Ins and outs of AlphaFold2 transmembrane protein structure predictions

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.

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.

An agnostic analysis of the human AlphaFold2 proteome using local protein conformations

Knowledge of the 3D structure of proteins is a valuable asset for understanding their precise biological mechanisms. However, the cost of production of 3D structures and experimental difficulties limit their obtaining. The proposal of 3D structural models is consequently an appealing alternative. The release of the AlphaFold Deep Learning approach has revolutionized the field. The recent near-complete human proteome proposal makes it possible to analyse large amounts of data and evaluate the results of the approach in greater depth. The 3D human proteome was thus analysed in light of the classic secondary structures, and many less-used protein local conformations (PolyProline II helices, type of γ-turns, of β-turns and of β-bulges, curvature of the helices, and a structural alphabet). Without questioning the global quality of the approach, this analysis highlights certain local conformations, which maybe poorly predicted and they could therefore be better addressed.

Restriction of access to the central cavity is a major contributor to substrate selectivity in plant ABCG transporters

ABCG46 of the legume Medicago truncatula is an ABC-type transporter responsible for highly selective translocation of the phenylpropanoids, 4-coumarate, and liquiritigenin, over the plasma membrane. To investigate molecular determinants of the observed substrate selectivity, we applied a combination of phylogenetic and biochemical analyses, AlphaFold2 structure prediction, molecular dynamics simulations, and mutagenesis. We discovered an unusually narrow transient access path to the central cavity of MtABCG46 that constitutes an initial filter responsible for the selective translocation of phenylpropanoids through a lipid bilayer. Furthermore, we identified remote residue F562 as pivotal for maintaining the stability of this filter. The determination of individual amino acids that impact the selective transport of specialized metabolites may provide new opportunities associated with ABCGs being of interest, in many biological scenarios.

Secondary and Topological Structural Merge Prediction of Alpha-Helical Transmembrane Proteins Using a Hybrid Model Based on Hidden Markov and Long Short-Term Memory Neural Networks

Alpha-helical transmembrane proteins (αTMPs) play essential roles in drug targeting and disease treatments. Due to the challenges of using experimental methods to determine their structure, αTMPs have far fewer known structures than soluble proteins. The topology of transmembrane proteins (TMPs) can determine the spatial conformation relative to the membrane, while the secondary structure helps to identify their functional domain. They are highly correlated on αTMPs sequences, and achieving a merge prediction is instructive for further understanding the structure and function of αTMPs. In this study, we implemented a hybrid model combining Deep Learning Neural Networks (DNNs) with a Class Hidden Markov Model (CHMM), namely HDNNtopss. DNNs extract rich contextual features through stacked attention-enhanced Bidirectional Long Short-Term Memory (BiLSTM) networks and Convolutional Neural Networks (CNNs), and CHMM captures state-associative temporal features. The hybrid model not only reasonably considers the probability of the state path but also has a fitting and feature-extraction capability for deep learning, which enables flexible prediction and makes the resulting sequence more biologically meaningful. It outperforms current advanced merge-prediction methods with a Q4 of 0.779 and an MCC of 0.673 on the independent test dataset, which have practical, solid significance. In comparison to advanced prediction methods for topological and secondary structures, it achieves the highest topology prediction with a Q2 of 0.884, which has a strong comprehensive performance. At the same time, we implemented a joint training method, Co-HDNNtopss, and achieved a good performance to provide an important reference for similar hybrid-model training.

AlphaFold2 and its applications in the fields of biology and medicine

AlphaFold2 (AF2) is an artificial intelligence (AI) system developed by DeepMind that can predict three-dimensional (3D) structures of proteins from amino acid sequences with atomic-level accuracy. Protein structure prediction is one of the most challenging problems in computational biology and chemistry, and has puzzled scientists for 50 years. The advent of AF2 presents an unprecedented progress in protein structure prediction and has attracted much attention. Subsequent release of structures of more than 200 million proteins predicted by AF2 further aroused great enthusiasm in the science community, especially in the fields of biology and medicine. AF2 is thought to have a significant impact on structural biology and research areas that need protein structure information, such as drug discovery, protein design, prediction of protein function, et al. Though the time is not long since AF2 was developed, there are already quite a few application studies of AF2 in the fields of biology and medicine, with many of them having preliminarily proved the potential of AF2. To better understand AF2 and promote its applications, we will in this article summarize the principle and system architecture of AF2 as well as the recipe of its success, and particularly focus on reviewing its applications in the fields of biology and medicine. Limitations of current AF2 prediction will also be discussed.

Structures of Plasmodium falciparum Chloroquine Resistance Transporter (PfCRT) Isoforms and Their Interactions with Chloroquine

Using a recently elucidated atomic-resolution cryogenic electron microscopy (cryo-EM) structure for the Plasmodium falciparum chloroquine resistance transporter (PfCRT) protein 7G8 isoform as template [Kim, J.; Nature 2019, 576, 315-320], we use Monte Carlo molecular dynamics (MC/MD) simulations of PfCRT embedded in a 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC) membrane to solve energy-minimized structures for 7G8 PfCRT and two additional PfCRT isoforms that harbor 5 or 7 amino acid substitutions relative to 7G8 PfCRT. Guided by drug binding previously defined using chloroquine (CQ) photoaffinity probe labeling, we also use MC/MD energy minimization to elucidate likely CQ binding geometries for the three membrane-embedded isoforms. We inventory salt bridges and hydrogen bonds in these structures and summarize how the limited changes in primary sequence subtly perturb local PfCRT isoform structure. In addition, we use the "AlphaFold" artificial intelligence AlphaFold2 (AF2) algorithm to solve for domain structure that was not resolved in the previously reported 7G8 PfCRT cryo-EM structure, and perform MC/MD energy minimization for the membrane-embedded AF2 structures of all three PfCRT isoforms. We compare energy-minimized structures generated using cryo-EM vs AF2 templates. The results suggest how amino acid substitutions in drug resistance-associated isoforms of PfCRT influence PfCRT structure and CQ transport.

Scalable Inhibitors of the Nsp3-Nsp4 Coupling in SARS-CoV-2

The human Betacoronavirus SARS-CoV-2 is a novel pathogen claiming millions of lives and causing a global pandemic that has disrupted international healthcare systems, economies, and communities. The virus is fast mutating and presenting more infectious but less lethal versions. Currently, some small-molecule therapeutics have received FDA emergency use authorization for the treatment of COVID-19, including Lagevrio (molnupiravir) and Paxlovid (nirmaltrevir/ritonavir), which target the RNA-dependent RNA polymerase and the 3CLpro main protease, respectively. Proteins downstream in the viral replication process, specifically the nonstructural proteins (Nsps1-16), are potential drug targets due to their crucial functions. Of these Nsps, Nsp4 is a particularly promising drug target due to its involvement in the SARS-CoV viral replication and double-membrane vesicle formation (mediated via interaction with Nsp3). Given the degree of sequence conservation of these two Nsps across the Betacoronavirus clade, their protein-protein interactions and functions are likely to be conserved as well in SARS-CoV-2. Through AlphaFold2 and its recent advancements, protein structures were generated of Nsp3 and 4 lumenal loops of interest. Then, using a combination of molecular docking suites and an existing library of lead-like compounds, we virtually screened 7 million ligands to identify five putative ligand inhibitors of Nsp4, which could present an alternative pharmaceutical approach against SARS-CoV-2. These ligands exhibit promising lead-like properties (ideal molecular weight and log P profiles), maintain fixed-Nsp4-ligand complexes in molecular dynamics (MD) simulations, and tightly associate with Nsp4 via hydrophobic interactions. Additionally, alternative peptide inhibitors based on Nsp3 were designed and shown in MD simulations to provide a highly stable binding to the Nsp4 protein. Finally, these therapeutics were attached to dendrimer structures to promote their multivalent binding with Nsp4, especially its large flexible luminal loop (Nsp4LLL). The therapeutics tested in this study represent many different approaches for targeting large flexible protein structures, especially those localized to the ER. This study is the first work targeting the membrane rearrangement system of viruses and will serve as a potential avenue for treating viruses with similar replicative function.

Machine learning in computational modelling of membrane protein sequences and structures: From methodologies to applications

Membrane proteins mediate a wide spectrum of biological processes, such as signal transduction and cell communication. Due to the arduous and costly nature inherent to the experimental process, membrane proteins have long been devoid of well-resolved atomic-level tertiary structures and, consequently, the understanding of their functional roles underlying a multitude of life activities has been hampered. Currently, computational tools dedicated to furthering the structure-function understanding are primarily focused on utilizing intelligent algorithms to address a variety of site-wise prediction problems (e.g., topology and interaction sites), but are scattered across different computing sources. Moreover, the recent advent of deep learning techniques has immensely expedited the development of computational tools for membrane protein-related prediction problems. Given the growing number of applications optimized particularly by manifold deep neural networks, we herein provide a review on the current status of computational strategies mainly in membrane protein type classification, topology identification, interaction site detection, and pathogenic effect prediction. Meanwhile, we provide an overview of how the entire prediction process proceeds, including database collection, data pre-processing, feature extraction, and method selection. This review is expected to be useful for developing more extendable computational tools specific to membrane proteins.

Virtual screening and activity evaluation of human uric acid transporter 1 (hURAT1) inhibitors

Hyperuricemia is a disease caused by disorder of purine metabolism, mainly due to insufficient renal excretion of uric acid. Urate transporter 1 (URAT1) is the most widely studied target of urate transporters, and used for uric acid (UA) reabsorption. This study used the AlphaFold2 algorithm to predict the structure of URAT1. Virtual screening and biological evaluation were used to discover novel URAT1 inhibitors that target the critical amino acids. Seven compounds were screened from the T2220 database and validated as URAT1 inhibitors by cell biology experiments. The IC50 values of benbromarone, NP023335, TN1148, and TN1008 were 6.878, 18.46, 24.64, and 53.04 μM, respectively. Molecular dynamics simulation was used to investigate the binding mechanism of URAT1 to NP023335, which forms stable contact with Ser35, Phe365, and Arg477. These interactions are essential for maintaining the biological activity of NP023335. The three compounds' pharmacokinetic characteristics were predicted, and NP023335's properties matched those of an empirical medication with the benefits of high solubility, low cardiotoxicity, good membrane permeability, and oral absorption. The natural product NP023335 will serve as a promising hit compound for facilitating the further design of novel URAT1 inhibitors.

The LIV-1 Subfamily of Zinc Transporters: From Origins to Present Day Discoveries

This review explains the origin of the LIV-1 family of zinc transporters, paying attention to how this family of nine human proteins was originally discovered. Structural and functional differences between these nine human LIV-1 family members and the five other ZIP transporters are examined. These differences are both related to aspects of the protein sequence, the conservation of important motifs and to the effect this may have on their overall function. The LIV-1 family are dependent on various post-translational modifications, such as phosphorylation and cleavage, which play an important role in their ability to transport zinc. These modifications and their implications are discussed in detail. Some of these proteins have been implicated in cancer which is examined. Furthermore, some additional areas of potential fruitful discovery are discussed and suggested as worthy of examination in the future.

Characterization of the renal tubular transport of creatinine by activity-based protein profiling and transport kinetics

Serum creatinine is widely used to adjust the dosing of drugs eliminated by the kidney in patients with renal dysfunction, as it is a readily accessible indicator of kidney function. However, there are many limitations for drug dosage adjustment based on serum creatinine levels, one of which is the limited understanding of creatinine's tubular transport. Thus, we aimed to complement and advance the renal tubular transport of creatinine by activity-based protein profiling (ABPP) and transporter-overexpression technology. Renal tubular transporters were not identified via ABPP due to the low-affinity interaction between transporters and creatinine. The uptake of isotopically labeled d3-creatinine was significantly increased in OCT2-overexpressing cell lines (p<0.01), and the K_m and V_max of d3-creatinine uptake mediated by OCT2 was 3.1 mM and 408 pmol/mg protein/min, respectively. In the OCT2-overexpressing cell lines, the IC50 of creatinine for d3-creatinine uptake was 10.3 mM, and that of the OCT2 inhibitor cimetidine for d3-creatinine uptake was 99.04 μM. Different dosages of creatinine did not affect the renal excretion of d3-creatinine in mice (p>0.05), while cimetidine significantly reduced the renal excretion of d3-creatinine (p<0.01) without affecting the glomerular filtration rate. Molecular docking in silico showed that the OCT2 amino acid GLN242 could form a hydrogen bond of 2.5 Å with creatinine, and there may be a π-π interaction between TYR362 and creatinine. A site mutation experiment demonstrated that TYR362 and GLN242 were important sites for the OCT2-creatinine interaction. These results demonstrate that OCT2 mediates the renal tubular secretion of creatinine with low affinity and is a minor contributor to creatinine secretion.