles that participate in many aspects of cellular homeostasis plus other vital functions. LD biogenesis is a complex, highly-regulated process with assembly occurring on the endoplasmic reticulum although aspects of the underpinning molecular mechanisms remain elusive. For example, it is unclear how many enzymes participate in the biosynthesis of the neutral lipid components of LDs and how this process is coordinated in response to different metabolic cues to promote or suppress LD formation and turnover. In addition to enzymes involved in the biosynthesis of neutral lipids, various scaffolding proteins play roles in coordinating LD formation. Despite their lack of ultrastructural diversity, LDs in different mammalian cell types are involved in a wide range of biological functions. These include roles in membrane homeostasis, regulation of hypoxia, neoplastic inflammatory responses, cellular oxidative status, lipid peroxidation, and protection against potentially toxic intracellular fatty acids and lipophilic xenobiotics. Herein, the roles of mammalian LDs and their associated proteins are reviewed with a particular focus on their roles in pathological, immunological and anti-toxicological processes.

Structural modeling of cytokine-receptor-JAK2 signaling complexes using AlphaFold Multimer

Homodimeric class 1 cytokine receptors include the erythropoietin (EPOR), thrombopoietin (TPOR), granulocyte colony-stimulating factor 3 (CSF3R), growth hormone (GHR), and prolactin receptors (PRLR). They are cell-surface single-pass transmembrane (TM) glycoproteins that regulate cell growth, proliferation, and differentiation and induce oncogenesis. An active TM signaling complex consists of a receptor homodimer, one or two ligands bound to the receptor extracellular domains and two molecules of Janus Kinase 2 (JAK2) constitutively associated with the receptor intracellular domains. Although crystal structures of soluble extracellular domains with ligands have been obtained for all the receptors except TPOR, little is known about the structure and dynamics of the complete TM complexes that activate the downstream JAK-STAT signaling pathway. Three-dimensional models of five human receptor complexes with cytokines and JAK2 were generated using AlphaFold Multimer. Given the large size of the complexes (from 3220 to 4074 residues), the modeling required a stepwise assembly from smaller parts with selection and validation of the models through comparisons with published experimental data. The modeling of active and inactive complexes supports a general activation mechanism that involves ligand binding to a monomeric receptor followed by receptor dimerization and rotational movement of the receptor TM α-helices causing proximity, dimerization, and activation of associated JAK2 subunits. The binding mode of two eltrombopag molecules to TM α-helices of the active TPOR dimer was proposed. The models also help elucidating the molecular basis of oncogenic mutations that may involve non-canonical activation route. Models equilibrated in explicit lipids of the plasma membrane are publicly available.

Template-free prediction of a new monotopic membrane protein fold and assembly by AlphaFold2

AlphaFold2 (AF2) has revolutionized the field of protein structural prediction. Here, we test its ability to predict the tertiary and quaternary structure of a previously undescribed scaffold with new folds and unusual architecture, the monotopic membrane protein caveolin-1 (CAV1). CAV1 assembles into a disc-shaped oligomer composed of 11 symmetrically arranged protomers, each assuming an identical new fold, and contains the largest parallel β-barrel known to exist in nature. Remarkably, AF2 predicts both the fold of the protomers and the interfaces between them. It also assembles between seven and 15 copies of CAV1 into disc-shaped complexes. However, the predicted multimers are energetically strained, especially the parallel β-barrel. These findings highlight the ability of AF2 to correctly predict new protein folds and oligomeric assemblies at a granular level while missing some elements of higher-order complexes, thus positing a new direction for the continued development of deep-learning protein structure prediction approaches.

Itch receptor MRGPRX4 interacts with the receptor activity-modifying proteins

Cholestatic itch is a severe and debilitating symptom in liver diseases with limited treatment options. The class A G protein-coupled receptor (GPCR) Mas-related GPCR subtype X4 (MRGPRX4) has been identified as a receptor for bile acids, which are potential cholestatic pruritogens. An increasing number of GPCRs have been shown to interact with receptor activity-modifying proteins (RAMPs), which can modulate different aspects of GPCR biology. Using a combination of multiplexed immunoassay and proximity ligation assay, we show that MRGPRX4 interacts with RAMPs. The interaction of MRGPRX4 with RAMP2, but not RAMP1 or 3, causes attenuation of basal and agonist-dependent signaling, which correlates with a decrease of MRGPRX4 cell surface expression as measured using a quantitative NanoBRET pulse-chase assay. Finally, we use AlphaFold Multimer to predict the structure of the MRGPRX4-RAMP2 complex. The discovery that RAMP2 regulates MRGPRX4 may have direct implications for future drug development for cholestatic itch.

Deorphanizing Peptides Using Structure Prediction

Many endogenous peptides rely on signaling pathways to exert their function, but identifying their cognate receptors remains a challenging problem. We investigate the use of AlphaFold-Multimer complex structure prediction together with transmembrane topology prediction for peptide deorphanization. We find that AlphaFold's confidence metrics have strong performance for prioritizing true peptide-receptor interactions. In a library of 1112 human receptors, the method ranks true receptors in the top percentile on average for 11 benchmark peptide-receptor pairs.

Metastable alpha-rich and beta-rich conformations of small Aβ42 peptide oligomers

Probing the structures of amyloid-β (Aβ) peptides in the early steps of aggregation is extremely difficult experimentally and computationally. Yet, this knowledge is extremely important as small oligomers are the most toxic species. Experiments and simulations on Aβ42 monomer point to random coil conformations with either transient helical or β-strand content. Our current conformational description of small Aβ42 oligomers is funneled toward amorphous aggregates with some β-sheet content and rare high energy states with well-ordered assemblies of β-sheets. In this study, we emphasize another view based on metastable α-helix bundle oligomers spanning the C-terminal residues, which are predicted by the machine-learning AlphaFold2 method and supported indirectly by low-resolution experimental data on many amyloid polypeptides. This finding has consequences in developing novel chemical tools and to design potential therapies to reduce aggregation and toxicity.

The opportunities and challenges posed by the new generation of deep learning-based protein structure predictors

The function of proteins can often be inferred from their three-dimensional structures. Experimental structural biologists spent decades studying these structures, but the accelerated pace of protein sequencing continuously increases the gaps between sequences and structures. The early 2020s saw the advent of a new generation of deep learning-based protein structure prediction tools that offer the potential to predict structures based on any number of protein sequences. In this review, we give an overview of the impact of this new generation of structure prediction tools, with examples of the impacted field in the life sciences. We discuss the novel opportunities and new scientific and technical challenges these tools present to the broader scientific community. Finally, we highlight some potential directions for the future of computational protein structure prediction.